Emerging Drugs and Targets for Multiple Sclerosis 1788014502, 9781788014502

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Emerging Drugs and Targets for Multiple Sclerosis

Drug Discovery Series Editor-­in-­chief

David Thurston, King's College, UK

Series editors:

David Fox, Vulpine Science and Learning, UK Ana Martinez, Centro de Investigaciones Biologicas-­CSIC, Spain David Rotella, Montclair State University, USA Hong Shen, Roche Innovation Center Shanghai, China

Editorial advisor:

Ian Storer, AstraZeneca, UK

Titles in the Series:

1: Metabolism, Pharmacokinetics and Toxicity of Functional Groups 2: Emerging Drugs and Targets for Alzheimer's Disease; Volume 1 3: Emerging Drugs and Targets for Alzheimer's Disease; Volume 2 4: Accounts in Drug Discovery 5: New Frontiers in Chemical Biology 6: Animal Models for Neurodegenerative Disease 7: Neurodegeneration 8: G Protein-­coupled Receptors 9: Pharmaceutical Process Development 10: Extracellular and Intracellular Signaling 11: New Synthetic Technologies in Medicinal Chemistry 12: New Horizons in Predictive Toxicology 13: Drug Design Strategies: Quantitative Approaches 14: Neglected Diseases and Drug Discovery 15: Biomedical Imaging 16: Pharmaceutical Salts and Cocrystals 17: Polyamine Drug Discovery 18: Proteinases as Drug Targets 19: Kinase Drug Discovery 20: Drug Design Strategies: Computational Techniques and Applications 21: Designing Multi-­target Drugs 22: Nanostructured Biomaterials for Overcoming Biological Barriers 23: Physico-­chemical and Computational Approaches to Drug Discovery 24: Biomarkers for Traumatic Brain Injury 25: Drug Discovery from Natural Products 26: Anti-­inflammatory Drug Discovery 27: New Therapeutic Strategies for Type 2 Diabetes: Small Molecules

28: Drug Discovery for Psychiatric Disorders 29: Organic Chemistry of Drug Degradation 30: Computational Approaches to Nuclear Receptors 31: Traditional Chinese Medicine 32: Successful Strategies for the Discovery of Antiviral Drugs 33: Comprehensive Biomarker Discovery and Validation for Clinical Application 34: Emerging Drugs and Targets for Parkinson's Disease 35: Pain Therapeutics; Current and Future Treatment Paradigms 36: Biotherapeutics: Recent Developments using Chemical and Molecular Biology 37: Inhibitors of Molecular Chaperones as Therapeutic Agents 38: Orphan Drugs and Rare Diseases 39: Ion Channel Drug Discovery 40: Macrocycles in Drug Discovery 41: Human-­based Systems for Translational Research 42: Venoms to Drugs: Venom as a Source for the Development of Human Therapeutics 43: Carbohydrates in Drug Design and Discovery 44: Drug Discovery for Schizophrenia 45: Cardiovascular and Metabolic Disease: Scientific Discoveries and New Therapies 46: Green Chemistry Strategies for Drug Discovery 47: Fragment-­based Drug Discovery 48: Epigenetics for Drug Discovery 49: New Horizons in Predictive Drug Metabolism and Pharmacokinetics 50: Privileged Scaffolds in Medicinal Chemistry: Design, Synthesis, Evaluation 51: Nanomedicines: Design, Delivery and Detection 52: Synthetic Methods in Drug Discovery: Volume 1 53: Synthetic Methods in Drug Discovery: Volume 2 54: Drug Transporters: Role and Importance in ADME and Drug Development 55: Drug Transporters: Recent Advances and Emerging Technologies 56: Allosterism in Drug Discovery 57: Anti-­aging Drugs: From Basic Research to Clinical Practice 58: Antibiotic Drug Discovery: New Targets and Molecular Entities 59: Peptide-­based Drug Discovery: Challenges and New Therapeutics 60: Drug Discovery for Leishmaniasis 61: Biophysical Techniques in Drug Discovery 62: Acute Brain Impairment Through Stroke: Drug Discovery and Translational Research 63: Theranostics and Image Guided Drug Delivery 64: Pharmaceutical Formulation: The Science and Technology of Dosage Forms

65: Small-­molecule Transcription Factor Inhibitors in Oncology 66: Therapies for Retinal Degeneration: Targeting Common Processes 67: Kinase Drug Discovery: Modern Approaches 68: Advances in Nucleic Acid Therapeutics 69: MicroRNAs in Diseases and Disorders: Emerging Therapeutic Targets 70: Emerging Drugs and Targets for Multiple Sclerosis

How to obtain future titles on publication:

A standing order plan is available for this series. A standing order will bring delivery of each new volume immediately on publication.

For further information please contact:

Book Sales Department, Royal Society of Chemistry, Thomas Graham House, Science Park, Milton Road, Cambridge, CB4 0WF, UK Telephone: +44 (0)1223 420066, Fax: +44 (0)1223 420247 Email: [email protected] Visit our website at www.rsc.org/books

Emerging Drugs and Targets for Multiple Sclerosis Edited by

Ana Martinez

Centro de Investigaciones Biologicas-­CSIC, Spain Email: [email protected]

Drug Discovery Series No. 70 Print ISBN: 978-­1-­78801-­450-­2 PDF ISBN: 978-­1-­78801-­607-­0 EPUB ISBN: 978-­1-­78801-­806-­7 Print ISSN: 2041-­3203 Electronic ISSN: 2041-­3211 A catalogue record for this book is available from the British Library © The Royal Society of Chemistry 2019 All rights reserved Apart from fair dealing for the purposes of research for non-­commercial purposes or for private study, criticism or review, as permitted under the Copyright, Designs and Patents Act 1988 and the Copyright and Related Rights Regulations 2003, this publication may not be reproduced, stored or transmitted, in any form or by any means, without the prior permission in writing of The Royal Society of Chemistry or the copyright owner, or in the case of reproduction in accordance with the terms of licences issued by the Copyright Licensing Agency in the UK, or in accordance with the terms of the licences issued by the appropriate Reproduction Rights Organization outside the UK. Enquiries concerning reproduction outside the terms stated here should be sent to The Royal Society of ­ Chemistry at the address printed on this page. Whilst this material has been produced with all due care, The Royal Society of Chemistry ­ cannot be held responsible or liable for its accuracy and completeness, nor for any ­ consequences arising from any errors or the use of the information contained in this ­ publication. The publication of advertisements does not constitute any endorsement by The Royal Society of Chemistry or Authors of any products advertised. The views and opinions advanced by contributors do not necessarily reflect those of The Royal Society ­ of Chemistry which shall not be liable for any resulting loss or damage arising as a result of reliance upon this material. The Royal Society of Chemistry is a charity, registered in England and Wales, Number 207890, and a company incorporated in England by Royal Charter (Registered No. RC000524), registered office: Burlington House, Piccadilly, London W1J 0BA, UK, Telephone: +44 (0) 20 7437 8656. For further information see our web site at www.rsc.org Printed in the United Kingdom by CPI Group (UK) Ltd, Croydon, CR0 4YY, UK

Preface In the 21st century, human health remains fragile and every day we are discovering many events that impact it severely, such as aging, environment, diet and resistance to antibiotics. Our current therapeutic arsenal is completely insufficient to be effective against many severe diseases, and drug discovery is more urgent than ever to improve human life in our global world. Among these pathological conditions, multiple sclerosis (MS) is a growing field, being one of the more disabling chronic disorders among young adults and prevailing two to three times more in women than in men. MS is a disease that affects the central nervous system (brain, spinal cord and optic nerves). There is a complete lack of knowledge about how it originates, and it is impossible to predict how the pathology will progress. There is no cure, but current treatment can relieve symptoms and help people with MS to manage their daily living. Multiple sclerosis means “scar tissue in multiple areas,” and these “scars” or scleroses are produced by the lack of myelin sheaths in multiple areas. As a result, the electrical impulses from the brain do not flow smoothly to the target nerve. Despite the great work by pharmaceutical researchers to discover and develop treatments for MS, today we need more human and financial efforts over the next decades to stop this pathology – which affects more than three million people worldwide today with an increasing prevalence – both in terms of better diagnostic tools and life expectancy. This monographic volume collects some of the most outstanding theories and examples of new drugs, treatments and targets currently under pharmaceutical development or validation. The first part of the book includes five chapters, by the authors García-­Merino, Hafler, Villar, Alonso, and Mestre   Drug Discovery Series No. 70 Emerging Drugs and Targets for Multiple Sclerosis Edited by Ana Martinez © The Royal Society of Chemistry 2019 Published by the Royal Society of Chemistry, www.rsc.org

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and Guaza. It is aimed at providing a deep overview of the disease, genetics, biomarkers, and tools for diagnosis and drug discovery such as animal models. In the second part of the book, new drugs and therapeutic strategies under development are commented on with clear ideas about therapies for primary progressive MS, B-­cell-­based therapies and the emergent field of protein kinase inhibitors. The authors for these aspects are Cohen, Milo, and Martinez and Gil. Two complementary chapters written by Williams and de Castro are grouped in the third part of the book, focused on the new era of remyelinating agents. Finally, the fourth section of the book offers visions about new targets such as sigma receptors and the endocannabinoid system, diagnostic and/or therapeutic tools such as non-­coding RNAs and the influence that diet and microbiota may have in the prevention and treatment of MS. The authors of this last section are Pryce, Collina, Otaegui, and Ochoa-­Repáraz. I would like personally to express my great gratitude to all of these contributors for their faith in this project, and the time and work they dedicated to it. I would also like to thank my family and my colleagues and students of my research group for their patience while I have been preparing this edition. Finally, but not less importantly, I would like to thank the support of Diego Fernandez in the translational research for some MS drug discovery projects and the Royal Society of Chemistry staff, mainly Katie Morrey and Drew Gwilliams, for their support in bringing the book to completion. I hope that this book provides an excellent, hands-­on resource to scientists, both in industry and academia, who are looking to find a solution for many patients worldwide waiting for effective drugs. Ana Martinez Centro de Investigaciones Biologicas-­CSIC ANKAR PHARMA (www.ankarpharma.com) Madrid, Spain

Contents Part I: General Information Chapter 1 Multiple Sclerosis: Epidemiology, Genetics, Symptoms, and Unmet Needs  Irene Moreno-­Torres, Julia Sabín-­Muñoz and Antonio García-­Merino

1.1 Introduction  1.2 Epidemiology and Genetics of Multiple Sclerosis  1.2.1 Susceptibility Genes for MS in the MHC  1.2.2 Susceptibility Genes for MS Outside of the MHC  1.2.3 Protective Genes in MS  1.3 Environmental Factors in Multiple Sclerosis  1.3.1 Infections and Multiple Sclerosis Risk  1.3.2 Lifestyle and Multiple Sclerosis  1.3.3 The 25-­hydroxy-­vitamin D and Multiple Sclerosis Risk  1.4 Clinical Manifestations of Multiple Sclerosis  1.4.1 Phenotypes of Multiple Sclerosis  1.4.2 Clinical Manifestations of Multiple Sclerosis in Initial Stages  1.4.3 Clinical Symptoms of MS in the Established Phase  1.5 Unmet Needs  References 

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3 4 7 9 10 11 11 13 13 14 14 16 18 24 25

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Chapter 2 Genetics of Multiple Sclerosis  Ahmad Abulaban, David A. Hafler and Erin E. Longbrake

2.1 Introduction  2.2 The Evolution of Multiple Sclerosis as a Genetic Disease  2.2.1 Familial and Linkage Studies  2.2.2 Genome-­wide Association Studies  2.2.3 The Immunochip and Beyond  2.3 A Shared Genetic Background for Autoimmunity  2.4 Genetics, Environment and Multiple Sclerosis Risk  2.4.1 Gene/Environment Interactions  2.4.2 Putative Mechanisms for Genetic/ Environmental Interactions  2.4.3 The Microbiome as a Mediator for Gene/ Environment Interactions  2.5 Linking Genotype and Phenotype in Multiple Sclerosis  2.5.1 Pharmacogenetics of Beta-­interferons  2.5.2 Pharmacogenetics of Glatiramer Acetate  2.5.3 Genetic Risk Scores and Clinical Phenotype  2.6 Conclusions  Disclosures  Acknowledgements  References  Chapter 3 Biomarkers for Multiple Sclerosis  Amalia Tejeda Velarde, Silvia Medina Heras and Luisa María Villar Guimerans



3.1 Introduction  3.2 Susceptibility/Risk Biomarkers  3.3 Diagnostic Biomarkers  3.3.1 IgG Oligoclonal Bands  3.3.2 AQP4-­IgG and MOG-­IgG  3.3.3 Free Light Chains  3.4 Prognostic Biomarkers  3.4.1 IgM Oligoclonal Bands  3.4.2 Neurofilament Light Chains  3.4.3 Chitinase 3-­like 1 

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3.4.4 Chemokines: CXCL13  3.4.5 MicroRNAs  3.5 Biomarkers for a Personalized Treatment  3.5.1 Biomarkers of Response to Treatment  3.5.2 Biomarkers Monitoring Treatment Side Effects  References  Chapter 4 Optical Coherence Tomography in Multiple Sclerosis  Ricardo Alonso and Leila Cohen



4.1 Introduction  4.2 The Role of Optical Coherence Tomography After Acute Optic Neuritis  4.3 The Importance of Optical Coherence Tomography as Axonal Damage Biomarker  4.3.1 Relationship Between OCT, Clinical and Cognitive Impairment, and Atrophy in Magnetic Resonance Imaging  4.4 Optical Coherence Tomography as a Tool for Monitoring Treatment  4.5 Conclusions  References  Chapter 5 Experimental In Vivo Models for Drug Discovery in Multiple Sclerosis  Leyre Mestre and Carmen Guaza



5.1 Introduction  5.2 “Immune-­mediated” Model: Experimental Autoimmune Encephalomyelitis (EAE)  5.3 Theiler's Virus Model  5.4 Toxin-­induced Demyelination Models  5.4.1 Ethidium Bromide (EtBr)  5.4.2 Lysolecithin (LPC)  5.4.3 Cuprizone (CPZ)  5.5 Animal Models as a Tool for New Therapy Development  5.6 Strategies for New Therapy Development for Progressive MS  5.7 Conclusion  Acknowledgements  References 

60 61 62 62 66 68 76 76 77 80 81 82 84 84 88 88 89 91 93 94 94 95 96 100 103 104 104

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Part II: New Drugs in Development for Multiple Sclerosis Chapter 6 Progressive Multiple Sclerosis: Drug Discovery  Ebtesam Alshehri and Jeffery A. Cohen

6.1 Introduction  6.2 Diagnosis of Progressive MS  6.3 Monitoring Progressive MS  6.4 Pathogenesis of Progressive MS  6.5 Clinical Trials of Anti-­inflammatory Treatment Strategies  6.5.1 Interferon-­beta  6.5.2 Glatiramer Acetate  6.5.3 Mitoxantrone  6.5.4 Natalizumab  6.5.5 Sphingosine 1-­phosphate Receptor Modulators: Fingolimod and Siponimod  6.5.6 Anti-­CD20 Monoclonal Antibodies: Rituximab and Ocrelizumab  6.6 Clinical Trials of Putative Neuroprotective and Repair-­promoting Strategies  6.6.1 Sodium Channel Blockers  6.6.2 Erythropoietin  6.6.3 Dronabinol  6.6.4 Simvastatin  6.6.5 High-­dose Biotin  6.6.6 Ibudilast  6.6.7 Alpha Lipoic Acid  6.6.8 Opicinumab  6.6.9 Cell-­based Therapeutic Strategies  6.7 Additional Aspects of Treatment of Progressive MS  6.8 Conclusions  Acknowledgements  References  Chapter 7 B Cell-­based Therapies for Multiple Sclerosis  Michael Osherov  and Ron Milo



7.1 Introduction  7.2 A Snapshot of B Cell Development  7.3 B Cells in MS  7.3.1 Emerging View of Immune Cell Interactions in MS  7.3.2 B Cells and the CNS  7.3.3 Role of B Cells in MS 

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7.4 Therapies Targeting B Cells  7.4.1 Anti-­CD20 mAbs  7.4.2 Anti CD19 mAbs  7.4.3 Cytokine Blockers  7.4.4 Plasmapheresis  7.4.5 Targeting Bruton's Tyrosine Kinase  7.4.6 Approved Therapies with Partial or Indirect Effects on B Cells  7.5 Summary and Future Directions  References  Chapter 8 Protein Kinase Inhibitors for the Treatment of Multiple Sclerosis  Ana Martinez and Carmen Gil



8.1 Introduction  8.2 Tyrosine Kinase Inhibitors for MS Therapy  8.2.1 Receptor Tyrosine Kinase Inhibitors  8.2.2 Non-­receptor Tyrosine Kinase Inhibitors  8.3 Serine/Threonine Kinases  8.3.1 Protein Kinase CK2  8.3.2 Phosphoinositide 3-­kinase (PI3K)  8.3.3 Rho-­associated Protein Kinase (ROCK)  8.3.4 SGK-­1  8.3.5 Mitogen-­activated Protein Kinase (MAPK) Family  8.3.6 Glycogen Synthase Kinase-­3 (GSK-­3)  8.3.7 IKappa B Kinase (IKKB)  8.3.8 Transforming Growth Factor-­β-­activated Kinase 1 (TAK1)  8.3.9 Miscellaneous  8.4 Conclusions  References 

141 142 153 155 156 156 157 161 162 170 170 171 172 174 178 180 180 180 182 182 183 185 186 187 188 189

Part III: Remyelinating Therapies Chapter 9 Emerging Drugs and Targets for Remyelination in Multiple Sclerosis  Laura J. Wagstaff and Anna Williams

9.1 Why Do We Want Therapies to Promote Remyelination in Multiple Sclerosis?  9.2 Modelling MS in Preclinical Animal Studies  9.2.1 Experimental Autoimmune Encephalomyelitis (EAE)  9.2.2 Toxin-­induced Demyelinating Models 

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9.3 Why Does Remyelination Fail in MS?  9.3.1 Oligodendrocyte Precursor Cell Migration into Demyelinated MS Lesions  9.3.2 OPC Maturation into Myelinating Oligodendrocytes  9.4 Identifying and Screening Future Targets  9.4.1 High-­throughput Drug Screening in Rodent Cultures  9.5 Difficulties in Assessing Remyelination in Human Patients  9.6 Future Targets of Preclinical Research  References 

Chapter 10 Regulation of Oligodendrocyte Differentiation: New Targets for Drug Discovery in Remyelination  Fernando de Castro and Fernando Josa-­Prado

10.1 Introduction  10.2 Oligodendrogliogenesis and Oligodendrocyte Differentiation  10.3 Oligodendrogliogenic Pathways Modified in Human MS  10.4 Chemical Agents Differentiating Adult Human OPCs: The Paths to Remyelinate in MS  10.5 Conclusions  Acknowledgements  References 

203 204 204 208 209 213 214 216 222 222 223 225 227 233 233 233

Part IV: Other Therapeutic Approaches Chapter 11 Cannabinoids as a Therapeutic Approach in Multiple Sclerosis  Gareth Pryce and David Baker

11.1 Introduction  11.2 Cannabis and the Endocannabinoid System  11.2.1 The Endocannabinoid System  11.2.2 Cannabinoid Receptors  11.2.3 Endocannabinoids, Synthesis and Degradation  11.3 Cannabinoids and Multiple Sclerosis  11.3.1 Cannabinoids as Symptom-­modifying Agents in MS 

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11.3.2 Experimental Evidence  11.3.3 Clinical Evidence  11.4 Cannabinoids as Neuroprotective Agents in MS  11.4.1 Experimental Evidence  11.4.2 Clinical Evidence  11.5 Cannabinoids and Bladder Dysfunction in MS  11.6 Cannabinoids and Pain in MS  11.6.1 Experimental Evidence  11.6.2 Clinical Evidence  11.7 Cannabinoids and Immunomodulation in MS  11.7.1 Experimental Evidence  11.7.2 Clinical Evidence  11.8 Conclusions  References 

250 251 253 253 254 255 256 256 256 257 257 257 258 258

Chapter 12 Sigma Receptors as New Target for Multiple Sclerosis  264 Marta Rui, Giacomo Rossino, Daniela Rossi and Simona Collina

12.1 Introduction  12.2 Sigma 1 Receptor  12.2.1 Sigma 1 Receptor and MS  12.2.2 Sigma 1 Receptor and Its Modulators  12.3 Conclusions  Acknowledgement  References 

Chapter 13 Non-­coding RNA and Multiple Sclerosis: New Targets for Drug Discovery  Iñaki Osorio-­Querejeta, Maider Muñoz-­Culla and David Otaegui

13.1 Genome Organization: Outside of the Exome  13.2 miRNA and snoRNA  13.3 Circular RNA  13.4 ncRNA and Their Therapeutic Possibilities  13.4.1 ncRNA as Immunomodulators in MS Therapy  13.4.2 ncRNA as Remyelination Promoters  13.4.3 ncRNA Delivery to the Central Nervous System  13.5 Conclusion  References 

264 265 267 272 278 278 278 285

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Chapter 14 Diet, Gut Microbiome and Multiple Sclerosis  Lacey B. Sell and Javier Ochoa-­Repáraz

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302 304



14.1 Multiple Sclerosis  14.2 Autoimmunity, Diet and the Gut Microbiome  14.3 Experimental Evidence for the Gut Microbiome and Multiple Sclerosis Connection  14.4 The Gut Microbiome of Multiple Sclerosis  14.5 Dietary-­microbiome Interventions in Multiple Sclerosis  14.6 Concluding Remarks  Acknowledgements  References 

Subject Index 

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Part I General Information

         

Chapter 1

Multiple Sclerosis: Epidemiology, Genetics, Symptoms, and Unmet Needs Irene Moreno-­Torresa,b, Julia Sabín-­Muñozc and Antonio García-­Merino*a,b,c a

Neuroimmunology Unit, Puerta de Hierro-­Segovia de Arana Health Research Institute, Manuel de Falla 1, 28222 Madrid, Spain; bAutonomous University of Madrid, Madrid, Spain; cPuerta de Hierro University Hospital, Neuroimmunology Unit, Manuel de Falla 1, 28222 Madrid, Spain *E-­mail: [email protected]

1.1 Introduction Multiple sclerosis (MS) is a chronic inflammatory demyelinating disease that affects the central nervous system (CNS)1 and is thought to be autoimmune in nature. It is characterized by the appearance of areas of demyelination in the white and gray substances of the CNS,2 infiltration of inflammatory cells in the parenchyma, glial reaction, and axonal damage.3,4 All these processes occur from the early stages of the disease with the consequent accumulation of disability. Although the underlying cause of MS still remains unknown, there is increasing evidence that immune dysregulation may play a role in genetically susceptible individuals. Some risk factors related to immune dysregulation

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have been identified, such as infections (Epstein–Barr virus, varicella/zoster, and HHV-­6), environmental factors (latitude, vitamin D), and epigenetic factors (post-­genomic rearrangements or somatic mutations). The sum of one or more risk factors on a genetic susceptibility leads to the loss of homeostasis between the inflammatory response and self-­tolerance, tilting the balance towards autoimmunity directed against the CNS.3 Between 1831 and 1842, Robert Carswell and Jean Cruveilhier presented their collections of autopsy material of MS patients in London and Paris, respectively. They described in vivo the brain lesions on plates for the first time. Cruveilhier was the first to relate these lesions to clinical findings and called it the medullar disease with paraplegia. Despite some preliminary clinical descriptions, made by Friedrich Theodor von Frerichs (1849), his pupil Valentiner (1856), Carl Rokitansky (1857) and Eduard Rindfleisch (1863), it was not until 1865 when Jean-­Martin Charcot made the first detailed description of the disease.5 This description included plaque-­like lesions disseminated in time and space with a predominant myelin involvement, mainly in the optic nerve, the periventricular region, and spinal cord, which were correlated with clinical manifestations alternating periods of exacerbation and remission. He explained the most characteristic clinical signs of the disease: oculomotor disorders, ataxia and dysarthria. In this way, MS was recognized for the first time as an entity distinct from other diseases. The term “sclérose en plaques disseminées” was coined by Edmé Félix Alfred Vulpian in 1866.6 Later, his friend and collaborator Charcot reduced the term to “sclérose en plaques,” and the term “multiple sclerosis” was introduced in the medical literature by Edward Seguin in 1878,5 but it was not until McAlpine, Compston, and Lumsden's classic publication in 1955 that the term gained international usage. In Diseases of the Nervous System, a book published in 1933, the author Russell Brain reported data on the incidence and course of MS, together with precise explanations on the underlying pathophysiology, some of which continue to be valid today. Since then, a series of clinical criteria have been used and updated periodically to make the diagnosis of the disease.7 Magnetic resonance imaging (MRI) techniques and cerebrospinal fluid (CSF) analysis have been incorporated into the diagnostic algorithms. This has allowed an improvement in the criteria sensitivity and specificity, resulting in the current McDonald criteria 2017.4

1.2 Epidemiology and Genetics of Multiple Sclerosis Age at onset of MS can vary from childhood to adult life, and the average is between 20 and 40 years. MS is the first cause of non-­traumatic disability in young patients, which has a great impact on the quality of life, a high health cost, and important social repercussions.8 MS is estimated to affect some 700 000 people in Europe and 2.3 million across the world. Longitudinal studies have revealed an increase in MS prevalence in recent years, but

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this does not necessarily carry an increased risk of MS. The increase in prevalence is related to a higher life expectancy in MS patients (as in the general population) over the last decades as a result of the improvement in healthcare, better disability rehabilitation, and access to health resources. The continuous review of McDonald's criteria has allowed earlier diagnosis, which also increases the incidence of MS. An increase in Europe and North America  of 0.064 per 100 000 each year has been reported.9 MS patients have a 7-­year shorter life expectancy and two to three times higher mortality compared with the general population.10 MS is universally found to be more prevalent in women than men, and the MS prevalence ratio of women to men has increased markedly during the last 60 years.9,11 According to a Canadian study published in 2006, the sex ratio increased from 2 : 1 in 1936–1940 to 3.2 : 1 in 1976–1980.12 Similar data supporting this increase have been published by other groups in Germany, France, and Norway.9,11,13 An analysis carried out in Europe and the United States indicated that the sex ratio not only changes over time but has also been shown to be negatively correlated with latitude.9 There is unequivocal evidence to support genetic susceptibility as an important factor involved in the occurrence of MS. In the general population, the MS risk is about 0.1–0.25%13 and increases up 2–5% in individuals with an affected family member in proportion to the number of shared genes. The published study with the largest number of patients  in this regard involved 15 000 Canadian patients with MS and their families.14 It has been demonstrated that first-­degree relatives (parents, children, and siblings) of an affected individual have a risk of approximately 2–5%, which can increase up to 30% if both parents are affected or up to 27% in monozygotic twins. The concordance between dizygotic twins is 3.5%, and up to 14% of asymptomatic monozygotic twins have MRI lesions compatible with demyelination. Second-­degree relatives (aunts and uncles) have a risk of about 1–2%, and third-­degree relatives (cousins) have a risk of less than 1%. The risk of MS decreases with age, and after 43 years it does not exceed 0.5%. A variation in MS risk has also been observed according to geographic area, being 2.4% in high prevalence areas and 0.1% in low prevalence areas.14 It has been suggested that the origin of the disease is the result of genetic mutations in the Scandinavian population during the first millennium, which were spread through the offspring by the Vikings during invasions and migrations to the rest of the known world.15 This hypothesis can explain the highest prevalence rates for MS in the Scandinavian peninsula and the countries settled by their descendants, such as Canada, Australia, New Zealand, and the United States16 as well as the greater prevalence among the mestizo population compared with the native population in the Americas. There is a clear relationship between latitude and prevalence of MS17 since a latitudinal gradient has been demonstrated, with the prevalence of MS increasing as one moves farther from the equator. The most extensive

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survey on MS worldwide was published as the Atlas of MS 2013 and was conducted by The MS International Federation.18 Data on prevalence were available from more than 50 countries through official and unofficial sources. According to this report, the prevalence of MS varies between the different regions from above 100–200 cases per 100 000 population in high latitudes to below 5 cases per 100 000 in the regions near the tropics (Figure 1.1). Subsequent publications have been reporting new data of prevalence in different countries. The highest prevalence of MS is found in the Canadian population, with 291 per 100 000 population, followed by San Marino (250), Sweden (189), Hungary (176), Cyprus (175), United Kingdom (164), Czech Republic (160), Norway (160), Denmark (154), and Germany (149). The lowest prevalence has been reported in sub-­Saharan Africa (2.1), Eastern Asia (2.2), and the equator region (3).18–24 The reason for this latitude gradient is not yet fully understood but is probably related to genetic factors, hygiene of populations, and environmental factors such as the contribution of vitamin D, which will be discussed in detail later. However, this gradient is changing as more studies of geographical areas where prevalence was considered low are been published. In Latin America, for example, an increase in prevalence has been observed due to factors related to improvement in diagnosis, accessibility to MRI and to different healthcare resources.19 As a result, great interest has been generated in searching MS-­related genes to explain the latitude gradient. The strongest and most consistent genetic determinant identified in MS is the major histocompatibility

Figure 1.1 World prevalence of multiple sclerosis. The map was designed using the online program Mapchart available at https://mapchart.net/detworld.html using the prevalence data obtained from the Atlas of MS 2013 (Multiple Sclerosis International Federation)18 plus more recent publications in different countries.20–24

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complex (MHC), which is located on chromosome 6p-­21-­23 and includes the Human Leukocyte Antigen (HLA) genes. The MHC type I region includes the genes HLA-­A, HLA-­B, HLA-­C, HLA-­E, HLA-­F, and HLA-­G and the type II region includes the genes HLA-­DM, HLA-­DO, HLA-­ DP, HLA-­DQ, and HLA-­DR. These molecules participate in the antigenic presentation to CD4+ (MHC type II) and CD8+ (MHC type I) lymphocytes. The genetic variability of this region is the highest in humans, with a large number of polymorphisms that allow them to serve as antigens of differentiation between different individuals.

1.2.1 Susceptibility Genes for MS in the MHC The first studies that demonstrated an increased frequency of some types of HLA (HLA-­B7, HLA-­A3, and HLA-­A9) in multiple sclerosis were published in 1972.25 Subsequent publications continued to document an increase in the expression of some alleles of the HLA-­DRB1, HLA-­DRA, HLA-­DQA1, HLA-­DQB1, HLA-­DMB, and TCRB genes as well as an increased frequency of combinations of alleles (haplotypes) in patients with MS compared to healthy controls in different populations.26–28 The alleles most frequently found in MS are HLA-­DRB1*15:01, HLA-­DRB1*13:03, HLA-­DRB1*03:01, HLA-­ DRB1*08:01, HLA-­DQA1*01:02, and HLA-­DQB1*03:02,29 and the haplotype most strongly related to MS is DRB*1501-­DQA1*0102-­DQB1*0602, defined serologically as DR15 and abbreviated as HLA-­DR2 or HLA-­DRB1. The presence of HLA-­DR2 significantly increases the risk of MS, especially in populations where this haplotype is more frequent, as in Caucasians of northern European descent30 and to a lesser extent in southern regions of Europe and in the Brazilian population from Rio de Janeiro to Sao Paolo.31 There has also been reported a greater concordance of these associations between monozygotic twins compared with dizygotic twins.32 On the other hand, the allele HLA-­DRB1*15:03 has been related to susceptibility to MS in the African-­American population33 and in the mulatto population of Brazil but not in the black population.34 An association between the HLA-­DRB1*17 allele and the susceptibility to MS in the Swedish and Canadian populations has also been found.35,36 The risk of MS among the Latin American community is generally low to medium, but the frequencies are increasing. The mestizos are the most representative ethnic population in Latin America and are the product of centuries of interracial mixing between Native Americans (or Amerindians), European whites, and African blacks. Epidemiological studies show an extremely low prevalence of MS among non-­mixed Amerindians.33 This has been attributed to Mongolian ancestral protective genetics and possibly to environmental factors. The mestizos and the biracial community of Latin America with African ancestry have more susceptibility to MS, apparently due to the historical introduction of the European HLA-­DR2 haplotype. Latin populations with a predominant European background (Argentina or Puerto Rico) seem to have a higher frequency of this haplotype.19

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The populations of Sardinia and Sicily have a prevalence of MS and other autoimmune diseases similar to those of the northern European countries despite their geographical location and the low frequency of the HLA-­DR2 haplotype. In these populations, as well as in populations of the Canary Islands and Turkey, there is a stronger association with other haplotypes such as DR4 and DR3.37,38 Figure 1.2 summarizes the relationship between HLA-­DR1 allele frequency, MS prevalence, and latitude with data obtained from the Atlas of MS 2013 18 and more recent publications in the different countries.20–24,31,39–67

Figure 1.2 Multiple sclerosis prevalence, latitude, and HLA-­DR1 allele frequency.

Prevalence data were obtained from the Atlas of MS 2013 (Multiple Sclerosis International Federation)18 plus more recent publications in different countries.20–24 HLA-­DR1 allele frequencies were obtained from different publications in each country.31,39–67

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1.2.2 Susceptibility Genes for MS Outside of the MHC In 2007, the first genome-­wide association study (GWAS) using DNA microarray technology was completed. In that study, carried out by the international MS genetics consortium (IMSGC), the common single nucleotide polymorphisms (SPN) were identified and tested for disease association in 931 family trios (consisting of a person with MS and both parents) and 2431 healthy controls from the UK and USA. For replication purposes, another 609 trios (2322 case subjects) and 789 control subjects were genotyped from another source. The final analysis involved 12 360 subjects. Forty-­nine single nucleotide polymorphisms (SNPs) were found associated with MS, most in the HLA genes. However, new genes outside the MHC related to MS were also described, such as IL2RA (CD25 chr 10p15), IL7R (CD127 chr 5p13), EVI5 (ectopic viral integration site 5 chr 1p22), and KIAA0350 (CLEC16A chr 16p13).68 Alleles of IL2RA and IL7RA genes have been associated with regulatory T cells, which are dysfunctional in MS. IL-­2 is a cytokine related to the development of several autoimmune diseases, since it participates as an inductor of the differentiation69 and proliferation70 of autoreactive T cells. The IL-­2-­related proliferation process of T lymphocytes is carried out through the interaction of the cytokine with the α subunit of the interleukin-­2 receptor (IL-­2RA or CD25) on the surface of monocytes and lymphocytes. CD25 has also been described as a surface marker of regulatory T lymphocytes.71 CD127 interacts with the cytokine IL-­7, an essential process for the proliferation of B and T cells. Subsequently, several GWAS were published in different countries and populations (The Netherlands, Switzerland, Australia, Germany, Spain, and Sardinia), confirming the previous findings and describing new associations between SNPs of genes outside the MHC and MS, such as TNFRSF1A (chr 12p13), IRF8 (chr 16q24), CD6 (chr 11q12 ), GPC5 (chr 13q31), and IL2A (chr 4 26q27).72–79 The TNFRSF1A gene codes for the tumor necrosis factor receptor alpha (TNF-­α), which exerts a regulatory effect on inflammation, having anti-­apoptotic properties, by stimulating NFKB. The IRF8 and CD6 molecules are involved in the myeloid and lymphoid differentiation, respectively, and the GPC5 in a heparan sulfate proteoglycan. Two meta-­analyses of all GWAS were published in 2011 and 2012, assembling data of 5545 and 2619 patients, respectively. These analyses confirmed the previous associations and added new genes (EOMES, MLADA, THADA).80,81 A new generation of GWAS was implemented in 2011 with the possibility of analyzing 10–15 times more patients and allowing the discovery of a greater number of genes.81–84 In 2011 and 2013, the IMSGC published two studies carried out with 9972 and 14 498 patients, respectively, in the US, Europe, and Australia. From these two studies the list of genes related to MS was expanded by more than 100.82,85 The last GWAS meta-­analysis was published in 2017 with data from 47 341 MS patients and 68 248 healthy controls

Chapter 1

10

Figure 1.3 Multiple sclerosis gene discovery. A list of the main genes related to

MS in order of temporary discovery as the GWAS advance from the first reported association between HLA and MS to the second generation of GWAS.

and from which MS association data were obtained for 200 SNPs outside the MHC, 1 variant on the X chromosome, and 32 associations with variants of the MHC.86 Figure 1.3 shows a summary of the main genes discovered in order of appearance.

1.2.3 Protective Genes in MS On the island of Malta, the prevalence of MS is remarkably low compared with that of neighboring Sicily. Initially, it was thought that a low frequency of the HLA-­DR2 haplotype was related to the low susceptibility of the Maltese population, but in a study published in 2008 it was demonstrated that the HLA-­DRB1*15 allele is also significantly present in MS patients on Malta. The apparent inhibition of MS risk in the Maltese has been related to the

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presence of an apparently strong protective factor, the HLA-­DR11 haplotype.64 The HLA-­B*44:02 is frequently expressed in people of Northern European ancestry, but carriers have a 46% reduction in the risk of developing MS. Other haplotypes described as protective factors are HLA-­A*02:01, HLA-­B*38:01, and HLA-­B*55:01.29 Ancestral communities such as the Sami in Laponia,87 the Uzbeks in Central Asia (Uzbekistan and Afghanistan), the Kirguis in Kirguistan, the Siberians, and African black natives have a low prevalence of MS, and Bedouin ethnicity appears to be protective.88,89 The frequency of the DR2 haplotype is significantly reduced among Sami people compared with non-­Sami Norwegian controls, contributing to the low prevalence of MS in the Sami.89 According to an Australian study, the CD58 gene (chr 1p13), whose product binds to CD2 to facilitate the entry of immune cells into the CNS, and the DBC1 gene have been found to be up-­regulated during remission.90 The CD58 gene has also been linked to the inhibition of endothelial adhesion and the decrease in the penetration of inflammatory cells in the CNS.

1.3 Environmental Factors in Multiple Sclerosis Data obtained from migratory dynamic studies32,91 have confirmed that individuals under 15 years who emigrate from high-­risk to low-­risk zones are significantly less likely to develop MS than those who migrate at an older age, suggesting that the environmental component plays a very important role in the risk of MS. More than 40 environmental factors have been tested in relation to the risk of MS in numerous studies,92 including infections, vaccines, toxic agents, weather agents and serum markers. A systematic review of meta-­analyses published in 2015 assessed the statistical weight of each of the environmental risk factors according to the number of patients and the methodology they used and found that the only three factors that meet the requirements to be unequivocally considered MS risk factors are: seropositivity in antibodies against EBV type IgG, infectious mononucleosis, and smoking. The other factors require further investigation to differentiate simple association with genuine pathogenesis.92

1.3.1 Infections and Multiple Sclerosis Risk There is a large body of evidence implicating Epstein–Barr virus (EBV) infection in the pathogenesis of MS, although its exact role is incompletely understood. Approximately 95% of the world's population is thought to have been infected by EBV at some point in their lives. Why only some of those people develop MS is an unresolved question. On the other hand, a meta-­analysis of multiple studies with different EBV test methodologies confirmed 100% of MS patients as seropositive when two different methods were used.93 The hygiene hypothesis suggests that contact with the virus at a young age reduces the risk of MS. This contact is almost 90% at 4 years in

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tropical countries and almost null in individuals from developed countries before adolescence.94 It is not clear whether the infection is directly related to the pathophysiology of the MS or is only a reflection of the gradient in hygiene and health inequalities among children. An individual who suffers a primary infection from EBV that results in infectious mononucleosis has a 2–3 times higher risk of MS than one who presents an asymptomatic contagion.95 Likewise, there are statistically significant data from large studies that link serum antibody titers against the nuclear protein 1 of EBV (EBNA1) with the risk of MS.96–98 Although the data about the EBV infection related to a higher risk of conversion from the first demyelinating episode (clinically isolated syndrome, CIS) to MS are contradictory,99,100 in established MS, the serum level of IgG antibodies to EBNA1 correlates with the number of gadolinium-­enhanced lesions in MRI and with the total number of T2 MRI brain lesions.101,102 In addition to elevated levels of EBNA1 antibodies in the serum, patients with MS also have elevated levels of IgG antibodies to EBNA1 in the CSF, suggesting a high EBV-­specific intrathecal immune response related to MS, in particular at the onset of the disease.103,104 EBV-­specific CD4+ and CD8+ lymphocytes are enriched in the serum and CSF of patients with MS compared with healthy controls.105–107 However, the presence of EBV in MS lesions in the CNS has been controversial108–111 and this is probably due to the lack of unification in pathological techniques. Recently, a study was published with the largest number of CNS samples (101 patients and more than 1000 tissue samples), in which the presence of the virus was documented in 90% of B cells, microglia, and astrocytes of patients with MS and only in a small proportion of healthy controls.112 EBV infection also interacts with other risk factors in MS: women with DRB1*15 and higher anti-­EBNA-­1 levels had a nine-­fold increased risk of MS compared to those with lower EBNA-­1 levels and DRB1*15 presence.113 The human cytomegalovirus (CMV) belongs to the herpes virus family and has been widely studied as a possible etiological agent in MS. It is present in 60–100% of the world population as a latent infection. In post-­ mortem studies of demyelinating lesions of patients with MS, the presence of CMV-­specific CD8+ cells has been documented.114 Additionally, anti-­CMV IgG titers in CSF are higher in patients with MS than in healthy controls.115 However, unlike EBV, CMV appears to have a protective role in MS, and two meta-­analyses concluded that the seropositivity for CMV confers a decrease in the risk of developing MS.116,117 It has also been described that high titers of anti-­CMV antibodies in MS patients correlate negatively with relapses and T2 lesion load in MRI118 and that the expansion of a CMV-­induced NK cell type carries a lower risk of progression in MS.119 Parasitic infections seem to be another environmental factor, with a theoretically protective role against MS. Infection by some species of helminths and other parasites such as Trypanosoma cruzi and Paracoccidioides

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have been related to a decrease in the appearance of relapses and radiological activity, as well as to an increase in the differentiation of IL-­10-­ producing regulatory lymphocytes (CD4+CD25+FoXP3+IL-­10+).120 However, there is not enough evidence to infer that parasitism, despite its attractive role in the hygiene hypothesis, can have an essential impact on the epidemiology of MS.19

1.3.2 Lifestyle and Multiple Sclerosis Multiple studies have shown the relationship between smoking and the increased risk of MS.121–125 The increased risk is estimated at 40%, with a dose-­response effect from 20% for moderate smokers to 60% for heavy smokers.94 Obesity at early ages of life and increased salt intake have also been risk factors related to MS in several studies.126–128

1.3.3 The 25-­hydroxy-­vitamin D and Multiple Sclerosis Risk Several investigations have suggested that low exposure to ultraviolet light and the consequent decrease in vitamin D skin synthesis, measured as 25-­hydroxy-­vitamin D [25 (OH) D], is an important environmental risk factor implicated in the onset of demyelination at an early age,129 and it has even been proposed that the month of birth, the maternal exposure to ultraviolet light and 25(OH)D levels during pregnancy have an effect on MS risk.130 It has been reported that a decreased risk of MS in individuals with high levels of 25(OH)D compared to those who have low levels and different cut-­off points have been proposed to establish an increased risk of MS.92,131,132 The concentration of vitamin D fluctuates with the seasons and the implication of this variation in MS risk has not yet been established. According to these findings, sun exposure has also been positively correlated with a low risk of MS,133,134 and the prevalence of MS is inversely proportional to the solar radiation of a determined population135 being a risk factor 20 times more important than latitude according to a Scandinavian study.136 However, it is a fact that solar radiation depends mainly on latitude, and this could partially explain the geographical gradient of MS incidence in relation to vitamin D. In MS patients, low levels of vitamin D have been correlated with an increased risk of relapses and long-­ term disability.137 A prospective longitudinal study showed that women who take a dietary supplement of vitamin D with a content greater than 400 IU/d have a 40% lower risk of developing MS138 as well as those individuals who regularly consumed cod liver oil, an important source of vitamin D, during childhood.139,140 However, a recent meta-­analysis of similar studies found no evidence of the therapeutic benefit of vitamin D as an add-­on treatment in MS.141

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1.4 Clinical Manifestations of Multiple Sclerosis 1.4.1 Phenotypes of Multiple Sclerosis MS is a disease in which there is an accumulation of demyelinating lesions and axonal loss in the CNS that generates progressive neurological disability. It is clinically heterogeneous, which leads to the definition of different forms of presentation.

1.4.1.1 Relapsing Forms of MS at Onset The relapsing–remitting form of MS (RRMS) is the most frequent (90% of patients) and is characterized by episodes of a focal neurological deficit (relapses) of greater or lesser magnitude that alternate with periods of neurological remission or stability. Patients may accumulate secondary disability, depending on the number, severity, and level of recovery after each relapse. Following this course, within 20–25 years, 60–70% of RRMS patients transform into secondary progressive multiple sclerosis (SPMS), which is characterized by progressive neurological decline (mediated by the cumulative damage of the relapses and the underlying degenerative component of the disease). Age markedly influences the phenotype before progression. RRMS patients who are younger at onset are more likely to display a predominantly inflammatory course, although the number of relapses does not affect the age at onset of progression.142 In general, SPMS is diagnosed retrospectively, since there are no clinical and imaging criteria for establishing the transition from RRMS. Clinically isolated syndrome (CIS) is a recently added category, although the term has been used for years. It refers to the first typical focal event of the disease, accompanied, in many cases, by the evidence of a possible demyelinating disease (positive MRI with typical abnormalities of the CSF) but has yet to fulfill criteria of dissemination in time. Clinical trials in MS have shown that CIS coupled with brain MRI lesions carry a high risk for a second relapse and therefore for MS conversion.1 For that reason, the current clinical criteria allow some patients with CIS and radiological evidence of dissemination to be diagnosed with MS.143

1.4.1.2 Progressive Form of MS at Onset There is a clinical form in which the accumulation of neurological disability occurs from the onset of the disease without the presence of clinically evident relapses. It has been called primary progressive multiple sclerosis (PPMS) and represents between 10 and 15% of patients with MS. It frequently manifests as a slowly progressive asymmetric spastic paraparesis or symptoms of cerebellar dysfunction. According to the North American Research Committee on Multiple Sclerosis (NARCOMS) survey, the age at

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onset and diagnosis of PPMS is greater than that of relapsing forms: 36 and 44 years old, respectively.144 Although their course is progressive, PPMS patients are not free from acute exacerbations or worsening consistent with relapses. The natural history of disability progression in PPMS seems identical to SPMS when compared from the beginning of the progressive phase. These data indicate that progressive forms, regardless of their start, have a similar progress.145 Age and bilateral motor symptoms at onset are the most important predictors of disability accumulation in PPMS throughout its course.146

1.4.1.3 Active and Progressive Disease MS phenotypes can be categorized as active or progressive. According to the description by Lublin et al. in 2014, clinical activity is defined as the occurrence of new relapses frame and imaging activity is defined as the occurrence of contrast-­enhancing T1 hyperintense lesions or new or unequivocally enlarging T2 hyperintense lesions in a determined time.147 In addition to the concept of activity, it is important to define progression since it would mark the beginning of the secondary progressive form in an RRMS patient or a suboptimal response to treatment in a patient with an established progressive form. Clinical progression is defined as a progressive increase of neurological dysfunction/disability, objectively documented, without unequivocal recovery.147 The appearance of clinical progression does not necessarily imply a continuous course: stationary periods of greater or lesser duration throughout the disease are possible. The term “confirmed progression” describes an increase in neurological dysfunction confirmed throughout a defined time interval (3, 6, or 12 months). The most common definition of progression used in clinical trials is the worsening of more than one point on Kurtzke's scale of neurological disability (EDSS) confirmed for more than 6 months. The diagnosis of MS must include the clinical form and the presence or absence of activity or progression. It is recommended that the assessments for disease activity and progression be conducted at least annually by clinical and MRI examinations.147 This periodic evaluation is necessary, especially because of the availability of highly effective therapies that can be used when the response is suboptimal.

1.4.1.4 Other Presentation Forms The increasing availability of MRI in medicine has led to an increase in incidental abnormal findings suggestive of multiple sclerosis. The detection of demyelinating lesions in individuals with no neurological symptoms and whose presence is not explained by other pathologies (vascular, toxic, neuro-­ infections, etc.) has been called isolated radiological syndrome (RIS). Approximately one-­third of these patients will develop MS within a 5-­year follow-­up period.148

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The study with the largest number of individuals with RIS revealed that the main risk factors associated with conversion to MS are younger age (70%) are increased micturition frequency, urinary urgency, and incontinence secondary to hyperactivity of the detrusor muscle, which is present in about two-­thirds of MS patients who undergo formal urodynamic testing.164 The hyperactivity of the muscle is caused by a loss of inhibition of the detrusor reflex, which involves the contraction of the detrusor in coordination with the urethral sphincter relaxation. It is also common to observe a delay or difficulty in emptying the bladder, urinary retention, increased post-­voiding residue or overflow incontinence as a result of inappropriate detrusor muscle contraction or a detrusor-­sphincter dyssynergia (20–25% of the patients). Detrusor

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overactivity and detrusor-­sphincter dyssynergia often coexist in the same patient. The alteration of bowel function referred as constipation is also frequent and may be aggravated by the anticholinergic drugs used in the treatment of urinary disorders. Fecal incontinence and retention are infrequent.164 Erectile dysfunction is a common symptom present in up to 75% of men165 with MS and correlated with high degrees of paraparesis and sphincter disorders. In women, a decrease in libido and anorgasmia are also frequent. Depression, motor symptoms, severe spasticity, and perineal sensory disturbances can aggravate sexual problems.162

1.4.3.7 Other Symptoms in MS Gait disturbance is one of the main causes of disability and diminished quality of life in patients with MS. The cause is multifactorial, including paresis, spasticity, and proprioceptive alteration of the lower limbs, truncal ataxia and gait, and visual disorders. The most frequent patterns are spastic and cerebellar gait. Dysphagia is present in up to 30% of patients with MS and is accompanied by other signs of brainstem dysfunction such as dysarthria or diplopia. In advanced stages, it can generate problems of nutrition or pulmonary infections requiring a multidisciplinary treatment. Epilepsy affects up to 4% of MS patients and is thought to be due to cortical or juxta-­cortical lesions. The most frequent seizures are focal, with or without secondary generalization. Paroxysmal phenomena are episodes of sudden onset and very short duration consisting of repetitive neurological symptoms such as tonic or dystonic spasms, hemifacial spasm, facial myokymias, dysarthria or paroxysmal diplopia, Lhermitte phenomenon, sudden muscular atony, or kinesigenic choreoathetosis. The associated pathophysiological mechanisms are not known. Uhthoff phenomenon is defined as the worsening of any focal symptom during processes in which body temperature rises, such as physical exercise or fever, secondary to a blockage of conduction in the optic nerve induced by heat when exceeding the safety threshold. This mechanism is not unique to the optic nerve, and patients with MS often complain of worsening of various neurological symptoms when there are increases in body temperature due to exercise, fever, or hot environments. MS has been termed the disease of “the thousand faces” since it can affect any region of the CNS and produce a large number of symptoms and signs. However, there are symptoms that, due to their low frequency of presentation in MS, make it necessary to consider alternative diagnoses. Table 1.1 presents a summary of the typical and atypical symptoms of the disease.

Chapter 1

24

Table 1.1 Typical symptoms of MS-­RR and symptoms suggestive of an alternative diagnosis.

Typical presentations

Atypical presentations

Optic neuritis (ON) unilateral Diplopia due to paralysis of the sixth cranial nerve or internuclear ophthalmoplegia Sensory symptoms with distribution of CNS involvement Loss of facial sensation or typical trigeminal neuralgia Cerebellar ataxia and nystagmus Incomplete transverse myelitis Asymmetric limb weakness Lhermitte phenomenon Urinary urgency with urinary incontinence or erectile dysfunction Bilateral ON or unilateral ON with poor recovery of vision Fluctuating alteration of ocular motility Nausea, vomiting or uncontrollable hiccups Complete transverse myelitis, with bilateral motor and sensory involvement Alteration of mental state (encephalopathy) Cognitive impairment of subacute evolution Headache with meningism Asthenia or fatigue isolated Constitutional syndrome

1.5 Unmet Needs Despite the available armamentarium of DMTs for MS, current agents are effective in reducing relapses and radiological activity but have a limited impact on the accumulation of disability and have not been shown to be effective in progressive forms of the disease. The existing agents are directed to reduce inflammation but lack efficacy for repairing the existing damage, restoring function, or inducing remyelination. One difficulty in conducting studies in progressive forms of MS is the lack of a primary outcome of progression that can be reliably measured early in the disease. Monitoring disability progression over time is a need, which can require longer and more costly trials. Disability progression has been defined as an increase in the EDSS score of 0.5–1.0 point after 3 or 6 months. The EDSS often has problems of reliability and validity because interrater variation has been reported to be greater than a 1-­point increase in about 40% of times. Therefore, the EDSS may be inaccurate at determining disease progression in RRMS patients after a short time period and new outcomes of disability progression are needed. Loss of brain volume is greater in patients with MS than in healthy individuals and is independent of the clinical phenotypes of the disease.166 Brain atrophy has generated great expectation as a measure of neurodegeneration, and its reduction is proposed as a therapeutic objective. Additionally, new techniques and MRI sequence advances such as magnetization transfer

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(MTR), diffusion tensor images (DTI), and magnetic resonance spectroscopy (MRS) may serve to quantify demyelination and remyelination processes. However, both brain atrophy measures and new MRI techniques are not available in clinical practice yet. A substantial percentage of patients show a suboptimal and unpredictable response and continue to accumulate disability. There are no accurate markers of response to treatments that can be applied in daily clinical practice. Furthermore, the best measure of response to treatment in MS is still to be determined. The absence of relapses is a good indicator of stability, but it does not consider the appearance of inflammation in areas of the CNS that do not manifest clinically. MRI has become very important in the follow-­up of inflammatory activity, since new lesions are 5–10 times more frequent than relapses and, nowadays, radiological activity is considered a virtual surrogate marker of activity.167 However, the best response data are obtained through a combination of clinical and radiological measures. Different response measurement schemes with combined variables have been proposed such as Río criteria,168 modified Río criteria,169 Canadian model,170 German model,171 NEDA3,172,173 and NEDA4 status,174 but there is no general agreement on the best way to measure the response or what is the expected time to determine suboptimal response to each treatment. A high efficacy is achieved with high levels of immunosuppression, increasing the risk of toxicity problems, of long-­term adverse events as well as of potential effects on protective autoimmunity. An unmet need exists for new medications, with more specific treatment targets, that provide efficacy while avoiding risk for the adverse events associated with deep immunosuppression. As previously described, a large number of symptoms may be experienced by MS patients and only a few medications are available to treat them. Effective symptom control without adverse effects is still a challenge.

References 1. A. J. Thompson, S. E. Baranzini, J. Geurts, B. Hemmer and O. Ciccarelli, Lancet, 2018, 6736, 1. 2. C. F. Lucchinetti, B. F. Popescu, R. F. Bunyan, N. M. Moll, S. F. Roemer and H. Lassmann, et al., N. Engl. J. Med., 2011, 365, 2188. 3. C. A. Dendrou, L. Fugger and M. A. Friese, Nat. Rev. Immunol., 2015, 9, 545. 4. D. S. Reich, C. F. Lucchinetti and P. A. Calabresi, N. Engl. J. Med., 2018, 378, 169. 5. M. A. Moreira, C. P. Tilbery, M. A. Lana-­Peixoto, M. F. Mendes and D. R. Callegaro, Rev. Neurol., 2002, 34, 379. 6. T. J. Murray, J. Neurol. Sci., 2009, 277, S3. 7. C. M. Poser and V. V. Brinar, Clin. Neurol. Neurosurg., 2004, 106, 147.

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8. X. Montalban, R. Gold, A. J. Thompson and O. R. Susana, Mult. Scler. J., 2018, 24, 96. 9. N. Koch-­Henriksen and P. S. Sørensen, Lancet Neurol., 2010, 9, 520. 10. H. M. B. Lunde, J. Assmus, K. M. Myhr, L. Bø and N. Grytten, J. Neurol., Neurosurg. Psychiatry, 2017, 88, 621. 11. J. Sellner, J. Kraus, A. Awad, R. Milo, B. Hemmer and O. Stüve, Autoimmun. Rev., 2011, 10, 495. 12. S. M. Orton, B. M. Herrera, I. M. Yee, W. Valdar, S. V. Ramagopalan and A. D. Sadovnick, et al., Lancet Neurol., 2006, 5, 932. 13. R. Milo and E. Kahana, Autoimmun. Rev., 2010, 9, A387. 14. G. C. Ebers, A. D. Sadovnick and N. J. Risch, Nature, 1995, 377, 150. 15. C. M. Poser, Ann. Neurol., 1994, 36, S231. 16. P. Browne, D. Chandraratna, C. Angood, H. B. Tremlett, T. Chris and V. Bruce, et al., Neurology, 2014, 83, 1022. 17. S. Simpson, L. Blizzard, P. Otahal, I. Van Der Mei and B. Taylor, J. Neurol., Neurosurg. Psychiatry, 2011, 82, 1132. 18. Atlas of MS 2013, Mapping Multiple Sclerosis Around the World, Multiple Sclerosis International Federation, London, 2013, Available at, http://www.msif.org/about-­ms/publications-­and-­resources/, Accessed September 01, 2018. 19. V. M. Rivera, Curr. Neurol. Neurosci. Rep., 2017, 17, 57. 20. P. Flachenecker, G. Kobelt, J. Berg, D. Capsa and M. Gannedahl, Mult. Scler. J., 2017, 23, 78. 21. M. Caniglia-­Tenaglia, S. Guttmann, C. Monaldini, D. Manzaroli, M. Volpini and M. Stumpo, et al., Neurol. Sci., 2018, 39, 1231. 22. A. Boyko, N. Smirnova, S. Petrov, E. Gusev, K. Z. Bakhtiiarova and R. V. Magzhanov, et al., Mult. Scler. Relat. Disord., 2016, 1, 13. 23. K. Kapica-­Topczewska, W. Brola, M. Fudala, J. Tarasiuk, M. Chorazy and K. Snarska, et al., Mult. Scler. Relat. Disord., 2018, 21, 51. 24. M. A. Battaglia and D. Bezzini, Neurol. Sci., 2017, 38, 473. 25. J. A. Sachs, Proc. R. Soc. Med., 1977, 70, 869. 26. A. Spurkland, K. S. Ronningen, B. Vandvik, E. Thorsby and F. Vartdal, Hum. Immunol., 1991, 30, 69. 27. B. H. Bennetts, S. M. Teutsch, M. M. Buhler, R. N. Heard and G. J. Stewart, Hum. Immunol., 1999, 60, 886. 28. K. J. Gogolin, V. J. Kolaga, L. Baker, R. P. Lisak, C. M. Zmijewski and R. S. Spielman, Ann. Hum. Genet., 1989, 53, 327. 29. L. Moutsianas, L. Jostins, A. H. Beecham, A. T. Dilthey, D. K. Xifara and M. Ban, et al., Nat. Genet., 2015, 47, 1107. 30. H. Schmidt, D. Williamson and A. Ashley-­Koch, Am. J. Epidemiol., 2007, 165, 1097. 31. S. V. Alves-­Leon, R. Papais-­Alvarenga, M. Magalhães, M. Alvarenga, L. C. S. Thuler and O. Fernández Y Fernandez, Acta Neurol. Scand., 2007, 115, 306. 32. G. C. Ebers, Lancet Neurol., 2008, 7, 268.

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33. J. R. Oksenberg, L. F. Barcellos, B. A. C. Cree, S. E. Baranzini, T. L. Bugawan and O. Khan, et al., Am. J. Hum. Genet., 2004, 74, 160. 34. D. G. Brum, A. A. Barreira, P. Louzada-­Junior, C. T. Mendes-­Junior and E. A. Donadi, J. Neuroimmunol., 2007, 189, 118. 35. S. V. Ramagopalan, A. P. Morris, D. A. Dyment, B. M. Herrera, G. C. DeLuca and M. R. Lincoln, et al., PLoS Genet., 2007, 3, 1607. 36. A. Campbell, Am. J. Hum. Genet., 2004, 74, 1322. 37. M. G. Marrosu, M. R. Murru, G. Costa, F. Cucca, S. D. A. Dyment, A. Dessa Sadnovich and G. C. Ebers, Lancet Neurol., 2004, 3, 104. 38. D. A. Dyment, A. Dessa Sadnovich and G. C. Ebers, Lancet Neurol., 2004, 3, 104–110. 39. G. Dean, T. W. Yeo, A. Goris, C. J. Taylor, R. S. Goodman and M. Elian, et al., Neurology, 2008, 70, 101. 40. G. Garavito, E. J. Yunis, E. Egea, L. A. Ramirez, C. Malagón and A. Iglesias, et al., Hum. Immunol., 2004, 65, 359. 41. M. D. Rossman, B. Thompson, M. Frederick, M. Maliarik, M. C. Iannuzzi and B. A. Rybicki, et al., Am. J. Hum. Genet., 2003, 73, 720. 42. A. H. Schmidt, U. V. Solloch, A. Stahr, R. Wassmuth and G. Ehninger, et al., Tissue Antigens, 2010, 76, 362. 43. M. Cerna, H. Friedman, E. Raimondi, A. Maccagno and M. Fernández-­ Vina, et al., Hum. Immunol., 1993, 37, 213. 44. C. Naugler and R. Liwski, Leuk. Lymphoma, 2009, 50, 254. 45. M. Zajacova, A. Kotrbova-­Kozak and M. Cerna, Hum. Immunol., 2016, 77, 365. 46. C. Schäfer, J. Sauter, T. Riethmüller, Z. M. Kashi, A. H. Schmidt and F. J. Barriga, Hum. Immunol., 2016, 77, 622. 47. E. A. Trachtenberg, H. A. Erlich, O. Rickards, G. F. DeStefano and W. Klitz, Am. J. Hum. Genet., 1995, 57, 415. 48. N. G. de Groot, N. Otting, J. Robinson, S. G. E. Marsh and R. E. Bontrop, Tissue Antigens, 2008, 71, 265. 49. A. Kapitány, E. Zilahi, S. Szántó, G. Szücs, Z. Szabó and A. Végvári, et al., Ann. N. Y. Acad. Sci., 2005, 1051, 263. 50. W. Thomson, Rheumatology, 2002, 41, 1183. 51. W. Fionnuala and M. Derek, Hum. Immunol., 2015, 76, 395. 52. S. Rendine, N. M. Ferrero, N. Sacchi, C. Costa, S. Pollichieni and A. Amoroso, Hum. Immunol., 2012, 73, 399. 53. C. Capittini, A. De Silvestri, M. Guarene, A. Pasi, C. Tinelli and C. Perotti, Hum. Immunol., 2017, 78, 412. 54. J. Link, A. R. Lorentzen, I. Kockum, K. Duvefelt, B. A. Lie and E. G. Celius, et al., J. Neuroimmunol., 2010, 226, 172. 55. F. Williams, A. Meenagh, R. Single, M. McNally, P. Kelly and M. P. Nelson, et al., Hum. Immunol., 2004, 65, 66. 56. M. Wawrzynowicz-­Syczewska, J. A. Underhill, M. A. Clare, A. Boron-­ Kaczmarska, I. G. McFarlane and P. T. Donaldson, Liver, 2000, 20, 234. 57. A. H. Schmidt, U. V. Solloch, J. Pingel, D. Baier, I. Böhme and K. Dubicka, et al., Hum. Immunol., 2011, 72, 558.

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Chapter 1

58. F. Coraddu, S. Sawcer, R. Feakes, J. Chataway, S. Broadley and H. B. Jones, et al., Neurogenetics, 1998, 2, 24. 59. E. Kuzminova, E. Khamaganova, T. Gaponova and V. Savchenko, Hum. Immunol., 2018, 79, 709. 60. Å. Johansson, M. Ingman, S. J. Mack, H. Erlich and U. Gyllensten, Eur. J. Hum. Genet., 2008, 16, 1341. 61. M. Paximadis, T. Y. Mathebula, N. L. Gentle, E. Vardas, M. Colvin and C. M. Gray, et al., Hum. Immunol., 2012, 73, 80. 62. S. Vidal, M. P. Morante, E. Moga, A. M. Mosquera, S. Querol and J. García, et al., Eur. J. Immunogenet., 2002, 29, 75. 63. M. T. Vuong, S. Lundberg, I. Gunnarsson, L. Wramner, E. Lundström and A. Fernström, et al., Hum. Immunol., 2013, 74, 957. 64. S. Yarman, F. Oguz and M. Carin, Int. J. Immunogenet., 2007, 34, 23. 65. M. S. Leffell, W. S. Cherikh, G. Land and A. A. Zachary, Transplantation, 2007, 83, 964. 66. M. C. Tracey and J. M. Carter, Tissue Antigens, 2006, 68, 297. 67. S. Buhler, J. M. Nunes, G. Nicoloso, J. M. Tiercy and A. Sanchez-­Mazas, PLoS One, 2012, 7, 1. 68. D. A. Hafler, C. Alastair, S. Stephen, E. S. Lander, M. J. Daly and P. L. Jagger, et al., N. Engl. J. Med., 2007, 357, 851. 69. H. Qiu, H. Wu, V. Chan, C. S. Lau and Q. Lu, Autoimmunity, 2017,  50, 71. 70. R. Martin, Clin. Immunol., 2012, 142, 9. 71. X.-­J. Wang, D. Leveson-­Gower, K. Golab, L. J. Wang, N. Marek-­ Trzonkowska and A. Krzyxtyniak, et al., Int. Immunopharmacol., 2013, 16, 364. 72. P. L. De Jager, X. Jia, J. Wang, P. I. W. De Bakker, L. Ottoboni and N. T. Aggarwal, et al., Nat. Genet., 2009, 41, 776. 73. S. E. Baranzini, J. Wang, R. A. Gibson, N. Galwey, Y. Naegelin and F. Barkhof, et al., Hum. Mol. Genet., 2009, 18, 767. 74. S. Nischwitz, S. Cepok, A. Kroner, C. Wolf, M. Knop and F. Müller-­ Sarnowski, et al., J. Neuroimmunol., 2010, 227, 162. 75. E. Jakkula, V. Leppä, A. M. Sulonen, T. Varilo, S. Kallio and A. Kemppinen, et al., Am. J. Hum. Genet., 2010, 86, 285. 76. M. Comabella, D. W. Craig, M. Carmiña-­Tato, C. Morcillo, C. López and A. Navarro, et al., PLoS One, 2008, 3, 1. 77. M. Bahlo, D. R. Booth, S. A. Broadley, M. A. Brown, S. J. Foote and L. R. Griffiths, et al., Nat. Genet., 2009, 41, 824. 78. Y. S. Aulchenko, I. A. Hoppenbrouwers, S. V. Ramagopalan, L. Broer, N. Jafari and J. Hillert, et al., Nat. Genet., 2008, 40, 1402. 79. S. Sanna, M. Pitzalis, M. Zoledziewska, I. Zara, C. Sidore and R. Murru, et al., Nat. Genet., 2010, 42, 495. 80. N. A. Patsopoulos, F. Esposito, J. Reischl, S. Lehr, D. Bauer and J. Heubach, et al., Ann. Neurol., 2011, 70, 897. 81. D. Manousaki, T. Dudding, S. Haworth, Y. H. Hsu, C. T. Liu and C. Medina-­Gómez, et al., Am. J. Hum. Genet., 2017, 101, 227.

Multiple Sclerosis: Epidemiology, Genetics, Symptoms, and Unmet Needs

29

82. S. Sawcer, G. Hellenthal, M. Pirinen, C. Spencer, N. A. Patsopoulos and L. Moutsonianas, et al., Nature, 2011, 476, 214. 83. F. Matesanz, A. González-­Pérez, M. Lucas, S. Sanna, J. Gayán and E. Urcelay, et al., PLoS One, 2012, 7, 1. 84. T. F. M. Andlauer, D. Buck, G. Antony, A. Bayas, L. Bechmann and A. Berthele, et al., Sci. Adv., 2016, 2, e1501678. 85. N. A. Patsopoulos, D. K. Xifara, M. F. Davis, A. Kemppinen and C. Cotsapas, Nat. Genet., 2013, 45, 1353. 86. N. Patsopoulos, S. Baranzini, A. Santaniello, P. Shoostari, C. Cotsapas and G. Wong, bioRxiv, 2017, 143933, 1. Prepint. 87. S. A. Grønlie, E. MyrvolL, G. Hansen, M. Grønning and S. I. Mellgren, J. Neurol., 2000, 247, 129. 88. G. Rosati, Neurol. Sci., 2001, 22, 117. 89. S. N. Al-­Shammri, M. G. Hanna, A. Chattopadhyay and A. O. Akanji, PLoS One, 2015, 10, e0132106. 90. J. Liu, X. Liu, Y. Liu, S. Deng, H. Huang and Q. Chen, et al., Meta Gene, 2016, 9, 97. 91. J. G. McLeod, S. R. Hammond and J. F. Kurtzke, J. Neurol., 2011, 258, 1140. 92. L. Belbasis, V. Bellou, E. Evangelou, J. P. A. Lonnidis and I. Tzoulaki, Lancet Neurol., 2015, 14, 263. 93. J. Pakpoor, G. Disanto, J. E. Gerber, R. Dobson, U. C. Meier and G. Giovannoni, et al., Mult. Scler. J., 2013, 19, 162. 94. A. Ascherio and K. L. Munger, Semin. Neurol., 2016, 36, 103. 95. A. Ascherio and K. L. Munger, Ann. Neurol., 2007, 61, 288. 96. A. Ascherio, K. L. Munger, E. T. Lennette, D. Spiegelman, M. A. Hernán and M. J. Olek, et al., JAMA, 2001, 286, 3083. 97. L. Levin, K. Munger, M. Rubertone, C. A. Peck, E. T. Lennette and D. Spiegelman, et al., J. Am. Med. Assoc., 2005, 293, 2496. 98. A. Ascherio and M. Munch, Epidemiology, 2000, 11, 220. 99. J. D. Lünemann, M. Tintoré, B. Messmer, T. Strowig, A. Rovira and H. Perkal, et al., Ann. Neurol., 2010, 67, 159. 100. K. L. Munger, K. C. Fitzgerald, M. S. Freedman, H. P. Hartung, D. H. Miller and X. Montalbán, et al., Neurology, 2015, 85, 1694. 101. R. A. Farrell, D. Antony, G. R. Wall, D. A. Clark, L. Fisniku and J. Swanton, et al., Neurology, 2009, 73, 32. 102. S. Kvistad, K.-­M. Myhr, T. Holmøy, S. Bakke, A. G. Beiske and K. S. Bjerve, et al., Mult. Scler. J., 2014, 20, 1833. 103. E. Jaquiéry, S. Jilek, M. Schluep, P. Meylan, A. Lysandropoulos and G. Pantaleo, et al., Eur. J. Immunol., 2010, 40, 878. 104. M. Castellazzi, C. Tamborino, A. Cani, E. Negri, E. Baldi and S. Seraceni, et al., Mult. Scler. J., 2010, 16, 883. 105. G. P. Van Nierop, J. Mautner, J. G. Mitterreiter, R. Q. Hintzen and G. M. Verjans, Mult. Scler. J., 2016, 22, 279. 106. J. D. Lünemann, N. Edwards, P. A. Muraro, S. Hayashi, J. I. Cohen and C. Münz, et al., Brain, 2006, 129, 1493.

30

Chapter 1

107. A. Lossius, J. N. Johansen, F. Vartdal, H. Robins, B. J. Šaltyte and T. Holmøy, et al., Eur. J. Immunol., 2014, 44, 3439. 108. L. A. N. Peferoen, F. Lamers, L. N. R. Lodder, W. H. Gerritsen, I. Huitinga and J. Melief, et al., Brain, 2010, 133, 1. 109. B. Serafini, B. Rosicarelli, D. Franciotta, R. Magliozzi, R. Reynolds and P. Cinque, et al., J. Exp. Med., 2007, 204, 2899. 110. R. Magliozzi, B. Serafini, B. Rosicarelli, G. Chiappetta, C. Veroni and R. Reynolds, et al., J. Neuropathol. Exp. Neurol., 2013, 72, 29. 111. S. A. Sargsyan, A. J. Shearer, A. M. Ritchie, M. P. Burgoon, S. Anderson and B. Hemmer, et al., Neurology, 2010, 74, 1127. 112. A. Hassani, J. R. Corboy, S. Al-­Salam and G. Khan, PLoS One, 2018, 13, 1. 113. S. Burnard, J. Lechner-­Scott and R. J. Scott, Mult. Scler. Relat. Disord., 2017, 16, 24. 114. J. Djelilovic-­Vranic and A. Alajbegovic, Med. Arch., 2012, 66, 37. 115. N. Sanadgol, N. Ramroodi, G. A. Ahmadi, M. Komijani, A. Moghtaderi and M. Bouzari, et al., New Microbiol., 2011, 34, 263. 116. J. Pakpoor, G. Disanto, G. Giovannoni and S. V. Ramagopalan, J. Neurol., 2013, 260, 1658. 117. E. Sundqvist, T. Bergström, H. Daialhosein, M. Nyström, P. Sundström and J. Hillert, et al., Mult. Scler., 2014, 20, 165. 118. R. Zivadinov, D. Nasuelli, M. A. Tommasi, M. Serafin, A. Bratina and M. Ukmar, et al., Neurol. Res., 2006, 28, 262. 119. J. E. Martinez-­Rodriguez, A. Cobo-­Calvo, L. M. Villar, E. Munteis, Y. Blanco and R. Rasal, et al., Mult. Scler. J., 2016, 22, 741. 120. J. Correale and M. Farez, Ann. Neurol., 2007, 61, 97. 121. A. K. Hedström, M. Bäärnhielm, T. Olsson and L. Alfredsson, Neurology, 2009, 73, 696. 122. J. Salzer, G. Hallmans, M. Nyström, H. Stenlund, G. Wadell and P. Sundström, Mult. Scler. J., 2013, 19, 1022. 123. B. Farren, L. Shen, P. Ramsay, P. Ramsay, H. Quach and A. Bernstein, et al., Epidemiology, 2014, 25, 605. 124. P. Sundström, L. Nyström and G. Hallmans, Eur. J. Neurol., 2008, 15,  579. 125. M. L. Degelman and K. M. Herman, Mult. Scler. Relat. Disord., 2017, 17, 207. 126. Data from RNA-­seq experiments available at the primary archive of high-­throughput sequencing data (SRA), https://www.ncbi.nlm.nih. gov/sra/SRP132699. 127. A. K. Hedström, T. Olsson and L. Alfredsson, Mult. Scler. J., 2012, 18, 1334. 128. M. F. Farez, M. P. Fiol, M. I. Gaitán, F. J. Quintana and J. Correale, J. Neurol., Neurosurg. Psychiatry, 2015, 86, 26. 129. T. Islam, W. J. Gauderman, W. Cozen and T. M. Mack, Neurology, 2007, 69, 381. 130. R. Dobson, G. Giovannoni and S. Ramagopalan, J. Neurol., Neurosurg. Psychiatry, 2013, 84, 427.

Multiple Sclerosis: Epidemiology, Genetics, Symptoms, and Unmet Needs

31

131. K. L. Munger, L. I. Levin, B. W. Holls, N. S. Howard and A. Ascherio, JAMA, 2006, 296, 2832. 132. J. Salzer, G. Hallmans, M. Nyström, H. Stenlund, G. Wadell and P. Sundström, Neurology, 2012, 79, 2140. 133. M. Bäärnhielm, A. K. Hedström, I. Kockum, E. Sundqvist, S. A. Gustafsson and J. Hillert, et al., Eur. J. Neurol., 2012, 19, 955. 134. K. Bjørnevik, T. Riise, I. Casetta, J. Drulovic, E. Granieri and T. Holmøy, et al., Mult. Scler. J., 2014, 20, 1042. 135. C. Pierrot-­Deseilligny and J. C. Souberbielle, Mult. Scler. Relat. Disord., 2017, 14, 35. 136. S. Sloka, C. Silva, W. Pryse-­Phillips, S. Patten, L. Metz and V. W. Yong, J. Neurol. Sci., 2011, 38, 98. 137. J. Smolders, P. Menheere, A. Kessels, J. Damoiseaux and R. Hupperts, Mult. Scler. J., 2008, 14, 1220. 138. K. L. Munger, S. M. Zhang, E. O'Reilly, M. A. Hernán, M. J. Olek and W. C. Willett, et al., Neurology, 2004, 62, 60. 139. M. Cortese, T. Riise, K. Bjørnevik, T. Holmøy, M. T. Kampman and S. Magalhaes, et al., Mult. Scler. J., 2015, 21, 1856. 140. M. Bäärnhielm, T. Olsson and L. Alfredsson, Mult. Scler. J., 2014, 20, 726. 141. C. Zheng, L. He, L. Liu, J. Zhu and T. Jin, Mult. Scler. Relat. Disord., 2018, 23, 56. 142. A. Scalfari, C. Lederer, M. Daumer, R. Nicholas, G. Ebers and P. Muraro, Mult. Scler. J., 2016, 22, 1750. 143. A. J. Thompson, B. L. Banwell, F. Barkhof, W. M. Carroll, T. Coetzee and G. Comi, et al., Lancet Neurol., 2018, 17, 162. 144. A. Salter, N. P. Thomas, T. Tyry, G. R. Cutter and R. A. Marrie, Mult. Scler. J., 2018, 24, 951. 145. M. Kremenchutzky, G. P. A. Rice, J. Baskerville, D. M. Wingerchuk and G. C. Ebers, Brain, 2006, 129, 584. 146. M. W. Koch, J. Greenfield, O. Javizian, S. Deighton, W. Wall and L. M. Metz, J. Neurol., Neurosurg. Psychiatry, 2015, 86, 615. 147. F. D. Lublin, S. C. Reingold, J. Cohen, G. R. Cutter, A. J. Thompson and J. S. Wolinsky, et al., Neurology, 2014, 83, 278. 148. C. Lebrun, Arch. Neurol., 2009, 66, 841. 149. B. Yamout and M. Al Khawajah, Mult. Scler. Relat. Disord., 2017, 17, 234. 150. L. J. Balcer, N. Engl. J. Med., 2006, 354, 1273. 151. J. L. Keltner, Arch. Ophthalmol., 1993, 111, 231. 152. B. Ford, D. Tampieri and G. Francis, Neurology, 1992, 42, 250. 153. I. Kister, T. E. Bacon, E. Chamot, A. R. Salter, G. R. Cutter and J. T. Kalina, et al., Int. J. MS Care, 2013, 15, 146. 154. L. Barin, A. Salmen, G. Disanto, H. Babačić, P. Calabrese and A. Chan, et al., Mult. Scler. Relat. Disord., 2018, 25, 112. 155. R. J. Swingler and D. A. Compston, Q. J. Med., 1992, 83, 325. 156. I. Moreno-­Torres, A. J. Sanchez and A. Garcia-­Merino, Expert Rev. Neurother., 2014, 14, 1243.

32

Chapter 1

157. A. Wilkins, Front. Neurol., 2017, 8, 1. 158. A. J. C. Eijlers, Q. van Geest, I. Dekker, M. D. Steenwijk, K. A. Meijer and H. E. Hulst, et al., Brain, 2018, 141, 2605. 159. P. L. Jelinek, S. Simpson, C. R. Brown, G. A. Jelinek, C. H. Marck and A. M. De Livera, et al., Eur. J. Neurol., 2019, 26, 142–154. 160. G. DiGiuseppe, M. Blair and S. A. Morrow, Int. J. MS Care, 2018, 20, 153. 161. E. S. Gromisch, J. M. Fiszdon and M. M. Kurtz, Neuropsychol. Rehabil., 2018, 5, 1–20. 162. M. A. Chalah and S. S. Ayache, J. Inflammation Res., 2018, 11, 253. 163. C. Solaro, G. Gamberini and F. G. Masuccio, CNS Drugs, 2018, 32, 117. 164. G. F. Nakipoglu, A. Z. Kaya, G. Orhan, O. Tezen, H. Tunc and N. Ozgirgin, et al., J. Clin. Neurosci., 2009, 16, 1321. 165. R. Balsamo, D. Arcaniolo, M. Stizzo, E. Illiano, R. Autorino and F. Natale, et al., Cent. Eur. J. Urol., 2017, 70, 289. 166. N. De Stefano, A. Giorgio, M. Battaglini, M. Rovaris, M. P. Sormani and F. Barkhof, et al., Neurology, 2010, 74, 1868. 167. T. Ziemssen, N. De Stefano, M. P. Sormani, B. Van Wijmeersch, H. Wiendl and B. C. Kieseier, Mult. Scler. Relat. Disord., 2015, 4, 460. 168. J. Río, J. Castilló, A. Rovira, M. Tintoré, J. Sastre-­Garriga and A. Horga, et al., Mult. Scler. J., 2009, 15, 848. 169. M. P. Sormani and N. De Stefano, Nat. Rev. Neurol., 2013, 9, 504. 170. M. S. Freedman, D. Selchen, D. L. Arnold, A. Prat, B. Banwell and M. Yeung, et al., Can. J. Neurol. Sci., 2013, 40, 307. 171. M. Stangel, I. K. Penner, B. A. Kallmann, C. Lukas and B. C. Kieseier, Ther. Adv. Neurol. Disord., 2015, 8, 3. 172. G. Giovannoni, D. Tomic, J. R. Bright and E. Havrdovà, Mult. Scler. J., 2017, 23, 1179. 173. D. L. Rotstein, B. C. Healy, M. T. Malik, T. Chitnis and H. L. Weiner, JAMA Neurol., 2015, 72, 152. 174. L. Kappos, N. De Stefano, M. S. Freedman, B. Cree, E. Radue and T. Sprenger, et al., Mult. Scler. J., 2016, 22, 1297.

Chapter 2

Genetics of Multiple Sclerosis Ahmad Abulabana, David A. Haflera,b,c and Erin E. Longbrake*a,d a

Department of Neurology, Yale School of Medicine, New Haven, CT, USA; Department of Immunobiology, Yale School of Medicine, New Haven, CT, USA; cBroad Institute of MIT and Harvard, Cambridge, MA, USA; dCenter for Neuroepidemiology and Clinical Neurological Research, Yale School of Medicine, New Haven, CT, USA *E-­mail: [email protected]

b

2.1  Introduction Multiple sclerosis (MS) is an autoimmune disease of the central nervous system (CNS), characterized pathologically by extensive demyelination, axonal damage and immune cell infiltrates. For many years, the pathophysiology of MS was debated, with some characterizing the disease as a primary neurodegenerative disease and others framing it as an autoimmune disease affecting the CNS. Genetic analyses of individuals with MS and other autoimmune diseases have been integral to our evolving understanding of MS pathogenesis, and it has now become clear that MS is a complex autoimmune disease wherein environmental triggers act to trigger autoimmune pathology in genetically susceptible individuals. The recognition that many genetic risk variants are shared across a wide spectrum of autoimmune diseases was instrumental in developing this model of MS

  Drug Discovery Series No. 70 Emerging Drugs and Targets for Multiple Sclerosis Edited by Ana Martinez © The Royal Society of Chemistry 2019 Published by the Royal Society of Chemistry, www.rsc.org

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pathophysiology. Despite the integral role of genetic studies for developing a working model for MS pathophysiology, efforts to leverage this genetic data to predict individual-­level responses to MS therapies or disease severity have been far less successful. In this chapter, we will first review the emergence of MS as a genetic disease and examine how the technological evolution of genetic analyses led to a massive expansion of the genetic risk variants identified for MS. We will then discuss the frontiers of genetics, evaluating how incorporation of epigenetic and microbiome data continue to support progress in this field. Finally, we will review efforts to use individual-­level genetics and genetic risk scores to predict disease severity and responses to immunomodulatory therapies.

2.2  T  he Evolution of Multiple Sclerosis as a Genetic Disease 2.2.1  Familial and Linkage Studies Twin studies were instrumental in establishing that MS has a strong genetic component.1–8 Such studies demonstrated a monozygotic concordance rate of up to 30% and a dizygotic rate of 5%.9–11 In contrast, the concordance of MS was about 2.5% among non-­t win siblings and overall risk in the general population was approximately 0.1%.11 Approximately 15–20% of MS patients have a family history of MS, and when both parents are affected with MS, 9% of children develop the disease.12–14 The lifetime risk for any first-­degree relative of individuals with MS is approximately 3%.15,16 Nevertheless, large extended pedigrees are uncommon, illustrating that while a genetic predisposition is likely necessary for MS pathogenesis, it is not sufficient. Loci within the HLA complex were identified as the strongest risk factors for MS in the early 1970s.17–20 HLA genes encode the major histocompatibility complex (MHC) proteins and are integral to the adaptive immune system. MHC class I proteins are expressed on all nucleated eukaryotic cells and are encoded by three major and three minor HLA genes: HLA-­A, HLA-­B, HLA-­C (major) and HLA-­E, HLA-­F and HLA-­G (minor). These class I subunits bind with β2 microglobulin to form heterodimers, and the resulting MHC class I complexes are responsible for presenting peptides corresponding to intracellular antigens on the cell surface. MHC class II complexes, on the other hand, present peptides from extracellular antigens and are expressed only on antigen-­presenting cells, which include monocytes, macrophages (circulating and tissue-­resident), dendritic cells and B-­cells. MHC-­II proteins are encoded by three major and two minor HLA genes: HLA-­DP, HLA-­DQ and HLA-­DR (major) and HLA-­DM and HLA-­DO (minor). The HLA alleles are among the most variable in the human genome, with several loci (e.g. HLA-­A and HLA-­DRB1) containing over 100 alleles.

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Moreover, the four megabases containing the HLA complex exhibit very long-­range linkage disequilibrium. This makes study of the HLA region complicated. Genetic material within this large complex tends to be inherited as a block, making it difficult to identify specific causative variants within the region. Early genetic studies proposed that the HLA haplotype HLA DRB1*15 : 01-­DQA1*01 : 02-­DQB1*06 : 02 conferred the greatest risk of MS.21 Unsurprisingly, these genetic risks were dependent upon the ethnicity of the population being studied,22 and while the HLA-­DRB1*15 : 01 haplotype was consistently associated with MS among Caucasians, other HLA types were associated with other ethnicities. For example, HLA-­ DRB*15 : 03 was associated with MS in individuals of African descent23–26 and DPB1 alleles were reported in individuals with African American and Asian ethnicities.27–30 Early candidate gene studies attempting to identify non-­HLA risk variants used small patient cohorts to evaluate candidate genes including the T-­cell receptor alpha31,32 and beta33,34 loci, immunoglobulin heavy chains35,36 and the gene for myelin basic protein37,38, among others. These studies yielded inconsistent results, and it quickly became clear that studying much larger populations would be necessary to confidently identify genetic variants associated with small effect sizes. Indeed, when the International Multiple Sclerosis Genetics Consortium (IMSGC) and others performed linkage analyses in several hundred families with MS, they were unable to identify any additional genetic risk variants outside of the HLA complex.39,40

2.2.2  Genome-­wide Association Studies The early 2000s marked the advent of numerous technological developments that were critical to refining the role of genetics in MS. Key achievements include the sequencing of the human genome and the haplotype map (HapMap) project.41 These tools enabled the rise of chip-­based technology and genome-­wide association studies (GWAS), mapping single nucleotide polymorphisms (SNPs) across thousands of individuals. GWAS afforded an unbiased approach to scanning the genome and identifying haplotypes associated with risk of developing disease. In so doing, they provided an alternative to classical linkage analysis and allowed greater statistical power for detecting variants associated with only modest disease risk.42,43 This led to the first GWAS scans in MS, performed by the IMSGC, which identified two clear genetic variants associated with risk of developing disease.44 This was followed by large collaborative efforts, such as the IMSGC and the Wellcome Trust Case Control Consortium, which were integral to the success of GWAS in MS. Only through leveraging international cohorts and tens of thousands of cases could risk variants with a small effect size be identified.45–54 GWAS successfully refined the HLA-­associated risk variants associated with MS. Prior to GWAS, our understanding of the association and linkage

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of MS with respect to MHC was limited to alleles and haplotypes on chromosome 6p21. Through deeper analysis of the major HLA loci, the risk haplotype (HLA DRB1*15 : 01-­DQA1*01 : 02-­DQB1*06 : 02) was refined and it became evident that the HLA DRB1*15 : 01 allele drove MS risk and that the other alleles' effect was due to linkage disequilibrium.55,56 Caucasians carrying HLA DRB1*15 : 01 (previously also referred to as DR2, DR2b and DR15 23) had an approximately three-­fold greater risk of MS than those without it.56,57 In contrast, the HLA-­A*02 : 01 allele, encoding a class I gene, conferred a protective effect, with individuals carrying this variant being about 30% less likely to develop MS.55,56 The overall contribution of HLA-­ complex genes was estimated to represent 10.5% of the total genetic variance underlying risk of MS.57 GWAS technology and large cross-­sectional studies also allowed non-­HLA-­ associated loci to be identified as MS risk variants for the first time, albeit with much lower odds ratios (typically less than 1.2). Variants in the IL-­2 and IL-­7 receptors were among the earliest non-­HLA loci identified,44 and successive GWAS iteratively added variants in proportion to the number of cases studied.55,58 By 2011, 55 common gene variants had been linked to MS risk and independently replicated45,47–53,55,59,60 and it was becoming clear that these known variants explained only a fraction of MS risk attributable to genetic factors. Examination of the identified non-­HLA risk variants revealed that many were important for T-­cell activation and proliferation. The majority was overwhelmingly expressed by immune cells, and many were also associated with other autoimmune diseases or were in linkage disequilibrium with genes that were.55

2.2.3  The Immunochip and Beyond Linkage disequilibrium was a major barrier to identifying causal nucleotide variants within loci identified by GWAS. Identified risk loci might actually encode dozens of proteins and contain additional noncoding transcripts and regulatory elements, any of which could confer risk of disease. As with GWAS, the evolution of genetic technology and the development of the Immunochip was instrumental for addressing this issue. Where GWAS tagged SNPs across the entire genome, the Immunochip employed denser genotyping of the 186 known non-­MHC loci previously linked to autoimmune disease (104 425 SNPs, median coverage 486 SNPs/ region) and also provided lighter coverage of genomic regions where suggestive associations had previously been observed (49 198 SNPs).61,62 Using the Immunochip, multiple prior GWAS of autoimmune diseases were replicated, including MS,58 celiac disease,63 autoimmune thyroid disease,64 primary biliary cirrhosis,65 ankylosing spondylitis,66 atopic dermatitis,67 primary sclerosing cholangitis,68 juvenile idiopathic arthritis,69 psoriasis,70 inflammatory bowel disease71 and type 1 diabetes.72 Immunochip data were then used to identify candidate causal SNPs for each disease phenotype using Bayesian analyses.73 Extensions of this work

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allowed our group to develop a novel algorithm for fine-­mapping causal variants based on genetic evidence.74 Probabilistic identification of causal SNPs (PICS) is a Bayesian algorithm modeled on dense genotyping data in MS.58 Using PICS, we assigned explicit probabilities of causality to each SNP within GWAS loci based on the strength of disease association and haplotype structure. This technique also allowed us to impute data from the 1000 Genomes Project to predict causal variants for GWAS data when dense genotyping data were unavailable.75 PICS was therefore integral to distilling GWAS loci for a wide range of diseases and phenotypes to a tractable set of candidate causal SNPs.61 Using these techniques, it became evident that the majority of MS susceptibility variants did not localize to coding regions of the genome but rather to gene regulatory regions.76 Susceptibility variants affecting the RNA expression of nearby genes (cis expression quantitative trait loci effects, or cis-­eQTL) are enriched in promotor and 3′ UTR regions, with a particular preference for immune enhancers.74 Incorporation of these cis-­eQTLs allowed linkage of causal non-­coding variants to specific gene pathways. For example, two SNPs were identified in the IKZF3 locus; this encodes an IKAROS family transcription factor with important roles in lymphocyte differentiation and function, and the risk variant SNPs regulate IKZF3 expression.74,77 We identified cis-­eQTLs in numerous tissue types, including immune cell subsets and CNS tissue and found that cis-­eQTLs had profound effects on numerous immune cell subsets, including naïve T-­cells and B-­cells.54,74 Innate immune cells, including NK cells and dendritic cells, were also strongly implicated.54 When cortical brain tissue containing a mixture of CNS cell types was evaluated, several cis-­eQTLs appeared to be specific for a particular CNS cell type. For example, the effect of the risk variant at SLC12A5, a potassium/chloride transporter expressed on neurons, appeared to be enhanced in neurons. Similarly, the effect of the CLECL1 locus was enhanced in microglia.54 While the implications are not fully clear, these data support the idea that while MS is most likely triggered by perturbation of adaptive immunity, the functional responses of innate immune cells and CNS cells may also be affected and play a role in targeting the autoimmune process to the brain and spinal cord. Modern genetic analytic techniques were able to further refine the role of the HLA complex and identify additional HLA polymorphisms associated with MS. In addition to the *15:01 allele on HLA DRB1, *03:01, *13:03, *04:04, *04:01 and *14:01 were independently associated with MS risk.56 MHC class I alleles, including HLA-­A*02:01, HLA-­B*44:02 and HLA-­B*38:01 were identified as protective,56,78,79 and interaction effects between HLA-­DQA1*01:01 and HLA-­DRB1*15:01 as well as HLA-­DQB1*03:01 and HLA-­DQB1*03:02 78 were identified. The most recent IMSGC study assembled genetic data from 47 351 MS patients and 68 284 controls, using these data to establish a reference map of the genetic architecture of MS. At last count, this included 200 common autosomal susceptibility variants outside the MHC, one chromosome X

38

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variant, and 32 independent associations within the extended MHC. Most of these risk variants had modest effect sizes and all currently identified risk variants are thought to cumulatively explain up to 50% of the heritability associated with MS54,80 with much of the remaining heritability thought to also be attributable to common risk variants. Recent work by Cotsapas' group estimates that an additional 5% of MS heritability may be attributed to low-­frequency variation in gene coding, or rare variants. Using over 68 000 cases and controls, they were able to identify four rare variants driving MS risk which were not detectable with conventional GWAS designs.80 These rare variants included PRF1, which encodes perforin (a key mediator of granzyme-­mediated cytotoxicity utilized by neutrophils and some lymphocyte subsets), and HDAC7, a histone deacetylase that potentiates the effects of Foxp3 (the master regulator governing the differentiation of regulatory T-­cells). The remaining rare variants identified were for PRKRA and NLRP8; these genes also play key roles in immune cell development and function. While additional genetic risk remains to be explained, the resources required to support the increasingly large sample sizes needed to identify the remaining risk variants may ultimately limit capacity for continuing these studies.

2.3  A Shared Genetic Background for Autoimmunity Recent work aimed at uncovering the nuances of autoimmune genetics confirmed that genetic commonalities underlie most autoimmune diseases81 regardless of the affected tissue type. Among the MS-­specific risk variants, at least one-­third are also associated with other autoimmune conditions,59 while there is no similar overlap with CNS diseases.82 These genetic data corroborate longstanding clinical observations, as it has been recognized for decades that not only are MS patients at risk for other inflammatory diseases83 but that diverse autoimmune diseases tend to cluster in the same families.81 Several key immune pathways have been implicated by analysis of autoimmune risk variants.74 For example, the transcription factor NF-­κB is a central regulator of inflammation, and variants within the NF-­κB signaling pathway are risk factors for both MS and inflammatory bowel disease. The MS-­associated variants proximal to NFKB1 (rs228614) and in TNFRSF1A (TNFR1, rs1800693) increase NF-­κB signaling in response to TNF-­α stimulation and increase degradation of IκBα (a negative regulator of NF-­κB).84 The variant proximal to NFKB1 alters expression of NF-­κB itself, with the GG genotype expressing 20-­fold more p50 NF-­κB. Thus, genetic variants implicated in autoimmune disease alter NF-­κB signaling pathways and enhance NF-­κB activation, leading to greater responsiveness to inflammatory stimuli. Another shared risk pathway is the IL2-­receptor signaling pathway. Variants in this complex pathway have been associated with a variety of autoimmune diseases. The IL2 receptor is a heterotrimer comprised of the IL2Rβ

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chain (CD122), the common γ chain (CD132), and the high-­affinity IL-­2Rα chain (CD25). CD25 is upregulated by activated T-­cells and is constitutively expressed on regulatory T-­cells (T-­regs). Signaling through CD25 stimulates T-­cell proliferation via STAT5 signaling pathways. Several CD25 variants have been implicated in autoimmunity, with most of the risk variants affecting regulatory, rather than coding, sequences. This may be a “super-­enhancer” locus containing a dense cluster of regulatory elements important for T-­cell function.85 The IL2 pathway has been implicated in a variety of autoimmune diseases. For example, the rs2104286 risk allele is associated with both MS and type 1 diabetes and increases CD25 expression on effector T-­cells and T-­regs.86–88 The variant is also associated with decreased STAT5 signaling downstream of IL-­2 87 and increased expression of GM-­CSF in response to IL-­2 stimulation.86 Risk variants do not always produce the same effect across autoimmune diseases. For example, SNPs within the TAGAP gene locus have been implicated in MS, type 1 diabetes, rheumatoid arthritis and celiac disease, but have the opposite direction of effect in celiac disease compared to the other autoimmune diseases.59 Genetic studies have provided convincing evidence that autoimmune diseases are interrelated and share a common substrate of allelic variants that cause subtle but important variations in immune function. Mechanisms by which this can occur are varied and include detectable effects of transcription factor binding, gene expression, and chromatin state of regulatory elements89 as well as changes in immune cell number.90 Understanding which combinations of genes confer the greatest risk of developing autoimmunity, determining how allelic variation dictates the type of autoimmune susceptibility and developing models to illustrate the overarching interconnectivity of various autoimmune disease mechanisms are central goals of ongoing research.

2.4  G  enetics, Environment and Multiple Sclerosis Risk 2.4.1  Gene/Environment Interactions As noted previously, genetic risk alone is insufficient to explain MS pathophysiology, and environmental risk factors also contribute to disease. Known environmental risk factors for MS include prior exposure to the Epstein–Barr virus (EBV), smoking, Vitamin D deficiency and childhood/adolescent obesity.91 These environmental risk factors can be far more potent when present in an individual with a susceptible genetic background – in other words, interactions between genetic and environmental risk factors can multiplicatively increase the risk of disease.91 One such interaction effect is observed between HLA loci and smoking. Individuals with HLA-­DRB*15 : 01 have an odds ratio (OR) of approximately 3 for developing MS, while HLA-­A*02 reduces risk of MS (OR of ∼0.6). An

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Chapter 2

individual who is both HLA-­DRB1*15 : 01 positive and HLA-­A*02 negative has an OR of ∼5 for developing MS. Similarly, smokers have an approximately 1.5x greater risk of MS than nonsmokers.92 A smoker with the above genetic profile, however, has a 14x greater chance of developing MS than a nonsmoker, illustrating an interaction effect between HLA loci and smoking.93 A separate interaction between smoking and the non-­HLA gene NAT1 has also been demonstrated (this gene encodes an enzyme needed to metabolize smoke products).94 The contribution of smoking to the risk of developing MS, therefore, depends in large part on the genetic background of the smoker. Exposure to EBV, the etiologic agent for infectious mononucleosis, has also been repeatedly linked to increased MS risk. While this common virus can be detected in 83–90% of the general population,95 it is near-­ubiquitous among MS patients.96,97 Epidemiologic studies find that individuals with a history of infectious mononucleosis are at two-­fold higher risk for subsequently developing MS98 and individuals with MS are more likely than those without MS to have high levels of antibodies directed against EBNA1, an EBV nuclear antigen.99,100 Similar to smoking, the effect of EBV exposure is enhanced among the HLA-­DRB1*1501-­positive and HLA-­A*02 negative population with a combined OR of approximately 15.100,101 Adolescent obesity (body mass index of >27 by age 20) has been associated with an approximately two-­fold increase in risk for developing MS. Genetic determinants of a high BMI are likewise associated with increased MS risk.102,103 Once again, individuals with a high adolescent BMI who also carry the at-­risk HLA haplotype (HLA-­DRB1*15 : 01 positive/HLA-­A*02 negative) are 14-­fold more likely to develop MS than those without the same genetic susceptibility.104 The mechanisms for this interaction have not been fully fleshed out, but may involve the low-­grade baseline inflammation, with elevated pro-­inflammatory cytokines and adipokines, known to characterize the obese state. Low levels of circulating vitamin D have long been associated with increased risk for developing MS. High-­quality epidemiologic evidence demonstrated that individuals with low levels of vitamin D in early life/ adolescence were more likely to develop MS105,106 and other studies suggested that even children of mothers who had low vitamin D levels while they were in utero were more susceptible to MS.107,108 A number of potential connections between vitamin D status and genetic risk variants have been proposed. Polymorphisms within CYP27B1, an important enzyme for metabolizing vitamin D, have been linked to MS risk109 and other genes regulating vitamin D levels also appear to impact MS.110,111 Moreover, a vitamin D response element lies within the HLA-­DRB1 promoter region112 and was proposed as a link between the environmental and genetic risk factors. Nevertheless, evidence has not confirmed an interaction effect between the HLA status and vitamin D.113 Associations between HLA alleles and disease phenotype and progression have been reported, but most of these relationships are not firmly

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114

established. Several have reported that the presence of HLA-­DRB1*15 : 01 correlated with younger age of MS onset,115–120 and another study121 found that children of European ancestry with an initial demyelinating syndrome who carried one or two copies of DRB1*15 : 01 were more than twice as likely to subsequently develop MS compared to children without the at-­risk HLA allele. No differences in DRB1*15 : 01 expression have been identified between relapsing-­and progressive-­onset MS.122–125

2.4.2  P  utative Mechanisms for Genetic/Environmental Interactions The mechanisms through which genetics and environmental risk factors interact remain to be fully elucidated. Possibilities include epigenetic modifications as well as alterations in the composition or function of an individual's microbiome. Epigenetics, or the study of chromatin structure, investigates how changes in gene regulation lead to differential gene expression without changing the genetic code. Many epigenetic modifications involve changes in chromatin structure. Chromatin consists of nuclear DNA wrapped around histone proteins; these can be modified in many ways to either promote or inhibit gene expression. Some of the best understood histone modifications include methylation, acetylation and phosphorylation.126 Other epigenetic tools include noncoding RNAs, such as microRNAs. These are small RNA molecules (about 22 base pairs long) that negatively regulate gene expression at a post-­transcriptional level. Histone methylation leads to “closed” regions of chromatin where DNA is tightly wound around histones, preventing the transcription machinery from accessing the DNA. Transcription is unlikely to occur in methylated regions. In contrast, acetylation “opens” the histone structure, allowing access to DNA and increasing the likelihood of transcription in that region. Epigenetic modifications are unique depending on the cell type involved and are critical for cell-­specific regulation of regulatory elements including promoters and enhancers. Immune cell subsets have characteristic cis-­regulatory landscapes, including distinct sets of enhancers that may be distinguished by their chromatin states127–131 and associated enhancer RNAs.132 Since, as previously noted, the majority of MS causal variants map to enhancer-­like non-­coding regions, changes in histone modification and non-­coding RNA transcription may be important mechanisms by which these genetic variants are implemented.74 Environmental stimuli such as smoking may directly affect the epigenetic modifications at work in a given cell type,133 affording one possible mechanism by which environmental and genetic factors could interact. A variety of epigenetic changes have been associated with MS, including differences in DNA methylation patterns134 and unique circulating micro-­ RNAs.135,136 Nevertheless, the impact of epigenetic changes on MS is still being studied. This is challenging in part because epigenomes vary widely across cell types and therefore must be mapped in diverse human cell types

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and under multiple physiological conditions to identify the full range of regulatory elements and expression programs that may be affected by noncoding variants to cause disease. We previously generated a large epigenomic resource of active regulatory elements in well-­defined, primary immune cells in both resting and stimulated conditions which enabled us to map disease variants to regulatory elements active in specialized cell types, especially stimulated CD4+ T cell subsets.61,74 Generating epigenomic data for more cell types under more conditions will continue to complement genetic data for generating a comprehensive map of human diseases by pathogenic cell signatures.

2.4.3  T  he Microbiome as a Mediator for Gene/Environment Interactions The number of bacteria colonizing the human body equals or surpasses the number of human cells, and the microbiome expresses over 100 times as many genes as human eukaryotic cells.137 Intestinal commensals prevent pathogen colonization, enable digestion, produce essential vitamins, and metabolize drugs.138–141 Gut bacteria are also essential for normal development of B-­ and T-­cell subsets.142 Certain bacterial species, including E. coli and Bifidobacterium, are particularly important for memory B-­cell development while other bacterial species influence T-­regulatory, Th-­17 and follicular T-­cells.143–148 In turn, cells of the immune system shape and prune the gut microbiota. The gut microbiota are affected by many of the same environmental factors that predispose to MS149–152 and the high inter-­individual variability in commensal microorganisms reflects heterogeneous lifetime exposures. Nevertheless, within a given adult, the gut microbiome remains relatively stable unless externally manipulated via major dietary changes or medications. The gut microbiota likely play an under-­appreciated role in the development of autoimmunity. Interactions between specific gut microbes (or their metabolic byproducts) and immune cells mediate normal T-­cell development, and dysbiosis predisposes to autoimmunity.142,153,154 Bacterial metabolites may also affect chromatin structure and thus the expression of regulatory elements.155 As an environmentally responsive variable that directly affects immune function, the gut microbiome is another putative mediator of the genetic/environmental interplay in MS. This subject is reviewed in more detail later in this book.

2.5  L  inking Genotype and Phenotype in Multiple Sclerosis Despite the contributions of genetics to our understanding of MS pathogenesis, the contributions of genetics to disease progression and the inter-­ individual heterogeneity of the disease have not yet been elucidated. MS

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patients have widely divergent phenotypes; some become wheelchair bound within years while others have no apparent disability after decades. Similarly, over a dozen immunomodulatory medications are available to treat MS, and each medication has patients who respond well to it and those who do not respond. There has been no way to identify a priori which patients would respond optimally to a given medication. The corollary of this “trial and error” treatment approach is that patients are susceptible to failure of their initial immunomodulatory medication within the first months or years of treatment. They are consequently at risk for disease progression or permanent disability. For decades, researchers and clinicians have hoped that elucidating the genetic basis of MS would form the basis of personalized medicine for this disease and explain the observed clinical heterogeneity. Many studies have attempted to tease out these hypothesized relationships, but overall, results have been negative or inconclusive. No strong data yet support the utility of genetic testing to assist in selecting an appropriate immune modulating medication. Moreover, genetic risk scores attempting to sum up the overall burden of genetic susceptibility to MS have not predicted clinical disease course. It is possible that the identified genetic markers associated with risk of disease simply represent a different group of genes than those responsible for disease heterogeneity and response to treatment. The existing GWAS and Immunochip studies were designed to address disease pathogenesis. If equally robust studies were instead designed to study MS patients at divergent ends of the clinical spectrum, it is possible that an entirely new set of SNPs might be identified. Such studies are improbable, however, for several reasons. First, disease heterogeneity is not a discrete outcome, and it is difficult to categorize patients into homogeneous groups for comparison. Moreover, existing clinical rating scales are labor-­intensive yet insensitive; much clinical variability is not well captured by these tools. The practical costs associated with these caveats decrease the likelihood that the tens of thousands of cases required to explore the genetic basis of disease heterogeneity will be assembled in the near future. Small studies have attempted to use genetic data to predict which patients are most likely to positively respond to a variety of MS therapies, including beta-­interferons and glatiramer acetate. Several genetic associations have been identified, although these data have not had a profound clinical impact.

2.5.1  Pharmacogenetics of Beta-­interferons Pharmacogenomics is the study of how genes affect the clinical response to a drug. Meaningful pharmacogenetic biomarkers can allow physicians to design individualized therapy plans, selecting therapies that are likely to be successful for a given individual. They may also enable treating physicians

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to anticipate adverse responses and avoid medications likely to cause harm. Pharmacogenomic biomarkers have had the largest clinical impact in oncology, but attempts have been made to translate those successes to a variety of other disease states, including MS. Identifying pharmacogenetic markers of a positive treatment response requires longitudinal follow-­up data. For a chronic disease like MS, years, if not decades, of data are required to correctly classify responders and nonresponders. Thus, pharmacogenomics studies have been attempted primarily on the immunomodulatory therapies that have been widely used for decades (e.g. beta interferons and glatiramer acetate). Similar studies have not been attempted for most of the modern, highly effective immunotherapies which have been widely used for only a few years. Studies of individuals taking interferon beta identified approximately 15 genes associated with a favorable response to treatment by using a candidate gene approach and selecting genes with a known contribution to IFN mechanisms. Identified markers included the type I IFN receptor, IFN response elements and regulatory transcription factors and cytokine genes.156,157 Not all of these associations were subsequently validated. Indeed, a recent study of ten interferon “responders” and ten “nonresponders” who were rigorously selected after 5 years of treatment failed to confirm that baseline expression of 25 interferon-­regulated genes had predictive value.158 Five GWAS incorporating several hundred patients have also been conducted, attempting to identify polymorphisms associated with an interferon-­beta response.159–163 A handful of genes were associated with either treatment response or lack of response [summarized in ref. 156], although the implicated genes were not consistent across studies. Once again, larger studies are needed.

2.5.2  Pharmacogenetics of Glatiramer Acetate Glatiramer acetate is a complex mixture of synthetic polymers composed of random sequences of four amino acids. It exerts immunomodulatory effects in MS, although its mechanism of action is not fully understood. GWAS using mouse splenocytes and cultured cells revealed that glatiramer modulates thousands of genes.164 Candidate gene studies identified ten genetic polymorphisms associated with the clinical response to glatiramer acetate,157,165–169 with several HLA-­II genes and genes involved in T-­cell activation and antigen recognition being conspicuously present. Only one GWAS comparing glatiramer responders and nonresponders has been performed so far. This study evaluated around 2600 patients treated with glatiramer in late-­phase clinical trials and identified a four-­SNP signature associated with a positive treatment response.170 Only about 10% of studied patients had this genetic signature, but those individuals appeared to have fewer clinical relapses and fewer new MRI lesions while treated with glatiramer.

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2.5.3  Genetic Risk Scores and Clinical Phenotype In 2016, Isobe and colleagues attempted to leverage the wealth of genetic data produced via the IMSGC datasets to evaluate the genetic contributions of the HLA alleles to MS phenotype among a cohort of European-­ ancestry MS patients.171 Several studies had previously associated the HLA-­DRB1*1501 allele with phenotypic features of the disease including female sex and presence of CSF-­restricted oligoclonal bands172,173 but had not supported a strong influence on features of disease course including MS subtype, disease progression, relapse rate and clinical severity.172,174,175 Isobe used the IMSGC data to calculate a composite HLA-­specific genetic burden score which incorporated all known HLA risk variants for MS. They then correlated this score with patients' clinical disease course and selected MRI measures of disease severity. They were able to validate the previously reported associations of HLA alleles with MS risk and sex using this approach. They found that women with a high HLA genetic burden developed MS at a younger age than those with a low HLA genetic burden and that women with a clinically isolated syndrome and a high HLA genetic burden developed clinically definite MS more rapidly than women with a low HLA genetic burden. Baseline MRI scans also demonstrated that women with a high HLA genetic burden had a lower subcortical gray matter volume than those with a low genetic burden, and this finding was reproduced on follow-­up MRI a year later. The effect was driven by HLA DRB1*1501. Interestingly, the HLA genetic burden was not associated with brain MRI metrics among men. Earlier work by Okuda and colleagues demonstrated that patients with HLA DRB1*15:01 had an increased volume of white matter lesions, decreased normalized brain parenchymal volume and decreased N-­ acetyl-­aspartate concentration within normal appearing white matter when compared to patients without this allele.118 These data suggested that although the effects of HLA subtype may not be detectable using coarse measures of clinical disability such as the estimated disability status scale (EDSS) or multiple sclerosis severity scale (MSSS), there may be subtle effects on disease progression that can be identified using sensitive MRI metrics. The clinical significance of these effects, however, is uncertain. Others have attempted to calculate non-­HLA specific genetic risk scores based on the total burden of known genetic variants. While a few have reported modest associations between genetic risk scores and phenotypic features such as relapse rate, progressive onset and disease severity,175,176 most have not found consistent connections between the genetic background and clinical disease phenotype or response to treatment177,178. There is therefore currently no clinical utility to genetic testing in terms of risk prediction.

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2.6  Conclusions Over the last four decades, the genetic architecture of MS has been slowly revealed as a complex and variegated landscape containing hundreds of risk variants. Although each individual allele holds only a modest disease association, the comprehensive genetic architecture has been fundamental to elucidating disease pathology and has provided the intellectual foundation for the expansion in treatment options observed over the last decade. We anticipate that current research efforts, including fine tuning of causative risk variants, expanding pathway analyses and improving data on the epigenetic basis for disease will continue to identify targets for effective diagnostic and therapeutic intervention. In contrast, the genetic contribution to individual-­level disease heterogeneity and treatment response remains in its infancy, without strong data to support efficacy. Although identifying these connections remains a long-­term goal of MS genetic research, much groundwork is still needed. First, we must be able to accurately distill out phenotypically homogeneous groups of MS patients for genetic comparisons. To do this, concrete biomarkers must be developed that effectively distinguish disease subtypes and treatment responders/nonresponders. Second, large-­scale genetic studies will need to be designed to specifically evaluate genotype/phenotype relationships. Existing genetic datasets were developed to compare MS patients to healthy individuals and therefore address a fundamentally different question. Finally, long-­term phenotypic data are needed to allow an accurate assessment of disease course and treatment efficacy. To successfully address these issues, it will be necessary to document thorough phenotypic data on large, longitudinal cohorts of MS patients. Such characterization is labor intensive due to the multi-­ faceted nature of the disease and the imperfect nature of the clinical metrics available to track its progression. While there is consensus about the need to deeply characterize the clinical phenotype of research cohorts, the resources available to support this can be scarce, and therefore multinational collaborative efforts, following the model provided by the IMSGC and others, will be critical to our eventual progress towards understanding genotype/phenotype connections in MS.

Disclosures AA: reports no disclosures. DAH: has in the past 10 years consulted for the following companies: Bayer Pharmaceuticals, Biohaven Pharmaceuticals, Bristol Myers Squibb, Compass Therapeutics, Eisai Pharmaceuticals, EMD Serono, Genentech, Juno Therapeutics, McKinsey & Co., MedImmune/AstraZeneca, Mylan, Pharmaceuticals, Neurophage Pharmaceuticals, NKT Therapeutics, Novartis Pharmaceuticals, Proclara Biosciences, Questcor Pharmaceuticals, Roche, Sage Therapeutics, Sanofi Genzyme, Toray Industries, Versant Venture. Dr Hafler’s work was generously supported by grants from the National Institutes

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of Health (U19 AI089992, R25 NS079193, P01 AI073748, U24 AI11867, R01 AI22220, UM 1HG009390, P01 AI039671, P50 CA121974, R01 CA227473), and the National Multiple Sclerosis Society (NMSS) (CA 1061-A-18, RG-180230153). DAH is also supported by grants from the National Institute of Neurological Disorders and Stroke and the Nancy Taylor Foundation for Chronic Diseases. In addition, Dr Hafler has received funding for his lab from Bristol Myers Squibb, Genentech, Novartis, Questcor, Sanofi Genzyme and EraseMS. EEL: has received honoraria for consulting from Genentech, Genzyme, Biogen, Celegene, Teva and EMD Serono.

Acknowledgements Dr Abulaban would like to thank the Saudi Arabian Cultural Mission and King Saud bin-­Abdulaziz University for Health Sciences for their support. This work was supported by grants from the National Institutes of Health (P01 AI045757, U19 AI046130, U19 AI070352, and P01 AI039671, P01AI073748), the Nancy Taylor Foundation for Chronic Diseases and the Nancy Davis Center Without Walls.

References 1. R. P. Mackay and N. C. Myrianthopoulos, Arch. Neurol., 1966, 15, 449–462. 2. A. Williams, R. Eldridge, H. McFarland, S. Houff, H. Krebs and D. McFarlin, Neurology, 1980, 30, 1139–1147. 3. A. D. Sadovnick, A. Heltberg and N. I. Holm, Lancet, 1982, 1, 1068. 4. G. C. Ebers, D. E. Bulman, A. D. Sadovnick, D. W. Paty, S. Warren and W. Hader, et al., N. Engl. J. Med., 1986, 315(26), 1638–1642. 5. E. Kinnunen, M. Koskenvuo, J. Kaprio and K. Aho, Multiple sclerosis in a nationwide series of twins, Neurology, 1987, 37, 1627–1629. 6. French Research Group on Multiple Sclerosis, Ann. Neurol., 1992, 32(6), 724–727. 7. Oral session 14: Multiple sclerosis (1), J. Neurol., 1992, 239(2), S62–S63. 8. C. J. Mumford, N. W. Wood, H. Kellar-­Wood, J. W. Thorpe, D. H. Miller and D. A. Compston, Neurology, 1994, 44(1), 11–15. 9. F. F. Holmes, D. W. Stubbs and W. E. Larsen, Arch. Intern. Med., 1967, 119(3), 302–304. 10. A. D. Sadovnick, H. Armstrong, G. P. Rice, D. Bulman, L. Hashimoto and D. W. Paty, et al., Ann. Neurol., 1993, 33(3), 281–285. 11. C. O'Gorman, R. Lin, J. Stankovich and S. A. Broadley, Neuroepidemiology, 2013, 40(1), 1–12. 12. W. E. Hogancamp, M. Rodriguez and B. G. Weinshenker, Mayo Clin. Proc., 1997, 72(9), 871–878. 13. A. D. Sadovnick, D. Dyment and G. C. Ebers, Epidemiol. Rev., 1997, 19(1), 99–106.

48



Chapter 2

14. A. D. Sadovnick, Adv. Neurol., 2006, 98, 17–25. 15. A. Compston and A. Coles, Lancet, 2002, 359(9313), 1221–1231. 16. S. Sawcer, R. J. Franklin and M. Ban, Lancet Neurol., 2014, 13(7), 700–709. 17. C. Jersild, A. Svejgaard and T. Fog, Lancet, 1972, 1(7762), 1240–1241. 18. S. Naito, N. Namerow, M. R. Mickey and P. I. Terasaki, Tissue Antigens, 1972, 2(1), 1–4. 19. C. Jersild, A. Svejgaard, T. Fog and T. Ammitzboll, Tissue Antigens, 1973, 3(4), 243–250. 20. P. I. Terasaki, M. S. Park, G. Opelz and A. Ting, Science, 1976, 193(4259), 1245–1247. 21. D. A. Dyment, A. D. Sadovnick and G. C. Ebers, Hum. Mol. Genet., 1997, 6(10), 1693–1698. 22. G. Ebers and D. W. T. Paty, Clin. Neurol. Neurosurg., 1979, 81(2), 69–86. 23. J. M. Greer, Curr. Top. Behav. Neurosci., 2015, 26, 1–27. 24. B. A. Johnson, J. Wang, E. M. Taylor, S. J. Caillier, J. Herbert and O. A. Khan, et al., Genes Immun., 2010, 11(4), 343–350. 25. M. T. Miranda, E. Suarez, M. Abbas, A. Chinea, R. Tosado and I. A. Mejias, et al., Bol. -­Asoc. Med. P. R., 2013, 105(1), 18–23. 26. J. R. Oksenberg, L. F. Barcellos, B. A. Cree, S. E. Baranzini, T. L. Bugawan and O. Khan, et al., Am. J. Hum. Genet., 2004, 74(1), 160–167. 27. J. Kira, Lancet Neurol., 2003, 2(2), 117–127. 28. T. Matsuoka, T. Matsushita, M. Osoegawa, Y. Kawano, M. Minohara and F. Mihara, et al., Mult. Scler., 2008, 14(9), 1181–1190. 29. J. P. McElroy, B. A. Cree, S. J. Caillier, P. K. Gregersen, J. Herbert and O. A. Khan, et al., Hum. Mol. Genet., 2010, 19(15), 3080–3088. 30. X. M. Wu, C. Wang, K. N. Zhang, A. Y. Lin, J. Kira and G. Z. Hu, et al., Mult. Scler., 2009, 15(12), 1422–1430. 31. M. Martell, A. Marcadet, J. Strominger, J. Dausset and D. Cohen, C. R. Acad. Sci. III, 1987, 304(5), 105–110. 32. J. R. Oksenberg, M. Sherritt, A. B. Begovich, H. A. Erlich, C. C. Bernard and L. L. Cavalli-­Sforza, et al., Proc. Natl. Acad. Sci. U. S. A., 1989, 86(3), 988–992. 33. S. S. Beall, P. Concannon, P. Charmley, H. F. McFarland, R. A. Gatti and L. E. Hood, et al., J. Neuroimmunol., 1989, 21(1), 59–66. 34. E. Seboun, M. A. Robinson, T. H. Doolittle, T. A. Ciulla, T. J. Kindt and S. L. Hauser, Cell, 1989, 57(7), 1095–1100. 35. C. N. Gaiser, M. J. Johnson, G. de Lange, L. Rassenti, L. L. Cavalli-­Sforza and L. Steinman, J. Clin. Invest., 1987, 79(1), 309–313. 36. M. A. Walter, W. T. Gibson, G. C. Ebers and D. W. Cox, J. Clin. Invest., 1991, 87(4), 1266–1273. 37. J. Rose, S. Gerken, S. Lynch, P. Pisani, T. Varvil and B. Otterud, et al., Lancet, 1993, 341(8854), 1179–1181. 38. P. J. Tienari, J. Wikstrom, A. Sajantila, J. Palo and L. Peltonen, Lancet, 1992, 340(8826), 987–991. 39. S. Sawcer, M. Ban, M. Maranian, T. W. Yeo, A. Compston and A. Kirby, et al., Am. J. Hum. Genet., 2005, 77(3), 454–467. 40. L. F. Barcellos and G. Thomson, J. Neuroimmunol., 2003, 143(1–2), 1–6.

Genetics of Multiple Sclerosis

49

41. S. A. Tishkoff and B. C. Verrelli, Curr. Opin. Genet. Dev., 2003, 13(6), 569–575. 42. N. Risch and K. Merikangas, Science, 1996, 273(5281), 1516–1517. 43. Q. Yang, M. J. Khoury, J. Friedman, J. Little and W. D. Flanders, Int. J. Epidemiol., 2005, 34(5), 1129–1137. 44. International Multiple Sclerosis Genetics Consortium, D. A. Hafler, A. Compston, S. Sawcer, E. S. Lander and M. J. Daly, et al., N. Engl. J. Med., 2007, 357(9), 851–862. 45. Wellcome Trust Case Control Consortium, Australo-­Anglo-­American Spondylitis C, P. R. Burton, D. G. Clayton, L. R. Cardon and N. Craddock, et al., Nat. Genet., 2007, 39(11), 1329–1337. 46. Y. S. Aulchenko, I. A. Hoppenbrouwers, S. V. Ramagopalan, L. Broer, N. Jafari and J. Hillert, et al., Nat. Genet., 2008, 40(12), 1402–1403. 47. M. Comabella, D. W. Craig, M. Camina-­Tato, C. Morcillo, C. Lopez and A. Navarro, et al., PLoS One, 2008, 3(10), e3490. 48. Australia, New Zealand Multiple Sclerosis Genetics Consortium, Nat. Genet., 2009, 41(7), 824–828. 49. S. E. Baranzini, J. Wang, R. A. Gibson, N. Galwey, Y. Naegelin and F. Barkhof, et al., Hum. Mol. Genet., 2009, 18(4), 767–778. 50. P. L. De Jager, X. Jia, J. Wang, P. I. de Bakker, L. Ottoboni and N. T. Aggarwal, et al., Nat. Genet., 2009, 41(7), 776–782. 51. E. Jakkula, V. Leppa, A. M. Sulonen, T. Varilo, S. Kallio and A. Kemppinen, et al., Am. J. Hum. Genet., 2010, 86(2), 285–291. 52. S. Nischwitz, S. Cepok, A. Kroner, C. Wolf, M. Knop and F. Muller-­ Sarnowski, et al., J. Neuroimmunol., 2010, 227(1–2), 162–166. 53. S. Sanna, M. Pitzalis, M. Zoledziewska, I. Zara, C. Sidore and R. Murru, et al., Nat. Genet., 2010, 42(6), 495–497. 54. N. Patsopoulos, S. E. Baranzini, A. Santaniello, P. Shoostari, C. Cotsapas and G. Wong, et al., bioRxiv, 2017, DOI: 10.1101/143933. 55. International Multiple Sclerosis Genetics Consortium, C. Wellcome Trust Case Control, S. Sawcer, G. Hellenthal, M. Pirinen and C. C. Spencer, et al., Nature, 2011, 476(7359), 214–219. 56. N. A. Patsopoulos, L. F. Barcellos, R. Q. Hintzen, C. Schaefer, C. M. van Duijn and J. A. Noble, et al., PLoS Genet., 2013, 9(11), e1003926. 57. J. A. Hollenbach and J. R. Oksenberg, J. Autoimmun., 2015, 64, 13–25. 58. International Multiple Sclerosis Genetics Consortium, A. H. Beecham, N. A. Patsopoulos, D. K. Xifara, M. F. Davis and A. Kemppinen, et al., Nat. Genet., 2013, 45(11), 1353–1360. 59. N. A. Patsopoulos, Bayer Pharma MS Genetics Working Group, Steering Committees of Studies Evaluating I-­b, a CCRA, Consortium AN and GeneMsa, et al., Ann. Neurol., 2011, 70(6), 897–912. 60. International Multiple Sclerosis Genetics Consortium, PLoS One, 2011, 6(4), e18813. 61. A. Marson, W. J. Housley and D. A. Hafler, J. Clin. Invest., 2015, 125(6), 2234–2241. 62. A. Cortes and M. A. Brown, Arthritis Res. Ther., 2011, 13(1), 101. 63. G. Trynka, K. A. Hunt, N. A. Bockett, J. Romanos, V. Mistry and A. Szperl, et al., Nat. Genet., 2011, 43(12), 1193–1201.

50

Chapter 2

64. J. D. Cooper, M. J. Simmonds, N. M. Walker, O. Burren, O. J. Brand and H. Guo, et al., Hum. Mol. Genet., 2012, 21(23), 5202–5208. 65. J. Z. Liu, M. A. Almarri, D. J. Gaffney, G. F. Mells, L. Jostins and H. J. Cordell, et al., Nat. Genet., 2012, 44(10), 1137–1141. 66. International Genetics of Ankylosing Spondylitis Consortium, A. Cortes, J. Hadler, J. P. Pointon, P. C. Robinson and T. Karaderi, et al., Nat. Genet., 2013, 45(7), 730–738. 67. D. Ellinghaus, H. Baurecht, J. Esparza-­Gordillo, E. Rodriguez, A. Matanovic and I. Marenholz, et al., Nat. Genet., 2013, 45(7), 808–812. 68. J. Z. Liu, J. R. Hov, T. Folseraas, E. Ellinghaus, S. M. Rushbrook and N. T. Doncheva, et al., Nat. Genet., 2013, 45(6), 670–675. 69. A. Hinks, J. Cobb, M. C. Marion, S. Prahalad, M. Sudman and J. Bowes, et al., Nat. Genet., 2013, 45(6), 664–669. 70. L. C. Tsoi, S. L. Spain, J. Knight, E. Ellinghaus, P. E. Stuart and F. Capon, et al., Nat. Genet., 2012, 44(12), 1341–1348. 71. G. B. Chen, S. H. Lee, M. J. Brion, G. W. Montgomery, N. R. Wray and G. L. Radford-­Smith, et al., Hum. Mol. Genet., 2014, 23(17), 4710–4720. 72. S. Onengut-­Gumuscu, W. M. Chen, O. Burren, N. J. Cooper, A. R. Quinlan and J. C. Mychaleckyj, et al., Nat. Genet., 2015, 47(4), 381–386. 73. Wellcome Trust Case Control Consortium, J. B. Maller, G. McVean, J. Byrnes, D. Vukcevic and K. Palin, et al., Nat. Genet., 2012, 44(12), 1294–1301. 74. K. K. Farh, A. Marson, J. Zhu, M. Kleinewietfeld, W. J. Housley and S. Beik, et al., Nature, 2015, 518(7539), 337–343. 75. Genomes Project Consortium, G. R. Abecasis, A. Auton, L. D. Brooks, M. A. DePristo and R. M. Durbin, et al., Nature, 2012, 491(7422), 56–65. 76. M. T. Maurano, R. Humbert, E. Rynes, R. E. Thurman, E. Haugen and H. Wang, et al., Science, 2012, 337(6099), 1190–1195. 77. F. J. Quintana, H. Jin, E. J. Burns, M. Nadeau, A. Yeste and D. Kumar, et al., Nat. Immunol., 2012, 13(8), 770–777. 78. L. Moutsianas, L. Jostins, A. H. Beecham, A. T. Dilthey, D. K. Xifara and M. Ban, et al., Nat. Genet., 2015, 47(10), 1107–1113. 79. B. C. Healy, M. Liguori, D. Tran, T. Chitnis, B. Glanz and C. Wolfish, et al., Neurology, 2010, 75(7), 634–640. 80. International Multiple Sclerosis Genetics Consortium, Cell, 2018, (18), 31261–31263, pii: S0092–8674. 81. C. Cotsapas, B. F. Voight, E. Rossin, K. Lage, B. M. Neale and C. Wallace, et al., PLoS Genet., 2011, 7(8), e1002254. 82. C. Brainstorm, V. Anttila, B. Bulik-­Sullivan, H. K. Finucane, R. K. Walters and J. Bras, et al., Science, 2018, 360(6395), eaap8757. 83. N. M. Nielsen, M. Frisch, K. Rostgaard, J. Wohlfahrt, H. Hjalgrim and N. Koch-­Henriksen, et al., Mult. Scler., 2008, 14(6), 823–829. 84. W. J. Housley, S. D. Fernandez, K. Vera, S. R. Murikinati, J. Grutzendler and N. Cuerdon, et al., Sci. Transl. Med., 2015, 7(291), 291ra93. 85. D. Hnisz, B. J. Abraham, T. I. Lee, A. Lau, V. Saint-­Andre and A. A. Sigova, et al., Cell, 2013, 155(4), 934–947. 86. F. J. Hartmann, M. Khademi, J. Aram, S. Ammann, I. Kockum and C. Constantinescu, et al., Nat. Commun., 2014, 5, 5056.

Genetics of Multiple Sclerosis

51

87. K. Cerosaletti, A. Schneider, K. Schwedhelm, I. Frank, M. Tatum and S. Wei, et al., PLoS One, 2013, 8(12), e83811. 88. C. A. Dendrou, V. Plagnol, E. Fung, J. H. Yang, K. Downes and J. D. Cooper, et al., Nat. Genet., 2009, 41(9), 1011–1015. 89. G. McVicker, B. van de Geijn, J. F. Degner, C. E. Cain, N. E. Banovich and A. Raj, et al., Science, 2013, 342(6159), 747–749. 90. V. Orru, M. Steri, G. Sole, C. Sidore, F. Virdis and M. Dei, et al., Cell, 2013, 155(1), 242–256. 91. T. Olsson, L. F. Barcellos and L. Alfredsson, Nat. Rev. Neurol., 2017, 13(1), 25–36. 92. A. K. Hedström, Mult. Scler. J., 2018, 1352458518801727. 93. A. K. Hedstrom, E. Sundqvist, M. Baarnhielm, N. Nordin, J. Hillert and I. Kockum, et al., Brain, 2011, 134(Pt 3), 653–664. 94. F. B. Briggs, B. Acuna, L. Shen, P. Ramsay, H. Quach and A. Bernstein, et al., Epidemiology, 2014, 25(4), 605–614. 95. A. B. Rickinson and D. J. Moss, Annu. Rev. Immunol., 1997, 15, 405–431. 96. A. Ascherio, K. L. Munger, E. T. Lennette, D. Spiegelman, M. A. Hernan and M. J. Olek, et al., JAMA, 2001, 286(24), 3083–3088. 97. E. L. Thacker, F. Mirzaei and A. Ascherio, Ann. Neurol., 2006, 59(3), 499–503. 98. A. E. Handel, A. J. Williamson, G. Disanto, L. Handunnetthi, G. Giovannoni and S. V. Ramagopalan, PLoS One, 2010, 5(9), e12496. 99. P. Sundstrom, M. Nystrom, K. Ruuth and E. Lundgren, J. Neuroimmunol., 2009, 215(1–2), 102–107. 100. E. Sundqvist, P. Sundstrom, M. Linden, A. K. Hedstrom, F. Aloisi and J. Hillert, et al., Genes Immun., 2012, 13(1), 14–20. 101. T. R. Nielsen, K. Rostgaard, J. Askling, R. Steffensen, A. Oturai and C. Jersild, et al., Mult. Scler., 2009, 15(4), 431–436. 102. L. E. Mokry, S. Ross, N. J. Timpson, S. Sawcer, G. Davey Smith and J. B. Richards, PLoS Med., 2016, 13(6), e1002053. 103. M. A. Gianfrancesco, M. M. Glymour, S. Walter, B. Rhead, X. Shao and L. Shen, et al., Am. J. Epidemiol., 2017, 185(3), 162–171. 104. A. K. Hedstrom, I. Lima Bomfim, L. Barcellos, M. Gianfrancesco, C. Schaefer and I. Kockum, et al., Neurology, 2014, 82(10), 865–872. 105. K. L. Munger, S. M. Zhang, E. O'Reilly, M. A. Hernan, M. J. Olek and W. C. Willett, et al., Neurology, 2004, 62(1), 60–65. 106. K. L. Munger, L. I. Levin, B. W. Hollis, N. S. Howard and A. Ascherio, JAMA, 2006, 296(23), 2832–2838. 107. J. Staples, A. L. Ponsonby and L. Lim, BMJ, 2010, 340, c1640. 108. K. L. Munger, J. Aivo, K. Hongell, M. Soilu-­Hanninen, H. M. Surcel and A. Ascherio, JAMA Neurol., 2016, 73(5), 515–519. 109. E. Sundqvist, M. Baarnhielm, L. Alfredsson, J. Hillert, T. Olsson and I. Kockum, Eur. J. Hum. Genet., 2010, 18(12), 1349–1352. 110. B. Rhead, M. Baarnhielm, M. Gianfrancesco, A. Mok, X. Shao and H. Quach, et al., Neurol.: Genet., 2016, 2(5), e97. 111. L. E. Mokry, S. Ross, O. S. Ahmad, V. Forgetta, G. D. Smith and D. Goltzman, et al., PLoS Med., 2015, 12(8), e1001866.

52

Chapter 2

112. S. V. Ramagopalan, N. J. Maugeri, L. Handunnetthi, M. R. Lincoln, S. M. Orton and D. A. Dyment, et al., PLoS Genet., 2009, 5(2), e1000369. 113. M. Baarnhielm, A. K. Hedstrom, I. Kockum, E. Sundqvist, S. A. Gustafsson and J. Hillert, et al., Eur. J. Neurol., 2012, 19(7), 955–962. 114. M. J. Chao, M. C. Barnardo, M. R. Lincoln, S. V. Ramagopalan, B. M. Herrera and D. A. Dyment, et al., Proc. Natl. Acad. Sci. U. S. A., 2008, 105(35), 13069–13074. 115. R. Balnyte, D. Rastenyte, A. Vaitkus, D. Mickeviciene, E. Skrodeniene and A. Vitkauskiene, et al., BMC Neurol., 2013, 13, 77. 116. L. F. Barcellos, J. R. Oksenberg, A. B. Begovich, E. R. Martin, S. Schmidt and E. Vittinghoff, et al., Am. J. Hum. Genet., 2003, 72(3), 710–716. 117. T. Masterman, A. Ligers, T. Olsson, M. Andersson, O. Olerup and J. Hillert, Ann. Neurol., 2000, 48(2), 211–219. 118. D. T. Okuda, R. Srinivasan, J. R. Oksenberg, D. S. Goodin, S. E. Baranzini and A. Beheshtian, et al., Brain, 2009, 132(Pt 1), 250–259. 119. S. V. Ramagopalan, J. K. Byrnes, D. A. Dyment, C. Guimond, L. Handunnetthi and G. Disanto, et al., J. Hum. Genet., 2009, 54(9), 547–549. 120. C. Smestad, B. Brynedal, G. Jonasdottir, A. R. Lorentzen, T. Masterman and E. Akesson, et al., Eur. J. Neurol., 2007, 14(8), 835–840. 121. G. Disanto, S. Magalhaes, A. E. Handel, K. M. Morrison, A. D. Sadovnick and G. C. Ebers, et al., Neurology, 2011, 76(9), 781–786. 122. R. Balnyte, D. Rastenyte, I. Uloziene, D. Mickeviciene, E. Skordeniene and A. Vitkauskiene, Medicina, 2011, 47(7), 368–373. 123. I. Kouri, S. Papakonstantinou, V. Bempes, H. S. Vasiliadis, A. P. Kyritsis and S. H. Pelidou, J. Neurol. Sci., 2011, 308(1–2), 28–31. 124. C. C. Vasconcelos, O. Fernandez, L. Leyva, L. C. Thuler and R. M. Alvarenga, J. Neuroimmunol., 2009, 214(1–2), 101–103. 125. J. S. Wu, I. James, Q. Wei, A. Castley, F. T. Christiansen and W. M. Carroll, et al., Mult. Scler., 2010, 16(5), 526–532. 126. T. Kouzarides, Cell, 2007, 128(4), 693–705. 127. R. M. Samstein, A. Arvey, S. Z. Josefowicz, X. Peng, A. Reynolds and R. Sandstrom, et al., Cell, 2012, 151(1), 153–166. 128. R. D. Hawkins, A. Larjo, S. K. Tripathi, U. Wagner, Y. Luu and T. Lonnberg, et al., Immunity, 2013, 38(6), 1271–1284. 129. G. Vahedi, H. Takahashi, S. Nakayamada, H. W. Sun, V. Sartorelli and Y. Kanno, et al., Cell, 2012, 151(5), 981–993. 130. C. M. Rivera and B. Ren, Cell, 2013, 155(1), 39–55. 131. R. Ostuni, V. Piccolo, I. Barozzi, S. Polletti, A. Termanini and S. Bonifacio, et al., Cell, 2013, 152(1–2), 157–171. 132. M. T. Lam, H. Cho, H. P. Lesch, D. Gosselin, S. Heinz and Y. Tanaka-­ Oishi, et al., Nature, 2013, 498(7455), 511–515. 133. F. Marabita, M. Almgren, L. K. Sjoholm, L. Kular, Y. Liu and T. James, et al., Sci. Rep., 2017, 7(1), 14589. 134. T. Liggett, A. Melnikov, S. Tilwalli, Q. Yi, H. Chen and C. Replogle, et al., J. Neurol. Sci., 2010, 290(1–2), 16–21.

Genetics of Multiple Sclerosis

53

135. A. Keller, P. Leidinger, J. Lange, A. Borries, H. Schroers and M. Scheffler, et al., PLoS One, 2009, 4(10), e7440. 136. A. Waschbisch, M. Atiya, R. A. Linker, S. Potapov, S. Schwab and T. Derfuss, PLoS One, 2011, 6(9), e24604. 137. R. Sender, S. Fuchs and R. Milo, Cell, 2016, 164(3), 337–340. 138. N. Kamada, G. Y. Chen, N. Inohara and G. Nunez, Nat. Immunol., 2013, 14(7), 685–690. 139. H. J. Flint, S. H. Duncan, K. P. Scott and P. Louis, Proc. Nutr. Soc., 2015, 74(1), 13–22. 140. A. L. Kau, P. P. Ahern, N. W. Griffin, A. L. Goodman and J. I. Gordon, Nature, 2011, 474(7351), 327–336. 141. T. Sousa, R. Paterson, V. Moore, A. Carlsson, B. Abrahamsson and A. W. Basit, Int. J. Pharm., 2008, 363(1–2), 1–25. 142. M. Levy, A. A. Kolodziejczyk, C. A. Thaiss and E. Elinav, Nat. Rev. Immunol., 2017, 17(4), 219–232. 143. A. C. Lundell, V. Bjornsson, A. Ljung, M. Ceder, S. Johansen and G. Lindhagen, et al., J. Immunol., 2012, 188(9), 4315–4322. 144. L. Jones, W. Q. Ho, S. Ying, L. Ramakrishna, K. G. Srinivasan and M. Yurieva, et al., Sci. Rep., 2016, 6, 30784. 145. H. Chu, A. Khosravi, I. P. Kusumawardhani, A. H. Kwon, A. C. Vasconcelos and L. D. Cunha, et al., Science, 2016, 352(6289), 1116–1120. 146. K. E. Block, Z. Zheng, A. L. Dent, B. L. Kee and H. Huang, J. Immunol., 2016, 196(4), 1550–1557. 147. F. Teng, C. N. Klinger, K. M. Felix, C. P. Bradley, E. Wu and N. L. Tran, et al., Immunity, 2016, 44(4), 875–888. 148. E. Cekanaviciute, B. B. Yoo, T. F. Runia, J. W. Debelius, S. Singh and C. A. Nelson, et al., Proc. Natl. Acad. Sci. U. S. A., 2017, 10713–10718. 149. P. J. Turnbaugh, R. E. Ley, M. A. Mahowald, V. Magrini, E. R. Mardis and J. I. Gordon, Nature, 2006, 444(7122), 1027–1031. 150. C. A. Mbakwa, J. Penders, P. H. Savelkoul, C. Thijs, P. C. Dagnelie and M. Mommers, et al., Obesity, 2015, 23(12), 2508–2516. 151. L. Biedermann, J. Zeitz, J. Mwinyi, E. Sutter-­Minder, A. Rehman and S. J. Ott, et al., PLoS One, 2013, 8(3), e59260. 152. R. V. Luthold, G. R. Fernandes, A. C. Franco-­de-­Moraes, L. G. Folchetti and S. R. Ferreira, Metabolism, 2017, 69, 76–86. 153. M. Wlodarska, A. D. Kostic and R. J. Xavier, Cell Host Microbe, 2015, 17(5), 577–591. 154. T. Van de Wiele, J. T. Van Praet, M. Marzorati, M. B. Drennan and D. Elewaut, Nat. Rev. Rheumatol., 2016, 12(7), 398–411. 155. Y. Qin and P. A. Wade, J. Biochem., 2018, 163(2), 105–112. 156. P. K. Coyle, Current and Future Perspectives, Mult. Scler. Int., 2017, 2017, 6198530. 157. I. Grossman, V. Knappertz, D. Laifenfeld, C. Ross, B. Zeskind and S. Kolitz, et al., Prog. Neurobiol., 2017, 152, 114–130. 158. S. Martire, N. D. Navone, F. Montarolo, S. Perga and A. Bertolotto, J. Neuroimmunol., 2016, 292, 34–39.

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159. E. Byun, S. J. Caillier, X. Montalban, P. Villoslada, O. Fernandez and D. Brassat, et al., Arch. Neurol., 2008, 65(3), 337–344. 160. F. Clarelli, G. Liberatore, M. Sorosina, A. M. Osiceanu, F. Esposito and E. Mascia, et al., Pharmacogenomics J., 2017, 17(1), 84–91. 161. M. Comabella, D. W. Craig, C. Morcillo-­Suarez, J. Rio, A. Navarro and M. Fernandez, et al., Arch. Neurol., 2009, 66(8), 972–978. 162. F. Esposito, M. Sorosina, L. Ottoboni, E. T. Lim, J. M. Replogle and T. Raj, et al., Ann. Neurol., 2015, 78(1), 115–127. 163. S. Mahurkar, M. Moldovan, V. Suppiah, M. Sorosina, F. Clarelli and G. Liberatore, et al., Pharmacogenomics J., 2017, 17(4), 312–318. 164. T. Hasson, S. Kolitz, F. Towfic, D. Laifenfeld, S. Bakshi and O. Beriozkin, et al., J. Neuroimmunol., 2016, 290, 84–95. 165. E. Tsareva, O. Kulakova, A. Boyko and O. Favorova, Pharmacogenet. Genomics, 2016, 26(3), 103–115. 166. R. Gross, B. C. Healy, S. Cepok, T. Chitnis, S. J. Khoury and B. Hemmer, et al., J. Neuroimmunol., 2011, 233(1–2), 168–174. 167. C. Fusco, V. Andreone, G. Coppola, V. Luongo, F. Guerini and E. Pace, et al., Neurology, 2001, 57(11), 1976–1979. 168. S. Dhib-­Jalbut, R. M. Valenzuela, K. Ito, M. Kaufman, M. Ann Picone and S. Buyske, Mult. Scler. Relat. Disord., 2013, 2(4), 340–348. 169. E. Y. Tsareva, O. G. Kulakova, A. N. Boyko, S. G. Shchur, D. Lvovs and A. V. Favorov, et al., Pharmacogenomics, 2012, 13(1), 43–53. 170. C. J. Ross, F. Towfic, J. Shankar, D. Laifenfeld, M. Thoma and M. Davis, et al., Genome Med., 2017, 9(1), 50. 171. N. Isobe, A. Keshavan, P. A. Gourraud, A. H. Zhu, E. Datta and R. Schlaeger, et al., JAMA Neurol., 2016, 73(7), 795–802. 172. E. G. Celius, H. F. Harbo, T. Egeland, F. Vartdal, B. Vandvik and A. Spurkiand, J. Neurol. Sci., 2000, 178(2), 132–135. 173. H. F. Harbo, N. Isobe, P. Berg-­Hansen, S. D. Bos, S. J. Caillier and M. W. Gustavsen, et al., Mult. Scler., 2014, 20(6), 660–668. 174. L. F. Barcellos, S. Sawcer, P. P. Ramsay, S. E. Baranzini, G. Thomson and F. Briggs, et al., Hum. Mol. Genet., 2006, 15(18), 2813–2824. 175. K. Hilven, N. A. Patsopoulos, B. Dubois and A. Goris, Mult. Scler., 2015, 21(13), 1670–1680. 176. A. D. Sadovnick, A. L. Traboulsee, Y. Zhao, C. Q. Bernales, M. Encarnacion and J. P. Ross, et al., Clin. Immunol., 2017, 180, 100–105. 177. H. B. Sondergaard, E. R. Petersen, M. Magyari, F. Sellebjerg and A. B. Oturai, Mult. Scler. Relat. Disord., 2017, 13, 25–27. 178. M. F. George, F. B. Briggs, X. Shao, M. A. Gianfrancesco, I. Kockum and H. F. Harbo, et al., Neurol.: Genet., 2016, 2(4), e87.

Chapter 3

Biomarkers for Multiple Sclerosis Amalia Tejeda Velarde†a,b,c, Silvia Medina Heras†a,b,c and Luisa María Villar Guimerans*a,b,c a

Immunology Department, Ramón y Cajal University Hospital, Madrid, Spain; bInstituto Ramón y Cajal para la Investigación Sanitaria (IRYCIS), Spain; cRed Española de Esclerosis Múltiple (REEM), Spain *E-­mail: [email protected]

3.1  Introduction According to the FDA-­NIH Biomarker Working Group, a biomarker is “a defined characteristic that is measured as an indicator of normal biological processes, pathogenic processes, or responses to an exposure or intervention, including therapeutic interventions”. Biomarkers can include molecular, histologic, radiographic, or physiologic characteristics, but not an assessment of how an individual feels, functions, or survives.1 Different categories of biomarkers comprise: susceptibility/risk, diagnostic, monitoring, prognostic, predictive, pharmacodynamic/response and safety biomarkers (Table 3.1). Multiple sclerosis (MS) is a complex disease characterized by inflammation, demyelination and axonal damage. Its diagnosis is still a challenge, due †

Both authors contributed equally to this work.

  Drug Discovery Series No. 70 Emerging Drugs and Targets for Multiple Sclerosis Edited by Ana Martinez © The Royal Society of Chemistry 2019 Published by the Royal Society of Chemistry, www.rsc.org

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Table 3.1  Categories  of biomarkers and their definitions according to the FDA-­NIH Biomarker Working Group.

Biomarker categories

Definition

Susceptibility/risk

A biomarker that indicates the potential for developing a disease or medical condition in an individual who does not currently have clinically apparent disease or the medical condition. A biomarker used to detect or confirm presence of a disease or condition of interest or to identify individuals with a subtype of the disease. A biomarker measured serially for assessing status of a disease or medical condition or for evidence of exposure to (or effect of) a medical product or an environmental agent. A biomarker used to identify likelihood of a clinical event, disease recurrence or progression in patients who have the disease or medical condition of interest. A biomarker used to identify individuals who are more likely than similar individuals without the biomarker to experience a favourable or unfavourable effect from exposure to a medical product or an environmental agent. A biomarker used to show that a biological response has occurred in an individual who has been exposed to a medical product or an environmental agent. A biomarker measured before or after an exposure to a medical product or an environmental agent to indicate the likelihood, presence, or extent of toxicity as an adverse effect.

Diagnostic Monitoring

Prognostic Predictive

Pharmacodynamic/ response Safety

to its heterogeneity in clinical, radiological, pathological, and therapeutic response features. In addition, prognosis data are important to establish early clinical and therapeutic decisions. In this way, biomarker research in MS is crucial, because it can allow an early diagnosis and better patient care and disease managing. In this chapter we describe biomarkers with susceptibility, diagnostic, and prognostic functions and biomarkers for a personalized treatment, and we focus on biomarkers that can be found or measured in body fluids.

3.2  Susceptibility/Risk Biomarkers MS is considered a multifactorial disease, which is supposed to appear in genetically susceptible individuals, but stochastic events and environmental factors influence disease risk. A susceptibility/risk biomarker is defined as “a biomarker that indicates the potential for developing a disease or medical condition in an individual who does not currently have clinically apparent disease or that medical condition”.1 Thus, susceptibility biomarkers could be measured in asymptomatic individuals in order to know their disease risk. There are two major susceptibility biomarkers in MS: HLA DRB1*15:01 and

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antibodies against Epstein–Barr virus nuclear antigens (anti-­EBNA). Genetic variation explains only 30% of the disease risk, but HLA DRB1*15:01 shows the strongest effect. Other major histocompatibility complex alleles also display a more modest contribution.2 Moreover, similar to other autoimmune diseases, genetic factors interact with environmental issues, such as nutrition, climate, or previous infections. Among infectious diseases, Epstein– Barr virus (EBV) has a key role, since seronegative individuals have a very low risk for MS.3 In this way, a recent meta-­analysis published that IgG seropositivity to (EBNA) impacts on MS risk (odds ratio (OR) = 4.46, 95% confidence interval (CI) = 3.26–6.09; p = 1.5 × 10−19).4 The authors also described an effect for infectious mononucleosis (OR = 2.17, CI = 1.97–2.39; p = 3.1 × 10−50), and smoking (OR = 1.52, 1.39–1.66; p = 1.7 × 10−18).4

3.3  Diagnostic Biomarkers A diagnostic biomarker is defined as “a biomarker used to detect or confirm the presence of a disease or condition of interest or to identify individuals with a subtype of the disease”.1 The McDonald criteria are widely used in MS research and clinical practice. They combine clinical, imaging, and laboratory data and are reviewed continuously in order to get an earlier and more precise MS diagnosis. The last revision was made in 2017. The 2017 McDonald criteria emphasized the importance of cerebrospinal fluid (CSF) examination and laboratory data, although they may not be necessary in all cases.5

3.3.1  IgG Oligoclonal Bands The 2017 McDonald criteria recognize that the evidence of IgG intrathecal synthesis contributes to MS diagnosis, although it is not specific for MS.5 They also support that the qualitative demonstration of two or more IgG oligoclonal bands (OCGBs) are more specific markers of intrathecal synthesis than other tests, such as IgG index.5 They are based on several studies, which evidenced that after clinically isolated syndrome (CIS), OCGBs are an independent predictor factor of the risk of a second relapse when controlling for demographic, clinical, treatment, and magnetic resonance imaging (MRI) variables.6,7 Moreover, there is some evidence showing that the presence of OCGBs is also a risk factor of conversion to MS after a radiologically isolated syndrome (RIS).8 Due to this evidence, 2017 McDonald criteria propose that the presence of CSF OCGBs could substitute the demonstration of dissemination in time in the resonance.5 However, they highlight the importance of using appropriate and standardized technology and they recognize that agarose gel electrophoresis with isoelectric focusing and immunoblotting or immunofixation for IgG is the most sensitive test.5,7 The analysis of paired CSF and serum samples is necessary to confirm the CSF specificity of these bands.5,7

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3.3.2  AQP4-­IgG and MOG-­IgG Neuromyelitis optica (NMO) is a severe inflammatory and demyelinating disease of the central nervous system that preferentially affects the optic nerve and the spinal cord. The serum detection of anti-­aquaporin-­4 IgG (AQP4-­IgG) antibodies by indirect immunofluorescence constitutes a specific biomarker of NMO.9 These antibodies recognize a water channel expressed in the central nervous system and they are both markers of the disease and pathogenic factors.9 Thus, AQP4-­IgG antibodies were included in the revised NMO diagnostic criteria.10 Moreover, the term NMO spectrum disorders (NMOSD) was introduced to include AQP4-­IgG-­seropositive patients with limited or initial forms, or with manifestations outside of the optic nerve and spinal cord. Thus, the detection of AQP4-­IgG antibodies generally allows differentiation between MS and NMOSD,11,12 disorders that can sometimes overlap. Some AQP4 seronegative patients with NMOSD symptoms show antibodies against the myelin oligodendrocyte glycoprotein (MOG). Recent data suggest that patients with these antibodies show different clinical features from those of NMOSD.13,14 However, the sensitivity and specificity of anti-­MOG antibodies have not been totally validated.5

3.3.3  Free Light Chains Free immunoglobulin light chains are usually secreted together with the intact IgG molecule, and their detection in CSF has been associated with immune activation in the central nervous system.15 There are several studies showing that MS patients have increased levels of kappa chain in the CSF.16,17 Moreover, the appearance of these higher levels of kappa chain is associated with a higher risk of developing MS after a CIS.16,17 Some studies also evaluate the CSF-­serum ratio of kappa chain (Q KFLC), and they observe that it is elevated in all patients with MS, 86.8% of CIS patients converting to MS, and 61.5% of remaining CIS patients.17 Additionally, Q KFLC has been analysed together with OCGB, showing that Q KFLC is significantly higher in CIS with OCGB.17 OCGB detection using isoelectric focusing and immunoblotting requires methodological experience and is difficult to standardize, while the measurement of kappa chain is a rapid and quantitative assay and is easy to standardize. On the other hand, some patients with other neurological diseases different from MS can also have high values of CSF kappa chains. This suggests they could have lower specificity than OCGB for MS diagnosis.

3.4  Prognostic Biomarkers According to the FDA-­NIH Biomarker working group, a prognostic biomarker is defined as “a biomarker used to identify likelihood of a clinical event, disease recurrence or progression in patients who have the disease or medical

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1

condition of interest”. Thus, accurate biomarkers that allow early prediction of clinical conversion to MS after a CIS episode or the prognosis of MS patients are of great clinical interest, because they are important to establish quick clinical and therapeutic decisions, especially since early treatment is beneficial to avoid disease progression in MS patients.18

3.4.1  IgM Oligoclonal Bands Oligoclonal band detection in paired CSF and serum samples is the best method for investigating the presence of IgM synthesis. Oligoclonal IgM bands are associated with a highly inflammatory disease course.19 Moreover, the impact of OCMBs is even higher when IgM antibodies recognize myelin lipids, phosphatidylcholine being the most frequently recognized.20 Lipid-­specific IgM oligoclonal bands (LS-­OCMBs) predict an aggressive disease course, with a shorter period to a second relapse, a higher number of relapses and increased disability.20 LS-­OCMBs also predict an earlier clinical conversion to MS after optic neuritis (ON), despite the fact that this CIS presentation is considered to be associated with a better prognosis.21 The presence of LS-­OCMBs in MS is associated with specific immunological mechanisms that can play an important role in the physiopathology of the disease. They correlate with an increased percentage of CD5+ B lymphocytes in the CSF,22 so these cells could be involved in the intrathecal production of IgM antibodies. Moreover, IgM is the most efficient immunoglobulin for complement fixation, and IgM antibodies co-­ localize in MS lesions on axons and oligodendrocytes in the brain of MS patients.23

3.4.2  Neurofilament Light Chains Neurofilaments are the main components of intermediate filaments in the cytoplasm of neurons. Together with microtubules and microfilaments, they constitute the neuronal cytoskeleton, which forms and maintains neuronal structure and cell shape, and participates in axonal transport. They are composed of three principal subunits: neurofilament light chain (NFL), neurofilament medium chain (NFM), and neurofilament high chain (NFH). Abnormal expression, accumulation or post-­translational modifications of neurofilament proteins are found in numerous neurological diseases, such as amyotrophic lateral sclerosis, Parkinson's disease, Alzheimer's disease, and Charcot-­Marie-­Tooth disease. In MS, the accumulation of NFL in the CSF is considered as a biomarker of axonal damage and is also associated with conversion to MS in CIS patients.24,25 NFL have been also related to cognitive impairment at early phases of MS26 and clinical conversion to MS after an RIS.8 Additionally, higher CSF NFL levels have been associated with the presence of LS-­OCMBs.24 In those studies the CSF levels of NFL were measured using a commercial enzyme-­linked immunosorbent assay (ELISA), but more

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recently, single molecule array (Simoa) technology has been used to measure these molecules with higher sensitivity. This has allowed quantification of NFL levels not only in CSF, but also in serum samples. Therefore, serum NFL levels have arisen as a promising biomarker for axonal damage in MS patients.27,28

3.4.3  Chitinase 3-­like 1 Chitinase is a defensive enzyme in plants that cleaves chitin and protects against pathogens. Chitinase-­like proteins (CLPs) are evolutionarily conserved in mammals but they do not have the enzymatic activity to degrade chitin. By contrast, they are important in the development and progression of Th2 inflammation, parasitic infections, and cancer.29 Chitinase 3-­like 1 (CHI3L1) has been highlighted in cancer since it is expressed in breast cancer cells, activated macrophages, chondrocytes, neutrophils, and synovial cells.29 In brain tissue from MS patients, CHI3L1 is greatly expressed in chronic active lesions with high inflammatory activity, due to the expression by reactive astrocytes and macrophages/microglial cells. By contrast, CHI3L1 is lowly expressed in lesions with low inflammatory activity because it is restricted to the cytoplasm of few macrophages/microglial cells.30 Moreover, the major CSF cell subset expressing CHI3L1 corresponds with monocytes with low expression of CD14.30 CSF CHI3L1 levels are increased in patients with CIS who convert to MS and are associated with a quick development of disability.30,31 Additionally, CHI3L1 levels are also higher in patients with gadolinium enhancing lesions30 and with cognitive impairment at early stages of the disease.32 However, CSF CHI3L1 levels do not identify RIS patients at risk of conversion to MS.8,33 Understanding processes playing a role in the progressive courses of MS is a major challenge in the field. It is also important to determine when a patient has progressed to this second phase after the relapsing–remitting phase. Plasma levels of CHI3L1 have been related to the progressive forms. They are significantly increased in progressive MS compared with relapsing– remitting MS (RRMS).34 Moreover, a polymorphism of the CHI3L1 gene has been also associated with primary progressive MS (PPMS) and higher plasma levels.34

3.4.4  Chemokines: CXCL13 Chemokines constitute a family of cytokines with similar structure that stimulate leukocyte migration. The main function of CXCL13 is to regulate the migration of B lymphocytes and follicular helper CD4+ T cells to the follicles within secondary lymphoid organs. CXCL13 is produced by follicular dendritic cells and other stromal cells and binds to its receptor CXCR5. CXCL13 is important for the differentiation of B cells, and B cells play an

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important role in the physiopathology of MS, due to antibody secretion, antigen presentation, or cytokine production. The role of B cells in MS was clearly demonstrated by the beneficial effect shown by B cell depletion in clinical trials.35 Several studies have shown elevated levels of CXCL13 in the CSF of MS patients compared to controls.36,37 However, CXCL13 is not exclusive of MS and it is also elevated in the CSF of other inflammatory neurological diseases and viral encephalitis.38,39 Regarding MS, CXCL13 levels have been associated with increased relapse rate, and disability worsening and higher number of MRI lesions.40 Additionally, CXCL13 levels have been correlated with CSF cell count, CSF total protein, CSF IgG Index, and with the presence of OCGBs and LS-­OCMBs.22,40 This last association could suggest that oligoclonal bands and CXCL13 could be related and it could also support the importance of B lymphocyte in MS pathogenesis. Finally, CSF CXCL13 levels are also higher in CIS patients who convert to MS39–41 and they did not differ between PPMS and RRMS in the stable phase without relapses,37 although CSF CXCL13 levels are higher during relapses.22,39

3.4.5  MicroRNAs MicroRNAs (miRNAs) are non-­coding single stranded RNAs, which contain about 22 nucleotides.42 They regulate gene expression by degradation of messenger RNA (mRNA) or by inhibiting protein translation.42 One-­third of human genes are negatively regulated by miRNAs and participate in important biological processes, such as differentiation, proliferation, or apoptosis.42 MiRNAs have been also implicated in several diseases, such as cancer and autoimmunity. Recently, miRNA profile has been studied in different samples from MS patients, and they have shown a specific miRNA profile associated with MS.43,44 For example, in serum samples miR-­484 can differentiate between MS patients and controls43 and hsa-­miR-­337-­3p correlates negatively with disability progression. Additionally, in peripheral blood mononuclear cells, a specific expression profile associated with relapse has been described.44 In this way, hsa-­miR-­ 18b and hsa-­miR-­599 could be relevant during relapse, while hsa-­miR-­96 could be important during remission.44 Moreover, miR-­21-­5p, miR-­26b-­5p, miR-­29b-­3p, miR-­142-­3p, and miR-­155-­5p are down-­regulated in secondary progressive MS patients.45 MiRNAs can be detected in numerous body fluids, so they have been studied as potential biomarkers in MS patients. All these findings support the use of miRNAs as promising biomarkers to diagnose and monitor MS patients. In CSF, miR-­150 is elevated in MS patients and in CIS who convert to MS.46 In addition, miR-­150 levels correlate with CSF cell count, and CXCL13, MMP-­9, OPN, and NFL levels.46 This indicates that it can be a prognostic marker of aggressive MS course. This was also confirmed by its association with the presence of LS-­OCMBs.47

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3.5  Biomarkers for a Personalized Treatment The number of treatments approved in MS is evolving rapidly and there is a great variety of available drugs to treat MS patients at present. In the same way, the investigation of biomarkers to evaluate the treatment response and help selecting the drug with the best benefit/risk profile for individual patients is a major field in MS research. To predict which patients are going to benefit from a particular treatment is of great interest. So, an optimal treatment is based on personalized therapy. We will review here biomarkers predicting treatment response as well as biomarkers predicting adverse effects to current treatments in MS.

3.5.1  Biomarkers of Response to Treatment Biomarkers are used to identify patients with a high probability of responding to an individual treatment. Response biomarkers are defined by the U.S. Food and Drug Administration (FDA) as “biomarkers used to show that a biological response has occurred in an individual who has been exposed to a medical product or an environmental agent”.1

3.5.1.1 Neutralizing Antibodies Administration of a protein drug often induces an antibody response that can decrease the treatment effectiveness because it interferes in the binding of the molecule with its receptor. IFN-­beta was the first disease-­modifying drug used for MS treatment; therefore there is more information available for neutralizing antibodies (NAb) against this drug. The frequencies of NAb anti IFN-­beta depend on the primary structure and glycosylation of the protein, the administration route, and the dose.48 All long-­term trials of RRMS show evidence of a negative effect of high and persistent titers of NAbs on relapses, disease activity, and disease progression;49 therefore these NAb are used to identify non-­responders.50,51 Several studies associated high titers of Nabs with HLA-­DRB1*04:01, HLA-­ DRB1*04:08,52,53 and CXCL10.54 The presence of NAbs can be investigated by the cytopathic effect assay.55 A decrease in Myxovirus resistance protein A (MxA), a protein up-­regulated upon IFN-­beta injection, also correlates with Nab levels.56 A measurement of both MxA and NAbs after 1 year of treatment predicted the risk of new relapses.50 A switch to alternative therapy should be considered when lack of MxA induction and high levels of NAbs are present. The presence of antibodies to other medications used in MS has also been investigated. Antibodies to glatiramer acetate (GA) appear in 48–100% of patients treated with this drug.57 However, their neutralizing ability remains controversial and the consensus opinion is that anti-­GA antibodies do not decrease the therapeutic benefit of GA.58

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Antibodies against natalizumab are found in 4–14% of natalizumab-­ treated MS patients, but only the persistent antibodies are associated with a decrease in efficacy or with adverse reactions to infusions.59,60

3.5.1.2 Soluble Molecules Associated with Inflammation 3.5.1.2.1  Biomarkers of Response to IFN Beta.  IL-­17, a pro-­inflammatory cytokine, is down-­regulated by IFN-­beta treatment in MS.61 It was proposed that high serum levels of IL-­17 at baseline could be associated with a suboptimal response to this drug, thus indicating that a more aggressive disease course mediated by T helper-­17 cells predicts a poor response to IFN-­beta.61 However, in a validation study using a bigger cohort, researchers found high levels of IL-­17 in only 4% of the patients. Although these levels associated with a suboptimal response to IFN-­beta, this biomarker did not identified the majority of non-­responder patients.62 Baseline IL-­10 levels in serum were found to be lower in patients responding positively to IFN-­beta and augmented during this treatment.63 By contrast, patients who continue having clinical relapses and MRI activity when treated with IFN-­beta had a decrease in serum IL-­10 levels during treatment.64 Another molecule implicated in response to IFN-­beta is tumor necrosis factor (TNF)-­related apoptosis-­inducing ligand (TRAIL), a proapoptotic member of the TNF family of type II membrane proteins that plays a role in T cell cytotoxicity.65 IFN-­beta is able to induce TRAIL in T cells65,66 and monocytes.67 Responders show early and sustained induction of TRAIL in serum.68 Therefore, TRAIL could be a candidate for predicting IFN-­beta response. In this line, IFN-­beta receptors (IFNR) were proposed as bioactivity biomarkers, and a decreased IFNAR expression is indicative of the bioavailability of IFN. Patients who have a good response to treatment experience a decrease in INFR1 and IFNR2 expression.69 Furthermore, a study of gene profiles showed an overexpression of the IFN-­induced genes in non-­responder patients.70 These data indicate that the decrease of IFNR levels could be a marker of the effectiveness of the drug. Levels of interleukin‐1 receptor‐associated kinase 3 (IRAK3), a negative regulator of TLR4 signaling primarily expressed in monocytes, were described to be low in the serum of responders to IFN-­beta. This molecule could also contribute to the early identification of patients with a high probability of responding to this drug.71 The problem with all the molecules involved in the mechanism of action of IFN-­beta treatment is that none have been confirmed to date. On the other hand, the absence of OCMBs reacting against myelin lipids identifies patients with a high probability of showing an optimal response to IFN-­beta.72 The main problem with this biomarker is that it has to be assayed in CSF.

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3.5.1.2.2  Soluble Biomarkers for Monitoring Response to Other Treatments.  Inhibition of intrathecal IgM synthesis is associated with a complete therapeutic response to natalizumab.73 Levels of CXCL13, a potent chemoattractant of B cells, increase in the CSF of MS patients and show a correlation with B cell numbers, so it could be an interesting biomarker for B-­cell-­depleting therapies.74 In this line, it was shown that rituximab induces a decline in CSF CXCL13 and CCL19 correlated with the decrease in B and T cells.75 Natalizumab has shown the same effect in CXC13.74 Although this molecule has not been validated, it seems a promising biomarker for response to these monoclonal antibodies in MS. CHI3L1, a potential marker for conversion of CIS to MS, decreases in CSF after a year of natalizumab treatment. This suggests a possible use as a biomarker of therapeutic response.76 CSF levels of Fetuin-­A decrease after 6 and 12 months of natalizumab treatment in responders.77 Validation studies are needed to confirm the role of this protein as biomarker of natalizumab response. However, it requires serial lumbar punctures, which are inconvenient and undesirable following treatment in MS.

3.5.1.3 Leukocyte Subsets as Biomarkers Peripheral blood leukocytes play an important role in the pathogenesis of immune-­mediated diseases and are modified by treatments. Thus, the study of cellular immune subsets by flow cytometry can be a useful tool to monitor response to treatment. 3.5.1.3.1  Biomarkers of Response to IFN-­beta.  Pretreatment percentages below 3% of CD19+ CD5+ cells or above 2.6% of CD8+ perforin+ T cells are associated with a high probability of optimal response to IFN-­beta.78 In addition, optimal responders to this drug experience an expansion of CD56 bright natural killer (NK) cells, suggesting a relationship with the therapeutic benefit of this therapy.79 Furthermore, IFN-­beta therapy restores suppressive function of CD4+ CD25+ regulatory T cells. An increased frequency of these cells is related to optimal response.80 3.5.1.3.2  Biomarkers of Response to Glatiramer Acetate.  Changes in circulating antigen-­presenting cells and CD4+ T cells were observed in MS patients treated with this glatiramer acetate.81 A low CD40 expression on dendritic cells is significantly associated with a higher risk of relapses.81 3.5.1.3.3  Biomarkers of Response to Fingolimod.  High percentages of recent thymic emigrants, transitional B cells, double negative T cells, CD4/ CD8 ratio and central memory Th1 Th17 cells at baseline correlate with a suboptimal response to treatment with fingolimod.82 On the other hand, patients with high percentages of NK bright cells and plasmablasts at baseline show

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a good response to the drug. These biomarkers can contribute to stratifying patients as responders or non-­responders prior to treatment.82 Moreover, higher percentages of central memory CD4+ T cells under this treatment are associated with the appearance of relapses.84 This can be detected after 2 weeks of fingolimod treatment.84 3.5.1.3.4  Biomarkers of Response to Dimethyl Fumarate.  Patients who have an optimal response to treatment with dimethyl fumarate show a significant reduction in central memory CD4+ and CD8+ T cells, memory B cells, and proinflammatory cytokine production 6 months after drug administration.85 Furthermore, these patients experience an expansion of CD56 bright NK cells.85 In the same way, the percentage of CD8+ T cells and B cells at 6 months after treatment was proposed as a response to treatment predictor.86 This variation in percentages of particular lymphocyte subsets suggest they may be related to the therapeutic benefit of dimethyl fumarate. 3.5.1.3.5  Biomarkers of Response to Natalizumab.  Several studies showed a reduction in CD49 expression by peripheral blood mononuclear cells (PBMC) of patients treated with natalizumab, which correlated with a decrease in their migration capacity. It has been suggested as a putative biomarker.87–89 Furthermore; a recent study described a method to determine the percentage of CD49d molecules bound to natalizumab. An optimal percentage of CD49d receptor occupancy associated with a favorable clinical, radiological, and immunological response.90 Using this method it is possible to identify patients who are over/under treated. This allows the possibility of defining a personalized dosage regime.

3.5.1.4 Biomarkers of Neurodegeneration As stated above, the levels of NFL in CSF are a useful tool to detect inflammation and neurodegeneration in MS.24 They have also been used to detect response to different treatments. Thus, fingolimod showed a reduction in NFL levels at month 12, which correlated with an improvement in relapse and MRI outcomes.91 Natalizumab and rituximab also demonstrated a decrease in CSF NFL after treatment. However, they did not correlate with the response to treatment.92,93 Actually, the new ultrasensitive method (SIMOA) that allows the detection of NFL in serum (sNFL), has made it possible to explore neurodegeneration in MS in serum samples.27 The first reports have appeared describing its variations upon immunomodulatory treatment in MS with IFN beta, glatiramer acetate, dimethyl fumarate, teriflunomide, and fingolimod.28,94 It is necessary to confirm these data in larger cohorts to determine its utility as a predictive biomarker of response to treatment. Furthermore, sNFL could be a critical tool to study therapeutic effects with the arrival of neuroprotection and remyelination therapies.

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3.5.1.5 Emerging Biomarkers Extracellular vesicles (EVs) are membrane-­bound particles involved in intercellular communication that carry miRNAs, among other molecules.95 They are deregulated in MS and different reports described changes in miRNA expression as putative biomarkers of response to treatment. In this line, fingolimod is able to alter miRNA concentration of EVs at 5 hours post-­ treatment, suggesting their implication in the treatment mechanism of action and their potential use for monitoring.96 In addition, miR-­15b, −23a and 223 levels are down-­regulated in MS and fingolimod normalizes their levels.97 On the other hand, mir-­155, which is up-­regulated in MS, decreases after natalizumab treatment as well as miR-­26a.98 In this line, dicer, which is a cleavage mediator of precursor miRNA, is also decreased in MS patients and induced in optimal responders to IFN-­beta.99 Although these studies require validation, they suggest that a therapeutic response may be reflected by restoration of deregulated miRNAs.

3.5.2  Biomarkers Monitoring Treatment Side Effects Identifying markers to detect the patients most susceptible to developing treatment side effects contributes to a personalized therapy with a better benefit/risk balance. Safety biomarkers are defined by the FDA as “biomarkers measured before or after an exposure to a medical product or an environmental agent to indicate the likelihood, presence, or extent of toxicity as an adverse effect”.1

3.5.2.1 Biomarkers Monitoring Natalizumab Side Effects Natalizumab is a very efficacious therapy for patients with aggressive MS. However, it presents some side effects. The most important one is progressive multifocal leukoencephalopathy (PML), a serious opportunistic infection caused by John Cunningham virus (JCV), which appears in about four of every 1000 treated patients. It often results in severe impairment and can result in patient death in a number of cases.98 Different biomarkers have proven to be useful in stratifying PML risk in patients treated with natalizumab. The presence of anti-­JCV antibodies in serum is clearly associated with PML risk.100,101 The infection rarely appears in seronegative patients. An anti-­JCV index further contributes to stratification of PML risk, since the infection rarely appears in patients with levels lower than 0.9.103 However, a number of patients have negative or low titers of anti-­JC antibodies that turn into positive/high titers during natalizumab treatment. Thus, these antibodies have to be monitored periodically, and other biomarkers are needed.100–102 Previous immunosuppressant use and natalizumab treatment duration are additional risk factors to stratifying PML risk.104 Decreased expression of L-­selectin (CD62L) on peripheral blood CD4+ T cells has been proposed as a PML biomarker in patients treated with

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105

105

natalizumab. A clear reduction was observed in the pre-­PML samples. Unfortunately, these results have not been confirmed.106 The presence of LS-­OCMBs in CSF, a marker of high inflammatory disease, is associated with a decreased PML risk, thus suggesting that patients with highly inflammatory disease are less prone to have an excess of immunosuppression under natalizumab treatment.107 On the other hand, natalizumab can induce infusion-­related anaphylactoid reactions, caused by the appearance of anti-­drug antibodies. They may be severe and cause treatment to be discontinued.108 HLA-­DRB1*13 and HLA-­DRB1*14 alleles were increased in patients who developed anaphylactic reactions. In contrast, the HLA-­DRB1*15 allele was significantly more represented in patients who did not develop these reactions.109 HLA-­DRB1 genotyping before treatment could help to identify patients with risk for developing serious systemic hypersensitivity reactions.109

3.5.2.2 Biomarkers for Monitoring Fingolimod Side Effects Fingolimod can increase the risk of serious infections by varicella zoster virus.110 Patients who are going to start fingolimod treatment should be tested for antibodies against varicella zoster virus, and in seronegative patients vaccination is recommended.111

3.5.2.3 Biomarkers for Monitoring Alemtuzumab Side Effects Alemtuzumab is highly effective in reducing disease activity in MS but its main drawback is the development of autoimmunity in more than one-­ third of treated patients, which may be serious in about 1% of the cases. A preliminary study showed that IL-­21 serum levels could identify patients with increased risk of secondary autoimmunity after treatment with alemtuzumab.112 Patients who developed autoimmunity showed higher levels of serum IL-­21 after treatment. The authors proposed that IL-­21 increases the probability of generating self-­reactive T cells by driving cycles of T cell expansion and death to excess.112 However, these results could not be validated, as currently available IL-­21 ELISA kits are not useful to discriminate patients at high risk of secondary autoimmunity during alemtuzumab treatment.113

3.5.2.4 Biomarkers for Monitoring Dimethyl Fumarate Side Effects Lymphopenia is a major concern in MS patients treated with dimethyl fumarate, as it increases the risk of PML.114 Lymphopenia occurs more likely in older patients, with lower baseline absolute lymphocytes counts, and those previously treated with natalizumab.115 Therefore, there is a need for lymphocyte monitoring in dimethyl-­fumarate-­treated patients, particularly in older ones and those switching from natalizumab.115

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References 1. FDA-­NIH Biomarker Working Group, BEST (Biomarkers, EndpointS, and other Tools) Resource, Silver Spring (MD), Food and Drug Administration (US), 2016, Co-­published by National Institutes of Health (US), Bethesda (MD). 2. M. R. Lincoln, S. V. Ramagopalan, M. J. Chao, B. M. Herrera, G. C. Deluca, S. M. Orton, D. A. Dyment, A. D. Sadovnick and G. C. Ebers, Proc. Natl. Acad. Sci. U. S. A., 2009, 106, 7542. 3. A. Ascherio and K. L. Munger, Ann. Neurol., 2007, 61, 288. 4. L. Belbasis, V. Bellou, E. Evangelou, J. P. Ioannidis and I. Tzoulaki, Lancet Neurol., 2015, 14, 263. 5. A. J. Thompson, B. L. Banwell, F. Barkhof, W. M. Carroll, T. Coetzee, G. Comi, J. Correale, F. Fazekas, M. Filippi, M. S. Freedman, K. Fujihara, S. L. Galetta, H. P. Hartung, L. Kappos, F. D. Lublin, R. A. Marrie, A. E. Miller, D. H. Miller, X. Montalban, E. M. Mowry, P. S. Sorensen, M. Tintoré, A. L. Traboulsee, M. Trojano, B. M. J. Uitdehaag, S. Vukusic, E. Waubant, B. G. Weinshenker, S. C. Reingold and J. A. Cohen, Lancet Neurol., 2018, 17, 162. 6. M. Tintore, A. Rovira, J. Río, S. Otero-­Romero, G. Arrambide, C. Tur, M. Comabella, C. Nos, M. J. Arévalo, L. Negrotto, I. Galán, A. Vidal-­Jordana, J. Castilló, F. Palavra, E. Simon, R. Mitjana, C. Auger, J. Sastre-­Garriga and X. Montalban, Brain, 2015, 138, 1863. 7. J. Masjuan, J. C. Alvarez-­Cermeño, N. García-­Barragán, M. Díaz-­Sánchez, M. Espiño, M. C. Sádaba, P. González-­Porqué, J. Martínez San Millán and L. M. Villar, Neurology, 2006, 66, 576. 8. C. Matute-­Blanch, L. M. Villar, J. C. Álvarez-­Cermeño, K. Rejdak, E. Evdoshenko, G. Makshakov, V. Nazarov, S. Lapin, L. Midaglia, A. Vidal-­ Jordana, J. Drulovic, A. García-­Merino, A. J. Sánchez-­López, E. Havrdova, A. Saiz, S. Llufriu, R. Alvarez-­Lafuente, I. Schroeder, U. K. Zettl, D. Galimberti, L. Ramió-­Torrentà, R. Robles, E. Quintana, H. Hegen, F. Deisenhammer, J. Río, M. Tintoré, A. Sánchez, X. Montalban and M. Comabella, Brain, 2018, 141, 1085. 9. V. A. Lennon, D. M. Wingerchuk, T. J. Kryzer, S. J. Pittock, C. F. Lucchinetti, K. Fujihara, I. Nakashima and B. G. Weinshenker, Lancet, 2004, 364, 2106. 10. D. M. Wingerchuk, V. A. Lennon, S. J. Pittock, C. F. Lucchinetti and B. G. Weinshenker, Neurology, 2006, 66, 1485. 11. D. M. Wingerchuk, V. A. Lennon, C. F. Lucchinetti, S. J. Pittock and B. G. Weinshenker, Lancet Neurol., 2007, 6, 805. 12. M. Sepúlveda, T. Armangué, N. Sola-­Valls, G. Arrambide, J. E. Meca-­ Lallana, C. Oreja-­Guevara, M. Mendibe, A. Alvarez de Arcaya, Y. Aladro, B. Casanova, J. Olascoaga, A. Jiménez-­Huete, M. Fernández-­ Fournier, L. Ramió-­Torrentà, A. Cobo-­Calvo, M. Viñals, C. de Andrés, C. Meca-­Lallana, A. Cervelló, C. Calles, M. B. Rubio, C. Ramo-­Tello,

Biomarkers for Multiple Sclerosis









69

A. Caminero, E. Munteis, A. R. Antigüedad, Y. Blanco, P. Villoslada, X. Montalban, F. Graus and A. Saiz, Neurol. Neuroimmunol. Neuroinflamm., 2016, 3, e225. 13. D. K. Sato, D. Callegaro, M. A. Lana-­Peixoto, P. J. Waters, F. M. de Haidar, T. Takahashi, I. Nakashima, S. L. Apostolos-­Pereira, N. Talim, R. F. Simm, A. M. Lino, T. Misu, M. I. Leite, M. Aoki and K. Fujihara, Neurology, 2014, 82, 474. 14. R. Höftberger, M. Sepulveda, T. Armangue, Y. Blanco, K. Rostásy, A. C. Calvo, J. Olascoaga, L. Ramió-­Torrentà, M. Reindl, J. Benito-­León, B. Casanova, G. Arrambide, L. Sabater, F. Graus, J. Dalmau and A. Saiz, Mult. Scler., 2015, 21, 866. 15. A. Vakaet and E. J. Thompson, J. Neurol., Neurosurg. Psychiatry, 1985, 48, 995. 16. L. M. Villar, M. Espiño, L. Costa-­Frossard, A. Muriel, J. Jiménez and J. C. Alvarez-­Cermeño, Clin. Chim. Acta, 2012, 413, 1813. 17. M. Senel, H. Tumani, F. Lauda, S. Presslauer, R. Mojib-­Yezdani, M. Otto and J. Brettschneider, PLoS One, 2014, 9, e88680. 18. G. Comi, M. Filippi, F. Barkhof, L. Durelli, G. Edan, O. Fernández, H. Hartung, P. Seeldrayers, P. S. Sørensen, M. Rovaris, V. Martinelli and O. R. Hommes, Lancet, 2001, 357, 1576. 19. L. M. Villar, J. Masjuan, P. González-­Porqué, J. Plaza, M. C. Sádaba, E. Roldán, A. Bootello and J. C. Alvarez-­Cermeño, Ann. Neurol., 2003, 53, 222. 20. L. M. Villar, M. C. Sádaba, E. Roldán, J. Masjuan, P. González-­Porqué, N. Villarrubia, M. Espiño, J. A. García-­Trujillo, A. Bootello and J. C. Alvarez-­ Cermeño, J. Clin. Invest., 2005, 115, 187. 21. A. Tejeda-­Velarde, L. Costa-­Frossard, S. Sainz de la Maza, Á. Carrasco, M. Espiño, C. Picón, I. Toboso, P. E. Walo, D. Lourido, A. Muriel, J. C. Álvarez-­Cermeño and L. M. Villar, Eur. J. Neurol., 2018, 25, 614. 22. L. M. Villar, M. Espiño, M. L. Cavanillas, E. Roldán, E. Urcelay, E. G. de la Concha, M. C. Sádaba, R. Arroyo, P. González-­Porqué and J. C. Alvarez-­ Cermeño, Clin. Immunol., 2010, 137, 51. 23. M. C. Sádaba, J. Tzartos, C. Paíno, M. García-­Villanueva, J. C. Alvarez-­ Cermeño, L. M. Villar and M. M. Esiri, J. Neuroimmunol., 2012, 247, 86. 24. L. M. Villar, C. Picón, L. Costa-­Frossard, R. Alenda, J. García-­Caldentey and M. Espiño, Eur. J. Neurol., 2015, 22, 1169. 25. G. Arrambide, C. Espejo, H. Eixarch, L. M. Villar, J. C. Alvarez-­Cermeño, C. Picón, J. Kuhle, G. Disanto, L. Kappos, J. Sastre-­Garriga, D. Pareto, E. Simon, M. Comabella, J. Río, C. Nos, C. Tur, J. Castilló, A. Vidal-­Jordana, I. Galán, M. J. Arévalo, C. Auger, A. Rovira, X. Montalban and M. Tintore, Neurology, 2016, 87, 1076. 26. E. Quintana, C. Coll, J. Salavedra-­Pont, M. Muñoz-­San Martín, R. Robles-­Cedeño, J. Tomàs-­Roig, M. Buxó, C. Matute-­Blanch, L. M. Villar, X. Montalban, M. Comabella, H. Perkal, J. Gich and L. Ramió-­Torrentà, Eur. J. Neurol., 2018, 25, 1189.

70

Chapter 3

27. G. Disanto, C. Barro, P. Benkert, Y. Naegelin, S. Schädelin, A. Giardiello, C. Zecca, K. Blennow, H. Zetterberg, D. Leppert, L. Kappos, C. Gobbi and J. Kuhle, Ann. Neurol., 2017, 81, 857. 28. N. Siller, J. Kuhle, M. Muthuraman, C. Barro, T. Uphaus, S. Groppa, L. Kappos, F. Zipp and S. Bittner, Mult. Scler., 2018, 1352458518765666. 29. D. H. Kim, H. J. Park, S. Lim, J. H. Koo, H. G. Lee, J. O. Choi, J. Oh, S. J. Ha, M. J. Kang, C. M. Lee, C. G. Lee, J. A. Elias and J. M. Choi, Nat. Commun., 2018, 9, 503. 30. E. Cantó, M. Tintoré, L. M. Villar, C. Costa, R. Nurtdinov, J. C. Álvarez-­ Cermeño, G. Arrambide, F. Reverter, F. Deisenhammer, H. Hegen, M. Khademi, T. Olsson, H. Tumani, E. Rodríguez-­Martín, F. Piehl, A. Bartos, D. Zimova, J. Kotoucova, J. Kuhle, L. Kappos, J. A. García-­Merino, A. J. Sánchez, A. Saiz, Y. Blanco, R. Hintzen, N. Jafari, D. Brassat, F. Lauda, R. Roesler, K. Rejdak, E. Papuc, C. de Andrés, S. Rauch, M. Khalil, C. Enzinger, D. Galimberti, E. Scarpini, C. Teunissen, A. Sánchez, A. Rovira, X. Montalban and M. Comabella, Brain, 2015, 138, 918. 31. M. Comabella, M. Fernández, R. Martin, S. Rivera-­Vallvé, E. Borrás, C. Chiva, E. Julià, A. Rovira, E. Cantó, J. C. Alvarez-­Cermeño, L. M. Villar, M. Tintoré and X. Montalban, Brain, 2010, 133, 1082. 32. E. Quintana, C. Coll, J. Salavedra-­Pont, M. Muñoz-­San Martín, R. Robles-­Cedeño, J. Tomàs-­Roig, M. Buxó, C. Matute-­Blanch, L. M. Villar, X. Montalban, M. Comabella, H. Perkal, J. Gich and L. Ramió-­Torrentà, Eur. J. Neurol., 2018, 25, 1189. 33. E. Thouvenot, G. Hinsinger, C. Demattei, U. Uygunoglu, G. Castelnovo, S. Pittion-­Vouyovitch, D. Okuda, O. Kantarci, D. Pelletier, S. Lehmann, P. Marin, A. Siva and C. Lebrun, Mult. Scler., 2018, 1352458518767043. 34. E. Cantó, F. Reverter, C. Morcillo-­Suárez, F. Matesanz, O. Fernández, G. Izquierdo, K. Vandenbroeck, A. Rodríguez-­Antigüedad, E. Urcelay, R. Arroyo, D. Otaegui, J. Olascoaga, A. Saiz, A. Navarro, A. Sanchez, C. Domínguez, A. Caminero, A. Horga, M. Tintoré, X. Montalban and M. Comabella, Mult. Scler., 2012, 18, 983. 35. S. L. Hauser, E. Waubant, D. L. Arnold, T. Vollmer, J. Antel, R. J. Fox, A. Bar-­Or, M. Panzara, N. Sarkar, S. Agarwal, A. Langer-­Gould, C. H. Smith and HERMES Trial Group, N. Engl. J. Med., 2008, 358, 676. 36. M. Stilund, M. C. Gjelstrup, T. Petersen, H. J. Møller, P. V. Rasmussen and T. Christensen, PLoS One, 2015, 10, e0119681. 37. P. Iwanowski, J. Losy, L. Kramer, M. Wójcicka and E. Kaufman, J. Neurol. Sci., 2017, 380, 22. 38. E. Alvarez, L. Piccio, R. J. Mikesell, E. C. Klawiter, B. J. Parks, R. T. Naismith and A. H. Cross, Mult. Scler., 2013, 19, 1204. 39. M. Khademi, I. Kockum, M. L. Andersson, E. Iacobaeus, L. Brundin, F. Sellebjerg, J. Hillert, F. Piehl and T. Olsson, Mult. Scler., 2011, 17, 335. 40. D. Ferraro, V. Galli, F. Vitetta, A. M. Simone, R. Bedin, C. Del Giovane, F. Morselli, M. M. Filippini, P. F. Nichelli and P. Sola, J. Neuroimmunol., 2015, 283, 64.

Biomarkers for Multiple Sclerosis

71

41. J. Brettschneider, A. Czerwoniak, M. Senel, L. Fang, J. Kassubek, E. Pinkhardt, F. Lauda, T. Kapfer, S. Jesse, V. Lehmensiek, A. C. Ludolph, M. Otto and H. Tumani, PLoS One, 2010, 5, e11986. 42. B. P. Lewis, I. H. Shih, M. W. Jones-­Rhoades, D. P. Bartel and C. B. Burge, Cell, 2003, 115, 787. 43. K. Regev, B. C. Healy, A. Paul, C. Diaz-­Cruz, M. A. Mazzola, R. Raheja, B. I. Glanz, P. Kivisäkk, T. Chitnis, M. Jagodic, F. Piehl, T. Olsson, M. Khademi, S. Hauser, J. Oksenberg, S. J. Khoury, H. L. Weiner and R. Gandhi, Neurol. Neuroimmunol. Neuroinflamm., 2018, 5, e491. 44. D. Otaegui, S. E. Baranzini, R. Armañanzas, B. Calvo, M. Muñoz-­Culla, P. Khankhanian, I. Inza, J. A. Lozano, T. Castillo-­Triviño, A. Asensio, J. Olaskoaga and A. López de Munain, PLoS One, 2009, 4, e6309. 45. K. A. Sanders, M. C. Benton, R. A. Lea, V. E. Maltby, S. Agland, N. Griffin, R. J. Scott, L. Tajouri and J. Lechner-­Scott, Clin. Epigenet., 2016, 8, 87. 46. P. Bergman, E. Piket, M. Khademi, T. James, L. Brundin, T. Olsson and F. Piehl, Neurol. Neuroimmunol. Neuroinflamm., 2016, 3, e219. 47. E. Quintana, F. J. Ortega, R. Robles-­Cedeño, M. L. Villar, M. Buxó, J. M. Mercader, J. C. Alvarez-­Cermeño, N. Pueyo, H. Perkal, J. M. Fernández-­ Real and L. Ramió-­Torrentà, Mult. Scler., 2017, 23, 1716. 48. C. Ross, K. M. Clemmesen, M. Svenson, P. S. Sørensen, N. Koch-­ Henriksen, G. L. Skovgaard and K. Bendtzen, Ann. Neurol., 2000, 48, 706. 49. D. Hesse and P. S. Sørensen, Eur. J. Neurol., 2007, 14, 850. 50. C. H. Polman, A. Bertolotto, F. Deisenhammer, G. Giovannoni, H. P. Hartung, B. Hemmer, J. Killestein, H. F. McFarland, J. Oger, A. R. Pachner, J. Petkau, A. T. Reder, S. C. Reingold, H. Schellekens and P. S. Sørensen, Lancet Neurol., 2010, 9, 740. 51. R. A. Rudick, J. C. Lee, J. Simon, R. M. Ransohoff and E. Fisher, Ann. Neurol., 2004, 56, 548. 52. D. Buck, S. Cepok, S. Hoffmann, V. Grummel, A. Jochim, A. Berthele, H. P. Hartung, R. Wassmuth and B. Hemmer, Arch. Neurol., 2011, 68, 480. 53. S. Hoffmann, S. Cepok, V. Grummel, K. Lehmann-­Horn, J. Hackermüller, J. Hackermueller, P. F. Stadler, H. P. Hartung, A. Berthele, F. Deisenhammer, R. Wassmuth and B. Hemmer, Am. J. Hum. Genet., 2008, 83, 219. 54. H. Hegen, A. Millonig, A. Bertolotto, M. Comabella, G. Giovanonni and M. Guger, et al., Mult. Scler., 2014, 20, 577. 55. M. Garcia-­Montojo, M. I. Dominguez-­Mozo, V. de las Heras, M. Bartolome, A. Garcia-­Martinez and R. Arroyo, Eur. J. Neurol., 2010, 17, 470. 56. A. R. Pachner, A. Bertolotto and F. Deisenhammer, Neurology, 2003, 61, S24. 57. H. H. Salama, J. Hong, Y. C. Zang, A. El-­Mongui and J. Zhang, Brain, 2003, 126, 2638. 58. D. Teitelbaum, T. Brenner, O. Abramsky, R. Aharoni, M. Sela and R. Arnon, Mult. Scler., 2003, 9, 592.

72

Chapter 3

59. A. Vennegoor, T. Rispens, E. M. Strijbis, A. Seewann, B. M. Uitdehaag, L. J. Balk, F. Barkhof, C. H. Polman, G. Wolbink and J. Killestein, Mult. Scler., 2013, 19, 593. 60. M. Lundkvist, E. Engdahl, C. Holmén, R. Movérare, T. Olsson, J. Hillert and A. Fogdell-­Hahn, Mult. Scler., 2013, 19, 757. 61. R. C. Axtell, B. A. de Jong, K. Boniface, L. F. van der Voort, R. Bhat, P. De Sarno, R. Naves, M. Han, F. Zhong, J. G. Castellanos, R. Mair, A. Christakos, I. Kolkowitz, L. Katz, J. Killestein, C. H. Polman, R. de Waal Malefyt, L. Steinman and C. Raman, Nat. Med., 2010, 16, 406. 62. H. P. Hartung, L. Steinman, D. S. Goodin, G. Comi, S. Cook, M. Filippi, P. O'Connor, D. R. Jeffery, L. Kappos, R. Axtell, V. Knappertz, T. Bogumil, S. Schwenke, E. Croze, R. Sandbrink and C. Pohl, JAMA Neurol., 2013, 70, 1017. 63. H. Bartosik-­Psujek and Z. Stelmasiak, Clin. Neurol. Neurosurg., 2006, 108, 644. 64. J. J. Graber, D. Ford, M. Zhan, G. Francis, H. Panitch and S. Dhib-­Jalbut, J. Neuroimmunol., 2007, 185, 168. 65. N. Kayagaki, N. Yamaguchi, M. Nakayama, H. Eto, K. Okumura and H. Yagita, J. Exp. Med., 1999, 189, 1451. 66. N. Arbour, E. Rastikerdar, E. McCrea, Y. Lapierre, J. Dörr, A. Bar-­Or and J. P. Antel, Mult. Scler., 2005, 11, 652. 67. J. A. Zula, H. C. Green, R. M. Ransohoff, R. A. Rudick, G. R. Stark and A. H. H. van Boxel-­Dezaire, Proc. Natl. Acad. Sci. U. S. A., 2011, 108, 19689. 68. K. P. Wandinger, J. D. Lünemann, O. Wengert, J. Bellmann-­Strobl, O. Aktas, A. Weber, E. Grundström, S. Ehrlich, K. D. Wernecke, H. D. Volk and F. Zipp, Lancet, 2003, 361, 2036. 69. B. Oliver, C. Mayorga, V. Fernández, L. Leyva, A. León, G. Luque, J. C. López, J. A. Tamayo, M. J. Pinto-­Medel, E. de Ramon, E. Blanco, A. Alonso and O. Fernández, J. Neuroimmunol., 2007, 183, 225. 70. M. Comabella, J. D. Lünemann, J. Río, A. Sánchez, C. López, E. Julià, M. Fernández, L. Nonell, M. Camiña-­Tato, F. Deisenhammer, E. Caballero, M. T. Tortola, M. Prinz, X. Montalban and R. Martin, Brain, 2009, 132, 3353. 71. M. F. Bustamante, N. Fissolo, J. Río, C. Espejo, C. Costa, M. J. Mansilla, I. Lizasoain, M. A. Moro, M. Carmen Edo, X. Montalban and M. Comabella, Ann. Neurol., 2011, 70, 634. 72. I. Bosca, L. M. Villar, F. Coret, M. J. Magraner, M. Simó-­Castelló, J. C. Alvarez-­Cermeño and B. Casanova, Mult. Scler., 2010, 16, 810. 73. L. M. Villar, M. I. García-­Sánchez, L. Costa-­Frossard, M. Espiño, E. Roldán, D. Páramo, M. Lucas, G. Izquierdo and J. C. Álvarez-­Cermeño, Arch. Neurol., 2012, 69, 191. 74. F. Sellebjerg, L. Börnsen, M. Khademi, M. Krakauer, T. Olsson, J. L. Frederiksen and P. S. Sørensen, Neurology, 2009, 73, 2003. 75. L. Piccio, R. T. Naismith, K. Trinkaus, R. S. Klein, B. J. Parks, J. A. Lyons and A. H. Cross, Arch. Neurol., 2010, 67, 707.

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76. M. P. Stoop, V. Singh, C. Stingl, R. Martin, M. Khademi, T. Olsson, R. Q. Hintzen and T. M. Luider, J. Proteome Res., 2013, 12, 101. 77. V. K. Harris, N. Donelan, Q. J. Yan, K. Clark, A. Touray, M. Rammal and S. A. Sadiq, Mult. Scler., 2013, 19, 1462. 78. R. Alenda, L. Costa-­Frossard, R. Alvarez-­Lafuente, C. Espejo, E. Rodríguez-­Martín, S. Sainz de la Maza, N. Villarrubia, J. Río, M. I. Domínguez-­Mozo, X. Montalban, J. C. Álvarez-­Cermeño and L. M. Villar, J. Neurol., 2018, 265, 24. 79. J. E. Martínez-­Rodríguez, M. López-­Botet, E. Munteis, J. Rio, J. Roquer, X. Montalban and M. Comabella, Clin. Immunol., 2011, 141, 348. 80. A. Namdar, B. Nikbin, M. Ghabaee, A. Bayati and M. Izad, J. Neuroimmunol., 2010, 218, 120. 81. F. Sellebjerg, D. Hesse, S. Limborg, H. Lund, H. B. Søndergaard, M. Krakauer and P. S. Sørensen, Mult. Scler., 2013, 19, 179. 82. B. Quirant-­Sánchez, J. V. Hervás-­García, A. Teniente-­Serra, L. Brieva, E. Moral-­Torres, A. Cano, E. Munteis, M. J. Mansilla, S. Presas-­Rodriguez, J. Navarro-­Barriuso, C. Ramo-­Tello and E. M. Martínez-­Cáceres, CNS Neurosci. Ther., 2018, 24, 1175. 83. I. Moreno-­Torres, C. González-­García, M. Marconi, A. García-­Grande, L. Rodríguez-­Esparragoza, V. Elvira, E. Ramil, L. Campos-­Ruíz, R. García-­ Hernández, F. Al-­Shahrour, C. Fustero-­Torre, A. Sánchez-­Sanz, A. García-­ Merino and A. J. Sánchez López, Front. Immunol., 2018, 9, 1693. 84. Z. Y. Song, R. Yamasaki, Y. Kawano, S. Sato, K. Masaki, S. Yoshimura, D. Matsuse, H. Murai, T. Matsushita and J. Kira, PLoS One, 2014, 10, e0124923. 85. S. Medina, N. Villarrubia, S. Sainz de la Maza, J. Lifante, L. Costa-­ Frossard, E. Roldán, C. Picón, J. C. Álvarez-­Cermeño and L. M. Villar, Mult. Scler., 2018, 24(10), 1317. 86. V. Fleischer, M. Friedrich, A. Rezk, U. Bühler, E. Witsch, T. Uphaus, S. Bittner, S. Groppa, B. Tackenberg, A. Bar-­Or, F. Zipp and F. Luessi, Mult. Scler., 2018, 24, 632. 87. M. Niino, C. Bodner, M. L. Simard, S. Alatab, D. Gano, H. J. Kim, M. Trigueiro, D. Racicot, C. Guérette, J. P. Antel, A. Fournier, F. Grand'Maison and A. Bar-­Or, Ann. Neurol., 2006, 59, 748. 88. P. Wipfler, K. Oppermann, G. Pilz, S. Afazel, E. Haschke-­Becher, A. Harrer, M. Huemer, A. Kunz, S. Golaszewski, W. Staffen, G. Ladurner and J. Kraus, Mult. Scler., 2011, 17, 163. 89. G. Defer, D. Mariotte, N. Derache, O. Toutirais, H. Legros, B. Cauquelin and B. Le Mauff, J. Neurol. Sci., 2012, 314, 138. 90. J. Puñet-­Ortiz, J. V. Hervás-­García, A. Teniente-­Serra, A. Cano-­Orgaz, M. J. Mansilla, B. Quirant-­Sánchez, J. Navarro-­Barriuso, M. A. Fernández-­ Sanmartín, S. Presas-­Rodríguez, C. Ramo-­Tello and E. M. Martínez-­ Cáceres, Cytometry, Part B, 2018, 94, 327. 91. J. Kuhle, G. Disanto, J. Lorscheider, T. Stites, Y. Chen, F. Dahlke, G. Francis, A. Shrinivasan, E. W. Radue, G. Giovannoni and L. Kappos, Neurology, 2015, 84, 1639.

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92. M. Gunnarsson, C. Malmeström, M. Axelsson, P. Sundström, C. Dahle, M. Vrethem, T. Olsson, F. Piehl, N. Norgren, L. Rosengren, A. Svenningsson and J. Lycke, Ann. Neurol., 2011, 69, 83. 93. M. Axelsson, C. Malmeström, M. Gunnarsson, H. Zetterberg, P. Sundström, J. Lycke and A. Svenningsson, Mult. Scler., 2014, 20, 43. 94. F. Piehl, I. Kockum, M. Khademi, K. Blennow, J. Lycke, H. Zetterberg and T. Olsson, Mult. Scler., 2018, 24, 1046. 95. G. Raposo and W. Stoorvogel, J. Cell Biol., 2013, 200, 373. 96. M. Sáenz-­Cuesta, A. Alberro, M. Muñoz-­Culla, I. Osorio-­Querejeta, M. Fernandez-­Mercado, I. Lopetegui, M. Tainta, Á. Prada, T. Castillo-­ Triviño, J. M. Falcón-­Pérez, J. Olascoaga and D. Otaegui, Int. J. Mol. Sci., 2018, 19, E2448. 97. C. Fenoglio, M. De Riz, A. M. Pietroboni, A. Calvi, M. Serpente and S. M. G. Cioffi, et al., J. Neuroimmunol., 2016, 299, 81. 98. G. Mameli, G. Arru, E. Caggiu, M. Niegowska, S. Leoni, G. Madeddu, S. Babudieri, G. P. Sechi and L. A. Sechi, PLoS One, 2016, 11, e0157153. 99. W. J. Magner, B. Weinstock-­Guttman, M. Rho, D. Hojnacki, R. Ghazi, M. Ramanathan and T. B. Tomasi, J. Neuroimmunol., 2016, 292, 68. 100. G. Bloomgren, S. Richman, C. Hotermans, M. Subramanyam, S. Goelz, A. Natarajan, S. Lee, T. Plavina, J. V. Scanlon, A. Sandrock and C. Bozic, N. Engl. J. Med., 2012, 366, 1870. 101. T. Plavina, M. Subramanyam, G. Bloomgren, S. Richman, A. Pace, S. Lee, B. Schlain, D. Campagnolo, S. Belachew and B. Ticho, Ann. Neurol., 2014, 76, 802. 102. C. Antoniol and B. Stankoff, Front. Immunol., 2014, 5, 668. 103. O. Outteryck, H. Zéphir, J. Salleron, J. C. Ongagna, A. Etxeberria, N. Collongues, A. Lacour, M. C. Fleury, F. Blanc, M. Giroux, J. de Seze and P. Vermersch, Mult. Scler., 2014, 20, 822. 104. P. R. Ho, H. Koendgen, N. Campbell, B. Haddock, S. Richman and I. Chang, Lancet Neurol., 2017, 16, 925. 105. N. Schwab, T. Schneider-­Hohendorf, V. Posevitz, J. Breuer, K. Göbel, S. Windhagen, B. Brochet, P. Vermersch, C. Lebrun-­Frenay, A. Posevitz-­ Fejfár, R. Capra, L. Imberti, V. Straeten, J. Haas, B. Wildemann, J. Havla, T. Kümpfel, I. Meinl, K. Niessen, S. Goelz, C. Kleinschnitz, C. Warnke, D. Buck, R. Gold, B. C. Kieseier, S. G. Meuth, J. Foley, A. Chan, D. Brassat and H. Wiendl, Neurology, 2013, 81, 865. 106. L. A. Lieberman, W. Zeng, C. Singh, W. Wang, K. L. Otipoby, C. Loh, T. Plavina, L. Gorelik, R. M. Ransohoff and E. Cahir-­McFarland, Neurology, 2016, 86, 375. 107. L. M. Villar, L. Costa-­Frossard, T. Masterman, O. Fernandez, X. Montalban, B. Casanova, G. Izquierdo, F. Coret, H. Tumani, A. Saiz, R. Arroyo, K. Fink, L. Leyva, C. Espejo, M. Simó-­Castelló, M. I. García-­Sánchez, F. Lauda, S. Llufriú, R. Álvarez-­Lafuente, J. Olascoaga, A. Prada, A. Oterino, C. de Andrés, M. Tintoré, L. Ramió-­Torrentà, E. Rodríguez-­Martín, C. Picón, M. Comabella, E. Quintana, E. Agüera, S. Díaz, R. Fernandez-­ Bolaños, J. A. García-­Merino, L. Landete, M. Menéndez-­González, L.

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Navarro, D. Pérez, F. Sánchez-­López, P. J. Serrano-­Castro, A. Tuñón, M. Espiño, A. Muriel, A. Bar-­Or and J. C. Álvarez-­Cermeño, Ann. Neurol., 2015, 77, 447. 108. R. A. Rudick and M. A. Panzara, Biologics, 2008, 2, 189. 109. B. de la Hera, E. Urcelay, D. Brassat, A. Chan, A. Vidal-­Jordana, A. Salmen, L. M. Villar, J. C. Alvarez-­Cermeño, G. Izquierdo, O. Fernández, B. Oliver, A. Saiz, J. R. Ara, A. G. Vigo, R. Arroyo, V. Meca, S. Malhotra, N. Fissolo, A. Horga, X. Montalban and M. Comabella, Neurol. Neuroimmunol. Neuroinflamm., 2014, 1, e47. 110. A. Uccelli, F. Ginocchio, G. L. Mancardi and M. Bassetti, Neurology, 2011, 76, 1023. 111. M. Loebermann, A. Winkelmann, H. P. Hartung, H. Hengel, E. C. Reisinger and U. K. Zettl, Nat. Rev. Neurol., 2012, 8, 143. 112. J. L. Jones, C. L. Phuah, A. L. Cox, S. A. Thompson, M. Ban, J. Shawcross, A. Walton, S. J. Sawcer, A. Compston and A. J. Coles, J. Clin. Invest., 2009, 119, 2052. 113. L. Azzopardi, S. A. J. Thompson, K. E. Harding, M. Cossburn, N. Robertson, A. Compston, A. J. Coles and J. L. Jones, J. Neurol., Neurosurg. Psychiatry, 2014, 85, 795. 114. R. Gold, L. Kappos, D. L. Arnold, A. Bar-­Or, G. Giovannoni, K. Selmaj, C. Tornatore, M. T. Sweetser, M. Yang, S. I. Sheikh, K. T. Dawson and DEFINE Study Investigators, N. Engl. J. Med., 2012, 367, 1098. 115. E. E. Longbrake, R. T. Naismith, B. J. Parks, G. F. Wu and A. H. Cross, Mult. Scler. J. Exp. Transl. Clin., 2015, 1, 205521731559699.

Chapter 4

Optical Coherence Tomography in Multiple Sclerosis Ricardo Alonso*a,b and Leila Cohena,c a

Clinical of Multiple Sclerosis, Neurology Department, Hospital Jose María Ramos Mejía, Urquiza 609, Buenos Aires, Argentina; bClinic of Multiple Sclerosis and Neuroimmunology, Neurology Department, University Hospital Sanatorio Guemes, Buenos Aires, Argentina; c Neurophthalmology Section, Neurology Department, Hospital Jose María Ramos Mejía, Buenos Aires, Argentina *E-­mail: [email protected]

4.1  Introduction The retina consists of multiple layers of different types of cells. The inner layer is known as the retinal nerve fiber layer (RNFL) and is formed mainly of unmyelinated optic nerve axons coming from the retinal ganglion cells. Optical coherence tomography (OCT) is able to detect subtle changes in the thickness of the retina and macula by infrared light reflection.1 The newest OCT technology is the Spectral Domain OCT, which enables three-­dimensional tissue imaging by combining hundreds of nearly simultaneous laser scans. Each scan is taken in one plane, allowing a cross-­sectional view of structures. In the retina, this allows the visualization of each unique layer and generates images of its histological tissue. All the information is processed by computer software, and a measure of the thickness and volume of each retinal layer can

  Drug Discovery Series No. 70 Emerging Drugs and Targets for Multiple Sclerosis Edited by Ana Martinez © The Royal Society of Chemistry 2019 Published by the Royal Society of Chemistry, www.rsc.org

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be obtained. Usually, OCT has two applications: macular OCT focuses on the macula center in the fovea, and peripapillary OCT measures the retinal layers around the optic disc. OCT has become a useful tool for ophthalmologists to understand and study retinal pathologies, and neurologists have started to explore its uses in studying optic nerve damage in neurological conditions.2 One of the first studies on this subject was carried out in 1974 by Frisen et al.3 The authors described qualitative changes on the RNFL in patients with multiple sclerosis (MS). Further post-­mortem studies ratified their hypothesis, confirming RNFL atrophy in 35 out of 49 eyes with optic nerve atrophy.4 A possible explanation for retinal damage in patients with MS is anterograde and retrograde axonal degeneration. Another one is the demyelination of the optic nerve axons, which leads to the retrograde degeneration of the RNFL, with subsequent degeneration of the macular ganglion cell layer (mGCL).5 Other studies have also shown multiple pathological retinal changes, as well as axonal loss, synaptic loss, microglia activation, neuronal soma shrinkage and inflammation.6 In recent years, different studies have suggested that retinal OCT is a sensitive and useful tool to measure axonal damage after optic neuropathy and to understand the process of neurodegeneration in MS patients using retinal changes as a window to the brain. The most important findings regarding OCT in MS patients are described in this chapter.

4.2  T  he Role of Optical Coherence Tomography After Acute Optic Neuritis Acute optic neuritis (ON) is a relatively common disease, with a prevalence estimated at 0.6/1000 and an incidence estimated at 1–5/100 000. Young Caucasian women (mean age 31–32 years) are most frequently affected by this condition.7 In 20% of MS patients, this can be the first symptom, while approximately 50–70% of patients will experience ON sooner or later in the course of the disease.8 Multiple sclerosis-­related optic neuritis (MSON) is described as a typical ON with an acute onset of ocular pain as a consequence of eye movements, unilateral loss of vision, impaired color vision, decreased contrast sensitivity, and defects in the central field. Recovery starts after 3–5 weeks and there is usually a good prognosis, as a result of a demyelination-­remyelination process.8 Nevertheless, after an episode of ON there is some degree of axonal injury, which varies in each patient depending on its severity, and it correlates with visual outcome.9 In recent years, OCT has become a useful tool to measure axonal damage after ON, and several studies were developed to understand optic nerve compromise in MS patients. It is believed that after an episode of acute ON, there could be not only axonal loss but also death of some of the retinal ganglion cells. This is represented in OCT exams as changes in the peripapillary RNFL (pRNFL), total macular volume (TMV) and mGCL in eyes with MSON. In this direction, researchers found that OCT was a non-­invasive method for diagnostics, prognosis and monitoring treatment in MS.10–12

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The first report on RNFL assessment in patients with MS was done in 1999 by Parisi et al.13 using Time Domain OCT. They examined 14 patients with MS with a history of retrobulbar ON and 14 healthy subjects, and found a mean RNFL thinning of 46% in the compromised eyes, with RNFL thickness 26% lower compared with healthy controls. Afterwards, with the advance of technology and the Spectral Domain OCT, other authors confirmed pRNFL thinning in MS patients' eyes with a history of ON to be a consequence of axonal and retinal ganglion cell damage after an inflammation episode, and that it was a biomarker of neurodegeneration in patients without ON.14 During the acute phase of ON, pRNFL measurements made closer in time to the acute episode could underestimate the true damage due to a swollen optic disc as a result of the inflammation process, and mask the real pRNFL loss. In order to get an early prognosis for the episode, it was found that thickness of the mGCL and decrease in TMV can be detected in the first month, since they are not influenced by optic disc edema. These parameters could be used as early markers of severity and damage. These findings are in concordance with the pRNFL thickness detectable in longitudinal follow-­up, and they also correlate with visual outcome, confirming the value of this exam as a prognosis tool in patients with MSON.15–17 The decrease in pRNFL thickness is highest 3–6 months after the acute episode, and stabilization is observed at 7–12 months.18 At this point, the OCT can show the axonal damage severity as a consequence of a chronic demyelination event due to optic nerve inflammation. It is an objective parameter measured through the thinning in pRNFL, TMV and mGCL due to retrograde degeneration and also direct injury to the retina ganglion cells.19 When analyzing retina compromise by quadrants, it has been described that the temporal quadrant is most often affected in eyes with MSON.20 The reason is believed to be that in the retinal sector there are more parvocellular cells of small-­diameter axons, which are more susceptible to damage after energy depletion due to mitochondrial injury. In addition, a smaller diameter could mean that they remyelinate less efficiently due to biochemical reasons, such as the decreased expression of membrane-­bound axonal factors, or due to biophysical reasons, such as axonal scaffold size. In contrast, the retinal nasal quadrant tends to be preserved in eyes with MSON, where magnocellular axons of larger size are in more quantity, with the ability to remyelinate quicker than the smaller axons.21 In the last few years, Gelfand et al.22 described a new indication in the OCT of eyes with MSON. This is the microcystic macular edema (MME), which is characterized by the presence of cystic areas of hyporeflectivity in the inner nuclear layer (INL) of the retina, visible in the OCT scan. Microcystics are associated with increased INL thickness, and are more common in eyes with MSON than in those with no history of ON. They suggested that this showed a breakdown of the blood–retinal barrier or microglia activation, although a possible connection to Müller cell pathology has also been proposed.23 This theory is supported by the fact that Müller cells help maintain water

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homeostasis in the retina, possibly leading to microcystic formation when dysfunctional. This finding is not specific to eyes with MSON, but it has also been described in patients with neuromyelitis optica spectrum disorders (NMOSD) after acute ON, in a higher percentage of cases.24,25 Another issue concerning the use of OCT arises when trying to take a differential diagnostic approach towards MSON and other acute ON that have similar clinical presentations, such as the ON related to neuromyelitis optica spectrum disorders (NMOSD-­ON). Evidently, this is a very important matter, because those two conditions have different courses and treatments, and above all, because it has been reported that the drugs used for disease-­modifying therapy in MS worsen the course of NMOSD.26 It is well documented that NMOSD-­ON is usually an atypical ON due to its severity, poor recovery rate, and in some cases, the field defects pattern.27 However, in some MS or NMOSD cases, these differences are not so clear, and for patients who show ON as first symptom, achieving a correct diagnosis becomes a real challenge—for neurologists specializing in this area too. During the last year, the discovery of autoantibodies related to NMOSD has been helpful,28 and today it is mandatory to look for AQP4-­IgG and MOG-­ IgG antibodies when studying a patient with an atypical ON.29 Despite this, a wide percentage of patients do not have a diagnosis or are included in the seronegative NMOSD group.30 The frequent compromise of optic nerve in MS and NMOSD patients may be caused by a more reduced blood–brain barrier (BBB) function. Furthermore, the optic nerve expresses high levels of supramolecular aggregation of AQP4. The combination of enhanced AQP4 supramolecular aggregation and a higher BBB permeability may contribute to the specific pattern of tissue damage in NMOSD patients.31,32 Studies using OCT are useful to find differences between these two conditions after acute ON in order to make a correct diagnosis, choose the indicated treatment, and measure the severity of the optic nerve damage.33 After some research, it is believed that NMOSD-­ON affects the entire pRNFL, especially the nasal, superior and inferior quadrants, due to the severity of the inflammation process in this condition, affecting all types of cells.34 In contrast, in eyes with MSON the thickness is more often seen in the temporal quadrant, as described above.20 In NMOSD-­ON, the OCT scan can show INL thinning due to Müller cell degeneration, which is not present in MSON. NMOSD patients with history of ON tend to have lower pRNFL thickness and more severe macular thinning than the eyes with MSON, correlating with a poor visual recovery observed after ON in NMOSD patients.35 Another frequent alteration highlighted by OCT in NMOSD-­ON is a more pronounced thickness in the mGCL than in MSON, owing to intense inflammation and necrosis with prominent neuronal and axonal damage.36 The higher compromise of the superior quadrant of the mGCL, as seen after anterior ischemic optic neuropathy, suggests vascular and ischemic damage of the optic nerve in NMOSD patients.37 Recent research shows foveal thinning in NMOSD patients with or without ON, indicating direct damage to the retina, perhaps because Müller cells and

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Box 4.1  OCT after acute optic neuritis   

●● During the acute phase of optic neuritis, pRNFL measurements made closer

in time to the acute episode could underestimate the true damage due to a swollen optic disc as a result of the inflammation process, and mask the real pRNFL loss. ●● Thickness of the mGCL and decrease in TMV can be detected in the first month since they are not influenced by optic disc edema. These parameters could be used as early markers of severity and damage. ●● Thickness is highest 3–6 months after the acute episode, and stabilization is observed at 7–12 months. ●● OCT could be a useful tool when trying to make a differential diagnosis approach in optic neuritis, for example due to NMOSD.   

retinal astrocytes are within reach of AQP4 canals susceptible to damage in NMOSD.38 In addition to this, MME is observed in up to 25% of patients with NMOSD-­ON, in contrast with the 5% previously described in patients with MSON.24 With this information, and in light of different treatment strategies for NMOSD and MS, an early and accurate diagnosis is key for optimal patient management, and OCT may have potential value to help clinicians differentiate between these two pathologies, particularly when ON is the initial clinical symptom. However, each of these findings has to be interpreted with caution, given the small sample size. Confirmatory studies using larger patient populations are needed before they can be used to guide clinical decision-­making33 (Box 4.1).

4.3  T  he Importance of Optical Coherence Tomography as Axonal Damage Biomarker Peripapillary RNFL provides direct assessment of axonal injury and could be used as a marker for neurodegeneration, given that evidence supports that in MS, degeneration affects different retinal layers. Macular GCL thickness measurements also provide an estimate of neurodegeneration in MS and could be more specific than pRNFL measurement. Distinguishing between mGCL and the internal plexiform layer (IPL) can be challenging, so a combination of mGCL and IPL (GCIPL) is measured. Different studies suggest that GCIPL atrophy may be of greater utility than tracking pRNFL atrophy, because GCIPL thickness may have improved structure–function compared to pRNFL thickness in MS.39 Some studies have also analyzed other measures such as TMV, outer plexiform layer (OPL), outer nuclear layer (ONL) and INL.40–45 Although these measures could be included as targets in OCT studies in MS, the largest and most robust evidence was found in the pRNFL and GCIPL. Beyond MS, different authors have related retinal atrophy with other neurodegenerative diseases, such as Alzheimer's disease46,47

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48,49

and Parkinson's disease. Several MS studies show that retinal atrophy goes beyond the inflammatory process of the optic nerve. As previously described, patients with MS and a history of ON have an average of pRNFL thickness reduction compared with patients without ON. However, pRNFL thinning is significantly greater than expected for normal aging even in MS patients without ON.50 Injury of the inner retinal layers is found during the early stages of the disease and these findings are irrespective of previous episodes of ON.51 Regarding GCIPL, patients with no history of ON also have greater thinning of the macula compared with healthy controls, which could be attributed to axonal degeneration.52

4.3.1  R  elationship Between OCT, Clinical and Cognitive Impairment, and Atrophy in Magnetic Resonance Imaging In different studies, OCT showed correlation with the Expanded Disability Status Scale (EDSS). In a 5 year follow-­up study, Garcia-­Martin et al.53 showed correlation between temporal and superior quadrant pRNFL thinning and the EDSS. Fisher et al.54 assessed 90 patients with MS and 36 healthy participants. They found a relationship between pRNFL thinning and impairment measured through two MS validated scales (EDSS and Multiple Sclerosis Functional Composite, MSFC). Britze et al.55 also obtained similar results relating pRNFL thinning to disability through the EDSS. On the other hand, a recent meta-­analysis showed that specific thinning of GCIPL was associated with visual function and EDSS scores in several studies. A multicenter cohort study with 879 patients showed that patients with pRNFL thinning had double the risk of worsening disability at any time after the first and up to the third year of follow-­up. The risk increased nearly four times after the third and up to the fifth year of follow-­up.56 Besides physical disability, some studies have also compared OCT with cognitive parameters. It was reported that 20% of the patients with cognitive impairments had normal OCT, while 71.4% who were cognitively healthy had normal OCT; the difference between both groups was statistically significant.57 A correlation was found between OCT and different cognitive tests. In a study with 217 patients and 59 healthy controls, the authors found a significant relationship between pRNFL and mGCL thickness, and cognitive impairment in the MS group.58 Ashtari et al.59 found a significant relationship between verbal and total IQ and RNFL thickness. Stellmann et al.60 identified correlations between pRNFL and mGCL with divided attention, semantic fluency, and the Paced Auditory Serial Addition Task 3 seconds (PASAT-­3). Toledo et al.61 found a significant correlation between pRNFL atrophy and the Symbol Digit Modalities Test (SDMT). MS subtypes can be distinguished by OCT. The evidence on this matter shows that patients with progressive MS (PMS) have more compromised RNFL and TMV than relapsing MS (RMS) patients and healthy controls. This result could suggest that PMS strongly relates to neurodegeneration (axonal

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Box 4.2  OCT and neurodegeneration   

●● MS patients even without clinical evidence of optic neuritis have RNFL and

mGCL atrophy over time.

●● Physical disability and cognitive disorder are related to RNFL and mGCL

thinning in OCT.

●● MS subtypes can be distinguished by OCT. Progressive MS patients have

more RNFL and mGCL compromised than relapsing MS patients and healthy controls ●● Changes in the retina measured by OCT are a marker of overall axonal loss and neurodegeneration. RNFL and mGCL could be used as a marker for neurodegeneration.   

and neuronal loss), a phenomenon observed in the OCT scan.62–64 In this direction, Behbehani et al. studied 113 MS patients (29 with PMS and 84 with RMS) and concluded that the pRNFL, GCIPL and OPL were significantly thinner in PMS patients compared with RMS patients, and that there was a significant correlation between ONL thickness and EDSS scores in patients with PMS.43 A large-­scale study with 414 MS patients showed that pRNFL thickness was lower in secondary progressive MS (SPMS) patients compared to RMS patients. At the same time, TMV was more reduced in primary progressive MS (PPMS) patients than in RMS patients.44 A follow-­up study showed that GCIPL thickness and whole-­brain atrophy by magnetic resonance imaging (MRI) rates were more strongly associated in PMS than in RMS patients. Over time, GCIPL atrophy seems to mirror whole-­brain atrophy, especially in gray matter atrophy.44 There is evidence of the relation between MRI changes and the OCT scan. Saidha et al.65 found that whole-­brain atrophy and gray matter atrophy were related to the thinning rate of mGCL thickness. In addition to this, pRNFL and the composite pRNFL/mGCL were associated with cortical gray matter and caudate volumes. On the other hand, Stellmann et al.60 found that focal cortical volume (insular and cingulate cortex) was associated with pRNFL and mGCL thickness. Cilingir et al.66 found a correlation between corpus callosum volume and RNFL thickness, indicating a connection to the cerebral white matter. These data show that the axonal changes observed in the retina can also imply progressive neurodegeneration of the central nervous system of MS patients (Box 4.2).

4.4  O  ptical Coherence Tomography as a Tool for Monitoring Treatment Spectral Domain OCT has become the most useful tool for the diagnosis of macular edema, which can appear as a side effect in MS patients during treatment with fingolimod. Its frequency is low (0.3%) and it typically disappears

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after completion of therapy. In this regard, an ophthalmic examination is required before starting this treatment, in addition to OCT monitoring every 3–4 months, to be able to provide an early diagnosis and end the treatment if macular edema appears.18 Given the sensitivity and reliability of OCT measurement, as well as the insight gained from retinal measures by OCT related to the global MS course of the disease, there is ample support for the potential role of OCT in monitoring neurodegeneration and, consequently, neuroprotection in MS. While nowadays most studies of disease-­modifying treatments (DMTs) in MS routinely examine the impact of brain atrophy and T2 injury accumulation on MRI measures, there is a lack of studies assessing the effects of DMTs on retinal layer thickness. OCT has been included in the most recent MS clinical trials, and is being considered as a tool to measure effectiveness and response to treatment by quantifying the axonal and neuronal loss, expected to diminish after therapy through neuroprotective or myelin repair mechanisms.68 Button et al.69 examined the differential effects of interferon beta-­1a, both subcutaneously (IFNSC, n = 35) and intramuscularly (IFNIM, n = 28), glatiramer acetate (n = 48), and natalizumab (n = 46) on rates of retinal layer atrophy in patients with RMS over a mean follow-­up period of approximately 3 years. The results of this study provide wide support for the application of OCT in monitoring neuroprotective therapeutic effects. Adjusting for age, sex, disease duration and baseline thickness, patients treated with natalizumab exhibited the lowest rates of mGCL thinning, with a rate of 0.17 µm year−1, which was not significantly different to the rate of mGCL atrophy observed in healthy controls (p = 0.720). In contrast, the rate of mGCL thinning was 0.37 µm year−1 (pG0.001) and 0.14 µm year−1 (p = 0.035) in patients treated with IFNSC and glatiramer acetate, respectively. While the study by Button et al.69 suggests that mGCL thickness measures may be useful in assessing the neuroprotective effect of MS DMTs, Knier et al.70 suggest that INL measures could be used to assess the impact on inflammatory activity. They observed globally reduced INL volumes across different types of immunomodulatory therapies 6–9 months after initializing treatment, which could be a sign that inflammatory control is achieved, given that INL thickening is linked with inflammatory activity in MS. Retinal layer measurements of patients treated with fingolimod have been examined by a few small-­scale studies. Differences in certain layers of retinal thickness were reported in MS patients treated with fingolimod, who had lower values than healthy controls but significantly higher values than newly diagnosed treatment-­naive MS patients.71,72 The effect of monoclonal antibodies other than natalizumab on retinal layer changes in MS certainly requires further study. Interestingly, a small-­ scale prospective study by Nguyen et al. presented at the European Committee for Treatment and Research in Multiple Sclerosis (ECTRIMS) 2015 conference reported an RNFL thickening of around 1.5 µm over a period of 2 years in 26 RMS patients treated with alemtuzumab.73

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Evidence of the utility of OCT in monitoring therapeutic response in MS is growing. Further larger longitudinal studies of oral DMTs and monoclonal antibody treatments, including alemtuzumab and ocrelizumab, in MS are required.68 A combination of pRNFL and mGCL thickness measurements would provide information about MS activity and optic nerve disease, and its association with the treatment.15

4.5  Conclusions Recently, the use of OCT in the follow-­up of patients with MS has increased exponentially. After an acute episode of optic neuritis, OCT could be used by clinicians as a non-­invasive tool for diagnostics, monitoring response to treatment, and prognosis in MS patients. On the other hand, considering the impact of neurodegeneration in the pathophysiology of MS and its connection to physical deterioration and cognitive impairment, the use of neurodegeneration biomarkers could be useful to monitor patients in clinical practice and to measure outcomes in clinical trials. The evidence presented in this review shows the promising use of OCT for monitoring MS patients.

References 1. K. Lidster and D. Baker, CNS Neurol. Disord.: Drug Targets, 2012, 11, 518. 2. B. Reed, D. Glabe and L. Yudcovitch, Ocular Coherence Tomography Guide, Faculty Scholarship (COO), 2011, Paper 12, Available at http://commons.pacificu.edu/coofac/12. 3. L. Frisen and W. F. Hoyt, Arch. Ophthalmol., 1974, 92, 91. 4. J. B. Kerrison, T. Flynn and W. R. Green, Retina, 1994, 14, 445. 5. I. Gabilondo, E. H. Martinez-­Lapiscina, E. Martinez-­Heras, E. Fraga-­Pumar, S. Llufriu, S. Ortiz, S. Bullich, M. Sepulveda, C. Falcon, J. Berenguer, A. Saiz, B. Sanchez-­Dalmau and P. Villoslada, Ann. Neurol., 2014, 75, 98. 6. A. J. Green, S. McQuaid, S. L. Hauser, I. V. Allen and R. Lyness, Brain, 2010, 133, 1591. 7. T. M. Jenkins and A. T. Toosy, Curr. Opin. Neurol., 2017, 30, 61. 8. L. J. Balk and A. Petzold, Neurodegener. Dis. Manage., 2014, 4, 165. 9. R. C. Nolan, O. Akhand, J. R. Rizzo, S. L. Galetta and L. J. Balcer, J. Neuroophthalmol., 2018, 38, 202. 10. E. Frohman, F. Costello, R. Zivadinov, O. Stuve, A. Conger, H. Winslow, A. Trip, T. Frohman and L. Balcer, Lancet Neurol., 2006, 5, 853. 11. K. Kallenbach and J. Frederiksen, Eur. J. Neurol., 2007, 14, 841. 12. T. Oberwahrenbrock, S. Schippling, M. Ringelstein, F. Kaufhold, H. Zimmermann, N. Keser, K. L. Young, J. Harmel, H. P. Hartung, R. Martin, F. Paul, O. Aktas and A. U. Brandt, Mult. Scler. Int., 2012, 2012, 530305. 13. V. Parisi, G. Manni, M. Spadaro, G. Colacino, R. Restuccia, S. Marchi, M. G. Bucci and F. Pierelli, Invest. Ophthalmol. Visual Sci., 1999, 40, 2520.

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85

14. J. Kucharczuk, Z. Maciejek and B. L. Sikorski, Neurol. Neurochir. Pol., 2017, 52, 140. 15. G. Rebolleda, L. Diez-­Alvarez, A. Casado, C. Sánchez-­Sánchez, E. de Dompablo, J. J. González-­López and F. J. Muñoz-­Negrete, Saudi J. Ophthalmol., 2015, 29, 9. 16. I. Gabilondo, E. H. Martínez-­Lapiscina, E. Fraga-­Pumar, S. Ortiz-­Perez, R. Torres-­Torres, M. Andorra, S. Llufriu, I. Zubizarreta, A. Saiz, B. Sanchez-­ Dalmau and P. Villoslada, Ann. Neurol., 2015, 77, 517. 17. G. Rebolleda, E. de Dompablo and F. J. Muñoz-­Negrete, J. Neuroophthalmol., 2015, 35, 165. 18. R. E. Iorga, A. Moraru, M. R. Ozturk and D. Costin, Rom. J. Ophthalmol., 2018, 62, 3. 19. F. Costello, S. Coupland, W. Hodge, G. R. Lorello, J. Koroluk, Y. I. Pan, M. S. Freedman, D. H. Zackon and R. H. Kardon, Ann. Neurol., 2006, 59, 963. 20. J. Mateo, O. Esteban, M. Martínez, A. Grzybowski and F. J. Ascaso, Front. Neurol., 2017, 8, 493. 21. U. Birkeldh, A. Manouchehrinia, M. A. Hietala, J. Hillert, T. Olsson, F. Piehl, I. S. Kockum, L. Brundin, O. Zahavi, M. Wahlberg-­Ramsay, R. Brautaset and M. Nilsson, Front. Neurol., 2017, 8, 675. 22. J. M. Gelfand, R. Nolan, D. M. Schwartz, J. Graves and A. J. Green, Brain, 2012, 135, 1786. 23. A. Petzold, Lancet Neurol., 2012, 11, 933. 24. E. Schneider, H. Zimmermann, T. Oberwahrenbrock, F. Kaufhold, E. M. Kadas, A. Petzold, F. Bilger, N. Borisow, S. Jarius, B. Wildemann, K. Ruprecht, A. U. Brandt and F. Paul, PLoS One, 2013, 8(6), e66151. 25. J. M. Gelfand, B. A. Cree, R. Nolan, S. Arnow and A. J. Green, J. JAMA Neurol., 2013, 70, 629. 26. D. M. Wingerchuk and B. G. Weinshenker, Neurology, 2003, 60(5), 848. 27. H. Merle, S. Olindo, M. Bonnan, A. Donnio, R. Richer, D. Smadja and P. Cabre, Ophthalmology, 2007, 114, 810. 28. S. Jarius, F. Paul, D. Franciotta, P. Waters, F. Zipp, R. Hohlfeld, A. Vincent and B. Wildemann, Nat. Clin. Pract. Neurol., 2008, 4, 202. 29. A. Petzold, M. P. Wattjes, F. Costello, J. Flores-­Rivera, C. L. Fraser, K. Fujihara, J. Leavitt, R. Marignier, F. Paul, S. Schippling, C. Sindic, P. Villoslada, B. Weinshenker and G. T. Plant, Nat. Rev. Neurol., 2014, 10, 447. 30. D. M. Wingerchuk, B. Banwell, J. L. Bennett, P. Cabre, W. Carroll, T. Chitnis, J. de Seze, K. Fujihara, B. Greenberg, A. Jacob, S. Jarius, M. Lana-­ Peixoto, M. Levy, J. H. Simon, S. Tenembaum, A. L. Traboulsee, P. Waters, K. E. Wellik and B. G. Weinshenker, Neurology, 2015, 85, 177. 31. P. Hofman, P. Hoyng, F. VanderWerf, G. F. Vrensen and R. O. Schlingemann, Invest. Ophthalmol. Visual Sci., 2001, 42, 895. 32. M. Matiello, J. Schaefer-­Klein, D. Sun and B. G. Weinshenker, JAMA Neurol., 2013, 70, 1118. 33. J. L. Bennett, J. de Seze, M. Lana-­Peixoto, J. Palace, A. Waldman, S. Schippling, S. Tenembaum, B. Banwell, B. Greenberg, M. Levy, K. Fujihara, K. H. Chan, H. J. Kim, N. Asgari, D. K. Sato, A. Saiz, J. Wuerfel, H. Zimmermann, A. Green, P. Villoslada and F. Paul, Mult. Scler., 2015, 21, 678.

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34. R. T. Naismith, N. T. Tutlam, J. Xu, E. C. Klawiter, J. Shepherd, K. Trinkaus, S. K. Song and A. H. Cross, Neurology, 2009, 72, 1077. 35. J. N. Ratchford, M. E. Quigg, A. Conger, T. Frohman, E. Frohman, L. J. Balcer, P. A. Calabresi and D. A. Kerr, Neurology, 2009, 73, 302. 36. M. Bertsch-­Gouta, R. Loeba, A. K. Finch, A. Javed and J. Bernard, J. Neurol. Sci., 2018, 384, 61. 37. A. J. Green and B. A. Cree, J. Neurol., Neurosurg. Psychiatry, 2009, 80, 1002. 38. K. A. Park, J. Kim and S. Y. Oh, Acta Ophthalmol., 2014, 92, 57. 39. J. Britze, G. Pihl-­Jensen and J. L. Frederiksen, J. Neurol., 2017, 264, 1837. 40. M. Pulicken, E. Gordon-­Lipkin, L. J. Balcer, E. Frohman, G. Cutter and P. A. Calabresi, Neurology, 2007, 69, 2085. 41. A. P. D. Henderson, S. A. Trip, P. G. Schlottmann, D. R. Altmann, D. F. Garway-­Heath, G. T. Plant and D. H. Miller, J. Neurol., 2010, 257, 1083. 42. R. Behbehani, A. A. Al-­Hassan, A. Al-­Khars, D. Sriraman and R. Alroughani, J. Neurol. Sci., 2015, 359, 305. 43. R. Behbehani, A. Al-­Hassan, A. Al-­Salahat, D. Sriraman, J. D. Oakley and R. Alroughani, PLoS One, 2017, 12, e0172120. 44. T. Oberwahrenbrock, S. Schippling, M. Ringelstein, F. Kaufhold, H. Zimmermann, N. Keser, K. L. Young, J. Harmel, H. P. Hartung, R. Martin, F. Paul, O. Aktas and A. U. Brandt, Mult. Scler., 2012, 2012, 530305. 45. G. Yousefipour, Z. Hashemzahi, M. Yasemi and P. Jahani, Acta Med. Iran., 2016, 54, 382. 46. Y. Lu, Z. Li, X. Zhang, B. Ming, J. Jia, R. Wang and D. Ma, Neurosci. Lett., 2010, 480, 69. 47. C. Paquet, M. Boissonnot, F. Roger, P. Dighiero, R. Gil and J. Hugon, Neurosci. Lett., 2017, 420, 97. 48. M. E. Hajee, W. F. March, D. R. Lazzaro, A. H. Wolintz, E. M. Shrier, S. Glazman and I. G. Bodis-­Wollner, Arch. Ophthalmol., 2009, 127, 737. 49. S. L. Hauser and J. R. Oksenberg, Neuron, 2006, 52, 61. 50. A. Petzold, J. F. de Boer, S. Schippling, P. Vermersch, R. Kardon, A. Green, P. A. Calabresi and C. Polman, Lancet Neurol., 2010, 9, 921. 51. L. J. Balk, A. Cruz-­Herranz, P. Albrecht, S. Arnow, J. M. Gelfand, P. Tewarie, J. Killestein, B. M. Uitdehaag, A. Petzold and A. J. Green, J. Neurol., 2016, 263, 1323. 52. S. A. Trip, P. G. Schlottmann, S. J. Jones, D. R. Altmann, D. F. Garway-­Heath, A. J. Thompson, G. T. Plant and D. H. Miller, Ann. Neurol., 2005, 58, 383. 53. E. Garcia-­Martin, J. R. Ara and J. Martin, Ophthalmology, 2017, 124, 688. 54. J. B. Fisher, D. A. Jacobs, C. Markowitz, S. L. Galetta, N. J. Volpe, M. L. Nano-­Schiavi, M. L. Baier, E. M. Frohman, H. Winslow, T. C. Frohman, P. A. Calabresi, M. G. Maguire, G. R. Cutter and L. J. Balcer, Ophthalmology, 2006, 113, 324. 55. J. Britze, G. Pihl-­Jensen and J. L. Frederiksen, J. Neurol., 2017, 264, 1837. 56. E. H. Martinez-­Lapiscina, S. Arnow and J. A. Wilson, Lancet Neurol., 2016, 15(6), 574. 57. B. Sedighi, M. A. Shafa, Z. Abna, A. K. Ghaseminejad, R. Farahat and N. Nakhaee, J. Mult. Scler., 2014, 1, 2376.

Optical Coherence Tomography in Multiple Sclerosis

87

58. D. Coric, L. J. Balk, M. Verrijp, A. Eijlers, M. M. Schoonheim, J. Killestein, B. M. Uitdehaag and A. Petzold, Mult. Scler., 2018, 24, 158. 59. F. Ashtari, P. Emami and M. Akbari, Adv. Biomed. Res., 2015, 4, 223. 60. J. P. Stellmann, H. Cetin, K. L. Young, S. Hodecker, J. Pöttgen, D. Bittersohl, A. Hassenstein, T. Oberwahrenbrock, C. Heesen and S. Siemonsen, Brain Behav., 2016, 7, e00614. 61. J. Toledo, J. Sepulcre, A. Salinas Almaran, A. García-­Layana, M. Murie-­ Fernandez, B. Bejarano and P. Villoslada, Mult. Scler., 2008, 14, 906. 62. C. Bjartmar, J. R. Wujek and B. D. Trapp, J. Neurol. Sci., 2003, 206, 165. 63. J. M. Gelfand, D. S. Goodin, W. J. Boscardin, R. Nolan, A. Cuneo and A. J. Green, PLoS One, 2012, 7, e36847. 64. M. Pulicken, E. Gordon-­Lipkin, L. J. Balcer, E. Frohman, G. Cutter and P. A. Calabresi, Neurology, 2007, 69, 2085. 65. S. Saidha, O. Al-­Louzi, J. N. Ratchford, P. Bhargava, J. Oh, S. D. Newsome, J. L. Prince, D. Pham, S. Roy, P. van Zijl, L. J. Balcer, E. M. Frohman, D. S. Reich, C. Crainiceanu and P. A. Calabresi, Ann. Neurol., 2015, 78, 801. 66. V. Cilingir, M. Batur, M. D. Bulut, A. Milanlioglu, A. Yılgor, A. Batur, T. Yasar and T. Tombul, Neurol. Sci., 2017, 38, 1223. 67. M. A. Zarbin, L. M. Jampol, R. D. Jager, A. T. Reder, G. Francis, W. Collins, D. Tang and X. Zhang, Ophthalmology, 2013, 120, 1432. 68. J. Lambe, O. C. Murphy and S. Saidha, Curr. Treat. Options Neurol., 2018, 20, 9. 69. J. Button, O. Al-­Louzi, A. Lang, P. Bhargava, S. D. Newsome, T. Frohman, J. Prince, P. A. Calabresi and S. Saidha, Neurology, 2017, 88, 525. 70. B. Knier, P. Schmidt, L. Aly, D. Buck, A. Berthele, M. Muhlau, C. Zimmer, B. Hemmer and T. Korn, Brain, 2016, 139, 2855. 71. A. Kal, M. Oğuz Ulusoy, B. Horasanlı, S. Cezairlioğlu and O. Kal, Mult. Scler. Relat. Disord., 2017, 14, 4. 72. R. Nolan, J. M. Gelfand and A. J. Green, Neurology, 2013, 80, 139. 73. A. L. Nguyen, J. Lam, R. White, R. Carruthers and A. Traboulsee, Neurology, 2016, 86, 3083.

Chapter 5

Experimental In Vivo Models for Drug Discovery in Multiple Sclerosis Leyre Mestre* and Carmen Guaza Instituto Cajal (CSIC), Av. Dr. Arce 37, 28002 Madrid, Spain *E-­mail: [email protected]

5.1  Introduction Multiple sclerosis (MS) is a chronic neuroinflammatory demyelinating disease characterized by perivascular inflammatory infiltration into the brain parenchyma and meninges, demyelinating plaques, axonal damage and gliosis. It entails a progressive accumulation of disability. MS affects over 2.5 million people world-­wide, with approximately 700 000 cases in Europe; this implies that more than one million people are affected by this condition taking into account both caregivers and family members (European MS platform: http://www.emsp.org).1 At present, MS is considered to be a disease autoimmune in nature, although there is no evidence that it may be due to an intrinsic primary alteration of the immune system, but rather to a normal response to an inadequate antigen or inappropriate antigenic exposure. Research for MS treatments has two approaches: either to tackle the cause that triggers it or to eliminate the symptomatology. To date, MS etiology remains unknown, hence experimental animal models try to reproduce the   Drug Discovery Series No. 70 Emerging Drugs and Targets for Multiple Sclerosis Edited by Ana Martinez © The Royal Society of Chemistry 2019 Published by the Royal Society of Chemistry, www.rsc.org

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histopathological signs observed in patients, focusing mainly on inflammation and demyelination. The validation of an animal model is more complicated as less is known about the origin of the pathology; therefore it is common to use models that replicate most symptoms of pathology, or in some cases it is necessary to use different models that replicate one specific sign of the disease. Although no model fully replicates the processes that occur in MS, there are several established experimental models that reflect the heterogeneity of MS and resemble its pathology. These models include “immune-­mediated”, “virus-­induced” and “toxin-­induced” models. The “immune-­mediated” models are based on the autoimmune reaction developed both after immunization with myelin protein fragments and passive transfer with autoreactive T cells against myelin epitopes. Experimental autoimmune encephalomyelitis (EAE) is, by far, the most exploited model for studying the autoimmune processes in the central nervous system (CNS).2,3 The “virus-­induced” models allow not only the study of the pathophysiology of the disease but they also take into account the hypothesis that some environmental factors, such as viral infections, are involved in MS and may be a trigger of the disease.4 The main exponent of this model is Theiler's murine encephalomyelitis virus-­ induced demyelinated disease (TMEV-­IDD).5 Lastly, the “toxin-­induced” models are not considered reference models of MS, since they try to model only one feature of this pathology; however, they are especially suitable for studying the de-­and re-­myelination processes.

5.2  “ Immune-­mediated” Model: Experimental Autoimmune Encephalomyelitis (EAE) EAE is one of the most studied animal models of MS and is based on the reaction of the immune system against CNS-­specific antigens.6 This reaction induces inflammation and destruction of the antigen-­carrying structures, resulting in neurological and pathological features that resemble those observed in MS patients.7,8 The first attempt to induce EAE dates back to 1933 and was carried out in primates by the intramuscular injection of a rabbit homogenate.9 In the 1940s, the technique was perfected by observing a greater reproducibility in the results when emulsifying the CNS antigens in complete Freund's adjuvant (CFA) together with Mycobacterium.10,11 This method would later be known as “active induction of EAE” as the inducer and effector phases occur in the same animal. Some decades later, “the passive induction of EAE” was developed by injecting encephalitogenic T lymphocytes from an immunized animal12 and more recently, a spontaneous EAE model has been developed based on TCR-­transgenic mice directed against myelin antigens such as PLP139–151,13 MOG35–55 14 and MOG92–106.15 This type of model allows the study of autoimmune mechanisms without exogenous manipulation. However, the percentage of incidence is variable and frequently low, so it is not usually used.

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It is important to notice that the pathology of lesions, and therefore the progression of the disease, depends on the animal strain, the antigen inoculated or the induction pathway16–18 leading to monophasic-­, chronic-­ or relapsing–remitting EAE. In this line, Moreno et al. summarized in 2012 the different models of EAE and their applications for research in the field.19 For most strains and peptides, disease appears between the second and fourth weeks post-­immunization and is characterized by an ascending hind-­limb paralysis that begins in the tail and spreads to the limbs. The disease can be graded on a scale from 0 to 5, with 0 being no differences in naïve mice and 5 being when mice are moribund and show a paralysis in all limbs.20,21 Several reviews detailed different protocols for EAE induction, both in rats22 and mice.23,24 The active induction of EAE consists in the subcutaneous immunization of susceptible strains of rodents (mice, rats or guinea pigs) or nonhuman primates with spinal cord homogenates, myelin-­related antigen or peptide emulsified in CFA. To generate a disease, mice require additional injections of pertussis toxin given on the day of immunization and 48 hours later.21,25 This inoculation facilitates immune cell entry to the CNS as well as promotes proliferation and cytokine production by T cells and breaks T cell tolerance.26 After immunization, the immune response leads to the appearance of activated autoreactive T cells that proliferate in secondary lymphoid organs. The effector phase is characterized by the extravasation of both T lymphocytes through the blood–brain barrier (BBB) to the CNS, and peripheral monocytes/macrophages attracted by chemokines and cytokines released by myelin-­specific T cells. Examples of these molecules are IFNγ, IL-­4, IL-­10, TNFβ, TNFα and IL-­17.27,28 The activation of the phagocytic capacity not only of the infiltrated monocytes/macrophages, but also of the microglia resident in the CNS, is involved in the demyelination of the characteristic axonal tracts in this pathology.24 Passive or adoptive EAE are induced in recipient animals by transferring myelin-­specific CD4+ encephalitogenic T cells generated in a donor animal by active immunization.29 Last year a new protocol for passive induction of EAE by adoptive transfer of dendritic cells (DCs) primed with myelin antigen into naïve mice was reported.30 Although the clinical features of disease induced by adoptive EAE are identical to those induced by active EAE, passive EAE presents some advantages, such as: (1) it allows the study of the effector phase independently from the induction stage; (2) it has high reproducibility, by injecting the same amount of self-­reactive cells to each animal, thus avoiding the variability of activation in the inducing phase; (3) there is no antigen depot to present leading to continuous de novo activation of naïve T cells; (4) it can be used to track encephalitogenic T cells from the CNS. Whereas the active and passive models of EAE have limited the possibility of identifying the immunological mechanisms that initiate or control relapses of disease, spontaneous models of EAE allow these problems to

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be sidestepped. A spontaneous EAE model has been developed based on TCR-­transgenic mice mainly in CD4+ T cells. The most frequently used are the 2D2 mice who express TCR specific for the MOG35–55 on the C57B1/6 background.14 CD8+ T cells are involved in MS disease, but the introduction of MBP-­specific receptors in mouse CD8 TCR did not result in spontaneous disease.31 However, GFAP-­specific CD8+T cell receptor transgenic (BG1) mice develop spontaneous relapsing–remitting EAE.32 The involvement of B cell receptors in spontaneous EAE has been evaluated by double transgenic carrying MOG-­specific CD4 T cells and a heavy chain of MOG-­binding immunoglobulin.33 One of the main goals of the spontaneous model of EAE was to discover the critical relevance of gut microbiota in MS development. SJL/J anti-­MOG92–106 TCR transgenic relapsing–remitting mice kept in germ-­free conditions did not develop spontaneous EAE, but recolonization with conventional commensal microbiota from SPF mice induced an increased incidence of up to 80%.34 Regardless of the histopathology of EAE, initially the disease can be characterized by CD4+ T cell and F4/80+ cell perivascular infiltration mainly in the white matter of the CNS. In most EAE models, the pattern of infiltration tends to concentrate in the thoracic section of the spinal cord.21 During EAE the inflammatory response is accompanied by microglia and astrocyte activation, demyelination and axonal loss. In 2005, Day et al. reviewed in detail the specific histopathological features of EAE that occur in SJL/J, C57B1/6 and NOD mice as well as in Lewis rat, rhesus monkey and marmoset.35 Cortical demyelination is a histopathologic feature of MS that has been of great importance in recent years. In this sense, although toxic models have been developed to study focal demyelination in different brain areas, several EAE models could be a useful tool to address this feature. In the past, only a few experiments have demonstrated the presence of cortical damage in the EAE model similar to that seen in MS patients;36,37 more recent studies in MOG-­induced EAE mouse model confirmed the presence of cortical and callosal abnormalities that resemble those observed in progressive MS.38 The pending subject of this model and of others that we will develop later is that many of the effective therapies in EAE cannot be extrapolated to the clinic. This fact makes it necessary to design studies aimed at delving into knowledge of the pathogenic mechanisms that occur in the EAE, both in the initial phases of the process and in chronic advanced situations—perhaps with the induction of a moderate EAE that allows the maintenance of the animal for longer periods of time.39

5.3  Theiler's Virus Model Historic prevalence and epidemiological studies of MS allowed observation of an irregular worldwide distribution of this pathology, with mayor frequencies between 40–60 degrees latitude in the north and in the south.40

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The recent study “Atlas of MS 2013” also corroborates this and therefore supports the idea that MS etiology involves a genetic susceptibility and environmental trigger factors.41 Infections with coronavirus-­like murine hepatitis virus (MHV),42 herpesvirus-like human herpesvirus 6 (HHV-6)43,44 or the Epstein–Barr virus45–48 have been suggested to contribute to MS. In addition, genetic evidence points towards the endogenous retroviruses HERV-­Fc, HERV-­K13 and HERV-­K18 as playing an essential role in the causation of MS and other autoimmune diseases.49–51 Although the exact mechanism by which a viral infection triggers an autoimmune pathology is unknown, three theories have been proposed. First, the theory of viral superantigens involving the non-­specific activation of autoreactive T cells by immunostimulatory molecules produced by the virus that do not bind to the trimolecular T-­antigen-­MHC receptor complex.52 Second, the theory of epitope spreading involving activation of autoreactive T cells against myelin-­specific antigens after tissue destruction as a consequence of the specific immune response of the infectious agent.8 Third, a theory called molecular mimicry involves the activation of autoreactive T cells by epitopes that, although encoded by the infectious agent, have a certain homology in their sequence or in their structure with myelin epitopes.53,54 While several viruses (murine hepatitis virus (MHV),55,56 Semliki Forest virus (SFV)57 and sindbis virus (SV))58 have been described to model multiple sclerosis signs, the best characterized virus-­induced demyelination model is Theiler's murine encephalomyelitis virus (TMEV)-­induced demyelinating disease (TMEV-­IDD). The model was described by Max Theiler in 1934 when he isolated this natural enteric virus from the CNS of mice having flaccid hind-­ leg paralysis.59 In 1952 Daniels et al. isolated another TMEV strain, named Daniels (DA), from spontaneously paralyzed mice that were shown to induce spinal cord demyelination.60 It was not until 1975 that Lipton described an experimental mouse model of TMEV-­DA-­induced demyelination.61 A complete description of the protocol to induce the TMEV-­IDD model was published by Miller's laboratory.21 Theiler virus is a non-­enveloped, positive-­stranded RNA virus that belongs to the Cardiovirus genus from the Picornaviridae family. It consists of 8100 nucleotides that code for 12 proteins, four of the viral capsid (VP1, VP2, VP3, VP4), the first two of which are involved in the persistence of the virus, seven essential for its replication (2A, 2B, 2C, 3A, 3B, 3C, 3D),62 and the leader protein (L) related to persistence, modulation of gene expression and cell death.63 Different strains of this virus are distinguished according to their neurovirulence as: (1) extremely virulent or GDVII strains (including GDVII and FA strains) that induce acute encephalomyelitis, which in most cases is lethal and does not persist in the few animals that survive; and (2) less virulent or Theiler's original that consists of BeAn 8386 and DA strains, which fail to induce severe encephalitis60 but cause persistent infection in the CNS.23 Although BeAn and DA virus strains induce a biphasic demyelinating disease in susceptible mice, the pathophysiologic mechanism and therefore the kinetics of the disease caused by the two strains are slightly different. BeAn-­ infected SJL mice develop clinical signs between 30 and 40 days post-­infection

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(dpi) whereas in DA-­infected mice clinical signs appear at approximately 140 to 180 dpi62 Moreover, the DA strain induced higher frequency and extent of demyelination in the spinal cord and more virus antigen-­positive cells during chronic stages of the disease.64 TMEV-­induced biphasic disease consists of an early acute phase (3–12 dpi) characterized by a multifocal inflammation involving CNS gray matter, with monocytes, CD4 and CD8 T cells, macrophages and a few B cells and plasmatic cells infiltrated,65 rapid virus replication in neurons and neuronal apoptosis.66–68 During this early phase, white matter of the spinal cord remains unaffected,62 underpinning the hypothesis that axonal damage (inside) provokes the immune system to recruit proinflammatory mediators than generates lastly the demyelination (outside) (known as the inside-­out model).69 Depending on the strain and the dose of the virus, in most of cases this early acute disease does not show clinical signs. Susceptible mice strains fail to completely clear the virus, which persists in monocytes/macrophages, microglia, astrocytes and oligodendrocytes, triggering a chronic demyelinating disease in which the main feature is progressive spinal cord atrophy and axonal loss with ensuing neurological deterioration70 that includes a symptomatology resembling that observed in MS patients (spasticity, incontinence, weakness in the extremities and finally paralysis of the hind legs).71,72 One feature which is particularly evident in SJL/J mice is the presence of large numbers of macrophages loaded with myelin debris which quickly populate the spinal cord columns.73 Similar to the appearance of oligoclonal bands in cerebrospinal fluid of MS patients, intracerebral infection with Theiler virus of strains of susceptible mice induces intrathecal production of antibodies.74 It should be noted that intracranial inoculation of TMEV to susceptible mice (SJL/J) must be performed in a temporal window between 4-­ and 8 week-­old mice to induce the chronic demyelination phase described in MS patients. This fact is consistent with the hypothesis that the origin of MS could be due to a viral infection during childhood and early adolescence. In summary, the Theiler's virus model is the key model to investigate virus-­ mediated mechanisms of acute and primary progressive MS. Risk factors for MS include genetic background, mainly MHC antigens, and environmental factors such as viral infections. TMEV-­IDD mimics human MS in a variety of aspects that include chronic inflammation involving CD4+ and CD8+ T cells, B cells and myeloid cells; sex influenced disease course and symptomatology such as gait alterations and incontinence. Histopathology shows clear signs of axonal degeneration and spinal cord atrophy during the chronic phase of TMEV-­IDD. Therefore, TMEV results are useful to reproduce acute or chronic phases of progressive MS.

5.4  Toxin-­induced Demyelination Models Although MS is considered an autoimmune disease, it is not clear whether it is the consequence or the origin of the main histopathological characteristic, the axonal damage and demyelination. Thus it is important

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to develop models that can study demyelination independently from the autoimmune component and in a localized area. Accordingly, toxin-­ induced demyelination models are really useful tools to study demyelination/remyelination processes. Demyelination can be induced by direct injections of gliotoxins such as ethidium bromide (EtBr) and lysolecithin (LPC), or systematically administered toxins, such as cuprizone, in the white matter.

5.4.1  Ethidium Bromide (EtBr) The first evidence of EtBr-­induced demyelination in the spinal cord dates from 1982 when Blakemore injected this toxin into the dorsal column of the spinal cord of cats and observed demyelinated areas between 8 and 14 days later.75 Actually we know that the stereotaxic injection of EtBr into specific white tracts preferentially compromises mtDNA transcription in glial cells than neurons and endothelial cells.76 The hallmark of EtBr delivery to white matter to induce lesions is astrocyte and oligodendrocyte loss, while axons remain unaffected. In the absence of oligodendrocytes, Schwann cells migrate into the lesion and remyelinate.75 Recently, focal intrahippocampal injection of EtBr has been proposed as a simple model to study cognitive deficit and gray matter demyelination.77 While the EtBr model allows analysis of a localized area of demyelination, this model is not totally lacking immunological components,76 and remyelination declines with age.78

5.4.2  Lysolecithin (LPC) In 1972 Hall observed a fibre damage consistent with Wallerian degeneration, changes in the lamellar pattern of the myelin sheath, myelin debris in vacuoles in perivascular macrophages and a rapid glial response 48 hours after LPC injection in the dorsal column of the spinal cord of mice,79 being the first evidence of the demyelinating effect of LPC. Historically several mechanisms have been proposed to explain this effect, such as:    1. Receptor-­mediated pathways: There is no known receptor that binds directly to LPC; however, LPC can act through G protein coupled receptors or RhoA signalling or even GPR4 receptors.80,81 2. Lysophosphatidic acid (LPA): This is a hydrolysis product of LPC mediated by autotaxin. Evidence that supports this is that autotoxin is present in oligodendrocytes82 and that LPA injection into the spinal cord white matter is enough to induce demyelination.83 3. Inflammation-­mediated demyelination: This should not be ruled out since LPC injection increases the release of free radicals from microglia.84 4. The non-­specific lipid disruption properties of LPC.85   

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All of the above putative mechanisms have been recently and elegantly reviewed by Yong's group, concluding that receptor-­mediated mechanisms and inflammation were unlikely to drive primary demyelination following LPC injection. LPC-­induced toxicity is likely due to its lipid disrupting properties and requires free, unbuffered, LPC.86 Usually 1% LPC solution is injected into the dorsal column of the spinal cord, caudal cerebellar peduncle or corpus callosum to study the demyelination/remyelination processes. The early changes due to LPC involve a reduction in cells of the oligodendrocyte lineage at 4 hours after injection, which is nearly disappeared at 24 hours. At this time point sparse Iba1 immunoreactivity for microglia was described. However, at 72 hours a return of oligodendrocyte progenitor cells (OPCs) adjacent to the injury site with increased microglia immunoreactivity and a completely lost of GPAF staining was found.86 Generally, a lack of myelin sheaths is observed in the site of injection after 7 days post-­injection, and thinly myelinated sheaths appear throughout the lesion at day 14.87 As in the EtBr model, the age is a factor that can slow down remyelination, which begins only after the myelin debris is removed.88 This model presents the disadvantage of the absence of the immune response that is observed in MS.

5.4.3  Cuprizone (CPZ) The oxalic acid bis [cyclohexylidene hydrazide] (Cuprizone) is a low molecular copper-­chelating agent toxic to myelin sheath that induce reversible demyelination in both white and gray matter in the murine brain.89 Signs of cuprizone intoxication, such as small lesions throughout the brain, spongy demyelination and astrogliosis, were first described by Carlton.90 It is assumed that cuprizone intake causes dysfunction of mitochondrial enzymes with the subsequent oxidative stress leading to oligodendrocyte apoptosis. However, neurons, astrocytes, microglia or OPCs seems to be unaffected by CPZ, at least in in vitro studies.91 Several factors are contributing to the specific vulnerability to oxidative stress of oligodendrocytes, such as the low levels of manganese superoxide dismutase or the reduced cuproenzyme activity during CPZ treatment as well as the glutathione (see review92). Two experimental setups are generally used to study the de-­ and re-­ myelination processes, as well as micro and astrogliosis following CPZ intake. First, to study spontaneous remyelination after acute demyelination: for this, C56BL/6 mice are fed with 0.2% CPZ-­supplemented diet for 4–6 weeks followed by recovery on a normal diet. Second, to study chronic demyelination the same strain of mice are fed with 0.2% CPZ-­supplemented diet for 12 weeks.93 Certainly, some variability occurs in CPZ-­induced demyelination; therefore alternative protocols have been described. An example is mTOR inhibition by rapamycin injection at the same time as the CPZ-­supplemented

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diet to eliminate the attempt of OPCs to remyelinate axons and provide a greater demyelination period.94 To avoid differences in the CPZ intake of each animal that could explain discrepancies in demyelination, oral gavage of CPZ has been reported, minimizing demyelination variability.95 According to the pattern 3 of demyelination described by Lucchinetti in MS patients,96 demyelination induced by CPZ shows active demyelinating lesions with a high presence of microglia/macrophages and sparse involvement of T cells. Signs of metabolic stress and mitochondrial dysfunction that could lead to oligodendrocyte apoptosis have been also observed. In the chronic demyelination schedule, CPZ-­induced fails in remyelination despite the presence of OPCs as occurs in MS. Together with the control over the length of CPZ intoxication and thus over remyelination, the CPZ model offers several advantages over other models of MS.

5.5  A  nimal Models as a Tool for New Therapy Development Experimental animal models are an extremely useful tool for research in MS in terms of deepening the knowledge of its etiology and pathogenic mechanisms and hence devising and testing new therapies. However, the translation of scientific discoveries in experimental models into effective treatments for MS patients often fails, suggesting that current models for preclinical research have insufficient predictive value. It is a priority to develop and improve the animal models in use so that they reproduce as much as possible the pathophysiology of MS for any translational application from the laboratory to the clinic. Table 5.1 summarizes the possible applications, advantages and disadvantages of each animal model described before. As no single experimental model reproduces all aspects of MS, it is necessary to use different models depending on the objective of the study. For example, toxic models of LPC, BrEt or cuprizone are critical for the discovery of therapeutic drugs with potential efficacy on remyelination. The TMEV-­IDD model reflects key features of MS-­like inflammatory demyelination, although the specific mechanism of demyelination is still unknown. While some researchers advocate an immune-­mediated mechanism (outside-­in model), others defend an initial neuron infection in the grey matter which in turn leads to axonal degeneration and secondary demyelination (inside-­out model). The EAE model is the most used to validate the therapeutic effect of new compounds in MS. It is mainly induced by auto-­reactive CD4 T cells, but several pathological data and results from clinical trials in MS reflect that CD8+ T cells97 and B-­lymphocytes98,99 play an important role in propagating inflammation and tissue damage in established MS. These facts could explain discrepancies in efficacy observed in the EAE model and MS clinical trials. Recently, toxin-­induced demyelination models have been highly useful for unravelling mechanisms of de-­ and remyelination, but do not reflect other important aspects of MS pathology and pathogenesis.

Model

Applications

Advantage

Disadvantage

Passive-­EAE

●● Analysis of immune surveillance of the

●● Encephalitogenic T cells are already

●● Mainly restricted to CD4+ T cell

●● Analysis of the molecular mechanisms

●● Possibility to study T cell ­phenotypes ●● Without widespread focal primary

CNS

involved in T-­cell-­mediated brain inflammation ●● Preclinic validation of new drugs Active-­EAE

●● ●● ●● ●●

Spontaneous-­ ●● EAE ●●

TMEV-­IDD

●● ●● ●●

raised and expanded

(Th1, TH17)-­induced disease

●● The outcome of brain i­nflammation

response

demyelination

(continued)

97

is not influenced by specific immune activation in the periphery ●● Adjuvants such as CFA or Analysis of the molecular mechanisms ●● Easy to induce ●● The waiting time for the appearance involved in T-­cell mediated brain ­additional treatments like inflammation ­pertussis toxin are required of clinical signs is short (days) Study of subpial cortical demyelinated ●● Possibility of using transgenic mice ●● Primary demyelination is sparse lesions (in case of rat model) of C57BL/6 genetic background or absent ●● Pathology mainly confined to the Study of plaques of primary ­demyelination (in rat, guinea pig or spinal cord, with low affect on the ­primate models) brainstem and the cerebellum and very little inflammation or Preclinic validation of new drugs tissue damage in the fore brain ●● Spontaneous start and relapse dis●● Sparse primary demyelinated First model to study the role of gut microbiota in the induction of brain ease process lesions ●● Low incidence inflammation ●● Great variety of timing of Analysis of the mechanisms of antigen/ epitope spreading in the induction of ­outcome disease chronic brain inflammation. ●● Demyelinated disease is not induced ●● No MS-­specific virus infection has Study mechanisms of virus clearance from the CNS by specific T cell immunogen been demonstrated to date ●● Disease pathogenesis is complex Analysis of virus-­induced inflammatory ●● Support viral etiology of MS ●● Useful model of progressive MS demyelinating disease and difficult to dissect ●● CD8+T cells involved in axonal ●● The waiting time (latency) for the Preclinic validation of new drugs for progressive MS pathogenesis appearance of clinical signs is long (months)

Experimental In Vivo Models for Drug Discovery in Multiple Sclerosis

Table 5.1  Summary  of different in vivo models for multiple sclerosis.

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Table 5.1  (continued) Model

Applications

Advantage

Disadvantage

Toxic models by cuprizone

●● Useful for analysis of the timing of

●● Allows the study of the basic

●● Rapid remyelination after toxic

demyelination/remyelination processes

­ echanisms of de-­and m ­remyelination, mainly in the corpus callosum

cessation

●● Demyelination mainly restricted ●●

●● Allows the study of focal area of Toxic models ●● Analysis of demyelination/­ by BrEt remyelination processes demyelination where astrocytes and ●● Analysis of cognitive deficits and oligodendrocytes are lost and axons remain unaffected grey matter alteration (injection in hippocampus) ●● Allows the study of focal area of Toxic models ●● Analysis of demyelination/­ by LPC remyelination processes in spinal cord, demyelination cortex, cerebellum and corpus callosum ●● OPCs are not affected

●● ●● ●●

to corpus callosum, which does not resemble that of MS patients Absence of inflammatory ­processes present in MS patients Age and strain dependent Remyelination could be mediated by Schwann cells Spontaneous recovery decreases with age

●● Rapid remyelination

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Axonal degeneration, nerve cell loss and dendritic/synapsis injury also occur in many other inflammatory conditions in the brain without primary demyelination; hence they are not MS-­specific markers. The key feature distinguishing MS from other inflammatory diseases of the brain is the subpial demyelinated lesions in the cerebral cortex described by Bo in 2003.100 Improved knowledge on the physiopathology of MS has led to addressing the issue of the reproducibility of MS pathology in experimental models.101 Specifically, interferon beta (IFNβ) was one of the first molecules investigated in EAE that offered satisfactory results in terms of its therapeutic activity. This same compound in the TMEV-­IDD model was also effective in the short-­term modulation of the expression of the major histocompatibility complex of class I (MHC-­I) and therefore the action of CD8+ T lymphocytes. However, other preclinical trials have had variable success depending on the animal model used. Following literature reviews,102,103 effective therapies in TMEV-­IDD seem to be more easily extrapolated to the clinic than those developed in the EAE model. The most appropriate is the validation of innovative therapies for MS in two or more models, as this point is critical. Actually, numerous studies support the usefulness of EAE variants for the discovery of new therapies for MS such as B cell depletion (ocrelizumab);104,105 Th1/Th2 balance regulation (laquinimod),106 reset of T cells by cladribine,107 sphingosine 1-­phosphate receptor modulation (siponimod),108 phospodiesterase 7 inhibitors109 and gut microbiota alterations by probiotics.110–112 Among its main advantages are the neuropathological, clinical and immunological similarities with MS; in addition, it is a fast model and has high reproducibility. Most of therapies approved for the treatment of MS are effective in EAE, and specifically two of them, glatiramer acetate and natalizumab, were developed from this model. However, not all effective treatments in EAE have been extrapolated to the clinic, either because of demonstrating some toxicity (blockade of TNF-­α) or not presenting the same efficacy. These discrepancies may be due to the fact that treatments in animals last for weeks or at most months, which may not be enough time to unmask toxic side effects that could arise from chronic treatment for years in MS patients. In this sense, the administration of the TNF-­α blocker (infliximab) resulted in an increase in the activity recorded by MRI113 and the treatment with linomide led in some cases to the appearance of heart disease.114 As a general rule, animal experimentation does not contemplate the appearance of opportunistic infections, so it does not predict the development of secondary diseases derived from treatment. An example of this occurred after the clinical application of natalizumab, observing the appearance of several cases of progressive multifocal leukoencephalopathy due to the infection of the JC40 virus.115 Other reasons responsible for the disparity of results between preclinical and clinical trials include: (1) immunological differences between mouse and human, such as the percentage of lymphocytes in peripheral mouse blood is double that of humans, MHC-­II expression is absent on murine T and endothelial cells but present in humans and CD52 is present in human lymphocytes but not

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in mouse; (2) in most cases each experiment includes animals from the same litter, ignoring the genetic variability present among MS patients; (3) in many cases researchers use animals with specific genetic modifications, which cannot be extrapolated to humans; (4), generally EAE is induced by a known protein antigen, which does not mimic one of the main characteristics of MS, its unknown etiology. A question that we cannot forget whenever animal models are used is to what extent they reproduce the mechanisms of induction or pathogenesis of the disease they model and the differences between species, for example, in the immune system. As previously mentioned, EAE is a disease mainly mediated by CD4+ T lymphocytes Th1, Th2 and myelin-­specific CD8+ T lymphocytes; however, studies carried out in recent years increasingly give prominence to the CD4+T lymphocyte Th17.116 In fact, this cell type, as well as CD4+ Th1 cells, could be behind the variable efficacy of IFNβ treatment, as it decreases the clinical signs of mice with EAE-­induced Th1 cells while exacerbating them in mice with EAE passively induced with Th17 cells. This observation could clarify the lack of response of some patients with relapsing–remitting MS (RRMS) to treatment with IFNβ.

5.6  S  trategies for New Therapy Development for Progressive MS Most of the therapeutic targets have been discovered for RRMS forms, as illustrated in Figure 5.1. However, progressive forms of MS involve the accumulation of neurological disability without a clear knowledge of the pathologic process that drives this disability progression, although it is possible to delineate some events such as compartmentalized inflammation, mitochondrial dysfunction, oxidative injury, accelerated neurodegeneration and ageing-­related immune changes, among others. Disease-­modifying drugs have mostly failed as treatments for progressive MS. Management of progressive MS just aims to minimize symptoms, making the development of therapeutics a priority challenge. The main difficulty in modelling the progressive forms of MS is replicate the chronicity and irreversibility that characterize these forms together with the presence of the peculiar cortical lesions. TMEV-­IDD is one of the best experimental models that mimic the features of progressive MS. Even so, in recent years several animal models have been developed, combining EAE variants and focal injection of cytokines in the rat or mouse cortex.117 In addition, the EAE model in marmoset monkeys shows pathological hallmarks of progressive MS-­like demyelination of cortical grey matter with microglia activation, oxidative stress and redistribution of iron.118 It is obvious that most clinical trials have employed immunomodulating or immunosuppressant drugs effective in RRMS, but the most negative data to date suggest that the focus of progressive MS trials should shift to a primary neuroprotective stance. Recently, there has been some early modest success with siponimod in secondary progressive MS and ocrelizumab

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Figure 5.1  Therapeutic  targets for the development of new drugs for multiple sclerosis.

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in primary progressive MS, but this might not work for older patients in the most advanced stage of disease. However, apart from those two immunomodulatory agents, there is not much in the late-­stage pipeline for progressive MS. At this point, it seems clear that MS might require stage-­dependent therapeutic strategies. Several therapies include immune ablation with subsequent bone marrow transplantation (stem cells) for compartmentalized inflammation.119,120 In this context, there is gaining strength for the notion that progressive MS is driven by innate immune cells such as microglia and astrocytes in the CNS.121 Because disease mechanisms in the last stage of MS are similar to those of brain ageing, the therapeutic goal should be both functional improvement and reduction in speed of neurodegeneration. Examples of treatments are the use of high doses of biotin to counteract energy deficiency.122 Additionally, some results suggest that the progression of neurodegeneration may be reduced by simvastatin.123 Actually, a new Phase III study (MS-­STAT2) in the UK is ongoing to evaluate whether simvastatin can slow down disability progression. An alternative strategy is the promotion of remyelination and repair124 by pharmacological approaches or by cell transplantation. Despite the enormous difficulties involved in progressive MS, the researcher must use or develop the preclinical experimental model that best fits the objectives pursued. Ageing is a risk factor related to an increase in susceptibility to developing diseases with more severe alterations and chronicity due to a lack of an appropriate organism response, which hinders homeostasis recovery. Regarding the immune system, ageing is associated with a progressive decrease in immunological efficiency, or immunosenescence, which implies a remodelling of the innate and acquired immunity that can alter the incidence, severity and susceptibility of autoimmune diseases including MS. Chronological age is an important fact in MS, both juvenile (children and adolescents) and can determine the course of the disease with faster transformation to the secondary progressive phase; thus, the conversion and onset of progressive MS could be influenced by age rather than factors related to the disease itself. Ageing also impairs regenerative processes, including remyelination, by affecting myeloid cell motility and phagocytosis of myelin debris.125 In addition, age influences susceptibility and symptomatology during the course of experimental models of MS. As commented before, the EAE model is specific to species and strain, but also highly dependent on the age at which the antigen is inoculated as well as the age of evaluation of the immunological parameters. Is there a critical period of response to the induction of the disease? In the Lewis rat model, an increase in the incidence of acute neurological deficits and relapses as well as chronicity of symptoms and a greater number of lesions in older animals have been described. However, in the Fisher rat there is a clear decrease in the

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susceptibility to EAE with increasing age. In SJL/J mice, a marked reduction in the incidence of EAE has been reported with increasing age. In other experimental models of progressive MS, such as the TMEV-­IDD there is a need to explore the changes associated with age on the effectiveness and function of the immune system. Currently, a topic of intense research is the exploration of the consequences of ageing in animal models of MS from a translational perspective to cover preclinical studies able to reproduce the pathological features of older MS patients. To date there is no information about the use of animal models of ageing such us transgenic mice with altered genes involved, for example, in oxidative stress and then subjected to diverse EAE variants. Cumulative evidence shows that neuronal mitochondrias are damaged in progressive MS. A number of studies reported a decrease in nuclear coded transcripts of mitochondrial respiratory chain complexes,126 mitochondrial DNA deletions127 and impaired anterograde transport of mitochondria, which is critical for replenishing the axon with healthy mitochondria in physiological situations. In progressive MS the deficiency of respiratory chain complex compromises the capacity of axon to generate ATP, resulting in an energy failure state in a situation with high energy demand by the demyelinated axon, particularly in long projection axons such as the corticospinal tracts.128 The impaired mitochondrial axonal transport has been confirmed in animal models of MS.129 Whether the manipulation of mitochondria protects demyelinated axons requires further investigation in preclinical systems that model the mitochondrial alterations in neurons as described in progressive MS.

5.7  Conclusion The development of MS animal models is critical to clarify the pathological mechanisms and to explore novel therapeutic agents for the treatment of the different clinical and pathological phenotypes of this extremely heterogeneous disease. Noting the intrinsic advantages and limitations of each animal model, it is possible to summarize that EAE, the most studied animal model of human MS, presents enormously versatility and can model acute and chronic courses of the disease, but also the relapsing–remitting course which affects the majority of patients. The TMEV-­IDD model is a particularly useful tool to investigate primary progressive MS and how a virus can induce autoimmunity. Toxin-­induced models that bypass the autoimmune component are more adequate to investigate the dynamics of demyelination and remyelination. The choice of the experimental model is essential for study design and depends on the objectives pursued with the therapy to be tested. Finally, when translating from animal model to human pathology, it is important to standardize the models to improve the success of the preclinical findings.

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Acknowledgements The authors thank Miriam Mecha, Ana Feliú and Francisco J. Carrillo-­Salinas for their reading and discussion of this manuscript. The authors gratefully acknowledge support from the Spanish Network of Multiple Sclerosis Research, REEM, RD16/0015/0021 sponsored by the Fondo de Investigación Sanitaria (FIS) and SAF2016-­76449-R from Ministry of the Economy and Competitiveness (MINECO, Spain).

References 1. http://www.emsp.org. 2. C. S. Constantinescu, N. Farooqi, K. O'Brien and B. Gran, Br. J. Pharmacol., 2011, 164, 1079–1106. 3. A. P. Robinson, C. T. Harp, A. Noronha and S. D. Miller, Handb. Clin. Neurol., 2014, 122, 173–189. 4. C. J. Willer and G. C. Ebers, Curr. Opin. Neurol., 2000, 13, 241–247. 5. J. E. Libbey and R. S. Fujinami, in Multiple Sclerosis: Immunology, Pathology and Pathopysiology, ed. R. M. Herndon, New York, NY, 2003, pp. 125–133. 6. D. Baker and S. Amor, Mult. Scler. Relat. Disord., 2014, 3, 555–564. 7. H. Lassmann, J. Neurol., 1983, 229, 207–220. 8. S. D. Miller and W. J. Karpus, Immunol. Today, 1994, 15, 356–361. 9. T. M. Rivers, D. H. Sprunt and G. P. Berry, J. Exp. Med., 1933, 58, 39–53. 10. E. A. Kabat, A. Wolf and A. E. Bezer, Science, 1946, 104, 362–363. 11. I. M. Morgan, J. Exp. Med., 1947, 85, 131–140. 12. I. Tsunoda and R. S. Fujinami, J. Neuropathol. Exp. Neurol., 1996, 55, 673–686. 13. H. Waldner, M. J. Whitters, R. A. Sobel, M. Collins and V. K. Kuchroo, Proc. Natl. Acad. Sci. U. S. A., 2000, 97, 3412–3417. 14. E. Bettelli, M. Pagany, H. L. Weiner, C. Linington, R. A. Sobel and V. K. Kuchroo, J. Exp. Med., 2003, 197, 1073–1081. 15. B. Pollinger, G. Krishnamoorthy, K. Berer, H. Lassmann, M. R. Bosl, R. Dunn, H. S. Domingues, A. Holz, F. C. Kurschus and H. Wekerle, J. Exp. Med., 2009, 206, 1303–1316. 16. H. Lassmann, Rev. Neurol., 2007, 163, 651–655. 17. B. Gran, K. O'Brien, D. Fitzgerald and A. Rostami, in Handbook of Neurochemistry and Molecular Neurobiology, ed. A. Lajtha, Springer, New York, USA, 2008. 18. S. Palumbo and S. Pellegrini, in Multiple Sclerosis: Perspectives in Treatment and Pathogenesis, ed. I. S. Zagon and P. J. McLaughlin, Brisbane, AU, 2017, DOI: 10.15586/codon.multiplesclerosis.2017.ch11. 19. B. Moreno, C. Espejo, L. Mestre, M. Suardiaz, D. Clemente, F. de Castro, O. Fernandez-­Fernandez, X. Montalban, P. Villoslada, C. Guaza and Spanish Network for MS, Rev. Neurol., 2012, 54, 114–124.

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20. S. D. Miller and W. J. Karpus, Curr. Protoc. Immunol., 2007, 77(1), 15.1.1–15.1.18. 21. D. P. McCarthy, M. H. Richards and S. D. Miller, Methods Mol. Biol., 2012, 900, 381–401. 22. M. Mannie, R. H. Swanborg and J. A. Stepaniak, Curr. Protoc. Immunol., 2009, 85(1), 15.2.1–15.2.15. 23. K. G. Fuller, J. K. Olson, L. M. Howard, J. L. Croxford and S. D. Miller, Methods Mol. Med., 2004, 102, 339–361. 24. S. D. Miller, W. J. Karpus and T. S. Davidson, Curr. Protoc. Immunol., 2010, 88(1), 15.1.1–15.1.20. 25. I. M. Stromnes and J. M. Goverman, Nat. Protoc., 2006, 1, 1810–1819. 26. H. Waldner, M. Collins and V. K. Kuchroo, J. Clin. Invest., 2004, 113, 990–997. 27. C. Rohowsky-­Kochan, D. Molinaro and S. D. Cook, Mult. Scler., 2000, 6, 69–77. 28. Y. Cao, B. A. Goods, K. Raddassi, G. T. Nepom, W. W. Kwok, J. C. Love and D. A. Hafler, Sci. Transl. Med., 2015, 7, 287ra274. 29. I. M. Stromnes and J. M. Goverman, Nat. Protoc., 2006, 1, 1952–1960. 30. H. J. Wei, T. K. Pareek, Q. Liu and J. J. Letterio, Sci. Rep., 2017, 7, 9886. 31. A. Perchellet, I. Stromnes, J. M. Pang and J. Goverman, Nat. Immunol., 2004, 5, 606–614. 32. K. Sasaki, A. Bean, S. Shah, E. Schutten, P. G. Huseby, B. Peters, Z. T. Shen, V. Vanguri, D. Liggitt and E. S. Huseby, J. Immunol., 2014, 192, 3029–3042. 33. G. Krishnamoorthy, H. Lassmann, H. Wekerle and A. Holz, J. Clin. Invest., 2006, 116, 2385–2392. 34. K. Berer, M. Mues, M. Koutrolos, Z. A. Rasbi, M. Boziki, C. Johner, H. Wekerle and G. Krishnamoorthy, Nature, 2011, 479, 538–541. 35. M. Day, in Experimental Models of Multiple Sclerosis, ed. E. Lavi and C. Constantinescu, Springer Science+Business Media Inc., New York, USA, 2005, ch. A3, pp. 25–43. 36. D. Merkler, T. Ernsting, M. Kerschensteiner, W. Bruck and C. Stadelmann, Brain, 2006, 129, 1972–1983. 37. M. K. Storch, J. Bauer, C. Linington, T. Olsson, R. Weissert and H. Lassmann, J. Neuropathol. Exp. Neurol., 2006, 65, 1137–1142. 38. F. Girolamo, G. Ferrara, M. Strippoli, M. Rizzi, M. Errede, M. Trojano, R. Perris, L. Roncali, M. Svelto, T. Mennini and D. Virgintino, Neurobiol. Dis., 2011, 43, 678–689. 39. M. R. Emerson, R. J. Gallagher, J. G. Marquis and S. M. LeVine, Comp. Med., 2009, 59, 112–128. 40. J. F. Kurtzke, Clin. Microbiol. Rev., 1993, 6, 382–427. 41. P. Browne, D. Chandraratna, C. Angood, H. Tremlett, C. Baker, B. V. Taylor and A. J. Thompson, Neurology, 2014, 83, 1022–1024. 42. E. Lavi, A. Suzumura, L. A. Lampson, R. M. Siegel, D. M. Murasko, D. H. Silberberg and S. R. Weiss, Adv. Exp. Med. Biol., 1987, 218, 219–222.

106

Chapter 5

43. R. Alenda, R. Alvarez-­Lafuente, L. Costa-­Frossard, R. Arroyo, S. Mirete, J. C. Alvarez-­Cermeno and L. M. Villar, Eur. J. Neurol., 2014, 21, 1096–1101. 44. A. Pormohammad, T. Azimi, F. Falah and E. Faghihloo, J. Cell. Physiol., 2018, 233, 2850–2862. 45. S. Fernandez-­Menendez, M. Fernandez-­Moran, I. Fernandez-­Vega, A. Perez-­Alvarez and J. Villafani-­Echazu, J. Neurol. Sci., 2016, 361, 213–219. 46. C. Otto, J. Hofmann and K. Ruprecht, Med. Hypotheses, 2016, 91, 109–113. 47. A. Mostafa, S. Jalilvand, Z. Shoja, A. Nejati, S. Shahmahmoodi, M. A. Sahraian and S. M. Marashi, J. Med. Virol., 2017, 89, 1309–1313. 48. A. Hassani, J. R. Corboy, S. Al-­Salam and G. Khan, PLoS One, 2018, 13, e0192109. 49. B. de la Hera, J. Varade, M. Garcia-­Montojo, J. R. Lamas, A. de la Encarnacion, R. Arroyo, B. Fernandez-­Gutierrez, R. Alvarez-­Lafuente and E. Urcelay, PLoS One, 2013, 8, e62090. 50. B. de la Hera, J. Varade, M. Garcia-­Montojo, A. Alcina, M. Fedetz, I. Alloza, I. Astobiza, L. Leyva, O. Fernandez, G. Izquierdo, A. Antiguedad, R. Arroyo, R. Alvarez-­Lafuente, K. Vandenbroeck, F. Matesanz and E. Urcelay, PLoS One, 2014, 9, e90182. 51. B. A. Nexo, Immunol. Res., 2018, 336–339. 52. M. T. Scherer, L. Ignatowicz, G. M. Winslow, J. W. Kappler and P. Marrack, Annu. Rev. Cell Biol., 1993, 9, 101–128. 53. R. S. Fujinami and M. B. Oldstone, Science, 1985, 230, 1043–1045. 54. J. K. Olson, J. L. Croxford, M. A. Calenoff, M. C. Dal Canto and S. D. Miller, J. Clin. Invest., 2001, 108, 311–318. 55. C. C. Bergmann, T. E. Lane and S. A. Stohlman, Nat. Rev. Microbiol., 2006, 4, 121–132. 56. T. E. Lane and M. P. Hosking, Crit. Rev. Immunol., 2010, 30, 119–130. 57. J. K. Fazakerley and H. E. Webb, Br. J. Exp. Pathol., 1987, 68, 101–113. 58. F. Mokhtarian, D. Grob and D. E. Griffin, J. Immunol., 1989, 143, 633–637. 59. M. Theiler, Science, 1934, 80, 122. 60. J. B. Daniels, A. M. Pappenheimer and S. Richardson, J. Exp. Med., 1952, 96, 517–530. 61. H. L. Lipton, Infect. Immun., 1975, 11, 1147–1155. 62. E. L. Oleszak, J. R. Chang, H. Friedman, C. D. Katsetos and C. D. Platsoucas, Clin. Microbiol. Rev., 2004, 17, 174–207. 63. G. D. Ghadge, L. Ma, S. Sato, J. Kim and R. P. Roos, J. Virol., 1998, 72, 8605–8612. 64. L. J. Zoecklein, K. D. Pavelko, J. Gamez, L. Papke, D. B. McGavern, D. R. Ure, M. K. Njenga, A. J. Johnson, S. Nakane and M. Rodriguez, Brain Pathol., 2003, 13, 291–308. 65. I. Tsunoda, L. Q. Kuang and R. S. Fujinami, J. Virol., 2002, 76, 12834–12844.

Experimental In Vivo Models for Drug Discovery in Multiple Sclerosis

107

66. M. Rodriguez, C. F. Lucchinetti, R. J. Clark, T. L. Yakash, H. Markowitz and V. A. Lennon, J. Immunol., 1988, 140, 800–806. 67. E. L. Oleszak, J. Kuzmak, R. A. Good and C. D. Platsoucas, Immunol. Res., 1995, 14, 13–33. 68. I. Tsunoda and R. S. Fujinami, J. Neuroimmune Pharmacol., 2010, 5, 355–369. 69. I. Tsunoda and R. S. Fujinami, Springer Semin. Immunopathol., 2002, 24, 105–125. 70. H. L. Lipton and M. L. Jelachich, Intervirology, 1997, 40, 143–152. 71. D. B. McGavern, L. Zoecklein, K. M. Drescher and M. Rodriguez, Exp. Neurol., 1999, 158, 171–181. 72. B. S. Kim, M. A. Lyman, B. S. Kang, H. K. Kang, H. G. Lee, M. Mohindru and J. P. Palma, Immunol. Res., 2001, 24, 121–130. 73. M. C. Dal Canto, R. W. Melvold, B. S. Kim and S. D. Miller, Microsc. Res. Tech., 1995, 32, 215–229. 74. A. R. Pachner, L. Li and K. Narayan, J. Neuroimmunol., 2007, 185, 57–63. 75. W. F. Blakemore, Neuropathol. Appl. Neurobiol., 1982, 8, 365–375. 76. N. J. Kuypers, K. T. James, G. U. Enzmann, D. S. Magnuson and S. R. Whittemore, Exp. Neurol., 2013, 247, 615–622. 77. M. Goudarzvand, S. Choopani, A. Shams, M. Javan, Z. Khodaii, F. Ghamsari, N. Naghdi, A. Piryaei and A. Haghparast, Basic Clin. Neurosci., 2016, 7, 63–72. 78. C. Ibanez, S. A. Shields, M. El-­Etr, E. E. Baulieu, M. Schumacher and R. J. Franklin, Neuropathol. Appl. Neurobiol., 2004, 30, 80–89. 79. S. M. Hall, J. Cell Sci., 1972, 10, 535–546. 80. J. Qiao, F. Huang, R. P. Naikawadi, K. S. Kim, T. Said and H. Lum, Am. J. Physiol.: Lung Cell. Mol. Physiol., 2006, 291, L91–L101. 81. A. M. Sheikh, A. Nagai, J. K. Ryu, J. G. McLarnon, S. U. Kim and J. Masuda, Glia, 2009, 57, 898–907. 82. Y. Zhang, K. Chen, S. A. Sloan, M. L. Bennett, A. R. Scholze, S. O'Keeffe, H. P. Phatnani, P. Guarnieri, C. Caneda, N. Ruderisch, S. Deng, S. A. Liddelow, C. Zhang, R. Daneman, T. Maniatis, B. A. Barres and J. Q. Wu, J. Neurosci., 2014, 34, 11929–11947. 83. E. Santos-­Nogueira, C. Lopez-­Serrano, J. Hernandez, N. Lago, A. M. Astudillo, J. Balsinde, G. Estivill-­Torrus, F. R. de Fonseca, J. Chun and R. Lopez-­Vales, J. Neurosci., 2015, 35, 10224–10235. 84. T. Schilling and C. Eder, Cell. Immunol., 2010, 265, 87–90. 85. M. le Maire, P. Champeil and J. V. Moller, Biochim. Biophys. Acta, 2000, 1508, 86–111. 86. J. R. Plemel, N. J. Michaels, N. Weishaupt, A. V. Caprariello, M. B. Keough, J. A. Rogers, A. Yukseloglu, J. Lim, V. V. Patel, K. S. Rawji, S. K. Jensen, W. Teo, B. Heyne, S. N. Whitehead, P. K. Stys and V. W. Yong, Glia, 2018, 66, 327–347. 87. M. B. Keough, S. K. Jensen and V. W. Yong, J. Visualized Exp., 2015, DOI: 10.3791/52679.

108

Chapter 5

88. I. Bjelobaba, V. Begovic-­Kupresanin, S. Pekovic and I. Lavrnja, J. Neurosci. Res., 2018, 96, 1021–1042. 89. J. Goldberg, T. Clarner, C. Beyer and M. Kipp, J. Mol. Neurosci., 2015, 57, 166–175. 90. W. W. Carlton, Life Sci., 1967, 6, 11–19. 91. K. Benardais, A. Kotsiari, J. Skuljec, P. N. Koutsoudaki, V. Gudi, V. Singh, F. Vulinovic, T. Skripuletz and M. Stangel, Neurotoxicol. Res., 2013, 24, 244–250. 92. J. Praet, C. Guglielmetti, Z. Berneman, A. Van der Linden and P. Ponsaerts, Neurosci. Biobehav. Rev., 2014, 47, 485–505. 93. G. K. Matsushima and P. Morell, Brain Pathol., 2001, 11, 107–116. 94. H. H. Sachs, K. K. Bercury, D. C. Popescu, S. P. Narayanan and W. B. Macklin, ASN Neuro, 2014, 6, 175909141455195. 95. W. Zhen, A. Liu, J. Lu, W. Zhang, D. Tattersall and J. Wang, ASN Neuro, 2017, 9, 1759091417725174. 96. C. Lucchinetti, W. Bruck, J. Parisi, B. Scheithauer, M. Rodriguez and H. Lassmann, Ann. Neurol., 2000, 47, 707–717. 97. M. Clement, J. A. Pearson, S. Gras, H. A. van den Berg, A. Lissina, S. Llewellyn-­Lacey, M. D. Willis, T. Dockree, J. E. McLaren, J. Ekeruche-­ Makinde, E. Gostick, N. P. Robertson, J. Rossjohn, S. R. Burrows, D. A. Price, F. S. Wong, M. Peakman, A. Skowera and L. Wooldridge, Sci. Rep., 2016, 6, 35332. 98. D. Baker, M. Marta, G. Pryce, G. Giovannoni and K. Schmierer, EBioMedicine, 2017, 16, 41–50. 99. J. J. Sabatino Jr, S. S. Zamvil and S. L. Hauser, Cold Spring Harbor Perspect. Med., 2018, a032037. 100. L. Bo, C. A. Vedeler, H. I. Nyland, B. D. Trapp and S. J. Mork, J. Neuropathol. Exp. Neurol., 2003, 62, 723–732. 101. H. Lassmann and M. Bradl, Acta Neuropathol., 2017, 133, 223–244. 102. A. L. Nelson, A. J. Bieber and M. Rodriguez, Int. MS J., 2004, 11, 95–99. 103. M. A. Friese, X. Montalban, N. Willcox, J. I. Bell, R. Martin and L. Fugger, Brain, 2006, 129, 1940–1952. 104. D. C. Anthony, A. M. Dickens, N. Seneca, Y. Couch, S. Campbell, B. Checa, V. Kersemans, E. A. Warren, M. Tredwell, N. R. Sibson, V. Gouverneur and D. Leppert, Ann. Clin. Transl. Neurol., 2014, 1, 659–669. 105. Y. S. Kap, N. van Driel, J. D. Laman, P. P. Tak and B. A. t Hart, J. Immunol., 2014, 192, 4242–4253. 106. C. Wegner, C. Stadelmann, R. Pfortner, E. Raymond, S. Feigelson, R. Alon, B. Timan, L. Hayardeny and W. Bruck, J. Neuroimmunol., 2010, 227, 133–143. 107. A. Musella, G. Mandolesi, A. Gentile, S. Rossi, V. Studer, C. Motta, H. Sepman, D. Fresegna, N. Haji, A. Paolillo, G. Matarese and D. Centonze, J. Neuroimmunol., 2013, 264, 8–13. 108. A. Gentile, A. Musella, S. Bullitta, D. Fresegna, F. De Vito, R. Fantozzi, E. Piras, F. Gargano, G. Borsellino, L. Battistini, A. Schubart, G. Mandolesi and D. Centonze, J. Neuroinflammation, 2016, 13, 207.

Experimental In Vivo Models for Drug Discovery in Multiple Sclerosis

109

109. R. Martin-­Alvarez, N. Paul-­Fernandez, V. Palomo, C. Gil, A. Martinez and G. Mengod, J. Chem. Neuroanat., 2017, 80, 27–36. 110. T. Secher, S. Kassem, M. Benamar, I. Bernard, M. Boury, F. Barreau, E. Oswald and A. Saoudi, Front. Immunol., 2017, 8, 1096. 111. S. Lavasani, B. Dzhambazov, M. Nouri, F. Fak, S. Buske, G. Molin, H. Thorlacius, J. Alenfall, B. Jeppsson and B. Westrom, PLoS One, 2010, 5, e9009. 112. H. K. Kwon, G. C. Kim, Y. Kim, W. Hwang, A. Jash, A. Sahoo, J. E. Kim, J. H. Nam and S. H. Im, Clin. Immunol., 2013, 146, 217–227. 113. B. W. van Oosten, F. Barkhof, L. Truyen, J. B. Boringa, F. W. Bertelsmann, B. M. von Blomberg, J. N. Woody, H. P. Hartung and C. H. Polman, Neurology, 1996, 47, 1531–1534. 114. I. L. Tan, G. J. Lycklama a Nijeholt, C. H. Polman, H. J. Ader and F. Barkhof, Mult. Scler., 2000, 6, 99–104. 115. T. Berger and F. Deisenhammer, N. Engl. J. Med., 2005, 353(16), 1744–1746. 116. A. M. McGinley, S. C. Edwards, M. Raverdeau and K. H. G. Mills, J. Autoimmun., 2018, 97–108. 117. M. Ucal, M. T. Haindl, M. Z. Adzemovic, J. Strasser, L. Theisl, M. Zeitelhofer, K. Kraitsy, S. Ropele, U. Schafer, F. Fazekas and S. Hochmeister, Exp. Neurol., 2017, 294, 32–44. 118. B. A. t Hart, J. Dunham, B. W. Faber, J. D. Laman, J. van Horssen, J. Bauer and Y. S. Kap, Front. Immunol., 2017, 8, 804. 119. H. L. Atkins, M. Bowman, D. Allan, G. Anstee, D. L. Arnold, A. Bar-­Or, I. Bence-­Bruckler, P. Birch, C. Bredeson, J. Chen, D. Fergusson, M. Halpenny, L. Hamelin, L. Huebsch, B. Hutton, P. Laneuville, Y. Lapierre, H. Lee, L. Martin, S. McDiarmid, P. O'Connor, T. Ramsay, M. Sabloff, L. Walker and M. S. Freedman, Lancet, 2016, 388, 576–585. 120. P. A. Muraro, R. Martin, G. L. Mancardi, R. Nicholas, M. P. Sormani and R. Saccardi, Nat. Rev. Neurol., 2017, 13, 391–405. 121. R. Gandhi, A. Laroni and H. L. Weiner, J. Neuroimmunol., 2010, 221, 7–14. 122. A. Tourbah, C. Lebrun-­Frenay, G. Edan, M. Clanet, C. Papeix, S. Vukusic, J. De Seze, M. Debouverie, O. Gout, P. Clavelou, G. Defer, D. A. Laplaud, T. Moreau, P. Labauge, B. Brochet, F. Sedel, J. Pelletier and MS-­SPI study group, Mult. Scler., 2016, 22, 1719–1731. 123. J. Chataway, N. Schuerer, A. Alsanousi, D. Chan, D. MacManus, K. Hunter, V. Anderson, C. R. Bangham, S. Clegg, C. Nielsen, N. C. Fox, D. Wilkie, J. M. Nicholas, V. L. Calder, J. Greenwood, C. Frost and R. Nicholas, Lancet, 2014, 383, 2213–2221. 124. R. J. Franklin and V. Gallo, Glia, 2014, 62, 1905–1915. 125. K. S. Rawji, J. Kappen, W. Tang, W. Teo, J. R. Plemel, P. K. Stys and V. W. Yong, J. Neurosci., 2018, 38, 1973–1988. 126. R. Dutta, J. McDonough, X. Yin, J. Peterson, A. Chang, T. Torres, T. Gudz, W. B. Macklin, D. A. Lewis, R. J. Fox, R. Rudick, K. Mirnics and B. D. Trapp, Ann. Neurol., 2006, 59, 478–489.

110

Chapter 5

127. G. R. Campbell, Y. Kraytsberg, K. J. Krishnan, N. Ohno, I. Ziabreva, A. Reeve, B. D. Trapp, J. Newcombe, R. Reynolds, H. Lassmann, K. Khrapko, D. M. Turnbull and D. J. Mahad, Acta Neuropathol., 2012, 124, 209–220. 128. G. Campbell and D. J. Mahad, FEBS Lett., 2018, 592, 1113–1121. 129. C. D. Sorbara, N. E. Wagner, A. Ladwig, I. Nikic, D. Merkler, T. Kleele, P. Marinkovic, R. Naumann, L. Godinho, F. M. Bareyre, D. Bishop, T. Misgeld and M. Kerschensteiner, Neuron, 2014, 84, 1183–1190.

Part II New Drugs in Development for Multiple Sclerosis

         

Chapter 6

Progressive Multiple Sclerosis: Drug Discovery Ebtesam Alshehri and Jeffery A. Cohen* Mellen Center for Multiple Sclerosis Treatment and Research, Neurological Institute, Cleveland Clinic, Cleveland, OH, USA *E-­mail: [email protected]

6.1 Introduction Multiple sclerosis (MS) is an inflammatory, demyelinating, and neurodegenerative disease of the central nervous system (CNS) affecting approximately 2.3 million people worldwide.1 It is a major cause of non-­traumatic neurologic disability in young adults.2 In approximately 85% of patients with  MS, the initial course is relapsing–remitting (RR), characterized by recurrent episodes of neurologic dysfunction thought to reflect the development of focal inflammatory lesions of the CNS.3 About two-­thirds of patients with RRMS eventually transition to a secondary progressive (SP) course within two decades of onset,4 in which disability accumulates gradually independent of relapses, although during the transition from RRMS to SPMS relapses may still occur.5 In primary progressive MS (PPMS), which accounts for about 15% of patients with MS, disability worsens gradually from onset, with or without subsequent superimposed relapses.5 Although some disability accrues in MS due to incomplete recovery from relapses,6 most disability accumulates

  Drug Discovery Series No. 70 Emerging Drugs and Targets for Multiple Sclerosis Edited by Ana Martinez © The Royal Society of Chemistry 2019 Published by the Royal Society of Chemistry, www.rsc.org

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during the progressive phases. Therapies that effectively prevent disability worsening and promote recovery of function in progressive MS remain major unmet needs.

6.2 Diagnosis of Progressive MS A fundamental requirement to developing effective therapies for progressive MS is accurate diagnosis of MS. The 2017 revisions of the McDonald Criteria emphasize diagnostic rigor to allow early diagnosis of patients with a high likelihood of MS and to lessen the risk of misdiagnosis.8 The McDonald Criteria initially were developed to make the diagnosis in patients with a clinically isolated syndrome at onset, i.e. with an RR or SP course. The criteria were later modified to incorporate diagnostic criteria for patients with a PP course.9 In the 2017 revision of the McDonald Criteria, the criteria for PPMS were unchanged from previous versions, aside from the removal of the distinction between symptomatic and asymptomatic lesions and that both juxtacortical and cortical lesions can be used to satisfy the magnetic resonance imaging (MRI) criteria for dissemination in space. As compared to the criteria for attack-­onset MS, the criteria for PPMS continue to place somewhat greater emphasis on spinal cord MRI lesions and cerebrospinal fluid (CSF)-­ specific oligoclonal bands. In 2013, the consensus MS phenotypes based on disease course were revised (Figure 6.1).5 The distinctions between RR and progressive courses and between PPMS (progressive from disease onset) and SPMS (a progressive course following an RR course) were retained. Two qualifiers were

Figure 6.1 Subcategories of progressive MS. Adapted from ref. 5 with permission from Wolters Kluwer Health, Inc., Copyright © 2014, American Academy of Neurology.

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Box 6.1  Evidence suggesting that relapsing–remitting/secondary progressive MS and primary progressive MS are part of the same MS spectrum rather than separate entities97–101    

●● Similar age of onset of the progressive phase and rate of progression ●● Overlapping clinical manifestations ●● Superimposed relapses and MRI lesion activity can occur in both, particu-

larly near the onset of the progressive phase

●● A similar proportion of patients with radiologically isolated syndrome ●● ●● ●● ●● ●●

develop progression as the initial clinical manifestation (i.e. a primary progressive course) as in the overall MS population Similar genetic risk factors Overlapping immunologic features Overlapping pathology Similar findings on conventional and advanced MRI Similar (lack of) benefit of anti-­inflammatory treatment strategies in the absence of inflammatory disease features (relapses and MRI lesion activity)

   

added – presence or absence of activity (occurrence of a clinical relapse, or development of a new/enlarged T2-­hyperintense or gadolinium-­ enhancing MRI lesion) and presence or absence of progression (gradual worsening independent of relapses). These qualifiers were intended to be reassessed over time (e.g. annually) with patients potentially changing category based on recent disease behavior, including as a result of therapy. As a result, the progressive relapsing phenotype was subsumed under PPMS as PP with activity. This new classification will be useful in testing and utilizing potential therapies in progressive MS by emphasizing two separate aspects of progressive disease – inflammatory lesion activity and neurodegeneration. Specifically, the presence of recent or ongoing inflammatory lesion activity indicates a higher likelihood of efficacy of medications with a predominantly anti-­inflammatory mechanism of action. Conversely, therapeutic strategies for progressive MS without activity represent a major unmet need. The current phenotypic categories distinguish PPMS from SPMS. Although the precise relationship between these forms of progressive MS remains uncertain, the current consensus is that PPMS is part of the MS disease spectrum rather than a separate entity (Box 6.1). The therapeutic implications are similar for both forms of progressive MS, so in this chapter they will be discussed together.

6.3 Monitoring Progressive MS The second key requirement to allow development of effective treatment strategies for progressive MS is the ability to quantify worsening and improvement. Although relapses occur in progressive MS, they occur less

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frequently compared with RRMS, making them less informative as an outcome measure. Conversely, the hallmark of progressive MS is gradually worsening disability, independent of relapses. Traditionally, the disability measure most commonly used in clinical trials and non-­interventional studies has been the Expanded Disability Status Scale (EDSS).10 The EDSS is based on the standard neurologic examination, assesses a wide range of neurologic functions, and permits comparison of individuals or groups cross-­sectionally and over time. An alternative approach is the MS Functional Composite (MSFC), a multidimensional battery of quantitative neuroperformance tests assessing ambulation (Timed 25-­foot Walk [T25FW]), upper extremity motor function (Nine-­Hole Peg Test [9HPT]), vision (Low-­Contrast Letter Acuity [LCLA]), and cognition (Paced Auditory Serial Addition Test [PASAT] or Symbol Digit Modalities Test [SDMT]).10 Some recent clinical trials in progressive MS, e.g. the INFORMS trial of fingolimod,11 have utilized a composite outcome measure combining the EDSS with components of the MSFC to improve sensitivity to detect worsening events. It is likely that for the foreseeable future a clinical outcome measure such as the EDSS or MSFC will be required by regulatory agencies as the primary outcome measure for pivotal clinical trials leading to approval of therapies intended to lessen disability worsening or augment disability improvement. However, these measures are inherently insensitive, in part because of their measurement properties but more importantly due to the typical slow rate of progression.10 Therefore, there is a great need for other approaches to quantify manifestations of progressive MS, particularly for proof-­of-­concept Phase II trials. Like relapses, MRI lesion activity occurs early in progressive MS but becomes less prominent over time. Therefore, standard MRI, focusing on acute lesion activity (new/enlarged T2-­hyperintense lesions or gadolinium-­enhancing lesions) and quantitation of the volumes of T2-­hyperintense and T1-­hypointense lesions burden in brain, tends not to be informative in progressive MS. Currently, the best validated MRI techniques to measure disease worsening are atrophy of whole brain,12,13 cerebral cortex14 deep gray matter,15,16 and spinal cord.17 Other promising techniques including direct demonstration of cortical lesions through special sequences18 or ultra-­high field MRI,19 diffusion tensor imaging (DTI),20 magnetization transfer imaging to measure magnetization transfer ratio (MTR),21 magnetic resonance spectroscopy,22 and magnetic resonance fingerprinting.23 The best validated non-­imaging biomarker to monitor disease activity, tissue injury, and response to treatment in MS is neurofilament light chain concentration (NfL-­c) in CSF or blood.24 NfL-­c reflects ongoing axonal damage irrespective of cause or anatomic location. In MS, NfL-­c relates most closely to inflammatory lesion activity and, to a lesser extent, the neurodegeneration processes underlying progression.

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Figure 6.2 Evolution of therapeutic strategies in different stages of MS.

6.4 Pathogenesis of Progressive MS RRMS is characterized by the development of focal inflammatory lesions with perivenular accumulation of T and B lymphocytes and blood–brain barrier disruption accompanied by myelin damage, oligodendrocyte injury, and axonal transection.25 This process leads to demyelination and neurodegeneration in the plaques and in the normal-­appearing white and gray matter.26 Targeting this process leads to the success achieved in development treatments for RRMS. The pathogenesis leading to tissue damage and gradual disability accrual in progressive MS is less well understood. In early progressive disease, focal lesion activity occurs, but becomes less prominent over time and is replaced by neurodegeneration, which explains why the efficacy of therapies approved for RRMS is primarily limited in progressive disease to patients early in the progressive phase. An important implication is that it is likely that other therapeutic strategies will be necessary to effectively treat patients with progressive MS without ongoing inflammatory features (Figure 6.2). A wide range of mechanisms has been postulated to contribute to the neurodegenerative process thought to underlie progression (Box 6.2).26–34 These processes can be summarized as increasingly compartmentalized CNS inflammation with continued involvement of adaptive immune mechanisms but increasing involvement of innate immune mechanisms, diffuse damage in white and gray matter, chronic demyelination, increased energy demand, and impaired energy production.35,36 The net result is virtual hypoxia and chronic axonal and neuronal degeneration. The pathologic sequence conceivably is propagated via Wallerian degeneration with anterograde and retrograde trans-­synaptic neurodegeneration. Many aspects of these processes represent potential therapeutic targets.37

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Box 6.2  M  ultifactorial processes that contribute to virtual hypoxia, neurodegeneration, and accumulation of tissue damage in progressive MS.    

●● Focal inflammatory lesions with perivenular accumulation of T and B lym●● ●● ●● ●● ●●

phocytes, blood–brain barrier disruption, demyelination, acute axonal transection Meningeal inflammatory cell infiltrates Diffuse pathology in white matter Diffuse pathology in cortical and deep gray matter with microglial activation, demyelination, neuritic transection, reduced presynaptic terminals, neuronal death Accumulation of toxic inflammatory mediators, and reactive oxygen and iron species Consequences of chronic axonal demyelination

    – Loss of structural and trophic support – Increased exposure to toxic components in the microenvironment – Increased energy demand from continuous rather than saltatory nerve impulse conduction – Increased sodium accumulation and reverse operation of the sodium–calcium exchanger – Increased cytoplasmic calcium accumulation resulting in activation of calpains and cytoskeletal proteolysis     ●● Mitochondrial dysfunction and impaired energy production     – Impaired transport – Oxidative injury – Accumulation of mutations in mitochondrial DNA        

6.5 Clinical Trials of Anti-­inflammatory Treatment Strategies A sizable number of medications shown to be efficacious in RRMS have been tested as potential therapy for progressive MS. In general, efficacy has been modest, and appears largely to be due to benefit in patients with ongoing inflammatory features.

6.5.1 Interferon-­beta Clinical trials of interferon beta (IFNβ) in SPMS have produced differing results. The European trial of IFNβ-­1b achieved its primary endpoint with a 22% relative reduction in the proportion of patients with confirmed EDSS worsening in the IFNβ group compared with placebo.38 The North

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American study of interferon IFNβ-­1b in SPMS showed reductions in clinical relapses, newly active MRI lesions, and accumulated burden of disease on T2-­weighted MRI but found no effect on time to confirmed EDSS worsening.39 The discrepancy in the results of these two trials, which tested identical medication with nearly identical study protocol, is probably due to differences in the baseline progression rates and ongoing disease activity in the two study populations. The SPECTRIMS study of subcutaneous IFNβ-­1a also did not show benefit on EDSS worsening. The IMPACT trial of intramuscular IFNβ-­1a showed significant though modest benefit on disability worsening measured by the MSFC, possibly due to its increased sensitivity. Two small studies of IFNβ in PPMS also found no benefit on disease progression.40,41 Subsequent analyses of the studies of IFNβ in progressive MS indicated that trials enriched for younger patients with recent relapses and MRI lesion activity tended to show benefit, while trials enrolling older patients without recent inflammatory disease activity tended not to show benefit.42,43 Subsequent trials of other anti-­inflammatory therapies have yielded similar results.

6.5.2 Glatiramer Acetate Glatiramer acetate is a complex mixture of random polypeptides, thought to be effective in MS through functioning as an altered peptide ligand and promoting tolerance and immune deviation in CNS-­autoreactive T lymphocyte populations.44 It also has been postulated to have neuroprotective effects and to stimulate remyelination,45 providing the rationale for testing in progressive MS. PROMiSe was a 3-­year, randomized, double-­blind trial comparing the efficacy of glatiramer acetate 20 mg by daily subcutaneous injection versus placebo in 943 participants with strictly defined PPMS.46 No benefit was demonstrated for confirmed EDSS worsening (hazard ratio [HR] 0.87, p = 0.1753), although the glatiramer acetate group had fewer gadolinium-­enhancing lesions in year 1 and smaller increases in T2-­hyperintense lesion volumes in years 2 and 3 compared with the placebo group. Superior efficacy was shown for men in a post-­hoc subgroup analysis.

6.5.3 Mitoxantrone Mitoxantrone is an anthracycline analog that inhibits the activation and proliferation of T and B lymphocytes (Figure 6.3).47 The largest trial of mitoxantrone in MS, MIMS, was a multicenter, double-­blind trial of 194 participants with worsening RRMS or SPMS.48 Patients were randomly assigned to treatment with intravenous mitoxantrone (5 mg m−2 or 12 mg m−2) or placebo every 3 months for 2 years. Mitoxantrone-­treated participants demonstrated fewer relapses, modest benefit on confirmed EDSS worsening, and no differences

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Figure 6.3 Small molecule anti-­inflammatory medications tested in progressive multiple sclerosis.

in the number of MRIs with gadolinium-­enhancing lesions. Mitoxantrone was approved by the US Food and Drug Administration (FDA) in 2000 for the treatment of SPMS, progressive-­relapsing MS, and worsening RRMS. Mitoxantrone is well tolerated by many patients with PP or SPMS.49 However, its current use is limited in MS because of concerns about amenorrhea, cardiotoxicity, and malignancies.50

6.5.4 Natalizumab Natalizumab is a humanized monoclonal antibody against the cell-­surface adhesion molecule α4 integrin, which mediates leukocyte migration across the blood–barrier barrier and chemokine-­mediated inflammatory cell recruitment into the CNS.51 It is highly efficacious in RRMS.52,53 ASCEND was a randomized, double-­blind, placebo-­controlled Phase III trial, which tested whether natalizumab is effective in SPMS.54 Participants were randomized to intravenous natalizumab (n = 440, 300 mg monthly) or placebo (n = 449) for 2 years. The primary outcome was proportion of participants with 6-­month confirmed worsening on any component of a multidimensional disability outcome measure comprising EDSS, T25FW, and 9HPT. No benefit was demonstrated with 44% of natalizumab-­treated participants and 48% of placebo-­treated participants demonstrating disability worsening (odds ratio 0.86, 95% confidence interval [CI] 0.66–1.13, p = 0.287). Although no treatment effect was demonstrated on EDSS or T25FW alone, benefit was seen on 9HPT worsening (odds ratio 0.56, 95% CI 0.40–0.80, p = 0.001). In addition, benefit on the disability measures appeared to emerge later as participants continued in the open-­label extension up to 108 weeks, during which participants originally randomized to natalizumab continued therapy and participants originally randomized to placebo switched to natalizumab.

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6.5.5 Sphingosine 1-­phosphate Receptor Modulators: Fingolimod and Siponimod Fingolimod is a sphingosine 1-­phosphate receptor (S1PR) modulator, which binds to subtypes 1, 3, 4, and 5, reduces egress of lymphocytes from lymphoid tissues, and prevents recirculation of peripheral lymphocytes to the CNS (Figure 6.3).55 There was a strong rationale for testing fingolimod in progressive MS, including efficacy in three Phase III trials in RRMS,56–58 good penetration into the CNS,59 and direct effects on astrocytes and oligodendrocytes potentially beneficial in MS.60 The efficacy of fingolimod compared to placebo in PPMS was tested in INFORMS, a 5-­year randomized, double-­blind, placebo-­controlled Phase III study.11 Fingolimod reduced new MRI lesions but, somewhat surprisingly, failed to meet the primary clinical endpoint of 3-­month confirmed disability worsening on any component of a composite outcome (EDSS, T25FW, and 9HPT). It also failed to decrease whole brain volume loss. Siponimod is a selective S1PR modulator, which binds to receptor subtypes 1 and 5 (Figure 6.3).55 EXPAND, a randomized, double-­blind, placebo-­ controlled Phase III trial involving 1645 participants with SPMS randomized 2 : 1 to receive siponimod or placebo.61 The trial was scheduled to run for 3 years or until a pre-­specified number of confirmed disability worsening events occurred. Relative to placebo, siponimod reduced the risk of 3-­month EDSS worsening, the primary endpoint, by 21% (HR 0.79, 95% CI 0.65– 0.95; p = 0·013). Benefit also was demonstrated on 6-­month confirmed EDSS worsening (26% relative reduction), whole brain volume loss (23%), and annualized relapse rate (55% at 12 and 24 months). No significant difference was found between siponimod and placebo on two other measures of ambulation (T25FW and MS Walking Scale-­12). Although younger patients with more active disease demonstrated greater benefit, the study also showed benefit in patients with less active disease. Lymphopenia, increased liver transaminase concentration, bradycardia and bradyarrhythmia at treatment initiation, macular edema, hypertension, varicella zoster reactivation, and convulsions occurred more frequently with siponimod than with placebo.

6.5.6 Anti-­CD20 Monoclonal Antibodies: Rituximab and Ocrelizumab Rituximab is a chimeric monoclonal antibody that binds the CD20 surface antigen expressed by B lymphocytes but not pro-­B-­cells or plasma cells.62 It potently depletes circulating B lymphocytes through complement-­dependent cytotoxicity and, to a lesser extent, antibody-­dependent cellular cytotoxicity.62 Immune system memory is preserved due to lack of CD20 expression on differentiated plasma cells. OLYMPUS was a randomized, double‐blind, placebo‐controlled 96-­week trial of 439 participants with PPMS.63 Rituximab slowed

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the change in lesion volume but failed to decrease confirmed EDSS worsening in the overall study population. In a subgroup analysis, rituximab treatment was associated with potent slowing of disability worsening in participants younger than 50 years with gadolinium-­enhancing lesions at baseline. Based on the growing body of evidence that supports the role of the meningeal ectopic lymphoid follicles in the pathogenesis of progressive MS,64 a small Phase II trial, RIVITALISE, evaluated the efficacy of intrathecal rituximab.65 Although CSF B lymphocytes were effectively depleted, the effect was transient. Interestingly, there was potent, long-­lived depletion of circulating B lymphocytes. There was no benefit on several biomarkers, including CSF NfL-­c. The negative results were attributed to insufficient saturation of CD20, lack of lytic complement in the CNS, and paucity of CD56dim natural killer cells. Ocrelizumab is a humanized monoclonal antibody also targeting CD20.62 Ocrelizumab binds to an epitope that overlaps that of rituximab and has somewhat greater antibody-­dependent cellular cytotoxicity compared to rituximab. It also kills B lymphocytes, to a lesser extent, via complement-­ dependent cytotoxicity, antibody-­dependent cellular phagocytosis, and apoptosis. The efficacy of ocrelizumab was tested in PPMS in ORATORIO, a randomized, double-­blind, placebo-­controlled Phase III trial.66 In this trial, 732 participants (mean age approximately 45 years) with PPMS were randomized 2 : 1 to intravenous ocrelizumab 600 mg (two 300-­mg infusions 14 days apart) or placebo every 24 weeks for at least 120 weeks. The primary endpoint was proportion of participants with 3-­month confirmed EDSS worsening, which was reduced to 32.9% in the ocrelizumab group compared with 39.3% with placebo (HR 0.76; 95% CI 0.59–0.98, p = 0.03). Six-­month confirmed EDSS worsening also was reduced (29.6% versus 35.7%, HR 0.75, 95% CI 0.58–0.98, p = 0.04). Benefit also was demonstrated on T25FW, mean decline in performance 38.9% versus 55.1% with placebo (p = 0.04), T2 MRI lesion volume (p < 0.001), and whole brain volume loss (p = 0.02). Participants with gadolinium-­enhancing lesions at baseline appeared to have a greater benefit of treatment. The most common adverse events with ocrelizumab treatment were infusion reactions, upper respiratory tract infection, herpes infection, and reactivation of hepatis B. Neoplasms were more frequent with ocrelizumab compared with placebo (2.3% versus 0.8%). Based on these results, the FDA approved ocrelizumab for the treatment of adult patients with PPMS in March 2017, representing the first medication with regulatory approval for PPMS.

6.6 Clinical Trials of Putative Neuroprotective and Repair-­promoting Strategies 6.6.1 Sodium Channel Blockers Studies in acute and chronic experimental autoimmune encephalomyelitis demonstrated potent neuroprotective effects of the anticonvulsant phenytoin, which blocks the voltage-­gated sodium channel (Figure 6.4).67 A

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Figure 6.4 Small molecule medications with neuroprotective or repair-­promoting actions.

randomized, double-­blind, placebo-­controlled Phase II trial tested whether oral phenytoin (4 mg kg−1 or 6 mg kg−1 daily) lessened residual optic nerve damage in 86 participants randomized within 2 weeks of onset of acute optic  neuritis.68 The primary endpoint was retinal nerve fiber layer (RNFL) thinning  on optical coherence tomography (OCT) over 6 months, which was reduced by 30% in the phenytoin group compared with the placebo group. This positive result contrasts with the negative results of an earlier clinical trial with 120 participants with SPMS of another oral sodium channel blocker lamotrigine (400 mg daily), which failed to slow whole brain volume loss over 24 months.69 The lamotrigine group demonstrated accelerated brain volume loss in the first year, suggesting an unanticipated pseudoatrophy effect, which may have obscured a beneficial treatment effect.

6.6.2 Erythropoietin Erythropoietin has been shown to have neuroprotective effects in an animal model of distal peripheral axonopathy70 and in experimental autoimmune encephalomyelitis.71 Phase II studies in CNS demyelinating disease have yielded mixed results. A randomized, placebo-­controlled Phase II trial randomized 40 participants with acute optic neuritis to intravenous recombinant human erythropoietin (33 000 IU) or placebo daily for 3 days, both in combination with intravenous methylprednisolone.72 The primary endpoint was change in RNFL thickness on OCT at 16 weeks. Median RNFL thickness decreased by 7.5 microns in the erythropoietin group compared with 16.0 microns in the placebo group (p = 0.0357). The beneficial effect on OCT was supported by superior efficacy on retrobulbar optic nerve diameter on MRI (p = 0.0112) and visual evoked potential (VEP) P100 latency (p = 0.0011) with a nonsignificant trend on tests of visual function. A second randomized,

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double-­blind, placebo-­controlled Phase II study enrolled 52 participants with PP or SPMS, EDSS 4.0–6.5, and relapse-­free progression in the previous 2 years.73 Participants were randomized to intravenous recombinant human erythropoietin (48 000 IU) or placebo administered 17 times over 24 weeks. In contrast to the earlier trial, no benefit was demonstrated on the primary endpoint, change in a composite measure of maximum walking distance, hand dexterity measured by 9HPT, or cognition measured by TRAIL making B from baseline to 24 weeks (p = 0.22), or on any of the other clinical (SDMT, MSFC, EDSS, EDSS pyramidal function system score, T25FW), MRI (whole brain volume change, T2 lesion volume), or patient-­reported (Short Form-­36) outcomes.

6.6.3 Dronabinol Several lines of evidence indicate that cannabinoids, particularly those with agonist effects on the CB2 receptor, have anti-­inflammatory, neuroprotective, and remyelinating effects.74 Dronabinol (Δ9-­tetrahydrocannabinol) is an oral CB1 and CB2 agonist (Figure 6.4). The CAMS trial focused on symptomatic effects of dronabinol on spasticity, pain, and tremor.75 In the 12-­month follow-­up study, there was a suggestion of benefit on disability measured by EDSS and Rivermead Mobility Index.76 CUPID was a randomized, double-­blind, placebo-­controlled trial that more directly assessed the potential neuroprotective effects of dronabinol in MS.77 Participants with PP or SPMS were randomized 2 : 1 to dronabinol (up to 28 mg daily, n = 329) or placebo (n = 164) for 36 months. No benefit was demonstrated on the primary endpoints, time to 6-­month confirmed EDSS worsening (HR 0.92, 95 CI 0.68–1.23, p = 0.57) or annual change from baseline in the physical impact subscale of the 29-­item Multiple Sclerosis Impact Scale (HR −0.91, 95 CI −2.01 to 0.19, p = 0.11). The low rate of EDSS worsening in the placebo group decreased the ability of the study to demonstrate benefit on this endpoint. However, other endpoints, including MSFC, MS Walking Scale-­12, Rivermead Mobility Index, or MRI (whole brain volume change, new/enlarging T2 lesions, new/enlarging T1 lesions), did not show benefit either.

6.6.4 Simvastatin Statins have a wide range of biologic actions besides lowering cholesterol that might be of benefit in MS, including immunomodulatory and neuroprotective effects (Figure 6.4).78 Clinical trial results in RRMS have been disappointing.79 Results in progressive MS have been somewhat more promising. MS-­STAT was a Phase II, double-­blind, placebo-­controlled trial of 140 participants with SPMS.80 Treatment with simvastatin (80 mg daily) led to significant reduction in the mean annualized rate of whole brain volume loss to 0.29% versus 0.58% for the placebo group and a significant reduction

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in some secondary measures of disability. A randomized, double-­blind, placebo-­controlled Phase III trial, MS-­STAT2 (ClinicalTrials.gov Identifier: NCT03387670), is currently in progress.

6.6.5 High-­dose Biotin Biotin (vitamin B7) is a cofactor for several carboxylases involved in fatty acid synthesis and cellular energy production. In high doses, it is hypothesized to stimulate axonal remyelination via augmented myelin production and energy production (Figure 6.4).81 A small study investigating the effectiveness of oral high-­dose biotin (MD1003, 100–300 mg daily) in PPMS and SPMS showed improvement in some of disease markers, particularly visual markers.82 MS-­SPI, a randomized, double-­blind, placebo-­controlled trial enrolled 154 subjects with PPMS or SPMS, excluding those with clinical or radiologic evidence of inflammatory activity within the previous year, and assigned them in a 2 : 1 ratio to oral high-­dose biotin (100 mg three times daily) or placebo for 12 months. Overall, 12·6% of biotin-­treated participants had improvement in a composite measure of disability (EDSS and T25FW) at month 9 confirmed at month 12 compared with none in the placebo group (p = 0.005).83 Although it was not statistically significant (p = 0.36), new MS lesions on brain MRI were more frequent in the biotin group (23%, versus 13% for placebo). A randomized, double-­blind, placebo-­ controlled Phase III trial, SPI2 (ClinicalTrials.gov Identifier: NCT02936037), comparing oral high-­dose biotin (100 mg three times daily) to placebo in progressive MS is in progress.

6.6.6 Ibudilast Ibudilast (MN-­166) is a nonselective phosphodiesterase inhibitor approved in Japan and other Asian countries for treatment of pulmonary airway diseases (Figure 6.4). It has immunomodulatory and neuroprotective actions, including blocking macrophage migration inhibitory factor,84 and has been studied as a potential neuroprotective agent in several neurological diseases including MS and stroke. In a Phase II study in RRMS patients, ibudilast was ineffective in reducing MRI lesion activity or clinical relapses, but it showed benefit in reduction of whole brain volume loss and rate of conversion of active MRI lesions to persistent T1-­hypointense lesions.85 This observation led to SPRINT-­MS, a randomized, double-­blind, placebo-­ controlled Phase II clinical trial in PP and SPMS.86 In the study, 255 participants were randomized 1 : 1 to receive oral ibudilast (100 mg daily) or placebo for 96 weeks. There was a 48% slowing (p = 0.04) in the rate of annualized whole brain volume loss measured by brain parenchymal fraction with ibudilast (0.0010 per year) compared to placebo (−0.0019 per year). The beneficial effects of ibudilast on whole brain volume loss were supported by results on cortical thickness and MTR in normal-­appearing

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white matter. The main side effects of ibudilast included gastrointestinal symptoms, headache, and depression. In addition to showing promising results for a specific putative neuroprotective agent, ibudilast, SPRINT-­MS was of interest by virtue of implementing a wide range of outcome measures, including advanced MRI methods, OCT, and quantitative neuroperformance tests, in a multicenter trial, which will allow comparison of their usefulness as outcome measures in Phase II trials of neuroprotective and repair-­promoting strategies.

6.6.7 Alpha Lipoic Acid Lipoic acid is an endogenously produced antioxidant with several potential neuroprotective effects (Figure 6.4).87 The efficacy of lipoic acid in 51 participants with SPMS was evaluated in a 2-­year, randomized, double-­blind, placebo controlled Phase II trial of daily oral lipoic acid (1200 mg daily) versus placebo.88 The study showed a 68% decrease (p = 0.002) in annualized percentage change in whole brain volume in the lipoic acid group (−0.21) compared with the placebo group (−0.65). There was trend for benefit on the T25FW. The most common adverse effects of lipoic acid were gastrointestinal symptoms. Laboratory abnormalities were limited to asymptomatic elevations in alkaline phosphatase, which improved after discontinuation of lipoic acid. Two participants treated with lipoic acid developed kidney-­related adverse effects: one participant with abnormal baseline creatinine developed renal failure and another developed proteinuria due to glomerulonephritis.

6.6.8 Opicinumab Leucine-­rich repeat and immunoglobulin domain-­containing neurite outgrowth inhibitor receptor-­interacting protein-­1 (LINGO-­1) is a membrane-­ associated protein expressed on oligodendrocytes and axons that inhibits myelination and remyelination.89 Opicinumab (BIIB033), a monoclonal antibody that blocks LINGO-­1, is under development as a repair-­promoting therapy. Results of two Phase II studies have been promising but somewhat equivocal. RENEW was a randomized, double-­blind, placebo-­controlled Phase II study comparing the ability of intravenous opicinumab (100 mg kg−1 every 4 weeks for six doses) to improve recovery from acute optic neuritis compared to placebo in 82 participants.90 Benefit was not demonstrated for the intention-­to-­treat cohort on the primary endpoint, P100 latency on full-­field VEP at week 24 (mean treatment difference −3.5 msec, 95% CI −10.6 to 3.7, p = 0.33). Pre-­specified additional analyses suggested benefit: per protocol cohort at week 24 (mean treatment difference −7.6 msec, 95% CI −15.1 to 0.0, p = 0.050), intention-­to-­treat cohort at week 32 (mean treatment difference −6.1 msec, −12.7 to 0.5, p = 0.071), and per protocol cohort at week 32 (mean treatment difference −9.1 msec, 95% CI −16.1 to −2.1, p = 0.011). No benefit was demonstrated on MRI, OCT, or LCLA.

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SYNERGY was a randomized, double-­blind, placebo-­controlled multiple dose Phase II study, which assessed whether intravenous opicinimab (3, 10, 30, or 100 mg kg−1 every 4 weeks for 19 doses) improved disability compared to placebo, both in combination with intramuscular IFNβ-­1a.91 The study population included 418 participants with active RR or SPMS with EDSS 2.0–6.0. The primary endpoint was percentage of participants with 3-­month confirmed improvement in disability using a multidimensional outcome measure comprising EDSS, T25FW, 9HPT, and PASAT. The linear trend test for the primary endpoint was not significant (p = 0.89). The dose–response curve had an inverted U shape, suggesting benefit for the 10-­mg and 30-­mg doses but not the 3-­mg and 100-­mg doses. There also was a suggestion of benefit in participants with disease duration ADCC, apoptosis

IV

Anti-­CD20 humanized IgG1 mAb

Type of MS Main clinical outcomes Main MRI outcomes

9

RR HERMES (NCT00097188) Phase II, DB, PC, ­48 w ­ eek; 104 pts OLYMPUS67 PP (NCT00087529) Phase II/III, DB, PC, 96 week; 439 pts

Anti-­CD20 ­chimeric IgG1 mAb

Ocrelizumab

Trial, design

ADCC>CDC, apoptosis

OPERA I68 RR (NCT01247324) and OPERA II68 (NCT01412333) Phase III, DB, DD, 96  week comparator-­ controlled (SC IFNβ-­1a); 1656 ­pts ORATORIO73 PP (NCT01194570)

Phase III, DB, PC, 120 w ­ eek. 732 pts, 2 : 1 OCR-­placebo

↓ARR (Week 24, 14.5% vs. 34.3%, p = 0.02; week 48, 20.3% vs. 40.0%, p = 0.04)

↓Total Gd+ lesions (p < 0.001) ↓Total new gadolinium-­ enhancing lesions ­ (p < 0.001) ↓Lesion volume on T2WI No difference in 12 week ↓Increase in T2 lesion burden CDP; Delayed time to CDP in patients aged No difference in brain ADCC, apoptosis

Achieved by defucosylation of the Fc region

Phase-­II, 48 w ­ eek, DB, PC, 48 pts

↓ARR No difference in ­disability outcomes No data available yet

ARR -­0.07; Relapse-­free – 93% CDP at week 24–7% CDI at week 24 – 17% NEDA – 74%

Main adverse events

↓Mean rate of cumulative Injection-­related new Gd+ lesions (65% ­reactions – 97% (mild for all doses between to moderate) weeks 0–12, p < 0.01) No data available yet No data available yet

↓100% in Gd+ lesions ↓10% in mean T2 lesion volume

IAR (mild to moderate)

Inebilizumab (MEDI-­ 551)

IV or SC

Anti-­CD19 glycoengineered humanized IgG1κ mAb SC

Enhanced ADCC NCT01585766 94 activity

RR

a

IAR 40% of patients on inebilizumab or placebo; Injection site reactions in 17%; Infections

Similar mean numbers of Gd+ T1 lesions per scan in all groups

Injection site reactions

Phase-­I, 24 week, PC, dose-­ escalation, 28 pts

Reduction in RR ATAMS101 ­B cell differ(NCT00642902) entiation, Phase II, DB, ­maturation PC, 36  week, and survival; 255 pts ATON102 Fusion protein Reduction in ON circulating (extracellu(NCT00624468) immunolar domain Phase II, DB, PC, globulins of the TACI 36 week, 34 pts concentrations receptor and Fc domain of human IgG1) NCT02975349 111 Evobrutinib Oral Prevention of RR activation of Phase-­II, 48 week, BTKi BCR signalDB, PC, 267 pts ling pathways; Inhibition of B cell ­activation and survival

Atacicept (TACI-­Ig)

↓New Gd+ and new or newly enlarging T2 MRI lesions

↑ARR in all 3 atacicept groups Trial prematurely terminated

More atacicept-­treated NA patients converted to clinically-­definite RRMS (35.2%) than placebo-­treated patients (17.6%) despite having less retinal axonal loss A trend towards a reduc- ↓T1 Gd+ lesions tion in ARR

More SAE in the atacicept groups Injection site reactions No SAE

↑ liver enzymes (asymptomatic)

 bbreviations: ADCC – antibody-­dependent cellular cytotoxicity; ARR – annualized relapse rate; BCR – B cell receptor; BTKi – Bruton's tyrosine kinase inhibitor; CDC – A complement-­dependent cytotoxicity; CDI – confirmed disability improvement; CDP – confirmed disability progression; DB – double-­blind; DD – double dummy; Gd – gadolinium; IAR – infusion-­associated reactions; IFN – interferon; IV – intravenous; NA – not available; NEDA – no evidence of disease activity; NEPAD – no evidence of progression or active disease; OCR – ocrelizumab; ON – optic neuritis; PC – placebo-­controlled; PP – primary-­progressive; pts – patients; RR – relapsing–remitting; SAE – serious adverse events; SC–subcutaneous.

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Table 7.2  Effects  of other approved disease-­modifying therapies on B cells.a Drug name

Target/Mode of action

Effect on B cells

Depletion phase: ↓ B cells115 Reconstitution phase: ↑ B cells (tB cells, nB cells, Breg)13,115–121 Interferon-­β Immunomodulation, ↓ mB cells, nB cells expressing CD86 ­anti-­inflammatory effects and CCR5 13,123–125 on various immune cells ↑ IL10-­producing Breg, TGF-­β126 Glatiramer Immunomodulation ↓ mB cells, CXCR5and ICAM-­3 in B Acetate cells;130 ↓ IL-­6, LT-­α and TNF-­α ↑ IL10 producing Breg56,129–131 Natalizumab VLA-­4 Blood: ↓ nB cells; ↑Breg cells, mB cells105 Prevention of leukocyte CSF: ↓ B cells, Immunoglobulins, trans-­migration to the OCBs105,133 CNS Fingolimod S1P-­R modulation Blood: ↓ nB cells, mB cells CSF: Minor decrease only in the number of B cells, ↑ Breg140–142 Prevention of lymphocyte Abrogation of B cell aggregate egress from lymph nodes ­formation in the CNS (EAE)143 Teriflunomide Inhibition of DHODH and ↓ B cell proliferation and activation, pyrimidine synthesis ↓ B cells in blood, ↓ IL-­6, IL-­8 144,145 Dimethyl Activation of NRF2 ­pathway, ↓ mB cells, ↓ GM-­CSF, IL-­6, TNFα; Fumarate inhibition of NFκB ↑ Breg147 pathway Cladribine Impairment of DNA Depletion phase: ↓ B cells in blood149 ­synthesis, lymphocyte Reconstitution phase: ↓ mB cells150 apoptosis. Alemtuzumab CD52 Lymphocyte depletion

a

 bbreviations: Breg – regulatory B cells; CCR5 – C–C chemokine receptor 5; CNS – central A nervous system; CSF – cerebrospinal fluid; DHODH – dihydro orotate dehydrogenase; GM-­CSF – granulocyte-­macrophage colony-­stimulating factor; ICAM-­3 – intracellular adhesion molecule-­3; IL – interleukin; mB cells – memory B cells; nB cells – naïve B cells; NRF2 – nuclear factor erythroid 2-­related factor 2; NF-­κB – nuclear factor kappa light chain enhancer of activated B cells; OCB – oligoclonal bands; S1P-­R – sphingosine-­1-­phosphate receptor; tB cells – transitional B cells; VLA-­4 – very late antigen 4.

which demonstrates increased binding capacity to CD20 and enhanced target cell killing through ADCC. These mAbs differ from each other not only by their structure and immunogenicity (chimeric, humanized, fully human or glycoengineered, respectively), but also by the relative degree of ADCC and CDC they exert and the CD20 epitope they recognize. The bioavailability of various anti-­CD20 mAbs and the expression of CD20 do not differ between B cell-­containing compartments; however, while B cell depletion is fast and nearly complete in the circulation (which contains only 2% of total B cells in humans), anti-­CD20 mAbs do not effectively remove B cells residing in protective niches within secondary lymphoid structures or the peritoneum.61,62 This effect is attributed to the surrounding microenvironment of B cells in different compartments,62 and raises the question

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of how the treatment works, if B cells residing in pathogenic niches, such as the CNS in MS, or synovium in rheumatoid arthritis (RA), are relatively protected from anti-­CD20 therapy. The most likely explanation is that sustained depletion of circulating B cells, which in autoimmune disease likely includes recirculating, restimulated memory B cells destined to return to the target tissue, prevents their re-­entry into focal white matter regions in MS or joint tissue in RA.63,64

7.4.1.1 Rituximab RTX is a chimeric IgG1 mAb depleting B cells primarily through CDC that is approved for the treatment of B cell lymphoma and RA resistant to TNF-­ α blocking agents. It was first tested for safety, tolerability, pharmacodynamics and activity of B cell depletion in a 72 week, open-­label, Phase I, multicenter clinical trial in 26 RRMS patients who received infusions of RTX 1000 mg at weeks 0, 2, 24 and 26.65 No serious adverse events (SAEs) were noted, mild to moderate infusion-­associated reactions (IARs) were experienced by 65.4% of patients and tended to decrease with subsequent infusions. Mild to moderate infections were reported in 64.1% of patients, and none led to withdrawal. Fewer new gadolinium-­enhancing (Gd+) or T2 lesions were seen starting from week 4 and through week 72. An apparent reduction in relapses was also observed over the 72 weeks compared with the year before therapy. B cell depletion was near complete (99.8%) by week 2 and sustained through week 48. B cells reconstituted to a mean of 34.5% of baseline by week 72; the majority were naïve (CD27−) rather than memory (CD27+) B cells. The HERMES trial (ClinicalTrials.gov identifier: NCT00097188) was a 48 ­week, Phase II, double-­blind trial that included 104 patients with RRMS who were randomized to receive either 1000 mg of intravenous (IV) RTX (n = 69) or placebo (n = 35) on days 1 and 15.9 Compared with patients who received placebo, patients who received RTX had reduced counts of total Gd+ lesions (p < 0.001) and of total new gadolinium-­enhancing lesions (p < 0.001) at weeks 12, 16, 20, 24 and 48. The proportion of patients in the RTX group with relapses was significantly reduced at week 24 (14.5% versus 34.3%, p = 0.02) and week 48 (20.3% versus 40.0%, p = 0.04). More patients in the RTX group than in the placebo group had adverse events (AEs) within 24 hours after the first infusion, most of which were mild-­to-­ moderate events; after the second infusion, the numbers of IARs were similar in the two groups. Infections were reported in about 70% of the two groups but no opportunistic infections were observed. SAEs were experienced by 5.7% of placebo-­treated and 2.9% of RTX-­treated patients. This study showed that a single course of RTX significantly and rapidly reduced both radiologic and clinical evidence of inflammatory activity for 48 weeks, and provided support for the theory of B cell involvement in the immuno-­ pathogenesis of MS.

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The third trial was a 52 ­week Phase II add-­on MRI-­blinded study enrolling 35 active patients with RRMS who experienced at least one relapse within the past 18 months and had at least 1 Gd+ lesion on any of three monthly pretreatment MRI images despite treatment with injectable drugs for MS.66 Patients received IV RTX (375 mg m2) at weeks 1, 2, 3 and 4 and underwent MRI at weeks 12, 16 and 20. The number of Gd+ lesions was significantly reduced in post-­treatment scans compared with pre-­treatment ones, with 74% of post-­treatment MRI scans being free of Gd activity compared with 26% free of Gd activity at baseline (p < 0.0001). The Multiple Sclerosis Functional Composite (MSFC, a composite measure of walking speed, upper-­limb function, and cognition) was improved by RTX treatment. In combination with standard injectable therapies, rituximab was well tolerated with no SAEs. In the OLYMPUS Phase II/III, 96 ­week, double-­blind, multicenter study (ClinicalTrials.gov identifier: NCT00087529), 439 patients with PPMS were randomized 2 : 1 to receive either four courses of two 1000 ­mg intravenous RTX or placebo infusions every 24 weeks.67 Although the time to confirmed disability progression (CDP) sustained for 12 weeks (the primary endpoint) did not reach statistical difference, rituximab patients had less increase in T2 volume load on MRI (p < 0.001). Subgroup analysis showed that time to CDP was delayed in patients aged 99% in all cohorts. Gd+ lesions were reduced to zero (100% reduction) at week 24, and maintained at week 48. Mean T2 lesion volume decreased by 7.3% and 10.6% at weeks 24 and 48, respectively. ARR was 0.07, 7% of subjects had 24 ­week CDP and 17% met criteria for 24 week confirmed disability improvement. Seventy-­four percent of subjects fulfilled the criteria for NEDA. Most common AEs were mild to moderate IARs which showed no increase in incidence with faster infusion times, and no SAEs were reported. The study showed that a rapid infusion time, as low as one hour, of 450 mg UTX was well tolerated and produced high levels of B cell depletion. This regimen is now being studied in two identical Phase III ULTIMATE trials.86 Collectively, trials with anti-­CD20 mAbs provide clinical and radiological evidence for the efficacy and safety of selective CD20+ B cell depletion in the treatment of both relapsing and progressive forms of MS.

7.4.2  Anti CD19 mAbs CD19, similar to CD20, is a B cell surface molecule of the Ig superfamily that is involved in B cell activation and differentiation and contributes to signal transduction following B cell-­receptor activation through its highly preserved cytoplasmic domains. Compared to CD20, CD19 has a wider range of expression, from pro-­B cells in the bone marrow to antibody-­ producing plasmablasts and some plasma cells after CD20 expression is

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lost. Preclinical data suggest that the elimination of CD19+ plasmablasts and some plasma cells could provide a significant decline in total and autoreactive antibody counts and a greater clinical effect in B cell-driven autoimmune diseases.88 In addition, treatment with anti-­CD19 mAbs leads to nearly complete depletion of B cells from the circulation and spleen and a more effective depletion of BM-­resident B cells compared to anti-­CD20 mAbs.89 Patients with MS were shown to have greater numbers of circulating CD19+ B cells than healthy controls.90 Short-­lived plasmablasts expressing CD19 have been suggested to be a primary effector B cell population involved in ongoing active inflammation in patients with MS.91 Therefore, there is a scientific rationale for depleting CD19+ B cells in patients with relapsing forms of MS.

7.4.2.1 Inebilizumab (MEDI-­551) Inebilizumab is a humanized glycoengineered IgG1κ monoclonal antibody that binds to and depletes CD19+ B cells. It lacks a fucose residue in the Fc region, a modification that enables tighter binding to FcγRIIIa and significantly enhanced effector cell-­mediated ADCC.92 In a mouse EAE model, administration of inebilizumab reduced the incidence and severity of the disease and prevented the development of EAE when given prior to induction.93 In addition, inebilizumab was shown to preferentially deplete autoreactive B cells, spare protective regulatory B cells, reduce T-­cell number in the spinal cord lesions of mice with EAE and expand myelin-­specific Foxp3+ regulatory T-­cells.93 Inebilizumab was tested in a Phase I placebo-­controlled dose–escalation study to determine the safety, tolerability, pharmacokinetics, pharmacodynamics and immunogenicity of ascending IV and SC doses of the drug in patients with relapsing forms of MS (ClinicalTrials.gov identifier: NCT01585766).94 Results showed complete B cell depletion across all doses, with higher inebilizumab doses causing longer duration of B cell depletion. Fewer new Gd+ and new or newly enlarging T2 MRI lesions were developed in inebilizumab-­treated patients compared with placebo-­treated patients. The safety and tolerability profile of inebilizumab was acceptable, with infusion or injection reactions reported in 40% of patients on active treatment or placebo and SAEs (including one death unrelated to inebilizumab) reported in three patients treated with inebilizumab.94 We could not find any information regarding further plans to develop inebilizumab for MS. Because of its ability to deplete later differentiation stages of B cells, inebilizumab is also currently being tested in a Phase III trial in neuromyelitis optica spectrum disorder (NMOSD), an antibody-­mediated demyelinating disorder affecting primarily the optic nerves and spinal cord. However, it is still unclear whether the broader range of depleted B cells entails greater clinical benefits (e.g., by a longer lasting depletion or elimination of later differentiation stages of B cells) or more potentially

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serious side effects (e.g., by delaying and/or negatively affecting B cell reconstitution after treatment cessation due to the elimination of earlier stages in the bone marrow or reducing humoral immunity by elimination of antibody-­producing cells).

7.4.3  Cytokine Blockers BAFF and APRIL are the main regulators of B cell survival, maturation and activation that are elevated in patients with MS.95,96 As mentioned above, BAFF binds strongly to the BAFF receptor, which is expressed on late stages of the B cell lineage and to TACI. Mice with EAE overexpressing BAFF show increased disease severity, while ligand knock-­out mice show decreased disease severity when compared with wild-­t ype mice,97 and treatment with anti-­BAFF antibodies ameliorates EAE in marmosets.98 BAFF is also involved with the formation of ectopic germinal centers under the meninges in MS.49 Taken together, targeting BAFF or APRIL or their receptors is a plausible approach that may inhibit B cell proliferation and survival and limit neuroinflammation. Several recombinant antibodies and fusion proteins targeting components of the BAFF/APRIL system have been developed, including belimumab and tabalumab (LY2127399), fully human mAbs to BAFF; VAY736, a fully human defucosylated mAb targeting the BAFF-­receptor; hBCMA-­Fc, a fusion protein composed of human BCMA (another receptor capable of binding both BAFF and APRIL) and human IgG1-­Fc, and atacicept, a fusion protein of the TACI receptor with human IgG1-­Fc. However, none of these have progressed past Phase II trials. Tabalumab is a potent and selective fully human IgG4 mAb with neutralizing activity against both soluble and membrane-­bound BAFF. A Phase II clinical trial investigating tabalumab in 245 patients with RRMS (ClinicalTrials.gov identifier: NCT00882999) started in April 2009 and was registered online as “completed”, but “canceled” in 2011 by investigators.99 It is not known whether this study has been terminated due to safety issues, clinical inefficacy as has been observed in systemic lupus erythematosus (SLE),100 or increased disease activity, as has been observed with atacicept. Atacicept (TACI-­Ig) is a fusion protein comprised of the extracellular domain of the naturally occurring TACI receptor (a binding site for BAFF and APRIL) and the Fc domain of human IgG1 (which increases the stability of the molecule). Two Phase II 36 ­week double-­blind, placebo-­ controlled clinical trials with atacicept – the ATAMS study in 255 patients with relapsing MS (ClinicalTrials.gov identifier: NCT00642902)101 and the ATON study in 34 (initially planned for 80) patients with unilateral optic neuritis (ClinicalTrials.gov identifier: NCT00624468)102 – have been prematurely terminated due to increased disease activity in the atacicept treatment group in ATAMS compared to placebo. In the ATAMS study, ARRs

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were higher in all three atacicept groups than in the placebo group and decreased until they were similar to that of the placebo group during the safety follow-­up period. Mean numbers of Gd+ T1 lesions per scan were similar in all groups.101 In ATON, more atacicept-­treated patients (35.2%) converted to clinically-­definite RRMS compared with placebo-­treated patients (17.6%), despite the same patients experiencing less retinal axonal loss after an optic neuritis event.102 Possible explanations for the differential effects of anti-­CD20 mAbs and atacicept in MS may include a broader depleting pattern of the former, while atacicept has a significant impact on Breg without sufficiently depleting pathogenic B cell subsets, or reduction of serum immunoglobulins by atacicept and disruption of non-­specific Fc receptor blockade, which could have a therapeutic benefit.103 In addition, receptors for BAFF and APRIL are also expressed on some T cells and regulatory cells and were found to have more pleiotropic roles, which may include protective pathways that may be disrupted by their blockade. There is also evidence that suggests APRIL as a negative regulator of autoimmunity and that atacicept preferentially targets naïve B cells, plasmablasts and plasma cells but have a lesser effect on memory B cells that are the relevant disease-­promoting subset, resulting in a relative increase in memory B cells after depletion of soluble BAFF and APRIL.104,105 The results of these studies with atacicept suggest that the role of B cells and humoral immunity in MS is complex. Investigators should therefore be cautious when testing new agents and employ rigorous monitoring for negative effects on clinical and MRI outcomes.

7.4.4  Plasmapheresis Plasmapheresis effectively removes pathogenic antibodies and pro-­ inflammatory cytokines from the plasma in autoimmune diseases. Plasmapheresis, plasma absorption, or therapeutic plasma exchange (TPE) have been used for decades for treatment of severe relapses in demyelinating CNS autoimmune disease and plasma exchange has a proven efficacy in severe relapses resistant to corticosteroids.106 This beneficial response is probably limited to patients exhibiting pattern II (T cell and antibody mediated) – the most common of four histopathological patterns of MS,107 however the mechanism is not completely understood and could be other than removal of autoreactive immunoglobulins.108

7.4.5  Targeting Bruton's Tyrosine Kinase Bruton's tyrosine kinase (BTK) is a key cytoplasmic enzyme that mediates B cell signaling via a variety of cell-­surface molecules resulting in multiple downstream immune effects.109 BTK is also present in innate

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immune cells but not T cells. Complete absence of BTK results in X-­linked agammaglobulinemia that can be managed by regular administration of replacement pooled human immunoglobulins. BTK is a key component of the BCR pathway and is involved in multiple other B cell signaling pathways such as CD40, toll-­like receptors (TLRs), Fc receptor and chemokine receptors. Administration of BTK inhibitors (BTKi) leads to B cell inhibition, which is rapidly reversible upon treatment cessation and to suppression of EAE disease activity.110 Results of the first proof-­of-­concept trial with evobrutinib, a highly specific, irreversible, oral BTKi in MS, were recently presented.111 This was a 48 ­week, Phase II, double-­blind, placebo-­ controlled trial (ClinicalTrials.gov identifier: NCT02975349) that randomized 267 patients with relapsing forms of MS to evobrutinib 25 mg QD, 75 mg QD, 75 mg BID, placebo or open-­label dimethyl fumarate. At week 24, T1 Gd+ lesions per scan (primary endpoint) were significantly reduced with the higher two doses versus placebo, with evidence of dose–response; a trend towards a reduction in ARR was seen with the higher two doses, with evidence of dose–response. Rates of AEs and SAEs were comparable with evobrutinib 25 and 75 mg QD and placebo, but higher with evobrutinib 75 mg BID (driven by asymptomatic increases in liver transaminases). Grade 3 AEs were more frequent with evobrutinib 75 mg BID; most were asymptomatic, reversible transaminase elevations with no Hy's Law cases. There were no serious infections with evobrutinib and no other emerging safety signals.111 These results support a role for evobrutinib in MS, warranting evaluation in larger trials.

7.4.6  A  pproved Therapies with Partial or Indirect Effects on B Cells The high diversity and complexity of the human immune system, along with its pleiotropy of functions, multiple interactions and cross-­talks among its different components, is also reflected in the complex multi-­player immune pathogenesis of MS that provides multiple sites for therapeutic intervention, and suggests that other immunomodulatory therapies may partially or indirectly affect the number and function of certain B cell populations in MS. The recent appreciation of the various contributions made by B cells to the pathogenesis of MS, namely production of autoantibodies, antigen presentation to and activation of T cells, production of pro-­inflammatory cytokines and the generation of ectopic lymphoid follicles in the CNS, and the role played by B cells in regulating the disease process, prompted research on the effects of other approved drugs for MS on B cells (Table 7.2). Indeed, while most MS therapies were designed to target T cells, essentially all of them affect B cell responses in ways that might be relevant for their therapeutic effects.13,56

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7.4.6.1 Alemtuzumab – anti-­CD52 mAb

Alemtuzumab (Lemtrada®, CAMPATH-­1H) is a lytic IgG1 mAb that targets CD52, a cell-­surface glycoprotein of unknown function present on >95% of T and B cells, monocytes and some dendritic cells, and to a lesser degree, on natural killer (NK) cells and other leukocytes.112 Two Phase III clinical trials in treatment-­naïve and treatment-­experienced RRMS patients demonstrated the high efficacy and superiority of alemtuzumab over SC IFNβ-­1a.113,114 Treatment with a single dose of alemtuzumab results in a rapid, profound and prolonged depletion of T and B cells by mechanisms of ADCC and CDC. Following depletion, gradual reconstitution of immune cells begins within weeks, with monocytes and B cells being first (median recovery time to baseline level – 1 and 3 months, respectively) and finally CD4+ T cells (median recovery time to baseline level – 61 months), leading to prolonged alteration of the immune repertoire. Specifically, first immature, then mature B cells repopulate and resurge above baseline levels before memory B cells reconstitute, while T cell subsets remain low for longer periods of time. This pattern of B cell reconstitution unbalanced by T-­cell regulation may explain the frequent B cell ­mediated autoimmunity following alemtuzumab treatment. Notably, qualitative, not only quantitative, changes in the reconstituted immune system occur, with a relative increase in T-­ and B-­regulatory cells, CD56+ regulatory NK cells, immature B cells and anti-­inflammatory cytokines, and relative decrease in Th1 and Th17 cells and pro-­inflammatory cytokines.115–121 In the EAE model, anti-­CD52 treatment resulted in near-­complete depletion of CNS infiltrates and B cell aggregates.122

7.4.6.2 Interferon-­β The mechanism of action of IFNβ, the first DMT approved for MS, is generally attributed to anti-­inflammatory effects on T cells and macrophages and to shift from Th1 to Th2 immune responses.123 IFNβ has recently been found to decrease the number of pathogenic memory B cells124 and the percentage of naïve B cells expressing the co-­stimulatory molecule CD86 and the chemokine receptor CCR5, thus decreasing their stimulation and motility.125 In addition, treatment of MS patients with IFNβ is associated with increase of IL-­10-­producing regulatory B cells.126

7.4.6.3 Glatiramer Acetate Glatiramer acetate (GA) is a mixture of random synthetic polypeptides composed of four amino acids (glutamate, lysine, alanine and tyrosine) that was designed to mimic the major myelin protein, myelin basic protein (MBP) chemically and immunologically.127 GA competes with myelin antigens for binding to the MHC class-­II groove on APCs, the results of which include suppression of myelin-­reactive T-­effector cells and

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generation of GA-­specific anti-­inflammatory Th2 cells that cross the BBB and exert by-­stander suppression of myelin-­reactive T cells in the CNS.123 GA has been shown to reduce the expression of MHC class-­II molecules on monocytic APCs and to suppress their potential to activate MBP-­reactive T cells in vitro.128 This mechanism could potentially be extrapolated to B cells, which are effective APCs as well. In addition, treatment with GA results in an increase in Breg-­derived IL-­10 in MS and EAE with a concomitant decrease in the production of IL-­6, LT-­α and TNF-­α56,129 as well as decreased expression of CXCR5 and intracellular adhesion molecule (ICAM)-­3 in B cells, reducing their ability to migrate,130 and a decrease in the number of circulating memory B cells and plasmablasts,38 likely due to the reduced levels of BAFF in the CNS.60 It has been hypothesized that these effects may be mediated by cross-­reactivity of B cell receptors for GA with an antigen (possibly myelin basic protein) expressed in the MS lesion.131

7.4.6.4 Natalizumab Natalizumab is a humanized mAb that blocks the adhesion molecule very late antigen (VLA)-­4 expressed on white blood cells, thus limiting their migration across the BBB into the CNS. Migration of B cells into the CNS seems to be reduced to a greater extent than T cells in MS patients treated with natalizumab,132 resulting in reduced B cells, immunoglobulins and OCBs in the CSF.105,133 In the periphery, treatment with natalizumab is associated with a decrease in naïve B cells and an increase in Breg as well as memory B cells, which may be problematic in light of the pathogenic role that memory B cells can play in MS.105 Natalizumab has also been shown to mobilize John-­Cunningham virus (JCV)-­infected CD34+ hematopoietic stem cells from the bone marrow that can differentiate to CD19+, which favors growth of JCV and to increase factors involve in B cell differentiation.134–137 These alterations, combined with the increase in pre-­B cells and B cells, the impaired immune surveillance in the CNS due to the reduced numbers of T cells and B cells in the CSF, and blocking the migration of cytotoxic CD8+ T cells into the CNS,138 are major contributors to the high prevalence of PML seen in MS patients treated with natalizumab.

7.4.6.5 Fingolimod Fingolimod, a modulator of the receptor to sphingosine-­1-­receptor (S1P), is the first oral DMT approved for the treatment of patients with RRMS. It acts primarily by preventing the egress of lymphocytes from lymph nodes, thus reducing the number of circulating lymphocytes.139 In spite of a significant reduction in the number of B cells in the circulation, fingolimod treatment results in only small reduction in the number of B cells in the CSF;140

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however, there is an increase in the frequency of Breg producing IL-­10 in the CSF, as well as in the periphery, and a shift to an anti-­inflammatory cytokine profile.60,141,142 In fingolimod-­treated patients, these Breg have enhanced capacity for transmigration, which may explain their increased frequency in the CSF.142 In a B cell-­antibody-­dependent EAE model in mice, fingolimod treatment resulted in abrogation of B cell aggregate formation in the CNS and reduction in their in vitro evolution into TLOs, without reduction in the numbers of B cells and plasma cells.143 This may suggest another potential mechanism involving effect on B cells through modulation of B cell-­receptor-­mediated signaling for this small molecule that can easily cross the BBB.

7.4.6.6 Teriflunomide Teriflunomide selectively and reversibly inhibits dihydro-­orotate dehydroge­ nase (DHODH), a key mitochondrial enzyme in the de novo pyrimidine synthesis pathway, leading to a reduction in proliferation of activated T and B lymphocytes without causing apoptotic cell death. Teriflunomide exerts dose-­dependent inhibition on both T cell and B cell proliferation144 and reduces the secretion of pro-­inflammatory cytokines, including IL-­6 and IL-­8.145

7.4.6.7 Dimethyl Fumarate The mechanism of action of dimethyl fumarate (DMF) is believed to involve activation of the major transcription factor nuclear factor erythroid 2-­related factor 2 (Nrf-­2) pathway that leads to activation of antioxidative pathways and promotion of cytoprotection against oxidative stress, along with inhibition of the pro-­inflammatory NF-­κB pathway.146 DMF treatment reduces the numbers of both T cells and B cells in the blood, and alters the profile of the remaining lymphocytes toward a more anti-­inflammatory state, including decreased number of CD27+ memory B cells, a reduction in GM-­CSF, IL-­6 and TNF-­α secreting B cells and increase in the numbers of Breg populations.147 A recent study in DMF-­treated MS patients showed a reduced frequency of follicular helper T (TFH) cells and an increased frequency of follicular regulatory T (TFR) cells.148 Studying the impact of monomethyl fumarate (MMF), the primary metabolite of DMF, on B cell effector function in vitro showed that MMF increased the frequency of TGF-­ β-­producing B cells and decreased the frequency of B cells secreting LT-­α, TNF-­α, IL-­6 and, to a lesser extent, IL-­10.148

7.4.6.8 Cladribine The recent addition to the expanding arsenal of effective MS therapies is oral cladribine. Cladribine is a chlorinated purine analog resistant to degradation by the enzyme adenosine deaminase (ADA). After entering

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the cell via nucleoside transporter, cladribine accumulates intracellularly due to protection from ADA, becomes phosphorylated and activated by specific kinases and impairs DNA synthesis and repair, causing apoptotic cell death primarily in lymphocytes that show a high ratio of kinase to phosphatase.149 Cladribine depletes B cells to a greater extent than T cells, probably because of the higher expression of kinases in B cells (particularly mature, memory and germinal center B cells) than in plasma cells and T cells. This may explain the marked long-­term depletion of memory-­B cells to levels comparable with alemtuzumab, but without the associated initial lymphopenia and with only modest CD3+ T-­cell depletion.150

7.5  Summary and Future Directions Progress made over the last decade in understanding the role and biology of B cells in MS, largely driven by the impressive success of anti-­CD20 therapies, has revealed the critical involvement of B cells in shaping inflammatory CNS autoimmunity and established their fundamental role in the pathogenesis of the disease. Selective B cell depletion with mAbs targeting CD20 is highly effective in suppressing disease activity in RRMS without significantly compromising normal immune reactivity. For the first time, the humanized anti-­CD20 mAb ocrelizumab has also demonstrated efficacy in PPMS. Another mAb that targets both T cells and B cells via CD52, alemtuzumab, has also shown high and durable efficacy in RRMS. Advances in mAb technology have resulted in the development of fully human and glycoengineered anti-­CD20 mAbs that promise to improve efficacy and safety in treating patients with MS. Inhibition of B cells using BTK inhibitors (being small molecules that can access the CNS) has the potential to limit both the contribution of peripheral B cells to the immunological cascades leading to MS relapses and the involvement of B cells within the CNS with CNS-­compartmentalized inflammation, which contributes to MS progression. On the other hand, blocking the effect of trophic factors that act on later stages in B-­cell development by atacicept has failed and even led to increased disease activity, highlighting the complex role of B cells and humoral immunity in MS and underscoring the need to better understand how B cells contribute to both regulation of normal autoimmunity and to immune-­mediated diseases. Several important questions still have to be addressed: First, it is not completely understood why inconsistent treatment outcomes are seen with B cell-­directed therapies and why some patients do not respond adequately to B cell depletion therapies. Possible explanations include the existence of polymorphisms in the Fc receptor regions between patients, which may reduce the binding of the depleting Ab to its receptor and decrease ADCC,50 the likelihood that different pathogenic mechanisms, some of which may not depend on B cells, contribute to the pathogenesis of MS in various patients,27 the depletion of protective Breg, and the

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inability of the depleting Abs to cross the BBB and reach pathogenic B cell populations within the CNS. The insufficient effect of these therapies in the brain may be overcome by exploiting receptor-­mediated transcytosis (RMT) as a way for mAbs to cross the BBB without its disruption (e.g. by engineering bispecific mAb where one Fab arm binds the target antigen, and the other Fab arm is directed against a physiological receptor on the BBB such as transferrin receptor, thus enhancing passage across the BBB).151 A second related question concerns which MS patients would benefit best from B cell-­directed therapies. There is a need to develop biomarkers that will identify patients with pathogenic B cell function prior to initiating such therapies and can be used for monitoring the response to treatment. Third, how long a patient with MS should be depleted of peripheral B cells and the long-­term safety of prolonged B cell depletion are still unknown, as potential risks may substantially increase with the duration of therapy. Fourth, which maintenance therapies should be developed and employed that would prevent re-­emergence of pathogenic B cells after cessation of anti-­B cell therapies? Although most current MS therapies show some effect on B cell function, newer treatments that would potentially prevent the development of pathogenic B cells or would divert them towards a regulatory profile are needed. Finally, the exact role of B cells in MS is still not completely understood. Despite major advances made recently towards a better understanding of various B cell sub-­populations, their functions and interactions with other components of the immune system, there is still much left to be explored and discovered. Development of more effective and safer therapies directed at B cells should focus on compounds that also target specific plasma cells or do not affect Breg and is dependent on enhanced understanding and further research into B cell biology as well as a better understanding of the pathogenesis of MS.

References 1. A. Compston and A. Coles, Lancet, 2008, 372, 1502. 2. R. Milo and E. Kahana, Autoimmun. Rev., 2010, 9, A387. 3. S. K. Yadav, J. E. Mindur, K. Ito and S. Dhib-­Jalbut, Curr. Opin. Neurol., 2015, 28, 206. 4. M. R. Lincoln, A. Montpetit, M. Z. Cader, J. Saarela, D. A. Dyment, M. Tiislar and V. Ferretti, et al., Nat. Genet., 2005, 37, 1108. 5. A. Ben-­Nun, H. Wekerle and I. R. Cohen, Eur. J. Immunol., 1981, 11, 195. 6. E. A. Kabat, D. H. Moore and H. Landow, J. Clin. Invest., 1942, 21, 571. 7. A. Lowenthal, M. van Sande and D. Karcher, J. Neurochem., 1960, 6, 51. 8. M. Colombo, M. Dono, P. Gazzola, S. Roncella, A. Valetto, N. Chiorazzi, G. L. Mancardi and M. Ferrarini, J. Immunol., 2000, 164, 2782.

B Cell-­based Therapies for Multiple Sclerosis

163

9. S. L. Hauser, E. Waubant, D. L. Arnold, T. Vollmer, J. Antel, R. J. Fox and A. Bar-­Or, et al., N. Engl. J. Med., 2008, 358, 676. 10. H. Eibel, H. Kraus, H. Sic, A. K. Kienzler and M. Rizzi, Curr. Allergy Asthma Rep., 2014, 14, 434. 11. H. Alexopoulos, A. Biba and M. C. Dalakas, Neurotherapeutics, 2016, 13, 20. 12. C. A. Dendrou, L. Fugger and M. A. Friese, Nat. Rev. Immunol., 2015, 15, 545. 13. R. Li , K. R. Patterson and A. Bar-­Or, Nat. Immunol., 2018, 19, 696. 14. E. Mefre, Ann. N. Y. Acad. Sci., 2011, 1246, 1. 15. V. Viglietta, C. Baecher-­Allan, H. L. Weiner and D. A. Hafer, J. Exp. Med., 2004, 199, 971. 16. K. Blauth, G. P. Owens and J. L. Bennett, Front. Immunol., 2015, 6, 565. 17. A. Louveau, I. Smirnov, T. J. Keyes, J. D. Eccles, S. J. Rouhani, J. D. Peske and N. C. Derecki, et al., Nature, 2015, 523(7560), 337. 18. J. N. Stern, G. Yaari, J. A. Vander Heiden, G. Church, W. F. Donahue, R. Q. Hintzen and A. J. Huttner, et al., Sci. Transl. Med., 2014, 6, 248ra107. 19. L. M. Villar, J. Masjuan, M. C. Sádaba, P. González-­Porqué, J. Plaza, A. Bootello and J. C. Alvarez-­C ermeño, Arch. Neurol., 2005, 62, 574–577. 20. R. Hohlfeld, K. Dornmair, E. Meinl and H. Wekerle, Lancet Neurol., 2016, 15, 317. 21. S. M. Brändle, B. Obermeier, M. Senel, J. Bruder, R. Mentele, M. Khademi and T. Olsson, et al., Proc. Natl. Acad. Sci. U. S. A., 2016, 113, 7864. 22. J. Kuhle, C. Pohl, M. Mehling, G. Edan, M. S. Freedman, H. P. Hartung and C. H. Polman, et al., N. Engl. J. Med., 2007, 356, 371. 23. R. Srivastava, M. Aslam, S. R. Kalluri, L. Schirmer, D. Buck, B. Tackenberg and V. Rothhammer, et al., N. Engl. J. Med., 2012, 367, 115. 24. E. Nerrant, C. Salsac, M. Charif, X. Ayrignac, C. Carra-­Dalliere, G. Castelnovo and R. Goulabchand, et al., Mult. Scler., 2014, 20, 1699. 25. A. H. Cross, J. L. Stark, J. Lauber, M. J. Ramsbottom and J. A. Lyons, J. Neuroimmunol., 2006, 180, 63. 26. C. Linington, M. Bradl, H. Lassmann, C. Brunner and K. Vass, Am. J. Pathol., 1988, 130(3), 443. 27. C. Lucchinetti, W. Brück, J. Parisi, B. Scheithauer, M. Rodriguez and H. Lassmann, Ann. Neurol., 2000, 47, 707. 28. D. Rodríguez-­Pinto, Cell. Immunol., 2005, 238, 67. 29. S. K. Pierce, J. F. Morris, M. J Grusby, P. Kaumaya, A. van Buskirk, M. Srinivasan, B. Crump and L. A. Smolenski, Immunol. Rev., 1988, 106, 149. 30. A. Rivera, C. C. Chen, N. Ron, J. P. Dougherty and Y. Ron, Int. Immunol., 2001, 13, 1583. 31. N. Molnarf, U. Schulze-­Topphoff, M. S. Weber, J. C. Patarroyo, T. Prod'homme, M. Varrin-­Doyer and A. Shetty, et al., J. Exp. Med., 2013, 210, 2921.

164

Chapter 7

32. A. Bar-­Or, E. M. Oliveira, D. E. Anderson, J. I. Krieger, M. Duddy, K. C. O'Connor and D. A. Hafler, J. Immunol., 2001, 167, 5669. 33. K. Genç, D. L. Dona and A. T. Reder, J. Clin. Invest., 1997, 99, 2664. 34. A. Ray, S. Basu, C. B. Williams, N. H. Salzman and B. N. Dittel, J. Immunol., 2012, 188, 3188. 35. I. Jelcic, F. Al Nimer, J. Wang, V. Lentsch, R. Planas, I. Jelcic and A. Madjovski, et al., Cell, 2018, 175, 1. 36. M. Mohme, C. Hotz, S. Stevanovic, T. Binder, J. H. Lee, M. Okoniewski and T. Eiermann, et al., Brain, 2013, 136, 1783. 37. A. Bar-­Or, L. Fawaz, B. Fan, P. J. Darlington, A. Rieger, C. Ghorayeb and P. A. Calabresi, et al., Ann. Neurol., 2010, 67, 452. 38. E. Staun-­Ram and A. Miller, Clin. Immunol., 2017, 184, 11. 39. T. A. Barr, P. Shen, S. Brown, V. Lampropoulou, T. Roch, S. Lawrieand and B. Fan, et al., J. Exp. Med., 2012, 209, 1001. 40. R. Li, A. Rezk, Y. Miyazaki, E. Hilgenberg, H. Touil, P. Shen and C. S. Moore, et al., Sci. Transl. Med., 2015, 7, 310ra166. 41. J. Correale and M. F. Farez, Front. Neurol., 2015, 6, 180. 42. A. Yoshizaki, T. Miyagaki, D. J. DiLillo, T. Matsushita, M. Horikawa, E. I. Kountikov and R. Spolski, et al., Nature, 2012, 491, 264. 43. M. Duddy, M. Niino, F. Adatia, S. Hebert, M. Freedman, H. Atkins, H. J. Kim and A. Bar-­Or, J. Immunol., 2007, 178, 6092. 44. Y. Iwata, T. Matsushita, M. Horikawa, D. J. Dilillo, K. Yanaba, G. M. Venturi and P. M. Szabolcs, et al., Blood, 2011, 117, 530. 45. K. Bjarnadóttir, M. Benkhoucha, D. Merkler, M. S. Weber, N. L. Payne, C. C. Bernard, N. Molnarfi and P. H. Lalive, Sci. Rep., 2016, 6, 34594. 46. R. X. Wang, C. R. Yu, I. M. Dambuza, R. M. Mahdi, M. B. Dolinska, Y. V. Sergeev and P. T. Wingfield, et al., Nat. Med., 2014, 20, 633. 47. R. Magliozzi, O. Howell, A. Vora, B. Serafini, R. Nicholas, M. Puopolo, R. Reynolds and F. Aloisi, Brain, 2007, 130, 1089. 48. D. L. Drayton, X. Ying, J. Lee, W. Lesslauer and N. H. Ruddle, J. Exp. Med., 2003, 197, 1153. 49. R. Magliozzi, S. Columba-­Cabezas, B. Serafini and F. Aloisi, J. Neuroimmunol., 2004, 148, 11. 50. J. L. Browning, Nat. Rev. Drug Discovery, 2006, 5, 564. 51. C. F. Lucchinetti, B. F. Popescu, R. F. Bunyan, N. M. Moll, S. F. Roemer, H. Lassmann, W. Brück and J. E. Parisi, et al., N. Engl. J. Med., 2011, 365, 2188. 52. S. R. Choi, O. W. Howell, D. Carassiti, D. Carassiti, R. Magliozzi, D. Gveric and P. A. Muraro, et al., Brain, 2012, 135, 2925. 53. M. Absinta, L. Vuolo, A. Rao, G. Nair, P. Sati, I. C. Cortese and J. Ohayon, et al., Neurology, 2015, 85, 18. 54. R. P. Lisak, J. A. Benjamins, L. Nedelkoska, J. L. Barger, S. Ragheb, B. Fan and N. Ouamara, et al., J. Neuroimmunol., 2012, 246, 85. 55. R. P. Lisak, L. Nedelkoska, J. A. Benjamins, D. Schalk, B. Bealmear, H. Touil, R. Li, G. Muirhead and A. Bar-­Or, J. Neuroimmunol., 2017, 309, 88.

B Cell-­based Therapies for Multiple Sclerosis

165

56. K. Lehmann-­Horn, S. Kinzel and M. S. Weber, Int. J. Mol. Sci., 2017, 18, E2048. 57. J. K. Riley and M. X. Sliwkowski, Semin. Oncol., 2000, 27(6 Suppl 12), 17. 58. A. Palanichamy, S. Jahn, D. Nickles, M. Derstine, A. Abounasr, S. L. Hauser and S. E. Baranzini, et al., J. Immunol., 2014, 193, 580. 59. P. Roll, A. Palanichamy, C. Kneitz, T. Dorner and H. P. Tony, Arthritis Rheumatol., 2006, 54, 2377. 60. J. J. Sabatino Jr, S. S. Zamvil and S. L. Hauser, Cold Spring Harbor Perspect. Med., 2018, a032037. 61. Q. Gong, Q. Ou, S. Ye, W. P. Lee, J. Cornelius, L. Diehl and W. Y. Lin, et al., J. Immunol., 2005, 174, 817. 62. Y. Hamaguchi, J. Uchida, D. W. Cain, G. M. Venturi, J. C. Poe, K. M. Haas and T. F. Tedder, J. Immunol., 2005, 174, 4389. 63. S. L. Hauser, Mult. Scler., 2015, 21, 8. 64. G. J. Silverman and D. L. Boyle, Immunol. Rev., 2008, 223, 175. 65. A. Bar-­Or, P. A. Calabresi, D. Arnold, C. Markowitz, S. Shafer, L. H. Kasper and E. Waubant, et al., Ann. Neurol., 2008, 63, 395. 66. R. T. Naismith, L. Piccio, J. A. Lyons, J. Lauber, N. T. Tutlam, B. J. Parks and K. Trinkaus, et al., Neurology, 2010, 74, 1860. 67. K. Hawker, P. O'Connor, M. S. Freedman, P. A. Calabresi, J. Antel, J. Simon and S. Hauser, et al., Ann. Neurol., 2009, 66, 460. 68. S. L. Hauser, A. Bar-­Or, G. Comi, G. Giovannoni, H. P. Hartung, B. Hemmer and F. Lublin, et al., N. Engl. J. Med., 2017, 376, 221. 69. G. Giovannoni, B. Turner, S. Gnanapavan, C. Offiah, K. Schmierer and M. Marta, Mult. Scler. Relat. Disord., 2015, 4, 329. 70. E. Havrdová, D. L. Arnold, A. Bar-­Or, G. Comi, H. P. Hartung, L. Kappos and F. Lublin, et al., Mult. Scler. J. Exp. Transl. Clin., 2018, 4(1), 2055217318760642. 71. D. L. Arnold, L. Kappos, S. L. Hauser, X. Montalban, A. Traboulsee, J. S. Wolinsky and M. Manfrini, et al., Mult. Scler., 2018, 24, 284. 72. S. L. Hauser, B. Brochet, X. Montalban, R. T. Naismith, J. S. Wolinsky, M. Manfrini and M. Garas, Mult. Scler., 2018, 24, 285. 73. X. Montalban, S. L. Hauser, L. Kappos, D. L. Arnold, A. Bar-­Or, G. Comi and J. de Seze, et al., N. Engl. J. Med., 2017, 376, 209. 74. J. S. Wolisnky, B. Brochet, X. Montalban, R. T. Naismith, M. Manfrini, M. Garas and P. Villoslada, et al., Mult. Scler., 2018, 24, 490. 75. J. Wolinsky, D. L. Arnold, A. Bar-­Or, J. de Seze, G. Giovannoni, B. Hemmer and K. W. Rammohan, et al., presented in part at ACTRIMS Forum, New Orleans, LA, 2016. 76. J. S. Wolinsky, X. Montalban, S. L. Hauser, G. Giovannoni, P. Vermersch, C. Bernasconi and G. Deol-­Bhullar, et al., Ann. Neurol., 2018, 84, 527. 77. G. Giovannoni, L. Airas, R. Bove, A. Boyko, G. Cutter, J. Hobart and J. Kuhle, et al., Mult. Scler., 2018, 24, 306. 78. S. Hughes, Medscape, 2017, http://www.medscape.com/viewarticle/880654, accessed 30 October 2018.

166

Chapter 7

79. P. Emery, W. Rigby, P. P. Tak, T. Dörner, E. Olech, C. Martin and L. Millar, et al., PLoS One, 2014, 9, e87379. 80. S. L. Hauser, L. Kappos, X. Montalban, R. Hughes, H. Koendgen, J. McNamara and A. Pradhan, et al., Mult. Scler., 2018, 24, 697. 81. R. Milo, Autoimmun. Rev., 2016, 15, 714. 82. P. S. Sorensen, S. Lisby, R. Grove, F. Derosier, S. Shackelford, E. Havrdova, J. Drulovic and M. Filippi, Neurology, 2014, 82, 573. 83. A. Bar-­Or, R. A. Grove, D. J. Austin, J. M. Tolson, S. A. VanMeter, E. W. Lewis and F. J. Derosier, et al., Neurology, 2018, 90, e1805. 84. J. P. Sharman, C. M. Farber, D. Mahadevan, M. T. Schreeder, H. D. Brooks, K. S. Kolibaba and S. Fanning, et al., Br. J. Haematol., 2017, 176, 412. 85. C. de Romeuf, C. A. Dutertre, M. Le Garff-­Tavernier, N. Fournier, C. Gaucher, A. Glacet and S. Jorieux, et al., Br. J. Haematol., 2008, 140, 635. 86. E. Fox, A. Lovett-­Racke, M. Gormley, Y. Liu, S. Wray, R. Shubin and M. Petracca, et al., Mult. Scler., 2018, 24, 87. 87. T. G. Forsthuber, D. M. Cimbora, J. N. Ratchford, E. Katz and O. Stuve, Ther. Adv. Neurol. Disord., 2018, 11, 175628641876169. 88. D. Chen, S. Gallagher, N. L. Monson, R. Herbst and Y. Wang, J. Clin. Med., 2016, 5, E107. 89. S. Gallagher, I. Yusuf, T. M. McCaughtry, S. Turman, H. Sun, R. Kolbeck, R. Herbst and Y. Wang, Arthritis Rheumatol., 2016, 68, 965. 90. J. Habib, J. Deng, N. Lava, W. Tyor and J. Galipeau, J. Mult. Scler., 2015, 2, 139. 91. S. Cepok, B. Rosche, V. Grummel, F. Vogel, D. Zhou, J. Sayn and N. Sommer, et al., Brain, 2005, 128, 1667. 92. R. Herbst, Y. Wang, S. Gallagher, N. Mittereder, E. Kuta, M. Damschroder and R. Woods, et al., J. Pharmacol. Exp. Ther., 2010, 35, 213. 93. D. Chen, M. Blazek, S. Ireland, S. Ortega, X. Kong, A. Meeuwissen and A. Stowe, et al., Immunology, 2014, 93, 4823. 94. M. A. Agius, G. Klodowska-­Duda, M. Maciejowski, A. Potemkowski, J. Li, K. Patra and J. Wesley, et al., Mult. Scler., 2019, 25, 235. 95. F. B. Vincent, D. Saulep-­Easton, W. A. Figgett, K. A. Fairfax and F. Mackay, Cytokine Growth Factor Rev., 2013, 24, 203. 96. F. Piazza, J. C. DiFrancesco, M. L. Fusco, D. Corti, L. Pirovano, B. Frigeni and L. Mattavelli, et al., J. Neuroimmunol., 2010, 220, 104. 97. X. Zhou, Z. Xia, Q. Lan, J. Wang, W. Su, Y. P. Han and H. Fan, et al., PLoS One, 2011, 6, e23629. 98. S. A. Jagessar, N. Heijmans, L. Oh, J. Bauer, E. L. Blezer, J. D. Laman and T. S. Migone, et al., J. Neuroimmune Pharmacol., 2012, 7, 557. 99. A. Deiß, I. Brecht, A. Haarmann and M. Buttmann, Expert Rev. Neurother., 2013, 13, 313. 100. F. A. Houssiau and A. Doria, Ann. Rheum. Dis., 2016, 75, 321. 101. L. Kappos, H. P. Hartung, M. S. Freedman, A. Boyko, E. W. Radü, D. D. Mikol and M. Lamarine, et al., Lancet Neurol., 2014, 13, 353.

B Cell-­based Therapies for Multiple Sclerosis

167

102. R. C. Sergott, J. L. Bennett, P. Rieckmann, X. Montalban, D. Mikol, U. Freudensprung and T. Plitz, et al., J. Neurol. Sci., 2015, 351, 174. 103. F. Lühder and R. Gold, Lancet Neurol., 2014, 13, 340. 104. H. P. Hartung and B. C. Kieseier, Ther. Adv. Neurol. Disord., 2010, 3, 205. 105. A. Negron, R. R. Robinson, O. Stüve and T. G. Forsthuber, Cell. Immunol., DOI: 10.1016/j.cellimm.2018.10.006. 106. B. G. Weinshenker, P. C. O'Brien, T. M. Petterson, J. H. Noseworthy, C. F. Lucchinetti, D. W. Dodick and A. A. Pineda, et al., Ann. Neurol., 1999, 46, 878. 107. C. Lucchinetti, W. Brück, J. Parisi, B. Scheithauer, M. Rodriguez and H. Lassmann, Ann. Neurol., 2000, 47, 707. 108. K. Lehmann-­Horn, S. Kinzel and M. S. Weber, Int. J. Mol. Sci., 2017, 18, E2048. 109. L. J. Crofford, L. E. Nyhoff, J. H. Sheehan and P. L. Kendall, Expert Rev. Clin. Immunol., 2016, 12, 763. 110. S. Torke, R. Grenningloh, U. Boschert and M. S. Weber, Mult. Scler., 2017, 23, 42. 111. X. Montalban, D. L. Arnold, M. S. Weber, I. Staikov, K. Piasecka-­ Stryczynska, J. Willmer and E. Martin, Mult. Scler., 2018, 24, 984. 112. S. Rao, J. Sancho, J. Campos-­Rivera, P. Boutin, P. Severy, T. Weeden and S. Shankara, et al., PLoS One, 2012, 7, e39416. 113. A. J. Coles, C. L. Twyman, D. L. Arnold, J. A. Cohen, C. Confavreux, E. J. Fox and H. P. Hartung, et al., Lancet, 2012, 380, 1829. 114. J. A. Cohen, A. J. Coles, D. L. Arnold, C. Confavreux, E. J. Fox, H. P. Hartung and E. Havrdova, et al., Lancet, 2012, 380, 1819. 115. D. Baker, S. S. C. Alvarez-­Gonzalez, G. Giovannoni and K. Schmierer, JAMA Neurol., 2017, 74, 961. 116. Y. Kim, G. Y. Kim, S. H. Kim, J. W. Hyun, H. J. Shin and H. J. Kim, Mult. Scler., 2017, 23(3 Suppl 1), 603. 117. S. De Mercanti, S. Rolla, A. Cucci, V. Bardina, E. Cocco, A. Vladic and S. Soldo-­Butkovic, et al., Neurol. Neuroimmunol. Neuroinflamm., 2016, 3, e194. 118. W. Gilmore, B. T. Lund, A. Traboulsee, A. Javed, J. E. Dunn, L. P. Weiner and P. Li, et al., Mult. Scler., 2015, 23(11 Suppl 1), 533. 119. L. H. Kasper, D. L. Arnold, A. J. Coles, H. P. Hartung, E. Havrdova, K. W. Selmaj and J. Palmer, et al., J. Neuroimmunol., 2014, 275, 63. 120. S. De Mercanti, M. Clerico, S. Rolla, V. Bardina, A. Cucci, D. Taverna and D. Cimino, et al., Mult. Scler., 2014, 20(1 Suppl 1), 484. 121. X. Zhang, Y. Tao, M. Chopra, M. Ahn, K. L. Marcus, N. Choudhary, H. Zhu and S. Markovic-­Plese, J. Immunol., 2013, 191, 5867. 122. M. Simon, R. Ipek, G. A. Homola, D. M. Rovituso, A. Schampel, C. Kleinschnitz and S. Kuerten, et al., J. Neuroinflammation, 2018, 15, 225. 123. S. Dhib-­Jalbut, Neurology, 2002, 58(Suppl 4), S3.

168

Chapter 7

124. F. Rizzo, E. Giacomini, R. Mechelli, M. C. Buscarinu, M. Salvetti, M. Severa and E. M. Coccia, Immunol. Cell Biol., 2016, 94, 886. 125. M. Niino, M. Hirotani, Y. Miyazaki and H. Sasaki, Neurosci. Lett., 2009, 464, 74. 126. R. D. Schubert, Y. Hu, G. Kumar, S. Szeto, P. Abraham, J. Winderl and J. M. Guthridge, et al., J. Immunol., 2015, 194, 2110. 127. D. Teitelbaum, R. Arnon and M. Sela, Cell. Mol. Life Sci., 1997, 53, 24. 128. R. Milo and H. Panitch, J. Neuroimmunol., 1995, 61, 185. 129. S. J. Ireland, A. A. Guzman, D. E. O'Brien, S. Hughes, B. Greenberg, A. Flores and D. Graves, et al., JAMA Neurol., 2014, 71, 1421. 130. J. Sellner, W. Koczi, A. Harrer, K. Oppermann, E. Obregon-­Castrillo, G. Pilz and P. Wipfler, et al., Clin. Exp. Immunol., 2013, 173, 381. 131. S. Kuerten, L. J. Jackson, J. Kaye and T. L. Vollmer, CNS Drugs, 2018, 32, 1039. 132. A. Alter, M. Duddy, S. Hebert, K. Biernacki, A. Prat, J. P. Antel and V. W. Yong, et al., J. Immunol., 2003, 170, 4497. 133. C. Warnke, M. Stettner, V. Lehmensiek, T. Dehmel, A. K. Mausberg, G. von Geldern and R. Gold, et al., Mult. Scler., 2015, 21, 1036. 134. F. Neumann, F. Zohren and R. Haas, Expert Opin. Biol. Ther., 2009, 9, 1099. 135. M. Krumbholz, I. Meinl, T. Kumpfel, R. Hohlfeld and E. Meinl, Neurology, 2008, 71, 1350. 136. E. M. Frohman, M. C. Monaco, G. Remington, C. Ryschkewitsch, P. N. Jensen, K. Johnson and M. Perkins, JAMA Neurol., 2014, 71, 596. 137. L. J. Marshall, L. Dunham and E. O. Major, J. Gen. Virol., 2010, 91(Pt 12), 3042. 138. A. Bellizzi, E. Anzivino, D. M. Rodio, A. T. Palamara, L. Nencioni and V. Pietropaolo, Clin. Dev. Immunol., 2013, 2013, 839719. 139. V. Brinkmann, A. Billich, T. Baumruker, P. Heining, R. Schmouder, G. Francis, S. Aradhy and P. Burtin, Nat. Rev. Drug Discovery, 2010, 9, 883. 140. M. C. Kowarik, H. L. Pellkofer, S. Cepok, T. Korn, T. Kumpfel, D. Buck and R. Hohlfeld, et al., Neurology, 2011, 76, 1214. 141. S. Blumenfeld, E. Staun-­Ram and A. Miller, J. Autoimmun., 2016, 70, 40. 142. B. Grutzke, S. Hucke, C. C. Gross, M. V. Herold, A. Posevitz-­Fejfar, B. T. Wildemann and B. C. Kieseier, et al., Ann. Clin. Transl. Neurol., 2015, 2, 119. 143. K. Bail, Q. Notz, D. M. Rovituso, A. Schampel, M. Wunsch, T. Koeniger and V. Schropp, et al., J. Neuroinflammation, 2017, 14, 148. 144. L. Li, J. Liu, T. Delohery, D. Zhang, C. Arendt and C. Jones, Neuroimmunology, 2013, 265, 82. 145. A. Bar-­Or, Exp. Neurol., 2014, 262(Pt A), 57. 146. R. A. Linker, D. H. Lee, S. Ryan, A. N. van Dam, R. Conrad, P. Bista and W. Zeng, et al., Brain, 2011, 134(Pt 3), 678.

B Cell-­based Therapies for Multiple Sclerosis

169

147. E. A. Mills, M. A. Ogrodnik, A. Plave and Y. Mao-­Draayer, Front. Neurol., 2018, 9, 5. 148. R. Holm Hansen, H. Højsgaard Chow, F. Sellebjerg and M. Rode von Essen, Mult. Scler., 2018, 1352458518790417. 149. T. P. Leist and R. Weissert, Clin. Neuropharmacol., 2011, 34, 28. 150. B. Ceronie, B. M. Jacobs, D. Baker, N. Dubuisson, Z. Mao, F. Ammoscato and H. Lock, et al., J. Neurol., 2018, 265, 1199. 151. V. Neves, F. Aires-­da-­Silva, S. Corte-­Real and M. A. R. B. Castanho, Trends Biotechnol., 2016, 34, 36.

Chapter 8

Protein Kinase Inhibitors for the Treatment of Multiple Sclerosis Ana Martinez* and Carmen Gil Centro de Investigaciones Biologicas-­CSIC, Ramiro de Maeztu 9, 28040 Madrid, Spain *E-­mail: [email protected]

8.1  Introduction Multiple sclerosis (MS) is a central nervous system (CNS) disease characterised by neuroinflammation, oligodendrocyte depletion and destruction of the myelin sheath and axonal damage, which results in neurodegeneration and consequently, the formation of sclerotic plaques in the brain and spinal cord.1 These processes lead to an impairment of axonal conduction and thereby to a development of severe disability in patients. Although the precise mechanisms underlying MS remain undefined, a pivotal role in pathogenesis of this illness is assigned to inappropriate or unregulated activation of the immune cells.2 Moreover, the relevant role of oligodendrocyte destruction is gaining importance as a pathological event. This lack of understanding is reflected in the current treatments for MS, most of which target only symptoms or are administered together with global immunosuppressants, which can have serious adverse side effects. Therefore, new therapies that target specific pathways involved in MS pathogenesis are needed.3   Drug Discovery Series No. 70 Emerging Drugs and Targets for Multiple Sclerosis Edited by Ana Martinez © The Royal Society of Chemistry 2019 Published by the Royal Society of Chemistry, www.rsc.org

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Protein kinases mediate protein phosphorylation, a major currency of signal transduction pathways. Protein phosphorylation is a fundamental component of cell signalling, with crucial roles in most signal transduction cascades: from cell growth, survival, proliferation and differentiation to the initiation and regulation of immunological responses. Dysregulation of the activity of protein kinases has been associated with numerous diseases, including cancer, CNS disorders and chronic inflammatory conditions.4 Protein kinases are therefore attractive drug targets and the most intensively pursued by both pharmaceutical industries and academic laboratories, with great success in the cancer field.5 Kinases can be targeted by small molecular weight compounds that act to inhibit the phosphorylation of proteins, thus preventing their activation. Today, more than 40 protein kinase inhibitors have been approved for cancer therapy.6 This success, coupled with a greater understanding of inflammatory signalling cascades, cell death and regeneration pathways, has led to kinase inhibitors taking centre stage in the pursuit for new drugs for the treatment of many unmet diseases, including MS.7 In this chapter, recent developments in protein kinase inhibitors for the future therapy of MS are collected, with special focus on those inhibitors in preclinical or clinical development.

8.2  Tyrosine Kinase Inhibitors for MS Therapy In addition to the proven efficacy of tyrosine kinase inhibitors (TKIs) for cancer treatment, several reports have provided experimental evidence for the important role of TKIs in several neurological and inflammatory disorders.8 The search for new therapies for the treatment of MS has focused on TKIs because of their involvement in signalling in many immune cells;7 therefore these kinases are important players in the pathological processes characteristic of MS. Moreover, these inhibitors have shown efficacy in the experimental autoimmune encephalomyelitis (EAE) model of MS.9 TKIs are commonly divided into two subgroups: receptor tyrosine kinase inhibitors (RTKIs) and non-­receptor tyrosine kinase inhibitors (NRTKIs). The members of the first group interact with ATP-­binding sites of the receptor tyrosine kinase (e.g. platelet-­derived growth factor receptors, c-­kit, colony-­ stimulating factor 1 receptors and others), a family of cell-­surface receptors that transduce signals to polypeptide and protein hormones, cytokines and growth factors, being key regulators of critical cellular processes that affect cell proliferation and differentiation, cell migration and growth and promote cell survival and apoptosis. NRTKIs are also ATP-­dependent, but structurally they possess a variable number of signalling domains, including a kinase one (Src family including e.g. Src, Fyn, Lyn, Lck and Abl family). Both types of TKIs have been recently reported to have immunomodulatory effects on immune cells implicated in MS; the most representative are listed in the next section, together with a short description of those inhibitors in preclinical or clinical development for MS therapy.

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8.2.1  Receptor Tyrosine Kinase Inhibitors 8.2.1.1 Platelet-­derived Growth Factor (PDGF) and Stem Cell Growth Factor (c-­kit) Receptor Tyrosine Kinase Inhibitors The platelet-­derived growth factor (PDGF) and the stem cell growth factor receptor, also known as c-­kit or CD117, are found in various types of immune cells. Some experimental evidence has shown that both PDGFR and c-­kit play a role in the pathogenesis of MS and may be good targets for immunotherapy of MS and other autoimmune diseases.10 Moreover, restoration of the blood– brain barrier (BBB) has been described in several neurological disorders by targeting PDGFR by small RTKIs.11 8.2.1.1.1  Imatinib.  Imatinib (Gleevec, Figure 8.1) is an orally administered 2-­phenylaminopyrimidine derivative which is a selective protein tyrosine kinase inhibitor developed to inhibit BCR-­ABL kinase activity in chronic myelogenous leukaemia. It can also block the activity of tyrosine kinases, such as FLT3, c-­Kit, PDGFR, c-­Fms, Lck and MAPK. Therefore, imatinib has antiproliferative activity and immunomodulatory effects on various cell types,12 so it can act on normal immune cells and modulate the differentiation, proliferation, activation and function of T lymphocytes, macrophages and dendritic cells.13 Imatinib has shown efficacy in animal models of MS by attenuation in the severity and a delay in the onset of disease.14 In vitro, imatinib inhibits cell proliferation, matrix metalloproteinase-­2 expression and activity and also attenuates the production of proinflammatory cytokines.12 It is also able to enhance the BBB integrity in an EAE animal model of MS. This treatment was accompanied by a decrease in neuroinflammation and demyelination and an especially reduced T-­cell recruitment.15 Based on the therapeutic effects and immunomodulatory properties of imatinib, it may be considered, after additional necessary tests and trials, as a therapeutic option for the treatment of MS.

Figure 8.1  Receptor  tyrosine kinase inhibitors in (A) preclinical or (B) clinical development for multiple sclerosis pharmacotherapy.

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Based on the imatinib-­induced amelioration of the neurological deficits present in a rare case of simultaneous chronic myeloblastic leukaemia and MS,16 a clinical trial to explore the efficacy of imatinib in MS has recently started. The main goal is to investigate if treatment with imatinib results in a better outcome than standard care in the form of methylprednisolone after MS-­associated relapses. The study, conducted by the Karolinska Institute, is now open to recruitment (ClinicalTrials.gov Identifier: NCT03674099) and completion is expected by summer of 2021. 8.2.1.1.2  Masitinib.  Masitinib mesilate (AB1010, Figure 8.1), is a phenylaminothiazole derivative synthesised during a medicinal chemistry program to improve the selectivity of TKIs belonging to the phenylaminopyrimidine family and developed by AB Science.17 As a result of this optimisation process, masitinib was found to be a potent and highly selective RTKI in comparison with the parent drug imatinib. It is an ATP-­competitive inhibitor of c-­Kit receptor with an IC50 of 200 nM. Moreover, it showed a higher selectivity than other TKIs and a better safety profile.18 Considering these results and its oral bioavailability, masitinib was envisaged to be effective for the treatment of c-­Kit-­dependent diseases, such as some kind of cancers and inflammatory processes.19,20 In fact, in 2008 it was approved by the European Medicine Agency (EMA) for the treatment of canine mast cell tumours.21 Nowadays it is being used under human clinical investigation in several oncology indications,22 but also in neurological diseases such as Alzheimer's, amyotrophic lateral sclerosis and MS.8,23 Studies in animals and humans have provided evidence of the pharmacokinetics of masitinib, such as oral absorption, 90% bound to plasma proteins and predominant metabolism by N-­demethylation.24 The interest of masitinib in non-­oncological treatments is mainly due to the involvement of the c-­Kit receptor in mast-­cell-­mediated inflammatory pathogenesis.25,26 The receptor tyrosine kinase c-­Kit (also called CD117 and stem cell factor receptor) is a key controller receptor for a number of cell types, including mast cells, being a signalling essential for mast cell development.27 Previous findings supporting the key role of mast cells in the pathogenesis of MS,28 together with the reported effects of masitinib in these kinds of cells,18 led to the exploration of the effects of this drug in a well-­known murine model of MS, the EAE model.29 After treatment of the animals with masitinib, a clear dose–response effect was observed together with a significant reduction of the clinical score. Considering its efficacy in the EAE model,29 and the good safety profile shown in clinical trials for different human diseases,30–32 masitinib was selected for an exploratory IIa study in patients with MS.29 The trial was multicentre, randomised and placebo-­controlled; the drug was administered orally at 3–6 mg kg−1 day in patients with primary progressive multiple sclerosis (PPMS) or relapse-­free secondary progressive multiple sclerosis (rfSPMS) for 12 months.

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Masitinib was well tolerated without any severe effects. The adverse events observed were the same associated frequently with TKIs, such as rash, nausea, oedema and diarrhoea. Regarding efficacy, a positive effect on the functional score was observed, although differences between the treatment groups and the placebo were not statistically significant. The results of this exploratory trial suggest that masitinib has a therapeutic benefit to PPMS and rfSPMS patients and supports a larger placebo-­ controlled study to decipher whether this drug could be a new treatment for this disease.29 At the moment, a phase III study is ongoing to compare the efficacy and safety of masitinib to placebo in the treatment of patients with PPMS or rfSPMS (ClinicalTrials.gov Identifier: NCT01433497) sponsored by AB Science.33

8.2.1.2 Colony-­stimulating Factor 1 (CSF-­1) Receptor Tyrosine Kinase Inhibitors The colony-­stimulating factor-­1 receptor (CSF-­1R) is a class III receptor tyrosine kinase activated by two homodimeric glycoprotein ligands, CSF-­1 and IL-­34. CSF-­1R is expressed on all microglia cells and plays important roles in development and in innate immunity by regulating the development of most tissue macrophages and osteoclasts, of Langerhans cells of the skin, of Paneth cells of the small intestine, and of brain microglia.34 It also regulates the differentiation of neural progenitor cells and controls functions of oocytes and trophoblastic cells in the female reproductive tract. Recently, CSF-­1R and its ligands have also been shown to play important roles in demyelinating diseases, neurodegeneration including Alzheimer's disease and brain tumours.35 In disease states, up-­regulation of CSF-­1 and CSF-­1R expression leads to the expansion of microglia and macrophages. The final outcome (i.e. amelioration or worsening the pathology) will depend on whether the local environment promotes trophic or inflammatory responses in phagocytes.36 In preclinical models of MS, CSF-­1R plays a pivotal role in the development of EAE and inhibitors of CSF-­1R may be drug candidates for the treatment of human MS (Figure 8.1). The selective CSF-­1R inhibitor Ki20227 significantly reduces the severity of EAE both preventively and therapeutically,37 while BLZ945 has a great impact on central myelination processes in the 5 week murine cuprizone model determined by non-­invasive and longitudinal magnetic resonance imaging and histology.38 Altogether, CSF-­1R inhibitors as microglia-­modulating therapies could be considered clinically for promoting myelination in combination with standard-­of-­care treatments in MS patients.

8.2.2  Non-­receptor Tyrosine Kinase Inhibitors Non-­receptor tyrosine kinase inhibitors are chemotherapy medications that block tyrosine kinases with the same mechanism of action. All of them are ATP-­competitive inhibitors at the catalytic binding site of the tyrosine kinase,

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Figure 8.2  Non-­  receptor tyrosine kinase inhibitors under preclinical and clin-

ical development for multiple sclerosis. (A) Non-­selective TKIs; (B) JAK inhibitors; (C) BTK inhibitor; (D) SYK inhibitor; (E) LcK inhibitor.

and differ from each other in the spectrum of targeted kinases, pharmacokinetics and substance-­specific adverse effects.39 The immunomodulatory mechanism of therapy in MS attenuation with tyrosine kinase inhibitors has been proven both in vitro and in vivo (Figure 8.2).40 Thus, the drugs approved by the US Food and Drug Administration (FDA), such as imatinib, sorafenib and dasatinib, have shown a therapeutic effect in the EAE mouse model of MS,9,41 having the potential to be novel therapeutics for the treatment of autoimmune demyelinating disease. More recently, attention has been directed to the modulation of specific kinases very related to specific steps of the immunomodulatory pathway.

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8.2.2.1 JAK/STAT Pathway and Tyrosine Kinase 2 (Tyk2) The Janus kinase/signal transducer and activators of transcription (JAK/STAT) signalling pathway is essential for the development and function of both innate and adaptive immunity.42 Thus, targeting JAKs has implications for treating autoimmune inflammation of the brain.43 The JAK1/2 inhibitor AZD1480 was used to investigate the therapeutic potential of inhibiting the JAK/STAT pathway in models of EAE. AZD1480 treatment inhibits disease severity in myelin oligodendrocyte glycoprotein-­induced classical and atypical EAE models by preventing the entry of immune cells into the brain, suppressing the differentiation of Th1 and Th17 cells, deactivating myeloid cells, inhibiting STAT activation in the brain and reducing expression of proinflammatory cytokines and chemokines.44 However, the dose-­limiting effects attributable to JAK2 inhibition, leads to the discovery of selective inhibitors of JAK3, JAK1 or Tyk2 as an opportunity to achieve clinical efficacy without side effects. Tyrosine kinase 2 (Tyk2) is a JAK family member that is crucial for signalling transduction in response to a wide variety of cytokines, including type I IFNs, IL-­6, IL-­10, IL-­12 and IL-­23.45 An appropriate expression of Tyk2-­ mediated signalling might be essential for maintaining normal immune responses, and several Tyk2 variants have been established as genetic risk factors for MS in a variety of populations.46,47 The modulation of Tyk2 activity might therefore represent a new therapeutic approach for the treatment of autoimmune diseases. It has been shown that Tyk2 is downregulated in response to IFN-­β or glatiramer acetate treatment and may be useful for evaluation of the T cell immunity and clinical response to these therapies in RRMS patients.48,49 However, until now selective Tyk2 inhibitors have been poorly developed. The availability of a selective Tyk2 inhibitor in the near future will provide the opportunity for better understanding of the physiological role of this kinase. Recent patent applications indicate that Tyk2 selectivity is achievable and that Tyk2 inhibitors have potential in the treatment of MS.50 8.2.2.1.1  Tofacitinib.  Therapies for MS that inhibit the immunogenic characters of dendritic cells (DCs) are currently being pursued. DCs are the most potent antigen-­presenting cells for naïve T cells that bridge the innate and adaptive immunity in autoimmune diseases. Tofacitinib, the first oral JAK1/3 inhibitor interfering with the JAK-­STAT signalling pathway and approved for chronic use in ulcerative colitis, prevented activation of the immune system through the modulation of the function of murine bone marrow-­derived dendritic cells (BMDCs). Furthermore, when these DCs treated with tofacitinib were loaded to the mice with established EAE, a reduction in disease severity and progression was observed while Th17 cells decreased and regulatory T cells (Tregs) increased.51 The cell therapy of antigen-­specific DCs treated with tofacitinib may represent a new avenue of research for the development of future clinical treatments that do not disturb the normal immune system and re-­shaping the Th17/Treg imbalance.52

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8.2.2.2 Bruton's Tyrosine Kinase (BTK) Bruton's tyrosine kinase (BTK) is a non-­receptor tyrosine kinase expressed in hematopoietic cell lineages. This enzyme is involved in various signalling pathways, including the B cell receptor (BCR) pathway in B lymphocytes, acting as a crucial regulator of cellular differentiation, activation, proliferation and survival. Moreover, aberrant BCR signalling is associated with autoimmune diseases.53 Based on the crucial role of BTK in B cell function, this enzyme is considered as a promising drug target for B-­cell related cancers and autoimmune diseases, and a number of inhibitors have already been developed.54 Remarkably, two covalent irreversible BTK inhibitors, ibrutinib and acalabrutinib, reached the US market in 2013 and 2017, respectively.55 These drugs are indicated for the treatment of diverse haematopoietic malignancies such as chronic lymphocytic leukaemia or mantle cell lymphoma. Lessons learnt from the first generations of BTK inhibitors led to the next generation with an improved safety profile, mainly due to enhanced selectivity.56 Besides clinical trials of new BTK inhibitors for different carcinomas, the interest of the therapeutic potential of these inhibitors is moving to autoimmune and inflammation diseases. Such is the case of ongoing clinical trials of BTK inhibitors for rheumatoid arthritis or MS. 8.2.2.2.1  Evobrutinib.  Evobrutinib, is a highly selective BTK inhibitor57 in Phase II for the treatment of patients with relapsing MS (ClinicalTrials.gov Identifier: NCT02975349). To understand the pharmacology of evobrutinib, a metabolism study using rat and human hepatocytes has been done.58 The results showed 23 different metabolites of evobrutinib due to metabolisation via hydroxylation, hydrolysis, O-­dealkylation, glucuronidation and GSH conjugation. Recently, Merck has announced positive results from its Phase IIb study of evobrutinib in relapsing MS. The study has met its primary endpoint, demonstrating that evobrutinib resulted in a clinically meaningful reduction of gadolinium-­enhancing T1 lesions measured at weeks 12, 16, 20 and 24 in comparison to patients receiving the placebo.59 These findings suggest that the dual mechanism of action of evobrutinib, which impacts on the pathogenic adaptive and innate immune cells in MS, could translate into clinical efficacy, being the first proof of the concept for BTK inhibitors in MS. The results of this study highlight the potential of BTK inhibitors as an oral disease-­modifying treatment for relapsing MS in the near future. 8.2.2.2.2  PRN2246.  PRN2246 is a potent and selective BTK inhibitor currently in clinical development at Principia Biopharma in collaboration with Sanofi. PRN-­2246 showed selectivity for BTK with >90% inhibition reported for 12 of 250 kinases tested at 1 µM.60 The drug is a good candidate for an oral therapy capable of penetrating the BBB for the treatment of CNS inflammatory diseases such as MS by modulating B-­cell function without depleting CD20-­expressing B cells. Furthermore, PRN2246 has just finished

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Phase I in healthy volunteers showing a safe profile with no serious adverse events.61 It is well-­tolerated following both single and multiple dose administration for 10 days. Moreover, pharmacologically-­relevant cerebral spinal fluid (CSF) exposure to PRN2246 was confirmed, highlighting the potential for PRN2246 to impact B-­cell driven inflammation in both the periphery and the CNS (Australian New Zeland Clinical Trial Register Nr ANZCTR 12617001457336).

8.2.2.3 Spleen Tyrosine Kinase (SYK) Spleen tyrosine kinase (SYK) has been involved in the control of degenerated myelin phagocytosis,62 and is a key integrator of intracellular signals triggered by activated immunoreceptors, including B cell receptors involved in the function of lymphoid cells.63 Thus, targeting SYK using orally active small molecules presents an attractive therapeutic strategy to modulate B cells. The ATP-­competitive inhibitor of SYK, designated RO9021, with an adequate kinase selectivity profile and oral bioavailability, is able to block osteoclastogenesis from mouse bone marrow macrophages in vitro. Moreover, Toll-­like Receptor (TLR) 9 signalling in human B cells and type I interferon production by human plasmacytoid dendritic cells upon TLR9 activation was inhibited. Inhibitors of SYK kinase activity may be the first step in a new avenue for the treatment of inflammation-­related and autoimmune-­related disorders.64

8.2.2.4 Miscellaneous Some other well-­known tyrosine kinases mainly studied in oncological pathologies have also been reported with immunomodulatory properties in vivo.65,66 Thus, their selective inhibitors may be of clinical use for the potential treatment of MS, but although several compounds targeting Abl2/Arg or Lck are in clinical use for different types of cancer, their selectivity is low, and more work in the medicinal chemistry field is needed to achieve a selective and brain penetrant compound. Derivative A-­420983, which inhibits Lck and Fyn but has selectivity with respect to non-­Src family kinases, inhibits antigen-­stimulated production of IFN-­γ and IL-­4 by mouse Th1 and Th2 cells, respectively.67 Orally dosed in an EAE model, A-­420983 inhibited TCR-­ mediated T cell activation and suppressed the disease course. These results support the hypothesis that small molecules with inhibitory activity on Src kinases family may be used for the treatment of T cell-­mediated disorders.

8.3  Serine/Threonine Kinases Serine/threonine kinases (STKs) phosphorylate the hydroxyl group of serine or threonine residues, which have similar sidechains, in their substrates. At least 125 of the more than 500 human protein kinases are STKs.

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Figure 8.3  Serine/threonine  protein kinase inhibitors with preclinical efficacy in

EAE mouse model. (A) CK2 inhibitors; (B) ROCK inhibitors; (C) PI3K inhibitors; (D) SGK-­1 inhibitors; (E) MAPK inhibitors.

They have been found in mammalian tissues and most are known to be expressed in neurons.68 The activity of these protein kinases can be regulated by specific events (e.g. DNA damage), as well as numerous chemical signals, including cAMP/cGMP, diacylglycerol and Ca+2/calmodulin. Most protein STKs undergo autophosphorylation, which is associated with an increase in kinase activity.69 Several STKs are involved with the pathogenesis or treatment of MS, being their inhibitors under preclinical or clinical development (Figure 8.3). The most relevant data are described in the next section.

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8.3.1  Protein Kinase CK2 The relevance of the highly conserved STK casein kinase II (CK2) in inflammatory disorders has been recently highlighted.70 CK2 is a major regulator of the Th17-­Treg axis relevant to many T cell-­driven autoimmune disorders, including MS.71,72 Using the EAE model and a specific CK2 inhibitor called CX-­4945, a significantly reduced disease severity was observed and was directly associated with the decrease of pathogenic Th17 cells in the CNS.73 Moreover, CK2 has been identified as a critical regulator of oligodendrocytic death pathways, and targeting CK2 with the inhibitor TBB prevents oligodendrocyte death in vitro. These results open the possibility for the use of CK2 inhibitors to prevent oligodendrocyte death in MS and other diseases involving CNS white matter.

8.3.2  Phosphoinositide 3-­kinase (PI3K) The phosphoinositide 3-­kinases (PI3Ks) are a family of lipid kinases. PI3Ks play an important role in immune regulation, existing in four different isoforms. Among them, PI3Kδ and PI3Kγ expression is mainly restricted to leukocytes. PI3Kδ function is critical for mature B cell development as well as effector T cell and Treg differentiation and function.74 Selective inhibition of PI3Kδ may promote anti-­inflammatory effects by inhibiting Th1 and Th17 cells.75 In fact, the selective PI3Kδ inhibitor IC87114 reduced the clinical symptoms, histopathology and cellular infiltration into the CNS in the EAE model together with reduction of IL-­1β, IL-­6, IL-­17 and IFN-­γ serum levels.76 Moreover, PI3Kγ regulates migration, proliferation and activation of inflammatory cells. The selective PI3Kγ inhibitor AS-­604850 significantly reduced the number of infiltrated leukocytes in the CNS and ameliorated the clinical symptoms of EAE mice. Moreover, it enhanced myelination and axon number in the spinal cord of EAE mice.77 Recently, a new family of selective PI3Kγ inhibitors has been designed as orally bioavailable and CNS penetrant drugs showing efficacy in the EAE model.78 Finally, ZSTK474, a novel PI3K inhibitor that exhibits potent anti-­t umour effects, significantly suppressed the human CD14(+) monocyte-­derived dendritic cell functions and ameliorated mouse EAE.79 ZSTK474 inhibited the phosphorylation of PI3K downstream signalling Akt and GSK-­3β in the dendritic cell. This data suggest that inhibitors of PI3K exert potent anti-­inflammatory and immunosuppressive properties via PI3K signalling and may serve as a potential therapeutic drug for MS and other autoimmune inflammatory diseases.

8.3.3  Rho-­associated Protein Kinase (ROCK) Rho-­associated protein kinase (ROCK) is an STK with new functions in diverse signalling pathways in neurons.80 ROCK inhibition increases neurite outgrowth, axonal regeneration and activation of pro-­survival Akt signalling

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pathway and may be a promising therapeutic target for the treatment of neurodegenerative disorders including MS.81 Experimental evidence shows that ROCK activity in MS serum was elevated compared with serum from healthy controls. Moreover in EAE, ROCK activity was also increased in serum, spleen, brain and spinal cord.82 In chronic disabling diseases, such as MS, demyelination and axonal damage are the major pathological changes contributing to neurological disability, and ROCK inhibition may offer a new way to minimise their impact. The protective effect of a specific ROCK inhibitor, Y-­39983, on demyelination and axonal damage in chronic EAE has been recently reported.83 Moreover, the ROCK inhibitor WAR-­5, alleviates the clinical symptoms, attenuates myelin damage and reduces CNS inflammatory responses in EAE mice at an extent similar to fasudil, while exhibiting fewer vasodilator and adverse reactions in vivo.84 Furthermore, evidence of modulation of the ROCK signalling pathway as a viable target for the induction of remyelination in demyelinating pathologies has been shown, and small molecules inhibitors of ROCK (Y-­27632 and GSK429286) are able to promote the transformation of oligodendrocyte precursor cells (OPCs) into mature, myelinating oligodendrocytes.85 These findings indicate that by direct inhibition of ROCK, the OPC differentiation program is activated, resulting in morphological and functional cell maturation, myelin formation and regeneration.

8.3.3.1 Fasudil and Chemically Related Derivatives Fasudil is the only clinically available ROCK inhibitor, having numerous beneficial roles comprising of vascular dilation, neuroprotection and axonal regeneration. Experimental evidence has shown its therapeutic potential in the EAE model, with a marked decrease of inflammatory cell infiltration, attenuated demyelination and acute axonal transaction.86 More recently, a significant down-­regulation of interleukin (IL)-­17, IL-­6 and MCP-­1 was also observed in vivo.87 These results, together with previous studies showing the inhibitory effect of fasudil by blocking astrocyte-­derived chemokine-­ mediated migration of inflammatory macrophages and pathogenic T cells,88 might expand its clinical application as a new therapy for MS by decreasing cell migration and regulating immune balance. Furthermore, as the axonal loss in MS may be related to increased ROCK activity, fasudil is able to promote synaptogenesis and to restore synaptic morphology of neurons when added to mouse cortical neurons cultured with serum of MS patients,82 and thus may contribute to prevent irreversible neurological disability associated with MS. However, fasudil has certain limitations, such as a relatively narrow safety margin and poor oral bioavailability, which limit its clinical use for long-­term treatment. Thus, the search for different and safer ROCK inhibitors is being explored to be used in chronic neurodegenerative diseases such as MS. That is the case for FaD-­1 and FSD-­C10, novel ROCK inhibitors, which selectively inhibit ROCK II and are mainly expressed in the CNS. Both

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compounds reduce the severity of the EAE animal model accompanied by the protection of demyelination and the inhibition of neuroinflammation in spinal cord of EAE.89,90 FSD-­C10 is able to induce neurite outgrowth of neurons enhancing the production of several neurotrophic factors in the same level of fasudil but with less cell cytotoxicity and vasodilation91 and can promote remyelination in a chemically induced demyelination model on organotypic slice culture.92 Thus, FaD-­1 and FSD-­C10 may be safer and more promising novel ROCK inhibitors for the treatment of several neurological disorders.

8.3.4  SGK-­1 The serum-­and glucocorticoid-­inducible kinase 1 (SGK-­1) is a downstream effector of PI3K, p38 MAPK and others signalling pathways.93 It has been recently found to be involved in the regulation of several genes, including immune genes associated with MS,94 playing a pivotal role in the inhibition of microglia pathological activation.95 Sodium, a major constituent of salt (NaCl) is essential for mammalian physiology. However, high salt intake may play a role in the development of autoimmune diseases. Several lines of evidence point toward the role of high sodium intake in reversing the suppressive effects of Tregs and instead promoting a cellular shift toward T-­helper (Th)-­1 and Th17 pro-­inflammatory phenotypes. These effects have been attributed to the cascade of events that ultimately results in downstream activation of SGK-­1.96 Pharmacological inhibition of SGK1 with GSK650394 significantly decreases IL-­17A production.97 Furthermore, very recently, experimental data have shown that EAE progression is associated with up-­regulation of major sodium transporters, which is most likely driven by increased expression of SGK-­1 and activation of ERK1/2 even under a normal NaCl diet.98 These data reinforce the new roles of SGK-­1 in MS and open up new avenues for the future therapeutic use of SGK-­1 inhibitors.

8.3.5  Mitogen-­activated Protein Kinase (MAPK) Family Another family of STKs that play an important role in the regulation of fundamental biological processes including the production of proinflammatory cytokines as well as the intracellular signalling cascades initiated when a cytokine binds to its corresponding receptor is the mitogen-­activated protein kinases (MAPKs). The MAPK superfamily can be broadly divided into conventional and atypical MAPKs. The conventional MAPKs consist of three major groups: (1) the p38 MAP kinases, (2) the extracellular signal-­regulated protein kinases (ERKs), ERK1 and ERK2, and (3) the c-­Jun N-­terminal kinases (JNKs). Treatment with several ERK inhibitors such as UO126, PD98059 and the commercially available MEK1/2 attenuated EAE, when administered either at the induction phase of acute EAE or during remission in the

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relapsing–remitting EAE model. This effect was associated with significant suppression of autoantigen-­specific Th17 and Th1 responses.99 Furthermore, oral administration of a highly specific p38α inhibitor called UR-­5269 to mice at the onset of EAE markedly suppressed the progression of EAE compared with a vehicle group,100 while the use of SB203580, a dual p38α and p38β inhibitor, showed a dynamic change of immune inflammation in EAE, supporting the hypothesis that the p38 MAPK pathway contributes to the demyelination in CNS caused by Th17 inflammatory responses.101 However, the utility of MAPK inhibitors as anti-­inflammatory therapies in the clinical setting remains unresolved.102

8.3.6  Glycogen Synthase Kinase-­3 (GSK-­3) Glycogen synthase kinase-­3 (GSK-­3) is a ubiquitous kinase that is part of multiple signalling pathways. It has neurotrophic/neuroprotective effects by mediating the actions of neurotrophic molecules in the brain, thus providing neuroprotection through modulation of energy metabolism. It is also a well-­established target for inflammatory and CNS diseases,103 as some of its mutations are susceptibility factors for MS.104 GSK-­3 was validated as a target for MS in 2008 when the knock-­in mice expressing constitutively active GSK-­3 were shown to develop EAE more rapidly and more severely than the wild type.105 Identification of GSK-­3 as a critical mediator of pathogenic Th17 cell production is also reported. Thus, decrease of GSK-­3 activity, either pharmacologically or molecularly, blocked Th17 cell production while an increase on GSK-­3 activity promoted polarization to Th17 cells. These findings provide a potential therapeutic intervention to control Th17-­mediated diseases trough GSK-­3 inhibitors.103 Moreover, in vivo lithium therapy, a well-­known GSK-­3 inhibitor, suppressed T cell differentiation, and IFN-­γ, IL-­6, and IL-­17 production while maintaining long-­ term (90 days after immunisation) protection in relapsing–remitting EAE induced with proteolipid protein peptide139-­151.106 GSK-­3 has two isoforms, α and β, both expressed in the brain at different percentages. Recently, an isoform-­selective effect of GSK-­3 on T cell generation has been reported: while Th17 cell production was sensitive to reduced levels of GSK-­3β and Th1 cell production was inhibited in GSK-­3α-­deficient cells. Administration of different GSK-­3 inhibitors as TDZD-­8, VP2.51, VP0.7 or L803-­mts significantly reduced the clinical symptoms of myelin oligodendrocyte glycoprotein35-­55-­induced EAE in mice, nearly eliminating the chronic progressive phase and reducing the number of Th17 and Th1 cells in the spinal cord (Figure 8.4).107 Furthermore, treatment with TDZD-­8 or L803-­mts after the initial disease episode alleviated the clinical symptoms in a relapsing–remitting model of proteolipid protein139-­151-­induced EAE. These results demonstrate the therapeutic effects of GSK-­3 inhibitors in EAE, as well as showing that GSK-­3 inhibition in T cells is sufficient to reduce the severity of EAE, suggesting that GSK-­3 may be a feasible target for developing new therapeutic interventions for MS.

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Figure 8.4  GSK-­  3 inhibitors with preclinical efficacy in EAE.

8.3.6.1 VP3.15 The 5-­imino-­1,2,4-­thiadiazole (ITDZ) named VP3.15 is an interesting small molecule under preclinical development for MS by ANKAR Pharma (Figure 8.4). It was designed as a substrate competitive inhibitor of GSK-­3 showing anti-­inflammatory and neurogenic profile in vitro using primary cultures of microglia and astrocytes or neuroespheres isolated from the hippocampus of adult rats.108 A related compound belonging to the same chemical family showed a clear neuroprotective effect in vivo using a kainate (KA) excitotoxicity model. Rats treated with VP1.14 showed a reduced inflammatory response after KA injection, and exhibited a significant reduction in pyramidal cell loss in the CA1 and CA3 areas of the hippocampus.109 Later on, a second biological activity was found for the ITDZ derivatives. Using a neuronal network training to predict phosphodiesterase 7 inhibition (PDE7), the ITDZs were found to be PDE7 inhibitors. PDE7 is a selective cAMP phosphodiesterase expressed both in CNS and peripheral cells and its inhibitors are able to increase intracellular levels of cAMP, showing efficacy in EAE models.110 Experimental studies corroborate this prediction; ITDZs and subsequently VP3.15 are the first dual GSK-­3/PDE7 inhibitors reported to date.111 ITDZs bind to PDE7, acting both at the cAMP site and also in a druggable allosteric pocket situated at the back of the active site.112 VP3.15 resulted in a hit-­to-­lead optimisation program directed to improve drug-­like properties and safety profile. Moreover a cross talk between GSK-­3 and PDE7 has been

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described, showing that the allosteric modulation of both targets are of great benefit for CNS diseases with increasing neuroprotective, anti-­inflammatory and neurogenic profiles.113 Thus, VP3.15 was assayed in the EAE model with a similar efficacy to that of fingolimod, an oral small molecule treatment in clinical use for MS.114 VP3.15 acts on peripheral lymphocytes, inhibiting their proliferation and TNFα secretion in a dose-­dependent manner. Moreover, VP3.15 has shown remyelinating properties in several in vitro, ex vivo and in vivo models.115 This small molecule is able to promote both adult mouse and adult human OPC differentiation, and to enhance myelinisation in cerebellar slices treated with lysophosphatidylcholine (LPC) and in two demyelinating mouse models, where demyelination occurs with minimal adaptive immune system contribution (using LPC or cuprizone). These relevant results led to the proposition of VP3.15, with good oral bioavailability and CNS penetration, as a neuroprotective and neuroreparative disease-­modifying treatment for MS. Recently, the activation of the PKA-­dependent signalling pathway has shown to play a key role to boost maturation of resident OPCs to overcome remyelination failure and halt disease progression.116 As VP3.15 increases cAMP levels, PKA signalling is activated and may be the reason for the potent remyelination profile shown by this protein kinase inhibitor with dual action on PDE7.117

8.3.7  IKappa B Kinase (IKKB) Nuclear factor kappa-­light-­chain-­enhancer of activated B cells (NF-­κB) signalling pathways are involved in cell immune responses, apoptosis and infections. In MS, NF-­κB pathways are changed, leading to increased levels of NF-­κB activation in cells playing a key role in T cell activation and survival during (auto)immune responses.118 NF-­κB signalling is complex, with many elements involved in its activation and regulation. The IkappaB kinase (IKKB) complex, which is composed of two catalytic subunits, IKKα and IKKβ, is a central component of the intracellular signalling pathway mediating NF-­ κB activation. The role of IKKβ in autoantigen-­specific T cell activation and induction of autoimmune disease using mice that lack this kinase specifically in T cells was studied. Treatment with the IKKβ-­inhibitory compound PS-­1145 reduced cytokine production of spleen cells in vitro and diminished clinical signs of EAE in vivo.119 Recently, the inhibition of NF-­κB activation by conditional deletion of IKKβ in myeloid cells resulted in alleviated clinical signs of EAE compared with wild-­t ype mice.120 Interestingly, these beneficial effects were associated with the suppression of Th1 and Th17 T cells, the up-­regulation of Treg cells, and reduced BBB damage.121 Recently, some natural compound inhibitors of IKK-­NFkB signalling have been shown to be beneficial in EAE models. Celastrol, a quinone methide triterpene pharmacologically active compound present in thunder god vine root extracts, and paeoniflorin, a monoterpene glycoside constituent of an herbal medicine derived from Paeonia lactiflora, both used to treat inflammatory and autoimmune diseases significantly attenuated EAE disease, down-­regulating cytokine

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Figure 8.5  Serine/threonine  protein kinase inhibitors with preclinical efficacy in EAE mouse model. (A) IKK inhibitors; (B) TAK1 inhibitors; (C) CDK inhibitors; (D) RSK2 inhibitors; (E) CaMKIIα inhibitor; (F) RIP1 inhibitor; (G) PKC-­θ inhibitor.

production in BMDCs and suppressing pathogenic Th17 responses.122,123 All these findings underscore the potential of therapeutic IKK inhibition in autoimmune diseases (Figure 8.5).

8.3.8  T  ransforming Growth Factor-­β-­activated Kinase 1 (TAK1) Transforming growth factor-­β-­activated kinase 1 (TAK1) is a member of the mitogen-­activated protein (MAP) kinase family and is an upstream signalling molecule of nuclear factor-­κB (NF-­κB). To date, TAK1 is a key

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upstream integrator of multiple pro-­inflammatory signalling pathways, mediating the production of pro-­inflammatory cytokines, chemokines and adhesion molecules.124 Thus, targeting TAK1 provides new therapeutic options for chronic inflammatory disorders, autoimmune diseases and cancer.125,126 Recently, the role of the TAK1 inhibitor 5Z-­7-­oxozeaenol in treating autoimmune demyelinating diseases has been shown.127 This kinase inhibitor exerts neuroprotective effects on EAE in mice by decreasing the levels of pro-­inflammatory cytokines in splenocytes and CNS, diminishing the number of activated microglia and inhibiting the p38 MAPK, JNK and ERK signalling pathways. These results together with the last results of takinib, a selective TAK1 inhibitor with preclinical efficacy in autoimmune diseases,128,129 suggest that TAK1 inhibition may provide a potent approach toward treating autoimmune demyelinating diseases (Figure 8.5).

8.3.9  Miscellaneous Many other STK inhibitors have been reported with efficacy in EAE mice models and modulation of Th17 cells (Figure 8.5). With the aim of identifying novel pathways involved in Th17 differentiation, 285 chemical inhibitors for known signalling pathways were screened. Among them, kenpaullone and roscovitine were found to suppress Th17 differentiation. Moreover, both compounds decreased severity in EAE, a typical Th17-­mediated autoimmune disease model.130 As both compounds are cyclin-­dependent kinase (CDK) inhibitors, experimental results may suggest that by targeting CDK the balance of Th17/Treg may be modified. Receptor interacting protein 1 (RIP1) elevation in leukocytes might be interpreted as the molecular equivalent of an elevated general inflammatory activity in MS patients compared to healthy control persons.131 Recently, a novel class of RIP1 kinase inhibitors with excellent pharmacokinetic profiles, being orally available and brain-­penetrating drug candidates, has been developed. Compound 22 significantly suppressed necroptotic cell death both in mouse and human cells and oral administration of 22 attenuated disease progression in the mouse EAE model of MS.132 Ribosomal S6 kinase 2 (RSK2) regulates Th17 differentiation by attenuating IL-­17A production. The pan-­RSK inhibitor BI-­D1870 inhibits Th17 differentiation and protects the EAE mice by reducing the infiltration of Th1 cells into the CNS.133 These results suggest that RSK2 inhibition is a promising strategy for the treatment of MS; moreover, very recently RSK2 has been proposed as the pharmacological target of dimethyl fumarate (DMF), a recently approved drug for the clinical treatment of MS.134 The biochemical and cell biological characteristics of DMF inhibition of RSK2 are consistent with the clinical protocols of DMF treatment. The crystal structure of the C-­terminal kinase domain of the mouse RSK2 inhibited by DMF decipher some clues for the next design of allosteric RSK2 inhibitors useful in MS pharmacotherapy.

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Finally, prophylactic or acute administration of KN93, a Ca /calmodulin-­ dependent protein kinase IIα (CaMKIIα) inhibitor, significantly reduced the clinical scores of EAE and attenuated mechanical allodynia and thermal hyperalgesia in EAE.135 Spinal CaMKIIα activity was enhanced in EAE, correlating with the development of ongoing spontaneous pain and evoked hypersensitivity to mechanical and thermal stimuli. As pain is a common and severe symptom in MS that decreases the patients' quality of life, targeting CaMKIIα may ultimately offer a new therapeutic approach for mitigating pain in MS. Many other serine/threonine specific protein kinases have been involved in autoimmunity processes. For example, PKC-­θ is predominantly expressed in the T cells and localised in the centre of immunological synapses upon T-­cell receptor (TCR) and CD28 signalling. Since these effector T helper cells are responsible for mediating autoimmunity, selective inhibition of PKC-­θ is considered a treatment for prevention of autoimmune diseases and allograft rejection.136 Regarding MS, PKC-­θ may play a neuroprotective role for oligodendrocytes interfering in peroxynitrite toxicity.137 Moreover, PKC-­θ-­deficient mice were completely resistant to the development of clinical EAE compared with wild-­t ype control mice, with a great reduction in IL-­17 expression.138 Pharmacological treatment with a selective PKC-­θ inhibitor (compound 4) showed a profound effect on the severity of EAE when dosed therapeutically in animals with ongoing disease.139 All these experimental data support the hypothesis that small molecules specifically designed to target this isoform of PKC may be a new approach for MS therapy.

8.4  Conclusions The main goal in the treatment of autoimmune diseases is to suppress the pathological inflammatory component, restoring immunological self-­ tolerance and preserving the ability to mount an appropriate immune response against invading pathogens. Considering that MS is classified as a CNS autoimmune disease, protein kinases as key drivers of many inflammatory-­mediated diseases represent an important and promising class of emerging therapeutic targets for MS. Furthermore, protein kinases are also involved in several cellular signals that protect cells from death. In that situation, they play a critical role not only for oligodendrocyte neuroprotection but also for OPC differentiation. Oligodendrocytes are the cells of CNS responsible for producing and maintaining myelin homeostasis, and they disappear progressively in MS, which is responsible for many of the debilitating clinical symptoms suffered by the patients. Targeting some protein kinases opens a new avenue for neuroprotective and remyelinating therapies for MS. Moreover, the success of protein kinase inhibitors in the treatment of cancer has spurred the search for kinase inhibitors for the treatment of inflammatory, autoimmune and CNS diseases, too. To date, only a few kinase inhibitors have reached the stage of FDA approval outside the cancer field,

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while others have had mixed results in clinical trials. Meanwhile, several kinase inhibitors, such as those collected in the present chapter, have also been developed and await testing in humans. It therefore remains to be determined whether or not protein kinases will indeed turn out to be ‘the major drug targets of the 21st century’, but all the protein kinase inhibitors developed up to now have a great value not only as emerging new drugs for MS but also as valuable pharmacological tools that improve our understanding of MS biology. In summary, the development of kinase-­targeted therapeutics for MS holds great promise in the future, as the BTK inhibitor evobrutinib in advanced clinical trials. With the growing number of protein kinases emerging as therapeutic targets for MS and the rapid advanced and growth in the development of protein kinase inhibitors, it is only a matter of time to witness whether the modulation of the protein kinases collected in this chapter may become a valuable option for the future treatment of MS.

References

1. B. D. Trapp and K. A. Nave, Annu. Rev. Neurosci., 2008, 31, 247. 2. H. F. McFarland and R. Martin, Nat. Immunol., 2007, 8, 913. 3. P. M. Matthews, Nat. Rev. Neurol., 2015, 11, 614. 4. Kinase Drug Discovery, ed. R. A. Ward and F. W. Goldberg, The Royal Society of Chemistry, Cambridge, UK, 2019. 5. F. Cruzalegui, Ann. Pharm. Fr., 2010, 68, 254. 6. R. Roskoski Jr, Pharmacol. Res., 2015, 100, 1–23. 7. A. Mirshafiey, G. Ghalamfarsa, B. Asghari and G. Azizi, Innovations Clin. Neurosci., 2014, 11, 23. 8. I. Gagalo, I. Rusiecka and I. Kocic, Curr. Neuropharmacol., 2015, 13, 836. 9. O. Crespo, S. C. Kang, R. Daneman, T. M. Lindstrom, P. P. Ho, R. A. Sobel, L. Steinman and W. H. Robinson, J. Clin. Immunol., 2011, 31, 1010. 10. F. Safavi, H. Li, P. Gonnella, E. R. Mari, J. Rasouli, G. X. Zhang and A. Rostami, Immunol. Res., 2015, 61, 294. 11. S. A. Lewandowski, L. Fredriksson, D. A. Lawrence and U. Eriksson, Pharmacol. Ther., 2016, 167, 108. 12. C. Leder, S. Ortler, R. Seggewiss, H. Einsele and H. Wiendl, Exp. Hematol., 2007, 35, 1266. 13. G. Azizi and A. Mirshafiey, Recent Pat. Inflammation Allergy Drug Discovery, 2013, 7, 259. 14. G. Azizi, M. R. Haidari, M. Khorramizadeh, F. Naddafi, R. Sadria, M. H. Javanbakht, R. Sedaghat, F. Tofighi Zavareh and A. Mirshafiey, Iran. J. Allergy, Asthma Immunol., 2014, 13, 198. 15. M. V. Adzemovic, M. Zeitelhofer, U. Eriksson, T. Olsson and I. Nilsson, PLoS One, 2013, 8, e56586. 16. B. Siroos, M. H. Harirchian and R. Abolfazli, Mult. Scler., 2013, 19, 1238. 17. A. Moussy, P. Reginault, F. Bellamy and A. Lermet, WO Pat., 2008098949, 2007.

190

Chapter 8

18. P. Dubreuil, S. Letard, M. Ciufolini, L. Gros, M. Humbert, N. Casteran, L. Borge, B. Hajem, A. Lermet, W. Sippl, E. Voisset, M. Arock, C. Auclair, P. S. Leventhal, C. D. Mansfield, A. Moussy and O. Hermine, PLoS One, 2009, 4, e7258. 19. U. Testa, Drugs Future, 2008, 33, 161. 20. L. Reber, C. A. Da Silva and N. Frossard, Eur. J. Pharmacol., 2006, 533, 327. 21. K. A. Hahn, G. Ogilvie, T. Rusk, P. Devauchelle, A. Leblanc, A. Legendre, B. Powers, P. S. Leventhal, J. P. Kinet, F. Palmerini, P. Dubreuil, A. Moussy and O. Hermine, J. Vet. Intern. Med., 2008, 22, 1301. 22. I. Marech, R. Patruno, N. Zizzo, C. Gadaleta, M. Introna, A. F. Zito, C. D. Gadaleta and G. Ranieri, Crit. Rev. Oncol. Hematol., 2014, 91, 98. 23. D. Petrov, C. Mansfield, A. Moussy and O. Hermine, Front. Aging Neurosci., 2017, 9, 68. 24. J. Folch, D. Petrov, M. Ettcheto, I. Pedros, S. Abad, C. Beas-­Zarate, A. Lazarowski, M. Marin, J. Olloquequi, C. Auladell and A. Camins, Expert Rev. Neurother., 2015, 15, 587. 25. S. D. Skaper, P. Giusti and L. Facci, FASEB J., 2012, 26, 3103. 26. R. Silver and J. P. Curley, Trends Neurosci., 2013, 36, 513. 27. K. Maeda, C. Nishiyama, H. Ogawa and K. Okumura, J. Immunol., 2010, 185, 4252. 28. J. P. Zappulla, M. Arock, L. T. Mars and R. S. Liblau, J. Neuroimmunol., 2002, 131, 5. 29. P. Vermersch, R. Benrabah, N. Schmidt, H. Zephir, P. Clavelou, C. Vongsouthi, P. Dubreuil, A. Moussy and O. Hermine, BMC Neurol., 2012, 12, 36. 30. M. Humbert, F. de Blay, G. Garcia, A. Prud'homme, C. Leroyer, A. Magnan, J. M. Tunon-­de-­Lara, C. Pison, M. Aubier, D. Charpin, I. Vachier, A. Purohit, P. Gineste, T. Bader, A. Moussy, O. Hermine and P. Chanez, Allergy, 2009, 64, 1194. 31. C. Paul, B. Sans, F. Suarez, P. Casassus, S. Barete, F. Lanternier, C. Grandpeix-­Guyodo, P. Dubreuil, F. Palmerini, C. D. Mansfield, P. Gineste, A. Moussy, O. Hermine and O. Lortholary, Am. J. Hematol., 2010, 85, 921. 32. F. Piette, J. Belmin, H. Vincent, N. Schmidt, S. Pariel, M. Verny, C. Marquis, J. Mely, L. Hugonot-­Diener, J. P. Kinet, P. Dubreuil, A. Moussy and O. Hermine, Alzheimer's Res. Ther., 2011, 3, 16. 33. E. Dolgin, Nature, 2016, 540, S7. 34. E. R. Stanley and V. Chitu, Cold Spring Harbor Perspect. Biol., 2014, 6, a021857. 35. V. Chitu, S. Gokhan, S. Nandi, M. F. Mehler and E. R. Stanley, Trends Neurosci., 2016, 39, 378. 36. Y. N. Gerber, G. P. Saint-­Martin, C. M. Bringuier, S. Bartolami, C. Goze-­ Bac, H. N. Noristani and F. E. Perrin, Front. Cell. Neurosci., 2018, 12, 368.

Protein Kinase Inhibitors for the Treatment of Multiple Sclerosis

191

37. Y. Uemura, H. Ohno, Y. Ohzeki, H. Takanashi, H. Murooka, K. Kubo and I. Serizawa, J. Neuroimmunol., 2008, 195, 73. 38. N. Beckmann, E. Giorgetti, A. Neuhaus, S. Zurbruegg, N. Accart, P. Smith, J. Perdoux, L. Perrot, M. Nash, S. Desrayaud, P. Wipfli, W. Frieauff and D. R. Shimshek, Acta Neuropathol. Commun., 2018, 6, 9. 39. J. T. Hartmann, M. Haap, H. G. Kopp and H. P. Lipp, Curr. Drug Metab., 2009, 10, 470. 40. E. Y. Moawad, Indian J. Clin. Biochem., 2014, 29, 491. 41. G. Azizi, M. Goudarzvand, S. Afraei, R. Sedaghat and A. Mirshafiey, Immunopharmacol. Immunotoxicol., 2015, 37, 287. 42. Z. Yan, S. A. Gibson, J. A. Buckley, H. Qin and E. N. Benveniste, Clin. Immunol., 2018, 189, 4. 43. E. N. Benveniste, Y. Liu, B. C. McFarland and H. Qin, J. Interferon Cytokine Res., 2014, 34, 577. 44. Y. Liu, A. T. Holdbrooks, P. De Sarno, A. L. Rowse, L. L. Yanagisawa, B. C. McFarland, L. E. Harrington, C. Raman, S. Sabbaj, E. N. Benveniste and H. Qin, J. Immunol., 2014, 192, 59. 45. Y. Liang, Y. Zhu, Y. Xia, H. Peng, X. K. Yang, Y. Y. Liu, W. D. Xu, H. F. Pan and D. Q. Ye, Expert Opin. Ther. Targets, 2014, 18, 571. 46. N. Couturier, F. Bucciarelli, R. N. Nurtdinov, M. Debouverie, C. Lebrun-­ Frenay, G. Defer, T. Moreau, C. Confavreux, S. Vukusic, I. Cournu-­ Rebeix, R. H. Goertsches, U. K. Zettl, M. Comabella, X. Montalban, P. Rieckmann, F. Weber, B. Muller-­Myhsok, G. Edan, B. Fontaine, L. T. Mars, A. Saoudi, J. R. Oksenberg, M. Clanet, R. S. Liblau and D. Brassat, Brain, 2011, 134, 693. 47. D. A. Dyment, M. Z. Cader, M. J. Chao, M. R. Lincoln, K. M. Morrison, G. Disanto, J. M. Morahan, G. C. De Luca, A. D. Sadovnick, P. Lepage, A. Montpetit, G. C. Ebers and S. V. Ramagopalan, Neurology, 2012, 79, 406. 48. M. Mazdeh, N. Moradi, E. Khoshroo, Z. Shayesteh, M. Taheri, A. Sayad, M. D. Omrani, M. Hajilooi, G. Roshanaei and G. Solgi, J. Neuroimmunol., 2018, 314, 24. 49. O. Kulakova, V. Bashinskaya, I. Kiselev, N. Baulina, E. Tsareva, R. Nikolaev, M. Kozin, S. Shchur, A. Favorov, A. Boyko and O. Favorova, Pharmacogenomics, 2017, 18, 1563. 50. P. Norman, Expert Opin. Ther. Pat., 2012, 22, 1233. 51. Y. Zhou, X. Leng, S. Luo, Z. Su, X. Luo, H. Guo, C. Mo, Q. Zou, Y. Liu and Y. Wang, J. Immunol. Res., 2016, 2016, 5021537. 52. M. Noack and P. Miossec, Autoimmun. Rev., 2014, 13, 668. 53. J. A. Di Paolo, T. Huang, M. Balazs, J. Barbosa, K. H. Barck, B. J. Bravo, R. A. Carano, J. Darrow, D. R. Davies, L. E. DeForge, L. Diehl, R. Ferrando, S. L. Gallion, A. M. Giannetti, P. Gribling, V. Hurez, S. G. Hymowitz, R. Jones, J. E. Kropf, W. P. Lee, P. M. Maciejewski, S. A. Mitchell, H. Rong, B. L. Staker, J. A. Whitney, S. Yeh, W. B. Young, C. Yu, J. Zhang, K. Reif and K. S. Currie, Nat. Chem. Biol., 2011, 7, 41.

192

Chapter 8

54. R. W. Hendriks, Nat. Chem. Biol., 2011, 7, 4. 55. C. Liang, D. Tian, X. Ren, S. Ding, M. Jia, M. Xin and S. Thareja, Eur. J. Med. Chem., 2018, 151, 315. 56. Z. Zhang, D. Zhang, Y. Liu, D. Yang, F. Ran, M. L. Wang and G. Zhao, Arch. Pharm., 2018, 351, e1700369. 57. B. L. Hodous, L. Liu-­Bujalski, R. Jones, D. Bankston, T. L. Johnson, I. Mochalkin, N. Nguyen, H. Qiu, A. Goutopoulos and N. Brugger, US Pat., 20140162983, 2011. 58. Z. Li, L. Zhang, Y. Yuan and Z. Yang, Drug Test. Anal., 2018, 129–139. 59. https://www.merckgroup.com/en/news/evobrutinib-­06-­03-­2018.html, Primary Analysis of a Randomised, Placebo-­Controlled, Phase II Study of the Bruton's Tyrosine Kinase Inhibitor Evobrutinib (M2951) in Patients with Relapsing Multiple Sclerosis, ECTRIMS, Berlin, 2018. 60. N. Diaz, Drugs Today, 2017, 53, 559. 61. https://multiplesclerosisnewstoday.com/2018/08/02/potential-­b -­cell-­ trageting-­m s-­t herapy-­b y-­p rincipia-­p rn2246-­s een-­t o-­r each-­b rain-­in-­ phase-­1-­study/. 62. S. Hadas, M. Spira, U. K. Hanisch, F. Reichert and S. Rotshenker, J. Neuroinflammation, 2012, 9, 166. 63. B. R. Wong, E. B. Grossbard, D. G. Payan and E. S. Masuda, Expert Opin. Invest. Drugs, 2004, 13, 743. 64. C. Liao, J. Hsu, Y. Kim, D. Q. Hu, D. Xu, J. Zhang, A. Pashine, J. Menke, T. Whittard, N. Romero, T. Truitt, M. Slade, C. Lukacs, J. Hermann, M. Zhou, M. Lucas, S. Narula, J. DeMartino and S. L. Tan, Arthritis Res. Ther., 2013, 15, R146. 65. F. A. Jacobsen, A. N. Scherer, J. Mouritsen, H. Bragadottir, B. Thomas Backstrom, S. Sardar, D. Holmberg, A. J. Koleske and A. Andersson, J. Neuroimmune Pharmacol., 2018, 13, 265. 66. J. S. Kamens, S. E. Ratnofsky and G. C. Hirst, Curr. Opin. Invest. Drugs, 2001, 2, 1213. 67. B. L. McRae, C. Wallace, K. F. Dixon, A. Roux, S. Mohan, Y. Jia, D. H. Presky, D. E. Tracey and G. C. Hirst, Int. Immunopharmacol., 2005, 5, 667. 68. T. Hunter, Cell, 1995, 80, 225. 69. H. Schulman, Curr. Opin. Neurobiol., 1995, 5, 375. 70. S. A. Gibson and E. N. Benveniste, Trends Immunol., 2018, 39, 82. 71. S. W. Jang, S. S. Hwang, H. S. Kim, K. O. Lee, M. K. Kim, W. Lee, K. Kim and G. R. Lee, Exp. Mol. Med., 2017, 49, e375. 72. A. Ulges, E. J. Witsch, G. Pramanik, M. Klein, K. Birkner, U. Buhler, B. Wasser, F. Luessi, N. Stergiou, S. Dietzen, T. J. Bruhl, T. Bohn, G. Bundgen, H. Kunz, A. Waisman, H. Schild, E. Schmitt, F. Zipp and T. Bopp, Proc. Natl. Acad. Sci. U. S. A., 2016, 113, 10145. 73. S. A. Gibson, W. Yang, Z. Yan, Y. Liu, A. L. Rowse, A. S. Weinmann, H. Qin and E. N. Benveniste, J. Immunol., 2017, 198, 4244. 74. A. K. Stark, S. Sriskantharajah, E. M. Hessel and K. Okkenhaug, Curr. Opin. Pharmacol., 2015, 23, 82.

Protein Kinase Inhibitors for the Treatment of Multiple Sclerosis

193

75. S. Haylock-­Jacobs, I. Comerford, M. Bunting, E. Kara, S. Townley, M. Klingler-­Hoffmann, B. Vanhaesebroeck, K. D. Puri and S. R. McColl, J. Autoimmun., 2011, 36, 278. 76. S. Q. Shang, J. Li, S. Q. Li and Y. L. Cao, Int. J. Clin. Exp. Med., 2015, 8, 20645. 77. H. Li, D. Park, P. M. Abdul-­Muneer, B. Xu, H. Wang, B. Xing, D. Wu and S. Li, Neuroscience, 2013, 253, 89. 78. J. H. Come, P. N. Collier, J. A. Henderson, A. C. Pierce, R. J. Davies, A. Le Tiran, H. O'Dowd, U. K. Bandarage, J. Cao, D. Deininger, R. Grey, E. B. Krueger, D. B. Lowe, J. Liang, Y. Liao, D. Messersmith, S. Nanthakumar, E. Sizensky, J. Wang, J. Xu, E. Y. Chin, V. Damagnez, J. D. Doran, W. Dworakowski, J. P. Griffith, M. D. Jacobs, S. Khare-­Pandit, S. Mahajan, C. S. Moody and A. M. Aronov, J. Med. Chem., 2018, 61, 5245. 79. Z. Xue, W. Li, H. Wang, B. Huang, Z. Ge, C. Gu, Y. Liu, K. Zhang, J. Yang, R. Han, M. Peng, Y. Li, D. Zhang, Y. Da, Z. Yao and R. Zhang, J. Mol. Med., 2014, 92, 1057. 80. P. V. LoGrasso and Y. Feng, Curr. Top. Med. Chem., 2009, 9, 704. 81. C. M. Chong, N. Ai and S. M. Lee, Curr. Drug Targets, 2017, 18, 455. 82. C. Chen, J. Z. Yu, Q. Zhang, Y. F. Zhao, C. Y. Liu, Y. H. Li, W. F. Yang, C. G. Ma and B. G. Xiao, NeuroMol. Med., 2015, 17, 454. 83. C. Gao, L. Huang, Y. Long, J. Zheng, J. Yang, S. Pu and L. Xie, NeuroImmunoModulation, 2013, 20, 334. 84. Y. H. Li, J. Z. Yu, Y. L. Xin, L. Feng, Z. Chai, J. C. Liu, H. Z. Zhang, G. X. Zhang, B. G. Xiao and C. G. Ma, Exp. Mol. Pathol., 2015, 99, 220. 85. C. E. Pedraza, C. Taylor, A. Pereira, M. Seng, C. S. Tham, M. Izrael and M. Webb, ASN Neuro, 2014, 6, 175909141453813. 86. X. Sun, M. Minohara, H. Kikuchi, T. Ishizu, M. Tanaka, H. Piao, M. Osoegawa, Y. Ohyagi, H. Shimokawa and J. Kira, J. Neuroimmunol., 2006, 180, 126. 87. J. Z. Yu, J. Ding, C. G. Ma, C. H. Sun, Y. F. Sun, C. Z. Lu and B. G. Xiao, J. Neurosci. Res., 2010, 88, 1664. 88. M. F. Guo, J. Meng, Y. H. Li, J. Z. Yu, C. Y. Liu, L. Feng, W. F. Yang, J. L. Li, Q. J. Feng, B. G. Xiao and C. G. Ma, J. Neurol. Sci., 2014, 343, 69. 89. Y. F. Zhao, X. Zhang, Z. B. Ding, X. W. Yang, H. Zhang, J. Z. Yu, Y. H. Li, C. Y. Liu, Q. Zhang, H. Z. Zhang, C. G. Ma and B. G. Xiao, J. Mol. Neurosci., 2015, 55, 725. 90. Y. H. Li, J. Z. Yu, C. Y. Liu, H. Zhang, H. F. Zhang, W. F. Yang, J. L. Li, Q. J. Feng, L. Feng, G. X. Zhang, B. G. Xiao and C. G. Ma, Immunology, 2014, 143, 219. 91. Y. L. Xin, J. Z. Yu, X. W. Yang, C. Y. Liu, Y. H. Li, L. Feng, Z. Chai, W. F. Yang, Q. Wang, W. J. Jiang, G. X. Zhang, B. G. Xiao and C. G. Ma, Biosci. Rep., 2015, 35, e00247. 92. Y. H. Li, C. Xie, Y. Zhang, X. Li, H. F. Zhang, Q. Wang, Z. Chai, B. G. Xiao, R. Thome, G. X. Zhang and C. G. Ma, Sci. Rep., 2017, 7, 41227. 93. A. Di Cristofano, Curr. Top. Dev. Biol., 2017, 123, 49.

194

Chapter 8

94. P. Martin, A. McGovern, J. Massey, S. Schoenfelder, K. Duffus, A. Yarwood, A. Barton, J. Worthington, P. Fraser, S. Eyre and G. Orozco, PLoS One, 2016, 11, e0166923. 95. H. Asai, K. Inoue, E. Sakuma, Y. Shinohara and T. Ueki, Int. J. Physiol., Pathophysiol. Pharmacol., 2018, 10, 115. 96. K. Sharif, H. Amital and Y. Shoenfeld, Autoimmun. Rev., 2018, 17, 1069. 97. M. Kleinewietfeld, A. Manzel, J. Titze, H. Kvakan, N. Yosef, R. A. Linker, D. N. Muller and D. A. Hafler, Nature, 2013, 496, 518. 98. X. Zhou, B. Packialakshmi, Y. Xiao, S. Nurmukhambetova and J. R. Lees, Cell. Immunol., 2017, 317, 18. 99. C. F. Brereton, C. E. Sutton, S. J. Lalor, E. C. Lavelle and K. H. Mills, J. Immunol., 2009, 183, 1715. 100. K. Namiki, H. Matsunaga, K. Yoshioka, K. Tanaka, K. Murata, J. Ishida, A. Sakairi, J. Kim, N. Tokuhara, N. Shibakawa, M. Shimizu, Y. Wada, Y. Tokunaga, M. Shigetomi, M. Hagihara, S. Kimura, T. Sudo, A. Fukamizu and Y. Kasuya, J. Biol. Chem., 2012, 287, 24228. 101. L. Wang, B. Li, M. Y. Quan, L. Li, Y. Chen, G. J. Tan, J. Zhang, X. P. Liu and L. Guo, Oncotarget, 2017, 8, 42808. 102. K. Birkner, B. Wasser, J. Loos, A. Plotnikov, R. Seger, F. Zipp, E. Witsch and S. Bittner, Int. J. Mol. Sci., 2017, 18, E1990. 103. E. Beurel, Front. Mol. Neurosci., 2011, 4, 18. 104. D. Galimberti, J. Macmurray, D. Scalabrini, C. Fenoglio, M. De Riz, C. Comi, D. Comings, F. Cortini, C. Villa, M. Serpente, C. Cantoni, E. Ridolfi, M. H. Fardipoor, M. Leone, F. Monaco, N. Bresolin and E. Scarpini, Neurosci. Lett., 2011, 497, 46. 105. P. De Sarno, R. C. Axtell, C. Raman, K. A. Roth, D. R. Alessi and R. S. Jope, J. Immunol., 2008, 181, 338. 106. M. Ahn, J. Kim, C. Park, J. Cho, Y. Jee, K. Jung, C. Moon and T. Shin, Anat. Cell Biol., 2017, 50, 48. 107. E. Beurel, O. Kaidanovich-­Beilin, W. I. Yeh, L. Song, V. Palomo, S. M. Michalek, J. R. Woodgett, L. E. Harrington, H. Eldar-­Finkelman, A. Martinez and R. S. Jope, J. Immunol., 2013, 190, 5000. 108. V. Palomo, D. I. Perez, C. Perez, J. A. Morales-­Garcia, I. Soteras, S. Alonso-­ Gil, A. Encinas, A. Castro, N. E. Campillo, A. Perez-­Castillo, C. Gil and A. Martinez, J. Med. Chem., 2012, 55, 1645. 109. C. Susin, J. A. Morales-­Garcia, D. Aguilar-­Morante, V. Palomo, M. Sanz-­ Sancristobal, S. Alonso-­Gil, C. Gil, A. Santos, A. Martinez and A. Perez-­ Castillo, J. Neurochem., 2012, 122, 1193. 110. C. Gonzalez-­Garcia, B. Bravo, A. Ballester, R. Gomez-­Perez, C. Eguiluz, M. Redondo, A. Martinez, C. Gil and S. Ballester, Br. J. Pharmacol., 2013, 170, 602. 111. M. Redondo, V. Palomo, J. Brea, D. I. Perez, R. Martin-­A lvarez, C. Perez, N. Paul-­Fernandez, S. Conde, M. I. Cadavid, M. I. Loza, G. Mengod, A. Martinez, C. Gil and N. E. Campillo, ACS Chem. Neurosci., 2012, 3, 793.

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112. M. Redondo, I. Soteras, J. Brea, A. Gonzalez-­Garcia, M. I. Cadavid, M. I. Loza, A. Martinez, C. Gil and N. E. Campillo, Eur. J. Med. Chem., 2013, 70, 781. 113. J. A. Morales-­Garcia, V. Palomo, M. Redondo, S. Alonso-­Gil, C. Gil, A. Martinez and A. Perez-­Castillo, ACS Chem. Neurosci., 2014, 5, 194. 114. R. Martin-­Alvarez, N. Paul-­Fernandez, V. Palomo, C. Gil, A. Martinez and G. Mengod, J. Chem. Neuroanat., 2017, 80, 27. 115. E. M. Medina-­Rodriguez, A. Bribian, A. Boyd, V. Palomo, J. Pastor, A. Lagares, C. Gil, A. Martinez, A. Williams and F. de Castro, Sci. Rep., 2017, 7, 43545. 116. J. Qin, A. H. Sikkema, K. van der Bij, J. C. de Jonge, K. Klappe, V. Nies, J. W. Jonker, J. W. Kok, D. Hoekstra and W. Baron, J. Neurosci., 2017, 37, 9925. 117. E. M. Medina-­Rodriguez, F. J. Arenzana, J. Pastor, M. Redondo, V. Palomo, R. Garcia de Sola, C. Gil, A. Martinez, A. Bribian and F. de Castro, Cell. Mol. Life Sci., 2013, 70, 3449. 118. S. M. Leibowitz and J. Yan, Front. Mol. Neurosci., 2016, 9, 84. 119. B. Greve, R. Weissert, N. Hamdi, E. Bettelli, R. A. Sobel, A. Coyle, V. K. Kuchroo, K. Rajewsky and M. Schmidt-­Supprian, J. Immunol., 2007, 179, 179. 120. W. Hao, Y. Decker, L. Schnoder, A. Schottek, D. Li, M. D. Menger, K. Fassbender and Y. Liu, Am. J. Pathol., 2016, 186, 1245. 121. M. J. Lee, S. J. Bing, J. Choi, M. Jang, G. Lee, H. Lee, B. S. Chang, Y. Jee, S. J. Lee and I. H. Cho, Mol. Neurodegener., 2016, 11, 54. 122. Y. Wang, L. Cao, L. M. Xu, F. F. Cao, B. Peng, X. Zhang, Y. F. Shen, G. Uzan and D. H. Zhang, J. Neuroimmune Pharmacol., 2015, 10, 506. 123. H. Zhang, Y. Qi, Y. Yuan, L. Cai, H. Xu, L. Zhang, B. Su and H. Nie, Sci. Rep., 2017, 7, 41887. 124. Y. Xiao, J. Jin, M. Chang, M. Nakaya, H. Hu, Q. Zou, X. Zhou, G. C. Brittain, X. Cheng and S. C. Sun, J. Exp. Med., 2014, 211, 1689. 125. T. Goldmann, P. Wieghofer, P. F. Muller, Y. Wolf, D. Varol, S. Yona, S. M. Brendecke, K. Kierdorf, O. Staszewski, M. Datta, T. Luedde, M. Heikenwalder, S. Jung and M. Prinz, Nat. Neurosci., 2013, 16, 1618. 126. H. Sakurai, Trends Pharmacol. Sci., 2012, 33, 522. 127. L. Lu, X. Zhang, H. Tong, W. Zhang, P. Xu and S. Qu, Front. Pharmacol., 2017, 8, 789. 128. J. Totzke, D. Gurbani, R. Raphemot, P. F. Hughes, K. Bodoor, D. A. Carlson, D. R. Loiselle, A. K. Bera, L. S. Eibschutz, M. M. Perkins, A. L. Eubanks, P. L. Campbell, D. A. Fox, K. D. Westover, T. A. J. Haystead and E. R. Derbyshire, Cell Chem. Biol., 2017, 24, 1029. 129. S. A. Scarneo, A. Mansourati, L. S. Eibschutz, J. Totzke, J. R. Roques, D. Loiselle, D. Carlson, P. Hughes and T. A. J. Haystead, Sci. Rep., 2018, 8, 17058. 130. H. Yoshida, H. Kotani, T. Kondo, I. Tani, X. Wei, S. Tsuruta, A. Kimura, M. Asakawa, M. Ito, S. Nagai and A. Yoshimura, Biochem. Biophys. Res. Commun., 2013, 435, 378. 131. R. Reuss, A. Mirau, M. Mistarz, J. Kraus, R. H. Bodeker and P. Oschmann, NeuroImmunoModulation, 2013, 20, 177.

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Chapter 8

132. M. Yoshikawa, M. Saitoh, T. Katoh, T. Seki, S. V. Bigi, Y. Shimizu, T. Ishii, T. Okai, M. Kuno, H. Hattori, E. Watanabe, K. S. Saikatendu, H. Zou, M. Nakakariya, T. Tatamiya, Y. Nakada and T. Yogo, J. Med. Chem., 2018, 61, 2384. 133. I. Takada, Y. Yogiashi and M. Makishima, Immunobiology, 2016, 221, 188. 134. J. L. Andersen, B. Gesser, E. D. Funder, C. J. F. Nielsen, H. Gotfred-­ Rasmussen, M. K. Rasmussen, R. Toth, K. V. Gothelf, J. S. C. Arthur, L. Iversen and P. Nissen, Nat. Commun., 2018, 9, 4344. 135. X. Hu, F. Huang and Z. J. Wang, J. Neurosci., 2018, 38, 232. 136. S. Chand, N. Mehta, M. S. Bahia, A. Dixit and O. Silakari, Curr. Pharm. Des., 2012, 18, 4725. 137. S. Li, W. Lin, F. Tchantchou, R. Lai, J. Wen and Y. Zhang, Mol. Cell. Neurosci., 2011, 48, 62. 138. S. L. Tan, J. Zhao, C. Bi, X. C. Chen, D. L. Hepburn, J. Wang, J. D. Sedgwick, S. R. Chintalacharuvu and S. Na, J. Immunol., 2006, 176, 2872. 139. A. Curnock, C. Bolton, P. Chiu, E. Doyle, D. Fraysse, M. Hesse, J. Jones, P. Weber and J. M. Jimenez, Biochem. Soc. Trans., 2014, 42, 1524.

Part III Remyelinating Therapies

         

Chapter 9

Emerging Drugs and Targets for Remyelination in Multiple Sclerosis Laura J. Wagstaff and Anna Williams* MRC Centre for Regenerative Medicine and MS Society Edinburgh   Centre for MS Research, University of Edinburgh, 5 Little France Drive,   Edinburgh EH16 4UU, UK *E-­mail: [email protected]

9.1 Why Do We Want Therapies to Promote Remyelination in Multiple Sclerosis? As we have learnt in previous chapters, multiple sclerosis (MS) is an inflammatory-­mediated, demyelinating neurodegenerative disease of the brain and spinal cord. The immune system attacks myelin sheaths, resulting in focal areas of demyelination and oligodendrocyte death, which are called MS lesions or plaques, and which are visible on magnetic resonance (MR) scans.1 These episodes of inflammation and demyelination often correlate with clinical symptoms and signs of neurological disability, which form the relapses of relapsing and remitting MS (RRMS).1 Although there is no cure for MS, current treatments focus on relieving symptoms associated with the disease and modifying disease processes (disease-­modifying therapies) by targeting the immune system to reduce inflammation. These therapies have been very successful at reducing the number of new lesions and relapses in MS, ranging from efficacies of 30% relapse reduction to around 80% relapse   Drug Discovery Series No. 70 Emerging Drugs and Targets for Multiple Sclerosis Edited by Ana Martinez © The Royal Society of Chemistry 2019 Published by the Royal Society of Chemistry, www.rsc.org

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reduction, depending on the immunomodulating therapy used. However, once a new demyelinated MS lesion occurs, altering the immune response does not appear effective. Furthermore, many patients with RRMS move to secondary progressive MS (SPMS), and some have primary progressive MS (PPMS), where relapses do not occur, and again immunomodulatory therapy does not appear effective. Instead, clinical symptoms gradually worsen over time due to persistent demyelination, resulting in neurodegeneration and increasing patient disability.1 We know that myelin is essential for nerve axons to conduct electrical impulses quickly, using saltatory conduction.2 Thus, demyelination reduces this ability, leading to clinical symptoms. In the central nervous system (CNS), myelin is made by oligodendrocytes, which produce processes with extensions of their cell membrane that wrap around the axon, electrically insulating it in segments, leaving the sodium channel-­rich node of Ranvier between the segments (internodes) for propagation of the nerve impulse.3 However, myelin fulfils a second functional role, which is to provide metabolic support for the underlying axon. Metabolites are passed through the cytoplasmic channels within the myelin wraps, and are delivered directly to the axon surface.4,5 Thus, axons that are demyelinated are vulnerable to neurodegeneration, as they no longer receive metabolic support. This is at least one of the reasons why there is increased neurodegeneration in MS patients, which is correlated to increased accumulation of disability, in the progressive phase of MS. Repair of the myelin sheath (remyelination) is possible and perhaps even rather frequent in MS patients, particularly at a younger age.6,7 In rodent or fish models of demyelination, remyelination is very efficient, and results from recruitment of oligodendrocyte precursor cells (OPCs) to the lesion, their maturation into myelin-­forming oligodendrocytes and their subsequent formation of myelin sheaths around axons. We think this happens similarly in humans, and remyelination is seen in post-­mortem MS patient brains, identified by slightly shorter and thinner myelin sheaths.8 However, with age and inevitably, remyelination is always insufficient in MS, and demyelinated plaques remain, leading to neurodegeneration (Figure 9.1). There is currently much research focusing on improving the efficiency of remyelination in MS, to restore fast nerve conduction velocities, but also to increase axon protection and reduce accumulation of disability. The rest of this chapter will focus on advances made in this area.

9.2 Modelling MS in Preclinical Animal Studies To be able to improve remyelination in MS, we must be able to model it over time in preclinical studies. As MS only occurs in humans, there is no natu­ ral animal model of the disease, and the research community has devel­ oped several models to study different features of the pathology and clinical course. MS is a complex, multifaceted disease and no model yet captures all aspects of it. Part of the problem is that the cause of MS is still unclear although several risk factors have been identified, including being female,

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Figure 9.1 The stages and importance of remyelination in MS. (A) Oligodendrocytes

are the myelinating cells of the CNS. Oligodendrocytes generate myelin sheaths forming multiple wraps around the axon facilitating nerve conduction and providing metabolic support. (B) In MS, the immune system attacks the myelin sheath, leading to demyelinated lesions. (C) When remyelination fails, the neuron degenerates due to a lack of metabolic support. (D) For remyelination to be successful, OPCs must first be recruited to the lesion. (E) Once at the lesion site, the OPC must differentiate to form a mature myelinating oligodendrocyte. (F) The newly generated oligodendrocytes will generate new thinner and shorter myelin sheaths to repair and remyelinate the lesion. Image generated in BioRender.

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being Caucasian, having genetic susceptibility, infectious agents, lifestyle and environmental factors. Although there have been several genes linked to the disease, MS is not considered to be hereditary, at least in a Mendelian sense, rather that single nucleotide polymorphisms (SNPs) in various genes contribute as a risk factor for disease development.9–11 Due to this complex interplay of factors causing MS, generating a reliable disease model has been challenging, with no one model fully replicating all of the clinical and pathological features of MS, but each with its own merit and limitation. The main two groups of models are those that model the T-­cell driven inflammatory component of MS, namely experimental autoimmune encephalomyelitis (EAE), and those that use myelin toxins to cause demyelination, isolating demyelination from the adaptive immune response.

9.2.1 Experimental Autoimmune Encephalomyelitis (EAE) EAE has until recently been the most widely used model to study MS, probably as it is a good model to study the immune components of the disease and the use of immunomodulatory agents. EAE is a T-­cell driven model, induced with the immunisation of myelin peptides or proteins such as myelin basic protein (MBP), myelin oligodendrocyte glycoprotein or myelin proteolipid protein combined with an adjuvant.12 This results in the activation of auto-­ reactive myelin targeting CD4+ T-­cells that enter the CNS. Peripheral monocytes/macrophages are recruited to the CNS, and resident microglia are activated. The combination of phagocytosis, inflammation and cytotoxicity results in demyelination.13 Many different EAE models have been developed to replicate aspects of MS disease pathology, including a relapsing–remitting model.14 However, it can be argued that the EAE models are not representative of the disease due to the controlled nature of the protocol, administration of adjuvants, lack of genetic heterogeneity and only modelling the inflammatory part of the disease rather than the subsequent neurodegeneration. Moreover, studies in human tissue now demonstrate that CD8+ T-­cells are the predominant immune cells found in MS lesions,15,16 and show the importance of B cells17,18 and neurodegeneration, which are not well modelled in EAE. This highlights the importance of studying human post-­mortem tissue to understand disease pathology and to generate reliable animal models. All of our current immunomodulatory therapies for MS have been tested in EAE models,19–26 but equally, many more agents developed and tested in EAE models have been found to be ineffective against the human disease, or even to worsen it.27 To find remyelination therapies, the EAE model is not the obvious choice, as it is an acute T-­cell-­driven adaptive immunity model, and instead we need a model where there is demyelination and axon loss that may be rescued by remyelination. Some types of EAE do not have demyelination (and so no remyelination), and where they do, remyelination co-­ exists with on-­going demyelination, complicating analysis. In spite of these caveats, some of the remyelination targeting drugs currently in clinical trials, discussed later in this chapter, were tested in EAE models.

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9.2.2 Toxin-­induced Demyelinating Models To specifically study remyelination, models that generate focal and global demyelination not mediated by an adaptive immune response have been developed. Injecting a few microlitres of phospholipase A2 activator lysophosphatidyl choline (LPC or lysolecithin) into the CNS is toxic to the myelin sheath, resulting in rapid demyelination in a focal area at the injection site.28 In young mice, this is followed by remyelination observed from 14 days post-­ injury;28 however, in aged mice, remyelination is delayed. It can be argued that induction of focal demyelinating lesions in aged mice (or rats) is a more clinically relevant model, due to the decrease in remyelination efficiency observed with age more closely mimicking MS disease pathology.29 To generate global white matter lesions, rodents are fed a diet containing a low percentage of cuprizone.30 Cuprizone is a copper chelator and therefore induces copper deficiency. It specifically targets oligodendrocytes, causing cell death, leaving other cells of the CNS relatively unaffected.31 When constantly ingested, demyelination occurs and remyelination is not observed; however, removal of cuprizone from the diet leads to active and complete remyelination within 4 weeks.32 Rodents can survive chronic toxicity of the diet over longer time periods, even after severe oligodendrocyte depletion, and axons may not appear damaged. Remyelination still occurs in this instance but is more limited.33 However, after some months post-­ cuprizone, there may be axonal degeneration, which may be due to direct neuronal toxicity of cuprizone or indirect toxicity via demyelination.34 Although cuprizone can cause alterations in liver function,35,36 a low dose avoids liver toxicity.37 With both the focal and the global demyelinating models, therapeutics can be introduced at different time-­points to try and improve the amount, speed or quality of remyelination. Focal models perhaps model the focal damage in MS better, allowing OPCs to be recruited from surrounding normal white matter, but they require stereotactic injection of the myelin toxin. Both models avoid an overlap of demyelination and remyelination occurring simultaneously, and have very-­well-­characterised disease courses. Despite the benefits of these focal or global demyelination disease models, using them is expensive and laborious and high numbers of animals are needed to account for variability and reproducibility. However, use of these models has led to much progress in understanding how remyelination occurs and how it may be manipulated therapeutically.

9.3 Why Does Remyelination Fail in MS? These models and examination of human post-­mortem brain tissue from MS patients have given us some clues to why remyelination is ultimately inefficient in MS patients. As oligodendrocytes are the cells that remyelinate axons, we will first concentrate on oligodendroglial biology in remyelination.

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9.3.1 Oligodendrocyte Precursor Cell Migration into Demyelinated MS Lesions The first stage in remyelination is recruitment of cells to the demyelinated lesion to initiate repair. Upon examination of human post-­mortem tissue, many demyelinated lesions contain oligodendroglia.37 Compared to other white matter regions, the number of oligodendroglia in lesions can be higher, demonstrating migration and recruitment to the lesion. However, 30% of lesions contain reduced numbers or an absence of oligodendroglia.38 This appears to be particularly true of demyelinated lesions classed as chronic active or chronic inactive ‘silent’ lesions,39,40 and these are the lesions thought to have a low propensity for remyelination. Chronic active lesions have been shown to express high levels of OPC chemorepellent factor Sema3A, which is primarily expressed in glial cells, particularly astrocytes, at both the protein and RNA level,39,41 which may account, at least in part, for this lack of OPCs. Mouse studies have demonstrated that addition of Sema3A to demyelinated lesions impairs OPC recruitment, subsequently impairing remyelination. Conversely, demyelinated lesions in a transgenic mouse knock-­out for Sema3A show increased numbers of OPCs as well as an increase in remyelination efficiency.39 Collectively, this highlights Sema3A and its signalling pathway as potential therapeutic targets to enhance oligodendroglial migration to lesions, thus enhancing remyelination. It is important to note, however, that enhancing OPC recruitment to a focal lesion has previously been found not to enhance remyelination in mice.42 However, rodents are very different from humans in that they do not normally have chronic active lesions and have very efficient remyelination, so there is not a limiting step. It is now possible to identify chronic active human MS lesions on MR scans and these seem not to remyelinate,43 and so it may be highly relevant to use pro-­recruitment therapy to repopulate these lesions with OPCs. However, a subsequent therapy may also be required to enhance maturation of OPC into myelin-­forming oligodendrocytes.

9.3.2 OPC Maturation into Myelinating Oligodendrocytes Although the majority (∼70%) of MS lesions contain high numbers of OPCs,38 they remain demyelinated and so the hypothesis is that many are under a maturation block and do not differentiate to generate myelin-­forming oligodendrocytes.44 In rodent, it is clear that replacement of oligodendrocytes occurs via differentiation of OPCs into remyelinating oligodendrocytes.45 This is assumed to be the same in humans, but has not been proven, and an alternative, at least theoretical, route may be a dedifferentiation of mature oligodendrocytes into less mature cells before remyelination. Against this dedifferentiation hypothesis, post-­mitotic human oligodendrocytes transplanted into demyelinated rat spinal cord lesions are unable to produce myelin sheaths.46 This emphasises the importance of OPC differentiation

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and the generation of new oligodendrocytes in remyelination. Determining the underlying cause of this arrest in human OPC maturation and promoting their differentiation is therefore one of the key areas of MS remyelination research.

9.3.2.1 Retinoic Acid Receptor Gamma (Rxrg) In order to identify factors that may promote OPC differentiation, research has examined transcriptional changes in lesions between demyelination and remyelination in vivo using rodent models. One such screen identified the up-­regulation of retinoid X receptor gamma (Rxrg) during the initiation of remyelination.47 Rxrg was also found to be up-­regulated in OPCs around the active borders of MS lesions in post-­mortem tissue. The down-­regulation of Rxrg in vitro with siRNA was found to inhibit OPC differentiation. Fewer mature MBP-­positive cells were generated in the knock-­down cultures and cells that were present displayed simple morphologies compared with the elaborate branching and membrane formation observed in controls. Similar results were obtained when inhibition of Rxr signalling was mediated by antagonists and a failure to myelinate axons in co-­cultures was also observed. Rxrg−/− mice myelinate and develop normally; however, upon demyelination, lesions contain more immature OPCs and fewer mature oligodendrocytes despite sufficient migration to the lesion. This work highlighted Rxrg as a target to promote remyelination in vivo.47 Rxrs are known to be activated by 9-­cis-­retinoic acid (9cRA),48 which in turn has previously been shown to transcriptionally activate gene expression of MBP.49 Further work has found a secondary role for Rxr activation in OPC maturation. Myelin debris created during demyelination inhibits OPC differentiation, thus impairing remyelination.50–52 The clearance of myelin debris declines with advancing age53,54 and has been linked to decreased remyelination and decreased expression of a different isoform of Rxr, Rxra in macrophages.55 Activation of Rxra with 9cRA enhances myelin phagocytosis in aged macrophages. Similarly, Rxr antagonism reduced phagocytic activity in young macrophages. MS patient-­derived monocytes display similar levels of myelin phagocytic activity to older healthy controls, regardless of patient age,55,56 suggesting an impairment in myelin debris clearance by macrophages in MS. However, following in vitro treatment with the Rxr agonist bexarotene, improved myelin phagocytosis was observed in these cells.55 Thus, agonists of these Rxr receptors may increase both OPC maturation into mature myelin-­forming oligodendrocytes and clearance of myelin debris by macrophages, both of which may be beneficial. Collectively, this work highlighted Rxr as a potential pharmaceutical target for clinical trials in humans. Bexarotene is an Rxr agonist currently used in the treatment of cancer. Bexarotene is therefore an appealing drug for assessing the effects of Rxr agonists on remyelination in MS as it is already approved by the US Food and Drug Administration (FDA)57 and easy to administer as it is taken orally. However, as Rxr forms heterodimers with other receptors,

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such as the thyroid receptor, and has been shown to regulate cholesterol absorption60 and lipid metabolism,61 side effects of stimulating these pathways are likely. Indeed, clinical trials investigating the use of bexarotene in Alzheimer's disease reported increases in triglyceride and cholesterol in the majority of patients and therefore treatment is not currently recommended outwith a carefully monitored clinical setting due to the increased risk of vascular disease.62 Despite these caveats, bexarotene is currently in clinical trial to enhance remyelination in MS patients (EudraCT number: 2014-­003145-­99). This trial involves careful monitoring of its effects on LDL, cholesterol and thyroid function, making it unlikely to be widely used as a pro-­remyelination drug unless its effect is profound. It may, however, pave the way for future pharmacological formulations of Rxr agonists to make it more specific with fewer potential side effects.

9.3.2.2 Anti-­LINGO-­1 Although the bexarotene clinical trial focuses on activating a pathway to promote OPC differentiation, other trials focus on inhibiting negative signals thought to prevent OPC differentiation. Leucine rich-­repeat (LRR) and immunoglobulin (Ig) domain–containing Nogo receptor–interacting protein (LINGO-­1) is a transmembrane protein that negatively regulates oligodendrocyte maturation.63,64 LINGO-­1 can be inhibited by induced expression of a truncated form of the cytoplasmic signalling domain. Oligodendrocyte cultures transduced with a lentivirus containing the truncated inactive and competitive form of LINGO-­1 induced cells with branched, intricate processes expressing greater levels of MBP. When these cells were co-­cultured with dorsal root ganglion neurons, increased axon myelination was observed compared with controls. LINGO-­1 pathway antagonism also promoted the maturation of OPCs by increasing O4 expression. When LINGO-­1 is knocked-­out in vivo, early postnatal LINGO-­1 KO mice had more myelinated axons in their spinal cords compared to wild-­t ype littermates, suggesting it is also important in myelin development.64 LINGO-­1 antagonism regulates OPC differentiation through signalling pathways that control cell shape and morphology. RhoA-­GTPases are known to regulate cell morphology and actin polymerisation. Inhibition of RhoA-­GTPase promotes oligodendrocyte differentiation, resulting in an increase in extended processes, whereas activation results in stunted outgrowth,65,66 suggesting that down-­regulation of Rho is required for the specialisation of the oligodendrocyte plasma membrane into myelin sheaths.67 Decreasing LINGO-­1 in oligodendrocytes in vitro led to down-­regulation of RhoA-­GTP protein expression, suggesting that LINGO-­1 inhibits oligodendrocyte maturation through the activation of RhoA-­GTP.64 LINGO-­1 has been further shown to regulate OPC differentiation by controlling cell morphology via actin remodelling. Cytoplasmic gelsolin (cGSN) is a downstream inhibitory target of LINGO-­1. When LINGO-­1 is down-­regulated, cGSN activity increases,

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resulting in OPC differentiation through the manipulation of actin dynamics.68 These promising results highlight LINGO-­1 as a target for enhancing remyelination. In EAE models of demyelination, administering an anti-­ LINGO-­1 antibody was found to have a prophylactic effect, with smaller demyelinated lesions and with rats achieving a better clinical score than isotype and control groups. However, as well as a decrease in demyelinated lesion size, a greater number of remyelinated axons were also observed in the treated group.69 This suggests that anti-­LINGO-­1 treatment can prevent demyelination and facilitate remyelination. Due to the positive results of this study, the anti-­LINGO-­1 drug opicinumab (BIIB033), a monoclonal antibody inhibiting LINGO-­1, was developed with the aim of aiding remyelination in MS. Phase I clinical trials administered opicinumab via intravenous infusion or subcutaneous injection to healthy volunteers or patients with RRMS or SPMS. No serious adverse events were reported with the treatment, and so opicinumab was approved to continue to a Phase II clinical trials.70 Two Phase II clinical trials were set up by Biogen, RENEW (clinical trial number NCT01721161) and SYNERGY (clinical trial number NCT01864148). RENEW administered opicinumab following acute unilateral optic neuritis to assess drug efficiency, safety and tolerability. Patients were recruited with optic neuritis only, as a first sign, without evidence of previous demyelinating events, and therefore without a diagnosis of MS, i.e. a clinically isolated syndrome. Participants were given six doses of opicinumab or placebo intravenously once every 4 weeks and followed up to week 32. The effect of opicinumab on remyelination of the optic nerve was assessed by measuring conduction velocity latency compared to the unaffected eye using full-­field visual evoked potentials. Similar incidents of adverse events were recorded in both groups with no clinical significant neurological changes observed. Unfortunately, the visual evoked potential results suggested that opicinumab did not have a noticeable pro-­remyelination effect. Concerns were also raised about adverse effects from prolonged exposure of such a high dose (100 mg kg−1) of opicinumab71 and whether the antibody crosses the blood–brain-­ barrier (BBB) at sufficient concentrations to affect OPCs. A second trial, SYNERGY, recruited patients with RRMS and SPMS. Subjects were given 3, 10, 30 or 100 mg kg−1 of intravenous opicinumab every 4 weeks for 72 weeks. This was supplemented with weekly injections of disease-­modifying Avonex (interferon beta 1) for 84 weeks. In line with previous findings, the adverse and serious adverse events reported were similar between placebo and treatment groups, concluding that the drug was satisfactorily tolerable.72 The key aim of the trial, however, was to assess the effect of treatment on improving and preventing patient disability. Endpoint testing examined expanded disability status scale (EDSS), the 25-­foot walk, the 9-­hole peg test and the 3 second paced auditory serial addition test. Although no improvements between placebo groups were seen with the lowest (3 mg kg−1) and highest (100 mg kg−1) dose of opicinumab, improvements were seen with the middle doses of 10 and 30 mg kg−1. Participants on these doses showed improvements of 65.6%

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and 68.8%, respectively, compared with placebo, which saw 51.6% improvement.73 These results may indicate a clinical effect; therefore Biogen plans to continue work with opicinumab in the medium dose range. Biogen are currently running a Phase II clinical trial in RRMS patients combining a middle dose of opicinumab with a disease-­modifying therapy (clinical trial number NCT03222973) with the hope that this will improve patient outcomes. The results are expected by the end of 2020. As the first molecule to be discovered to inhibit OPC differentiation, it is laudable that it has now reached clinical trials in humans.

9.4 Identifying and Screening Future Targets As the preclinical research into Rxr and Lingo-­1 has demonstrated, the identification of targets to promote remyelination can be a lengthy, laborious process. Due to the length of time required to take a treatment from bench to bedside, researchers are also focusing on trying to reduce preclinical research timescales, by developing better models to assess remyelination and to perform high throughput drug screens. In recent years, the zebrafish has been adopted as a model for studying various developmental and disease processes. The zebrafish embryo is an ideal model to study myelin development due to the transparent nature of its body and the development of transgenic zebrafish with fluorescently tagged myelin proteins and OPCs.74–78 This has allowed the development of high-­ throughput drug screens for drugs increasing myelin and myelin-­forming cells in embryos in the early days post-­fertilisation, a process which has now been fully automated.79 The compounds found to increase oligodendroglia numbers in early development can therefore be quickly identified before more laborious tests on remyelination in adult models are carried out, accelerating the drug discovery process. Zebrafish models of demyelination either early or in adulthood have also been developed. An adult EAE model has been described80 as well as cell abolition methods using lasers,77 drugs81 or LPC82 to induce demyelination. These have less advantages over rodent models, as live imaging through the transparent normal fish can only occur in embryos, which remyelinate extremely efficiently. As no animal except humans develops MS, differences between human disease and any animal model of the disease may mean that compounds successfully screened in models have no effect on human oligodendroglia. The case can therefore be argued for high-­throughput screening on human cells in vitro. Primary cultures of adult human oligodendroglia can sometimes be obtained from neurosurgical biopsies or resections,83 but this is not a predictable source and raises questions as to whether these cells are truly normal as they come from pathological brain. Instead, several methods have been published to generate oligodendroglia from human embryonic and induced pluripotent stem cells. These cells express markers of the oligodendroglia lineage, develop electrophysical properties suggestive of appropriate oligodendroglial function and myelinate hypomyelinated mouse brain

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84–86

if injected in vivo. However, due to the slow timescale of differentiation, variability of differentiation efficiency between cell lines and the inability of most cultures to generate myelin sheaths in vitro, library screening on human oligodendroglia is still in development. A recent study has, however, demonstrated the effect of small molecules on human oligodendrocytes in human spheroids.87

9.4.1 High-­throughput Drug Screening in Rodent Cultures High-­throughput screens in rodent-­derived oligodendroglia, however, have highlighted several targets that promote remyelination. These studies screened libraries of FDA-­approved drugs and measured their effect on oligodendrocyte maturation and MBP expression.88–90 Screening compounds that have already undergone FDA approval accelerates drug progression into human clinical trials as preclinical safety and tolerability profiles are already known.

9.4.1.1 Benztropine Two screens applying this method highlighted benztropine as a target that enhanced MBP expression88,89 (Figure 9.2A). Further testing demonstrated that, in vitro, benztropine enhanced OPC differentiation and myelination in OPC neuronal co-­culture models. Furthermore, in a cuprizone model in vivo, a similar finding of enhanced remyelination and OPC differentiation was documented. The authors further identified that benztropine promotes OPC differentiation through M1/M3 muscarinic receptor antagonism. Other M1/ M3 antagonists showed a similar response in OPCs; however, none were as potent as benztropine. Surprisingly, in vivo, administration of benztropine in an EAE model decreased clinical severity and virtually eliminated relapses,89 suggesting that it may have an anti-­inflammatory action as well. Due to the cross-­reactivity of anti-­muscarinic drugs on several receptors, further preclinical testing is required, and therefore clinical trials are not yet underway. M1/M3 receptors are distributed in various tissues throughout the body, and so the aim is to develop a more specific compound that will result in fewer side effects. This study does, however, highlight a new target pathway for future development.

9.4.1.2 Clobetasol and Miconazole Similar screening techniques have identified targets that have no previously known downstream signalling pathway in enhancing OPC differentiation. The topical medication miconazole (Figure 9.2B), an antifungal that inhibits cytochrome P450, and clobetasol (Figure 9.2C), a corticosteroid, were both found to increase MBP expression in vitro and enhance remyelination in an LPC-­induced demyelination model in rodents.88 Both drugs also improved

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Figure 9.2 The chemical structures of compounds found to enhance remyelination:

benztropine, an M1/M3 muscarinic receptor antagonist; antifungal miconazole; clobetasol, a corticosteroid; antimuscarinic, antihistamine clemastine, currently in Phase II clinical trials; GSK247246, an H3R antagonist; GSK239512, an H3R antagonist currently in Phase II clinical trials; thyroid hormone triiodothyronine (T3), the synthetic form of which, liothyronine, is currently in Phase I clinical trials; and thyroid hormone T4.

functional outcomes in an EAE model, with clobetasol, as a corticosteroid, also acting as an immunosuppressant.88 RNAseq and phosphoproteomic analyses suggest that clobetasol mediates OPC differentiation through the glucocorticoid receptor signalling pathway, whereas miconazole operates through a MEK-­dependent mechanism.88 Although this screen generated two new targets for remyelination therapies, these drugs are only approved for topical use in humans so far. Drug dose, delivery and specificity will have to be optimised for safety before human trials can begin. However, the ability of both drugs to cross the BBB mitigates many of the issues with drug delivery.

9.4.1.3 Antihistamines – Clemastine, GSK247246 and GSK239512 Although these in vitro screens have provided several new targets for remyelination therapies, these models use cells in a two-­dimensional space whereas in vivo oligodendrocytes exist in a three-­dimensional environment.

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Therefore, many compounds that are screened in vitro may fail to generate reproducible results in vivo. The ability to generate MBP is also not the only factor required for remyelination. Oligodendrocytes must be able to extend processes and wrap around axons, generating multiple compact myelin sheaths. The use of nanofibres and micropillar assays address this issue by providing a solid framework around which oligodendrocytes can extend and wrap myelin. These can be manufactured in 96 well plates, allowing for high-­throughput screening of compound libraries.91–93 Such a screen also highlighted a group of antimuscarinic compounds, including benztropine, as promoting myelin-­wrapping. This study, however, found that incubation with the antimuscarinic clemastine (Figure 9.2D), promoted MBP expression, wrapping and in vitro myelination to a greater extent than benztropine.91 As clemastine is FDA-­approved as an antihistamine and can be administered orally, clinical trials in MS patients are currently underway to examine its potential as a remyelinating agent. After passing safety and tolerability testing in Phase I trials, the Phase II ReBUILD trial started (clinical trial number: NCT02040298). The study recruited 50 patients with RRMS with chronic demyelinating optic neuropathy. Subjects were randomly assigned to receive 5.36 mg experimental drug or placebo twice daily for 90 days. Their treatment was then switched for 60 days to ensure both groups had received clemastine. Vision evoked potentials were measured at 1, 3 and 5 months into the trial. Clemastine was found to reduce the latency delay by 1.7 ms/eye, a relatively subtle amount, nevertheless suggesting that remyelination of the optic nerve had occurred and improved the nerve impulse conduction velocity. However, no clinical effect was seen, and the vision of patients did not improve over this timescale. The main side effect reported was fatigue, as may be expected with a sedating anti-­histamine, which is problematic as many MS patients already experience fatigue. However, no serious adverse effects were reported.94 This is certainly promising, but questions remain as to whether the correct subgroup of MS patients were chosen, whether the outcome measure is relevant and whether this length of time to measure the outcome is sufficient to allow remyelination and a clinical effect. Further clinical trials of clemastine are expected, and development of similar drugs with more targeted histamine receptor inhibitors, with fewer side effects. The encouraging results of this work have highlighted histamine receptor antagonism as a target for remyelination therapies. H3R is one of the histamine receptors and is a Gi/o protein-­coupled receptor that is highly expressed in neurons with moderate expression in OPCs. It has also been shown to be up-­regulated in oligodendroglia in MS lesions.95 In vitro, antagonism of H3R with commercial inverse agonists promotes OPC differentiation, as measured by increased MBP production. The most potent of these is compound GSK247246 (Figure 9.2E). In a cuprizone model, when GSK247246 was administered following the removal of cuprizone from the diet, enhanced remyelination was observed in the corpus callosum. The extent of remyelination observed was dose dependent and

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higher doses were even found to be neuroprotective, with a decreased in axonal loss observed in the lesions of this treatment group.95 Although this could be due to remyelination of axons, providing neuroprotection, it could also be a direct effect of the antagonists on neurons, in which H3R is highly expressed. GSK239512 (Figure 9.2F), a similar H3 antagonist, has previously been tested for safety and tolerability in Phase I trials of Alzheimer's patients where an oral dose of up to 80 µg was well tolerated.96 No adverse physiological symptoms were reported,96 and GSK239512 was found to cross the BBB and bind H3R with high affinity.97 Clinical trials of the compound GSK239512 are in progress to study remyelination in MS patients (clinical trial number: NCT01772199). A Phase II trial recruited RRMS patients who were monitored for 48 weeks on an increasing titration then steady administration of GSK239512 daily. Remyelination was determined by MRI measures. As previously documented, GSK239512 was well tolerated and a mild improvement in MR measures of remyelination was observed in experimental groups compared to placebo.98 However, despite this, no clinical improvements were reported.

9.4.1.4 Thyroid Hormones – T3 and T4 Micropillar assays have also highlighted triiodothyronine (T3) (Figure 9.2G), a thyroid hormone, as a promoter of OPC differentiation, myelin formation and wrapping.91 However, T3 elicits a reduced response compared to antimuscarinic drugs.89 This was not a novel finding, as thyroid hormones have been previously shown to increase oligodendrocyte number and myelin production in vitro99 and are standardly used in vitro in media to promote OPC differentiation.100 When removed in vitro, OPC differentiation is impaired but oligodendrocyte survival is unaffected, and this is therefore not T3-­dependent.101 Administration of the thyroid hormone T4 (Figure 9.2H) has been shown to improve remyelination in an EAE model through increased MBP expression and myelin sheath thickness.102 T3 administration has been shown to improve remyelination in both acute103 and chronic cuprizone models.104 Following chronic demyelination, T3 was even found to promote the generation of new OPC populations.104 Due to these promising studies, a synthetic form of T3, liothyronine, is currently in Phase I clinical trials to assess tolerability and safety in subjects with MS. T3 has a variety of physiological effects and therefore may not be a desirable candidate as a remyelination therapy due to side effects. A Phase I trial (clinical trial number NCT02760056) administered various doses of liothyronine to small groups of participants and measured the effects on blood pressure, cardiac output and placement on the hyperthyroid symptom scale. A further Phase I trial (clinical trial number NCT02506751), administered an increasing dose of liothyronine over a 24 week period and reported the incidence of adverse events. The results of each of these trials are still pending publication. If liothyronine is well tolerated, it is likely to continue to Phase II trials.

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9.4.1.5 Cholesterol Biosynthesis Recent work suggests that many of these compounds, although seemingly very different in their mechanisms of action, promote OPC differentiation through inhibiting enzymes involved in cholesterol biosynthesis.87 Screening of small-­molecule libraries found that imidazole antifungals, including miconazole, inhibit the enzyme CYP51, which is essential for sterol biosynthesis. This inhibition promoted OPC differentiation quantified by MBP expression. Further biochemical analysis found that inhibition of enzymes CYP51, TM7SF2, or EBP in the sterol biosynthesis pathway resulted in the differentiation of OPCs and the accumulation of 8,9-­unsaturated sterols. Inhibition of enzymes at other stages in the pathway did not enhance MBP expression.87 Compounds such as benztropine89 and clemastine91 that promote MBP expression (as already described in this chapter) may actually enhance OPC differentiation through EBP inhibition rather than through muscarinic or histamine receptors. This study further demonstrated the effect of 8,9-­unsaturated sterol accumulation on remyelination by documenting remyelination following an LPC lesion. Mice treated with small-­ molecule inhibitors of TM7SF2 and EBP were found to have increased levels of 8,9-­unsaturated sterols in their brains coupled with enhanced remyelination.87 The potential of translation of this work into humans was demonstrated by incubating human pluripotent stem cell-­derived cortical spheroids105 with miconazole. This was found to enhance the generation of oligodendrocytes, suggesting the pathway is conserved in humans.87 The variety of small molecules in this study all promoting 8,9-­unsaturated sterol accumulation and the conserved mechanism between humans and mice highlights this pathway as one to target in future trials to promote remyelination. Figure 9.2 illustrates the different chemical structures of the compounds discussed in this chapter. Although similarities in structure are observed within compound groups, the variety of compounds that can enhance remyelination demonstrate the diversity and breadth of targets currently under investigation.

9.5 Difficulties in Assessing Remyelination in Human Patients Although it is promising that remyelination therapies are entering clinical trials, one of the problems of trying to test pro-­remyelinating therapies is that is difficult to measure new myelin in the living organism. Even on tissue sections, it can be difficult to define, as replaced myelin (sometimes called ‘remyelin’) is very similar to the original. The constituents at the protein level appear the same, but as generally the remyelinated sheaths are thinner and shorter then myelin looks paler on stains such as luxol fast blue, leading to the name ‘shadow’ plaques in human post-­mortem tissue.106 However, it may be that the lipids present in ‘remyelin’ compared to myelin vary, as suggested

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by a pilot study using imaging mass spectroscopy, which may become a useful measure, as well as suggesting that the biology of these sheaths at the lipid level may be subtly different. The gold standard at detecting remyelinated axons in the preclinical model is electron microscopy within the site of the demyelinated lesion, showing thinner myelin sheaths for the axon size.39 Live imaging can be carried out in rodents through skull108 or spinal cord109 windows using genetic fluorescent reporters for myelin, detecting the return of myelin after demyelination; however, this is clearly not possible in humans. Ideally, for clinical trials in humans, we need a direct biomarker of remyelination. Magnetic resonance imaging is used with the magnetic transfer ratio (MTR), which has been shown to report remyelination reasonably well.110,111 This is commonly used as a readout in clinical trials94,98 (EudraCT number: 2014-­003145-­99). Neurofilament levels in either the cerebrospinal fluid (CSF) or the blood correlate with brain atrophy,112 but this is a rather indirect measure of remyelination success, with higher levels of neurofilament (and increased atrophy) suggesting reduced remyelination but equally could represent reduced demyelination. As remyelination improves nerve conduction velocity, then measuring this may indicate improved remyelination, although again not directly, and the robustness of repeated measures of this over time is in question. Generally, visual evoked potentials have been used, as they are the easiest to measure in humans, but this relies on there being a defect in the visual pathway, and it is an assumption that any improvement is definitely due to remyelination. However, this was used in the clemastine trial113 with a slight improvement in the treated group, although no clinical visual improvement was reported. It is therefore unknown what extent of remyelination is required to generate clinical improvement. Trials described in this chapter have varied in duration and clinical assessment time points. Trials may therefore fall short of achieving functional improvements. A measure that quantifies myelin levels more directly is positron emission tomography (PET) using a ligand for myelin, e.g. Pittsburgh compound B ([11C]PiB) superimposed on MR scans.114 Serial PET/MR scans of MS patients has shown that some demyelinated lesions increase their PET ligand signal for myelin over time, and others do not; and interestingly, it is this gain of signal indicating remyelination which correlates with reduced disability, rather than the amount of demyelination,115 adding strength to the argument that pro-­remyelination therapies will reduce disability. As yet, this PET/MR method has not been used in clinical trials of pro-­remyelination therapies, but hopefully this will change, as this seems the most promising method yet developed to measure the change in myelin over time.

9.6 Future Targets of Preclinical Research The majority of research in promoting remyelination targets OPC differentiation; the maturation of oligodendrocytes, the generation of the MBP and myelin sheaths. However, OPCs are not the only source of remyelinating

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cells. Following demyelination, cell populations in the subventricular zone (SVZ) have been shown to generate OPCs and oligodendrocytes in rodent models116 and in post-­mortem MS patient tissue.117 Recent work has focused on endogenous adult neural stem cells (NSCs) that reside in the SVZ and are known sources of remyelinating cells.118 Fate mapping of this population has demonstrated that, in a rodent cuprizone model, oligodendroglia of NSC origin are recruited to areas of demyelination.119 Oligodendrocytes generated from NSCs even remyelinated axons following cuprizone withdrawal. It was found that genetic and pharmacological inhibition of Gli1, a transcription factor activated by sonic hedgehog signalling, promoted recruitment and differentiation of NSCs following demyelination without affecting endogenous OPC differentiation. Although Gli1 inhibition did not have a prophylactic effect in an EAE model, suggesting it does not target the immune response, mice receiving a therapeutic treatment had improved clinical outcomes and reduced disease pathology.119 Future targeting of NSCs is therefore of therapeutic interest as dual targeting of endogenous OPCs and SVZ population sources is likely to further enhance repair. Remyelination from NSC sources may be of particular therapeutic benefit as myelin generated from these sources is suggested to be thicker than the myelin traditionally seen in remyelination, and therefore may better improve conduction velocity and metabolic support.118 Previous work has highlighted that T3 administration enhances emergence of remyelinating oligodendrocytes from the pool of proliferating cells residing in the SVZ103 and increases OPC numbers from here.104 Thyroid hormones may therefore enhance remyelination through dual targeting of endogenous OPCs and SVZ cell populations. The results of liothyronine clinical trials will demonstrate whether this further enhances remyelination. Despite the promising preclinical research described in this chapter, the translational abilities of this work may be limited, as it is unknown how cells in a chronic disease environment will respond. Recent work in other neurodegenerative diseases, such as Parkinson's disease120 and Alzheimer's disease,121 have highlighted the importance of targeting cellular senescence,122,123 a process characterised by arrest of the cell cycle and a secretary phenotype.124 Due to the differentiation block of OPCs observed in MS lesions, it is not absurd to hypothesise a role of senescence in MS, particularly in more progressive forms of the disease where remyelination is impaired. Furthermore, oligodendrocyte biology is proving to be more complex than previously thought. Recent work has highlighted the existence of heterogenous populations of oligodendrocytes in mice.125 Whether this is true in human tissue is still unknown. It may be possible that specific subgroups of oligodendrocytes are required for remyelination. MS may affect the regeneration of these subtypes, resulting in poor remyelination, and this may be different in different people. Therapies may therefore need to target the generation of specific subtypes of oligodendrocytes to promote remyelination rather than simply increasing the numbers of myelin-­ forming oligodendrocytes.

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As the work highlighted in this chapter demonstrates, a vast collection of preclinical research has focused on promoting remyelination, and the first remyelination therapies are currently in the early stages of clinical trials. There are also many promising targets that have been highlighted recently that have clinical potential. Now, we must overcome the limitations of how to reliably measure remyelination in vivo in humans, determine how long it takes to occur and have a clinical effect, but again we are making progress. These scientific and clinical advances provide evidence and hope for the future that a remyelinating therapy will one day be available.

References

1. A. Compston and A. Coles, Lancet, 2008, 372, 1502. 2. A. F. Huxley and R. Stämpfli, J. Physiol., 1949, 108, 315. 3. R. P. Bunge, Physiol. Rev., 1968, 48, 197. 4. U. Fünfschilling, L. M. Supplie, D. Mahad, S. Boretius, A. S. Saab and J. Edgar, et al., Nature, 2012, 485, 517. 5. Y. Lee, B. M. Morrison, Y. Li, S. Lengacher, M. H. Farah and P. N. Hoffman, et al., Nature, 2012, 487, 443. 6. P. Patrikios, C. Stadelmann, A. Kutzelnigg, H. Rauschka, M. Schmidbauer and H. Laursen, et al., Brain, 2006, 129(Pt 12), 3165. 7. R. Patani, M. Balaratnam, A. Vora and R. Reynolds, Neuropathol. Appl. Neurobiol., 2007, 33, 277. 8. J. W. Prineas and F. Connell, Ann. Neurol., 1979, 5, 22. 9. L. F. Barcellos, S. Sawcer, P. P. Ramsay, S. E. Baranzini, G. Thomson and F. Briggs, et al., Hum. Mol. Genet., 2006, 15, 2813. 10. M. F. George, F. B. S. Briggs, X. Shao, M. A. Gianfrancesco, I. Kockum and H. F. Harbo, et al., Neurol.: Genet., 2016, 2, e87. 11. International Multiple Sclerosis Genetics Consortium (IMSGC), A. H. Beecham, N. A. Patsopoulos, D. K. Xifara, M. F. Davis and A. Kemppinen, et al., Nat. Genet., 2013, 45, 1353. 12. S. Brocke, K. Gijbels and L. Steinman, Experimental autoimmune encephalomyelitis in the mouse, in Autoimmune Disease Models, 1994, pp. 1–14. 13. C. S. Constantinescu, N. Farooqi, K. O'Brien and B. Gran, Br. J. Pharmacol., 2011, 164, 1079. 14. F. D. Lublin, P. H. Maurer, R. G. Berry and D. Tippett, J. Immunol., 1981, 126, 819. 15. H. Babbe, A. Roers, A. Waisman, H. Lassmann, N. Goebels and R. Hohlfeld, et al., J. Exp. Med., 2000, 192, 393. 16. J. Booss, M. M. Esiri, W. W. Tourtellotte and D. Y. Mason, J. Neurol. Sci., 1983, 62, 219. 17. R. Magliozzi, O. Howell, A. Vora, B. Serafini, R. Nicholas and M. Puopolo, et al., Brain, 2007, 130(Pt 4), 1089. 18. B. Serafini, B. Rosicarelli, R. Magliozzi, E. Stigliano and F. Aloisi, Brain Pathol., 2004, 14, 164.

Emerging Drugs and Targets for Remyelination in Multiple Sclerosis

217

19. S. C. Ridge, A. E. Sloboda, R. A. McReynolds, S. Levine, A. L. Oronsky and S. S. Kerwar, Clin. Immunol. Immunopathol., 1985, 35, 35. 20. T. A. Yednock, C. Cannon, L. C. Fritz, F. Sanchez-­Madrid, L. Steinman and N. Karin, Nature, 1992, 356, 63. 21. D. Teitelbaum, A. Meshorer, T. Hirshfeld, R. Arnon and M. Sela, Eur. J. Immunol., 1971, 1, 242. 22. D. Teitelbaum, C. Webb, M. Bree, A. Meshorer, R. Arnon and M. Sela, Clin. Immunol. Immunopathol., 1974, 3, 256. 23. A. B. Keith, R. Arnon, D. Teitelbaum, E. A. Caspary and H. M. Wisniewski, J. Neurol. Sci., 1979, 42, 267. 24. H. Kataoka, K. Sugahara, K. Shimano, K. Teshima, M. Koyama and A. Fukunari, et al., Cell. Mol. Immunol., 2005, 2, 439. 25. C. A. Foster, D. Mechtcheriakova, M. K. Storch, B. Balatoni, L. M. Howard and F. Bornancin, et al., Brain Pathol., 2009, 19, 254. 26. S. Schilling, S. Goelz, R. Linker, F. Luehder and R. Gold, Clin. Exp. Immunol., 2006, 145, 101. 27. A. L. A. Nelson, A. J. Bieber and M. Rodriguez, Int. MS J., 2004, 11, 95. 28. S. M. Hall, J. Cell Sci., 1972, 10, 535. 29. F. J. Sim, C. Zhao, J. Penderis and R. J. M. Franklin, J. Neurosci., 2002, 22, 2451. 30. W. W. Carlton, Toxicol. Appl. Pharmacol., 1966, 8, 512. 31. W. F. Blakemore, J. Neurocytol., 1972, 1, 413. 32. W. F. Blakemore, J. Neurol. Sci., 1973, 20, 73. 33. S. K. Ludwin, Ann. Neurol., 1994, 36(Suppl), S143–S145. 34. N. Manrique-­Hoyos, T. Jürgens, M. Grønborg, M. Kreutzfeldt, M. Schedensack and T. Kuhlmann, et al., Ann. Neurol., 2012, 71, 227. 35. K. Suzuki and Y. Kikkawa, Am. J. Pathol., 1969, 54, 307. 36. K. Suzuki, Science, 1969, 163, 81. 37. M. M. Hiremath, Y. Saito, G. W. Knapp, J. P. Ting, K. Suzuki and G. K. Matsushima, J. Neuroimmunol., 1998, 92, 38. 38. C. Lucchinetti, W. Brück, J. Parisi, B. Scheithauer, M. Rodriguez and H. Lassmann, Brain, 1999, 122(Pt 12), 2279. 39. A. Boyd, H. Zhang and A. Williams, Acta Neuropathol., 2013, 125, 841. 40. A. Wegener, C. Deboux, C. Bachelin, M. Frah, C. Kerninon and D. Seilhean, et al., Brain, 2015, 138(Pt 1), 120. 41. A. Williams, G. Piaton, M.-­S. Aigrot, A. Belhadi, M. Théaudin and F. Petermann, et al., Brain, 2007, 130(Pt 10), 2554. 42. R. H. Woodruff, M. Fruttiger, W. D. Richardson and R. J. M. Franklin, Mol. Cell. Neurosci., 2004, 25, 252. 43. M. Absinta, P. Sati, M. Schindler, E. C. Leibovitch, J. Ohayon and T. Wu, et al., J. Clin. Invest., 2016, 126, 2597. 44. T. Kuhlmann, V. Miron, Q. Cui, C. Wegner, J. Antel and W. Brück, Brain, 2008, 131(Pt 7), 1749. 45. M. Zawadzka, L. E. Rivers, S. P. J. Fancy, C. Zhao, R. Tripathi and F. Jamen, et al., Cell Stem Cell, 2010, 6, 578.

218

Chapter 9

46. M. P. Targett, J. Sussman, N. Scolding, M. T. O'Leary, D. A. Compston and W. F. Blakemore, Neuropathol. Appl. Neurobiol., 1996, 22, 199. 47. J. K. Huang, A. A. Jarjour, B. Nait Oumesmar, C. Kerninon, A. Williams and W. Krezel, et al., Nat. Neurosci., 2011, 14, 45. 48. R. A. Heyman, D. J. Mangelsdorf, J. A. Dyck, R. B. Stein, G. Eichele and R. M. Evans, et al., Cell, 1992, 68, 397. 49. P. M. Pombo, D. Barettino, N. Ibarrola, S. Vega and A. Rodríguez-­Peña, Mol. Brain Res., 1999, 64, 92. 50. S. Robinson and R. H. Miller, Dev. Biol., 1999, 216, 359. 51. M. R. Kotter, C. Zhao, N. van Rooijen and R. J. M. Franklin, Neurobiol. Dis., 2005, 18, 166. 52. M. R. Kotter, W.-­W. Li, C. Zhao and R. J. M. Franklin, J. Neurosci., 2006, 26, 328. 53. S. A. Shields, J. M. Gilson, W. F. Blakemore and R. J. Franklin, Glia, 1999, 28, 77. 54. T. Wyss-­Coray, Nature, 2016, 539, 180. 55. M. S. Natrajan, A. G. de la Fuente, A. H. Crawford, E. Linehan, V. Nuñez and K. R. Johnson, et al., Brain, 2015, 138(Pt 12), 3581. 56. M. S. Natrajan, M. Komori, P. Kosa, K. R. Johnson, T. Wu and R. J. M. Franklin, et al., Ann. Clin. Transl. Neurol., 2015, 2, 1071. 57. J. E. Henney, JAMA, 2000, 284, 1640. 58. X. P. Lu, N. L. Eberhardt and M. Pfahl, Mol. Cell. Biol., 1993, 13, 6509. 59. S. Mader, J. Y. Chen, Z. Chen, J. White, P. Chambon and H. Gronemeyer, EMBO J., 1993, 12, 5029. 60. J. J. Repa, S. D. Turley, J. A. Lobaccaro, J. Medina, L. Li and K. Lustig, et al., Science, 2000, 289, 1524. 61. P. Tontonoz, L. Nagy, J. G. Alvarez, V. A. Thomazy and R. M. Evans, Cell, 1998, 93, 241. 62. J. L. Cummings, K. Zhong, J. W. Kinney, C. Heaney, J. Moll-­Tudla and A. Joshi, et al., Alzheimer's Res. Ther., 2016, 8, 4. 63. S. Mi, X. Lee, Z. Shao, G. Thill, B. Ji and J. Relton, et al., Nat. Neurosci., 2004, 7, 221. 64. S. Mi, R. H. Miller, X. Lee, M. L. Scott, S. Shulag-­Morskaya and Z. Shao, et al., Nat. Neurosci., 2005, 8, 745. 65. X. Liang, N. A. Draghi and M. D. Resh, J. Neurosci., 2004, 24, 7140. 66. R. M. Wolf, J. J. Wilkes, M. V. Chao and M. D. Resh, J. Neurobiol., 2001, 49, 62. 67. A. Kippert, K. Trajkovic, L. Rajendran, J. Ries and M. Simons, J. Neurosci., 2007, 27, 3560. 68. Z. Shao, X. Lee, G. Huang, G. Sheng, C. E. Henderson and D. Louvard, et al., J. Neurosci., 2017, 37, 3127. 69. S. Mi, B. Hu, K. Hahm, Y. Luo, E. S. Kam Hui and Q. Yuan, et al., Nat. Med., 2007, 13, 1228. 70. J. Q. Tran, J. Rana, F. Barkhof, I. Melamed, H. Gevorkyan and M. P. ­Wattjes, et al., Neurol. Neuroimmunol. Neuroinflammation, 2014, 1, e18.

Emerging Drugs and Targets for Remyelination in Multiple Sclerosis

219

71. D. Cadavid, L. Balcer, S. Galetta, O. Aktas, T. Ziemssen and L. Vanopdenbosch, et al., Lancet Neurol., 2017, 16, 189–199. 72. P. McCroskery, K. Selmaj, O. Fernandez and L. M. E. Grimaldi, Neurology, 2017, 88, P5.369. 73. M. Mellion, K. R. Edwards, R. Hupperts and J. Drulović, Neurology, 2017, 88, S33.004. 74. M. Yoshida and W. B. Macklin, J. Neurosci. Res., 2005, 81, 1. 75. C. E. Buckley, P. Goldsmith and R. J. M. Franklin, Dis. Models Mech., 2008, 1, 221. 76. S.-­H. Jung, S. Kim, A.-­Y. Chung, H.-­T. Kim, J.-­H. So and J. Ryu, et al., Dev. Dyn., 2010, 239, 592. 77. B. B. Kirby, N. Takada, A. J. Latimer, J. Shin, T. J. Carney and R. N. Kelsh, et al., Nat. Neurosci., 2006, 9, 1506. 78. J. Shin, H.-­C. Park, J. M. Topczewska, D. J. Mawdsley and B. Appel, Methods Cell Sci., 2003, 25, 7. 79. J. J. Early, K. L. Cole, J. M. Williamson, M. Swire, H. Kamadurai and M. Muskavitch, et al., eLife, 2018, 7, e35136. 80. P. Kulkarni, S. Yellanki, R. Medishetti, D. Sriram, U. Saxena and P. Yogeeswari, Mult. Scler. Relat. Disord., 2017, 11, 32. 81. S. Curado, R. M. Anderson, B. Jungblut, J. Mumm, E. Schroeter and D. Y. R. Stainier, Dev. Dyn., 2007, 236, 1025. 82. E. J. Münzel, C. G. Becker, T. Becker and A. Williams, Acta Neuropathol. Commun., 2014, 2, 77. 83. V. W. Yong and J. P. Antel, Culture of Glial Cells from Human Brain Biopsies, in Protocols for Neural Cell Culture, 1997, Humana Press, Totowa, NJ, pp. 157–172. 84. M. R. Livesey, D. Magnani, E. M. Cleary, N. A. Vasistha, O. T. James and B. T. Selvaraj, et al., Stem Cells, 2016, 34, 1040. 85. S. Wang, J. Bates, X. Li, S. Schanz, D. Chandler-­Militello and C. Levine, et al., Cell Stem Cell, 2013, 12, 252. 86. B.-­Y. Hu, Z.-­W. Du and S.-­C. Zhang, Nat. Protoc., 2009, 4, 1614. 87. Z. Hubler, D. Allimuthu, I. Bederman, M. S. Elitt, M. Madhavan and K. C. Allan, et al., Nature, 2018, 560, 372. 88. F. J. Najm, M. Madhavan, A. Zaremba, E. Shick, R. T. Karl and D. C. Factor, et al., Nature, 2015, 522, 216. 89. V. A. Deshmukh, V. Tardif, C. A. Lyssiotis, C. C. Green, B. Kerman and H. J. Kim, et al., Nature, 2013, 502, 327. 90. C. Eleuteri, S. Olla, C. Veroni, R. Umeton, R. Mechelli and S. Romano, et al., Sci. Rep., 2017, 7, 45780. 91. F. Mei, S. P. J. Fancy, Y.-­A. A. Shen, J. Niu, C. Zhao and B. Presley, et al., Nat. Med., 2014, 20, 954. 92. S. Lee, M. K. Leach, S. A. Redmond, S. Y. C. Chong, S. H. Mellon and S. J. Tuck, et al., Nat. Methods, 2012, 9, 917. 93. S. Lee, S. Y. C. Chong, S. J. Tuck, J. M. Corey and J. R. Chan, Nat. Protoc., 2013, 8, 771.

220

Chapter 9

94. A. J. Green, J. M. Gelfand, B. A. Cree, C. Bevan, W. J. Boscardin and F. Mei, et al., Lancet, 2017, 390, 2481. 95. Y. Chen, W. Zhen, T. Guo, Y. Zhao, A. Liu and J. P. Rubio, et al., PLoS One, 2017, 12, e0189380. 96. P. J. Nathan, R. Boardley, N. Scott, A. Berges, P. Maruff and T. Sivananthan, et al., Curr. Alzheimer Res., 2013, 10, 240. 97. S. Ashworth, A. Berges, E. A. Rabiner, A. A. Wilson, R. A. Comley and R. Y. K. Lai, et al., Br. J. Pharmacol., 2014, 171, 1241. 98. C. J. Schwartzbach, R. A. Grove, R. Brown and D. Tompson, J. Neurol., 2017, 264, 304. 99. G. Almazan, P. Honegger and J. M. Matthieu, Dev. Neurosci., 1985, 7, 45. 100. K. McCARTHY and J. De Vellis, J. Cell Biol., 1980, 85, 890. 101. B. A. Barres, M. A. Lazar and M. C. Raff, Development, 1994, 120, 1097. 102. M. Fernandez, A. Giuliani, S. Pirondi, G. D'Intino, L. Giardino and L. Aloe, et al., Proc. Natl. Acad. Sci. U. S. A., 2004, 101, 16363. 103. P. G. Franco, L. Silvestroff, E. F. Soto and J. M. Pasquini, Exp. Neurol., 2008, 212, 458. 104. L.-­A. Harsan, J. Steibel, A. Zaremba, A. Agin, R. Sapin and P. Poulet, et al., J. Neurosci., 2008, 28, 14189. 105. M. Madhavan, Z. S. Nevin, H. E. Shick, E. Garrison, C. Clarkson-­Paredes and M. Karl, et al., Nat. Methods, 2018, 15, 700. 106. H. Lassmann, Schriftenr Neurol, 1983, 25, 1. 107. M. S. Bergholt, A. Serio, J. S. McKenzie, A. Boyd, R. F. Soares and J. ­Tillner, et al., ACS Cent. Sci., 2018, 4, 39. 108. E. G. Hughes, J. L. Orthmann-­Murphy, A. J. Langseth and D. E. Bergles, Nat. Neurosci., 2018, 21, 696. 109. I. Nikić, D. Merkler, C. Sorbara, M. Brinkoetter, M. Kreutzfeldt and F. M. Bareyre, et al., Nat. Med., 2011, 17, 495. 110. K. Schmierer, F. Scaravilli, D. R. Altmann, G. J. Barker and D. H. Miller, Ann. Neurol., 2004, 56, 407. 111. F. Barkhof, W. Bruck, C. J. A. De Groot, E. Bergers, S. Hulshof and J. Geurts, et al., Arch. Neurol., 2003, 60, 1073. 112. M. Khalil, C. E. Teunissen, M. Otto and F. Piehl, Nat. Rev. Neurol., 2018, 14(10), 577–589. 113. A. J. Green, J. M. Gelfand, B. A. Cree and C. Bevan, Lancet, 2017, 390, 2481. 114. M. Veronese and B. Bodini, J. Cereb. Blood Flow Metab., 2015, 35, 1771. 115. B. Bodini, M. Veronese and D. García-­Lorenzo, Ann. Neurol., 2016, 79, 726. 116. B. Menn, J. M. Garcia-­Verdugo, C. Yaschine, O. Gonzalez-­Perez, D. Rowitch and A. Alvarez-­Buylla, J. Neurosci., 2006, 26, 7907. 117. B. Nait-­Oumesmar, N. Picard-­Riera, C. Kerninon, L. Decker, D. Seilhean and G. U. Höglinger, et al., Proc. Natl. Acad. Sci. U. S. A., 2007, 104, 4694. 118. Y. L. Xing, P. T. Röth, J. A. S. Stratton, B. H. A. Chuang, J. Danne and S. L. Ellis, et al., J. Neurosci., 2014, 34, 14128. 119. J. Samanta, E. M. Grund, H. M. Silva, J. J. Lafaille, G. Fishell and J. L. Salzer, Nature, 2015, 526, 448.

Emerging Drugs and Targets for Remyelination in Multiple Sclerosis

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120. S. J. Chinta, G. Woods, M. Demaria, A. Rane, Y. Zou and A. McQuade, et al., Cell Rep., 2018, 22, 930. 121. T. J. Bussian, A. Aziz, C. F. Meyer, B. L. Swenson, J. M. van Deursen and D. J. Baker, Nature, 2018, 562(7728), 578–582. 122. D. J. Baker and R. C. Petersen, J. Clin. Invest., 2018, 128, 1208. 123. B. G. Childs, M. Durik, D. J. Baker and J. M. van Deursen, Nat. Med., 2015, 21, 1424. 124. J.-­P. Coppé, C. K. Patil, F. Rodier, Y. Sun, D. P. Muñoz and J. Goldstein, et al., PLoS Biol., 2008, 6, 2853. 125. S. Marques, A. Zeisel, S. Codeluppi, D. van Bruggen, A. Mendanha ­Falcão and L. Xiao, et al., Science, 2016, 352, 132.

Chapter 10

Regulation of Oligodendrocyte Differentiation: New Targets for Drug Discovery in Remyelination Fernando de Castro*a and Fernando Josa-­Pradoa,b a

Grupo de Neurobiología del Desarrollo-­GNDe, Instituto Cajal, Spanish Research Council/CSIC-­Consejo Superior de Investigaciones Científicas, Madrid, Spain; bUniversidad Alfonso X El Sabio, Avenida de la Universidad, 1; 28691, Villanueva de la Cañada, Madrid, Spain *E-­mail: [email protected]

10.1  Introduction The only myelin-­forming cells in the central nervous system (CNS) are oligodendrocytes, originally identified as ‘glia with very few processes’ by brilliant Spanish neuroscientist Pío del Río-­Hortega, who also identified microglia.1–5 The first translation into English was published recently.6 These neural cells contact all the axons from CNS neurons and form myelin sheaths by rolling their cell membrane selectively around the axons with larger diameter.7 The cell membrane of oligodendrocytes has a high concentration of lipids as well as specific proteins (more than 80% of the protein content of CNS myelin are two structural proteins exclusive to oligodendrocytes: P0 and MBP). The way

  Drug Discovery Series No. 70 Emerging Drugs and Targets for Multiple Sclerosis Edited by Ana Martinez © The Royal Society of Chemistry 2019 Published by the Royal Society of Chemistry, www.rsc.org

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myelin sheaths are formed allows the insulation of large axons from the exposure of the extracellular environment and dangers, as well as the saltatory conduction of nerve impulses, which results in more efficient transmission and speeds of up to 50 times faster: essentially, myelin represents a major step forward phylogenetically when it was acquired about 425 million years ago by placoderms and cartilaginous fishes, basically because myelin allows organisms to economise space and energy, becoming more efficient.8–12 It is noteworthy that although thicker CNS axons are usually very long (they can reach meters in length), one single oligodendrocyte myelinates internodes (100–200 µm in length) from a variable number of different axons, depending on their emplacement, from just two to three in the spinal cord tracts to about 50 in the optic nerve.13–15 Biochemically and morphologically, CNS myelin seems identical all along the caudorostral axis, from the anterior telencephalic pole to the lower spinal cord, although we have recently demonstrated the dynamic change in the sulfatide components of myelin through life span.14,16 In the last decade, it has been demonstrated that myelin sheath is coupled with the wrapped axon, and different mechanisms present in oligodendrocytes facilitate macromolecules and different metabolites to reach the periaxonal space and eventually be internalized by axons.17 Thus, one myelinated axon and its myelin sheath form a kind of physiological/metabolic unit; not in vain, from the results obtained in different animal models of pathology it has been suggested that the disorganization of myelin giving rise to axon-­glia metabolic uncoupling has stronger degenerative effects on axons than the total loss of myelin,17 although this remains to be demonstrated in human demyelinating diseases. It is also relatively recently that the functional synaptic contacts from interneurons onto oligodendrocyte precursor cells (OPCs) have been described, and it is proposed that they form two different populations depending on their ability to trigger spikes or not.18 Altogether, it is clear that the physiology of oligodendroglia and the physiological and pathological impacts of the oligodendroglial lineage are significantly larger than commonly accepted about a decade ago: the implications of oligodendrocytes and OPCs will restructure our current under­standing of CNS plasticity and functioning, as well as open unfathomable opportunities for new therapeutic approaches for multiple sclerosis (MS) and other primary demyelinating diseases and for neuropsychiatric pathology in general.

10.2  O  ligodendrogliogenesis and Oligodendrocyte Differentiation In spite of myelin homogeneity within the CNS, oligodendrocytes are not a homogeneous population: they derive from different groups of OPCs that are generated along the entire neural tube during development. These are

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in different foci located both ventrally and dorsally within the tube, and differ in whether they depend or not on the main signals identified to date (Shh: sonic hedgehog; PDGFR: platelet-­derived growth factor receptor).14,19 OPCs migrate in close relationship with blood vessels and retain a certain degree of proliferative capacity that contributes to the spread of this cell type within the entire CNS parenchyme. They disperse in a mutual self-­ repulsive way that gives rise to a kind of homogeneous distribution in the different neural structures and, at the same time, is the best way to conserve their homeostasis in maturity.20–22 In recent years it has been demonstrated that OPCs regulate angiogenesis during development, even in low vascularized regions of the developmental CNS, by secreting TGFβ1, Wnt7 and maybe other factors.23,24 Signals triggering OPC differentiation in the normal myelination process remain far from fully understood. Mature oligodendrocytes start to be produced in the second half of embryonic development and remain active through maturity to adulthood, with relevant peaks just before birth and in early postnatal development.25,26 The progression along the different stages within the oligodendroglial lineage has been detailed in remarkable reviews.17,27–29 What should be emphasized is the very recent discovery of the high death rate of pre-­myelinating oligodendrocytes in the adult brain, which points to this oligodendroglial subpopulation as a very sensitive pool of cells available to complete a fast differentiation into myelin-­forming oligodendrocytes even after physiological experiences.30 The final differentiation step into myelin-­forming oligodendrocytes depends on different factors and intracellular pathways, from FGFRs, ERK1/2 and R-­Ras-­1/2, to MYRF and anosmin-­1.31–39 Other factors involved in the final step of myelin formation are also reported,37 and the motile processes show a dynamic process of actin assembly/disassembly that is regulated by the equilibrium between two characteristic proteins of the lineage: myelin basic protein (MBP), which favors actin disassembly and the formation of compact myelin, and 2′,3′-­cyclic nucleotide 3′-­phosphodiesterase (CNPase), which slows the latter – promoting, therefore, the formation of more myelin (Figure 10.1).40 Contrary to the general assumption that central myelin just wraps the long axons of projection neurons, it has been recently demonstrated that a relevant volume of myelin in the CNS corresponds to oligodendrocytes ensheathing axons from interneurons; since the work by Young and Richardson it has been demonstrated that new myelin-­forming oligodendrocytes are physiologically generated in the adult brain.26,41–43 Indeed, different patterns of myelination are key players in the transmission of nerve impulses and represent a fast efficient way to substantiate plasticity in the adult brain.37,43 Restriction in OPC differentiation has been suggested as the main determinant for remyelination failure in MS and maybe other demyelinating scenarios, and has been proposed to have a role in the pathogenesis of schizophrenia, too.14,44–46

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Figure 10.1  Representation  of the oligodendrogenesis pathway, including the

characteristic expression of different marker proteins for each stage and molecules that interfere with the developmental path of oligodendrocytes. Functions of mature oligodendrocytes and adult OPCs in normal physiology, homeostasis and a pathological context.27,114-116

10.3  O  ligodendrogliogenic Pathways Modified in Human MS Since close to 20 years ago, the presence of OPCs in demyelinating lesions has pointed to the replacement of the lost oligodendrocytes and generation of new myelin, in a process known as (re)myelination.47 This proceeds in three steps: first, endogenous OPCs are activated by microglia and astrocytes reacting after demyelination; then activated OPCs proliferate and migrate towards lesions; and lastly, recruited OPCs differentiate into (re)myelinating oligodendrocytes.14 These three steps are decrementing in effectiveness, and therefore (re)myelination, although extensive, is insufficient to prevent neurological symptoms and deterioration in many cases.14,48 All reactive astrogliosis, persistent inflammation (with both its positive and negative facets), defective OPC recruitment and axonal damage contribute to restrain effective (re)myelination. In this scenario, newly generated myelin sheaths show similar patterns to normal development, although adult OPC biology is lengthier.

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Among the pathways regulating oligodendrogliogenesis in human MS, the potential role of fibroblast growth factor 2 (FGF-­2) in demyelination and remyelination should be emphasized here: this cue is up-­regulated by microglia/macrophages in MS active lesions and in the periplaque of chronic-­active lesions, both regions where spontaneous (re)myelination takes place and significantly improves symptomatology in patients.49,50 More recent reports suggest that the highest potential for FGF-­2-­based therapies should be for chronic lesions, because in acute scenarios FGF-­2 would be redundant.34 This latter work completes the controversy that emerged from the clinical improvement after FGF-­2 gene therapy in the experimental autoimmune encephalomyelitis (EAE) animal model, the apparently clear neuroprotective of the agent, and the fact that FGF-­2 would inhibit OPC differentiation into myelinating oligodendrocytes.51–55 Recent reports spread controversy on other FGFs, too.56,57 Therefore, we can conclude that FGFs to treat MS are an encouraging field that needs further research and a final clarification of effects. A second pathway with special relevance for MS is that from the morphogen Shh. Different studies on mouse suggest Shh as a promoter of physiological myelination, as well as of remyelination in EAE treated with a combination of interferon β (IFN-­β) and vitamin B12.58–61 In MS patients, Shh is produced by hypertrophic astrocytes at the periplaque of chronic-­active lesions, and at the adjacent normal-­appearing white matter.62 Indeed, Shh has been suggested as a relevant actor for (re)myelination (by promoting OPC proliferation and differentiation) in the EAE model treated with the clinically available immune-­ modulator fingolimod.63,64 This latter cannot be discriminated from the anti-­inflammatory effects of the drug, as has been suggested in chronic MS lesions, where cell response to Shh is limited because of the down-­regulation of Gli signalling in chronic-­active and -­inactive lesions.62 In addition, it cannot be discarded that Shh pathway-­suggested effects of fingolimod would derive from the demonstrated role of Shh maintaining the integrity of the blood–brain barrier (BBB) in MS.65 Nevertheless, the demonstrated effects of Shh in inducing oligodendroglial cells from human embryos, as well as preliminary results from our laboratory, strongly suggested to us that the overall role of Shh in human MS lesions should be fully evaluated as a putative (re) myelinating target to achieve myelin repair.14,66,67 A third oligodendrogliogenic pathway interesting in human MS is semaphorins. This large family of chemotropic molecules first raised attention for our purpose in this chapter due to the dynamic roles of some of the secreted molecules guiding OPC migration during development.68 Different studies with human MS tissue have been published suggesting that Sema 3A would repel OPCs and Sema 3F attract OPCs for myelin repair (both detected in active lesions – Sema 3F in the most active lesions – as well as in the cortical neurons with demyelinated axons).69–72 To complete its negative impact on (re) myelination, Sema 3A could also block OPC differentiation.73 Nevertheless, since some of these studies were conducted the possibility that these secreted semaphorins were acting on immune cells and therefore participating in MS

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pathogenesis was increasingly likely: today, the spectrum of semaphorins acting on the immunopathological facet of MS has been increased to transmembrane semaphorins, such as Sema 7A or Sema 4A, too.74–76 The specific case of the immune semaphorin Sema 4D as proapoptotic and blocker for OPC differentiation77 is partially treated below in this chapter because of the humanized inmunoglobulin G 4 (IgG4) anti-­semaphorin 4D currently in clinical trials (Table 10.1). Components of the extracellular matrix implicated in the final stage of physiological myelination during CNS development have been also proposed to regulate (re)myelination in MS. Putative inhibitors of OPC differentiation are polysialylated neural cell adhesion molecule (PSA-­NCAM), leucine-­rich repeat and Ig-­domain-­containing neurite outgrowth inhibitor (NOGO) receptor-­interacting protein-­1 (LINGO-­1), and anosmin-­1, to name but a few.14,78 PSA-­NCAM is normally absent in the adult brain, but it is re-­ expressed on the demyelinated segment of axons within MS lesions and re-­disapears in remyelinated axons in MS shadow plaques.79 Recent discoveries suggest that the control of polysialylation in oligodendrocyte differentiation is more complex but confirm this as a target of interest to promote (re)myelination.80 LINGO-­1 was identified as an important inhibitor of oligodendrocyte differentiation and myelination during CNS development, and when antagonized with a monoclonal antibody (MAb 1A7) enhanced functional recovery in the EAE animal model of MS:81,82 These, and other preclinical studies, aimed to organize clinical trials with Anti-­LINGO-­1 that will be discussed in this chapter (see also Table 10.1). Another relevant extracellular matrix component involved in (re)myelination is anosmin-­1. Absent in active human MS lesions, anosmin-­1 is specifically up-­regulated in the core of chronic-­active and inactive lesions, both scenarios where (re)myelination does not happen, which suggests that this protein could impede OPCs from invading the lesions.14,49 Finally, other relevant pro-­myelinating molecules during development could also be relevant for (re)myelination in the adult brain, e.g. chemokines (CXCLs and their receptors, CXCRs), glial growth factor-­2 (GGF-­2), netrin-­1, neuregulin-­1 (and mainly its receptors ErbB2 and ErbB4) or retinoids, but we will stop here in the interests of brevity for the current chapter.83–85

10.4  C  hemical Agents Differentiating Adult Human OPCs: The Paths to Remyelinate in MS After years of hopes and tears, in October 2017 the very first randomized controlled clinical trial on MS patients was published showing positive effects for a remyelinating compound: this was performed with clemastine fumarate, a first generation of antihistaminic agents.86 The main (re)myelinating effects preclinically in vitro were achieved on off-­target antimuscarinic effects of clemastine fumarate, and it seems clear that it is purely (re)myelinating and it is not due to immunomodulation (Table 10.1).86–90 This is the main problem

Table 10.1  (Re)myelinating  agents in the pipeline in 2018. Drug (in alphabetic order)

Preclinical studies Mechanism of action

Human adult OPCs

Rodent OPCs

Cuprizone model

EAE model

Development status

Reference

M1 muscari-­nic acetylcho-­line receptor antagonist Inhibits dopa-­mine reup-­take Histamine H1 receptor antagonist Anti-­LINGO-­1 blocking antibody

Yes?

Yes (Rat P7-­9)

Yes

Yes

In clinical use for:

87

89

No

Yes

Yes

Yes

Clemastine

Histamine H1 receptor blockade Antimuscarinic

Yes?

Yes (Rat P7-­9)

Yes

No

GSK239512

Histamine H3 receptor blockade

No

Yes (Rat P2)

Yes

No

Parkinson's disease Extrapyramidal reactions Clinical trials: NCT01864148 NCT01721161 NCT01052506 NCT01244139 NCT02657915 In clinical use for allergy Clinical trials: NCT02521311 (Remyelinating agent in acute optic neuritis) NCT02040298 (Remyelinating agent in MS:) Clinical trials: NCT01772199 (very slight remyelination in RR-­MS).

Benztropine

BIIB033 (biological)

101

89 88 86

91 92

GNbAC1

Humanized antibody anti-­ENV protein

No

Yes (Rat P1)

No

No

Myaptavin-­3064 rHIgM22 (biological)

Aptamer (conjugated) Recombinant monoclonal human IgM22 autoantibody

No No

No No

No Yes

Yes No

TC3.6

PDE7 inhibition

Yes

Yes

No

Yes

Clinical trials: NCT01639300 (Safety in MS patients) NCT02782858 Preclinical Clinical trials: NCT01803867 & N CT02398461 (both for safety, tolerability & PK in MS patients) Preclinical

TDZD8

GSK3 inhibition

Yes

Yes

No

Yes

Preclinical

VP1.15 VP3.15

PDE7/GSK3 inhibition PDE7/GSK3 inhibition

Yes Yes

Yes Yes

Yes Yes

No Yes

Preclinical Preclinical

VX15/2503 (biological)

Humanized IgG4 anti-­semaphorin 4D

No

Yes

No

Yes

Clinical trials: NCT01764737 (safety, tolerability, & PK in MS patients)

107 106 99 95

116 111 117 118 111 111 111 112 110

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for the use of clemastine fumarate in clinics to treat MS. The effects obtained with the posology used where relatively limited, maybe because of the cited off-­target effect on muscarinic receptors: the use of clemastine fumarate at higher doses and as chronic treatment would trigger stronger secondary effects than the mere fatigue increase reported in part of the cohorts.86 This opens the gate to other potential remyelinating agents that have been demonstrated in preclinical experiments. To date, these are limited to a few agents (Table 10.1), but their variety in molecular nature will offer more possibilities for combining with immune-­modulators in the future. In the same high-­throughtput screening that detected clemastine as a candidate, benztropine was also identified as a putative candidate for remyelination.87,89 Benztropine is an antimuscarinic-­M1 agent currently used as an adjunct in the treatment of Parkinson's disease and extrapyramidal reactions to antipsychotics, but it also inhibits the reuptake of dopamine at nerve terminals via the dopamine transporter, and antagonizes histamine H1 receptor. This latter is why it cannot be distinguished to date from the remyelinating effects of clemastine.87,89 The histamine-­H3 receptor (H3R) has been demonstrated as relevant for OPC differentiation;87,89,91 just a few months before the cited work by Green and colleagues,86 the H3R blocker agent GSK239512 was reported to have a very mild non-­significant remyelinating effect on relapsing–remitting MS patients treated with immune-­modulators in a single-­blind Phase II clinical trial.92 Regarding this work, the scientists in charge of the preclinical studies with this molecule remained very elusive when repeatedly asked for details for the publication of the current chapter, as also happened in previous communications with the authors. In the case of further confirmation of the (re)myelinating effect of GSK239512, it should be interesting to check about the contribution of this particular effect on the published benefits on Alzheimer's disease and schizophrenia,93,94 where changes in myelin are normally underestimated. A recombinant version of one human monoclonal IgM autoantibody isolated in a screening of benign monoclonal gammapathies, named rHIgM22, with putative pro-­remyelinating capabilities, has been clinically tested to check safety, tolerability, pharmacokinetics and pharmacodynamics in MS.95 Although known for close to 20 years, repeated studies claiming its remyelinating effects in the Theiler's murine encephalomyelitis virus (TMEV), cuprizone, and lysolecythin animal models of demyelination (including corpus callosum and hippocampus). In very recent studies in search of rHIgM22's mechanism of action, it remained unclear whether the real effect observed in the animal models derived from a more efficient clearing of myelin debris by microglia, or from being a neuroprotective agent, more than OPC differentiation.96–100 As reported above for GSK239512, the request for details regarding direct effects on OPCs was never answered. The promising biological agent BIIB033, an anti-­LINGO-­1 blocking antibody commercialized as Opicinumab®, was used in a number of clinical trials to check its (re)myelinating effects that were abruptly abandoned in 2017, even after its safety and tolerability were reported in Phase I trials,101,102 and

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no information regarding a re-­start of these clinical trials has been reported to date. The first results of a clinical trial on acute optic neuritis have been recently released with no apparent effect on remyelination.103,104 GNbAC1 is a humanized IgG4 monoclonal antibody anti-­envelope protein (ENV) of the human endogenous retrovirus type W. This human endogenous retrovirus type W (HERV-­W) is also named as multiple sclerosis-­associated retrovirus (MSRV), and has been proposed to display a relevant role in MS pathogenesis since its isolation in the 1990s.105 Preclinically, it has been shown to abolish the ENV-­due inhibition of foetal and neonatal (P1) OPC maturation in vitro.106,107 OPCs were obtained from human foetus and from the brain cortex of rat P1 pups. GNbAC1 has passed safety, pharmacokinetic and pharmacodynamics double-­blinded trials,108 and the results of a Phase II clinical trial (CHANGE-­MS, NCT02782858) suggesting an effect on remyelination is under review at the moment of publishing this current chapter. Other humanized IgG4, in this case against Sema 4D, VX15/2503, also passed clinical trial for safety and tolerability in MS,109 based on preclinical studies on rodent OPCs in vitro and in the EAE animal model, where both pro-­BBB integrity and OPC (from P2 rats) pro-­survival and pro-­myelinating differentiation was reported.110 Aptamers and small molecules, in general, are especially promising compounds because they are easy to synthetize, their chemical structures are very stable, they have high target specificity, it is easy to generate similar components if unexpected problems arise (secondary effects, etc.), they can easily cross the BBB and therefore, altogether, their cost of industrial production is significantly lower than other agents, such as humanized recombinant antibodies.99 Aptamer streptavidin-­conjugate Myaptavin-­3064 (≈103 kDa) has been shown to promote strong remyelination in the TMEV model but not in EAE murine models, although the exact mechanism/s involved remain unknown and any other preclinical proofs have been published to date for a more detailed demonstration of the effect of this drug (Table 10.1).99 As confirmed by these authors, safety trials have not been launched yet, as they hesitate in replacing streptavidin in conjugation, which can trigger undesired immunogenic responses. Beside all these, and with the guarantee of more complete preclinical studies including in vitro effects on human adult OPCs (primary cell cultures from biopsies, not inmortalized cells) and three animal models of demyelination (with major immune component – EAE – and without it – cuprizone, lysolecythin), we got strong (re)myelinating effects on OPCs with two different small molecules inhibiting phosphodiesterase 7 (PDE7) and a protein kinase named glycogen synthase kinase 3 (GSK3) (Table 10.1).111,112 The dual inhibition of PDE7 and GSK3 would allow remyelination and immune-­modulation, respectively, in a single compound, such as VP3.15, even better than VP1.15, for example.111,112 It is remarkable that the positive effects observed with VP3.15 in EAE are similar to those of the licensed immune-­modulator fingolimod when administered either as preventive or treatment regimes.112 The exclusive inhibition of PDE7 with quinazoline TC3.6 promotes OPC differentiation and myelin formation, but less efficiently than the dual PDE7-­plus-­GSK3 inhibition, and targeting GSK3

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alone (with TDZ8, for example) does not promotes OPC differentiation, being limited to increasing the survival of this cell type.111,112 These dual compounds promote remyelination by specific targeting of OPCs, because neither microglia nor astrocytes seem affected in vivo.112 These dual PDE7-­GSK3 inhibitors can be administered orally and show a fast and effective penetration of the BBB, are not expected to induce immunogenic reactions, and do not have secondary effects (one major problem that discards the clinical use of anti-­PDE4): all these represent major advantages when compared with antibody treatments. Given that some specific PDE inhibitors are already used to treat other diseases, the pharmacological features of VP3.15 make this drug the ideal candidate among the dual PDE7-­GSK3 inhibitors for further development: it is currently being tested in regulatory toxicology studies and, once the ideal therapeutic window is identified, the first clinical trials should be launched (Figure 10.2). At the moment of writing the current chapter, a brand-­new approach has been published to induce OPCs towards myelin-­forming oligodendrocytes, based on the modification of the intracellular cholesterol metabolic pathway:113 these small molecules inhibit a few select enzymes (CYP51, TM7SF2, EBP) involved in the biosynthesis of cholesterol, giving rise to 8,9-­unsaturated sterol accumulation. This specific unsaturated sterol is known to favor OPC-­ to-­oligodendrocyte differentiation. This is very promising; no data on other preclinical studies or the state of play regarding the trials of these cholesterol-­ modifying small molecules is available to date, but these results suggest a unifying sterol-­based mechanism of action for most of the small molecules known to promote oligodendrocyte differentiation and open a new therapeutic avenue to achieve optimal remyelination.113

Figure 10.2  Small  molecules with (re)myelinating activity.

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One of the most attractive results obtained with the clemastine fumarate clinical trial is that partial “myelin repair can be achieved even following prolonged damage”.86 This is an obvious hope for patients, their families and the health systems, although it does not imply that no time window should be contemplated for the use of remyelinating agents in the future: when this kind of therapy will be a common reality, it is convenient to start using as soon as possible before the disease (the demyelinating lesions) also affects the axons.

10.5  Conclusions Although this trial shows obvious limitations, the collaborative work of Ari Green and Jonah Chan suggest that the potential of endogenous OPCs to be medically potentiated and achieve remyelination in adult MS patients is now a reality.86 Effective remyelination should protect axons from degeneration. All the candidates for remyelinating agents in the different phases of the pipeline in general act via different mechanisms/pathways than the currently available MS treatments in clinics (all of them, immune-­modulators): if some of them reach final approval for their clinical use, the number of synergistic combinations to approach MS patients would allow a much more accurate treatment (close to personalized treatment) and more efficient prevention of progression, and open doors to the unmet desire of reversing myelin loss and recovery from symptoms. In this sense, the technology of small molecules represents a major hope due to their versatility, pharmacodynamic characteristics, and easy and cheap production.99,112,113 The challenge for the field is to induce a Copernican transformation in the treatment of MS and, at last, attack the disease in a holistic way, covering immune-­and neuropathogenic aspects of disease.

Acknowledgements F.dC. research is currently supported by grants from the Spanish Ministry of Science, Research and Innovation (grants SAF2016-­77575-­R, RD RD16/0015 and SAF2015-­72325-­EXP) and Fundación Inocente Inocente (Spain), as well as by one contract-­donation from Asociación Española de Esclerosis Múltiple/AEDEM-­COCEMFE (Spain) and a contract of technological support by AptaTargets, S.L. (Spain).

References

1. P. del Río-­Hortega, Bol. Soc. Esp. Biol., 1919, VIII, 67–82. 2. P. del Río-­Hortega, Bol. Soc. Esp. Biol., 1919, VIII, 91–103. 3. P. del Río-­Hortega, Bol. Soc. Esp. Biol., 1919, VIII, 108–115. 4. P. del Río-­Hortega, Bol. Soc. Esp. Biol., 1919, VIII, 154–166. 5. P. del Río-­Hortega, Bol. Soc. Esp. Biol., 1921, XXI, 1–43.

234

Chapter 10

6. A. Sierra, F. de Castro, J. Del Rio-­Hortega, J. Rafael Iglesias-­Rozas, M. Garrosa and H. Kettenmann, Glia, 2016, 64, 1801–1840. 7. B. A. Barres and Y. Barde, Curr. Opin. Neurobiol., 2000, 10, 642–648. 8. B. Emery, Science, 2010, 330, 779–782. 9. R. D. Fields, Neuroscientist, 2005, 11, 528–531. 10. G. S. Tomassy, L. B. Dershowitz and P. Arlotta, Trends Cell Biol., 2016, 26, 135–147. 11. B. Zalc and D. R. Colman, Science, 2000, 288, 271–272. 12. B. Zalc, D. Goujet and D. Colman, Curr. Biol., 2008, 18, R511–R512. 13. C. Bjartmar, C. Hildebrand and K. Loinder, Glia, 1994, 11, 235–244. 14. F. de Castro and B. Zalc, in Cellular Migration and Formation of Neuronal Connections, ed. J. L. R. Rubenstein and P. Rakic, Academic Press, Oxford, 2013, pp. 417–429. 15. A. Peters, S. Palay and H. F. Webster, The Fine Structure of the Nervous System: Neurons and Their Supporting Cells, Oxford University Press, Oxford, 3rd edn, 1991. 16. M. Pintado-­Sierra, I. Garcia-­Alvarez, A. Bribian, E. M. Medina-­Rodriguez, R. Lebron-­Aguilar, L. Garrido, F. de Castro, A. Fernandez-­Mayoralas and J. E. Quintanilla-­Lopez, Anal. Chim. Acta, 2017, 951, 89–98. 17. M. Simons and K. A. Nave, Cold Spring Harbor Perspect. Biol., 2015, 8, a020479. 18. N. Frohlich, B. Nagy, A. Hovhannisyan and M. Kukley, J. Anat., 2011, 219, 18–32. 19. N. Kessaris, M. Fogarty, P. Iannarelli, M. Grist, M. Wegner and W. D. Richardson, Nat. Neurosci., 2006, 9, 173–179. 20. E. G. Hughes, S. H. Kang, M. Fukaya and D. E. Bergles, Nat. Neurosci., 2013, 16, 668–676. 21. D. P. Kelenis, E. Hart, M. Edwards-­Fligner, J. E. Johnson and T. Y. Vue, Glia, 2018, 66, 1862–1880. 22. H. H. Tsai, J. Niu, R. Munji, D. Davalos, J. Chang, H. Zhang, A. C. Tien, C. J. Kuo, J. R. Chan, R. Daneman and S. P. Fancy, Science, 2016, 351, 379–384. 23. J. H. Seo, T. Maki, M. Maeda, N. Miyamoto, A. C. Liang, K. Hayakawa, L. D. Pham, F. Suwa, A. Taguchi, T. Matsuyama, M. Ihara, K. W. Kim, E. H. Lo and K. Arai, PLoS One, 2014, 9, e103174. 24. T. J. Yuen, J. C. Silbereis, A. Griveau, S. M. Chang, R. Daneman, S. P. J. Fancy, H. Zahed, E. Maltepe and D. H. Rowitch, Cell, 2014, 158, 383–396. 25. X. Zhu, R. A. Hill, D. Dietrich, M. Komitova, R. Suzuki and A. Nishiyama, Development, 2011, 138, 745–753. 26. K. M. Young, K. Psachoulia, R. B. Tripathi, S. J. Dunn, L. Cossell, D. Attwell, K. Tohyama and W. D. Richardson, Neuron, 2013, 77, 873–885. 27. F. de Castro and A. Bribian, Brain Res. Rev., 2005, 49, 227–241. 28. L. Dimou and M. Simons, Curr. Opin. Neurobiol., 2017, 47, 73–79.

Regulation of Oligodendrocyte Differentiation

235

29. V. E. Miron, T. Kuhlmann and J. P. Antel, Biochim. Biophys. Acta, 2011, 1812, 184–193. 30. E. G. Hughes, J. L. Orthmann-­Murphy, A. J. Langseth and D. E. Bergles, Nat. Neurosci., 2018, 21, 696–706. 31. H. Bujalka, M. Koenning, S. Jackson, V. M. Perreau, B. Pope, C. M. Hay, S. Mitew, A. F. Hill, Q. R. Lu, M. Wegner, R. Srinivasan, J. Svaren, M. Willingham, B. A. Barres and B. Emery, PLoS Biol., 2013, 11, e1001625. 32. D. Fortin, E. Rom, H. Sun, A. Yayon and R. Bansal, J. Neurosci., 2005, 25, 7470–7479. 33. M. Furusho, J. L. Dupree, K. A. Nave and R. Bansal, J. Neurosci., 2012, 32, 6631–6641. 34. M. Furusho, A. J. Roulois, R. J. Franklin and R. Bansal, Glia, 2015, 63, 1714–1728. 35. A. Ishii, M. Furusho and R. Bansal, J. Neurosci., 2013, 33, 175–186. 36. A. Ishii, S. L. Fyffe-­Maricich, M. Furusho, R. H. Miller and R. Bansal, J. Neurosci., 2012, 32, 8855–8864. 37. V. Murcia-­Belmonte, P. F. Esteban, J. Martinez-­Hernandez, A. Gruart, R. Lujan, J. M. Delgado-­Garcia and F. de Castro, Brain Struct. Funct., 2016, 221, 1365–1385. 38. M. Sanz-­Rodriguez, A. Gruart, J. Escudero-­Ramirez, F. de Castro, J. M. Delgado-­Garcia, F. Wandosell and B. Cubelos, J. Neurosci., 2018, 38, 5096–5110. 39. J. Xiao, A. H. Ferner, A. W. Wong, M. Denham, T. J. Kilpatrick and S. S. Murray, J. Neurochem., 2012, 122, 1167–1180. 40. N. Snaidero, C. Velte, M. Myllykoski, A. Raasakka, A. Ignatev, H. B. Werner, M. S. Erwig, W. Mobius, P. Kursula, K. A. Nave and M. Simons, Cell Rep., 2017, 18, 314–323. 41. K. D. Micheva, D. Wolman, B. D. Mensh, E. Pax, J. Buchanan, S. J. Smith and D. D. Bock, eLife, 2016, 5, e15784. 42. J. Stedehouder, J. J. Couey, D. Brizee, B. Hosseini, J. A. Slotman, C. M. F. Dirven, G. Shpak, A. B. Houtsmuller and S. A. Kushner, Cereb. Cortex, 2017, 27, 5001–5013. 43. G. S. Tomassy, D. R. Berger, H. H. Chen, N. Kasthuri, K. J. Hayworth, A. Vercelli, H. S. Seung, J. W. Lichtman and P. Arlotta, Science, 2014, 344, 319–324. 44. T. Kuhlmann, V. Miron, Q. Cui, C. Wegner, J. Antel and W. Bruck, Brain, 2008, 131, 1749–1758. 45. N. A. Murphy and R. J. M. Franklin, Prog. Brain Res., 2017, 231, 135–163. 46. J. Stedehouder and S. A. Kushner, Mol. Psychiatry, 2017, 22, 4–12. 47. J. M. Levine, R. Reynolds and J. W. Fawcett, Trends Neurosci., 2001, 24, 39–47. 48. S. Bramow, J. M. Frischer, H. Lassmann, N. Koch-­Henriksen, C. F. Lucchinetti, P. S. Sorensen and H. Laursen, Brain, 2010, 133, 2983–2998. 49. D. Clemente, M. C. Ortega, F. J. Arenzana and F. de Castro, J. Neurosci., 2011, 31, 14899–14909.

236

Chapter 10

50. E. M. Frohman, M. K. Racke and C. S. Raine, N. Engl. J. Med., 2006, 354, 942–955. 51. D. R. Goddard, M. Berry, S. L. Kirvell and A. M. Butt, Mol. Cell. Neurosci., 2001, 18, 557–569. 52. R. C. Armstrong, T. Q. Le, N. C. Flint, A. C. Vana and Y. X. Zhou, J. Neuropathol. Exp. Neurol., 2006, 65, 245–256. 53. R. C. Armstrong, T. Q. Le, E. E. Frost, R. C. Borke and A. C. Vana, J. Neurosci., 2002, 22, 8574–8585. 54. A. Rottlaender, H. Villwock, K. Addicks and S. Kuerten, Immunology, 2011, 133, 370–378. 55. F. Ruffini, R. Furlan, P. L. Poliani, E. Brambilla, P. C. Marconi, A. Bergami, G. Desina, J. C. Glorioso, G. Comi and G. Martino, Gene Ther., 2001, 8, 1207–1213. 56. M. Lindner, K. Thummler, A. Arthur, S. Brunner, C. Elliott, D. McElroy, H. Mohan, A. Williams, J. M. Edgar, C. Schuh, C. Stadelmann, S. C. Barnett, H. Lassmann, S. Mucklisch, M. Mudaliar, N. Schaeren-­Wiemers, E. Meinl and C. Linington, Brain, 2015, 138, 1875–1893. 57. H. Mohan, A. Friese, S. Albrecht, M. Krumbholz, C. L. Elliott, A. Arthur, R. Menon, C. Farina, A. Junker, C. Stadelmann, S. C. Barnett, I. Huitinga, H. Wekerle, R. Hohlfeld, H. Lassmann, T. Kuhlmann, C. Linington and E. Meinl, Acta Neuropathol. Commun., 2014, 2, 168. 58. L. Bouslama-­Oueghlani, R. Wehrle, M. Doulazmi, X. R. Chen, F. Jaudon, Y. Lemaigre-­Dubreuil, I. Rivals, C. Sotelo and I. Dusart, PLoS One, 2012, 7, e49015. 59. F. G. Mastronardi, W. Min, H. Wang, S. Winer, M. Dosch, J. M. Boggs and M. A. Moscarello, J. Immunol., 2004, 172, 6418–6426. 60. P. Merchan, A. Bribian, C. Sanchez-­Camacho, M. Lezameta, P. Bovolenta and F. de Castro, Mol. Cell. Neurosci., 2007, 36, 355–368. 61. M. C. Ortega, O. Cases, P. Merchan, R. Kozyraki, D. Clemente and F. de Castro, Glia, 2012, 60, 851–866. 62. Y. Wang, J. Imitola, S. Rasmussen, K. C. O'Connor and S. J. Khoury, Ann. Neurol., 2008, 64, 417–427. 63. M. A. Sanchez, G. M. Sullivan and R. C. Armstrong, Neurobiol. Dis., 2018, 115, 145–156. 64. J. Zhang, Z. G. Zhang, Y. Li, X. Ding, X. Shang, M. Lu, S. B. Elias and M. Chopp, Neurobiol. Dis., 2015, 76, 57–66. 65. J. I. Alvarez, A. Dodelet-­Devillers, H. Kebir, I. Ifergan, P. J. Fabre, S. Terouz, M. Sabbagh, K. Wosik, L. Bourbonniere, M. Bernard, J. van Horssen, H. E. de Vries, F. Charron and A. Prat, Science, 2011, 334, 1727–1731. 66. J. A. Ortega, N. V. Radonjic and N. Zecevic, Front. Cell. Neurosci., 2013, 7, 254. 67. M. C. Ortega, D. Clemente and F. de Castro, Glia, 2013, 61(S1), S136. 68. N. Spassky, F. de Castro, B. Le Bras, K. Heydon, F. Queraud-­LeSaux, E. Bloch-­Gallego, A. Chedotal, B. Zalc and J. L. Thomas, J. Neurosci., 2002, 22, 5992–6004.

Regulation of Oligodendrocyte Differentiation

237

69. A. Boyd, H. Zhang and A. Williams, Acta Neuropathol., 2013, 125, 841–859. 70. G. Piaton, M. S. Aigrot, A. Williams, S. Moyon, V. Tepavcevic, I. Moutkine, J. Gras, K. S. Matho, A. Schmitt, H. Soellner, A. B. Huber, P. Ravassard and C. Lubetzki, Brain, 2011, 134, 1156–1167. 71. G. Piaton, A. Williams, D. Seilhean and C. Lubetzki, Prog. Brain Res., 2009, 175, 453–464. 72. A. Williams, G. Piaton, M. S. Aigrot, A. Belhadi, M. Theaudin, F. Petermann, J. L. Thomas, B. Zalc and C. Lubetzki, Brain, 2007, 130, 2554–2565. 73. Y. A. Syed, E. Hand, W. Mobius, C. Zhao, M. Hofer, K. A. Nave and M. R. Kotter, J. Neurosci., 2011, 31, 3719–3728. 74. C. Costa, E. Martinez-­Saez, A. Gutierrez-­Franco, H. Eixarch, Z. Castro, A. Ortega-­Aznar, Y. C. S. Ramon, X. Montalban and C. Espejo, Mult. Scler., 2015, 21, 1632–1643. 75. A. Gutierrez-­Franco, C. Costa, H. Eixarch, M. Castillo, E. M. Medina-­ Rodriguez, A. Bribian, F. de Castro, X. Montalban and C. Espejo, Clin. Immunol., 2016, 163, 22–33. 76. A. Gutierrez-­Franco, H. Eixarch, C. Costa, V. Gil, M. Castillo, L. Calvo-­ Barreiro, X. Montalban, J. A. Del Rio and C. Espejo, Mol. Neurobiol., 2017, 54, 4820–4831. 77. P. Giraudon, P. Vincent, C. Vuaillat, O. Verlaeten, L. Cartier, A. Marie-­ Cardine, M. Mutin, A. Bensussan, M. F. Belin and L. Boumsell, J. Immunol., 2004, 172, 1246–1255. 78. D. Kremer, O. Aktas, H. P. Hartung and P. Kury, Ann. Neurol., 2011, 69, 602–618. 79. P. Charles, M. P. Hernandez, B. Stankoff, M. S. Aigrot, C. Colin, G. Rougon, B. Zalc and C. Lubetzki, Proc. Natl. Acad. Sci. U. S. A., 2000, 97, 7585–7590. 80. S. Werneburg, H. L. S. Fuchs, I. Albers, H. Burkhardt, V. Gudi, T. Skripuletz, M. Stangel, R. Gerardy-­Schahn and H. Hildebrandt, J. Neurosci., 2017, 37, 8131–8141. 81. S. Mi, B. Hu, K. Hahm, Y. Luo, E. S. Kam Hui, Q. Yuan, W. M. Wong, L. Wang, H. Su, T. H. Chu, J. Guo, W. Zhang, K. F. So, B. Pepinsky, Z. Shao, C. Graff, E. Garber, V. Jung, E. X. Wu and W. Wu, Nat. Med., 2007, 13, 1228–1233. 82. S. Mi, R. H. Miller, W. Tang, X. Lee, B. Hu, W. Wu, Y. Zhang, C. B. Shields, Y. Zhang, S. Miklasz, D. Shea, J. Mason, R. J. Franklin, B. Ji, Z. Shao, A. Chedotal, F. Bernard, A. Roulois, J. Xu, V. Jung and B. Pepinsky, Ann. Neurol., 2009, 65, 304–315. 83. K. M. Omari, G. R. John, S. C. Sealfon and C. S. Raine, Brain, 2005, 128, 1003–1015. 84. V. Tepavcevic, C. Kerninon, M. S. Aigrot, E. Meppiel, S. Mozafari, R. Arnould-­Laurent, P. Ravassard, T. E. Kennedy, B. Nait-­Oumesmar and C. Lubetzki, Ann. Neurol., 2014, 76, 252–268. 85. J. K. Huang, A. A. Jarjour, B. Nait Oumesmar, C. Kerninon, A. Williams, W. Krezel, H. Kagechika, J. Bauer, C. Zhao, A. Baron-­Van Evercooren, P.

238

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Chambon, C. Ffrench-­Constant and R. J. M. Franklin, Nat. Neurosci., 2010, 14, 45. 86. A. J. Green, J. M. Gelfand, B. A. Cree, C. Bevan, W. J. Boscardin, F. Mei, J. Inman, S. Arnow, M. Devereux, A. Abounasr, H. Nobuta, A. Zhu, M. Friessen, R. Gerona, H. C. von Budingen, R. G. Henry, S. L. Hauser and J. R. Chan, Lancet, 2017, 390, 2481–2489. 87. V. A. Deshmukh, V. Tardif, C. A. Lyssiotis, C. C. Green, B. Kerman, H. J. Kim, K. Padmanabhan, J. G. Swoboda, I. Ahmad, T. Kondo, F. H. Gage, A. N. Theofilopoulos, B. R. Lawson, P. G. Schultz and L. L. Lairson, Nature, 2013, 502, 327–332. 88. J. Liu, J. L. Dupree, M. Gacias, R. Frawley, T. Sikder, P. Naik and P. Casaccia, J. Neurosci., 2016, 36, 957–962. 89. F. Mei, S. P. J. Fancy, Y. A. Shen, J. Niu, C. Zhao, B. Presley, E. Miao, S. Lee, S. R. Mayoral, S. A. Redmond, A. Etxeberria, L. Xiao, R. J. M. Franklin, A. Green, S. L. Hauser and J. R. Chan, Nat. Med., 2014, 20, 954–960. 90. F. Mei, K. Lehmann-­Horn, Y. A. Shen, K. A. Rankin, K. J. Stebbins, D. S. Lorrain, K. Pekarek, A. S. Sagan, L. Xiao, C. Teuscher, H. C. von Budingen, J. Wess, J. J. Lawrence, A. J. Green, S. P. Fancy, S. S. Zamvil and J. R. Chan, eLife, 2016, 5, e18246. 91. Y. Chen, W. Zhen, T. Guo, Y. Zhao, A. Liu, J. P. Rubio, D. Krull, J. C. Richardson, H. Lu and R. Wang, PLoS One, 2017, 12, e0189380. 92. C. J. Schwartzbach, R. A. Grove, R. Brown, D. Tompson, F. Then Bergh and D. L. Arnold, J. Neurol., 2017, 264, 304–315. 93. L. F. Jarskog, M. T. Lowy, R. A. Grove, R. S. Keefe, J. P. Horrigan, M. P. Ball, A. Breier, R. W. Buchanan, C. S. Carter, J. G. Csernansky, D. C. Goff, M. F. Green, J. T. Kantrowitz, M. S. Keshavan, M. Laurelle, J. A. Lieberman, S. R. Marder, P. Maruff, R. P. McMahon, L. J. Seidman and M. A. Peykamian, Schizophr. Res., 2015, 164, 136–142. 94. M. Kubo, T. Kishi, S. Matsunaga and N. Iwata, J. Alzheimer's Dis., 2015, 48, 667–671. 95. A. Eisen, B. M. Greenberg, J. D. Bowen, D. L. Arnold and A. O. Caggiano, Mult. Scler. J. Exp. Transl. Clin., 2017, 3, 2055217317743097. 96. C. Cui, J. Wang, A. P. Mullin, A. O. Caggiano, T. J. Parry, R. W. Colburn and E. Pavlopoulos, Brain Res., 2018, 1694, 73–86. 97. H. N. Lemus, A. E. Warrington, A. Denic, B. Wootla and M. Rodriguez, Hum. Antibodies, 2017, 25, 121–129. 98. A. P. Mullin, C. Cui, Y. Wang, J. Wang, E. Troy, A. O. Caggiano, T. J. Parry, R. W. Colburn and E. Pavlopoulos, Neurobiol. Dis., 2017, 105, 142–155. 99. M. K. Perwein, J. A. Smestad, A. E. Warrington, R. M. Heider, M. W. Kaczor, L. J. Maher 3rd, B. Wootla, A. Kunbaz and M. Rodriguez, Expert Opin. Biol. Ther., 2018, 18, 545–560. 100. Y. Zorina, J. Stricker, A. O. Caggiano and D. C. Button, Sci. Rep., 2018, 8, 9392. 101. S. Ruggieri, C. Tortorella and C. Gasperini, Expert Rev. Neurother., 2017, 17, 1081–1089.

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102. J. Q. Tran, J. Rana, F. Barkhof, I. Melamed, H. Gevorkyan, M. P. Wattjes, R. de Jong, K. Brosofsky, S. Ray, L. Xu, J. Zhao, E. Parr and D. Cadavid, Neurol. Neuroimmunol. Neuroinflamm., 2014, 1, e18. 103. D. Cadavid, L. Balcer, S. Galetta, O. Aktas, T. Ziemssen, L. Vanopdenbosch, J. Frederiksen, M. Skeen, G. J. Jaffe, H. Butzkueven, F. Ziemssen, L. Massacesi, Y. Chai, L. Xu, S. Freeman and R. S. Investigators, Lancet Neurol., 2017, 16, 189–199. 104. J. Petrillo, L. Balcer, S. Galetta, Y. Chai, L. Xu and D. Cadavid, J. Neuroophthalmol., 2018, 1. 105. H. Perron, J. A. Garson, F. Bedin, F. Beseme, G. Paranhos-­Baccala, F. Komurian-­Pradel, F. Mallet, P. W. Tuke, C. Voisset, J. L. Blond, B. Lalande, J. M. Seigneurin and B. Mandrand, Proc. Natl. Acad. Sci. U. S. A., 1997, 94, 7583–7588. 106. D. Kremer, M. Forster, T. Schichel, P. Gottle, H. P. Hartung, H. Perron and P. Kury, Mult. Scler., 2015, 21, 1200–1203. 107. D. Kremer, T. Schichel, M. Forster, N. Tzekova, C. Bernard, P. van der Valk, J. van Horssen, H. P. Hartung, H. Perron and P. Kury, Ann. Neurol., 2013, 74, 721–732. 108. T. Derfuss, F. Curtin, C. Guebelin, C. Bridel, M. Rasenack, A. Matthey, R. Du Pasquier, M. Schluep, J. Desmeules, A. B. Lang, H. Perron, R. Faucard, H. Porchet, H. P. Hartung, L. Kappos and P. H. Lalive, J. Neuroimmunol., 2015, 285, 68–70. 109. C. LaGanke, L. Samkoff, K. Edwards, L. Jung Henson, P. Repovic, S. Lynch, L. Stone, D. Mattson, A. Galluzzi, T. L. Fisher, C. Reilly, L. A. Winter, J. E. Leonard and M. Zauderer, Neurol. Neuroimmunol. Neuroinflamm., 2017, 4, e367. 110. E. S. Smith, A. Jonason, C. Reilly, J. Veeraraghavan, T. Fisher, M. Doherty, E. Klimatcheva, C. Mallow, C. Cornelius, J. E. Leonard, N. Marchi, D. Janigro, A. T. Argaw, T. Pham, J. Seils, H. Bussler, S. Torno, R. Kirk, A. Howell, E. E. Evans, M. Paris, W. J. Bowers, G. John and M. Zauderer, Neurobiol. Dis., 2015, 73, 254–268. 111. E. M. Medina-­Rodriguez, F. J. Arenzana, J. Pastor, M. Redondo, V. Palomo, R. Garcia de Sola, C. Gil, A. Martinez, A. Bribian and F. de Castro, Cell. Mol. Life Sci., 2013, 70, 3449–3462. 112. E. M. Medina-­Rodriguez, A. Bribian, A. Boyd, V. Palomo, J. Pastor, A. Lagares, C. Gil, A. Martinez, A. Williams and F. de Castro, Sci. Rep., 2017, 7, 43545. 113. Z. Hubler, D. Allimuthu, I. Bederman, M. S. Elitt, M. Madhavan, K. C. Allan, H. E. Shick, E. Garrison, M. T. Karl, D. C. Factor, Z. S. Nevin, J. L. Sax, M. A. Thompson, Y. Fedorov, J. Jin, W. K. Wilson, M. Giera, F. Bracher, R. H. Miller, P. J. Tesar and D. J. Adams, Nature, 2018, 560, 372–376. 114. R. M. Bove and A. J. Green, Neurotherapeutics, 2017, 14, 894–904. 115. R. Pepper, L. C. Cullen, K. A. Pitman and K. M. Young, Front. Cell. Neurosci., 2018, 12, 399.

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116. C. Gonzalez-­Garcia, B. Bravo, A. Ballester, R. Gomez-­Perez, C. Eguiluz, M. Redondo, A. Martinez, C. Gil and S. Ballester, Br. J. Pharmacol., 2013, 170, 602–613. 117. L. Mestre, M. Redondo, F. J. Carrillo-­Salinas, J. A. Morales-­García, S. Alonso-­Gil, A. Pérez-­Castillo, C. Gil, A. Martínez and C. Guaza, Br. J. Pharmacol., 2015, 172, 4277–4290. 118. E. Beurel, O. Kaidanovich-­Beilin, W. I. Yeh, L. Song, V. Palomo, S. M. Michalek, J. R. Woodgett, L. E. Harrington, H. Eldar-­Finkelman, A. Martinez and R. S. Jope, J. Immunol., 2013, 190, 5000–5011.

Part IV Other Therapeutic Approaches

         

Chapter 11

Cannabinoids as a Therapeutic Approach in Multiple Sclerosis Gareth Pryce* and David Baker Centre for Neuroscience and Trauma, Blizard Institute, Barts and the London School of Medicine and Dentistry, Queen Mary University of London, London, E1 2AT, United Kingdom *E-­mail: [email protected]

11.1 Introduction Cannabis has been used for its medicinal properties in a number of medical conditions for millennia. More recently, there has been interest in the potential use of cannabis or cannabinoid compounds for the treatment of multiple sclerosis (MS). As a consequence of nerve damage and degeneration, people with MS (pwMS) can develop increasing disability. Associated with this are a variety of symptoms, including tremor, limb spasticity, pain, and bladder and sexual dysfunction that can greatly diminish quality of life for the individual.1 Interest in cannabis treatment for some of these symptoms was initially based on a wealth of anecdotal reports from pwMS taking cannabis in the absence of effective disease or symptom control. These anecdotal reports were confirmed by a study of pwMS self-­medicating with smoked cannabis reporting perceived benefits from cannabis for a variety of symptoms, including limb spasticity and pain.2 A more recent large-­scale survey (5481 pwMS) revealed that: 47% had considered using cannabis for their MS; 26% had used cannabis for their MS; 20% had spoken to their clinician about the   Drug Discovery Series No. 70 Emerging Drugs and Targets for Multiple Sclerosis Edited by Ana Martinez © The Royal Society of Chemistry 2019 Published by the Royal Society of Chemistry, www.rsc.org

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use of cannabis for their MS; and 16% were currently using cannabis for their MS. Over 90% of responders were in favour of the legalisation of cannabis for medicinal use.3 Subsequent discoveries revealing the biology of cannabis and the endocannabinoid system have added weight to the perceptions of pwMS and has underpinned research into the medicinal manipulation of the endocannabinoid system for the treatment of MS. This research has led to the licensing of a cannabis-­based medicine for use in MS and also the legalisation, in an increasing number of countries, of medicinal cannabis for a number of indications, including MS. In the UK, it has finally been acknowledged by the medical establishment and the UK Government that cannabis does have medicinal properties, including for the treatment of MS, in a rescheduling review by the Chief Medical Officer.4 This report and a follow-­up review by an expert panel recommended removing cannabis-­based medicines from schedule 1 (little or no therapeutic benefit, not used medicinally) to schedule 2, a controlled drug capable of prescription in a similar manner to opiate analgesics and major stimulants such as amphetamine. It is clear that after decades of prohibition cannabis and cannabis-­derived therapeutics may be about to re-­enter the pharmacopoeia of therapeutic drugs for the treatment of MS and other conditions.

11.2 Cannabis and the Endocannabinoid System The prototypic cannabinoid Δ9-­tetrahydrocannabinol (THC), the main psychoactive and medicinally relevant component of Cannabis sativa, was identified in 1964.5 The cannabinoids belong to the terpenoid chemical family. Before the identification of specific receptors, it was postulated that cannabinoids exerted their effects by interaction with and perturbation of membrane lipids and associated proteins. Cannabis contains a large number of other chemical constituents (more than 60); the other main constituent of cannabis of current medicinal interest is the non-­psychoactive cannabidiol (CBD), which is in development as an anti-­epileptic agent and has anti-­psychotic potential (Figure 11.1).

11.2.1 The Endocannabinoid System The cannabinoid system is a relatively novel regulatory pathway that was revealed, like the opioid system, following the study of plant-­derived narcotics, and it is now clear that it is a fundamental element of the biology of the nervous system and many other areas of the body. Since the identification and cloning of the predominantly neuronally expressed cannabinoid receptor CB1,6,7 there has been a huge increase in research in this area. The cloning of a second, peripheral cannabinoid receptor, CB2,8 which is expressed primarily on cells of the immune system, revealed the ubiquity of cannabinoid receptor signalling in many physiological processes. These are the classical

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Figure 11.1 Cannabinoids, endocannabinoids and synthetic cannabinoids. cannabinoid receptors that are characterised by their agonism with the prototypic cannabinoid THC, which is a partial agonist for CB1 and CB2, with fewer efficacies at CB2 than CB1. Both of these receptors show constitutive activity, as evidenced by the inverse agonism of many CB receptor antagonists. The identification of endogenously produced fatty-­acid ligands for these receptors, such as anandamide (AEA)9 and 2-­arachidonoyl glycerol

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(2-­AG), termed endocannabinoids (Figure 11.1), for these receptors has identified a functional endogenous cannabinoid signalling system, raising the possibility of utilising aspects of the endocannabinoid system for potential therapeutic benefit, particularly in neurological disease. The endocannabinoid system consists of four components: the cannabinoid receptors, their ligands the endocannabinoids, enzymes that generate the endocannabinoids and the enzymes that degrade them.

11.2.2 Cannabinoid Receptors CB1 and CB2 receptors are members of the rhodopsin-­like, 7-­transmembrane, G protein-­coupled superfamily that appear to bind their ligands in the central core of the membrane-­spanning helices.12 CB1 is chiefly but not exclusively expressed by neuronal cells and the CB2 receptor is mainly but not exclusively expressed by cells of the immune cell lineage. The CB1 receptor sequence shows a high degree of conservation across mammalian species, whereas the CB2 receptor shows a greater degree of interspecies difference and less than 50% sequence homology with human CB1 and limited homology to CB2 receptors of other species.13 CB1 is the most highly expressed and ubiquitous G protein-­coupled receptor in the brain14 with densities similar to those of gamma aminobutyric acid (GABA) and glutamate receptors15 and is primarily located at pre-­synaptic axonal nerve terminals. Activation of the CB1 receptor leads to the inhibition of neurotransmitter release, principally by inhibition of adenylate cyclase-­ mediated production of cyclic adenosine monophosphate (cAMP), activation of potassium channels, inhibition of voltage-­gated calcium channels and, in addition, activation of extracellular signal-­related kinases.16 Activation of the CB2 receptor also inhibits adenylate cyclase activity and activates extracellular signal-­related kinases but does not appear to couple to ion channel inhibition.16 Exogenous agonists of CB2 have been reported to have anti-­inflammatory activity in a number of immune cell sub-­t ypes with downregulation of cytokine release, reactive oxygen species production and reduced cell migration. In addition, in several models of immune function, CB2 knockout mice show enhanced levels of inflammation with enhanced leucocyte recruitment (chiefly neutrophils) and enhanced pro-­inflammatory cytokine production.17 We have focused in this review on the two best characterised cannabinoid receptors, CB1 and CB2, which have perhaps the most direct relevance to MS; however, a further level of complexity exists, with the involvement of non-­ classical cannabinoid binding receptors and ligands. Other candidate cannabinoid receptors, characterised by similarities in ligand pharmacology rather than any similarities in sequence homology, are the previously “orphan” G protein-­coupled receptors: GPR18,18 which may be involved in microglial activation and migration in the central nervous system (CNS) in response to neuronal injury19 and which may be pertinent in relation to MS (GPR18 has been reported to be activated by THC20); GPR55,21 which is expressed

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in a number of different organ systems, including the CNS where it may have a role in synaptic plasticity,22 but is still poorly characterised as to its functional significance; and lastly, and perhaps less convincingly, GPR119, which is involved in appetite and obesity.23 There is evidence for endogenous ligands for these receptors that have similarities to or are derivatives of classical endocannabinoids.24 The transient receptor potential cation channel subfamily V member 1 (TRPV1) is a non-­selective cation channel that has been primarily associated with activation by noxious stimuli such as heat and hydrogen ions. TRPV1 is also activated by capsaicin, which is the pungent ingredient of chilli peppers. TRPV1 was initially reported as being expressed by sensory neurons, where opening of the channel triggers calcium influx, neurotransmitter release and transmission of painful or noxious stimuli. Numerous studies have demonstrated that the endocannabinoid anandamide can also activate the TRPV1 receptor, although the binding site may be at cytosolic sites of the receptor.25 Finally, experimental observations point to the potential activity of cannabinoids on the peroxisome proliferator-­activated receptors (PPAR) α and γ. PPARs hetero-­dimerise with the retinoid X receptor and bind to PPAR response elements of DNA sequences which trigger the transcription of target genes upon ligand activation of these receptors. Plant-­derived cannabinoids and endocannabinoids have been shown to activate PPAR α and γ.26 More recently, it has been reported that an anandamide derivative VSN16 (Figure 11.1), which does not have binding activity at conventional cannabinoid receptors, is a selective ligand for the neuronally expressed big conductance calcium-­activated potassium channel (BKCa), activation of which modulates neuronal excitability.27 The BKCa channel and potentially other channels in this class, may be additional members of the ever increasing family of receptors/channels that may modulated by endocannabinoids or endocannabinoid-­derived exogenous ligands.

11.2.3 Endocannabinoids, Synthesis and Degradation The most studied and probably most physiologically important endocannabinoids are AEA and 2-­AG. In the nervous system, endocannabinoids are synthesised “on demand” from the membrane of post-­synaptic neurons after an increase in neuronal activity due to neurotransmitter signalling and calcium ion influx. Endocannabinoids function as fast acting retrograde neuromodulators limiting further pre-­synaptic release of neurotransmitters from axonal terminals and are then rapidly degraded.13,28,29 AEA is synthesised from its likely precursor, the minor phospholipid component N-­arachidonoylphosphatidylethanolamine (NAPE) by the selective phospholipase D enzyme (NAPE-­PLD), which is preferentially located at post-­ synaptic dendrites.30 Three enzymes have been identified that are capable of hydrolysing AEA on its removal from the synaptic cleft and transport into the cell by fatty acid binding proteins. Fatty acid amide hydrolase (FAAH),

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FAAH2 and N-­acylethanolamine acid amidase (NAAA), hydrolyse AEA to arachidonic acid and ethanolamine. FAAH is located intracellularly and is the main enzyme responsible for AEA hydrolysis, as shown by the observation that FAAH knockout mice have a 15-­fold higher level of AEA in the brain compared with wild-­t ype mice.31 2-­AG is synthesised from the intracellular signalling component 1,2-­diacylglycerol which is generated by the enzyme phospholipase C. 2-­AG is synthesised from 1,2-­diacylglycerol by the enzyme diacylglycerol lipase (DAG-­Lipase), which is located post-­s ynaptically at dendrites and dendritic spines.30,32 The enzyme responsible for over 90% of the degradation of 2-­AG is monoacylglycerol lipase (MAGLipase), which is expressed at presynaptic axon terminals.33 The enzymes human α/β-­hydrolase domain containing 6 (ABHD6) and 12 (ABHD12) can also, to a much lesser extent hydrolyse 2-­AG.34 Under normal physiological conditions, it appears that the main endocannabinoid responsible for retrograde signalling is 2-­AG. Supporting evidence for this is the observation that MAGLipase inhibitors significantly prolonged the suppression in neurons of post-­s ynaptic potentials induced by exogenous 2-­AG inhibition of neurotransmitter release. This phenomenon was not observed with inhibitors of AEA degradation.35 AEA may assume more physiological significance in situations where there is neuronal perturbation or damage in neurological conditions such as stroke, head injury and neurodegenerative conditions such as MS. Moreover, a large number of synthetic agonists and antagonists of CB receptors, inhibitors of the enzymes that degrade endocannabinoids and mouse gene knockout technology have been developed. All these tools have greatly aided the study of the endocannabinoid system and pointed to the potential therapeutic manipulation of this system for the treatment of conditions such as MS.

11.3 Cannabinoids and Multiple Sclerosis Over recent years, axonal pathology during MS has been re-­examined and it has been established that CNS atrophy and axonal loss occurs, coincident with inflammatory lesion formation, early in the relapsing-­remitting phase. This may be accommodated initially by neuronal reserve, remodelling of neuronal circuits (neural plasticity) or an increase in the number of neural precursors in some lesioned areas contiguous with subventricular zones.36 However, as the disease continues, a threshold is reached beyond which permanent impairment and increasing disability is established.1,37 This suggests that axonal loss rather than myelin damage is the key determinant of progressive disability in MS. In addition, a doubling in the levels of glutamate, an excitatory amino acid that has been shown to be neurotoxic in excess, is seen in the cerebrospinal fluid of pwMS undergoing an inflammatory episode.38

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In MS, axonal loss correlates with permanent neurological disability39 and in experimental allergic encephalomyelitis (EAE), an animal model of MS induced by the development of autoimmunity against myelin antigens, 15–30% of spinal cord axons can be lost before permanent locomotor impairment is established.40 After a number of relapse events, permanent disability develops with significant axonal loss (40–80%, as also occurs in MS) in the spinal cord and the development of hind-­limb spasticity and tremor,40,41 which may reflect the preferential loss of inhibitory circuits in certain locations of the spinal cord and their influence on signalling to skeletal muscles. Whilst inflammatory events are associated with axonal transections, chronic demyelination may contribute to a slow degenerative process. As increasing numbers of axons are lost, this creates an extra burden on the remaining neurons and potential excitotoxicity due to increased activity on these neurons within the neural circuitry. Thus, a slow amplifying cascade of neuronal death may be triggered, which could occur independently of significant lymphocytic inflammation. This would be compatible with the slow progression in secondary progressive MS and the inability of potent immunosuppressive agents to significantly inhibit this aspect of disease. During all neurodegenerative diseases, symptoms occur because homeostatic control of neurotransmission is lost and may result from increased neurotransmission by excessive signalling of excitatory circuits or loss of inhibitory circuits, or vice versa. As it appears that an important function of the cannabinoid system is the modulation of neurotransmitter release via CB1 receptor expression at pre-­synaptic nerve terminals,28 this raises the possibility of therapeutic intervention in CNS events for symptom control by the manipulation of this system.

11.3.1 Cannabinoids as Symptom-­modifying Agents in MS The primary area of investigation of the cannabinoids in MS so far has been that of symptom relief, in particular bladder incontinence, pain, tremor and particularly limb spasticity. Spasticity is one of the most common reported symptoms of MS and can affect approximately 50% of patients to some degree, with significant deleterious effects of quality of life and the ability to function in daily life.42 A survey of pwMS in Spain reported the presence of spasticity in 65% of pwMS, with 40% rating this as moderate or severe and the severity increasing with the degree of disability.43 Current therapies for spasticity include the GABA receptor agonist Baclofen, Tizanidine and benzodiazepines.44 Intrathecal Baclofen is commonly used for the treatment of severe refractory spasticity.44 The anti-­convulsant Gabapentin and local administration of botulinum toxin have also shown efficacy in clinical trials.44 However, a study in Germany has reported that 55% of physicians were dissatisfied with current treatment options for spasticity and the chief patient-­reported negative effects were adverse side-­effects (92.5%) and poor efficacy (88%), with one third of patients seeking relief by self-­medication.45

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The pathophysiology of spasticity remains poorly understood but it may reflect a preferential loss of inhibitory circuitry in the spinal cord resulting in excessive levels of stimulatory signals. Under normal circumstances, inhibitory signals are sent via the corticospinal tract to the spinal cord, but following injury, damage to the corticospinal tract, a hallmark of MS, causes disinhibition of the stretch reflex leading to a reduction in the triggering threshold. This can result in excessive contraction of the muscles, sometimes even at rest.46–48 The hypertonic mouse mutant hyrt49 shows spastic signs in the hind limbs associated with a reduction in the level of inhibitory GABA A receptors in lower motor neurons. Loss of GABA-­ergic inputs or GABA-­receptor-­expressing neurons may result in the spasticity seen in MS as neurodegeneration progresses and explains the efficacy of GABA agonists such as Baclofen. Improved treatment options for spasticity are required as agents that directly interfere with neurotransmitter activity are often associated with undesirable side-­effects such as cognitive impairment.50

11.3.2 Experimental Evidence Experimental data in MS models in mice have proved the anti-­spastic and anti-­tremor effects of cannabinoids and CB1 receptor agonists,41,51,52 and any CB1 agonist that reaches the CNS has the potential to inhibit spasticity. Furthermore and importantly, antagonism of the cannabinoid system by CB1 receptor antagonist SR141716 (Figure 11.1) produces a transient worsening of these signs, indicating the presence of an endogenous cannabinoid tone, which is modulating these signs to some degree via the release of endocannabinoids in response to elevated neuronal excitation.41,51 The cannabis-­derived medication Sativex®, consisting of a 1 : 1 mixture of THC and CBD, in addition to other cannabinoids, has also demonstrated efficacy in the reduction of hind-­limb spasticity in an experimental model of MS.53 The AEA derivative VSN16, which has modulatory activity on the BKCa ion channel but no activity at CB1 or CB2 receptors, has also been shown to ameliorate limb spasticity and, importantly, without the psychoactive effects that are inevitably seen with agonists that act at the CB1 receptor.27 In addition, endocannabinoid (particularly AEA) levels are raised in the spinal cords and brains of chronic neurodegenerative EAE mice that show hind-­limb spasticity, but not in animals that have equivalent levels of neurodegeneration but without associated limb spasticity.51 This further indicates the presence of an endocannabinoid tone, which is elevated as a result of spasticity and tremor in these animals. Furthermore, administration of compounds that elevate endogenous anandamide or 2-­AG levels, either via the inhibition of endocannabinoid re-­uptake,54,55 or the inhibition of enzymatic degradation by FAAH or MAG Lipase,51,56 also reduces the level of hind-­limb spasticity in chronic EAE mice. It has also been demonstrated that CNS-­excluded CB1 receptor agonists, developed to limit the psychoactivity associated with CB1 receptor agonism in the CNS,

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are also capable of ameliorating hind-­limb spasticity in EAE mice via the action at CB1 receptors in the peripheral nervous system. The therapeutic anti-­spastic activity was lost when administered to spastic, fully congenic peripheral nerve CB1-­deficient mice, suggesting the therapeutic activity was within the peripheral nervous system.57 These observations provide clear, objective evidence to underpin patient perceptions of the efficacy of cannabis on MS symptoms.

11.3.3 Clinical Evidence A large-­scale patient survey in 2017 reported that 75.5% of responders using cannabis for their symptoms perceived cannabis as effective in treating their spasticity, which was the most common MS symptom reported.3 Historical clinical studies of the efficacy of cannabis on spasticity in MS were at best equivocal. However, more recent studies, with improved methodology have produced more robust evidence of efficacy. More weight has been given to a patient-­rated Numeric Rating Scale of improvements in spasticity as a result of treatment rather than relying on the unreliable Ashworth scale as the primary outcome measure. In the Cannabis in Multiple Sclerosis (CAMS) study,58 despite there being no significant improvement in spasticity as assessed by the Ashworth scale, there was a significant improvement in a 10 metre walking time test in patients treated with Marinol/dronabinol (a pure synthetic isomer of THC). Patient-­rated evaluations also reported significant improvements in spasticity and also pain and quality of sleep. In the extension study over a further period of 12 months in responders, a modest but significant improvement in spasticity assessed by the observer-­assessed Ashworth scale measurement was reported in addition to patient-­rated improvements in cannabinoid treatment groups.59 In light of the poor experience with the Ashworth scale due to its limited ability to detect positive improvements in spasticity, doubts have been raised as to its usefulness for spasticity assessment.60 Thus, the Multiple Sclerosis and Extract of Cannabis (MUSEC) trial was initiated using a category rating scale (CRS) measuring patient-­reported change in muscle stiffness from baseline after treatment with a standardised cannabis extract containing 2.5 mg THC per capsule. Participants received 5 mg to a maximum dose of 25 mg THC daily versus placebo. The study reported that cannabis extract to a maximum of 25 mg THC daily over 10 weeks demonstrated twice the amount of improvement of muscle stiffness compared with placebo. There were also reported improvements in spasms and sleep quality, and effective pain relief was also demonstrated by the cannabis extract, particularly in those pwMS with high baseline scores.61 In addition, a small-­scale Canadian study investigating the use of smoked cannabis for the treatment of spasticity in MS patients refractory to current medications reported highly significant reductions in spasticity as assessed by the modified Ashworth scale (reduction of 2.74 points compared with placebo) and also significant reductions in pain scores.62 The proviso here is that the patients smoking

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cannabis were significantly cognitively affected, which will be a negative indication for many patients. Indeed, it is likely that all patients reporting improvements in spasticity with cannabinoid therapeutics will have cognitive side effects to a greater or lesser degree due to the ubiquitous expression of the CB1 receptor in the brain. There are now a large number of documented clinical studies on the activity of the cannabis-­derived product Sativex® as a therapeutic agent in MS-­ related spasticity.63 A study investigating the effects of Sativex® on spasticity used a patient/clinician assessed visual numerical rating scale (NRS) with a range of 1–10 as the primary endpoint. Sativex®-­treated pwMS reported a significant decrease in their spasticity score of 1.18 compared with placebo. In comparison, and similar to many other spasticity studies, the decrease assessed by the Ashworth scale was not significant. Of the 40% of pwMS who responded to Sativex®, a reduction of at least 30% in the NRS score was reported.64 In a study that was enriched by the inclusion of early-­responding patients to treatment who were refractory to available anti-­spastic medication showed at least a 20% reduction in the NRS score compared with baseline after 4 weeks of treatment. In the second phase of the study (12 weeks), enriched with responders to treatment, a significant reduction in NRS score was maintained compared with placebo over this period together with a modest improvement in spasticity measured by the modified Ashworth scale. In addition to spasticity severity, Sativex® was found to improve other spasticity-­related symptoms, including spasm frequency, sleep disturbance, Barthel activities of daily living, Physician Global Impression of Change, Subject Global Impression of Change and Carer Global impression of Change.65 In a study of pwMS with treatment-­resistant spasticity, treated with Sativex®, significant improvements in the level of spasticity were reported after 3 months of treatment.66 This positive clinical evidence for the potential benefit of cannabis on MS-­associated spasticity led in June 2010 to the approval for prescription in the UK and elsewhere of the cannabis extract Sativex® for the treatment of spasticity in MS. A recent publication, however, raises the concern that this medication will not be available to a large percentage of MS patients who could benefit from cannabinoid treatment of their spasticity.67 The study highlights that due to issues of pricing versus quality of life measurements, the authors state “Sativex® appears unlikely to be considered cost effective by UK funders of healthcare for spasticity in MS. This is unfortunate, since it appears that Sativex® use is likely to benefit some patients in the management of this common consequence of MS.” This has led to a “post code lottery” in the UK, where some clinical commissioning groups approve the prescription of Sativex® for spasticity, whereas many others do not on cost grounds. This makes the recent approval in many countries of medicinal cannabis and also the positive recent change in the Government's attitude to medicinal cannabis in the UK significant, as many pwMS will seek this option to treat their MS symptoms, including spasticity, and it lessens the need to take the illegal street cannabis option.

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11.4 Cannabinoids as Neuroprotective Agents in MS The neurotoxic mechanisms during MS and experimental models are varied, with the potential agents of neuronal/axonal damage including oxidative damage to mitochondria, release of inflammatory cytokines, nitric oxide release from activated macrophages/microglia and excitotoxicity due to excessive glutamate signalling leading to toxic levels of calcium ion influx. There is increasing evidence that elevated levels of glutamate are seen in both MS and EAE, particularly during the active stages of disease,67,68 accompanied by an increase in the level of expression of Group 1 metabotropic glutamate receptors and excitatory amino acid transporters.68 Elevation of glutamate was also observed in the progressive phase of EAE concomitant with increased levels of neurodegeneration, further implicating glutamate excitotoxicity as a mechanism for neuronal degeneration in experimental MS.69 The ability of cannabinoids to down-­regulate the release of neurotransmitters such as glutamate is long established,70 leading to the hypothesis that they may have neuroprotective properties in neuroinflammatory disease.

11.4.1 Experimental Evidence The important role of the cannabinoid system in the protection against neurodegeneration was revealed in a mouse model of MS where the CB1 receptor was genetically deleted. In these mice, neuroinflammation resulted in an accelerated accumulation of neurological deficits compared with wild-­t ype animals.71,72 In wild-­t ype EAE mice, there is a reduction in the level and signalling efficiency of CB1 receptors in motor-­related areas of the CNS in the acute phase of disease and also to a more marked extent in the chronic phase of disease. In contrast, in cognition-­related areas such as the cerebral cortex and hippocampus, the level and signalling efficiency of CB1 receptors was unaffected in all phases of disease.73 Furthermore, administration of exogenous cannabinoid agonists, including the cannabis components THC and CBD, can significantly inhibit neurodegeneration due to neuroinflammation in acute and chronic disease models in wild-­ type mice in the absence of any overt immunosuppression, which would modify the level of neuronal insult.72,74,75 Endocannabinoid (AEA and 2-­AG) levels have been reported to be decreased in EAE in response to neuroinflammation;76 another study reported that 2-­AG was also decreased during EAE.77 However, in contrast, an increase in AEA but not 2-­AG has also been reported in EAE and also MS.78 In addition to the administration of CB1 agonists, the rate of neurodegeneration can be decreased by exogenous 2-­AG administration in acute and chronic EAE,79 via pharmacological inhibition of endocannabinoid (AEA) degradation/uptake,76 or by the genetic ablation of FAAH, the enzyme that degrades AEA.80,81 In another study, EAE was also ameliorated via the selective pharmacological inhibition of MAG lipase, thus enhancing 2-­AG levels in the CNS.82 Also, oligodendrocyte excitotoxicity and white matter damage has been reported to be ameliorated

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by the administration of a MAG lipase inhibitor via enhancing endogenous levels of 2-­AG, in contrast to the inhibition of FAAH, which had no effect in this animal model of MS.83 These observations would suggest that cannabinoid therapy may have a potential role in the slowing of neurodegeneration as a result of MS and may be considered as an adjunctive therapy to current disease-­modifying therapies.

11.4.2 Clinical Evidence There has to date been one study investigating the neuroprotective potential of synthetic THC (Dronabinol) to slow the development of disability in MS.84,85 This stemmed from follow-­up studies in spasticity trials that suggested a neuroprotective effect of THC.59 Participants in the neuroprotective trial were randomly assigned to receive THC capsules or placebo capsules, to be taken by mouth over a period of 3 years. Three hundred and twenty-­nine people were allocated to receive the THC capsules and 164 were allocated to the placebo group. For each participant, the first 4 weeks of the trial were devoted to establishing the best-­tolerated dose for study treatment. For the remainder of the study period, participants remained on a stable dose of treatment, as far as possible, before the dose was gradually reduced to zero at the end of the treatment period. The study was “double-­b lind”, meaning that neither the participants nor the doctors and nurses involved at the study sites knew which treatment group they were in. Despite the abundant experimental evidence that cannabinoid therapy has a neuroprotective role in a spectrum of neurological diseases, overall the study found no evidence that THC had an  effect on MS progression in either of the main outcomes, the Expanded Disability Status Scale (EDSS) neurological assessments conducted by doctors at the study clinics or the 29-­item multiple sclerosis impact scale (MSIS-­ 29) questionnaire responses provided by the participants. The EDSS and MSIS-­29 scores showed little change over the course of the study and no difference was found between the active and placebo groups. A confounding finding was that the placebo group had not progressed as expected, which complicated assessing the value of the trial. However, and potentially importantly, there was some evidence from subgroup analysis that THC might have a significant (P < 0.01) beneficial effect in participants at the lower end of the EDSS disability scale (11) trinucleotide AAT repeats of the CB1 receptor gene CNR1, located on chromosome 6 in both alleles in pwMS resulted in more severe disease, neurodegeneration and disease progression and cognitive impairment.87,88 This polymorphism is likely to be associated with a lower transcription efficiency of the CB1 receptor,89 with lower levels of CB1 expressed on lymphocytes from pwMS with long AAT repeats88 compared with those with short AAT repeats, and thus also potentially reducing the number of these neuroprotective receptors in the CNS. These observations again point to the potential of CB1 agonists such as cannabis to influence disease progression in MS and the need for more clinical studies in this area.

11.5 Cannabinoids and Bladder Dysfunction in MS Bladder dysfunction is one of the most common symptoms reported by pwMS,3 having a great impact on quality of life. It was found that 20.3% of 5481 respondents to a survey reported that smoked cannabis was effective in treating overactive bladder symptoms.3 Increased numbers of CB1-­expressing nerve fibres are seen in painful and overactive bladder disorders unrelated to MS, which significantly correlate with pain and urgency scores, indicating that cannabinoids may be useful in the treatment of bladder disorders.90 The first clinical study showed a positive effect of a cannabis extract on impaired bladder control in pwMS over a 2-­week treatment period.91 In another small-­ scale study, again with a medicinal cannabis extract, urinary urgency, the number and volume of incontinence episodes, frequency and nocturia all decreased significantly following treatment.92 In a larger-­scale, double-­blind, placebo-­controlled study with 135 participants with MS-­related overactive bladder activity treated with Sativex®, although the primary endpoint of a reduction in incontinence episodes was not met, nocturia, overall bladder condition, voids per day and patient's global impression of change were significantly improved with Sativex® compared with placebo.93 In treatment-­ resistant spasticity pwMS treated with Sativex®, improvements in bladder symptoms were reported after 3 months of treatment.66 A large-­scale study of 630 pwMS as an addition to the CAMS spasticity study,58 investigating the effects of cannabis extract or THC on urge incontinence episodes without affecting number of voids, reported a positive effect on incontinence with both cannabis extract and THC over placebo.94 In a study of pwMS with treatment-­resistant spasticity, treated with Sativex®, significant improvement in bladder dysfuction were reported after 3 months of treatment.66 Although the number of studies is still small, the evidence would suggest a positive therapeutic effect of cannabinoids on bladder dysfunction, particularly bladder over-­activity in MS.

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11.6 Cannabinoids and Pain in MS Along with spasticity, pain is a common problem in pwMS, with approximately two thirds reporting pain, particularly neuropathic pain and pain associated with spasticity and spasms associated with their MS.95 Common forms of neuropathic pain seen in MS patients include central neuropathic pain, Lhermitte's phenomenon and trigeminal neuralgia. Neuropathic pain, which can be difficult to treat effectively, arises from damage to nerves of the somatosensory nervous system and is likely to be associated with MS lesion location.

11.6.1 Experimental Evidence Numerous pre-­clinical animal studies have reported the effectiveness of CB1 and also CB2 receptor agonists in reducing the level of neuropathic pain,96 whereas peripheral nerve CB1 deletion, global CB2 receptor deletion or pharmacological antagonists of these receptors enhanced the level of induced neuropathic pain.96–98 Genetic deletion (of FAAH) or pharmacological inhibition of endocannabinoid degradation, thus boosting endogenous levels, have both been shown to attenuate neuropathic pain in animal models.99 However, in January 2016, there were serious adverse events (four subjects suffered brain damage and there was one fatality) found in a phase I pharmacology/safety study of a FAAH inhibitor in France, therefore it would seem that further pharmaceutical development of this class of compounds is unlikely. This may be despite the fact that it is now clear that this particular FAAH inhibitor had numerous off-­target effects on a number of other lipases, and this was proposed to cause metabolic dysregulation in the CNS.100

11.6.2 Clinical Evidence There are numerous studies that have reported the beneficial effects of cannabinoids on pain relating to a number of different conditions.101 Focusing specifically on MS, assessments of the effectiveness of cannabinoids on pain measurements have tended to be secondary outcomes associated with cannabinoids for the treatment of spasticity clinical trials. Both the CAMS and MUSEC spasticity studies58,61 examined secondary outcomes of patient-­ reported measurements of pain, and both studies reported improvements in pain with cannabinoid treatment compared with placebo. A study looking at a cannabis-­based (nabiximol) therapy specifically for pain in MS, found a 41% decrease in mean pain intensity in the nabiximol-­treated group (average THC dose of 26 mg day−1), compared with a 22% decrease in pain in the placebo-­treated group.102 A study looking at nabilone (a synthetic THC derivative, Figure 11.1), 1 mg twice daily as an add-­on to gabapentin treatment for pain in 15 pwMS, reported significant improvements

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in patient-­rated pain scores versus gabapentin and placebo. In a study of pwMS with treatment-­resistant spasticity, treated with Sativex®, significant improvement in patient-­perceived pain ratings were reported after 3 months of treatment.66 Despite an as-­yet limited number of studies in MS, cannabis does seem to have the potential to modulate pain arising as a result of MS.

11.7 Cannabinoids and Immunomodulation in MS Although there is clear clinical evidence for symptomatic benefit the question of whether there is additional disease-­modifying potential has yet to be adequately resolved. The relapsing course of MS is clearly driven by the influence of the immune system, as evidenced by response to therapy.104,105 Cells within the immune system both express CB1 and CB2;13 therefore, some influence on immune function may be possible in MS.

11.7.1 Experimental Evidence Indeed, there is evidence that both CB1 and CB2 agonism can inhibit the autoimmune response in experimental models of MS.106–109 Cannabinoid binding to CB1 receptors within nervous tissue may deliver immunosuppressive signals, possibly via glucocorticoid steroids to inhibit autoreactive cells,107,108,110 and there may be direct effects on the adaptive and innate immune response via CB2 receptors.109,111 However, the importance of these observations is debatable, as there has been considerable inconsistency in experimental outcomes.106,107,112,113 These effects may be due to adverse cannabimimetic reactions due to high experimental doses that are probably irrelevant to human use108 or the robustness of the animal model used, where effects are best seen in low susceptibility systems.109

11.7.2 Clinical Evidence Although there are some immunomodulating effects in chronic cannabis smokers,112 as Marinol (THC) is a licenced treatment in acquired immune deficiency syndrome (AIDS) it is envisioned that there is no marked immunosuppression effect that is relevant. To date trials of cannabinoids have been conducted to determine symptom control and have not been designed to examine disease-­modifying effect.58,59 Furthermore, such trials have often involved pwMS who have not been experiencing active, relapsing disease.58,59 It is unlikely that this immunosuppressive effect will be directly addressed in a formal major trial, given the cost of such studies and the fact that there are many therapeutic agents that target relapsing disease in MS, which clearly have greater efficacy than that achievable by cannabinoids in models of MS.108,114,115

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11.8 Conclusions There is now clear evidence that compounds within cannabinoids have therapeutic value in humans, notably in MS. The rescheduling of cannabis for medical use in a number of countries and the development of pharmaceutical cannabinoid variants will allow help us gain even greater evidence for benefit and help provide greater insight into the real therapeutic potential of the cannabinoid system, as much remains to be uncovered.

References 1. C. Confavreux and S. Vukusic, Clin. Neurol. Neurosurg., 2006, 108, 327. 2. P. Consroe, R. Musty, J. Rein, W. Tillery and R. Pertwee, Eur. Neurol., 1997, 38, 44. 3. S. S. Cofield, A. Salter, T. Tyry, C. Crowe, G. R. Cutter, R. I. Fox and R. A. Marrie, Neurol. Clin. Pract., 2017, 7, 333. 4. S. Davies, Cannabis scheduling review: part 1, 2018, https://www.gov.uk/ government/publications/cannabis-­scheduling-­review-­part-­1. 5. Y. Gaoni and R. Mechoulam, J. Am. Chem. Soc., 1964, 86, 1646. 6. W. A. Devane, F. A. Dysarz, M. R. Johnson, L. S. Melvin and A. C. Howlett, Mol. Pharmacol., 1988, 34, 605. 7. L. A. Matsuda, S. J. Lolait, M. J. Brownstein, A. C. Young and T. I. Bonner, Nature, 1990, 346, 561. 8. S. Munro, K. L. Thomas and M. Abu-­Shaar, Nature, 1993, 365, 61. 9. W. A. Devane, L. Hanus, A. Breuer, R. G. Pertwee, L. A. Stevenson, G. Griffin, D. Gibson, A. Mandelbaum, A. Etinger and R. Mechoulam, Science, 1992, 258, 1946. 10. R. Mechoulam, S. Ben-­Shabat, L. Hanus, M. Ligumsky, N. E. Kaminski, A. R. Schatz, A. Gopher, S. Almog, B. R. Martin, D. R. Compton, R. G. Pertwee, G. Griffin, M. Bayewitch, J. Barg and Z. Vogel, Biochem. Pharmacol., 1995, 50, 83. 11. N. Stella, P. Schweitzer and D. Piomelli, Nature, 1997, 388, 773. 12. S. D. McAllister, Q. Tao, J. Barnett-­Norris, K. Buehner, D. P. Hurst, F. Guarnieri, P. H. Reggio, K. W. Nowell-­Harmon, G. A. Cabral and M. E. Abood, Biochem. Pharmacol., 2002, 63, 2121. 13. A. C. Howlett, F. Barth, T. I. Bonner, G. Cabral, P. Casellas, W. A. Devane, C. C. Felder, M. Herkenham, K. Mackie, B. R. Martin, R. Mechoulam and R. G. Pertwee, Pharmacol. Rev., 2002, 54, 161. 14. G. Moldrich and T. Wenger, Peptides, 2000, 21, 1735. 15. M. Herkenham, A. B. Lynn, M. R. Johnson, L. S. Melvin, B. R. de Costa and K. C. Rice, J. Neurosci., 1991, 11, 563. 16. C. C. Felder, K. E. Joyce, E. M. Briley, J. Mansouri, K. Mackie, O. Blond, Y. Lai, A. L. Ma and R. L. Mitchell, Mol. Pharmacol., 1995, 48, 443. 17. C. Turcotte, M.-­R. Blanchet, M. Laviolette and N. Flamand, Cell. Mol. Life Sci., 2016, 73, 4449.

Cannabinoids as a Therapeutic Approach in Multiple Sclerosis

259

18. M. Kohno, H. Hasegawa, A. Inoue, M. Muraoka, T. Miyazaki, K. Oka and M. Yasukawa, Biochem. Biophys. Res. Commun., 2006, 347, 827. 19. D. McHugh, Br. J. Pharmacol., 2012, 167, 1575. 20. D. McHugh, J. Page, E. Dunn and H. B. Bradshaw, Br. J. Pharmacol., 2012, 165, 2414. 21. D. Baker, G. Pryce, W. L. Davies and C. R. Hiley, Trends Pharmacol. Sci., 2006, 27, 1. 22. B. A. Marichal-­Cancino, A. Fajardo-­Valdez, A. E. Ruiz-­Contreras, M. Mendez-­Díaz and O. Prospero-­García, Curr. Neuropharmacol., 2017, 15, 771. 23. K. B. Hansen, M. M. Rosenkilde, F. K. Knop, N. Wellner, T. A. Diep, J. F. Rehfeld, U. B. Andersen, J. J. Holst and H. S. Hansen, J. Clin. Endocrinol. Metab., 2011, 96, E1409. 24. A. P. Davenport, S. P. H. Alexander, J. L. Sharman, A. J. Pawson, H. E. Benson, A. E. Monaghan, W. C. Liew, C. P. Mpamhanga, T. I. Bonner, R. R. Neubig, J.-­P. Pin, M. Spedding and A. J. Harmar, Pharmacol. Rev., 2013, 65, 967. 25. L. De Petrocellis, T. Bisogno, M. Maccarrone, J. B. Davis, A. Finazzi-­Agro and V. Di Marzo, J. Biol. Chem., 2001, 276, 12856. 26. S. E. O'Sullivan, Br. J. Pharmacol., 2007, 152, 576. 27. D. Baker, G. Pryce, C. Visintin, S. Sisay, A. I. Bondarenko, W. S. Vanessa Ho, S. J. Jackson, T. E. Williams, S. Al-­Izki, I. Sevastou, M. Okuyama, W. F. Graier, L. A. Stevenson, C. Tanner, R. Ross, R. G. Pertwee, C. M. Henstridge, A. J. Irving, J. Schulman, K. Powell, M. D. Baker, G. Giovannoni and D. L. Selwood, Br. J. Pharmacol., 2017, 174, 2662. 28. R. I. Wilson and R. A. Nicoll, Science, 2002, 296, 678. 29. T. F. Freund, I. Katona and D. Piomelli, Physiol. Rev., 2003, 83, 1017. 30. L. Reguero, N. Puente, I. Elezgarai, A. Ramos-­Uriarte, I. Gerrikagoitia, J. L. Bueno-­López, F. Doñate and P. Grandes, Histochem. Cell Biol., 2014, 141, 543. 31. B. F. Cravatt, K. Demarest, M. P. Patricelli, M. H. Bracey, D. K. Giang, B. R. Martin and A. H. Lichtman, Proc. Natl. Acad. Sci. U. S. A., 2001, 98, 9371. 32. T. Yoshida, M. Fukaya, M. Uchigashima, E. Miura, H. Kamiya, M. Kano and M. Watanabe, J. Neurosci., 2006, 26, 4740. 33. A. I. Gulyas, B. F. Cravatt, M. H. Bracey, T. P. Dinh, D. Piomelli, F. Boscia and T. F. Freund, Eur. J. Neurosci., 2004, 20, 441. 34. J. L. Blankman, G. M. Simon and B. F. Cravatt, Chem. Biol., 2007, 14, 1347. 35. Y. Hashimotodani, T. Ohno-­Shosaku and M. Kano, J. Neurosci., 2007, 27, 1211. 36. A. Chang, M. C. Smith, X. Yin, R. J. Fox, S. M. Staugaitis and B. D. Trapp, Brain, 2008, 131, 2366. 37. C. Bjartmar, J. R. Wujek and B. D. Trapp, J. Neurol. Sci., 2003, 206, 165. 38. J. F. Stover, U. E. Pleines, M. C. Morganti-­Kossmann, T. Kossmann, K. Lowitzsch and O. S. Kempski, Eur. J. Clin. Invest., 1997, 27, 1038.

260

Chapter 11

39. C. Bjartmar, G. Kidd, S. Mork, R. Rudick and B. D. Trapp, Ann. Neurol., 2000, 48, 893. 40. J. R. Wujek, C. Bjartmar and E. Richer, et al., Axon loss in the spinal cord determines permanent neurological disability in an animal model of multiple sclerosis, J. Neuropathol. Exp. Neurol., 2002, 61, 23. 41. D. Baker, G. Pryce, J. L. Croxford, P. Brown, R. G. Pertwee, J. W. Huffman and L. Layward, Cannabinoids control spasticity and tremor in a multiple sclerosis model, Nature, 2000, 404, 84. 42. M. P. Barnes, R. M. Kent, J. K. Semlyen and K. M. McMullen, Neurorehabil. Neural Repair, 2003, 17, 66. 43. C. Oreja-­Guevara, D. González-­Segura and C. Vila, Int. J. Neurosci., 2013, 123, 400. 44. S. Otero-­Romero, J. Sastre-­Garriga, G. Comi, H. P. Hartung, P. Soelberg Sørensen, A. J. Thompson, P. Vermersch, R. Gold and X. Montalban, Mult. Scler., 2016, 22, 1386. 45. T. Henze, P. Flachenecker and U. K. Zettl, Nervenarzt, 2013, 84, 214. 46. M. M. Adams and A. L. Hicks, Spinal Cord, 2005, 43, 577. 47. P. Brown, J. Neurol., Neurosurg. Psychiatry, 1994, 57, 773. 48. J. B. Nielsen, C. Crone and H. Hultborn, Acta Physiol., 2007, 189, 171. 49. S. L. Gilbert, L. Zhang, M. L. Forster, J. R. Anderson, T. Iwase, B. Soliven, L. R. Donahue, H. O. Sweet, R. T. Bronson, M. T. davisson, R. L. Wollmann and B. T. Lahn, Nat. Genet., 2006, 38, 245. 50. S. Paisley, S. Beard, A. Hunn and J. Wight, Mult. Scler., 2002, 8, 319. 51. D. Baker, G. Pryce, J. L. Croxford, P. Brown, R. G. Pertwee, A. Makriyannis, A. Khanolkar, L. Layward, F. Fezza, T. Bisogno and V. Di Marzo, FASEB J., 2001, 15, 300. 52. G. Pryce and D. Baker, Br. J. Pharmacol., 2007, 150, 519. 53. A. Hilliard, C. Stott, S. Wright, G. Guy, G. Pryce, S. Al-­Izki, C. Bolton and G. Giovannoni, ISRN Neurol., 2012, 2012, 802649. 54. E. de Lago, A. Ligresti, G. Ortar, E. Morera, A. Cabranes, G. Pryce, M. Bifulco, D. Baker, J. Fernández-­Ruiz and V. Di Marzo, Eur. J. Pharmacol., 2004, 484, 249. 55. E. de Lago, J. Fernández-­Ruiz, S. Ortega-­Gutiérrez, A. Cabranes, G. Pryce, D. Baker, M. López-­Rodríguez and J. A. Ramos, Eur. Neuropsychopharmacol., 2006, 16, 7. 56. G. Pryce, A. Cabranes, J. Fernández-­Ruiz, T. Bisogno, V. Di Marzo, J. Z. Long, B. F. Cravatt, G. Giovannoni and D. Baker, Mult. Scler., 2013, 19, 1896. 57. G. Pryce, C. Visintin, S. V. Ramagopalan, S. Al-­Izki, L. E. De Faveri, R. A. Nuamah, C. A. Mein, A. Montpetit, A. J. Hardcastle, G. Kooij, H. E. de Vries, S. Amor, S. A. Thomas, C. Ledent, G. Marsicano, B. Lutz, A. J. Thompson, D. L. Selwood, G. Giovannoni and D. Baker, FASEB J., 2014, 28, 117. 58. J. Zajicek, P. Fox, H. Sanders, D. Wright, J. Vickery, A. Nunn, A. Thompson and UK MS Research Group, Lancet, 2003, 362, 1517. 59. J. P. Zajicek, H. P. Sanders, D. E. Wright, P. J. Vickery, W. M. Ingram, S. M. Reilly, A. J. Nunn, L. J. Teare, P. J. Fox and A. J. Thompson, J. Neurol., Neurosurg. Psychiatry, 2005, 76, 1664.

Cannabinoids as a Therapeutic Approach in Multiple Sclerosis

261

60. J. F. M. Fleuren, G. E. Voerman, C. V. Erren-­Wolters, G. J. Snoek, J. S. Rietman, H. J. Hermans and A. V. Nene, J. Neurol., Neurosurg. Psychiatry, 2010, 81, 46. 61. J. P. Zajicek, J. C. Hobart, A. Slade, D. Barnes, P. G. Mattison and MUSEC Research Group, J. Neurol., Neurosurg. Psychiatry, 2012, 83, 112511. 62. J. Corey-­Bloom, T. Wolfson, A. Gamst, S. Jin, T. D. Marcotte, H. Bentley and B. Gouaux, CMAJ, 2012, 184, 1143. 63. S. Giacoppo, P. Bramanti and E. Mazzon, Mult. Scler. Relat. Disord., 2017, 17, 22. 64. C. Collin, P. Davies, I. K. Mutiboko and S. Ratcliffe, Eur. J. Neurol., 2007, 14, 290. 65. A. Novotna, J. Mares, S. Ratcliffe, I. Novakova, M. Vachova, O. Zapletalova, C. Gasperini, C. Pozzilli, L. Cefaro, G. Comi, P. Rossi, Z. Ambler, Z. Stelmasiak, A. Erdmann, X. Montalban, A. Klimek and P. Davies, Eur. J. Neurol., 2011, 18, 1122. 66. P. Vermersch and M. Trojano, Eur. Neurol., 2016, 76, 216. 67. L. Lu, H. Pearce, C. Roome, J. Shearer, I. A. Lang and K. Stein, Pharmacoeconomics, 2012, 30, 1157. 68. G. Sulkowski, B. Dabrowska-­Bouta, B. Kwiatkowska-­Patzer and L. Struzyńska, Folia Neuropathol., 2009, 47, 329. 69. A. Marte, A. Cavallero, S. Morando, A. Uccelli, M. Raiteri and E. Fedele, J. Neurochem., 2010, 115, 343. 70. M. Shen and S. A. Thayer, Mol. Pharmacol., 1999, 55, 8. 71. S. L. Jackson, G. Pryce, L. T. Diemel and D. Baker, Neuroscience, 2005, 134, 261. 72. G. Pryce, Z. Ahmed, D. J. Hankey, S. J. Jackson, J. L. Croxford, J. M. Pocock, C. Ledent, A. Petzold, A. J. Thompson, G. Giovannoni and M. L. Cuzner, Brain, 2003, 126, 2191. 73. A. Cabranes, G. Pryce, D. Baker and J. Fernández-­Ruiz, Brain Res., 2006, 1107, 199. 74. J. L. Croxford, G. Pryce, S. J. Jackson, C. Ledent, G. Giovannoni, R. G. Pertwee, T. Yamamura and D. Baker, J. Neuroimmunol., 2008, 193, 120. 75. G. Pryce, D. R. Riddall, D. L. Selwood, G. Giovannoni and D. Baker, J. Neuroimmune Pharmacol., 2015, 10, 281. 76. A. Cabranes, K. Venderova, E. de Lago, F. Fezza, A. Sánchez, L. Mestre, M. Valenti, A. García-­Merino, J. A. Ramos, V. Di Marzo and J. Fernández-­ Ruiz, Neurobiol. Dis., 2005, 20, 207. 77. A. Witting, L. Chen, E. Cudaback, A. Straiker, L. Walter, B. Rickman, T. Möller, C. Brosnan and N. Stella, Proc. Natl. Acad. Sci. U. S. A., 2006, 103, 6362. 78. D. Centonze, M. Bari, S. Rossi, C. Prosperetti, R. Furlan, F. Fezza, V. De Chiara, L. Battistini, G. Bernardi, S. Bernardini, G. Martino and M. Maccarrone, Brain, 2007, 130, 2543. 79. A. Lourbopoulos, N. Grigoriadis, R. Lagoudaki, O. Touloumi, E. Polyzoidou, I. Mavromatis, N. Tascos, A. Breuer, H. Ovadia, D. Karussis, E. Shohami, R. Mechoulam and C. Simeonidou, Brain Res., 2011, 1390, 126.

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80. M. Webb, L. Luo, J. Y. Ma and C. S. Tham, Neurosci. Lett., 2008, 439, 106. 81. S. Rossi, R. Furlan, V. De Chiara, L. Muzio, A. Musella, C. Motta, V. Studer, F. Cavasinni, G. Bernardi, G. Martino, B. F. Cravatt, B. Lutz, M. Maccarrone and D. Centonze, Brain, Behav., Immun., 2011, 25, 1242. 82. G. Hernández-­Torres, M. Cipriano, E. Hedén, E. Björklund, Á. Canales, D. Zian, A. Feliú, M. Mecha, C. Guaza, C. J. Fowler, S. Ortega-­Gutiérrez and M. L. López-­Rodríguez, Angew. Chem., Int. Ed. Engl., 2014, 53, 13765. 83. A. Bernal-­Chico, M. Canedo, A. Manterola, M. Victoria Sánchez-­Gómez, A. Pérez-­Samartín, R. Rodríguez-­Puertas, C. Matute and S. Mato, Glia, 2015, 63, 163. 84. J. Zajicek, S. Ball, D. Wright, J. Vickery, A. Nunn, D. Miller, M. G. Cano, D. McManus, S. Mallik, J. Hobart and CUPID investigator group, Lancet Neurol., 2013, 12, 857. 85. S. Ball, J. Vickery, J. Hobart, D. Wright, C. Green, J. Shearer, A. Nunn, M. G. Cano, D. MacManus, D. Miller, S. Mallik and J. Zajicek, Health Technol. Assess., 2015, 19, 1. 86. E. Leray, J. Yaouanq, E. Le Page, M. Coustans, D. Laplaud, J. Oger and G. Edan, Brain, 2010, 133, 1900. 87. S. Rossi, F. Buttari, V. Studer, C. Motta, P. Gravina, M. Castelli, V. Mantovani, V. De Chiara, A. Musella, S. Fiore, S. Masini, G. Bernardi, M. Maccarrone, S. Bernardini and D. Centonze, Mult. Scler., 2011, 17, 281. 88. S. Rossi, M. Bozzali, M. Bari, F. Mori, V. Studer, C. Motta, F. Buttari, M. Cercignani, P. Gravina, N. Mastrangelo, M. Castelli, R. Mancino, C. Nucci, F. Sottile, S. Bernardini, M. Maccarrone and D. Centonze, PLoS One, 2013, 8, e82848. 89. Y. C. Li, A. B. Korol, T. Fahima and E. Nevo, Mol. Biol. Evol., 2004, 21, 991. 90. G. Mukerji, Y. Yiangou, S. K. Agarwal and P. Anand, Urology, 2010, 75, 1514. 91. D. T. Wade, P. Robson, H. House, P. Makela and J. Aram, Clin. Rehabil., 2003, 17, 21. 92. C. M. Brady, R. DasGupta, C. Dalton, O. J. Wiseman, K. J. Berkley and C. J. Fowler, Mult. Scler., 2004, 10, 425. 93. R. B. Kavia, D. De Ridder, C. S. Constantinescu, C. G. Stott and C. J. Fowler, Mult. Scler., 2010, 16, 1349. 94. R. M. Freeman, O. Adekanmi, M. R. Waterfield, A. E. Waterfield, D. Wright and J. Zajicek, Int. Urogynecol. J. Pelvic Floor Dysfunct., 2006, 17, 636. 95. P. L. Foley, H. M. Vesterinen, B. J. Laird, E. S. Sena, L. A. Colvin, S. Chandran, M. R. MacLeod and M. T. Fallon, Pain, 2013, 154, 632. 96. X. Nadal, C. La Porta, S. Andreea Bura and R. Maldonado, Eur. J. Pharmacol., 2013, 716, 142. 97. E. J. Rahn and A. G. Hohmann, Neurotherapeutics, 2009, 6, 713. 98. K. A. da Silva, A. F. Paszcuk, G. F. Passos, E. S. Silva, A. F. Bento, F. C. Meotti and J. B. Calixto, Pain, 2011, 152, 1872. 99. J. Z. Long, D. K. Nomura, R. E. Vann, D. M. Walentiny, L. Booker, X. Jin, J. J. Burston, L. J. Sim-­Selley, A. H. Lichtman, J. L. Wiley and B. F. Cravatt, Proc. Natl. Acad. Sci. U. S. A., 2009, 106, 20270.

Cannabinoids as a Therapeutic Approach in Multiple Sclerosis

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100. A. C. M. van Esbroeck, A. P. A. Janssen, A. B. Cognetta 3rd, D. Ogasawara, G. Shpak, M. van der Kroeg, V. Kantae, M. P. Baggelaar, F. M. S. de Vrij, H. Deng, M. Allarà, F. Fezza, Z. Lin, T. van der Wel, M. Soethoudt, E. D. Mock, H. den Dulk, I. L. Baak, B. I. Florea, G. Hendriks, L. De Petrocellis, H. S. Overkleeft, T. Hankemeier, C. I. De Zeeuw, V. Di Marzo, M. Maccarrone, B. F. Cravatt, S. A. Kushner and M. van der Stelt, Science, 2017, 356, 1084. 101. E. A. Romero-­Sandoval, J. E. Fincham, A. L. Kolano, B. N. Sharpe and P. A. Alvarado-­Vázquez, Pharmacotherapy, 2018, 38, 651. 102. D. J. Rog, T. J. Nurmikko, T. Friede and C. A. Young, Neurology, 2005, 65, 812. 103. D. Turcotte, M. Doupe, M. Torabi, A. Gomori, K. Ethans, F. Esfahani, K. Galloway and M. Namaka, Pain Med., 2015, 16, 149. 104. D. Baker, M. Marta, G. Pryce, G. Giovannoni and K. Schmierer, EBioMedicine, 2017, 6, 41. 105. D. Baker, G. Pryce, S. Amor, G. Giovannoni and K. Schmierer, Brain, 2018, 141, 2847. 106. A. Arévalo-­Martín, J. M. Vela, E. Molina-­Holgado, J. Borrell and C. Guaza, J. Neurosci., 2003, 23, 2511. 107. K. Maresz, G. Pryce, E. D. Ponomarev, G. Marsicano, J. L. Croxford, L. P. Shriver, C. Ledent, X. Cheng, E. J. Carrier, M. K. Mann, G. Giovannoni, R. G. Pertwee, T. Yamamura, N. E. Buckley, C. J. Hillard, B. Lutz, D. Baker and B. N. Dittel, Nat. Med., 2007, 13, 492. 108. J. L. Croxford, G. Pryce, S. J. Jackson, C. Ledent, G. Giovannoni, R. G. Pertwee, T. Yamamura and D. Baker, J. Neuroimmunol., 2008, 193, 120. 109. S. Sisay, G. Pryce, S. J. Jackson, C. Tanner, R. A. Ross, G. J. Michael, D. L. Selwood, G. Giovannoni and D. Baker, PLoS One, 2013, 8, e76907. 110. M. Moreno-­Martet, A. Feliú, F. Espejo-­Porras, M. Mecha, F. J. Carrillo-­ Salinas, J. Fernández-­Ruiz, C. Guaza and E. de Lago, Mult. Scler. Relat. Disord., 2015, 4, 505. 111. W. Kong, H. Li, R. F. Tuma and D. Ganea, Cell. Immunol., 2014, 287, 1. 112. E. Kozela, N. Lev, N. Kaushansky, R. Eilam, N. Rimmerman, R. Levy, A. Ben-­Nun, A. Juknat and Z. Vogel, Br. J. Pharmacol., 2011, 163, 1507. 113. D. M. Elliott, N. Singh, M. Nagarkatti and P. S. Nagarkatti, Front. Immunol., 2018, 9, 1782. 114. M. Gholamzad, M. Ebtekar, M. S. Ardestani, M. Azimi, Z. Mahmodi, M. J. Mousavi and S. Aslani, Inflammation Res., 2018, 68(1), 25–38. 115. S. Al-­Izki, G. Pryce, S. J. Jackson, G. Giovannoni and D. Baker, Mult. Scler., 2011, 17, 939.

Chapter 12

Sigma Receptors as New Target for Multiple Sclerosis Marta Rui, Giacomo Rossino, Daniela Rossi and Simona Collina* University of Pavia, Department of Drug Sciences, Via Taramelli 12, Pavia, 27100, Italy *E-­mail: [email protected]

12.1  Introduction During recent years, new advances in genomics and proteomics have led to the identification of new pharmacological targets, offering new opportunities to discover drugs that could cure several multifactorial pathologies, such as multiple sclerosis (MS), a widespread disease associated with genetic, environmental and sociocultural factors.1,2 Indeed, not only may specific genetic variations increase the risk to manifest the pathology (see Chapter 2),3 but also the exposure to infectious agents (e.g. human herpes viruses) and to smoking and stress (Figure 12.1).4,5 Accumulating evidence suggests that neurodegeneration, together with immune system abnormalities, represent leading events in the exacerbation of the disease.6,7 Oxidative stress, mitochondrial alterations and oligodendrocyte degeneration constitute triggering factors to promote dysfunctions within the central nervous system (CNS).7 The main pathophysiological features of MS are represented by the axon demyelination process, inflammation, occurring after the T cells attack the myelin sheaths, and breaching of the blood–brain barrier (BBB) that becomes permeable to T cells (Figure   Drug Discovery Series No. 70 Emerging Drugs and Targets for Multiple Sclerosis Edited by Ana Martinez © The Royal Society of Chemistry 2019 Published by the Royal Society of Chemistry, www.rsc.org

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Figure 12.1  Multiple  sclerosis features. 12.1).8,9 The pathological signs of MS depend on the locations of the lesions within the CNS. Loss of sensitivity, muscle weakness, blurred vision, ataxia, speech or swallowing problems, visual disturbances, fatigue, acute or chronic pain, and bladder and bowel difficulties are some of the main and debilitating MS clinical manifestations (Figure 12.1).3 To date, only few drugs that are able to delay the devastating outcome are available and an effective cure for MS is still missing. Accordingly, there is an urgent need to identify novel and potent remedies suitable to eradicate this debilitating disorder.10–14 Many efforts have been spent studying the molecular basis underlying this complex disease. The discovery of novel molecular targets and their signalling cascades may open the door to innovative therapeutic strategies. Among the eligible targets, sigma 1 receptor (S1R) has recently attracted the interest of scientists engaged in MS study, in virtue of its involvement in several neurodegenerative phenomena.7

12.2  Sigma 1 Receptor In the late 1970s, sigma receptors (SRs) were identified as an opioid receptor subtype for their capability to interact with the benzomorphan analogue (±)-­SKF-­10,047.15 Nevertheless, this classification was an erroneous assumption, since the opioid antagonists – naloxone and naltrexone – had no activity towards SRs.16–18 Subsequently, SRs were proposed as the binding site of phencyclidine, located on the ionic channel associated with the N-­methyl-­d-­ aspartate (NMDA) receptor. Also in this case the hypothesis was rejected.19

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Despite the SRs discovery pathway presenting several contradictions and wrong assumptions, the advances in biological and pharmacological fields eventually culminated in defining SRs as an orphan receptor family, divided into two receptor subtypes, S1R and S2R.20,21 They display a different tissue distribution and a distinct physiological and pharmacological profile.22 Throughout this chapter, the state-­of-­the-­art knowledge about S1R and its modulators will be disclosed, to offer an overview of S1R involvement in neurodegeneration, and how its ligands may be beneficial in counteracting MS. The gene encoding S1R has been cloned from several species, including humans, rats and mice, and it expresses an integral membrane protein composed of 223 amino acids (homologous only to yeast sterol isomerases), with a molecular weight of 25.3 kDa.23–26 The isolated S1R gene is 7 kbp long, localized on 13p band of human chromosome 9, which is a region related to psychiatric disorders.23–25,27,28 In recent decades, several possible structures of S1R have been postulated. In detail, at the beginning the analysis of the amino acid sequence of S1R suggested a single trans-­membrane segment.23,24,27 Subsequently, three hydrophobic domains have been proposed: two trans-­membrane-­spanning domains connected by a loop, and a third one protruding from the inner face of the membrane.29,30 Despite numerous attempts to elucidate the binding site structure, advanced proposals on this crucial element did not match up. Regarding this aspect, the breakthrough took place in 2016, when the three-­dimensional structure of S1R was published (co-­crystallized with PD144418 and 4-­IBP, PDB ID: 5HK1 and 5HK2, respectively). The crystal presents a trimeric architecture, with a single trans-­ membrane helix and a cytosolic domain for each protomer. The ligand binding pocket, placed in the β-­barrel region of the cytosolic domain, consists mainly of hydrophobic residues. The so-­obtained crystal structure possesses a high degree of similarity with the previous receptor models, with the exception of the single trans-­membrane domain, which is a structural motif in disagreement with the constructs reported in antecedent studies.31 At the subcellular level, S1R is localized at the endoplasmic reticulum/ mitochondria interface, in a region called the mitochondria-­associated endoplasmic reticulum (ER) membrane (MAM) and in 2007, its chaperone behaviour was clarified by Hayashi et al.32 In detail, under resting conditions, S1R is in a dormant state, forming a complex with another chaperone, the glucose-­regulated protein (BiP) whereas, under stressful conditions or in the case of pharmacological manipulations, S1R can translocate from the MAM to other cellular compartments, regulating the activity of other receptors, enzymes and ionic channels.33 Moreover, it plays a pivotal role in ensuring the cell's survival, regulating different signal cascades: (1) Ca2+ homeostasis control, by chaperoning the inositol triphosphate receptor (IP3-­R); (2) increase of antioxidant and anti-­stress protein production, by ensuring the correct transmission of ER stress into the nucleus, through the modulation of inositol requiring enzyme 1 (IRE1); (3) decrease of reactive oxygen species (ROS) formation, by promoting the nuclear factor (erythroid-­derived 2)-­like-­2 factor (Nrf2) signalling.34

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An interesting theory developed in the last decade concerns the ability of S1R to generate both homodimers and higher order oligomers. The first study, aimed at analysing this structural aspect, was published by Ramachandran et al., who showed how the S1R radiolabelled ligand, [3H]-­(+)-­ pentazocine, binds to the molecular target with a molar ratio of 1 : 2.35 An explanation of this phenomenon, according to which a molecule interacts with a single receptor subunit, can be found in the putative asymmetric dimerization between two S1R species, endowed with different molecular weights (26 kDa and 23 kDa).36 More recently, fluorescence resonance energy transfer (FRET) spectrometric analyses of S1R constructs, containing monomeric green fluorescent protein 2 (GFP2) and yellow fluorescent protein (YFP) C-­terminal fusions, have revealed the presence of higher monomers, dimers and oligomers.37 Lastly, the already mentioned trimeric form of the crystal obtained in 2016 confirmed the ability of S1R to form oligomers, although the mechanism of formation and the biological function of such forms are still unclear. Mechanistic models suggest that these structural changes seem strictly related to the ligands interacting with S1R. In particular, agonists stabilize S1R monomers and dimers that act as chaperones, whereas antagonists bind to higher oligomer complexes, maintaining them in repository forms.33,37–39 Macroscopically, S1R is ubiquitously expressed (liver, kidney, heart), but above all it is found in the CNS tissues (spinal cord, pons, cerebellum, hippocampus, hypothalamus, midbrain, cerebral cortex and pineal gland) and therefore is considered a potential therapeutic target for treating neurodegenerative pathologies.40–43

12.2.1  Sigma 1 Receptor and MS From the evidence reported so far, it emerges that S1R could be a viable and innovative target to treat MS, in virtue of its involvement in neurodegeneration, a condition occurring at the onset of MS in some patients.7 This molecular target plays a pivotal role in regulating synaptogenesis and myelination, as well as in controlling cellular homeostasis via different mechanisms, such as microglial activation, maintenance of mitochondrial integrity and regulation of oxidative stress.44–48 In this section, we will examine the numerous functions that this protein mediates in neuronal cells, along with the neuroprotective effects that can be obtained by regulating S1R and its therapeutic potential in the treatment of MS (Figure 12.2).

12.2.1.1 Calcium Regulation A microscopic condition, often associated with both chronic neurodegenerative diseases and acute CNS damages, involves an alteration of the intracellular Ca2+ levels. Several molecular cascades take part in restoring the cellular physiological condition, and S1R is one of the main actors in this

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Figure 12.2  S1R-­  mediated molecular cascades. sophisticated homeostasis control, avoiding pro-­apoptotic phenomena.49 As previously mentioned, S1R forms a quiescent complex with BiP and this structure is Ca2+-­dependent. Ca2+ depletion or decrease in glucose levels, as well as S1R agonism promote the complex dissociation and thus, the activation of S1R chaperonic activity.32 The inositol 1,4,5-­triphosphate receptor type 3 (IP3R) is a client protein of S1R chaperone and it regulates the Ca2+ influx into mitochondria for stimulating adenosine triphosphate (ATP) production through Krebs' cycle activation.50,51 Another mechanism overcoming the low level of Ca2+ takes advantages from the modulation of phospholipase C and protein kinase C. In detail, their S1R-­mediated stimulation activates the IP3R channels, increasing the level of IP3 in the cytoplasm and the release of Ca2+ from the ER.52–54 A cytoprotective effect guaranteed by S1R provides the acid-­sensing ion channel-­1a inhibition, arresting intracellular Ca2+ accumulation. Specifically, this neuronal Na+ channel, activated by fluctuation of extracellular H+ levels, is highly distributed in the central and peripheral nervous system of mammals and is selectively permeable to Ca2+. High levels of intracellular

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Ca cause membrane depolarization and increase Ca influx, activating the mitochondrial permeability transition pore (MPTP). Altogether this cascade provides a sequence of toxic events that culminate in ischaemic neuronal cell death.55,56 The ability of S1R to inhibit the MAPK/ERK pathway is another effective route to regulate the Ca2+-­associated downstream signalling cascades, which may trigger programmed cell death and neurotoxic damages.57

12.2.1.2 Glutamate Excitotoxicity Reduction Excitotoxicity is a pathological condition in which high levels of glutamate over-­activate NMDA receptors, causing high levels of Ca2+ to enter the cell, which in turn stimulate various enzymes involved in different mechanisms of programmed cell death. Excitotoxicity occurs in various neurodegenerative pathologies, and it has been correlated with S1R in virtue of its ability to modulate glutamate receptors.58 However, the underlying mechanisms are still unclear and seem to be numerous. For example, in a study exploiting a chronic restraint stress model of depression, it has been evidenced that S1R stimulation promotes glutamate release by increasing presynaptic intracellular concentration of Ca2+.59 This mechanism could be reverted by S1R agonists, which decrease Ca2+ entry through presynaptic voltage-­dependent Ca2+ channels and suppress PKC molecular pathways, causing a limited glutamate release from nerve terminals in the rat cerebral cortex.60 This effect can be achieved by preventing small conductance Ca2+-­activated K+ (SK) channel opening, which are strictly related to NMDA activation.61 NMDA receptors can be regulated by S1R both directly, by binding to specific subunits of the NMDA receptor, and indirectly, modulating the interaction of other proteins with NMDA receptors.62–64 In fact, S1R antagonists can inhibit the interaction between S1R and the cytosolic C-­terminal region of the NMDA receptor GluN1 subunit in recombinant cells, while activation of S1R can stimulate the binding to GluN2 subunit. When this occurs, NMDA receptors are translocated to the cell surface, thus increasing their availability at the plasma membrane.65 S1R agonists also increase the overexpression of GluN2A and GluN2B subunits of the NMDA receptor, influencing synaptic plasticity.66,67 In another study, conducted on an ischaemia/reperfusion vascular dementia model, S1R agonists were found to be able to increase brain-­derived neurotrophic factor (BDNF) levels, by modulating the GluN2A subunit.68 Concerning the modulation of NMDA receptors, via interaction with other proteins, it has recently been found that S1R agonists promote interaction between histidine triad nucleotide binding protein 1 (HINT1) and G-­protein coupled receptors (GPCRs), which eventually improve GPCR–NMDA interaction.69 Lastly, recent studies demonstrated that S1R agonism can modulate other glutamate receptors, besides NMDA receptors, such as kainate and alpha-­amino-­3-­hydroxy-­5-­methyl-­5 isoxazole propionic (AMPA) receptors to exert neuroprotection.70,71

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Overall, these results suggest that S1R can exert neuroprotection by modulation of glutamatergic neurotransmission, through numerous mechanisms which can be very complex and are not always completely understood.

12.2.1.3 Oxidative and ER Stresses and Mitochondrial Dysfunction ROS are natural by-­products of oxygen metabolism, promoting hormetic responses. In detail, low concentrations of ROS possess beneficial effects in maintaining cellular homeostasis, whereas a disequilibrium between their production and detoxification systems may lead to oxidative damage.72 Numerous altered conditions contribute in generating ROS, such as mitochondrial dysfunction and sustained neurotransmission, and they may cause severe side effects, damaging lipids, nucleic acids and proteins.73,74 The brain is particularly vulnerable to oxidative stress, since it has high oxygen demand, relatively low levels of the antioxidant glutathione and is enriched in polyunsaturated fatty acids that are substrates for lipid peroxidation.75 Accordingly, oxidative stress has been extensively associated with several CNS-­related diseases. Moreover, under ER stress, unfolded or misfolded proteins can accumulate within the ER lumen and promote the protective unfolded protein response (UPR), which involves three molecular cascades: (1) activating transcription factor 6 (ATF6); (2) protein kinase RNA like ER kinase (PERK); and (3) inositol requiring enzyme 1 alpha (IRE1α).76,77 Multiple studies show that S1R agonists interfere with ROS production and abnormal protein accumulation, modulating the UPR. More specifically, S1R attenuates the activation of PERK and ATF6, increasing cell survival, whereas it stabilizes IRE-­1α.78,79 This last enzyme oligomerizes to activate its cytoplasmic kinase and endoribonuclease domains, which initiate the splicing of the ER stress linked transcriptional factor x-­box binding protein 1 (XBP1) mRNA, which encodes for transcription factors necessary for the expression of specific genes to weaken ER stress.80,81 The IRE-­1α-­XBP1 pathway is involved in several neurodegenerative disorders, and high levels of spliced XBP1 mRNA were also identified in MS demyelinated lesions.82–85 Mitochondrial abnormalities (i.e. mitochondrial DNA mutations, bioenergetic and dynamic impairments) are often associated with neurodegeneration, and accumulating evidence suggests S1R as an effective protector against mitochondrial damage.86–88 S1R is able to influence the expression of both pro-­ and anti-­apoptotic signals targeting mitochondria. In detail, S1R agonists promote cell survival by increasing the expression of the anti-­ apoptotic protein Bcl-­2 – via the ROS/NF-­κB pathway – as well as by decreasing the activity of the pro-­apoptotic protein Bax.89–91

12.2.1.4 Oligodendrocyte Degeneration Oligodendrocytes (OLs) are non-­neuronal cells involved in myelin formation and regulation of myelin protein shunting. Glycosphingolipids are the main fatty substances surrounding axons and they are responsible for nerve

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conduction control, as well as for the terminal OL differentiation. Accordingly, an alteration of this refined framework may lead to irreversible CNS damage, causing neurodegeneration.92 The role played by S1R in controlling the intracellular lipid flow and the membrane reconstitution in lipid rafts is still clouded, although the ability of S1R to influence the compartmentalization of lipids into the ER lipid storage sites may provide a concrete explanation.44–51 Moreover, the up-­regulation of S1R affects the function of proteins residing in plasma membrane lipid rafts, such as trophic factor receptors. In this context, it has been highlighted that S1R promotes neurite sprouting by amplifying the activity of the neurotrophin NGF and this synergic effect is a consequence of lipid constituent changes at the plasma membrane lipid rafts. In addition, overexpression of S1R potentiates OL differentiation, while S1R knockdown avoids OL maturation.93 Recently, it has been evidenced that the destruction of OLs and the axonal demyelination may be limited, exploiting the inhibitory aptitude of S1R toward the altered response of CD3+ lymphocytes and cytokines (i.e. IL-­1, IL-­6, TNF-­α). Indeed, a single injection of S1R ligand in experimental autoimmune encephalomyelitis susceptible mice (animal model for MS) impedes mononuclear cell accumulation and demyelination in brain and spinal cord, reducing the clinical progression of the disease.94

12.2.1.5 Neuroinflammation Microglia are macrophage-­derived cells located in the CNS, where they play a key role as mediators of neuroinflammation. They are grouped into two subcategories: M1 microglia, with pro-­inflammatory properties involved in CNS damage, and M2 microglia, which are anti-­inflammatory and stimulate neuronal regrowth and repair.95,96 Accumulating evidence suggests that S1R can regulate microglial activity by strengthening the reparative microglia phenotype (M2), while it attenuates the inflammatory response (M1) and hence promotes neuroprotection. In particular, during a recent study neurotoxic dosing with methamphetamine was observed to activate M1 microglia responses in the mouse striatum, while pre-­treatment with S1R ligand prevented it. This process involves variations of the levels of pan-­macrophage markers, cluster of differentiation 68 (CD68) and ionized calcium binding adapter molecule 1 (IBA-­1).97 In another study, conducted on lipopolysaccharide (LPS)-­stimulated murine microglial BV2 cells, the authors observed that the S1R agonists suppress M1 microglial activation, also reducing the levels of pro-­inflammatory cytokines, such as interleukin (IL)-­1β, tumour necrosis factor-­α (TNF-­α), and inducible NOS.98 Similarly, other S1R agonists have shown the ability to prevent microglial activation and inflammatory cytokines release in response to a number of microglial activators such as LPS, ATP, uridine triphosphate and monocyte chemoattractant protein-­1.99 PRE-­084 was deeply investigated in different studies that confirmed its ability to counteract neuroinflammation: in an in vivo model of traumatic brain injury, PRE-­084 decreases IBA-­1 expression following controlled cortical impact, reduces lesion volume and improves behaviour in mice;100 in a

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mouse model of amyotrophic lateral sclerosis (ALS) it also reduces counts of IBA-­1 positive microglial cells;101 and in another study on animals with motor neuron disease, PRE-­084 increases the levels of pan-­macrophage marker CD68 and CD206, which is associated with M2 microglial responses.41 Another important pathological scenario in which S1R intervenes is leukocyte extravasation into the brain occurring upon disruption of the BBB by injury, a condition that exacerbates neuroinflammation. Being expressed in lymphocytes, S1R can suppress CD3 lymphocyte proliferation in vitro and LPS-­induced release of cytokines in vivo.102,103 This was confirmed by Oxombre et al. using an S1R ligand in a mouse autoimmune encephalitis model with peripheral leukocyte infiltration into the brain, demyelination and axonal loss. The ligand considerably reduced the clinical signs of encephalitis by preventing mononuclear cell accumulation and demyelination in the CNS, while also increasing the proportion of B-­cell subsets and regulatory T-­cells.94 Overall, these findings demonstrate the involvement of S1R in neuroinflammation and its potential to counteract this pathological condition.

12.2.2  Sigma 1 Receptor and Its Modulators Over time, the interest in S1R has increased. It represents an innovative target with a wide spectrum of therapeutic uses, and thus design and synthesis of S1R modulators have gained enormous importance.22 One critical aspect is represented by the lack of endogenous ligand. Neurosteroids (i.e. dehydroepiandrosterone (DHEA), pregnenolone, and progesterone) have been suggested as putative endogenous S1R ligands, despite their low binding affinities (0.3–10 µM).104,105 Their compound structures are shown in Figure 12.3. The long road to discovering a novel S1R modulator begins with the comprehension of the agonist/antagonist behaviour. Defining an S1R profile contributes to depicting the possible pharmacological role of an S1R modulator. A biological test has been set up that provides the evaluation of nerve growth factor (NGF)-­induced neurite outgrowth in PC-­12 cells (pheochromocytoma of the rat adrenal medulla), a widely accepted neuronal differentiation model. S1R agonists promote neurite elongation, demonstrating their neuroplastic effects. By contrast, S1R antagonists have no effect on this cell line, and several data show their involvement in the treatment of neuropathic pain and cancer conditions.106 Recently, three drugs in clinical development support this outstanding evidence: (1) ANAVEX 2-­73 (Figure 12.3), patented by Anavex Life Sciences Corp, is undergoing Phase III clinical trials for the treatment of Alzheimer's disease (AD);107 (2) AVP-­923 (Dextromethorphan-­Quinidine) (Figure 12.3), patented by Concert Pharmaceuticals Inc., is undergoing Phase II clinical trials to alleviate agitation in patients with AD;108 (3) S1A or MR309/ E-­52862 (Figure 12.3), patented by Esteve, is undergoing Phase II clinical trials for treating neuropathic pain, post-­operative pain and opioid analgesia enhancement.109

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Figure 12.3  Structures  of S1R endogenous ligands and S1R modulators under clinical trials.

In this chapter we will focus on the S1R agonists, as potential neuroprotective agents able to counteract MS.7 The growing interest in the S1R/neurodegenerative diseases correlation required the identification of molecules able to selectively bind this molecular target. For years this aspect has been a challenge, since several research groups have faced the lack of two fundamental milestones: knowledge of endogenous S1R ligands and the three-­dimensional receptor structure. As a result, ligand-­based drug design approaches were adopted to identify new molecules targeting S1R. The pioneer was Glennon, who described the first pharmacophore model, in which an ionizable amine site and two hydrophobic domains are essential structural elements.110 Later on, Gund et al. introduced an electronegative atom – an oxygen or a sulfur – as an additional pharmacophore element to the previous model.111 Nevertheless, in 2005, Laggner et al. demonstrated how this electronegative atom is a negligible element and is not required to increase the affinity of a ligand with S1R.112 Over the years, numerous S1R modulators have been designed, taking into account these structural common features and, the resulted compounds can be grouped into six chemical classes: (1) morpholine derivatives; (2) 1,3-­disubstituted guanidines; (3) piperazine-­based molecules; (4) spirocycles; (5) cyclopropanes; and (6) piperidine-­based compounds. The most representative molecules, belonging to the six different groups, are briefly described in this section.

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12.2.2.1 Morpholine Derivatives The main morpholine-­based compound is known as PRE-­084 (Figure 12.4). It was the first selective S1R ligand identified as an S1R agonist and has been employed to define the pathophysiological role played by S1R in different CNS-­related disorders. Nowadays is still commonly used as an S1R agonist reference standard in in vitro and in vivo assessments of new compound.113,114 In 2009, Seredinin et al. reported the high affinity of Afobazole (Figure 12.4) – a morpholine derivative with anxiolytic and neuroprotective properties – towards S1R.115 This molecule is also able to interact with S2R, and in a recent study the pan-­SRs affinity of Afobazole was useful to provide

Figure 12.4  S1R  agonists.

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superior long-­term outcomes compared with other S1R ligands in the rat middle cerebral artery occlusion (MCAO) stroke model, by enhancing glial cell survival, blocking ischaemia-­induced glial cell activation, and decreasing nitrosative stress.116

12.2.2.2 1,3-­Disubstituted Guanidines 1,3-­di-­o-­tolylguanidine (DTG) (Figure 12.4) belongs to the second category of S1R agonists. It was patented by researchers at the University of Oregon. In their investigation, the authors claim that the tritiated form, [3H]-­DTG, is an effective radioligand for SR binding assays.117 This molecule shows an equal affinity for both SRs, and thus it cannot be considered a selective S1R ligand. Nevertheless, it is a widely accepted reference molecule to perform in vitro evaluations, outclassing the previous non-­selective radiolabelled SR ligand, (±)-­[3H]-­SKF 10,047. Indeed, it is still used for determining binding affinity at S2R, in the presence of non-­tritiated pentazocine to mask the S1R binding site.118

12.2.2.3 Piperazine-­based Molecules This class of derivatives counts numerous molecules with affinity for S1R. Among them, SA4503, or Cutamesine (Figure 12.4), deserves to be mentioned. It was introduced in the pharmaceutical panorama by Matsuno et al., who described in 1996 this novel S1R agonist.119,120 SA4503 reached a phase II clinical trial for evaluating its safety, tolerability, dose range and functional effects in patients with ischaemic stroke. Although this study failed, since no significant effects on functional end points were seen in the treated population compared to the placebo control, post-­hoc analysis of subjects with severe or modest ischaemic stroke showed significant improvements. Accordingly, once patient characteristics are optimized to identify a potential responder population, further clinical evaluations will be performed.49 The same authors reported on another arylalkylpiperazine derivative in a constrained structure: BD1031 (Figure 12.4). Their work aimed at proposing a novel method to define the S1R agonist/antagonist profile, evaluating the burst duration of red nucleus neurons in the in vitro turtle brain. The study revealed that S1R agonists, including BD1031, increase burst duration and produce dystonic postures after microinjection into the red nucleus, whereas antagonists promote the opposite effect.121,122 Another member belonging to the piperazine class, structurally related to SA4503, is BHDP (Figure 12.4), defined as an S1R agonist. In a comparative study with other S1R ligands, Klouz et al. showed that BHDP possesses a high affinity for S1R in liver mitochondria and rat brain membranes.123 Moreover, the ability of BHPD to prevent hypoxia-­induced ATP depletion in cultured astrocytes suggests that this molecule is a potential cytoprotective agent.124

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12.2.2.4 Spirocycles The potent S1R agonist L-­687,384 (Figure 12.4), developed by Merck Sharp & Dohme, is characterized by a spirocycle structure.125 In 1994, it was also evidenced that this molecule promotes a negative effect on N-­methyl-­d-­ aspartate (NMDA)-­induced currents in cultured rat hippocampal pyramidal neurons and thus, it acts as an antagonist at the NMDA receptor–channel complex.126 L-­687,384-­inspired molecules, presenting a heteroatom in the aliphatic part of the tetraline substructure, were designed and synthesized by Wuensch's group. Compound 14a (Figure 12.4), so-­numbered in their work, belongs to a broad series of derivatives and shows high S1R affinity and good S1R/S2R-­selectivity.127

12.2.2.5 Cyclopropanes Among compounds characterized by a cyclopropane moiety, igmesine is worth noting (Figure 12.4). It is a potent and selective S1R ligand, endowed with neuroprotective and antidepressant-­like effects, comparable to those of fluoxetine.128,129 It completed Phase II clinical trials for the treatment of depression; however, it failed Phase III clinical trials.130 Cyclopropanes, also containing a piperidine motif, are endowed with S1R-­mediated neuroprotective effects, as demonstrated by Marrazzo's group. Of particular interest is trans-­(+)-­1d (so-­numbered in the work) (Figure 12.4), in virtue of its excellent S1R affinity and selectivity over S2R, and the authors proposed it as a promising tool for the study of S1R.131

12.2.2.6 Piperidine-­based Molecules Piperidine-­based molecules include 4-­IBP (Figure 12.4), a non-­selective SR modulator, which was co-­crystallized with S1R.31,132 It has been developed as radiopharmaceutical to bind to SRs on the MCF-­7 human breast carcinoma cell line. Moreover, it shows antiproliferative properties, by decreasing the migration of human cancer cells (e.g. glioblastoma cells) and sensitizing them to cytotoxic insults of pro-­apoptotic and pro-­autophagic drugs.133 4-­PPBP (Figure 12.4) is another compound belonging to this group. It was discovered by Yang et al., who reported the cytoprotective effect of this molecule, in virtue of its ability to control a mechanism involving the anti-­apoptotic protein bcl-­ 2.134 Additionally, this molecule made it possible to individuate a correlation between S1R and the mitogen-­activated protein kinase (MAPK) cascade in neuroprotection. Specifically, 4-­PPBP promotes the activation of extracellular signal-­regulated kinase (ERK1/2) – a subfamily of MAPKs – in glucose-­deprived cells (ischaemic models), via an S1R-­mediated phosphorylation mechanism.135 Lastly, a wide series of arylalkylpiperidines with neuroprotective effects have been prepared by Collina's research team.136–138 Of particular interest is enantiomeric (R)-­1-­[3-­(1,1′-­biphen)-­4-­yl]butylpiperidine hydrochloride (named RRC33, Figure 12.4), a selective S1R agonist with an excellent S1R affinity (Ki = 1.8 nM) along with high selectivity over other receptors

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and good in vitro metabolic stability. The ability of RRC-33 to promote the differentiation and the neurite elongation was verified using the rat dorsal root ganglia (DRG) experimental model. DRG neurite elongation is a well-­ accepted simple method to screen the neurotoxic or neuroprotective effect of a drug. The results obtained in vitro highlighted the positive neurotrophic role of RRC-33 on neurite outgrowth, and thus support and strengthen the hypothesis to test the effect of this compound in more complex in vitro models representing MS. Moreover, after systemic administration in mice, RRC-33 showed an excellent pharmacokinetic profile and CNS distribution. Taken together, these characteristics might potentially be the basis for finding effective treatments against MS. Accordingly, RRC-33 can be considered as a candidate for proof of concept in vivo studies in an animal model of MS.42,139,140 Another interesting compound is RC-­403 (Figure 12.4), a dual S1R agonist/ anti-­acetylcholinesterase ligand with antioxidant properties, currently under investigation in in vitro and in vivo neurodegenerative models.141 Before concluding, well-­established drugs, in clinical use for the treatment of other pathological conditions such as antitussives, antidepressants, antipsychotics, anti-­AD drugs, anti-­Parkinson's disease (anti-­PD) drugs and some drugs of abuse, are reported to act as S1R agonists.142–144 The most effective molecules belonging to the diverse groups have been reported in Figure 12.5.

Figure 12.5  Well-­  established drugs for the treatment of other pathological conditions, able to interact with S1R.

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Moreover, the experimental drug ANAVEX2-­73 (Figure 12.3), acting as a muscarinic and S1R agonist in clinical trials for AD, is of high interest.107 All these drugs may have a great relevance for understanding the potential of S1R agonists in MS. Based on the leading principle of translational medicine, which seeks to coordinate the use of new knowledge in clinical practice, clinical observations deriving from the use of such drugs in patients affected by MS may provide feedback about the applications of patient treatment with S1R agonists, confirming the scientific hypotheses of their potential efficacy.

12.3  Conclusions Over the years S1R agonists have become part of the pharmaceutical panorama; in particular, some of them have become full members of the drug candidates group for the treatment of neurodegenerative diseases. Throughout this work, we pointed out the inestimable value of S1R and its role in modulating different molecular cascades to prevent neurodegeneration, a clinical manifestation accompanying the immune system abnormalities in MS. Although the involvement of S1R in MS deserves to be further investigated, previous data clearly suggests the great potential of S1R as an eligible target to hit for offering an alternative to conventional therapies against MS. Moreover, S1R agonists may be exploited in combinatorial therapeutic strategies to reinforce the beneficial effects of the well-­known anti-­MS drugs. This synergism may decrease the side effects associated with the disease-­ modifying drugs in clinical use and improve the probability of success in the fight against MS. Lastly, S1R agonists could be effective molecules to provide a multi-­target therapy, in virtue of their ability to activate S1R, which in turn modulates the function of several client proteins.

Acknowledgement The authors gratefully acknowledge the University of Pavia for the research grant to M.R. and MIUR for the doctoral fellowships to G.R.

References 1. R. L. Zuvich, J. L. McCauley, M. A. Pericak-­Vance and J. L. Haines, Semin. Immunol., 2009, 21, 328. 2. C. C. Bernard and N. Kerlero de Rosbo, Curr. Opin. Immunol., 1992, 4, 760. 3. A. Compston and A. Coles, Lancet, 2008, 372, 1502. 4. J. O. Virtanen and S. Jacobson, CNS Neurol. Disord.: Drug Targets, 2012, 11, 528. 5. V. Pantazou, M. Schluep and R. Du Pasquier, Presse Méd., 2015, 44, e113. 6. W. W. Chen, X. Zhang and W. J. Huang, Mol. Med. Rep., 2016, 13, 3391.

Sigma Receptors as New Target for Multiple Sclerosis

279

7. S. Collina, M. Rui, S. Stotani, E. Bignardi, D. Rossi, D. Curti, F. Giordanetto, A. Malacrida, A. Scuteri and G. Cavaletti, Future Med. Chem., 2017, 9, 2029. 8. B. F. G. Popescu, I. Pirko and C. F. Lucchinetti, Continuum, 2013, 19, 901. 9. M. M. Goldenberg, Pharm. Ther., 2012, 37, 175. 10. R. Berkovich, Neurotherapeutics, 2013, 10, 97. 11. B. C. Kieseier and P. A. Calabresi, CNS Drugs, 2012, 26, 205. 12. W. Schrempf and T. Ziemssen, Autoimmun. Rev., 2007, 6, 469. 13. P. S. Rommer, A. Dudesek, O. Stüve and U. K. Zettl, Clin. Exp. Immunol., 2014, 175, 373. 14. S. R. Mehr and M. P. Zimmerman, Am. Health Drug Benefits, 2015, 8(426), 9. 15. W. R. Martin, C. G. Eades, J. A. Thompson, R. E. Huppler and P. E. Gilbert, J. Pharmacol. Exp. Ther., 1976, 197, 517. 16. T. P. Su, J. Pharmacol. Exp. Ther., 1982, 223, 284. 17. T. Maurice and B. P. Lockhart, Prog. Neuro-­Psychopharmacol., 1997, 21, 69. 18. D. B. Vaupel, Eur. J. Pharmacol., 1983, 92, 269. 19. G. Skuza, Pol. J. Pharmacol., 2003, 55, 923. 20. R. Quirion, W. D. Bowen, Y. Itzhak, J. L. Junien, J. M. Musacchio, R. B. Rothman, T. P. Su, S. W. Tam and D. P. Taylor, Trends Pharmacol. Sci., 1992, 13, 85. 21. S. B. Hellewell, A. Bruce, G. Feinstein, J. Orringer, W. Williams and W. D. Bowen, Eur. J. Pharmacol., 1994, 268, 9. 22. S. Collina, R. Gaggeri, A. Marra, A. Bassi, S. Negrinotti, F. Negri and D. Rossi, Expert Opin. Ther. Pat., 2013, 23, 597. 23. M. Hanner, F. F. Moebius, A. Flandorfer, H. G. Knaus, J. Striessnig, E. Kempner and H. Glossmann, Proc. Natl. Acad. Sci. U. S. A., 1996, 93, 8072. 24. P. Seth, Y. J. Fei, H. W. Li, W. Huang, F. H. Leibach and V. Ganapathy, J. Neurochem., 1998, 70, 922. 25. P. D. Prasad, Y. J. Fei, H. W. Li, W. Huang, F. H. Leibach and V. Ganapathy, J. Neurochem., 1998, 70, 443. 26. J. M. Walker, W. D. Bowen, F. O. Walker, R. R. Matsumoto, B. De Costa and K. C. Rice, Pharmacol. Rev., 1990, 42, 355. 27. R. Kekuda, P. D. Prasad, Y. J. Fei, F. H. Leibach and V. Ganapathy, Biochem. Biophys. Res. Commun., 1996, 229, 553. 28. J. Mei and G. W. Pasternak, Biochem. Pharmacol., 2001, 62, 349. 29. E. Laurini, V. D. Col, M. G. Mamolo, D. Zampieri, P. Posocco, M. Fermeglia, L. Vio and S. Pricl, Acs Med. Chem. Lett., 2011, 2, 834. 30. J. L. Ortega-­Roldan, F. Ossa, N. T. Amin and J. R. Schnell, FEBS Lett., 2016, 589, 659. 31. H. R. Schmidt, S. Zheng, E. Gurpinar, A. Koehl, A. Manglik and A. C. Kruse, Nature, 2016, 532, 527. 32. T. Hayashi and T. P. Su, Cell, 2007, 131, 596. 33. U. Chu and A. E. Ruoho, Mol. Pharmacol., 2016, 89, 142. 34. T. P. Su, T. C. Su, Y. Nakamura and S. Y. Tsai, Trends Pharmacol. Sci., 2016, 37, 262.

280

Chapter 12

35. S. Ramachandran, H. Lu, U. Prabhu and A. E. Ruoho, Protein Expression Purif., 2007, 51, 283. 36. U. B. Chu, S. Ramachandran, A. R. Hajipour and A. E. Ruoho, Biochem. J., 2013, 52, 859. 37. A. K. Mishra, T. Mavlyutov, D. R. Singh, G. Biener, J. Yang, J. A. Oliver, A. E. Ruoho and V. Raicu, Biochem. J., 2015, 466, 263. 38. K. A. Gromek, F. P. Suchy, H. R. Meddaugh, R. L. Wrobel, L. M. LaPointe, U. B. Chu, J. G. Primm, A. E. Ruoho, A. Senes and B. G. Fox, J. Biol. Chem., 2014, 289, 20333. 39. A. Tesei, M. Cortesi, A. Zamagni, C. Arienti, S. Pignatta, M. Zanoni, M. Paolillo, D. Curti, M. Rui, D. Rossi and S. Collina, Front. Pharmacol., 2018, 711. 40. T. Maurice and T. P. Su, Pharmacol. Ther., 2009, 124, 195. 41. M. Peviani, E. Salvaneschi, L. Bontempi, A. Petese, A. Manzo, D. Rossi, M. Salmona, S. Collina, P. Bigini and D. Curti, Neurobiol. Dis., 2014, 62, 218. 42. A. Marra, D. Rossi, L. Pignataro, C. Bigogno, A. Canta, N. Oggioni, A. Malacrida, M. Corbo, G. Cavaletti, M. Peviani, D. Curti, G. Dondio and S. Collina, Future Med. Chem., 2016, 8, 287. 43. S. Kourrich, T. P. Su, M. Fujimoto and A. Bonci, Trends Neurosci., 2012, 35, 762. 44. T. Hayashi and T. P. Su, Proc. Natl. Acad. Sci. U. S. A., 2004, 101, 14949. 45. A. A. Hall, Y. Herrera, C. T. Ajmo, J. Cuevas and K. R. Pennypacker, Glia, 2009, 57, 744. 46. L. Nguyen, N. Kaushal, M. J. Robson and R. R. Matsumoto, Eur. J. Pharmacol., 2014, 743, 42. 47. A. Pal, D. Fontanilla, A. Gopalakrishnan, Y. K. Chae, J. L. Markley and A. E. Ruoho, Eur. J. Pharmacol., 2012, 682, 12. 48. J. Wang, A. Shanmugam, S. Markand, E. Zorrilla, V. Ganapathy and S. B. Smith, Free Radicals Biol. Med., 2015, 86, 25. 49. L. Nguyen, B. P. Lucke-­Wolds, S. Mookerjee, N. Kaushal and R. R. Matsumoto, Adv. Exp. Med. Biol., 2017, 964, 133. 50. T. Hayashi, R. Rizzuto, G. Hajnoczky and T. P. Su, Trends Cell Biol., 2009, 19, 81. 51. T. Hayashi and T. P. Su, J. Pharmacol. Exp. Ther., 2003, 306, 726. 52. H. Ivanova, T. Vervliet, L. Missiaen, J. B. Parys, H. De Smedt and G. Bultynck, Biochim. Biophys. Acta, 2014, 1843, 2164. 53. F. P. Monnet, M. P. Morin-­Surun, J. Leger and L. Combettes, J. Pharmacol. Exp. Ther., 2003, 307, 705. 54. K. T. Tchedre, R. Q. Huang, A. Dibas, R. R. Krishnamoorthy, G. H. Dillon and T. Yorio, Invest. Ophthalmol. Visual Sci., 2008, 49, 4993. 55. T. W. Sherwood, E. N. Frey and C. C. Askwith, Am. J. Physiol.: Cell Physiol., 2012, 303, C699. 56. Y. Herrera, C. Katnik, J. D. Rodriguez, A. A. Hall, A. Willing, K. R. Pennypacker and J. Cuevas, J. Pharmacol. Exp. Ther., 2008, 327, 491. 57. T. Tuerxun, T. Numakawa, N. Adachi, E. Kumamaru, H. Kitazawa, M. Kudo and H. Kunugi, Neurosci. Lett., 2010, 469, 303.

Sigma Receptors as New Target for Multiple Sclerosis

281

58. A. L. Sheldon and M. B. Robinson, Neurochem. Int., 2007, 51, 333. 59. Y. Fu, S. Yu, X. Guo, X. Li, T. Li, H. Li and Y. Dong, Biochim. Biophys. Acta, 2012, 1823, 826. 60. C. W. Lu, T. Y. Lin and S. J. Wang, Neurochem. Int., 2010, 57, 168. 61. C. W. Lu, T. Y. Lin, C. C. Wang and S. J. Wang, J. Pharmacol. Exp. Ther., 2012, 341, 532. 62. D. Balasuriya, A. P. Stewart and J. M. Edwardson, J. Neurosci., 2013, 33, 18219. 63. M. Pabba and E. Sibille, Molecular Neuropsychiatry, 2015, 1, 47. 64. M. Martina, M. E. Turcotte, S. Halman and R. Bergeron, J. Physiol., 2007, 578, 143. 65. M. Pabba, A. Y. Wong, N. Ahlskog, E. Hristova, D. Biscaro, W. Nassrallah, J. K. Ngsee, M. Snyder, J. C. Beique and R. Bergeron, J. Neurosci., 2014, 34, 11325. 66. Y. Lin, V. A. Skeberdis, A. Francesconi, M. V. Bennett and R. S. Zukin, J. Neurosci., 2004, 24, 10138. 67. O. A. Shipton and O. Paulsen, Philos. Trans. R. Soc., B, 2014, 369, 20130163. 68. Q. Xu, X. F. Ji, T. Y. Chi, P. Liu, G. Jin, S. L. Gu and L. B. Zou, Psychopharmacology, 2015, 232, 1779. 69. M. Rodriguez-­Munoz, E. Cortés-­Montero, A. Pozo-­Rodrigálvarez, P. Sánchez-­Blázquez and J. Garzón-­Niño, Oncotarget, 2015, 6, 35458. 70. H. C. Kim, W. K. Jhoo, W. K. Kim, E. J. Shin, M. A. Cheon, C. Y. Shin and K. H. Ko, Life Sci., 2001, 69, 915. 71. E. J. Shin, S. Y. Nah, W. K. Kim, K. H. Ko, W. K. Jhoo, Y. K. Lim, J. Y. Cha, C. F. Chen and H. C. Kim, Br. J. Pharmacol., 2005, 144, 908. 72. P. D. Ray, B. W. Huang and Y. Tsuji, Cell Signaling, 2012, 24, 981. 73. S. Gandhi and A. Y. Abramov, Oxid. Med. Cell. Longevity, 2012, 428010. 74. G. H. Kim, J. E. Kim, S. J. Rhie and S. Yoon, Exp. Neurobiol., 2015, 24, 325. 75. R. Dringen and J. Hirrlinger, Biol. Chem., 2003, 384, 505. 76. H. Yoshida, FEBS J., 2007, 274, 630. 77. Y. Kimata and K. Kohno, Curr. Opin. Cell Biol., 2011, 23, 135. 78. T. Mori, T. Hayashi, E. Hayashi and T. P. Su, PLoS One, 2013, 8, e76941. 79. T. Hayashi, Psychiatry Clin. Neurosci., 2015, 69, 179. 80. J. Dunys, E. Duplan and F. Checler, Mol. Neurodegener., 2014, 9, 35. 81. D. Jiang, M. Niwa and A. C. Koong, Semin. Cancer Biol., 2015, 33, 48. 82. C. Hetz, P. Thielen, S. Matus, M. Nassif, F. Court, R. Kiffin, G. Martinez, A. M. Cuervo, R. H. Brown and L. H. Glimcher, Genes Dev., 2009, 23, 2294. 83. R. L. Vidal, A. Figueroa, F. A. Court, P. Thielen, C. Molina, C. Wirth, B. Caballero, R. Kiffin, J. Segura-­Aguilar, A. M. Cuervo, L. H. Glimcher and C. Hetz, Hum. Mol. Genet., 2012, 21, 2245. 84. S. Casas-­Tinto, Y. Zhang, J. Sanchez-­Garcia, M. Gomez-­Velazquez, D. E. Rincon-­Limas and P. Fernandez-­Funez, Hum. Mol. Genet., 2011, 20, 2144. 85. J. H. Lee, S. M. Won, J. Suh, S. J. Son, G. J. Moon, U. J. Park and B. J. Gwag, Exp. Mol. Med., 2010, 42, 386.

282

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86. A. Federico, E. Cardaioli, P. Da Pozzo, P. Formichi, G. N. Gallus and E. Radi, J. Neurol. Sci., 2012, 322, 254. 87. A. Johri and M. F. Beal, J. Pharmacol. Exp. Ther., 2012, 342, 619. 88. R. K. Chaturvedi and M. Flint Beal, Free Radicals Biol. Med., 2013, 63, 1. 89. J. Meunier and T. Hayashi, J. Pharmacol. Exp. Ther., 2010, 332, 388. 90. Y. Ha, Y. Dun, M. Thangaraju, J. Duplantier, Z. Dong, K. Liu, V. Ganapathy and S. B. Smith, Invest. Ophthalmol. Visual Sci., 2011, 52, 527. 91. A. A. Behensky, I. E. Yasny, A. M. Shuster, S. B. Seredenin, A. V. Petrov and J. Cuevas, J. Pharmacol. Exp. Ther., 2013, 347, 468. 92. F. Mei, S. Y. Christin Chong and J. R. Chan, Neurosci. Bull., 2013, 29, 177. 93. T. Hayashi and T. P. Su, Life Sci., 2005, 77, 1612. 94. B. Oxombre, C. Lee-­Chang, A. Duhamel, M. Toussaint, M. Giroux, M. Donnier-­Maréchal, P. Carato, D. Lefranc, H. Zéphir, L. Prin, P. Melnyk and P. Vermersch, Br. J. Pharmacol., 2015, 172, 1769. 95. V. H. Perry, J. A. Nicoll and C. Holmes, Nat. Rev. Neurol., 2010, 6, 193. 96. N. N. Burke, D. M. Kerr, O. Moriarty, D. P. Finn and M. Roche, Brain, Behav., Immun., 2014, 42, 147. 97. M. J. Robson, R. C. Turner, Z. J. Naser, C. R. McCurdy, J. D. Huber and R. R. Matsumoto, Exp. Neurol., 2013, 247, 134. 98. Z. Wu, L. Li, L. T. Zheng, Z. Xu, L. Guo and X. Zhen, J. Neurochem., 2015, 134, 904. 99. J. Cuevas, A. Rodriguez, A. Behensky and C. Katnik, J. Pharmacol. Exp. Ther., 2011, 339, 161. 100. H. Dong, Y. Ma, Z. Ren, B. Xu, Y. Zhang, J. Chen and B. Yang, Cell. Mol. Neurobiol., 2015, 36, 639. 101. R. Mancuso, S. Oliván, A. Rando, C. Casas, R. Osta and X. Navarro, Neurotherapeutics, 2012, 9, 814. 102. J. M. Derocq, B. Bourrié, M. Ségui, G. Le Fur and P. Casellas, J. Pharmacol. Exp. Ther., 1995, 272, 224. 103. P. Casellas, B. Bourrié, X. Canat, P. Carayon, I. Buisson, R. Paul, J. C. Brelière and G. Le Fur, J. Neuroimmunol., 1994, 52, 193. 104. A. Urani, F. J. Roman, V. L. Phan, T. P. Su and T. Maurice, J. Pharmacol. Exp. Ther., 2001, 298, 1269. 105. T. P. Su, E. D. London and J. H. Jaffe, Science, 1988, 240, 219. 106. M. Takebayashi, T. Hayashi and T. P. Su, J. Pharmacol. Exp. Ther., 2002, 303, 1227. 107. S. Macfarlane, M. Cecchi, D. Moore, P. Maruff, K. M. Capiak, A. Zografidis and C. U. Missling, New Alzheimer's Drug ANAVEX 2-­73: A Phase 2a Study, Clinical Safety, Tolerability and Maximum tolerated dose finding in mild-­to-­moderate Alzheimer's patients, https://www.anavex.com, last accessed July 2018. 108. Avanir Pharmaceuticals Announces Positive Phase II Trial Results for AVP-­923 in Treatment of Agitation in Patients with Alzheimer's Disease, https://www.avanir.com, last accessed July 2018.

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109.  Mundipharma and Purdue reach a new deal to in-­licence from Esteve full global rights and development responsibilities for novel first-­in-­ class Sigma-­1 antagonist (S1A or MR309/E-­52862). 110. R. A. Glennon, S. Y. Ablordeppey, A. M. Ismaiel, M. B. El-­Ashmawy, J. B. Fischer and K. Burke Howie, J. Med. Chem., 1994, 37, 1214. 111. T. M. Gund, J. Floyd and D. Jung, J. Mol. Graphics Modell., 2004, 22, 221. 112. C. Laggner, C. Schieferer, B. Fiechtner, G. Poles, R. D. Hoffmann, H. Glossmann, T. Langer and F. F. Moebius, J. Med. Chem., 2005, 48, 4754. 113. T. P. Su, X. Z. Wu, E. J. Cone, K. Shukla, T. M. Gund, A. L. Dodge and D. W. Parish, J. Pharmacol. Exp. Ther., 1991, 259, 543. 114. E. Griesmaier, A. Posod, M. Gross, V. Neubauer, K. Wegleiter, M. Hermann, M. Urbanek, M. Keller and U. Kiechl-­Kohlendorfer, Exp. Neurol., 2012, 237, 388. 115. S. B. Seredenin, T. A. Antipova, M. V. Voronin, S. Y. Kurchashova and A. N. Kuimov, Bull. Exp. Biol. Med., 2009, 148, 42. 116. C. Katnik, A. Garcia, A. A. Behensky, I. E. Yasny, A. M. Shuster, S. B. Seredenin, A. V. Petrov and J. Cuevas, J. Neurochem., 2016, 139, 497. 117. University of Oregon, Sigma Brain Receptor Ligands and Their Use, US Pat., 4709094, 1986. 118. University of Oregon, Substituted guanidines having high binding to the sigma receptor and the use thereof, WO Pat., 9118868, 1991. 119. K. Matsuno, M. Nakazawa, K. Okamoto, Y. Kawashima and S. Mita, Eur. J. Pharmacol., 1996, 306, 271. 120. J. R. Lever, J. L. Gustafson, R. Xu, R. L. Allmon and S. Z. Lever, Synapse, 2006, 59, 350. 121. R. R. Matsumoto, W. D. Bowen, B. R. De Costa and J. C. Houk, Brain Res. Bull., 1999, 48, 497. 122. R. R. Matsumoto, K. A. McCracken, M. J. Friedman, B. Pouw, B. R. De Costa and W. D. Bowen, Eur. J. Pharmacol., 2001, 419, 163. 123. A. Klouz, J. P. Tillement, M. F. Boussard, M. Wierzbicki, V. Berezowski, R. Cecchelli, S. Labidalle, B. Onténiente and D. Morin, FEBS Lett., 2003, 553, 157. 124. A. Klouz, D. B. Saïd, H. Ferchichi, N. Kourda, L. Ouanes, M. Lakhal, J. P. Tillement and D. Morin, Eur. J. Pharmacol., 2008, 578, 292. 125. D. N. Middlemiss, D. Billington, M. Chambers, P. H. Hutson, A. Knight, M. Russell, L. Thorn, M. D. Tricklebank and E. H. F. Wong, Br. J. Pharmacol., 1991, 102, 153P. 126. J. McLarnon, D. Sawyer and J. Church, Neurosci. Lett., 1994, 174, 181. 127. C. A. Maier and B. Wünsch, J. Med. Chem., 2002, 45, 438. 128. F. J. Roman, X. Pascaud, B. Martin, D. Vauché and J. L. Junien, J. Pharm. Pharmacol., 1990, 42, 439. 129. J. A. Fishback, M. J. Robson, Y. T. Xu and R. R. Matsumoto, Pharmacol. Ther., 2010, 127, 271. 130. T. Hayashi, S. Y. Tsai, T. Mori, M. Fujimoto and T. P. Su, Expert Opin. Ther. Targets, 2011, 15, 557.

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Chapter 12

131. E. Amata, A. Rescifina, O. Prezzavento, E. Arena, M. Dichiara, V. Pittalà, Á. Montilla-­García, F. Punzo, P. Merino, E. J. Cobos and A. Marrazzo, J. Med. Chem., 2018, 61, 372. 132. V. Mégalizzi, V. Mathieu, T. Mijatovic, P. Gailly, O. Debeir, N. De Neve, M. Van Damme, G. Bontempi, B. Haibe-­Kains, C. Decaestecker, Y. Kondo, R. Kiss and F. Lefranc, Neoplasia, 2007, 9, 358. 133. C. S. John, B. J. Vilner and W. D. Bowen, J. Med. Chem., 1994, 37, 1737. 134. S. Yang, A. Bhardwaj, J. Cheng, N. J. Alkayed, P. D. Hurn and J. R. Kirsch, Anesth. Analg., 2007, 104, 1179. 135. F. Tan, P. L. Guio-­Aguilar, C. Downes, M. Zhang, L. O'Donovan, J. K. Callaway and P. J. Crack, Neuropharmacology, 2010, 59, 416. 136. D. Rossi, A. Pedrali, M. Urbano, R. Gaggeri, M. Serra, L. Fernández, M. Fernández, J. Caballero, S. Ronsisvalle, O. Prezzavento, D. Schepmann, B. Wuensch, M. Peviani, D. Curti, O. Azzolina and S. Collina, Bioorg. Med. Chem., 2011, 19, 6210. 137. D. Rossi, M. Urbano, A. Pedrali, M. Serra, D. Zampieri, M. G. Mamolo, C. Laggner, C. Zanette, C. Florio, D. Schepmann, B. Wuensch, O. Azzolina and S. Collina, Bioorg. Med. Chem., 2010, 18, 1204. 138. D. Rossi, A. Marra, P. Picconi, M. Serra, L. Catenacci, M. Sorrenti, E. Laurini, M. Fermeglia, S. Pricl, S. Brambilla, N. Almirante, M. Peviani, D. Curti and S. Collina, Bioorg. Med. Chem., 2013, 21, 2577. 139. D. Rossi, A. Pedrali, A. Marra, L. Pignataro, D. Schepmann, B. Wünsch, L. Ye, K. Leuner, M. Peviani, D. Curti, O. Azzolina and S. Collina, Chiral­ ity, 2013, 25, 814. 140. A. Marra, D. Rossi, L. Maggi, F. Corana, B. Mannucci, M. Peviani, D. Curti and S. Collina, Biomed. Chromatogr., 2016, 30, 645. 141. M. Rui, G. Rossino, S. Coniglio, S. Monteleone, A. Scuteri, A. Malacrida, D. Rossi, L. Catenacci, M. Sorrenti, M. Paolillo, D. Curti, L. Venturini, D. Schepmann, B. Wünsch, K. R. Liedl, G. Cavaletti, V. Pace, E. Urban and S. Collina, Eur. J. Med. Chem., 2018, 158, 353. 142. N. Narita, K. Hashimoto, M. Iyo, Y. Minabe and K. Yamazaki, Eur. J. Pharmacol., 1995, 293, 277. 143. N. Narita, K. Hashimoto, S. Tomitaka and Y. Minabe, Eur. J. Pharmacol., 1996, 307, 117. 144. E. R. Whittemore, V. I. Ilyin and R. M. Woodward, J. Pharmacol. Exp. Ther., 1997, 282, 326.

Chapter 13

Non-­coding RNA and Multiple Sclerosis: New Targets for Drug Discovery Iñaki Osorio-­Querejeta, Maider Muñoz-­Culla and David Otaegui* Multiple Sclerosis Unit, Biodonostia Health Research Institute, Donostia, Spain *E-­mail: [email protected]

13.1  Genome Organization: Outside of the Exome The human genome is organized into 23 chromosome pairs (22 autologous and 2 sexual) plus the mitochondrial genome, and lately we can add also the microbiome (meaning the genome from all the microorganisms that form part of our organism). Classically we try to organize the information of our genome through its function, assuming that the genes that code for proteins (termed the exome) are the most important pieces of our genome. The reality is more complex than the axiom “one gene, one protein”. Instead of that we are discovering a complex network of transcription in which each gene can produce several (and different) transcripts through the splicing process. Moreover, huge collaborating projects such as the Human Genome Project and ENCODE Project have shown us that the rest of the genome hides important information in the form of genes that are transcribed to RNA that

  Drug Discovery Series No. 70 Emerging Drugs and Targets for Multiple Sclerosis Edited by Ana Martinez © The Royal Society of Chemistry 2019 Published by the Royal Society of Chemistry, www.rsc.org

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are not translated to proteins (ncRNA). These ncRNA have key importance as regulators of the system. In this model, the importance of a gene lays not only in the DNA sequence, but in the regulation of its expression by a complex network that can respond with flexibility to the internal and external signals. The latest statistics from Ensembl Genome Browser release 96.38 (November 2018) give a total number of 20 465 coding genes, 15 137 long ncRNA (lncRNA) genes and 4871 small non-­coding RNA (sncRNA) genes. The main proposed function, although not the only one, for these ncRNA is regulation at different levels and with different specific functions; however, we are only just beginning to scratch the surface, and the complexity behind these ncRNA is far from being completely understood. The understanding of how these regulators work could help us not only to have a more complete picture of the studied disease and to select these ncRNA as therapeutic targets but also to use the regulation potential of these molecules as therapeutic tools. In this chapter we will focus in the most studied subgroup of these ncRNA genes, the sncRNA. This subgroup of ncRNA can be subdivided in many families, although we will focus in the most studied ones: microRNA (miRNA), small nuclear RNA (snRNA) and small nucleolar RNA (snoRNA).

13.2  miRNA and snoRNA ncRNA have been related with multiple sclerosis (MS) in many characterization studies in which different expression levels of small ncRNA have been described between MS patients and healthy controls and also between different classes of MS. In this context, the main established relationship between sncRNA and MS has been through the potential interest of these molecules as biomarkers in MS. However, a functional role for sncRNA in the etiopathology of MS has been also postulated. miRNAs are short sequences of RNA (21–24 nt) that regulate gene expression by binding to target mRNAs and preventing their translation to protein. Canonical miRNAs are transcribed by RNA polymerase II as part of a longer transcript called pri-­miRNA. This primary transcript acquires a hairpin secondary structure which is processed by Microprocessor, a protein complex formed by one molecule of the Drosha endonuclease and two molecules of DGCR8. Drosha cuts at the base of the hairpin of the pri-­miRNA to generate a shorter RNA of about 60 nucleotides long. This stem-­loop structure called pre-­miRNA is transported to the cytoplasm by Exportin-­5,1 where it is further be processed by Dicer. This endonuclease generates an miRNA duplex after cutting both strands near the loop. Finally, this miRNA duplex is loaded into an Argonaute protein and only one of the two strands is kept in the complex (the functional strand) while the other strand is usually degraded. Once the miRNA is loaded into the silencing complex, it binds

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to the target mRNA by base-­pairing and either produces its degradation or inhibits its translation to protein.2 In animals, miRNA-­mediated repression requires the binding of the protein TNRC6, which recruits other proteins; depending on the context, these cause mRNA decay or translation repression.3 The predominant way of miRNA-­mediated regulation of transcription is through target mRNA decay,4,5 which means that the effects of miRNA regulation can be measured at the mRNA level. It has been described that miRNA take part in virtually all biological functions such as development, cell differentiation, proliferation, cell death and cell signaling. Taking into account that they are part of the gene expression regulation network, it is not surprising that the alteration of these small molecules has an impact on human health. In fact, an increasing number of articles are being published describing the relationship of miRNA deregulation with several human diseases such as cancer, cardiovascular diseases, neurological disorders and immune-­mediated diseases.6–10 All of this body of evidence has also contributed to the knowledge of MS, a disease in which we know now that miRNAs are involved. The first evidence of the implication of miRNA in multiple sclerosis was reported in 2009 by three groups.11–13 Otaegui and colleagues reported that miR-­18b and miR-­599 were overexpressed in leucocytes from MS patients during relapse episodes. Moreover, they proposed miR-­96 to be relevant for the remitting phase of the disease, which was identified by co-­expression networks.11 Shortly after having described the deregulation of some miRNA in leucocytes from MS patients, another group reported the dysregulation of 165 miRNA in MS patients compared with healthy controls and they found that miR-­145 was able to correctly classify 89.7% of the samples tested (from a total of 39).12 Interestingly, to improve these results, Keller and colleagues applied machine learning techniques to select a subset of miRNA that is able to distinguish both groups. This analysis yielded a group of 48 miRNA able to discriminate patients from healthy controls with an accuracy of 96.3%, a specificity of 95% and a sensitivity of 97.6%. After these first profiling studies, several studies have been published trying to elucidate the specific role of miRNA in MS. Those studies have analyzed miRNA expression in different sample types in humans (leucocytes, serum, plasma, specific lymphocyte subsets, cerebrospinal fluid and brain) and also in different tissues of the experimental autoimmune encephalomyelitis (EAE) model. These studies have provided a list of miRNA that may have a role in MS by regulating different processes in the immune cell development and differentiation as well as in neurodegeneration and remyelination (described in Section 13.4). More recently, a study of exosomal miRNA composition in patients has found that exosomal let-­7i regulates MS pathogenesis by blocking the IGF1R/TGFBR1 pathway on CD4+ T cells.14 This finding adds a new layer of regulation outside the cell, given that exosomes can carry different molecules which can exert their function in another cell of another tissue.

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miRNA have been the most widely studied family of sncRNA, but other types, such as snoRNA, have also been related to MS. snoRNA are grouped into two main families, C/D box and H/ACA box, according to the evolutionarily conserved sequence elements.15 The name snoRNA refers to the nucleolar localization of the first members of this family, as opposed to small nuclear RNA (snRNA), which are localized in the nucleoplasm. The function of the majority of these RNA is the maturation and post-­transcriptional modification of ribosomal RNA, which takes place in the nucleolus. Nonetheless, other functions and target RNA have also been assigned to these snoRNA and therefore, other subcellular localizations.16 snoRNA are essential players in important biological functions, such as protein translation and mRNA processing. The well-­known function of snoRNA is to guide 2′-­O-­methylation and pseudouridylation of the rRNA and snRNA, which is performed together with a protein complex. It has been described that these kinds of RNA modifications are essential for the correct functioning of the spliceosome, the complex which is responsible for mRNA maturation (splicing of the pre-­mRNA). Therefore, the regulation of snoRNA is a key factor in the cell molecular machinery.16 The deregulation of snoRNA, as with miRNA, has been associated with several human diseases.17 As for MS is concerned, Jernas and colleagues reported that 50 snoRNA were differentially expressed in T cells from MS patients compared with healthy controls.18 Almost all these snoRNA were found to be underexpressed in MS patients. Afterwards, Muñoz-­Culla et al. reported that several snoRNA were differentially expressed in leucocytes from MS patients. More importantly, a significant alteration of snoRNA during relapses of the disease compared to remission periods of the same patients has been found.19 Moreover, applying co-­expression and network analysis, Irizar et al. had identified several snoRNA, together with some miRNA as potential therapeutic targets for MS, SNORA40 being the most promising one given its central role in the disease-­specific network and the fact that it is differentially expressed in patients.20 Furthermore, several snoRNA have been identified in PBMC from interferon-­treated MS patients as the most abundant class of the total RNA pool.21 All these findings suggest that the snoRNA family also has an important role in the pathogenesis of MS and that the interactions with other types of RNA are crucial for the general transcriptome regulation program and cell homeostasis. In fact, we have just started to decipher these molecular interactions and new RNA families are being proposed to play a role in this complex regulation network. On top of that, the classification of different types of RNAs may not be such a permanent list, as new classes of RNAs are being described and the known ones redefined. For instance, a model has been proposed in which the RNA species included in the snoRNA and miRNA families would form a spectrum from classical snoRNA to prototypical miRNA: classical snoRNA ↔ snoRNA with miRNA-­like features ↔ dual function sno-­miRNA ↔ miRNA with snoRNA-­like features ↔ prototypical miRNA.22

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13.3  Circular RNA In the last decade, a new player has been added to the ever-­growing list of RNA species: circular RNA (circRNA). They were originally considered as secondary-­by-­products of linear mRNA splicing and therefore with no function or importance. However, with the new data generated by RNA sequencing technologies as well as ad hoc bioinformatic tools, circRNA have been shown to be a numerous, endogenous, abundant, stable and well-­conserved species of ncRNA in mammalian cells.23,24 CircRNA form through RNA splicing, resulting in a covalently closed loop structure characterized by a backspliced junction, which allows their identification. Their function remains unclear, although an miRNA sponge function has been proposed,25,26 suggesting that they may play an important role in the transcriptome regulation network. However, in recent years another important function has been proposed, demonstrating that these circRNA can produce functional proteins, different from the ones produced by the linear form of the gene.27 CircRNA differential expression has been related with several diseases, including cancer, diabetes, vascular diseases and neurological diseases.28–30 A profile study of 13 617 circRNA in MS demonstrated a specific transcriptional signature in MS patients, proposing the relevance of two specific circRNA coming from the ANXA2 gene.31 Another study shows the importance of a GSDMB-­circRNA in MS.32 Both studies show the relevance of circRNA in our schema to understand the complexity of MS; however, these studies focus in the biomarker capacity of circRNA but not in the function or in their druggability. CircRNA are so new and their functions so unknown that their classification is quite ambiguous: they have been classified as ncRNA, although as previously mentioned, some of them can produce proteins. They do not fit properly in the lncRNA family nor the sncRNA family; however, we include them in this chapter due to their growing importance in regulation and their potential not only as druggable molecules but also as therapeutic tools (Figure 13.1 and 13.2).

13.4  ncRNA and Their Therapeutic Possibilities The risk of developing MS has been, in part, associated with epigenetic modifications in which ncRNA are implicated.33 In this sense Drosha, Dicer and DGCR8 have been found to be overexpressed in relapsing–remitting MS patients, underlining the possible involvement that the dysregulation of miRNA might play in the pathogenesis of MS.34 In addition to this, lncRNA, which also regulate gene expression, have been associated with the regulation of oligodendrocyte precursor cell (OPC) differentiation and the development of MS animal models.35 Although lncRNA have been proposed as therapeutic targets in epigenetic diseases,36 their functions and potential as therapeutic mediators remain largely undefined,37 and no papers have been

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Figure 13.1  Simplified  scheme of the RNA classification.

Figure 13.2  Some  of the proposed functions of the sncRNA. miRNA can bind to

mRNA and inhibit or down-­regulate their translation to protein. CircularRNA can join to miRNA and sponge them, minimizing their effect and can also translate to protein. snoRNA and snRNA have an effect in the translation process and in the inhibition by miRNA process.

published elucidating the role that this kind of ncRNA may play in MS therapy. On the other hand, miRNA have been postulated as feasible and promising molecules to induce both immunomodulation and remyelination (the two main goals in MS therapy). The use of miRNA as targets or mediators of these effects will be addressed in the following sections.

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13.4.1  ncRNA as Immunomodulators in MS Therapy Several strategies have been used to identify potential targets and mediators for immunomodulatory ncRNA-­mediated therapy (Table 13.1). One of these consists in the use of expression network analysis, which has been used to identify dysregulated ncRNA in patients, both in relapse and remission, and in controls. This kind of approach can lead to a list of therapeutic ncRNA candidates.38 It has been shown that this dysregulation favours proinflammatory and T-­cell-­mediated autoimmunity, underlining the role that miRNA play in the development of the disease and opening therapeutic approaches in miRNA-­mediated immunotherapy for MS.39 Drug-­mediated modification of miRNA expression has been used as a biomarker of treatment efficiency and disease evolution. However, these studies have also been used to determine miRNA that can be targets of immunomodulatory therapies. This is the case of patients treated with glatiramer acetate, in which the expression of miR-­142-­3p and miR-­146a is restored after treatment administration. The physiological expression of these miRNA has been associated with the regulation of immune tolerance.40 In addition to this, the treatment of MS patients with autologous hematopoietic stem cell transplantation (AHSCT) was able to restore the expression patterns of miR-­16, miR-­155 and miR-­142-­3p, which have been found to be dysregulated in MS patients.41 The fact that AHSCT are reported to control autoimmunity and to induce clinical amelioration made these authors suggest that targeting miR-­16, miR-­155 and miR-­142-­3p might be a therapeutic opportunity for MS and perhaps for other autoimmune diseases. The dysregulation of miRNA founded in MS (and in different stages of the disease) opened the opportunity to the discovery of new therapeutic targets.42 For example, miR-­141, miR-­200a and miR-­448 have been found to be up-­regulated in MS patients. These miRNA regulate Th17 cell differentiation while inhibiting regulatory T cell (Treg) differentiation.43,44 Polarization of T cells to Th1, Th2 or Th17 is a critical process in cell-­mediated immunity, and these miRNA could be the targets for an immunomodulatory therapy in MS patients. Related to this, treatment of patient-­derived T cells with miRNA inhibitors for miR-­27, miR-­128 and miR-­340, which are overexpressed in MS patients, was able to restore Th2 cytokine production in vitro.39 Moreover, the role of miRNA as immunoregulators has also been addressed in vivo. miR-­155 is a positive regulator of central nervous system (CNS) autoimmune inflammation and miR-­155 knockout mice generated a less severe EAE by decreasing Th1 and Th17 response in the CNS. In addition, when EAE animals were treated with LNA-­modified anti-­miR-­155 mice, clinical severity was reduced.45 Moreover, the lentiviral infection of miR-­326 in EAE mice, an miRNA which is up-­regulated in MS patients, leads to an increase in disease severity by targeting ETS-­1, a known negative regulator of Th17 cells.46 These results indicate that miR-­155 and miR-­326 may be potential targets for MS patients.

Table 13.1  Summary  of the proposed druggable microRNA in multiple sclerosis. microRNA

Role in MS

Status

Therapeutic strategy

Expected resulta

Reference

miR-­16 miR-­23a miR-­26a

Autoimmunity control — Glutamate levels increase

Antagonist Agonist Antagonist

IS regulation Increased myelin thickness Neuroprotection

41 69 76

miR-­106a-­363 cluster miR-­125a-­3p

Inflammation and BBB breakdown Oligodendroglia maturation inhibition Th17 and Treg differentiation regulator Regulation of inmuno tolerance Regulation of inmuno tolerance Inhibition of oligodendrogenesis

Up-­regulated — Up-­regulated in IFN-­ B-­treated patients —

Agonist

IS regulation

47

Up-­regulated

Antagonist

Oligodendrocyte maturation

74

Up-­regulated

Antagonist

IS regulation

42–44

Down-­regulated Down-­regulated

Agonist Agonist

40,41 40,68 65,66

Up-­regulated Up-­regulated

Antagonist Antagonist

IS regulation IS regulation Oligodendrogenesis and myelination IS regulation IS regulation

—

Antagonist

72

Up-­regulated in OPC differentiation — —

Agonist

Oligodendrocyte maturation OPC differentiation/ remyelination Promotion of myelination Oligodendrocyte maturation

miR-­141 miR-­142-­3p miR-­146a miR-­155 miR-­200a miR-­212 miR-­219 miR-­221-­3p miR-­297c-­5p miR-­326 miR-­338 miR-­448 a

Positive autoimmunity regulator Th17 and Treg differentiation regulator Oligodendrocyte maturation inhibitor OPC differentiation promotor Myelination inhibitior Oligodendroglia maturation inhibition Th17 cell negative regulator OPC differentiation promotor Th17 and Treg differentiation regulator

IS: immune system; OPC: oligodendrocyte precursor cell.

Up-­regulated Up-­regulated in OPC differentiation Up-­regulated

Antagonist Antagonist Antagonist Agonist Antagonist

IS regulation OPC differentiation/ remyelination IS regulation

45 42–44

54–61 71 73 13 54,56,58 42–44

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More evidence about the role that miRNA can play as immunomodulators in MS therapy was provided by the induction of EAE in miR-­ 106a-­363 cluster knockout mice, which developed a severe disease course. These data correlated with histological analysis in which more lesions and immune cell infiltrates were detected when compared with wild-­ type mice.47 Interestingly, VEGFA and STAT3 are targets of this cluster: the first is involved in the regulation of autoimmune inflammation and blood–brain barrier breakdown48 while the second plays a role in Th17 cell differentiation.49 Finally, the up-­regulation of miR-­132 increases EAE symptoms, and it has been proposed as a therapeutic target for anti-­ inflammatory treatments in MS.50 Taking all this information together, we can summarize that miRNA are active players in the immune alteration that takes place in MS, and therefore their utilization as mediators or targets in future MS therapies is a promising field that is starting its first steps.

13.4.2  ncRNA as Remyelination Promoters In order to develop a remyelinating therapy, the reasons why remyelination tends to fail in late stages of the disease should be clarified. Although it is not totally understood why remyelination tends to fail with the progression of the disease, it is proposed that a lack of OPC, an insufficient migration and/ or a poor differentiation process might be behind the remyelination failure. Demyelination lesions often retain high numbers of undifferentiated OPC with the potential of generating new myelin. There are several hypotheses that try to explain why these cells do not remyelinate, and a common point is their incapability to differentiate to myelinating oligodendrocytes.51 It has been widely proposed that the stimulation of OPC differentiation could increase myelin regeneration, being a promising therapeutic approach to induce remyelination in MS. As it will be discussed below, miRNA play an important role in the precise and complex program that takes place in OPC differentiation and remyelination. Dicer, which is implicated in the maturation of miRNA, has shown to be modified in MS patients. There is controversy, and results might vary depending on the source of the sample, the disease evolution and other aspects. In this sense, Dicer protein showed lower levels in MS patients compared with controls52,53 whereas Dicer 1 miRNA has been reported to have increased levels in patients.34 Dicer knockout mice showed impaired myelination, which was related with a poor OPC differentiation, as the observations of proliferating OPC but the lack of oligodendrocytes suggested.54,55 Dicer 1 and Olig 1 knockout mice were used to search for the miRNA responsible for oligodendrocyte maturation. miR-­219 and miR-­338 expression were significantly reduced in both animals, indicating the role that these miRNA may be playing in OPC differentiation. In addition to this, inducible oligodendrocyte Dicer knockout mice generate demyelination and inflammatory gliosis. Interestingly, these oligodendrocytes reduce the

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production of miR-­219, highlighting the importance of this miRNA.56 In this sense, differentiating OPC and human white matter have been shown to be enriched in miR-­219, and conditional miR-­219 mutant mice showed reduced numbers of oligodendrocytes (OL) in the corpus callosum and optic nerve but not lower OPC levels.57,58 To reinforce the role that miR-­219 plays in OPC differentiation, OPC were obtained from a Dicer 1 knockout mouse being unable to differentiate to OL. However, when miR-­219 was transfected to these cells, they increased the expression of myelin basic proteins (MBP) and the overall expression of C-­t ype natriuretic peptide (CNP), MBP and MOG genes, which are associated with oligodendrogenesis.55 In addition, human endometrial-­derived stromal cells have been shown to differentiate to pre-­oligodendrocytes after lentiviruses mediated miR-­219 overexpression.59 Similar to this, mouse embryonic stem cells transfected with miR-­219 differentiated to OPC, which induced remyelination more efficiently than wild-­t ype OPC after their transplantation to a mice model of toxin demyelination.60 In the same model of cuprizone-­induced demyelination, lentiviruses overexpressing miR-­219 were intrathecally administered increasing MBP and CNP levels and decreasing demyelination in the model.61 In addition, miR-­219-­inducible knockout mice were demyelinated by injection of lysolecithin. Fewer remyelinating signs were detected, indicating that miR-­219 in critical for remyelination after a demyelinating insult. In order to confirm these results, miR-­219-­ overexpressing mutant mice were generated and lysolecithin-­induced demyelination was generated in the spinal cord. These animals were able to generate more OPC and to increase the percentage of remyelinated axons.57 Finally, when miR-­219 was administered intrathecally to the EAE animal model, a decrease in the clinical score was shown thanks to the generation of new myelin-­forming oligodendrocytes.57 These results suggest that the elevation of miR-­219 induces OPC differentiation and therefore remyelination, opening up a promising therapeutic approach. miR-­219 has been predicted and experimentally demonstrated to target PDGFRα, Sox6, FoxJ3 and ZFP238, all of which are related with OPC differentiation.55 The expression of PDGFRα induces OPC proliferation and Sox6 prevents their differentiation. Another proposed route of action for miR-­219 is related to the repression of Lingo1 expression, a protein that has been demonstrated to inhibit OPC differentiation. Although miR-­219-­5p is the most promising candidate to show remyelinating potential, it is not the only miRNA described to be involved in these processes. Both, miR-­219 and miR-­338 are overexpressed in perinatal stages coinciding with oligodendrocyte maturation,54 and miR-­338-­5p appears also to be up-­regulated during mouse brain maturation and to regulate OPC differentiation in humans.58 Moreover, the overexpression of miR-­338 in human endometrial stromal cells by lentiviruses induces their differentiation to OPC.62 Interestingly, miR-­338 and miR-­219 knockout mice did show lower myelin levels when compared with miR-­219 knockout

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mice, indicating that miR-­338 cooperates with miR-­219 in the regulation of oligodendrocyte maturation.57 As a consequence, the combination of both miRNA has seen to be more effective at inducing OPC differentiation in vitro.63,64 MiR-­146a has also been proposed to regulate this process. Primary OPC culture transfected with miR-­146a differentiates to oligodendrocytes, whereas the inhibition of this miRNA with hairpin inhibitors blocks their differentiation.65 This miRNA has shown to be up-­regulated during the first stages of a cuprizone-­induced demyelination model.66 In addition, the infusion of miR-­146a in the model stimulates the generation of oligodendrocytes and augments myelination,66 which has been related with the role that the miRNA might play in myelin restoration. This effect has shown to be mediated by interleukin-­1 receptor-­associated kinase 1 (IRAK1).65 Nevertheless, these results are somehow in contrast with later ones that showed higher numbers of oligodendrocytes and reduced demyelination in miR-­146a knockout mice.67 Mir-­146a has also been described as reducing the inflammatory response, and this might explain these contradictory results.68 To finish with the use of miRNA as remyelination inductors, the overexpression of miR-­23a in OPC leads to an increase in CNP (cyclic nucleotide 3′ phosphodiesterase, an oligodendrocyte marker)-­positive cells. Mice genetically engineered to overexpress miR-­23a not only increase myelin gene expression, but also myelin thickness in the corpus callosum.69 Interestingly, miR-­23a has shown to up-­regulate 2700046G09Rik, a lncRNA which increases the half-­life of miR-­23a. The presence of this lncRNA in oligodendroglia has been proposed to potentiate the activation of miR-­23a/PTEN/Akt/mTOR and MAPK cascades, regulating the expression of myelin related genes in OL.70 MiRNA have also been proposed to be targets of remyelinating drugs. miR-­221-­3p was proven to inhibit Schwann cell myelination in vivo by targeting Nab1.71 Similar to this, miR-­212 and miR-­297c-­5p were shown to inhibit the maturation of oligodendrocytes.72,73 In addition, the overexpression of miR-­125a-­3p has been described to impair rodent oligodendroglia maturation. More precisely, this miRNA reduces the number of MBP-­positive OL. Although MBP is not a predicted target of miR-­125a-­3p, by in silico analysis tools this miRNA has been reported to regulate myelination pathways up-­stream. Interestingly, miR-­125a-­3p has been shown to be up-­regulated in the CSF of MS patients, which could indicate that this miRNA is inhibiting remyelination.74 Although the role of this miRNA in CNS remyelination should be studied deeply, its down-­regulation may be a therapeutic approach to stimulate oligodendrocyte differentiation and remyelination. To conclude, miR-­26a, an miRNA that is overexpressed on IFN-­β treated patients, targets SLC1A1, which is involved in the glutamate receptor signaling pathway. Excessive glutamate is released in demyelinating lesions and this can be a cause of neuron toxicity.75 Targeting miR-­26a can promote neuroprotection or at least inhibit neuron toxicity in neurodegenerative diseases.76

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13.4.3  ncRNA Delivery to the Central Nervous System ncRNA have been shown to play a role as immunomodulators and myelin inductors, making them therapeutic candidates for MS. The use of sncRNA and more concretely miRNA as therapeutic mediators may involve the manipulation of miRNA levels by increasing or repressing their expression in the affected tissue. This could be done by miRNA mimic (agonist) or by anti-­miR (antagonist) increasing or decreasing target gene expression, respectively.77 In order to perform an effective and safe miRNA administration, two main considerations should be made: the route of administration and the delivery method. Regarding the first question, miRNA have been shown to be effective mediators in the CNS after direct administration in animal models.57 Nevertheless, direct administration by intrathecal injections should be avoided in human therapy, especially if repetitive administrations have to be made; therefore intravenous, intranasal or intraperitoneal administration have been studied as therapeutic approaches. However, the CNS is protected by the blood–brain barrier (BBB) and delivery of miRNA or their repressors to the CNS is a challenging question. In addition, nucleic acids can be degraded by enzymes and must be assisted to enter the target cells, avoiding the risk of being taken up by non-­target tissues. To cover all these, miRNA or their antagonists should be encapsulated to protect them and to cross the BBB and reach the CNS to produce the expected effect. In relation to this, several delivery methods have been studied. These range from viral vectors such as adenoviruses, retroviruses or lentiviruses to synthetic nanocarriers. Viruses have been demonstrated to be effective at delivering miRNA to the brain. However, safety questions should be taken into account, such as a possible oncogenic transformation of the recipient cell. In addition, this delivery method can stimulate the innate and adaptive immune responses, reducing its effectivity.78 On the other hand, synthetic nanocarriers have appeared to be promising miRNA vehicles due to the known composition, easy management and analysis and lower immunogenicity.79 Several delivery systems can be used, especially liposomes and nanoparticles. These have been shown to be able to cross the BBB and deliver a specific drug or molecule to the CNS.80 In addition, their surface can be bound to a concrete ligand, making them specific for a cell type or tissue. Interestingly, in recent years a new miRNA delivery method has appeared that is a middle step between virus vectors and synthetic nanocarriers. This consists in the use of extracellular vesicles (EV). EV are 100 to 1000 nm sized vesicles released constitutively by cells that play an important role in intercellular communication by delivering proteins and genetic materials, among others.81,82 They have been proven to cross the BBB,83 to take part in the transmission of information across the CNS84 and to be a potential therapeutic method for demyelination. Interestingly, EV derived from young rats have been shown to be able to induce myelin formation in aged rats. This effect was related with the miRNA

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cargo, and more concretely with the presence of miR-­219 in the vesicles, reinforcing the role that EV may play in miRNA delivery to the CNS for the treatment of MS.

13.5  Conclusion ncRNA have clearly been related with MS in several ways. They can be used as biomarkers not only of the disease but also of the evolution and of the treatment response. More interestingly, they play a function, not fully understood, in the biology of MS, and therefore they are potential targets for new treatments, and even more exciting, they can be used as therapeutic molecules with a promising effect in such fields as immunomodulation and remyelination. A growing body of evidence is being built with a lot of research in these tiny molecules, trying to elucidate what they do, how we can use them and how we can transport them. Results coming from this field can bring great joy to the MS community in the future, opening new research avenues that will help us to understand the disease, and improving our capacity to modulate it.

References 1. R. Yi, Y. Qin, I. G. Macara and B. R. Cullen, Genes Dev., 2003, 17, 3011–3016. 2. D. P. Bartel, Cell, 2018, 173, 20–51. 3. S. Jonas and E. Izaurralde, Nat. Rev. Genet., 2015, 16, 421–433. 4. D. Baek, J. Villen, C. Shin, F. D. Camargo, S. P. Gygi and D. P. Bartel, Nature, 2008, 455, 64–71. 5. H. Guo, N. T. Ingolia, J. S. Weissman and D. P. Bartel, Nature, 2010, 466, 835–840. 6. S. S. Hebert and B. De Strooper, Trends Neurosci., 2009, 32, 199–206. 7. A. Ceribelli, M. Satoh and E. K. Chan, Curr. Opin. Immunol., 2012, 24, 686–691. 8. N. Papoutsidakis, S. Deftereos, A. Kaoukis, G. Bouras, G. Giannopoulos, A. Theodorakis, C. Angelidis, G. Hatzis and C. Stefanadis, Curr. Top. Med. Chem., 2013, 13, 216–230. 9. M. Fernandez-­Mercado, L. Manterola and C. H. Lawrie, Curr. Genomics, 2015, 16, 349–358. 10. C. H. Lawrie, S. Gal, H. M. Dunlop, B. Pushkaran, A. P. Liggins, K. Pulford, A. H. Banham, F. Pezzella, J. Boultwood, J. S. Wainscoat, C. S. Hatton and A. L. Harris, Br. J. Haematol., 2008, 141, 672–675. 11. D. Otaegui, S. E. Baranzini, R. Armañanzas, B. Calvo, M. Muñoz-­Culla, P. Khankhanian, I. Inza, J. A. Lozano, T. Castillo-­Triviño, A. Asensio, J. Olaskoaga and A. López de Munain, PLoS One, 2009, 4, e6309. 12. A. Keller, P. Leidinger, J. Lange, A. Borries, H. Schroers, M. Scheffler, H. P. Lenhof, K. Ruprecht and E. Meese, PLoS One, 2009, 4, e7440.

298

Chapter 13

13. C. Du, C. Liu, J. Kang, G. Zhao, Z. Ye, S. Huang, Z. Li, Z. Wu and G. Pei, Nat. Immunol., 2009, 10, 1252–1259. 14. K. Kimura, H. Hohjoh, M. Fukuoka, W. Sato, S. Oki, C. Tomi, H. Yamaguchi, T. Kondo, R. Takahashi and T. Yamamura, Nat. Commun., 2018, 9, 10–15. 15. T. Kiss, Cell, 2002, 109, 145–148. 16. A. G. Matera, R. M. Terns and M. P. Terns, Nat. Rev. Mol. Cell Biol., 2007, 8, 209–220. 17. M. Esteller, Nat. Rev. Genet., 2011, 12, 861–874. 18. M. Jernas, C. Malmestrom, M. Axelsson, I. Nookaew, H. Wadenvik, J. Lycke and B. Olsson, BMC Immunol., 2013, 14, 32. 19. M. Muñoz-­Culla, H. Irizar, M. Sáenz-­Cuesta, T. Castillo-­Triviño, I. Osorio-­ Querejeta, L. Sepúlveda, A. López de Munain, J. Olascoaga and D. Otaegui, Sci. Rep., 2016, 6, 20126. 20. H. Irizar, M. Muñoz-­Culla, M. Sáenz-­Cuesta, I. Osorio-­Querejeta, L. Sepúlveda, T. Castillo-­Triviño, A. Prada, A. Lopez de Munain, J. Olascoaga and D. Otaegui, BMC Genomics, 2015, 16, 1–15. 21. B. De Felice, P. Mondola, A. Sasso, G. Orefice, V. Bresciamorra, G. Vacca, E. Biffali, M. Borra and R. Pannone, BMC Med. Genomics, 2014, 7, 1–9. 22. M. Brameier, A. Herwig, R. Reinhardt, L. Walter and J. Gruber, Nucleic Acids Res., 2011, 39, 675–686. 23. A. Rybak-­Wolf, C. Stottmeister, P. Glažar, M. Jens, N. Pino, M. Hanan, M. Behm, O. Bartok, R. Ashwal-­Fluss, M. Herzog, L. Schreyer, P. Papavasileiou, A. Ivanov, M. Öhman, D. Refojo, S. Kadener and N. Rajewsky, Mol. Cell, 2014, 58, 870–885. 24. W. R. Jeck, J. A. Sorrentino, K. Wang, M. K. Slevin, C. E. Burd, J. Liu, W. F. Marzluff and N. E. Sharpless, RNA, 2013, 19, 141–157. 25. E. Lasda and R. Parker, RNA, 2014, 20, 1829–1842. 26. F. R. Kulcheski, A. P. Christoff and R. Margis, J. Biotechnol., 2016, 238, 42–51. 27. N. R. Pamudurti, O. Bartok, M. Jens, R. Ashwal-­Fluss, C. Stottmeister, L. Ruhe, M. Hanan, E. Wyler, D. Perez-­Hernandez, E. Ramberger, S. Shenzis, M. Samson, G. Dittmar, M. Landthaler, M. Chekulaeva, N. Rajewsky and S. Kadener, Mol. Cell, 2017, 66, 9–21.e7. 28. X. Fan, X. Weng, Y. Zhao, W. Chen, T. Gan and D. Xu, BioMed Res. Int., 2017, 2017, 5135781. 29. H. Xu, S. Guo, W. Li and P. Yu, Sci. Rep., 2015, 5, 12453. 30. T.-­R. Li, Y.-­J. Jia, Q. Wang, X.-­Q. Shao and R.-­J. Lv, Int. J. Neurosci., 2016, 7454, 1–18. 31. L. Iparraguirre, M. Muñoz-­Culla, I. Prada-­Luengo, T. Castillo-­Triviño, J. Olascoaga and D. Otaegui, Hum. Mol. Genet., 2017, 26, 3564–3572. 32. G. Cardamone, E. M. Paraboschi, V. Rimoldi, S. Duga, G. Soldà and R. Asselta, Int. J. Mol. Sci., 2017, 18, 576. 33. M. W. Koch, L. M. Metz and O. Kovalchuk, Trends Mol. Med., 2013, 19, 23–30.

Non-­coding RNA and Multiple Sclerosis: New Targets for Drug Discovery

299

34. N. Jafari, H. Shaghaghi, D. Mahmoodi, Z. Shirzad, F. Alibeiki, S. Bohlooli and H. P. Dogaheh, J. Clin. Neurosci., 2015, 22, 200–203. 35. T. R. Mercer, I. A. Qureshi, S. Gokhan, M. E. Dinger, G. Li, J. S. Mattick and M. F. Mehler, BMC Neurosci., 2010, 11, 1–15. 36. B. Prabhakar, X. B. Zhong and T. P. Rasmussen, Yale J. Biol. Med., 2017, 90, 73–86. 37. M. Matsui and D. R. Corey, Nat. Rev. Drug Discovery, 2017, 70, 773–779. 38. H. Irizar, M. Muñoz-­Culla, M. Sáenz-­Cuesta, I. Osorio-­Querejeta, L. Sepúlveda, T. Castillo-­Triviño, A. Prada, A. Lopez de Munain, J. Olascoaga and D. Otaegui, BMC Genomics, 2015, 16, 250. 39. M. Guerau-­De-­Arellano, K. M. Smith, J. Godlewski, Y. Liu, R. Winger, S. E. Lawler, C. C. Whitacre, M. K. Racke and A. E. Lovett-­Racke, Brain, 2011, 134, 3575–3586. 40. A. Waschbisch, M. Atiya, R. A. Linker, S. Potapov, S. Schwab and T. Derfuss, PLoS One, 2011, 6, 1–5. 41. L. C. M. Arruda, J. C. C. Lorenzi, A. P. A. Sousa, D. L. Zanette, P. V. B. Palma, R. A. Panepucci, D. S. Brum, A. A. Barreira, D. T. Covas, B. P. Simões, W. A. Silva, M. C. Oliveira and K. C. R. Malmegrim, Bone Marrow Transplant., 2015, 50, 380–389. 42. Q. Huang, B. Xiao, X. Ma, M. Qu, Y. Li, P. Nagarkatti, M. Nagarkatti and J. Zhou, MicroRNAs Associated with the Pathogenesis of Multiple Sclerosis, Elsevier B.V., 2016, vol. 295–296. 43. R. Naghavian, K. Ghaedi, A. Kiani-­Esfahani, M. Ganjalikhani-­Hakemi, M. Etemadifar and M. H. Nasr-­Esfahani, PLoS One, 2015, 10, 1–15. 44. R. Wu, Q. He, H. Chen, M. Xu, N. Zhao, Y. Xiao, Q. qian Tu, W. Zhang and X. Bi, Biochem. Biophys. Res. Commun., 2017, 486, 759–766. 45. G. Murugaiyan, V. Beynon, A. Mittal, N. Joller and H. L. Weiner, J. Immunol., 2011, 187, 2213–2221. 46. C. Du, C. Liu, J. Kang, G. Zhao, Z. Ye, S. Huang, Z. Li, Z. Wu and G. Pei, Nat. Immunol., 2009, 10, 1252–1259. 47. J. Ingwersen, T. Menge, B. Wingerath, D. Kaya, J. Graf, T. Prozorovski, A. Keller, C. Backes, M. Beier, M. Scheffler, T. Dehmel, B. C. Kieseier, H. P. Hartung, P. Küry and O. Aktas, Ann. Clin. Transl. Neurol., 2015, 2, 43–55. 48. A. T. Argaw, B. T. Gurfein, Y. Zhang, A. Zameer and G. R. John, Proc. Natl. Acad. Sci. U. S. A., 2009, 106, 1977–1982. 49. T. J. Harris, J. F. Grosso, H.-­R. Yen, H. Xin, M. Kortylewski, E. Albesiano, E. L. Hipkiss, D. Getnet, M. V. Goldberg, C. H. Maris, F. Housseau, H. Yu, D. M. Pardoll and C. G. Drake, J. Immunol., 2007, 179, 4313–4317. 50. H. Hanieh and A. Alzahrani, Eur. J. Immunol., 2013, 43, 2771–2782. 51. S. A. Goldman and J. Osorio, Nat. Neurosci., 2014, 17, 483–485. 52. W. J. Magner, B. Weinstock-­Guttman, M. Rho, D. Hojnacki, R. Ghazi, M. Ramanathan and T. B. Tomasi, J. Neuroimmunol., 2016, 292, 68–78. 53. L. L. Aung and K. E. Balashov, Mult. Scler., 2015, 21, 1131–1138. 54. X. Zhao, X. He, X. Han, Y. Yu, F. Ye, Y. Chen, T. Hoang, X. Xu, Q. S. Mi, M. Xin, F. Wang, B. Appel and Q. R. Lu, Neuron, 2010, 65, 612–626.

300

Chapter 13

55. J. C. Dugas, T. L. Cuellar, A. Scholze, B. Ason, A. Ibrahim, B. Emery, J. L. Zamanian, L. C. Foo, M. T. McManus and B. A. Barres, Neuron, 2010, 65, 597–611. 56. D. Shin, J. Y. Shin, M. T. McManus, L. J. Ptáček and Y. H. Fu, Ann. Neurol., 2009, 66, 843–857. 57. H. Wang, A. L. Moyano, Z. Z. Ma, Y. Deng, Y. Lin, C. Zhao, L. Zhang, M. Jiang, X. He, Z. Z. Ma, F. Lu, M. Xin, W. Zhou, S. O. Yoon, E. R. Bongarzone and Q. R. Lu, Dev. Cell, 2017, 40, 566–582.e5. 58. O. de Faria, Q. L. Cui, J. M. Bin, S. J. Bull, T. E. Kennedy, A. Bar-­Or, J. P. Antel, D. R. Colman and A. S. Dhaunchak, Front. Genet., 2012, 3, 1–13. 59. S. Ebrahimi-­Barough, H. M. Kouchesfehani, J. Ai, M. Mahmoodinia, S. Tavakol and M. Massumi, Neurosci. Lett., 2013, 537, 65–70. 60. H.-­B. Fan, L.-­X. Chen, X.-­B. Qu, C.-­L. Ren, X.-­X. Wu, F.-­X. Dong, B.-­L. Zhang, D.-­S. Gao and R.-­Q. Yao, Sci. Rep., 2017, 7, 41407. 61. S. Liu, C. Ren, X. Qu, X. Wu, F. Dong, Y. K. Chand, H. Fan, R. Yao and D. Geng, Eur. J. Neurosci., 2017, 45, 249–259. 62. S. Ebrahimi-­Barough, M. Massumi, H. M. Kouchesfahani and J. Ai, J. Mol. Neurosci., 2013, 51, 337–343. 63. H. J. Diao, W. C. Low, Q. R. Lu and S. Y. Chew, Biomaterials, 2015, 70, 105–114. 64. H. J. Diao, W. C. Low, U. Milbreta, Q. R. Lu and S. Y. Chew, J. Controlled Release, 2015, 208, 85–92. 65. X. S. Liu, M. Chopp, W. L. Pan, X. L. Wang, B. Y. Fan, Y. Zhang, H. Kassis, R. L. Zhang, X. M. Zhang and Z. G. Zhang, Mol. Neurobiol., 2016, 54, 227–237. 66. J. Zhang, Z. Gang Zhang, M. Lu, X. Wang, X. Shang, S. B. Elias and M. Chopp, Neuroscience, 2017, 348, 252–263. 67. N. A. Martin, V. Molnar, G. T. Szilagyi, M. L. Elkjaer, A. Nawrocki, J. Okarmus, A. Wlodarczyk, E. K. Thygesen, M. Palkovits, F. Gallyas, M. R. Larsen, H. Lassmann, E. Benedikz, T. Owens, A. F. Svenningsen and Z. Illes, Front. Immunol., 2018, 9, 1–14. 68. M. P. Boldin, K. D. Taganov, D. S. Rao, L. Yang, J. L. Zhao, M. Kalwani, Y. Garcia-­Flores, M. Luong, A. Devrekanli, J. Xu, G. Sun, J. Tay, P. S. Linsley and D. Baltimore, J. Exp. Med., 2011, 208, 1189–1201. 69. S.-­T. Lin and Y.-­H. Fu, Dis. Models Mech., 2009, 2, 178–188. 70. S.-­T. Lin, Y. Huang, L. Zhang, M. Y. Heng, L. J. Ptácek and Y.-­H. Fu, Proc. Natl. Acad. Sci. U. S. A., 2013, 110, 17468–17473. 71. L. Zhao, Y. Yuan, P. Li, J. Pan, J. Qin, Y. Liu, Y. Zhang, F. Tian, B. Yu and S. Zhou, Neuroscience, 2018, 379, 239–245. 72. C. Y. Wang, B. Deneen and S. F. Tzeng, J. Neurochem., 2017, 143, 112–125. 73. N. J. Kuypers, A. N. Bankston, R. M. Howard, J. E. Beare and S. R. Whittemore, J. Neurosci., 2016, 36, 1698–1710. 74. D. Lecca, D. Marangon, G. T. Coppolino, A. M. Méndez, A. Finardi, G. D. Costa, V. Martinelli, R. Furlan, M. P. Abbracchio, M. R. Fabian, T. R. Sundermeier, N. Sonenberg, H. Yang, W. Zheng, W. Zhao, C. Guan, J. An, C. J. Huang, D. P. Bartel, J. Krol, I. Loedige, W. Filipowicz, J. P. Sluijter, P.

Non-­coding RNA and Multiple Sclerosis: New Targets for Drug Discovery

301

A. Doevendans, Y. M. Sun, K. Y. Lin, Y. Q. Chen, Y. K. Adlakha, N. Saini, C. Boissart, X. Nissan, K. Giraud-­Triboult, M. Peschanski, A. Benchoua, A. Shenoy, M. Danial, R. H. Blelloch, A. Jovicic, P. Lau, R. White, E. M. Kramer-­Albers, N. Kessaris, B. Emery, L. Ninio-­Many, H. Grossman, N. Shomron, D. Chuderland, R. Shalgi, R. White, B. Huang, Y. Dong, P. Li, Y. Ni, J. Zhao, Z. Liu, F. Yin, H. Takebayashi, M. Fumagalli, E. Boda, A. E. Warrington, S. E. Pfeiffer, L. Zhao, R. Reynolds, X. Liang, N. A. Draghi, M. D. Resh, C. Seiwa, M. F. Corsten, K. Ozawa, X. Cheng, J. C. Dugas, X. Zhao, W. Wu, M. Sun, G. M. Zou, J. Chen, P. Crociara, R. Parolisi, D. Conte, M. Fumagalli, L. Bonfanti, Y. Chen, S. Mitew, H. Nakatani, H. Franke, H. Y. Wu, M. R. Dawson, R. Reynolds, R. J. Hardy, D. A. Vouyiouklis, P. J. Brophy, N. Baumann, D. Pham-­Dinh, C. Klein, M. C. Ortega, U. Bruning, A. E. Pasquinelli, M. Bjarnadottir, S. Saman, C. Verderio, Y. Gui, H. Liu, L. Zhang, W. Lv, X. Hu, A. Reijerkerk, M. Munoz-­Culla, J. R. Christensen, M. Kumar, V. R. Nerurkar, S. Y. Leong, D. A. Galloway, C. S. Moore, P. Rao, E. Benito, A. Fischer, D. Corrada, F. Viti, I. Merelli, C. Battaglia, L. Milanesi, D. Szklarczyk, C. G. Ontology, H. Mi, A. Muruganujan, P. D. Thomas, Y. Chen, G. Obernosterer, J. Martinez, M. Alenius, R. J. Fox and C. H. Polman, Sci. Rep., 2016, 6, 34503. 75. P. Villoslada, C. Alonso, I. Agirrezabal, E. Kotelnikova, I. Zubizarreta, I. Pulido-­Valdeolivas, A. Saiz, M. Comabella, X. Montalban, L. Villar, J. C. Alvarez-­Cermeño, O. Fernández, R. Alvarez-­Lafuente, R. Arroyo and A. Castro, Neurol. Neuroimmunol. Neuroinflamm., 2017, 4, 1–10. 76. N. Potenza, N. Mosca, P. Mondola, S. Damiano, A. Russo and B. De Felice, Biochim. Biophys. Acta, Mol. Basis Dis., 2018, 1864, 317–323. 77. L. P. Garo and G. Murugaiyan, Cell. Mol. Life Sci., 2016, 73, 2041–2051. 78. M. M. Wen, Front. Mol. Neurosci., 2016, 9, 1–7. 79. Y. Zhang, Z. Wang and R. A. Gemeinhart, J. Controlled Release, 2013, 172, 962–974. 80. M. Pinzon-­Daza, I. Campia, J. Kopecka, R. Garzon, D. Ghigo and C. Rigant, Curr. Drug Metab., 2013, 14, 625–640. 81. H. Valadi, K. Ekström, A. Bossios, M. Sjöstrand, J. J. Lee and J. O. Lötvall, Nat. Cell Biol., 2007, 9, 654–659. 82. C. Théry, M. Boussac, P. Véron, G. Raposo, J. Garin and S. Amigorena, J. Immunol., 2001, 166, 7309–7318. 83. X. Zhuang, X. Xiang, W. Grizzle, D. Sun, S. Zhang, R. C. Axtell, S. Ju, J. Mu, L. Zhang, L. Steinman, D. Miller and H.-­G. Zhang, Mol. Ther., 2011, 19, 1769–1779. 84. C. Frühbeis, D. Fröhlich, W. P. Kuo and E.-­M. Krämer-­Albers, Front. Cell. Neurosci., 2013, 7, 182.

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Diet, Gut Microbiome and Multiple Sclerosis Lacey B. Sell and Javier Ochoa-­Repáraz* Department of Biology, Eastern Washington University, Cheney, WA 99004, USA *E-­mail: [email protected]

14.1  Multiple Sclerosis Multiple sclerosis (MS) is an autoimmune disease of the central nervous system (CNS). The neurological manifestations of MS have been attributed to exacerbated immune response and inflammation in the brain and spinal cord. In MS, CNS factors yet to be elucidated attack the myelin sheath that protects neuronal axons, causing demyelination, loss of axons, loss of the oligodendrocytes' ability to remyelinate, and eventually neuronal death.1 This neuronal damage is a direct result of immune cells' inability to discern between self and non-­self, causing an autoimmune reaction. The axonal demyelination in white and gray matter of the CNS causes a plethora of symptoms that include initial manifestations such as muscle weakness, loss of coordination, numbness, and double or blurring vision and at later stages can advance into severe paralysis, pain, mood disorders, depression and disturbances in urinary, sexual and gastrointestinal functions, among others.2 MS is a complex disease that is initiated by a clinically isolated syndrome (CIS) and followed by a pattern that can be differentiated into relapsing–remitting

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MS (RRMS), primary progressive MS (PPMS) or secondary progressive MS (SPMS).3 Approximately 80% of RRMS patients develop SPMS. In addition, it is considered that a proportion of MS patients develop a mild form of MS, defined as benign MS, although the clinical existence of this less severe form is still under some debate.4 MS has a prevalence of 50 to 300 per 100 000 individuals, and between two to three million people suffer from MS worldwide.5 This wide range in prevalence is due to geographical location. With only a few exceptions, the prevalence of MS is shown to be higher in countries in the northern hemisphere and rates increase with increasing latitude. Although the exact cause of this trend is still unknown, it provides evidence that there may be a link between geography and being at risk of developing MS.5 The female to male ratio, similar to other autoimmune diseases, is close to 3 : 1.6 As occurs with other human diseases, polymorphisms in the human leukocyte antigen (HLA) region of genes in Chromosome 6 have been linked to an increased risk for the development of MS. Genome-­ wide association studies (GWAS) suggest HLA regions but also suggest other immune system genes such as interleukin 2 receptor alpha (IL2RA) and interleukin 17 receptor alpha (IL17RA), among others that are known to be associated with MS.7 Although major advances have occurred in recent decades, the causative factor that triggers MS is still unknown. However, it has been posed that a combination of genetic susceptibilities8 and environmental risk factors is a major contributor for MS induction and perhaps progression.9 Multiple environmental risk factors have been proposed for MS, such as smoking, previous viral infections, vitamin D deficiency, and others, including adolescent obesity.9 Because of the numerous factors that must be considered and because comorbidities are common in MS, it is plausible that multiple different environmental factors merge together in the context of MS etiology. As we will discuss in the next section, many of the environmental factors that have been proposed to increase the risk of MS also have the capacity to affect the composition of the gut microbiome.10 Globally, all microbes of a given system are termed microbiota, while the combination of the microbiota within the host's factors is termed the microbiome. Among all of the environmental causes that possibly affect the composition of the gut microbiome, diet is perhaps the most relevant. With rising levels of childhood obesity coupled with the fact that higher body mass index (BMI) is a risk factor for developing MS, diet appears to play a crucial role.11–13 Therefore, next we will explore the link between the diet, the microbiome and MS in this chapter. We will discuss the main mechanisms by which diet has the capacity to modulate the composition of the microbiome, and cover current research on how these modifications may be altering the microbiome in those with MS. Finally, we discuss the most recent publications that are providing insights on the potential of diet and its effect on the gut microbiome, as a novel therapeutic approach for MS.

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14.2  Autoimmunity, Diet and the Gut Microbiome The microbiome in humans consists of hundreds of trillions of microorganisms including bacteria, archaea and eukaryotic microbes along with acellular infectious agents such as viruses. Different microbiomes are defined based on the environment in which the microbiota lives. In the context of human disease, the gut microbiome is the most applicable and therefore the most well studied. This is due to the important roles that have been described for intestinal microbes which regulate immune, endocrine and neural system development and functions. The microbial colonization of the gut starts during birth,14,15 although pre-­birth colonization is also proposed.16–19 Metagenomic studies indicate that the delivery method impacts the composition of the microbiome14 and some investigators are exploring the link between these differences with the occurrence of diseases later in life.20 The diversity of the microbiome increases with age until the infant is 2–3 years old.21 Early life events such as infections, use of antibiotics, and nutrition all have profound impacts on this developing microbiome.21 The environment where infants live may also impact future development of immune-­related diseases, such as asthma.22 The gut microbiome comprises all microbes harbored in the gastrointestinal tract, with the vast majority of them being located in the large intestine. Physical factors such as nutrient availability, pH, oxygen content and mucus thickness form the composition of the intestinal microbiota both longitudinally and transversally.23 In addition, host factors such as antimicrobial peptides or proteins and mucosal secreted IgA immunoglobulins can also impact the composition and symbiotic function of the gut microbiota.24,25 In addition, the part of the world one lives in can also have an impact on microbiome diversity, and studies have been done to compare the composition of the gut microbiome of individuals living in westernized and non-­ westernized countries.26–30 The studies suggest that, overall, the composition of the microbiome of western individuals is less diverse, with fewer species, than in non-­western microbiomes, and that the pattern of the most common phyla shifts. More recently, research has shown that those who have immigrated from East Asian countries to the US lose their native gut bacteria and eventually develop a microbiome that is more “westernized” and less diverse. In addition, these effects increase with duration of US residency and are exacerbated by obesity and across generations.31 However, seasonal variability as well as changes based on different factors such as sex or age still exist among populations.29 Despite the complexity of the studies, there are still numerous confounding factors that can modulate the gut microbiome.32 The effects of diet on the microbiome impact the relative abundances of taxa, as evidenced in studies of the composition of the gut microbiome in individuals depending on their geographical location.33,34 More controlled experimental studies provide evidence that suggests that the diet can promote changes in the composition of the gut microbiota, even in as little as a few hours.35

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The cellular and molecular mechanisms by which dietary factors impact the composition of the microbiome are largely unknown. As discussed above, a westernized diet, generally known to be rich in saturated fatty acids, processed sugars, red meats and salt, can impact the microbiota. Such diets favor the accumulation of cholesterol, which reduces the fluidity of cellular membranes and could result in cardiovascular diseases and inflammation.36 High sugar content diets can also result in arachidonic acid production that in turn would exacerbate proinflammatory responses.36 Furthermore, diets that are high in salt have been described to promote the induction of proinflammatory, IL-­17-­producing T helper-­17 (Th17) cells that could result in autoimmunity, as suggested based on animal models of disease including MS.37–39 Dietary fibers are known modulators of the gut microbiome.35 The bacterial fermentation of fiber carbohydrates produces short chain fatty acid (SCFAs) metabolites in the gut. There are three main types of SCFAs: acetate, propionate and butyrate. SCFAs are strong immunomodulatory microbial metabolites that impact the induction of regulatory T cells (Tregs)40 and epithelial and blood–brain barrier permeability.41 Humans use SCFAs as a source for energy, although to a lesser extent than other mammals, such as closely related primates. SCFAs are ligands of G-­protein-­coupled receptors (GPRs) that are expressed on epithelial cells and adipocytes, and also by immune cells. GPR43 signals to SCFAs produced by colonic microbiota to promote the expression of forkhead box P3 (Foxp3), a master regulator that polarizes the differentiation of naïve T cells into inducible Tregs (iTregs) in the periphery and natural Tregs (nTregs) in the thymus. Once Tregs are induced by SCFAs derived from gut microbes, the cells then produce anti-­inflammatory cytokines such as interleukin-­10 (IL-­10).42 Studies show that high-­fat-­diet-­ induced (HFD) mice that are deficient in GPR43 receptor become obese and diabetic in a process regulated by the gut microbiota, showing the importance of this pathway.43 The effects of fibers on CNS inflammatory demyelination were recently tested using a transgenic murine model of spontaneous experimental autoimmune encephalomyelitis (EAE).44 The main goal of the study performed by Berer et al. was to determine whether the supplementation of diet with non-­ fermentable fibers often present in vegetarian diets would result in changes in the outcome of EAE. The supplementation consisted of a fiber-­rich diet enhanced in cellulose. The results showed that the supplementation significantly impacted the composition of the gut microbiota and the metabolic pathways triggered, with an increase in long chain fatty acids, which in turn resulted in the promotion of Th2-­t ype immune responses that reduced the extent of the disease, reducing autoimmunity. Th2-­immune responses were characterized by IL-­4 and IL-­5-­producing CD4+ T cells.44 Gut microbes also play a role in the metabolism of tryptophan, an essential amino acid that must be obtained from the diet. Like all amino acids, tryptophan is necessary for protein biosynthesis, but it also serves as a precursor for serotonin (a neurotransmitter that will be discussed shortly) and it may play a significant role in the gut–brain axis. Tryptophan is also a precursor for

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vitamin B3 and the hormone melatonin, among other metabolites. The microbial catabolism of tryptophan by the enzyme indoleamine 2,3-­dioxygenase 1 (IDO1) provides anti-­inflammatory mechanisms by inducing Tregs through the activation of aryl hydrocarbon receptor (AhR), a transcription factor that regulates gene expression and leads to immunomodulation and control of inflammatory processes and epithelial integrity in the gut.45 A recent study demonstrates that a high-fat diet in mice can induce metabolic syndrome by reducing the gut-microbes ability to metabolize tryptophan and activate the aryl hydrocarbon receptor, ultimately leading to a negative impact on the integrity of the intestinal barrier.46 The process was mediated by hindering the catabolism of tryptophan by intestinal enteroendocrine L-cells, thereby reducing the production of glucagon-like peptide 1 (GLP-1), an incretin hormone important in glucose homeostasis and whose agonists are used in treating type 2 diabetes.47 Interestingly, treatment with an analog of GLP-­1 in EAE rats delays the onset of disease and reduces its severity in a mechanism proposed by the authors of the study to be dependent on the reduction in oxidative stress.48 Other dietary factors such as vitamin D could be of importance in the context of MS,49 since MS patients show reduced vitamin D levels in serum. Although sun exposure is a factor proposed for such deficiency, geographical locations with ample exposure to sunlight, such as Mediterranean countries, still present MS patients with low vitamin D levels.50 In this context, it is relevant to note that in obese individuals, low bioavailability of vitamin D is also observed.51 The diminished capacity of vitamin D availability suggests a dysfunction rather than a low exposure to sunlight. Dietary interventions have been shown to impact the most salient features of CNS inflammatory demyelination that characterize the animal models of MS. In EAE, axonal damage induced by active disease induction was significantly affected by the chow used for feeding.52 In this recent study investigators compared the progression of EAE and axonal damage in mice fed with conventional chow (Teklad 7012) or AIN-­93M chow. Although the progression of the disease was comparable in mice fed with either chow, the study showed that diet impacted the thickness of axons, showing a reduction in those animals fed with Teklad 7012 chow.52 Cycles of food intake reduction lessened the severity of disease in C57BL/6 mice, completely in 20% of the animals tested, when compared with conventional diet.53 In this study 3-­day cycles of a fasting mimicking diet with a low protein and caloric content were administered for 3 days every 7 days starting a few days after disease onset. The dietary approach reduced significantly the frequencies of proinflammatory Th1 and Th17 cells in periphery and proinflammatory cytokines detected in serum, reduced infiltration of immune cells into the CNS and had opposite effects in Tregs. Similarly, a continuous ketogenic diet also conferred protection against the disease. The dietary intervention also impacted the metabolic system, as evidenced by the increase in corticosterone levels found in the serum of mice. Interestingly the diet also had neuroprotective effects that resulted from increased oligodendrocyte precursor cell regeneration that caused remyelination in vivo. The positive impact of the diet was also observed therapeutically in mice

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with already established severe EAE. Furthermore, investigators performed a pilot experiment in 60 RRMS patients that were separated into three groups of 20 individuals and treated with control diet, ketogenic diet and a variant of the fasting mimicking diet administered for 7 days, followed by 6 months with a form of Mediterranean diet. Despite the limitations of a pilot study, the results indicated the potential safety of the intervention and a beneficial effect in patients treated with both dietary interventions.53 The failure to distinguish between self and non-­self is a hallmark of autoimmunity, and it is this malfunction that mediates the destructive response of immune cells to target otherwise healthy tissues. In MS, the immune cells target the CNS, and demyelination occurs as a result. Proinflammatory immune pathways associated with T cell and B cell subsets are detected in the CNS of MS patients. These pathways are characterized by the secretion of inflammatory mediators such as cytokines and by an increase in reactive oxygen and nitrogen species, which signal an immunological attack on the myelin which surrounds neuronal axons, and which promotes astrocyte and microglia activation. The attack results in neurodegeneration. Within months of the first attack the lesions can be observed by magnetic resonance imaging (MRI). Although natural remyelination can occur, the process becomes less efficient as the disease progresses.54 Because of the key involvement of proinflammatory responses triggered during MS, it is relevant to highlight the importance of the gut-­associated lymphoid tissue (GALT), since it serves as the main peripheral immune reservoir of the human body. In the GALT, specialized secondary lymphoid tissues such as Peyer's patches, diffuse lymphoid aggregates and mesenteric lymph nodes that are connected to the lymphatic system, serve as an enhanced area for host–microbe interactions and are essential for protection against pathogens and to help establish symbiotic relationships with commensal or mutualistic microbes. T cells are activated in the lymph nodes by antigen-­presenting cells (APCs) loaded with antigens. Activation promotes T cell clonal expansion and differentiation of T cells into different subsets. Differentiation depends on which cytokines are secreted during activation and may also depend on the nature of the antigen which was presented. Peripheral tolerance mechanisms ensure that the only T cells which proliferate are the ones that have been appropriately activated through the major histocompatibility (MHC) – peptide – T cell receptor (TCR) signal, costimulatory signal, and the cytokines, and those T cells that are autoreactive do not proliferate. Tregs also play a major role in peripheral tolerance by suppressing the proliferation of inflammatory cell subsets such as Th1 and Th17 cells and modulate the activation state of APCs. As we discuss in the next section, the gut microbiota also plays an essential role in regulating the balance of inflammatory and immunoregulatory responses. For example, segmented filamentous bacteria (SFB) in the gut trigger proinflammatory responses55 while others such as Bacteroides fragilis induce T-­cell-­mediated tolerogenic responses.56 Thus, changes in the composition of the gut microbiota cause alterations in immune homeostasis,57 which can then lead to exacerbated inflammatory diseases.58 Although the exact

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molecular mechanism by which the gut microbiota and autoreactive cells are driving MS remains to be elucidated. Recently published results propose that it may be through a molecular mimicry process. Molecular mimicry represents a scenario in which an epitope is shared between a microbe and the host, which triggers the activation of autoreactive cells in response to both microbe and target cell.59,60 We next cover the most salient findings that suggest the importance of the balance between pro-­ and anti-­inflammatory responses triggered by gut microbes, and how this balance can help regulate CNS inflammatory demyelination.

14.3  E  xperimental Evidence for the Gut Microbiome and Multiple Sclerosis Connection The evaluation of the potential impacts of the microbiome on MS began experimentally, using murine models of the disease. EAE is a murine disease characterized by CNS inflammatory demyelination that resembles some of the most prominent features of the human disease. EAE induced in mice, rats and non-­human primates is widely used in academia and industry to help study CNS inflammation of nucleated cells, gliosis and astrogliosis, demyelination and natural processes of remyelination, and peripheral immune mechanisms triggered during the disease, among other topics. In the context of the microbiome, the first studies using EAE were done in mice using the broad treatment of a mixture of several antibiotics61,62 and the induction of the disease in gnotobiotic or germ-­free (GF) mice. GF animals are maintained free of microbes and show important anatomical and immunological abnormalities when compared with animals housed conventionally. These differences are significantly important when considering that in GF mice there is a profound bias in the T helper cell differentiation pattern within the GALT that results in significantly lower frequency of Th17 cells.63 It has been postulated that the reduction in Th17 cells, proinflammatory cell subset characterized by the production of interleukin-­17 (IL-­17) cytokines, IL-­ 21, IL-­22 and granulocyte-­macrophage colony-­stimulating factor (GM-­CSF) in response to extracellular pathogens, reduces the susceptibility to disease in murine models of autoimmunity, such as diabetes,64 inflammatory bowel disease,65 and rheumatoid arthritis, when compared to conventional mice.66 In the context of MS research, GF mice show reduced susceptibility to EAE that is induced spontaneously in a transgenic murine model67 and actively induced with myelin oligodendrocyte glycoprotein 35–55 (MOG35–55) in wild-­ type C57BL/6 EAE.68 A marked reduction of proliferative responses in MOG-­ stimulated T cells isolated from TCR-­transgenic EAE mice when compared to wild-­t ype counterparts suggest that GF conditions impact the ability of autoantigen cells to proliferate, indicating a role of the microbiome in regulating the frequencies of autoreactive T cells in molecular mimicry conditions that stimulate autoimmunity.68

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The impact of broad alterations of the gut microbiota with antibiotics on the severity of the disease was described by Yokote et al.61 In their study, investigators proposed a mechanism of protection based on the effects of invariant natural killer (iNK) cells. Effects of Foxp3+CD25+CD4+ Tregs on the balance between pro-­ and anti-­inflammatory responses altered with antibiotics in EAE mice was also observed.62 EAE protection with antibiotics was reduced when mice were subjected to CD25 depletion, while the adoptive transfer of Tregs isolated from mice previously treated with antibiotics reduced the susceptibility to the disease of recipient mice.62 Although treatment with a mixture of broad spectrum antibiotics is not suitable for the treatment of the disease, these studies served as the foundation for later studies.69,70 In a murine model of autoimmune uveitis, a disease that also has genetic and environmental factors associated with increased risk, antibiotics reduced the susceptibility by reducing Th17 cells and increasing Tregs. The treatment reduced the frequency of members of the Firmicutes and Bacteroidetes phyla and Alphaproteobacteria class, while the frequency of Gammaproteobacteria increased.71 Results suggest that the murine model of CNS demyelinating inflammation affects the overall impact of large alterations in the microbiome, since the treatment does not affect severity of the disease in the Theiler's virus model of encephalomyelitis.72 Because of the reduced susceptibility to EAE in GF mice, the model has been used to determine the potential role of individual microbial species, by modulating the severity and progression of the disease. When GF mice are monocolonized with SFB, a Gram-­positive bacterium identified as a strong Th17 cell inducer, EAE severity is restored.68 An oral commensal bacterium, such as Porphyromonas gingivalis, exacerbates disease symptoms of EAE,73,74 while other gut microbes, such as Bifidobacterium animalis,75 Lactobacillus spp.76 and Prevotella histicola,77 promote protection. The common mechanism of action proposed is immunomodulation triggered by an expansion of Tregs and control of proinflammatory responses mediated by Th1 and Th17 cells. Tolerogenic dendritic cells that could result in Treg polarization and immunosuppressive macrophages induced by the oral treatment with P. histicola have also been recently documented.77 The studies of the immunomodulatory effects of gut microbial species resulted in the identification of symbiont factors produced by Bacteroides fragilis. B. fragilis is a Gram-­negative bacterium present in the mammalian gut. Research produced over the last decades indicate that B. fragilis serves as a human gut symbiont. The bacterium produces eight different extracellular polysaccharides that are necessary for its survival within the gut.78 One of these polysaccharides, polysaccharide A (PSA), promotes tolerogenic responses triggered by dendritic cells that result in the differentiation of T cells into IL-­10-­producing T cells, with either Foxp3+ or Foxp3− phenotypes. PSA is a zwitterionic molecule that is recognized by dendritic cells79 and plasmacytoid dendritic cells80 by recognition with Toll-­like receptor 2 (TLR2). It is presented to T cells through an MHC class II-­dependent mechanism.56 The

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monocolonization of GF with PSA-­producing B. fragilis results in the reinstatement of immune homeostasis in the gut of the Th2-­biased mice.81 PSA has been shown to be protective against a Helicobacter hepaticus model of experimental colitis,79 asthma82 and EAE.83–85 The protective effects of PSA are believed to be dependent on the induction of IL-­10-­producing Tregs,86 IL-­10-­producing Foxp3− CD4+ T cells79,82,87 and IL-­10-­producing CD39+ T cells.84,85 Although no evidence is currently available about the potential protective effects of PSA in humans, in vitro data using peripheral blood mononuclear cells (PBMCs) isolated from healthy individuals indicates that PSA also exerts immunomodulation.88 PSA exposure to human naïve CD4+ T cells and dendritic cells promotes T cell differentiation into Tregs.88 Interestingly, the Tregs induced have a CD39+ phenotype, a Treg subpopulation that appears dysfunctional in suppressing the proliferation of inflammatory T cells and the production of IL-­17 in MS populations.89 When circulating healthy human FoxP3+ CD4+ T cells were co-­cultured with PSA-­exposed dendritic cells the expression levels of CD39 were enhanced, as well as the capacity of the Tregs to produce IL-­10.88 These cells were immunosuppressive, as evidenced by the results obtained in vitro assays where tumor necrosis factor-­ alpha (TNF-­α) production by lipopolysaccharide (LPS)-­stimulated monocytes was evaluated.88 In CD4+ T cells isolated from MS patients, PSA exacerbates IL-­10 production.90 The expression levels of Foxp3 observed in MS cells stimulated with PSA were significantly higher than those observed in healthy cells stimulated with the polysaccharide.90 Although the highlighted results suggest that microbial antigens are mechanistically responsible for the immunomodulatory effects observed, important effects in neural and endocrine pathways and metabolism by the production of key metabolites such as SCFAs have also been observed when altering the microbiome. Some of these factors directly influence the progression of the biology within the CNS and disease outcomes and can be modified with diet, such as SCFAs, as previously described. The hypothalamic–pituitary–adrenal (HPA) axis mediates stress responses by producing hormones such as adrenocorticotrophic hormone (ACTH) and corticosteroids. It has been shown that inflammation is another modulator of HPA function and that HPA-­secreted hormones impact intestinal barrier integrity and the composition of the microbiome.91 Reciprocally, gut microbes control the metabolism of neurotransmitters such as serotonin92 and gamma-­ aminobutyric acid (GABA).93 Furthermore, the effects of intestinal microbes on the neural system can be direct, as PSA produced by B. fragilis has the ability to activate intestinal neurons.94 In the context of the neural system the importance of the vagus nerve should be addressed since it constitutes the most direct pathway of interaction within the gut–brain axis. Vagal nerve neurons express TLRs and it has been shown that microbial metabolites such as SCFAs can activate the vagal nerve.95 Thus, experimental evidences suggest that there are multiple potential avenues of interaction between the gut microbiome and the CNS. This likely multifactorial interaction combined

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with the individual's genetic makeup and the extreme variety of the overpopulated gut ecosystem results in a tremendous challenge when evaluating the potential impact of gut microbes on the disease. Another aspect to consider when evaluating the results obtained from experimental models of disease is whether the link between the gut microbiome and diseases are reciprocal. As murine studies performed in GF conditions suggest, the lack of microbes affect morphology,75 angiogenesis76 and, remarkably, the integrity of the intestinal barrier.96 This is of particular interest because the disruption of the intestinal barrier is also observed in response to disease induction. During EAE induced actively with self-­ antigens and passively by the adoptive transfer of autoreactive T cells isolated from EAE mice, a disruption in the intestinal epithelium barrier is observed.97 The effects observed are based on the loss of tight junction protein integrity, causing what it is called “leaky gut”. The treatment with probiotics96 and SCFA98 have been shown to reduce intestinal permeability and leaky gut. Whether the intestinal permeability is enhanced during MS is still unknown, although a recent pilot study suggests that it might be the case.99,100 One of the potential mediators for increased permeability is TNF-­α, enhanced in the CNS of EAE and MS patients.101 TNF-­α is a molecular mediator of the gene expression patterns that result in changes in the zonulin pathway and tight junction expression levels,102 which indicates that CNS inflammation could potentially impact the intestinal permeability. The impact of disease on the gut microbiota of mice was recently evaluated by our laboratory. Results show that when EAE is induced in non-­obese diabetic (NOD) mice a long-­term, biphasic, disease occurs in approximately 75% of the mice.70 We used this long-­term model of disease in order to compare the composition of the gut microbiota of EAE with control.70 We observed that those mice that would develop a more severe form of disease had a microbiota composition significantly different from the one isolated from control mice. The statistical analysis performed indicated that the differences were observed at early stages of disease (day 14 and 30) while at late stages of disease (day 58) the differences observed were not statistically significant. We next questioned whether the alteration of the gut microbiota with antibiotics at different stages of disease would affect the progression of EAE. Interestingly, only those mice subjected to early treatment (from day 0 to 14) showed reduced EAE severity while mice treated from days 30 to 44 and from 70 to 84 showed no changes in the EAE clinical score curves.70 Based on the results obtained, we concluded that disease induction affects the composition of the microbiota, which suggests a bidirectional flow of events between the gut microbiota and the disease, and that early interventions that impact the microbiota could result in later protective effects against the progression of the disease. Taken together, the earliest and most recent literature suggest that gut microbes and factors produced by gut microbes have the capacity to control the immune system in such a remarkable way that the effects

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modulate the severity of murine models of CNS inflammatory demyelination. However, is the gut microbiome of MS patients significantly different from the microbiome of healthy individuals? Moreover, if differences are observed, are those changes sufficient to functionally affect the onset or the progression of disease? These aspects will be covered in the next section of the chapter.

14.4  The Gut Microbiome of Multiple Sclerosis Over the last decade the number of published studies that use murine models of disease to research if there is a multifactorial association between the gut microbiome and MS have significantly increased.103 Furthermore, clinical investigations performed in the context of other autoimmune-­related diseases indicate that the gut microbiome of individuals suffering from conditions such as inflammatory bowel disease,104 rheumatoid arthritis,105 diabetes,106 psoriasis,107 systemic lupus erythematosus,108 neuromyelitis optica (NMO),109 Parkinson's disease,110 Alzheimer's disease and others harbor a distinct microbiota when compared with the composition of that of healthy individuals.111 The most commonly proposed hypothesis is that an altered microbiome can then significantly influence immune, metabolic and/or neuroendocrine homeostasis, resulting in pathologic pathways that lead to disease. This imbalance of the gut microbiota is a process globally known as dysbiosis. Despite the recent efforts, signature, disease-­specific, microbiomes have not been yet identified. Although the term enterotype was proposed for a given cluster of individuals sharing a microbiome pattern identified by specific taxa that serve as markers,112 others question its validity since it may be dependent on the statistical approach used to define it, as reviewed by Costea et al.113 These clinical studies require in-­depth control of the conditions of the experiment, collection, storage and processing of the samples, DNA extraction and manipulation and appropriate protocols for sequencing, meta-­analysis and statistical analysis, all of which can affect the results if not monitored properly.114,115 Furthermore, it is important to consider confounding factors that affect the composition of the microbiota, such as those described previously in this chapter. Results obtained in GF EAE mice suggest that dysbiotic alterations of the gut microbiome mediated by genetic and environmental interactions could impact immune homeostasis and the function of the immune system's ability to regulate autoimmunity through peripheral tolerance. Our immune system uses central and peripheral control mechanisms to minimize the deleterious consequences of allowing circulating autoreactive T and B cells. Central tolerance responses are triggered during T cell development in the thymus and B cell development in the bone marrow, while peripheral tolerance responses are triggered largely in part by regulatory T cells. As summarized previously, changes in the microbiome could impact the presence and function of proinflammatory cell subsets, which in the context of disease

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would exacerbate immunopathological pathways that lead to demyelination, oligodendrocyte cell killing, axonal loss and neuronal death. Concomitantly, the changes in microbiota composition could result in reduced regulatory T cell frequencies and dysfunction. In MS patients, peripheral tolerance mechanisms based on Treg function appear to be disrupted and are unable to control the proliferation of autoreactive T cells sufficiently.89,116–118 Another mechanism of action linked to dysbiosis is that during MS the intestinal barrier permeability might be disrupted.99 The leaky gut effect would result in increases in the concentration of microbial inflammatory factors such as endotoxin and microbial translocation to deep tissues, which promotes a state of chronic inflammation that has been observed in diet-­induced obesity,119 a scenario covered later in this section. When comparing the gut microbiome of MS patients with healthy individ­ uals, the overall structure remains unaltered; however, significant changes in the relative abundances of specific taxa are observed.120–124 Similar alterations in the microbiome have also been described in pediatric MS.125,126 Even with variability in the data, these results taken together suggest that during MS, even at early stages of life, the gut microbiome differs from the microbiome isolated from healthy individuals. Microbiome changes have been described for other diseases of the CNS such as Parkinson's disease,110,127,128 and for patients suffering from NMO.59 In NMO patients, an intestinal overabundance of Clostridum perfringens was observed and a mechanism of molecular mimicry between bacterial adenosine triphosphate (ATP) binding cassette transporters and an epitope of aquaporin-­4 was proposed based on in vitro proliferation assays with Th17 cells.109 Curiously, C. perfringens type B was previously isolated from the intestines of an MS patient.129 To search for a candidate autoantigen that may mediate molecular mimicry in MS, a recent study used brain-infiltrating and clonally expanded CD4+ T cells, isolated from cerebrospinal fluid from an MS patient to determine the antigen that could activate its pathogenic effects. It was not only found that the peptides of GDP-l-fucose synthase were the main specificity of these clones, but that cross-recognition of homologous bacterial GDP-l-fucose peptides from gut microbiota also activated the CD4+ T cells. There was shown to be a 40% similarity between the sequences of the human and bacterial peptides, indicating a potential role of molecular mimicry in the development of MS.60 More functional studies are required in order to assess the biological relevance of the observed changes on the microbiome and to better determine the impact of these changes on disease onset, progression and severity. Two recent studies provided further evidence for the potential functional impact that altered microbiota might have on MS. Both studies, performed using GF mice, evaluated the effects of the fecal microbiota transplantation (FMT) on the incidence and severity of EAE in mice.123,124 The mechanism of action of the microbiota transplanted was proposed to be immunomodulatory, by impacts on the regulatory of IL-­10-­producing Tregs and IL-­10-­producing CD4+ T cells.

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Evaluated together, the results discussed above would indicate that the microbiota and changes in its composition are essential components in the homeostatic balance of the immune system. As a result, one could hypothesize that environmental factors capable of altering specific microbial taxa of the intestine could, at least in part, be responsible for proinflammatory pathways associated with disease. The experimental evidence highlights the complex multifactorial and reciprocal nature of the gut and CNS disease interactions. Despite the complexity of such interactions, it is essential to consider specific mechanisms of action in order to advance the search for novel therapeutics. One possible mechanism of action is the increase in intestinal permeability that is observed in different models of disease and also in obesity, which can be regulated by caloric restriction.130 A detrimental impact on the integrity of the intestinal epithelium could result in chronic inflammation caused by microbial translocation and the crossing of microbial components such as endotoxin. When comparing the BMI that women reported to have at 18 years of age,11 BMI at ages 7 to 13 12 and findings of two case–control studies with BMI data,131 the risk for MS is two-­fold enhanced in individuals who are obese during adolescence.2 The association between early life obesity and MS incidence is strongest when BMI > 27.9,131 Another relevant risk factor proposed for MS is vitamin D deficiency, which could be a confounding factor when considering MS in previously obese patients, as obesity can have an affect by impacting the bioavailability of vitamin D.51 The studies also indicate that elevated BMI is also positively associated with pediatric MS. Because pediatric MS and MS patients both show significant differences in specific taxa of the gut microbiota when compared with the microbiota of healthy individuals, and diet is a critical environmental factor that regulates BMI and obesity, it is plausible to consider diet as a mechanism by which MS progression and even onset can be regulated. Furthermore, obesity and chronic inflammation are also positively associated, as evident by increases in proinflammatory cytokines and inflammatory pathways and decreases in immunoregulatory function of Tregs that has been observed experimentally and clinically in animal models. Furthermore, macrophages isolated from diet-­induced obese mice showed a proinflammatory gene expression profile characterized by increased expression of TNF-­α and inducible NO synthase (iNOS) as opposed to macrophages isolated from lean mice.132 LPS of Gram-­negative bacteria has been linked to diet-­induced obesity and metabolic disease characterized by glucose intolerance.119 The study also indicates that the composition of the microbiota of diet-­induced obese mice was significantly different from the microbiota of lean mice, and that diet-­induced obese mice had increased intestinal permeability, inflammation and increased expression associated with oxidative stress. The serum of diet-­induced obese animals showed a significant increase in the endotoxin levels, suggesting the impact of increased intestinal permeability on chronic inflammation.119 Murine models also show that a westernized diet promotes significant changes in the composition of the gut microbiota and that the transplantation of obese microbiota feces

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also transfers an obese phenotype to the GF recipient mice. Interestingly, GF mice show a reduced susceptibility to diet-­induced obesity and glucose intolerance.64,134 Early studies done in monozygotic and dizygotic twins also indicated that the microbiota of obese individuals is significantly different from lean individuals.135 The presence of a chronic, low-­grade inflammation in obese individuals is postulated based on the prolife of proinflammatory cytokines quantified in human adipose tissue136 and serum.137,138 As previously discussed, the microbiome and the endocrine system appear to be reciprocally associated. In this context, the hormone leptin is produced by adipose cells in response to food intake.139 As an anorexigenic factor, leptin interacts with the HPA and controls food intake. Mice deficient in leptin production are obese and show a microbiota with a composition significantly different from the composition of wild-­t ype counterparts.140 High leptin levels correlate with EAE onset,141 and having a significant deficiency in leptin production renders mice resistant to EAE. However, the treatment of EAE mice with neutralizing anti-­leptin antibodies protects mice against disease.142 Clinically, increased levels of leptin have also been observed in brains of MS biopsies when compared with control samples.143 This section has summarized some of the most relevant publications that suggest the association between the microbiome and MS. Since diet composition is one of the most relevant modifying factors on the microbiome, we will next discuss the therapeutic potential of dietary interventions against MS.

14.5  D  ietary-­microbiome Interventions in Multiple Sclerosis As recently revised by Thompson and colleagues, there are now 13 different disease-­modifying medications which are currently available for the treatment of MS. These treatments target inflammation, peripherally or within the CNS, with neuroprotection being a possible indirect outcome.2 The risks associated with these treatments are unfortunately directly proportional to their effectiveness and higher efficacy indicates the likelihood of more serious complications.2 Since the first drug for the treatment of relapsing MS was approved in 1995, a lot of progress has been made in searching for better therapies, but safer and more protective options are still in high demand. Furthermore, almost all approved drugs target relapsing forms of MS, which indicates that progressive MS is still a clinically unmet challenge. In this section we will solely focus on dietary interventions as a potential avenue for treating MS. Studies evaluating the effects of dietary habits on MS risk support the premise that diet is an environmental factor to be considered,144,145 and the impact of diet and dietary habits have been tested in murine models of CNS inflammatory demyelination, such as EAE.44,52,53 As recently reviewed by Riccio and colleagues, the experimental and clinical evidence summarized in previous sections suggest that the association between diet,

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the gut microbiome and MS is at least in part mediated by low-­grade inflammation.49 More recent studies also suggest the possibility of molecular mimicry induced in response to epitopes shared between the gut microbiota and target cells. The effects of intestinal permeability on these processes as well as direct effects on neuroendocrine systems also need further attention.100 A dietary intervention could possibly target potentially deleterious changes in the gut microbiota, intestinal permeability and inflammation, and may also affect metabolism including energy efficiency. The significance of the recent findings that correlate the gut microbiome and MS is highlighted by the numerous recent studies designed to evaluate impact of diet on disease. A pilot study performed in RRMS patients studied the effects of diet on relapse rates and Expanded Disability Status Scale (EDSS) scores as well as microbiota composition in individuals that based on their dietary habits of the previous 12 months were divided in two groups, one of patients following a high-­vegetable/low-­protein diet (n = 10) and a second group following a westernized diet (n = 10). The study reported changes on specific taxa such as an increase in the relative abundances of members of the family Lachnospiraceae in individuals following high-­vegetable/low-­protein diet that correlated positively with reductions in proinflammatory cell profiles and increases in anti-­inflammatory cells, including Tregs.146 Moreover, the pilot study, although based on a limited number of patients, indicated a beneficial impact on relapse rates and EDSS in RRMS patients.146 The supplementation of the diet has been tested in MS patients. A 30-­month pilot study tested the effects of omega-­3, and omega-­6 polyunsaturated fatty acids on the progression of the disease.147 The results provided preliminary evidence for the beneficial impact of the dietary intervention when comparing relapse rates per year. Riccio and colleagues performed a 7-­month intervention in MS patients (both relapsing and progressive MS forms) fed with a semi-­vegetarian calorie restricted diet, which included several supplements such as vitamin D.50 Treated patients were compared with placebo controls. The conclusions of the study suggest that the intervention reduced inflammatory parameters, such as serum metalloproteinase-­9 levels.50 The effects of calorie restriction have been already tested in MS patients.148 The study was performed in 36 patients diagnosed with RRMS that were subjected to either one of three dietary interventions based on calorie restriction: 22% daily reduction in energy needs, 75% reduction 2 days per week (and 0% reduction 5 days per week), and 0% reduction in energy needs. Weight loss was not significant among groups. The dietary interventions did not improve significantly the functional assessment of MS (FAMS) score nor fatigue or sleep quality. When associated with patients that had lost weight, the calorie restriction showed a significant increase in emotional well-­being scores.148 The study also indicated that the intervention was not associated with adverse effects. The importance of diet in the severity of diagnosed MS was further evaluated by the same group of investigators using a questionnaire-­based analysis

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of the nutritional habits of almost 7000 patients. Overall, patients using a high-­quality diet and healthy lifestyles showed a 20% reduction in the likelihood of scoring high at Patient Determined Disease Steps (PDDS) when compared with individuals exposed to low-­quality diets. High-­quality diets were considered those rich in fruits, vegetables, legumes and whole grains and low in red meat and sugars from sweets and beverages. The diets considered of highest quality and healthy lifestyles were associated with a reduction in the likelihood of suffering from more severe forms of depression, but not with pain, fatigue or cognitive symptoms. However, an increased intake of some nutritional types such as whole grains and total dairy showed a reduced likelihood of developing severe versus mild disability than those with low intake of those foods.149 The study suggests that the consumption of high quantities of some dietary factors could be associated with lower disability scores. A recent EAE study by Kap and colleagues performed in marmosets provides direct evidence for the protective effects that a dietary intervention could have in CNS inflammatory demyelinating diseases such as MS.150 In adult marmosets, EAE is induced actively with recombinant human MOG1–125 and incomplete Freund's adjuvant. Investigators used eight twin pairs of marmosets to compare the incidence and severity of disease induced after the long-­term treatment with conventionally used water-­based supplement (WBS) and with a new yogurt-­based supplement (YBS), with enhanced concentrations of yogurt, vitamin D, lemon juice, carrot juice and oatmeal, among others. The incidence of disease was reduced from 100% in WBS to 65% in YBS and reversed to 100% when returned to WBS supplementation. The reverse in disease incidence was accompanied by increased neuroinflammation and demyelination, changes in gene expression patterns and inflammatory pathways, as well as changes in the composition of the gut microbiota of the marmosets. The yogurt-­based supplement was associated with significant reductions in IL-­17A and IFN-­γ levels quantified in supernatants of MOG-­stimulated T cells isolated from mesenteric lymph nodes. Interestingly, the changes observed in the composition of the microbiota were only apparent after EAE induction and the activation of the immune responses that characterize the disease, which suggests a bidirectional interplay between the microbiota and disease151 and a critical impact of diet. Further mechanistic studies are needed to unravel the hypothesized beneficial impact of diet on disease incidence and progression.

14.6  Concluding Remarks Recent experimental and clinical evidence indicates that during MS the composition of the intestinal microbiota differs from that of healthy animals and individuals in specific taxa. Furthermore, some recent studies show that the qualitative and quantitative changes might be accompanied by functional effects that regulate the extent of experimental disease. Since diet is a major modulator of the microbiota composition and function and because early obesity constitutes a risk factor for MS, it is

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plausible to propose a dietary intervention that targets the gut microbiota to help treat this devastating disease of the CNS. Although more and larger clinical studies are needed to fully evaluate the impact of diet and dietary changes on MS pathology and progression, recent experimental data do suggest that a dietary intervention could be used to reduce the severity of the disease. This approach would constitute a significant advance in the quality of life for those suffering from MS, as it would most likely have less adverse effects when compared to the currently approved and prescribed disease-­modifying MS drugs on the market. Nevertheless, the study of diet and its impact on the gut microbiota as a possible avenue for novel therapeutic options in MS still carries significant challenges, such as confounding factors that modify the microbiota including the host's genetics and disease, as well as inter and intra-­individual variability.

Acknowledgements The authors wish to thank the Department of Biology at Eastern Washington University for their support.

References 1. C. A. Dendrou, L. Fugger and M. A. Friese, Nature, 2015, 15, 545–558. 2. A. J. Thompson, S. E. Baranzini, J. Geurts, B. Hemmer, O. Ciccarelli and FRCP, Lancet, 2018, 391, 1622–1636. 3. S. Hauser and J. Oksenberg, Neuron, 2006, 52, 61–76. 4. A. Sartori, M. Abdoli and M. S. Freedman, J. Neurol., 2017, 264, 1068–1075. 5. P. Browne, D. Chandraratna, C. Angood, H. Tremlett, C. Baker, B. V. Taylor and A. J. Thompson, Neurology, 2014, 83, 1022–1024. 6. F. Fazekas, C. Enzinger, M. Wallner-­Blazek, S. Ropele, A. Pluta-­Fuerst and S. Fuchs, J. Neurol. Sci., 2009, 286, 28–30. 7. S. E. Baranzini and J. R. Oksenberg, Trends Genet., 2017, 33, 960–970. 8. A. Compston and A. Coles, Lancet, 2008, 372, 1502–1517. 9. T. Olsson, L. F. Barcellos and L. Alfredsson, Nat. Rev. Neurol., 2017, 13, 25–36. 10. J. Ochoa-­Repáraz and L. H. Kasper, FEBS Lett., 2014, 588, 4214–4222. 11. K. L. Munger, T. Chitnis and A. Ascherio, Neurology, 2009, 73, 1543–1550. 12. K. L. Munger, J. Bentzen, B. Laursen, E. Stenager, N. Koch-­Henriksen, T. I. A. Sørensen and J. L. Baker, Mult. Scler., 2013, 19, 1323–1329. 13. A. K. Hedström, T. Olsson and L. Alfredsson, Mult. Scler., 2016, 22, 878–883. 14. J. Ravel, P. Gajer, Z. Abdo, G. M. Schneider, S. S. K. Koenig, S. L. McCulle, S. Karlebach, R. Gorle, J. Russell, C. O. Tacket, R. M. Brotman, C. C.

Diet, Gut Microbiome and Multiple Sclerosis







319

Davis, K. Ault, L. Peralta and L. J. Forney, Proc. Natl. Acad. Sci. U. S. A., 2011, 108(Suppl 1), 4680–4687. 15. M. G. Dominguez-­Bello, E. K. Costello, M. Contreras, M. Magris, G. Hidalgo, N. Fierer and R. Knight, Proc. Natl. Acad. Sci. U. S. A., 2010, 107, 11971–11975. 16. E. Jiménez, M. L. Marín, R. Martín, J. M. Odriozola, M. Olivares, J. Xaus, L. Fernández and J. M. Rodríguez, Res. Microbiol., 2008, 159, 187–193. 17. J. Hu, Y. Nomura, A. Bashir, H. Fernandez-­Hernandez, S. Itzkowitz, Z. Pei, J. Stone, H. Loudon and I. Peter, PLoS One, 2013, 8, e78257. 18. A. N. Ardissone, D. M. de la Cruz, A. G. Davis-­Richardson, K. T. Rechcigl, N. Li, J. C. Drew, R. Murgas-­Torrazza, R. Sharma, M. L. Hudak, E. W. Triplett and J. Neu, PLoS One, 2014, 9, e90784. 19. R. W. Walker, J. C. Clemente, I. Peter and R. J. F. Loos, Pediatr. Obes., 2017, 12, 3–17. 20. L. F. Stinson, M. S. Payne and J. A. Keelan, Front. Med., 2018, 5, 135. 21. J. E. Koenig, A. Spor, N. Scalfone, A. D. Fricker, J. Stombaugh, R. Knight, L. T. Angenent and R. E. Ley, Proc. Natl. Acad. Sci. U. S. A., 2011, 108(Suppl 1), 4578–4585. 22. M. J. Ege, M. Mayer, A. ­C. Normand, J. Genuneit, W. O. Cookson, C. Braun-­Fahrländer, D. Heederik, R. Piarroux, E. von Mutius and GABRIELA Transregio 22 Study Group, N. Engl. J. Med., 2011, 364, 701–709. 23. G. P. Donaldson, S. M. Lee and S. K. Mazmanian, Nat. Rev. Microbiol., 2015, 14, 20–32. 24. N. W. Palm, M. R. de Zoete, T. W. Cullen, N. A. Barry, J. Stefanowski, L. Hao, P. H. Degnan, J. Hu, I. Peter, W. Zhang, E. Ruggiero, J. H. Cho, A. L. Goodman and R. A. Flavell, Cell, 2014, 158, 1000–1010. 25. D. A. Peterson, N. P. McNulty, J. L. Guruge and J. I. Gordon, Cell Host Microbe, 2007, 2, 328–339. 26. J. C. Clemente, E. C. Pehrsson, M. J. Blaser, K. Sandhu, Z. Gao, B. Wang, M. Magris, G. Hidalgo, M. Contreras, O. Noya-­Alarcon, O. Lander, J. McDonald, M. Cox, J. Walter, P. L. Oh, J. F. Ruiz, S. Rodriguez, N. Shen, S. J. Song, J. Metcalf, R. Knight, G. Dantas and M. G. Dominguez-­Bello, Sci. Adv., 2015, 1, e1500183. 27. S. Rampelli, S. L. Schnorr, C. Consolandi, S. Turroni, M. Severgnini, C. Peano, P. Brigidi, A. N. Crittenden, A. G. Henry and M. Candela, Curr. Biol., 2015, 25, 1682–1693. 28. S. L. Schnorr, M. Candela, S. Rampelli, M. Centanni, C. Consolandi, G. Basaglia, S. Turroni, E. Biagi, C. Peano, M. Severgnini, J. Fiori, R. Gotti, G. De Bellis, D. Luiselli, P. Brigidi, A. Mabulla, F. Marlowe, A. G. Henry and A. N. Crittenden, Nat. Commun., 2014, 5, 3654. 29. G. K. Fragiadakis, S. A. Smits, E. D. Sonnenburg, W. Van Treuren, G. Reid, R. Knight, A. Manjurano, J. Changalucha, M. G. Dominguez-­Bello, J. Leach and J. L. Sonnenburg, Gut Microbes, 2018, 26, 1–12. 30. T. Yatsunenko, F. E. Rey, M. J. Manary, I. Trehan, M. G. Dominguez-­Bello, M. Contreras, M. Magris, G. Hidalgo, R. N. Baldassano, A. P. Anokhin, A. C. Heath, B. Warner, J. Reeder, J. Kuczynski, J. G. Caporaso, C. A.

320







Chapter 14

Lozupone, C. Lauber, J. C. Clemente, D. Knights, R. Knight and J. I. Gordon, Nature, 2012, 486, 222–227. 31. P. Vangay, A. J. Johnson, T. L. Ward, G. A. Al-­Ghalith, R. R. Shields-­Cutler, B. M. Hillmann, S. K. Lucas, L. K. Beura, E. A. Thompson, L. M. Till, R. Batres, B. Paw, S. L. Pergament, P. Saenyakul, M. Xiong, A. D. Kim, G. Kim, D. Masopust, E. C. Martens, C. Angkurawaranon, R. McGready, P. C. Kashyap, K. A. Culhane-­Pera and D. Knights, Cell, 2018, 175, 962–972.e10. 32. J. L. Sonnenburg and F. Bäckhed, Nature, 2016, 535, 56–64. 33. C. De Filippo, D. Cavalieri, M. Di Paola, M. Ramazzotti, J. B. Poullet, S. Massart, S. Collini, G. Pieraccini and P. Lionetti, Proc. Natl. Acad. Sci. U. S. A., 2010, 107, 14691–14696. 34. E. R. Davenport, J. G. Sanders, S. J. Song, K. R. Amato, A. G. Clark and R. Knight, BMC Biol., 2017, 15, 127. 35. L. A. David, C. F. Maurice, R. N. Carmody, D. B. Gootenberg, J. E. Button, B. E. Wolfe, A. V. Ling, A. S. Devlin, Y. Varma, M. A. Fischbach, S. B. Biddinger, R. J. Dutton and P. J. Turnbaugh, Nature, 2014, 505, 559–563. 36. M. Herieka and C. Erridge, Mol. Nutr. Food Res., 2014, 58, 136–146. 37. M. Veldhoen, K. Hirota, A. M. Westendorf, J. Buer, L. Dumoutier, J.-­C. Renauld and B. Stockinger, Nature, 2008, 453, 106–109. 38. M. Kleinewietfeld, A. Manzel, J. Titze, H. Kvakan, N. Yosef, R. A. Linker, D. N. Muller and D. A. Hafler, Nature, 2013, 496, 518–522. 39. S. Hucke, H. Wiendl and L. Klotz, Mult. Scler., 2016, 22, 133–139. 40. N. Bhaskaran, C. Quigley, C. Paw, S. Butala, E. Schneider and P. Pandiyan, Front. Microbiol., 2018, 9, 1995. 41. V. Braniste, M. Al-­Asmakh, C. Kowal, F. Anuar, A. Abbaspour, M. Tóth, A. Korecka, N. Bakocevic, L. G. Ng, N. L. Guan, P. Kundu, B. Gulyás, C. Halldin, K. Hultenby, H. Nilsson, H. Hebert, B. T. Volpe, B. Diamond and S. Pettersson, Sci. Transl. Med., 2014, 6, 263ra158. 42. P. M. Smith, M. R. Howitt, N. Panikov, M. Michaud, C. A. Gallini, Y.-­M. Bohlooly, J. N. Glickman and W. S. Garrett, Science, 2013, 341, 569–573. 43. I. Kimura, K. Ozawa, D. Inoue, T. Imamura, K. Kimura, T. Maeda, K. Terasawa, D. Kashihara, K. Hirano, T. Tani, T. Takahashi, S. Miyauchi, G. Shioi, H. Inoue and G. Tsujimoto, Nat. Commun., 2013, 4, 1–12. 44. K. Berer, I. Martínez, A. Walker, B. Kunkel, P. Schmitt-­Kopplin, J. Walter and G. Krishnamoorthy, Sci. Rep., 2018, 8, 10431. 45. T. Zelante, R. G. Iannitti, C. Cunha, A. De Luca, G. Giovannini, G. Pieraccini, R. Zecchi, C. D'Angelo, C. Massi-­Benedetti, F. Fallarino, A. Carvalho, P. Puccetti and L. Romani, Immunity, 2013, 39, 372–385. 46. J. M. Natividad, A. Agus, J. Planchais, B. Lamas, A. C. Jarry, R. Martin, M.-­L. Michel, C. Chong-­Nguyen, R. Roussel, M. Straube, S. Jegou, C. McQuitty, M. Le Gall, G. da Costa, E. Lecornet, C. Michaudel, M. Modoux, J. Glodt, C. Bridonneau, B. Sovran, L. Dupraz, A. Bado, M. L. Richard, P. Langella, B. Hansel, J.-­M. Launay, R. J. Xavier, H. Duboc and H. Sokol, Cell Metab., 2018, 28(5), 737–749.e4.

Diet, Gut Microbiome and Multiple Sclerosis

321

47. M. Gejl, J. Rungby, B. Brock and A. Gjedde, Basic Clin. Pharmacol. Toxicol., 2014, 115, 162–171. 48. B. DellaValle, G. S. Brix, B. Brock, M. Gejl, A. M. Landau, A. Møller, J. Rungby and A. Larsen, Front. Pharmacol., 2016, 7, 433. 49. P. Riccio and R. Rossano, Neurotherapeutics, 2018, 15, 75–91. 50. P. Riccio, R. Rossano, M. Larocca, V. Trotta, I. Mennella, P. Vitaglione, M. Ettorre, A. Graverini, A. De Santis, E. Di Monte and M. G. Coniglio, Exp. Biol. Med., 2016, 241, 620–635. 51. J. Wortsman, L. Y. Matsuoka, T. C. Chen, Z. Lu and M. F. Holick, Am. J. Clin. Nutr., 2000, 72, 690–693. 52. J. L. Dupree and D. L. Feinstein, J. Neuroimmunol., 2018, 322, 9–14. 53. I. Y. Choi, L. Piccio, P. Childress, B. Bollman, A. Ghosh, S. Brandhorst, J. Suarez, A. Michalsen, A. H. Cross, T. E. Morgan, M. Wei, F. Paul, M. Bock and V. D. Longo, Cell Rep., 2016, 15, 2136–2146. 54. R. J. M. Franklin and S. A. Goldman, Cold Spring Harbor Perspect. Biol., 2015, 7, a020594. 55. I. I. Ivanov, K. Atarashi, N. Manel, E. L. Brodie, T. Shima, U. Karaoz, D. Wei, K. C. Goldfarb, C. A. Santee, S. V. Lynch, T. Tanoue, A. Imaoka, K. Itoh, K. Takeda, Y. Umesaki, K. Honda and D. R. Littman, Cell, 2009, 139, 485–498. 56. B. A. Cobb and D. L. Kasper, Glycobiology, 2008, 18, 707–718. 57. I. I. Ivanov, R. de Llanos Frutos, N. Manel, K. Yoshinaga, D. B. Rifkin, R. B. Sartor, B. B. Finlay and D. R. Littman, Cell Host Microbe, 2008, 4, 337–349. 58. C. Petersen and J. L. Round, Cell. Microbiol., 2014, 16, 1024–1033. 59. J. A. Foster and K.-­A. M. Neufeld, Trends Neurosci., 2015, 1–8. 60. R. Planas, R. Santos, P. Tomas-­Ojer, C. Cruciani, A. Lutterotti, W. Faigle, N. Schaeren-­Wiemers, C. Espejo, H. Eixarch, C. Pinilla, R. Martin and M. Sospedra, Sci. Transl. Med., 2018, 10, eaat4301. 61. H. Yokote, S. Miyake, J. L. Croxford, S. Oki, H. Mizusawa and T. Yamamura, Am. J. Pathol., 2008, 173, 1714–1723. 62. J. Ochoa-­Reparaz, D. W. Mielcarz, L. E. Ditrio, A. R. Burroughs, D. M. Foureau, S. Haque-­Begum and L. H. Kasper, J. Immunol., 2009, 183, 6041–6050. 63. J. L. Round and S. K. Mazmanian, Nat. Rev. Immunol., 2009, 9, 313–323. 64. F. Bäckhed, J. K. Manchester, C. F. Semenkovich and J. I. Gordon, Proc. Natl. Acad. Sci. U. S. A., 2007, 104, 979–984. 65. R. K. Sellon, S. Tonkonogy, M. Schultz, L. A. Dieleman, W. Grenther, E. Balish, D. M. Rennick and R. B. Sartor, Infect. Immun., 1998, 66, 5224–5231. 66. H.-­J. Wu, I. I. Ivanov, J. Darce, K. Hattori, T. Shima, Y. Umesaki, D. R. Littman, C. Benoist and D. Mathis, Immunity, 2016, 32, 1–13. 67. K. Berer, M. Mues, M. Koutrolos, Z. A. Rasbi, M. Boziki, C. Johner, H. Wekerle and G. Krishnamoorthy, Nature, 2011, 479, 538–541. 68. Y. K. Lee, J. S. Menezes, Y. Umesaki and S. K. Mazmanian, Proc. Natl. Acad. Sci. U. S. A., 2011, 108(Suppl 1), 4615–4622.

322

Chapter 14

69. P. G. Miller, M. B. Bonn, C. L. Franklin, A. C. Ericsson and S. C. McKarns, J. Immunol., 2015, 195, 4668–4684. 70. S. L. Colpitts, E. J. Kasper, A. Keever, C. Liljenberg, T. Kirby, K. Magori, L. H. Kasper and J. Ochoa-­Reparaz, Gut Microbes, 2017, 8, 561–573. 71. Y. K. Nakamura, C. Metea, L. Karstens, M. Asquith, H. Gruner, C. Moscibrocki, I. Lee, C. J. Brislawn, J. K. Jansson, J. T. Rosenbaum and P. Lin, Invest. Ophthalmol. Visual Sci., 2016, 57, 3747–3758. 72. F. J. Carrillo-­Salinas, L. Mestre, M. Mecha, A. Feliú, R. Del Campo, N. Villarrubia, C. Espejo, X. Montalbán, J. C. Álvarez-­Cermeño, L. M. Villar and C. Guaza, Sci. Rep., 2017, 7, 44377. 73. L. Shapira, S. Ayalon and T. Brenner, J. Periodontol., 2002, 73, 511–516. 74. F. C. Nichols, W. J. Housley, C. A. O'Conor, T. Manning, S. Wu and R. B. Clark, Am. J. Pathol., 2009, 175, 2430–2438. 75. J. Ezendam, A. de Klerk, E. R. Gremmer and H. van Loveren, Clin. Exp. Immunol., 2008, 154, 424–431. 76. S. Lavasani, B. Dzhambazov, M. Nouri, F. Fåk, S. Buske, G. Molin, H. Thorlacius, J. Alenfall, B. Jeppsson and B. Weström, PLoS One, 2010, 5, e9009. 77. A. Mangalam, S. K. Shahi, D. Luckey, M. Karau, E. Marietta, N. Luo, R. S. Choung, J. Ju, R. Sompallae, K. Gibson-­Corley, R. Patel, M. Rodriguez, C. David, V. Taneja and J. Murray, Cell Rep., 2017, 20, 1269–1277. 78. H. M. Wexler, Clin. Microbiol. Rev., 2007, 20, 593–621. 79. S. K. Mazmanian, J. L. Round and D. L. Kasper, Nature, 2008, 453, 620–625. 80. S. Dasgupta, D. Erturk-­Hasdemir, J. Ochoa-­Reparaz, H.-­C. Reinecker and D. L. Kasper, Cell Host Microbe, 2014, 15, 413–423. 81. Q. Wang, R. M. McLoughlin, B. A. Cobb, M. Charrel-­Dennis, K. J. Zaleski, D. Golenbock, A. O. Tzianabos and D. L. Kasper, J. Exp. Med., 2006, 203, 2853–2863. 82. J. L. Johnson, M. B. Jones and B. A. Cobb, Glycobiology, 2015, 25, 368–375. 83. J. Ochoa-­Repáraz, D. W. Mielcarz, Y. Wang, S. Begum-­Haque, S. Dasgupta, D. L. Kasper and L. H. Kasper, Mucosal Immunol., 2010, 3, 487–495. 84. Y. Wang, K. M. Telesford, J. Ochoa-­Reparaz, S. Haque-­Begum, M. Christy, E. J. Kasper, L. Wang, Y. Wu, S. C. Robson, D. L. Kasper and L. H. Kasper, Nat. Commun., 2014, 5, 4432. 85. Y. Wang, S. Begum-­Haque, K. M. Telesford, J. Ochoa-­Reparaz, M. Christy, E. J. Kasper, D. L. Kasper, S. C. Robson and L. H. Kasper, Gut Microbes, 2014, 5, 552–561. 86. J. L. Round and S. K. Mazmanian, Proc. Natl. Acad. Sci. U. S. A., 2015, 1–6. 87. S. K. Mazmanian, C. H. Liu, A. O. Tzianabos and D. L. Kasper, Cell, 2005, 122, 107–118. 88. K. M. Telesford, W. Yan, J. Ochoa-­Reparaz, A. Pant, C. Kircher, M. A. Christy, S. Begum-­Haque, D. L. Kasper and L. H. Kasper, Gut Microbes, 2015, 6, 234–242.

Diet, Gut Microbiome and Multiple Sclerosis

323

89. J. M. Fletcher, R. Lonergan, L. Costelloe, K. Kinsella, B. Moran, C. O'Farrelly, N. Tubridy and K. H. G. Mills, J. Immunol., 2009, 183, 7602–7610. 90. J. N. Burgess, A. B. Pant, L. H. Kasper and S. Colpitts Brass, Ann. Clin. Transl. Neurol., 2017, 4, 825–829. 91. S. M. O'Mahony, J. R. Marchesi, P. Scully, C. Codling, A.-­M. Ceolho, E. M. M. Quigley, J. F. Cryan and T. G. Dinan, Biol. Psychiatry, 2009, 65, 263–267. 92. J. M. Yano, K. Yu, G. P. Donaldson, G. G. Shastri, P. Ann, L. Ma, C. R. Nagler, R. F. Ismagilov, S. K. Mazmanian and E. Y. Hsiao, Cell, 2015, 161, 264–276. 93. R. Mazzoli and E. Pessione, Front. Microbiol., 2016, 7, 1934. 94. Y. ­K. Mao, D. L. Kasper, B. Wang, P. Forsythe, J. Bienenstock and W. A. Kunze, Nat. Commun., 2013, 4, 1465. 95. J. A. Bravo, P. Forsythe, M. V. Chew, E. Escaravage, H. M. Savignac, T. G. Dinan, J. Bienenstock and J. F. Cryan, Proc. Natl. Acad. Sci. U. S. A., 2011, 108, 16050–16055. 96. H. Kozakova, M. Schwarzer, L. Tuckova, D. Srutkova, E. Czarnowska, I. Rosiak, T. Hudcovic, I. Schabussova, P. Hermanova, Z. Zakostelska, T. Aleksandrzak-­Piekarczyk, A. Koryszewska-­Baginska, H. Tlaskalova-­ Hogenova and B. Cukrowska, Cell. Mol. Immunol., 2015, 1–12. 97. M. Nouri, A. Bredberg, B. Weström and S. Lavasani, PLoS One, 2014, 9, e106335. 98. J. M. Mariadason, A. Catto-­Smith and P. R. Gibson, Gut, 1999, 44, 394–399. 99. M. C. Buscarinu, B. Cerasoli, V. Annibali, C. Policano, L. Lionetto, M. Capi, R. Mechelli, S. Romano, A. Fornasiero, G. Mattei, E. Piras, D. F. Angelini, L. Battisitni, M. Simmaco, R. Umeton, M. Salvetti and G. Ristori, Mult. Scler., 2016, 23(3), 442–446. 100. A. Mirza and Y. Mao-­Draayer, Clin. Immunol., 2017, 183, 213–224. 101. Y.-­K. Mao, D. L. Kasper, B. Wang, P. Forsythe, J. Bienenstock and W. A. Kunze, Nat. Commun., 2013, 4, 1465. 102. L. Shen, Ann. N. Y. Acad. Sci., 2012, 1258, 9–18. 103. D. W. Mielcarz and L. H. Kasper, Curr. Treat. Options Neurol., 2015, 17, 344. 104. D. Knights, M. S. Silverberg, R. K. Weersma, D. Gevers, G. Dijkstra, H. Huang, A. D. Tyler, S. van Sommeren, F. Imhann, J. M. Stempak, H. Huang, P. Vangay, G. A. Al-­Ghalith, C. Russell, J. Sauk, J. Knight, M. J. Daly, C. Huttenhower and R. J. Xavier, Genome Med., 2014, 6, 107. 105. J. Chen, K. Wright, J. M. Davis, P. Jeraldo, E. V. Marietta, J. Murray, H. Nelson, E. L. Matteson and V. Taneja, Genome Med., 2016, 8, 43. 106. I. Harsch and P. Konturek, Med. Sci. (Basel), 2018, 6, 32. 107. A. Tett, E. Pasolli, S. Farina, D. T. Truong, F. Asnicar, M. Zolfo, F. Beghini, F. Armanini, O. Jousson, V. De Sanctis, R. Bertorelli, G. Girolomoni, M. Cristofolini and N. Segata, NPJ Biofilms Microbiomes, 2017, 3, 1–11. 108. E. C. Rosser and C. Mauri, J. Autoimmun., 2016, 74, 85–93. 109. B. A. C. Cree, C. M. Spencer, M. Varrin-­Doyer, S. E. Baranzini and S. S. Zamvil, Ann. Neurol., 2016, 80, 443–447.

324

Chapter 14

110. A. Heintz-­Buschart, U. Pandey, T. Wicke, F. Sixel-­Döring, A. Janzen, E. Sittig-­Wiegand, C. Trenkwalder, W. H. Oertel, B. Mollenhauer and P. Wilmes, Mov. Disord., 2018, 33, 88–98. 111. S. V. Lynch and O. Pedersen, N. Engl. J. Med., 2016, 375, 2369–2379. 112. M. Arumugam, J. Raes, E. Pelletier, D. Le Paslier, T. Yamada, D. R. Mende, G. R. Fernandes, J. Tap, T. Bruls, J.-­M. Batto, M. Bertalan, N. Borruel, F. Casellas, L. Fernandez, L. Gautier, T. Hansen, M. Hattori, T. Hayashi, M. Kleerebezem, K. Kurokawa, M. Leclerc, F. Levenez, C. Manichanh, H. B. Nielsen, T. Nielsen, N. Pons, J. Poulain, J. Qin, T. Sicheritz-­Ponten, S. Tims, D. Torrents, E. Ugarte, E. G. Zoetendal, J. Wang, F. Guarner, O. Pedersen, W. M. de Vos, S. Brunak, J. Doré, MetaHIT Consortium, M. Antolín, F. Artiguenave, H. M. Blottiere, M. Almeida, C. Brechot, C. Cara, C. Chervaux, A. Cultrone, C. Delorme, G. Denariaz, R. Dervyn, K. U. Foerstner, C. Friss, M. van de Guchte, E. Guedon, F. Haimet, W. Huber, J. van Hylckama-­Vlieg, A. Jamet, C. Juste, G. Kaci, J. Knol, O. Lakhdari, S. Layec, K. Le Roux, E. Maguin, A. Mérieux, R. Melo Minardi, C. M'rini, J. Muller, R. Oozeer, J. Parkhill, P. Renault, M. Rescigno, N. Sanchez, S. Sunagawa, A. Torrejon, K. Turner, G. Vandemeulebrouck, E. Varela, Y. Winogradsky, G. Zeller, J. Weissenbach, S. D. Ehrlich and P. Bork, Nature, 2011, 473, 174–180. 113. P. I. Costea, F. Hildebrand, A. Manimozhiyan, F. Bäckhed, M. J. Blaser, F. D. Bushman, W. M. de Vos, S. D. Ehrlich, C. M. Fraser, M. Hattori, C. Huttenhower, I. B. Jeffery, D. Knights, J. D. Lewis, R. E. Ley, H. Ochman, P. W. O'Toole, C. Quince, D. A. Relman, F. Shanahan, S. Sunagawa, J. Wang, G. M. Weinstock, G. D. Wu, G. Zeller, L. Zhao, J. Raes, R. Knight and P. Bork, Nat. Microbiol., 2017, 3, 1–10. 114. S. E. Baranzini, Mult. Scler., 2018, 24, 1278–1279. 115. S. E. Baranzini, Mult. Scler., 2018, 24, 1419–1420. 116. J. Haas, A. Hug, A. Viehöver, B. Fritzsching, C. S. Falk, A. Filser, T. Vetter, L. Milkova, M. Korporal, B. Fritz, B. Storch-­Hagenlocher, P. H. Krammer, E. Suri-­Payer and B. Wildemann, Eur. J. Immunol., 2005, 35, 3343–3352. 117. K. Venken, N. Hellings, M. Thewissen, V. Somers, K. Hensen, J.-­L. Rummens, R. Medaer, R. Hupperts and P. Stinissen, Immunology, 2008, 123, 79–89. 118. K. Venken, N. Hellings, K. Hensen, J.-­L. Rummens, R. Medaer, M. B. D'hooghe, B. Dubois, J. Raus and P. Stinissen, J. Neurosci. Res., 2006, 83, 1432–1446. 119. P. D. Cani, S. Possemiers, T. Van de Wiele, Y. Guiot, A. Everard, O. Rottier, L. Geurts, D. Naslain, A. Neyrinck, D. M. Lambert, G. G. Muccioli and N. M. Delzenne, Gut, 2009, 58, 1091–1103. 120. S. Miyake, S. Kim, W. Suda, K. Oshima, M. Nakamura, T. Matsuoka, N. Chihara, A. Tomita, W. Sato, S.-­W. Kim, H. Morita, M. Hattori and T. Yamamura, PLoS One, 2015, 10, e0137429. 121. J. Chen, N. Chia, K. R. Kalari, J. Z. Yao, M. Novotna, M. M. P. Soldan, D. H. Luckey, E. V. Marietta, P. R. Jeraldo, X. Chen, B. G. Weinshenker, M. Rodriguez, O. H. Kantarci, H. Nelson, J. A. Murray and A. K. Mangalam, Sci. Rep., 2016, 6, 28484.

Diet, Gut Microbiome and Multiple Sclerosis

325

122. S. Jangi, R. Gandhi, L. M. Cox, N. Li, F. von Glehn, R. Yan, B. Patel, M. A. Mazzola, S. Liu, B. L. Glanz, S. Cook, S. Tankou, F. Stuart, K. Melo, P. Nejad, K. Smith, B. D. Topçuolu, J. Holden, P. Kivisäkk, T. Chitnis, P. L. De Jager, F. J. Quintana, G. K. Gerber, L. Bry and H. L. Weiner, Nat. Commun., 2016, 7, 12015. 123. K. Berer, L. A. Gerdes, E. Cekanaviciute, X. Jia, L. Xiao, Z. Xia, C. Liu, L. Klotz, U. Stauffer, S. E. Baranzini, T. Kümpfel, R. Hohlfeld, G. Krishnamoorthy and H. Wekerle, Proc. Natl. Acad. Sci. U. S. A., 2017, 114, 10719–10724. 124. E. Cekanaviciute, B. B. Yoo, T. F. Runia, J. W. Debelius, S. Singh, C. A. Nelson, R. Kanner, Y. Bencosme, Y. K. Lee, S. L. Hauser, E. Crabtree-­ Hartman, I. Katz Sand, M. Gacias, Y. Zhu, P. Casaccia, B. A. C. Cree, R. Knight, S. K. Mazmanian and S. E. Baranzini, Proc. Natl. Acad. Sci. U. S. A., 2017, 114, 10713–10718. 125. H. Tremlett, D. W. Fadrosh, A. A. Faruqi, F. Zhu, J. Hart, S. Roalstad, J. Graves, S. Lynch, E. Waubant and US Network of Pediatric MS Centers, Eur. J. Neurol., 2016, 23, 1308–1321. 126. H. Tremlett, D. W. Fadrosh, A. A. Faruqi, J. Hart, S. Roalstad, J. Graves, C. M. Spencer, S. V. Lynch, S. S. Zamvil and E. Waubant, BMC Neurol., 2016, 16, 1–9. 127. A. Keshavarzian, S. J. Green, P. A. Engen, R. M. Voigt, A. Naqib, C. B. Forsyth, E. Mutlu and K. M. Shannon, Mov. Disord., 2015, 30, 1351–1360. 128. S. Gerhardt and M. H. Mohajeri, Nutrients, 2018, 10, 708. 129. K. R. Rumah, J. Linden, V. A. Fischetti and T. Vartanian, PLoS One, 2013, 8, e76359. 130. B. Ott, T. Skurk, L. Hastreiter, I. Lagkouvardos, S. Fischer, J. Büttner, T. Kellerer, T. Clavel, M. Rychlik, D. Haller and H. Hauner, Sci. Rep., 2017, 7, 11955. 131. A. K. Hedström, I. Lima Bomfim, L. Barcellos, M. Gianfrancesco, C. Schaefer, I. Kockum, T. Olsson and L. Alfredsson, Neurology, 2014, 82, 865–872. 132. C. N. Lumeng, J. L. Bodzin and A. R. Saltiel, J. Clin. Invest., 2007, 117, 175–184. 133. P. J. Turnbaugh, F. Bäckhed, L. Fulton and J. I. Gordon, Cell Host Microbe, 2008, 3, 213–223. 134. F. Bäckhed, H. Ding, T. Wang, L. V. Hooper, G. Y. Koh, A. Nagy, C. F. Semenkovich and J. I. Gordon, Proc. Natl. Acad. Sci. U. S. A., 2004, 101, 15718–15723. 135. P. J. Turnbaugh, M. Hamady, T. Yatsunenko, B. L. Cantarel, A. Duncan, R. E. Ley, M. L. Sogin, W. J. Jones, B. A. Roe, J. P. Affourtit, M. Egholm, B. Henrissat, A. C. Heath, R. Knight and J. I. Gordon, Nature, 2009, 457, 480–484. 136. C. Caër, C. Rouault, T. Le Roy, C. Poitou, J. Aron-­Wisnewsky, A. Torcivia, J.-­C. Bichet, K. Clément, M. Guerre-­Millo and S. André, Sci. Rep., 2017, 7, 3000. 137. S. H. Jung, H. S. Park, K.-­S. Kim, W. H. Choi, C. W. Ahn, B. T. Kim, S. M. Kim, S. Y. Lee, S. M. Ahn, Y. K. Kim, H. J. Kim, D. J. Kim and K.-­W. Lee, J. Nutr. Biochem., 2008, 19, 371–375.

326

Chapter 14

138. A. J. Cox, N. P. West and A. W. Cripps, Lancet Diabetes Endocrinol., 2015, 3, 207–215. 139. A. Aguilar-­Valles, W. Inoue, C. Rummel and G. N. Luheshi, Neuropharmacology, 2015, 96, 124–134. 140. G. Matarese, A. Di Giacomo, V. Sanna, G. M. Lord, J. K. Howard, A. Di Tuoro, S. R. Bloom, R. I. Lechler, S. Zappacosta and S. Fontana, J. Immunol., 2001, 166, 5909–5916. 141. V. Sanna, A. Di Giacomo, A. La Cava, R. I. Lechler, S. Fontana, S. Zappacosta and G. Matarese, J. Clin. Invest., 2003, 111, 241–250. 142. V. De Rosa, C. Procaccini, A. La Cava, P. Chieffi, G. F. Nicoletti, S. Fontana, S. Zappacosta and G. Matarese, J. Clin. Invest., 2006, 116, 447–455. 143. G. Matarese, P. B. Carrieri, A. La Cava, F. Perna, V. Sanna, V. De Rosa, D. Aufiero, S. Fontana and S. Zappacosta, Proc. Natl. Acad. Sci. U. S. A., 2005, 102, 5150–5155. 144. F. Sedaghat, M. Jessri, M. Behrooz, M. Mirghotbi and B. Rashidkhani, Asia Pac. J. Clin. Nutr., 2016, 25, 377–384. 145. L. J. Black, C. Rowley, J. Sherriff, G. Pereira, A.-­L. Ponsonby, R. M. Lucas and Ausimmune Investigator Group, Mult. Scler., 2018, 76, 1352458518793524. 146. M. Saresella, L. Mendozzi, V. Rossi, F. Mazzali, F. Piancone, F. LaRosa, I. Marventano, D. Caputo, G. E. Felis and M. Clerici, Front. Immunol., 2017, 8, 1–11. 147. M. C. Pantzaris, G. N. Loukaides, E. E. Ntzani and I. S. Patrikios, BMJ Open, 2013, 3, e002170. 148. K. C. Fitzgerald, D. Vizthum, B. Henry-­Barron, A. Schweitzer, S. D. Cassard, E. Kossoff, A. L. Hartman, D. Kapogiannis, P. Sullivan, D. J. Baer, M. P. Mattson, L. J. Appel and E. M. Mowry, Mult. Scler. Relat. Disord., 2018, 23, 33–39. 149. K. C. Fitzgerald, T. Tyry, A. Salter, S. S. Cofield, G. Cutter, R. Fox and R. A. Marrie, Neurology, 2018, 90, e1–e11. 150. Y. S. Kap, C. Bus-­Spoor, N. van Driel, M. L. Dubbelaar, C. Grit, S. M. Kooistra, Z. C. Fagrouch, E. J. Verschoor, J. Bauer, B. J. L. Eggen, H. J. M. Harmsen, J. D. Laman and B. A. 't Hart, J. Immunol., 2018, 201(11), 3229–3243. 151. J. Ochoa-­Reparaz and L. H. Kasper, Neurotherapeutics, 2018, 173, 1714.

Subject Index acute myelitis, 17 acute optic neuritis, 77–80 afobazole, 274 alemtuzumab, 158 anandamide (AEA), 247–248 ANAVEX2-­73, 278 anti-­aquaporin-­4 IgG (AQP4-­IgG), 58 anti CD19 mAbs, 153–155 anti-­CD20 mAbs, 142, 146–153 ocrelizumab, 148–151 ofatumumab, 152 rituximab, 147–148 ublituximab, 153 anti-­CD20 monoclonal antibodies, 121–122 anti-­CD52 mAb, 158 antihistamines, 210–212 anti-­inflammatory treatment strategies, 118–122 anti-­CD20 monoclonal antibodies, 121–122 glatiramer acetate, 119 interferon beta (IFNβ), 118–119 mitoxantrone, 119–120 natalizumab, 120 sphingosine 1-­phosphate receptor (S1PR) modulator, 121 anti-­LINGO-­1, 206–208 2-­arachidonoyl glycerol (2-­AG), 248 atrophy, 20, 21, 81–82 autoimmunity, 304–308 shared genetic background, 38–39

B cell-­based therapies antibody production, 138–139 anti CD19 mAbs, 153–155 anti-­CD20 mAbs, 142–153 antigen presentation, 139–140 B cell development, snapshot, 135–136 Bruton's tyrosine kinase (BTK), targeting, 156–157 CNS and, 137 cytokine blockers, 155–156 cytokine production, 140–141 ectopic germinal centers formation, 141 immune cell interactions, emerging view, 136–137 plasmapheresis, 156 therapies target, B Cells, 141–161 therapies with partial or indirect effects, 157–161 benztropine, 209 biomarkers alemtuzumab side effects, monitoring, 67 antibodies, neutralizing, 62–63 categories of, 56 diagnostic, 57–58 dimethyl fumarate, response, 65 dimethyl fumarate side effects, monitoring, 67 emerging, 66 fingolimod, response, 64–65 327

328

biomarkers (continued) fingolimod side effects, monitoring, 67 glatiramer acetate, response, 64 IFN-­beta, response, 63, 64 leukocyte subsets as, 64–65 for monitoring response, 64 natalizumab, response, 65 natalizumab side effects, monitoring, 66–67 of neurodegeneration, 65 for personalized treatment, 62–67 prognostic, 58–61 of response to treatment, 62–66 soluble molecules, inflammation, 63–64 susceptibility/risk, 56–57 biotin (vitamin B 7), 125 blood–brain barrier (BBB), 79, 90, 137, 159, 162, 172, 226, 264 brain stem and cerebellar involvement, relapses, 17–18 Bruton's tyrosine kinase (BTK), 177–178 Ca2+/calmodulin-­dependent protein kinase IIα (CaMKIIα), 188 cannabinoids and bladder dysfunction, 255 clinical evidence, 251–252 experimental evidence, 250–251 and immunomodulation, 257 multiple sclerosis and, 248–252 and neuropathic pain, 256–257 as neuroprotective agents, 253–255 receptors, 246–247 as symptom-­modifying agents, 249–250 cannabis, 243, 244 cannabinoid receptors, 246–247

Subject Index

endocannabinoid system and, 244–246 cell-­based therapeutic strategies, 127 cerebellar disorders, 21 chemokines, CXCL13, 60–61 chitinase 3-­like 1 (CHI3L1), 60 cholesterol biosynthesis, 213 chromatin, 41 circular RNA (circRNA), 289 cladribine, 160–161 clemastine, 210–212 clinically isolated syndrome (CIS), 14, 58, 302 clinical manifestations, 14–25 of anti-­inflammatory treatment strategies, 118–122 of multiple sclerosis, established phase, 18–24 of multiple sclerosis, initial stages, 16–18 phenotypes, 14–16 of putative neuroprotective and repair-­promoting strategies, 122–127 clobetasol, 209–210 cognitive dysfunction, 22 cognitive impairment, 21–22 cognitive progressive syndromes, 18 colony-­stimulating factor-­1 receptor (CSF-­1R), 174 cortical demyelination, 91 cuprizone, 203 cutamesine, 275 cytokine, 9, 60, 90, 100 demyelination, 13. see also toxin-­induced demyelination models derivative A-­420983, 178 diagnostic biomarkers, 57–58 AQP4-­IgG and MOG-­IgG, 58 free immunoglobulin light chains, 58 IgG oligoclonal bands, 57 diet, 304–308

Subject Index

dietary-­microbiome interventions, 315–317 dimethyl fumarate (DMF), 160, 187 1,3-­di-­o-­tolylguanidine (DTG), 275 diplopia, 17 disability progression, 24 dizygotic twins, 5 dronabinol, 124 dysphagia, 23 endocannabinoids synthesis and degradation, 247–248 system, 244–246 environmental factors, 11–13 25-­hydroxy-­vitamin D, 13 infections, 11–13 lifestyle and, 13 epidemiology, 4–11 epilepsy, 23 epitope spreading theory, 92 Epstein–Barr virus (EBV), 11, 12, 57 erythropoietin, 123–124 evobrutinib, 177, 189 experimental autoimmune encephalomyelitis (EAE) model, 89–91, 100, 102, 103, 171, 315, 317 experimental in vivo models animal models, new therapy development tool, 96–100 experimental autoimmune encephalomyelitis (EAE), 89–91 immune-­mediated model, 89–91 progressive MS, new therapy development, 100–103 Theiler's virus model, 91–93 toxin-­induced demyelination models, 93–96 eye movements, 19–20 fatigue, 21–22 fibroblast growth factor 2 (FGF-­2), 226 fingolimod, 121, 159–160

329

gait disturbance, 23 gene/environment interactions, 39–41 microbiome as mediator for, 42 putative mechanisms for, 41–42 genetics, 4–11 beta-­interferons pharmacogenetics, 43–44 familial and linkage studies, 34–35 gene/environment interactions, 39–41 genetic risk scores and clinical phenotype, 45 genome-­wide association studies (GWAS), 35–36 genotype and phenotype, linking, 42–45 glatiramer acetate pharmacogenetics, 44 immunochip and beyond, 36–38 genetic susceptibility, 5 genome organization, 285–286 genome-­wide association study (GWAS), 9 glatiramer acetate (GA), 158–159 glycogen synthase kinase-­3 (GSK-­3), 183–185 GNbAC1, 231 GSK239512, 210–212 GSK247246, 210–212 gut microbiome, 304–308 immunomodulatory effects of, 309 and multiple sclerosis connection, 308–315 gut microbiota, 42 human cytomegalovirus (CMV), 12 human leukocyte antigen (HLA) complex, 7, 34, 35 25-­hydroxy-­vitamin D, 13

330

ibudilast (MN-­166), 125–126 IgG oligoclonal bands (OCGBs), 57, 58 IgM oligoclonal bands, 59 Ikappa B kinase (IKKB), 185–186 imatinib, 172–173 5-­imino-­1,2,4-­thiadiazole (ITDZ), 184 immune-­mediated model, 89–91 immunomodulation, cannabinoids clinical evidence, 257 experimental evidence, 257 immunosuppression, 25 inebilizumab (MEDI-­551), 154–155 infections, 11–13 interferon beta (IFNβ), 99, 158 International Multiple Sclerosis Genetics Consortium (IMSGC), 35, 37, 46 internuclear ophthalmoplegia (INO), 17–18 isolated radiological syndrome (RIS), 15, 16 Janus kinase/signal transducer and activators of transcription (JAK/STAT) pathway, 176 John-­Cunningham virus (JCV), 66, 159 leaky gut, 311 lifestyle, 13 lipoic acid, 126 lower monoparesis, 20 lymphopenia, 67 major histocompatibility complex (MHC) proteins, 6–7, 34 maltese, 10 masitinib, 173–174 miconazole, 209–210 microRNAs (miRNAs), 61, 286–288 mitogen-­activated protein kinase (MAPK) family, 182–183 modulators, sigma 1 receptor, 272–278

Subject Index

1,3-­disubstituted guanidines, 275 cyclopropanes, 276 morpholine derivatives, 274–275 piperazine-­based molecules, 275 piperidine-­based molecules, 276–278 spirocycles, 276 molecular mimicry theory, 92 monozygotic twins, 5 motor symptoms, 19 multiple sclerosis-­related optic neuritis (MSON), 77–79 murine models, 314 myaptavin-­3064, 231 myelin oligodendrocyte glycoprotein (MOG), 58 natalizumab, 159 ncRNA delivery to central nervous system, 296–297 as immunomodulators, 291–293 as remyelination promoters, 293–295 therapeutic possibilities, 289–297 neurodegeneration, 65 neurofilament light chain (NFL), 59–60 neuromyelitis optica (NMO), 58 neuromyelitis optica spectrum disorders (NMOSD), 79 neuropathic pain, cannabinoids, 256–257 clinical evidence, 256–257 experimental evidence, 256 neuroprotective agents, cannabinoids, 253–255 clinical evidence, 254–255 experimental evidence, 253–254 neuropsychiatric disorders, 21–22

Subject Index

non-­receptor tyrosine kinase inhibitors, 174–178 North American Research Committee on Multiple Sclerosis (NARCOMS), 18, 19 ocrelizumab, 121–122, 148–151, 161 adverse events with, 150–151 in PPMS, 149–150 in RRMS, 149 oculomotor palsy, 17 ofatumumab, 152 oligodendrocyte differentiation, 223 oligodendrocyte precursor cells (OPCs), 223, 226 oligodendrocytes, 188, 222 oligodendrogliogenesis, 223–225 oligodendrogliogenic pathways, 225–227 one-­and-­a-­half syndrome, 18 opicinumab®, 126–127, 230 optical coherence tomography (OCT) after acute optic neuritis, 77–80 atrophy, magnetic resonance imaging, 81–82 as axonal damage biomarker, 80–82 clinical and cognitive impairment, magnetic resonance imaging, 81–82 and neurodegeneration, 82 sensitivity and reliability of, 83 tool for monitoring treatment, 82–84 optic neuritis (ON), 16–17 paraparesis, 20 parasitic infections, 12 paroxysmal phenomena, 23 pharmacogenetics of beta-­interferons, 43–44 of glatiramer acetate, 44 phenotypes, 14–16

331

active and progressive disease, 15 other presentation forms, 15–16 progressive form of MS, 14–15 relapsing forms of MS, 14 phosphoinositide 3-­kinases (PI3Ks), 180 platelet-­derived growth factor (PDGF), 172–174 PRE-­084, 274 preclinical animal studies, modelling MS, 200–203 experimental autoimmune encephalomyelitis (EAE), 202 toxin-­induced demyelinating models, 203 primary progressive multiple sclerosis (PPMS), 14, 15. see also progressive multiple sclerosis initial symptoms in, 18 PRN2246, 177–178 probabilistic identification of causal SNPs (PICS), 37 prognostic biomarkers, 58–61 chemokines, CXCL13, 60–61 chitinase 3-­like 1 (CHI3L1), 60 IgM oligoclonal bands, 59 microRNAs (miRNAs), 61 neurofilament light chains, 59–60 progressive multifocal leukoencephalopathy (PML), 66 progressive multiple sclerosis diagnosis of, 114–115 monitoring, 115–116 pathogenesis of, 117–118 subcategories of, 114 treatment, additional aspects, 127–128 trials in, 128 protective genes, 10–11

332

protein kinase casein kinase II, 180 pyramidal pathway disorders, 20 quality of life (QoL), 19 receptor interacting protein 1 (RIP1), 187 receptor tyrosine kinase inhibitors, 172–174 relapsing–remitting MS (RRMS), 14, 62, 100, 117, 199 remyelination adult human OPCs, chemical agents, 227–233 failure, 203–208 in human patients, difficulties in assessing, 213–214 oligodendrocyte precursor cell migration, demyelinated MS lesions, 204 OPC maturation, myelinating oligodendrocytes, 204–208 preclinical animal studies, modelling MS, 200–203 preclinical research, targets of, 214–216 rodent cultures, high-­ throughput drug screening, 209–213 targets, identifying and screening, 208–213 therapies to promote, 199–200 retinal nerve fiber layer (RNFL), 77, 78 retinoic acid receptor gamma (Rxrg), 205–206 Rho-­associated protein kinase (ROCK), 180–182 ribosomal S6 kinase 2 (RSK2), 187 rituximab, 121–122, 147–148 SA4503, 275 secondary progressive multiple sclerosis (SPMS), 14, 15, 82, 118

Subject Index

sensory pathway disorders, 20–21 serine/threonine kinases (STKs), 178–188 fasudil and chemically related derivatives, 181–182 glycogen synthase kinase-­3 (GSK-­3), 183–185 Ikappa B kinase (IKKB), 185–186 mitogen-­activated protein kinase (MAPK) family, 182–183 phosphoinositide 3-­kinases (PI3Ks), 180 protein kinase casein kinase II, 180 Rho-­associated protein kinase (ROCK), 180–182 serum-­and glucocorticoid-­ inducible kinase 1 (SGK-­1), 182 transforming growth factor-­β-­activated kinase 1 (TAK1), 186–187 serum-­and glucocorticoid-­inducible kinase 1 (SGK-­1), 182 sex ratio, 5 sexual dysfunction, 22–23 short chain fatty acids (SCFAs), 305 sigma 1 receptor calcium regulation, 267–269 glutamate excitotoxicity reduction, 269–270 mitochondrial dysfunction and, 270 and modulators, 272–278 neuroinflammation, 271–272 oligodendrocyte degeneration, 270–271 oxidative and ER stresses, 270 simvastatin, 102, 124–125 single nucleotide polymorphisms (SNPs), 9 siponimod, 121

Subject Index

small nucleolar RNA (snoRNA), 286–288 sodium channel blockers, 122–123 sphincter disorders, 22–23 sphingosine 1-­phosphate receptor (S1PR) modulator, 121 spleen tyrosine kinase (SYK), 178 stem cell growth factor, 172–174 susceptibility genes for multiple sclerosis (MS) in MHC, 7–8 for multiple sclerosis (MS) outside of MHC, 9–10 susceptibility/risk biomarkers, 56–57 teriflunomide, 160 Theiler's murine encephalomyelitis virus-­induced demyelinated disease (TMEV-­IDD) model, 96, 100, 103 Theiler's virus model, 91–93 thyroid hormones – T3 and T4, 212 tofacitinib, 176

333

toxin-­induced demyelination models, 93–96 cuprizone (CPZ), 95–96 ethidium bromide (EtBr), 94 lysolecithin (LPC), 94–95 transforming growth factor­β-­activated kinase 1 (TAK1), 186–187 tumor necrosis factor receptor alpha (TNF-­α), 140, 310, 311 tyrosine kinase 2 (Tyk2), 176 tyrosine kinase inhibitors (TKIs) for MS therapy, 171–178 non-­receptor tyrosine kinase inhibitors, 174–178 receptor tyrosine kinase inhibitors, 172–174 ublituximab, 153 Uhthoff phenomenon, 23 unmet needs, MS, 24–25 viral superantigens theory, 92 visual disturbances, 19–20 VP3.15, 184–185