Rare Disease Drug Development: Clinical, Scientific, Patient, and Caregiver Perspectives 3030786048, 9783030786045

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Rare Disease Drug Development Clinical, Scientific, Patient, and Caregiver Perspectives Raymond A. Huml Editor

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Rare Disease Drug Development

Raymond A. Huml Editor

Rare Disease Drug Development Clinical, Scientific, Patient, and Caregiver Perspectives

Editor Raymond A. Huml Rare Disease Consortium Syneos Health Clinical Services Morrisville, NC USA

ISBN 978-3-030-78604-5    ISBN 978-3-030-78605-2 (eBook) https://doi.org/10.1007/978-3-030-78605-2 © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2021, corrected publication 2022 This work is subject to copyright. All rights are solely and exclusively licensed by the Publisher, whether the whole or part of the material is concerned, specifically the rights of translation, reprinting, reuse of illustrations, recitation, broadcasting, reproduction on microfilms or in any other physical way, and transmission or information storage and retrieval, electronic adaptation, computer software, or by similar or dissimilar methodology now known or hereafter developed. The use of general descriptive names, registered names, trademarks, service marks, etc. in this publication does not imply, even in the absence of a specific statement, that such names are exempt from the relevant protective laws and regulations and therefore free for general use. The publisher, the authors and the editors are safe to assume that the advice and information in this book are believed to be true and accurate at the date of publication. Neither the publisher nor the authors or the editors give a warranty, expressed or implied, with respect to the material contained herein or for any errors or omissions that may have been made. The publisher remains neutral with regard to jurisdictional claims in published maps and institutional affiliations. This Springer imprint is published by the registered company Springer Nature Switzerland AG The registered company address is: Gewerbestrasse 11, 6330 Cham, Switzerland

For Leslie Wile Huml (my wife), who taught me my deepest understanding of the phrase, “do unto others as you would have them do unto you,” to Nancy Elizabeth Kizer (my mother), who taught me “one person can, and does, change the world” and to Martha “Marty” Vance Wile (my mother-in-law) who taught me the true meaning of the words “thoughtfulness” and “philanthropy.”

Foreword

Hope. Sometimes it’s all that we have. We remember the day we needed hope all too well. I am not a superstitious person but having an appointment with a neurologist to discuss the findings of a muscle biopsy for our then 15-month-old daughter on a Friday the 13th just didn’t sit well. I feared that the muscle weakness we were seeing was a sign that she may never walk. As we entered the doctor’s office and he introduced a social worker, we knew this was not going to be good news. As it turns out, it was even worse than we feared. This experienced and caring neurologist, in an empathetic tone, let us know that our daughter Megan had a very rare muscular dystrophy known as Pompe disease, or Glycogen Storage Disease Type II. He said that she would become weaker, quickly losing the ability to eat or breathe on her own. And that she would only live to be a couple of years old. He apologized as he said “I am so very sorry. There is nothing we can do. Love her and spend as much time as you can with her.” He then also let us know that our then 7-day-old son, Patrick, sleeping in the car carrier seat at our feet, that he had a 25% chance of having Pompe disease as well and needed to be tested immediately. (A month later he too would test positive for Pompe.) In an instant, with that diagnosis for Megan and soon afterward for our Patrick, our lives changed forever. Our family’s journey into the world of rare diseases is similar to that of tens of millions of other families worldwide. It wasn’t supposed to happen to us. Rare diseases are something that happen to other people. Our family went very quickly through the shock, the grief, the denial, the anger. By late that first night though, after researching everything I could online about this disease that we had never heard of, I came upon a recent medical publication from Duke University about some encouraging studies in animals with Pompe. I understood little about science and technology at that time. I woke my wife in the middle of the night and began excitedly trying to summarize these findings. She looked and asked simply: “What does this mean?” I answered, “Aileen, it means that there is hope.” Late that Friday the 13th of March in 1998, that’s the only hope we had. But it was hope that maybe the course of the disease could be changed. Maybe we could beat this dreadful mistake of nature. And maybe we could beat time as well. Fear and anger turned to determination. Over the years, Megan and Patrick struggled with the effects of Pompe disease, eventually needing feeding tubes, wheelchairs, and ventilators to breath. I became an “accidental entrepreneur” and helped to start a biotech company focused on vii

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developing a treatment for Pompe disease, eventually relying upon the resources and expertise of a much larger biotechnology company to move the program forward. Finally, on January 9, 2003, on what would have been my Dad’s 63rd birthday, Megan and Patrick began to receive a life-saving enzyme replacement therapy. It fixed the enlargement of their hearts and for a time made them stronger. We thought for a while that we were done. But rarely in science and medicine do you have a “silver bullet” cure, and almost never in the rare diseases – at least not yet. As the positive effects of that first-generation enzyme therapy waned, we knew we had to go back to the drawing board. We had to find science to drive us toward even better therapies, and maybe one day to a cure. And so in 2005, I helped to found Amicus Therapeutics. We picked the name “Amicus” because it is the Latin word for “friend.” We wanted to be the most patient-focused and patient-friendly company in biotechnology. We had a big vision, the five of us back then, to create one of the world’s leading biotechnology companies focused on rare diseases. We realized that there was so much need in the rare diseases for new therapies and next-­ generation ones as well. We believed that we could be a force for good and build a valuable and sustainable business in rare diseases. It took many years and much resilience after countless setbacks but today we are beginning to realize that vision, with more than 500 employees worldwide, an approved precision medicine in Fabry disease, and a great pipeline of medicines for a host of rare diseases. And thankfully, we are not alone. Literally, hundreds of biotechnology companies now focus on rare diseases, including some of the world’s largest biopharmaceutical companies. Patient communities are far more engaged and welcomed throughout the drug development process than ever before. We as a rare disease community are on the cusp of a golden age of genetic medicine. The evolution of rare disease drug development is accelerating thanks to some of the scientific and technological tools marshaled against cancer. The future of rare disease therapeutics reflects advances in genomic screening, improved understanding of the role of gene variants and epigenetics in disease phenotype, and greater use of in silico modeling that can help predict patient-level variation in disease progression, reducing the large variability in key endpoints usually seen in rare disease trials. The development of medicines for rare diseases is a virtuous circle, beginning with some of the world’s great academic research institutions. It then moves along that circle to include entrepreneurs, financiers, consultants, manufacturers, clinicians, regulators, and countless others. But always at the center is the patient, their family, and their caregivers. They are the central actors on the stage of innovation. And they are the focus of our mission: to create great medicines, for as many patients, as quickly as possible. As new technologies and breakthrough medicines advance for rare diseases, we also need to ensure that regulatory science advances in lockstep. It needs to always ensure that we have safe and effective medicines and regulatory independence and integrity. We need high-quality data generation to enable regulatory decisions of risk and benefit. My hope is that during our lifetimes, we can change the paradigm. The days when a doctor tells a patient with a rare disease or their family that “I am sorry. There is nothing we can do” must come to an end. That is simply no longer

Foreword

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acceptable. We will in the years and decades ahead have the tools to alleviate an enormous amount of human suffering – to extend and enhance human life. These medical breakthroughs in rare genetic medicine will also have profound consequences to address some of the world’s most intractable health problems, especially in areas like brain diseases and neurodegeneration. Let’s always realize, too, that the patient is waiting and that time is always of the essence. Oh, and how are Megan and Patrick today? They are still in wheelchairs and still need ventilators to breathe. But despite these challenges, Patrick completed high school and works part-time in a flower store in Princeton, NJ. Megan graduated in 2019 from the University of Notre Dame with a double major. She is completing a Master’s in Social Work degree from the University of North Carolina at Chapel Hill. She wants to be a social worker for children with rare diseases. The new hope is that in the years ahead, Megan and other social workers will watch as a doctor tells a family: “Your child has a rare genetic disease. We have a cure and a plan.” That day cannot come soon enough. To achieve it, so much work lies ahead of us. But we are filled with hope as never before. That’s why this book, edited by Dr. Raymond Huml, himself a father of two children with rare diseases, is so important and such a remarkable resource for this rare disease community. It will help us together chart a path forward. Onward. John F. Crowley Amicus Therapeutics Philadelphia, PA, USA

About the Artist

Maryna Kolochavina Why Does Art Matter? We know that art relieves anxiety, stress, and depression. There is a growing body of evidence that art can help maintain our health, aid our recovery from illness, and enable us to live longer, healthier lives. Art influences the way we think and the way we feel. The influential American artist, Jeff Koons, expressed this nicely in comments on teaching about art and creativity. He said, “The art is never in the painting that you’re looking at or the surface of the sculpture. The art is inside you. When the lights at the museum go off, nothing is happening there. But within you everything exists.” As a patient advocate, I meet many people affected by rare and orphan conditions. Some are at the beginning of their patient journey. Others are newly diagnosed children, or parents of a rare disease patient who would like a second child but fear that the new baby might also be impacted. Still others are patients who are on a long odyssey toward a diagnosis, or who have no available treatment options. All have one thing in common: a laser focus on their particular rare disease. However, each person is much more than his or her condition. Realizing this fact was the moment of my epiphany – and I started to paint. My art shows people affected by rare and orphan diseases in everyday situations – living life to the fullest with their families. My paintings show the joys of family life – an adult playing with a child, brothers and sisters laughing together, or the miracle of being pregnant. My goal for these paintings is to empower patients and their families to share their everyday experiences with others. This does not deny the reality of the disease but rather emphasizes the full humanity of the rare disease patient – and the fact that, as with all of us, the art actually exists within them.

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About the Artist

Acknowledgments

I first wish to express my professional gratitude to Dr. Nicholas Kenny, PhD, Chief Scientific Officer of Syneos Health, for the opportunity to lead the Rare Disease Consortium within Syneos Health Clinical Solutions. In this position, I lead a virtual team drawn from across the organization to share best practices in all aspects of the development of new therapies for rare diseases. I am indebted to Dr. Cinzia Dorigo, the author of the chapter on the operational aspects of rare disease drug development and the founder of the Syneos Health Rare Disease Consortium. A collaborative and independent team intended to support all business lines in the development of medicines for rare diseases, this team shares best practices and represents Syneos Health in the community at large touched by rare diseases. A total of 17* authors for this book were drawn from a myriad of functional areas within Syneos Health that support rare disease drug development to get rare disease products to the market quickly and to be successful in the global marketplace. Although when this book commenced, I did not personally know some of my colleague authors, they graciously agreed to help author or coauthor a chapter. Together at Syneos Health, we are developing an innovative and kindred spirit to tackle the enormous challenges associated with rare disease drug development. I also wish to express my sincere appreciation to Alistair Macdonald, Chief Executive Officer (CEO) of Syneos Health, for his excellent leadership of the only company to combine a Contract Research Organization (CRO) and a Contract Commercial Organization (CCO); to Sir Dennis Gillings, Commander of the British Empire (CBE), FMedSci, founder of the CRO industry, former CEO and Chairman of Quintiles Transnational Corp, for his personal support and for the opportunity to learn about the complex processes of pharmaceutical drug development and risk-­ based investing in pharmaceutical products over a 20+ year time frame; to Thomas “Tom” H. Pike, former CEO of Quintiles, for his friendship and personal support of my rare disease publications – and for providing me with corporate communications support, which enabled me to write the Springer book on muscular dystrophy; to Professor Geoffrey Barker, former Chief Medical and Scientific Officer for Quintiles Transnational Corp for his friendship, coauthorship and astute medical advice; to Jonathan E. Tunnicliffe, Chief Investment Officer, Partner and Founder of NovaQuest, mentor and friend, and formerly my supervisor within Quintiles Corporate Due Diligence Group; to John F. Crowley, CEO of Amicus, kindred spirit and author of the book titled, “Chasing Miracles: The Crowley Family Journey of xiii

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Strength, Hope, and Joy,” whose family was the inspiration for the movie Extraordinary Measures and who is the author of the foreword for this book; to Lynn O’Connor Vos, former CEO of the MDA, for her friendship and support of the neuromuscular data hub/registry called MOVR (which I have wholeheartedly supported since its inception and helped build), and providing me with the inspiration and courage to face all of the muscular dystrophies and beyond; to Tara Britt, Founder and President of the Rare Disease Innovations Institute, for her compassion and her assistance both professionally and personally; to Pat Furlong, CEO and founder of PPMD, who lost two sons to DMD, for her friendship and outstanding muscular dystrophy and rare disease leadership (and author of the chapter on caregiving); to Dr. Jill Dawson for her unwavering friendship, compassion, rare disease coauthorship, and editorial leadership; to Dr. Rick Turner, for his friendship and editorial help spanning almost two decades; to Daniel Paul Perez, former CEO and founder of the FSHD Society, for his friendship and historical coauthoring support; to Mark A. Stone, current CEO of the FSHD Society, for his friendship and inspiration to strive for a disease modifying treatment for folks with FSHD by 2025; and to Brandon S. Neuman, Partner of Nelson Mullins Riley & Scarborough LLP, for his compassion, friendship, and astute legal advice. I also wish to express my personal gratitude to my incredible children, Meredith L. Huml, friend and author of the patient advocacy chapter for the Springer book on MD, coauthor of multiple peer-reviewed papers on FSHD, coauthor of the select patient narratives chapter of this book on rare diseases, and cofounder and Head of the North Carolina Chapter of the FHSD Society; and Jonathan R. Huml, friend, Eagle Scout, pilot, and coauthor of several peer-reviewed papers on the topic of rare diseases, recent graduate of the University of North Carolina at Chapel Hill, current student at Harvard University, and coauthor of the rare disease trial designs chapter in this book; to my deceased parents, Raymond G. Huml Jr. and Nancy E. Kizer, who always encouraged me to do more for others than for myself; to Pastor L. Raymond “Ray” Cobb, for more than two decades of his inspirational prayers and those of his Triangle Grace Church congregation; and finally, to Springer Publishing, whose royalties the authors will donate to the FSHD Society – a highly respected patient advocacy group that has earned 11 consecutive four-star awards from Charity Navigator and received a Platinum Seal of Transparency in 2019 from GuideStar. *One author, Dr. Cinzia Dorigo, was Vice President of Rare Diseases at Syneos Health at the time this manuscript commenced; she now works for Alexion Pharmaceuticals.

Editor’s Introduction

Collectively, the rare disease community is a rather large group. Although individual diseases are rare, combined, it is estimated that about one in 10 people in the USA has a rare disease. Although much progress has been made since the US Orphan Drug Act of 1983, it is really in the last 10 years that I have seen a burgeoning of rare disease drug development, unlike all previous decades. For the first time in history, cell and gene therapies have the potential to cure rare diseases, and we have witnessed promising approvals of Spinraza® for spinal muscular atrophy and Luxturna® for inherited blindness. I first seriously entered into the rare disease community when my daughter, Meredith, was diagnosed with facioscapulohumeral muscular dystrophy (FSHD) at Duke University’s Muscular Dystrophy Association (MDA) Center in 2003. Until then, my personal awareness of MD was limited to Duchenne muscular dystrophy (DMD) – with the desperate need for a cure showcased by actor Jerry Lewis during his half a century of telethons in conjunction with the MDA. My professional rare disease experience until 2003 was limited to working with sponsors of rare disease products while at Quintiles Transnational Corp (now IQVIA) or via biopharmaceutical investing while working for NovaQuest when it was part of Quintiles. My son, Jonathan, was diagnosed with the same affliction at the University of North Carolina at Chapel Hill Hospital in 2013. Since 2013, I have learned that FSHD is probably more common and more severe that originally reported in the literature. Indeed, due to his worsening scoliosis, Jonathan underwent spinal fusion surgery in 2018 at Duke University Hospital. As a result, he lost his remaining ability to walk and uses a wheel chair to ambulate. My daughter, Meredith, relies on her wheelchair to ambulate and has also been diagnosed with epilepsy. I am thankful to Sir Dennis Gillings and Tom Pike, both past CEOs of Quintiles Transnational Corp, who, understanding my personal situation, were incredibly supportive of my rare disease efforts and allowed me the opportunity to work with many rare disease sponsors and patient advocacy groups via Requests for Proposals (RFPs), Requests for Information (RFI), and the opportunity to publish multiple thought leadership pieces (e.g., FSHD papers, DMD papers, Limb Girdle MD papers, and papers on shared platform registries and proactive rare disease feasibility studies) while I gained experience in rare diseases over a 17-year period. However, it was not until I met Dr. Nicholas (“Nick”) Kenny from Syneos Health

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Clinical Solutions that I was offered the opportunity to work full-time in the area of rare diseases. This is my third book for Springer Publishing and my most comprehensive. My first book, part of the SpringerBrief series, entitled, “Competitive Intelligence for the Regulatory Affairs Professional,” was published in 2012; my second book, focused on the muscular dystrophies and more akin to the rare disease topics contained in this book, was entitled, “Muscular Dystrophy: A Concise Guide,” published in 2015. I remain grateful to the physicians and experts who contributed to the MD book – and especially to my daughter, Meredith, who provided an articulate and heartfelt patient perspective. This book on rare diseases entitled, “Rare Disease Drug Development: Clinical, Scientific, Patient and Caregiver Perspectives” – encompassing all aspects of rare disease drug development from research and development (R&D) through commercialization – uniquely includes the perspectives of the caregiver and the rare disease patient. These narratives add a perspective of humanity often absent in highly scientific treatises in the drug development space. The book also benefits from the fact that one of the authors – a doctoral-level biopharmaceutical industry professional, Dr. Maryna Kolochavina – is also a trained artist. She has graciously provided rare-­ disease artwork for each chapter, adding yet another aspect of humanity to the book. This rare disease book also includes the views and authorship of notable rare disease advocates who have advanced rare disease drug development, such as the MDA (Sharon Hesterlee), Parent Project Muscular Dystrophy (Pat Furlong), ex-­ FDAers (James Valentine), and members of the Drug Information Association (DIA) Adaptive Design Scientific Working Group (ADSWG) for rare disease clinical trial designs (Zoran Antonejevic, et al.). I am thankful that this prestigious group of biostatisticians allowed my son, Jon, to join their group and participate in their efforts. This diverse coauthorship clan brings a myriad of personal and professional experiences to the table. We all share the mantra that, in order for us to be successful, the patient voice must be included as a part of every rare disease drug development program.

Contents

1 Introduction to Rare Diseases and Market Overview����������������������������   1 Raymond A. Huml 2 The Patient Perspective ����������������������������������������������������������������������������  19 June Kinoshita 3 Select Patient Narratives ��������������������������������������������������������������������������  27 Meredith L. Huml and Kevin Schaefer 4 The Caregiver Perspective������������������������������������������������������������������������  53 Pat Furlong 5 The Critical, Multidimensional Role of Patient Advocacy Groups in Rare Disease ����������������������������������������������������������������������������  59 Keri McDonough 6 A Mental Health Perspective��������������������������������������������������������������������  75 Michelle Bailey 7 Investment Decisions Related to Rare Disease Drug Development������  89 Jonathan E. Tunnicliffe and Devin T. Rosenthal 8 Optimizing Rare Disease Registries and Natural History Studies�������� 109 Sharon Hesterlee 9 Novel Approaches to Clinical Trials in Rare Diseases���������������������������� 127 Rui (Sammi) Tang, Robert A. Beckman, Yi Liu, Heng Xu, Mercedeh Ghadessi, Cong Chen, and Zoran Antonijevic 10 Patient Benefits from Innovative Designs in Rare Diseases ������������������ 147 Zoran Antonijevic, Yi Liu, Rui (Sammi) Tang, Jonathan R. Huml, Robert A. Beckman, Cristiana Mayer, and Gianna McMillan 11 Central Nervous System Rare Disease Drug Development�������������������� 161 Jane Williams and Nermina Nakas 12 Oncologic Rare Disease Drug Development�������������������������������������������� 179 Keren Moss and Jozsef Palatka

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13 Hematologic Rare Disease Drug Development���������������������������������������� 197 Daniel Mazzolenis and Liat Vidal 14 Lessons From Rare Disease and Gene Therapy Clinical Studies in Ophthalmology�������������������������������������������������������������������������������������� 213 Nicholas Spittal 15 Rare Diseases in the Pediatric Population ���������������������������������������������� 233 Alexander Cvetkovic Muntañola 16 Cell and Gene Therapy in Rare Diseases������������������������������������������������ 249 Peter Robinson 17 The Feasibility Assessment������������������������������������������������������������������������ 263 Gloria Alley and Marie Emms 18 The Evolving Regulatory Space and the Advent of Patient-Focused Drug Development���������������������������������������������������� 277 James E. Valentine and Annie Kennedy 19 Operational Aspects of Rare Disease Drug Development���������������������� 291 Cinzia Dorigo 20 Accelerating Rare Disease Drug Development���������������������������������������� 303 Zizi Imatorbhebhe 21 Select Rare Disease Drug Approvals: Lessons Learned ������������������������ 311 Raymond A. Huml and Teresa Leon 22 A Rapid Market Access Strategy for Orphan Medicinal Products (OMPs) with Highlights Regarding the Pricing and Reimbursement Process and Barriers to Patient Use �������������������� 333 Maryna Kolochavina 23 Integrated Life Cycle Management for Rare and Orphan Products������������������������������������������������������������������������������������������������������ 351 Maryna Kolochavina 24 The Case for Real-World Data and Real-­World Evidence Generation in Rare and Orphan Medicinal Drug Development ���������� 371 Maryna Kolochavina 25 Closing Remarks���������������������������������������������������������������������������������������� 389 Raymond A. Huml  orrection to: Patient Benefits from Innovative Designs C in Rare Diseases. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .  C1 Comments on the Book�������������������������������������������������������������������������������������� 397 Index�������������������������������������������������������������������������������������������������������������������� 401

Contributors

Gloria Alley, RN  Syneos Health, Morrisville, NC, USA Zoran Antonijevic, MS  Abond CRO Inc., Allendale, MI, USA Michelle  Bailey, MD  Medical and Scientific Affairs, Syneos Health Clinical Solutions, Morrisville, NC, USA Robert  A.  Beckman, MD  Drug Information Association Innovative Design Scientific Working Group, Horsham, PA, USA Lombardi Comprehensive Cancer Center and Innovation Center for Biomedical Informatics, Georgetown University Medical Center, Washington, DC, USA Departments of Oncology and of Biostatistics, Bioinformatics, & Biomathematics, Georgetown University Medical Center, Washington, DC, USA Cong  Chen, MS, PhD  Biostatistics and Research Decision Sciences (BARDS), Merck & Co., Inc., Kenilworth, NJ, USA Cinzia Dorigo, PharmD  Alexion Pharmaceuticals, Milano, Italy Marie Emms, BA  Syneos Health, New York, NY, USA Pat  Furlong, RN, Middletown, OH, USA

BSN,

MS  Parent

Project

Muscular

Dystrophy,

Mercedeh  Ghadessi, MSc  Drug Information Association Innovative Design Scientific Working Group, Horsham, PA, USA Bayer U.S. LLC Pharmaceuticals, Whippany, NJ, USA Sharon Hesterlee, PhD  Muscular Dystrophy Association, New York, NY, USA Jonathan R. Huml, BSc, BA  Harvard University, Cambridge, MA, USA Meredith L. Huml  Disability Rights of North Carolina, Raleigh, NC, USA Raymond  A.  Huml, MS, DVM, RAC  Syneos Health Clinical Solutions, Morrisville, NC, USA Zizi  Imatorbhebhe, MS, MBA, PMP  Executive Board, Bios Health Group, Irvine, CA, USA xix

Contributors

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Annie  Kennedy, BSc  EveryLife Washington, DC, USA June  Kinoshita, BA  Research Lexington, MA, USA

&

Foundation Patient

for

Rare

Engagement,

Diseases,

FSHD

Society,

Maryna Kolochavina, PharmD, PMP  Syneos Health, Berlin, Germany Teresa  Leon, MD, Morrisville, NC, USA

PhD  CNS

Clinical

Development,

Syneos

Health,

Yi  Liu, MSc, PhD  Drug Information Association Innovative Design Scientific Working Group, Horsham, PA, USA Nektar Therapeutics, San Francisco, CA, USA Cristiana  Mayer, MSc, PhD  Janssen Research and Development, LLC, Raritan, NJ, USA Daniel  Mazzolenis, MD, MBA  Oncology Hematology, Syneos Health, Morrisville, NC, USA Keri  McDonough, MA  Head, Patient Voice Consortium, Syneos Health, New York City, NY, USA Gianna McMillan, DBe  LMU Bioethics Institute, Los Angeles, CA, USA Keren Moss, MD  Syneos Health, Tel Aviv, Israel Alex Cvetkovic Muntañola, MD  Syneos Health, Madrid, Spain Nermina  Nakas, MD, MPH  Medical Management and Scientific Services, Clinical Solutions, Syneos Health, Morrisville, NC, USA Jozsef Palatka, MS, MD  Syneos Health, Budapest, Hungary Peter Robinson, MBA  Syneos Health, Morrisville, NC, USA Devin T. Rosenthal, PhD, MBA  NovaQuest Capital Management, Raleigh, NC, USA Kevin Schaefer, BA  BioNews, Pensacola, FL, USA Nicholas Spittal, MBA, PMP  Syneos Health, Syneos Health Clinical Solutions, Morrisville, NC, USA Rui  (Sammi)  Tang, PhD  Drug Information Association Innovative Design Scientific Working Group, Horsham, PA, USA Servier Pharmaceuticals, Boston, MA, USA Jonathan  E.  Tunnicliffe, MS, MBA  NovaQuest Capital Management, Raleigh, NC, USA James  E.  Valentine, JD, MHS  Hyman, Phelps, & McNamara, P.C., Washington, DC, USA

Contributors

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Liat Vidal-Fisher, MD, MSc  Syneos Health, Tel Aviv, Israel Jane Williams, MD, MPH, FAAFP  Medical Management and Scientific Services, Syneos Health Clinical Solutions, Boston, MA, USA Heng Xu, PhD  Nektar Therapeutics, San Francisco, CA, USA

About the Editor

Raymond A. Huml, MS, DVM, RAC  is Vice President of Medical and Scientific Strategy and Head of Syneos Health’s Rare Disease Consortium. Dr. Huml is an 18-year member and strong supporter of the FSHD Society. He is a cofounder, along with his daughter, Meredith, of the North Carolina Chapter of the FSHD Society. Ray is a member of the Corporate Council of the National Organization for Rare Disorders (NORD), has represented patients with muscular dystrophy as part of MDStarNet in North Carolina, and is currently a member of the National Steering Committee for the State Rare Disease Education Initiative (STRiDE), a state-focused rare disease gene therapy educational campaign that provides programs and content to help stakeholders facilitate patient access to transformative cell and gene therapies. He is also a member of Berkeley Public Health’s “The Forum for Collaborative Research Rare Disease Forum: Bayesian Methods in Rare Diseases Working Group,” designed to address regulatory hurdles to facilitate safe and efficient drug development. Ray has over 30 years of experience in the clinical and biopharmaceutical industries and over 20 years in the Contract Research Organization (CRO) industry. He previously served as Head of the Global Biosimilars Center of Excellence at IQVIA and as Head of Global Due Diligence at Quintiles Transnational Corp. where he identified risks associated with global, product-based investments (including rare disease investments), which resulted in almost $3 billion in capital committed to partnerships of all sizes. Ray has authored or coauthored over 70 peer-reviewed articles, a dozen book chapters, and three books on topics such as due diligence, competitive intelligence, biosimilars, and muscular dystrophy. He was awarded North Carolina Veterinary Medical Association’s Young Veterinarian of the Year Award and Regulatory Affairs Professionals Society’s New Professional Award. He has also received numerous CRO awards from Quintiles including the Distinguished Performance Award as well as Clinical Development Services President’s Award and the Chairman’s Award. While at Syneos Health, he was recognized by his CEO in the company’s Spotlight Recognition Program for a video that was produced that included the Rare Disease Consortium, the FSHD Society, and an FSHD patient – his daughter, Meredith.

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About the Editor

Ray is a Returned Peace Corps Volunteer having served in Ghana, West Africa and currently sits on North Carolina State University’s Board of Visitors. He holds an MS in Biology from East Stroudsburg University and a DVM from North Carolina State University’s College of Veterinary Medicine, and has earned the RAC (US) Certification.

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Introduction to Rare Diseases and Market Overview Raymond A. Huml

Illustration 1 Artist’s rendition of the editor and his daughter, Meredith L. Huml, coauthor of Chap. 3, from the 2019 Facioscapulohumeral Muscular Dystrophy (FSHD) Society-­ sponsored “Walk and Roll” fundraising event – which occurred on the campus of North Carolina State University and raised over $29,000 for FSHD clinical research. Artwork courtesy of the artist.

R. A. Huml (*) Syneos Health Clinical Solutions, Morrisville, NC, USA e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Switzerland AG 2021 R. A. Huml (ed.), Rare Disease Drug Development, https://doi.org/10.1007/978-3-030-78605-2_1

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R. A. Huml

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Introduction to Rare Diseases Rare diseases were once a neglected aspect of pharmaceutical research relegated to nonprofits and philanthropic projects. However, in today’s research environment, blockbuster drugs are few and far between, and regulators are incentivizing developers to pursue unmet needs. As a result, drug targets are increasingly being discovered and pharmaceutical companies are investing billions in rare disease research – with some impressive results [1]. This is great news for people who suffer from rare diseases that often have few if any treatments. However, it creates significant challenges for the biopharma companies conducting this research, particularly if they are accustomed to running trials for common conditions that affect millions of people worldwide. Patient recruitment and retention are arguably the greatest challenges to the timely execution of clinical trials [2]. For rare diseases, recruitment success depends not only on finding enough patients but also on retaining them for the duration of the trial. Rare diseases have small and often widely scattered populations, so recruiting patients to trials can be extremely challenging. Historical success with patient recruitment – in addition to site qualifications – needs to be considered in parallel with current market competition and results in an ever-changing patient recruitment environment. Multiple companies supporting the biopharmaceutical industry, such as contract research organizations (CRO), have begun to leverage sophisticated data modeling tools and techniques to find additional patients for biosimilar and rare disease trials and/or to find patients more quickly. In addition, these companies seek to better understand the population of interest and refine their statistical assumptions when conducting clinical trials or real-world analyses. The largest and most well-established companies that support the biopharmaceutical industry now have unparalleled access to big data from clinical trials, electronic health records, medical claims, laboratory tests, and prescriptions. For rare diseases, there is typically a lack of natural history data, suitable animal models of disease, regulatory guidance, and agreement on primary and secondary endpoints, as well as genotypic and phenotypic variability within the same disease. These factors make it very difficult to define common treatment patterns, assess how those treatments are working, and identify gaps – all of which are vital to the clinical research process. There are no “silver bullet” solutions to these challenges, but they can be addressed, at least in part, through big data sources, such as patient registries. Between 5000 and 8000 rare diseases are known, and around 250–280 more are described each year [3, 4]. More than 70 percent of these are genetic disorders (and 70 percent of these genetic disorders have exclusively pediatric onset), affecting 3–4 percent of all births. Although the number of individuals suffering from a single rare disease is by definition small, the total number of individuals that rare diseases impact is significant, at approximately 25 million people in the United States (almost one in ten), 30 million in Europe, and a total of 400 million worldwide. It is difficult to estimate the number of patient advocacy groups (PAGs) in the rare disease space. One database identified over 1200 that had received money from biopharmaceutical companies [5], but this number is probably an underestimate because, as other groups who have surveyed PAGs that have received no money

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have discovered, “groups in our survey that received no industry funding seemed to be for diseases that drug companies have little opportunity to profit by [6].” Anecdotally, I’ve been told that less than half of the rare diseases identified to date are represented by a PAG. Regardless of the actual number, it is clear that many patients with rare diseases lack patient advocacy representation. Prior to the passage of the Orphan Drug Act of 1983, many of the more prevalent rare diseases, such as muscular dystrophy and certain childhood cancers, were only represented by a PAG. These groups worked tirelessly on behalf of their patients to vie for government dollars to study their diseases, determine its mechanism of action or genetic features, and attempt to identify approaches to target those mechanisms to modify or perhaps cure the disease. According to PhRMA’s (phrma.org) Fact Sheet entitled “Spurring Innovation in Rare Diseases” (posted February 28, 2019), in the decade prior to passage of the Orphan Drug Act, only ten rare diseases had approved therapies. According to the Health Promotion and Disease Prevention Amendments of 1984 (Pub. L. No. 98–551, 98 Stat. 2815 (Oct. 30, 1984): • A “rare disease” is defined by the Amendment to the Orphan Drug Act of 1983 as a condition affecting fewer than 200,000 Americans or a disease with a greater prevalence but for which no reasonable expectation exists that the costs of developing or distributing a drug can be recovered from the sale of the drug in the United States. Since then there has been a marked increase in rare disease research funding and development efforts, thanks to regulatory changes, multiple international initiatives, and development incentives. Today, more than 300 rare diseases have approved therapies. In 2019 alone, 22 of 48 novel drug approvals by the Food and Drug Administration (FDA) were for rare or orphan diseases, involving a total of 76 orphan indications [7, 8]. At present, almost 600 companies are developing novel rare disease therapies, and 729 gene therapies are being studied in 1855 clinical trials [9]. According to the FDA’s “Rare Diseases at FDA” website (www.fda.gov/ patients/rare-diseases-fda), the Orphan Drug Act is a law that incentivizes the development of drugs used to treat rare diseases. Companies and other drug developers can request orphan drug designation if the drug meets certain criteria. Orphan designation qualifies sponsors for various incentives, including: • Tax credits for qualified clinical (in humans) testing • Waiver of the Prescription Drug User Fee (currently at almost $3m for a new drug) • Potential for 7 years of market exclusivity after approval (which serves as additional patent protection)

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Along with regulatory incentives, there have been major advances in science, helping elucidate many more targets for rare disease therapies. Following a long unproductive spell, there have been some recent successes in the cell and gene therapy space. For the first time in decades, there is potential for a cure for many of the rare diseases that involve a single-gene mutation. Scientific progress, coupled with regulatory incentives and the promise of favorable pricing, has led to burgeoning of rare disease drug development. Rare diseases represent one of the fastest growing areas for biopharmaceutical R&D investment [10], yet the inherent challenges pose significant risks that sponsors must anticipate and navigate to be successful. These include the extreme heterogeneity among rare diseases; the many unknowns about rare disease pathophysiology and natural history; the very long, often frustrating, and emotionally wrenching diagnostic journey many patients and their families must travel; complex and changeable global regulatory frameworks; uncertain reimbursement landscapes; and the limited availability of rare disease research expertise and patients to participate in clinical trials. Worldwide orphan drug sales are forecast to grow at a compound annual growth rate of 12.3% from 2019 to 2024, which is approximately double the rate forecast for the non-orphan drug market [11]. By 2024, orphan drug sales are expected to reach $242bn and capture one-fifth of the worldwide prescription sales.

Introduction to Rare Disease Drug Development Rare disease drug development is all about the patient. All rare disease drug development should begin and end by thoughtfully and meaningfully incorporating feedback from rare disease patients. For patients who cannot communicate adequately, due to either their rare disease or a concurrent comorbidity, the voice of their caregivers and loved ones should be sought and incorporated. To obtain a cure – the ultimate goal of rare disease drug development – we need to conduct well-designed clinical trials. We need to, first, identify patients with rare disease and, second, provide them with a compelling reason to consider enrolling in a particular clinical trial (after understanding and balancing the potential risks and benefits involved). Absent a cure, we need to seek ways to treat, and care for, patients with rare diseases based on their priorities. To determine if candidate therapies are working, especially for disease-modifying treatments, we need to either: 1. Identify a surrogate (or biomarker) that, if modified, should, by extrapolation, correspond to a clinical improvement. 2. Determine if the rare disease product will alter the natural course of the disease when compared with lack of any treatment or treatment with the current standard of care.

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Side Bar 1: Patient- Focused Drug Development Meeting with the FDA [12]

Thankfully, regulatory agencies are increasingly supporting the patient voice in rare disease drug development. My daughter, Meredith Lindsay Huml, and I provided feedback to the FDA on June 29, 2020, during a patient-focused drug development (PFDD) meeting jointly sponsored by the FDA and the Facioscapulohumeral Muscular Dystrophy (FSHD) Society. At the time, this was one of the only three PFDD meetings held by the FDA during the COVID-19 pandemic – and provided testimony that the patient voice cannot wait until it is over. Based on over 20 years of combined experience, Meredith and I provided feedback to the FDA that: • FSHD is more common than originally thought  – and may even be the most common type of muscular dystrophy, though not the most well-­ known or understood. • The severity of the clinical signs of FSHD is probably underreported in the literature, meaning that it is more severe than originally thought (especially in regard to the use of – and time to use of – wheelchairs and the probability that certain surgeries, such as back surgery to correct scoliosis, is needed more often than reported). The meeting also highlighted that the fatigue, anxiety, and depression that often accompany a rare disease diagnosis are important clinical signs that need to be addressed – as well as the ability to slow down the progression of the disease until a cure is developed.

Incorporation of the patient voice should occur from inception of the idea of the candidate therapy, to address the particular culture and market, including prevailing or developing standards of care, and the type of patient that the therapy is intended to treat. For many rare diseases, including the muscular dystrophies, subtypes of patients may only be amenable to certain approaches (e.g., exon skipping therapies) or the condition may comprise a syndrome of diseases, as with the limb girdle muscular dystrophies (LGMDs), where the various proteins of subtypes targeted for correction may differ (e.g., sarcoglycan vs. dysferlin). The patient voice certainly needs to be incorporated prior to the recruitment phase. As an example, some leading entities in the muscular dystrophy space (e.g., CureDuchenne, Sarepta, and PTC Therapeutics) used a Web-based portal, such as DuchennXchange.com, to solicit both patient and caregiver feedback prior to finalizing and implementing a protocol. This risk/benefit analysis needs to be understood by the patient, their caregiver(s), and their entire healthcare team. Every barrier to successful enrollment (typically defined by the inclusion/exclusion criteria or the schedule of protocol assessments in a clinical protocol), whether identified through artificial intelligence methods, competitive intelligence methods, clinical experience, or historical site performance, should make sure that every procedure in the protocol’s schedule of events is matched with either a primary or secondary endpoint

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in the trial design. We need to keep in mind that if the proposed procedure does not add or contribute to the proposed study, it should be considered an unnecessary burden and removed. The recruitment phase, when patients may first become aware of the proposed clinical trial, may also require education on why the candidate therapy may work (e.g., promising preclinical data), what the risks entail (to determine the risk/benefit balance), and if the participant can take the product again following the trial. It is important to note that some approaches, such as the gene therapies utilizing adeno-­associated virus (AVV) vector technology, currently employ a “one and done” strategy – meaning that a patient can only participate in a single such trial. The patient voice continues to be needed after the approval of a drug to make sure that the diversity of patients that require the medication can afford and obtain access to the medication. This is especially important for highly expensive drugs, such as the gene therapies, which may limit access for certain patients. Once a product is approved for the treatment of a rare disease, the product life cycle continues until patent expiration and beyond. Some products may receive a tentative regulatory approval based on results from only a small number of patients, and contingent on conducting a larger confirmatory clinical trial. For others, such as the gene therapies, sponsors may be required to monitor patients for up to 15 years or even longer to better understand the long-term safety (and possibly efficacy) consequences of taking an RNA- or DNA-altering product via a vehicle, such as a cold virus.

The Rare Disease Journey: Diagnosis The rare disease patient journey may begin with a diagnostic test at birth due to genetic screening or, much more commonly, after an untoward clinical event occurs that mandates physician intervention. My daughter Meredith first exhibited a speech development problem when she was in the third grade (about 8  years old). This lessened as she grew older, probably due to her growing and increasing in size, but it was initially thought to have been resolved due to intervention by a speech pathologist, provided as part of public school services. We did not realize that her speech pathology was due to weakening facial muscles until, retrospectively, when she had problems with her dance routines. Her impaired coordination, combined with her inability to lift her arms above her shoulders, initiated a greater than 1-year odyssey that led to multiple incorrect tentative diagnoses, including Marfan syndrome, until she was finally clinically diagnosed with facioscapulohumeral muscular dystrophy (FSHD) at age 13 at Duke University Hospital’s MDA Care Center. There were no genetic tests available at that time to confirm her clinical diagnosis. For many folks with rare diseases, the diagnostic odyssey  – whether clinical or molecular – may take weeks, months, or even decades. For example, when preparing to give a talk at the First National Limb Girdle Muscular Dystrophy (LGMD) Conference in Chicago in August 2019, I discovered that many patients with LGMD did not obtain the correct diagnosis until decades after their initial symptoms appeared. It is hoped that, with newer genetic tests, this prolonged “time to correct diagnosis” can be avoided. See Chap. 4, The Caregiver Perspective by Pat Furlong, for more information about the environment surrounding an initial diagnosis if not provided at birth.

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Joining a Patient Advocacy Group and Enrolling in a Registry After diagnosis of a rare disease, the patient or caregiver will often seek to identify a PAG. These groups can be of enormous help with education about the disease, identifying available treatments (if any) and navigating the clinical trial landscape. In some cases, where a PAG does not exist, it is possible to join an umbrella organization, such as the National Organization for Rare Disorders (NORD) or the Muscular Dystrophy Association (MDA). There is also the option of setting up a new PAG.

Side Bar 2: The FSHD Society

I joined the FSHD Society as soon as Meredith was diagnosed with FSHD in 2003. I first met Carol Perez, who used to answer the phone at the time. Carol, a person with FSHD and mother of a child of FSHD, unfortunately, has since passed. I spent many hours with her on the phone discussing how one deals with such a diagnosis of FSHD.  We also discussed my concerns that my daughter might have the infantile onset form (which is more severe when compared with the two other known types: FSHD 1 and FSHD 2) and if my son might have the same affliction (he was diagnosed with FSHD in 2013 at the University of North Carolina Hospital’s MDA Care Center). Having the disease herself, Carol was kind, compassionate, courageous, and incredibly supportive. I then met Carol’s son, Daniel (“Dan”), founder of the FSHD Society and former CEO and Chairman, who is also afflicted with FSHD. I first thought Dan was a physician, as he knew so much about clinical, medical, and scientific muscular dystrophy issues (he is not). We became friends, even coauthoring the chapter on FSHD for the Springer book on all MDs. Dan testified almost 50 times before Congress and was well connected with others in the various muscular dystrophy communities and is conversant about almost every issue facing folks with FSHD as well as the many commonalities that folks with other rare diseases face on a daily basis. When my good friend, Carl Hellman’s wife, Shannon – who had increasing motor and cognitive clinical symptoms  – was diagnosed with earlyonset Alzheimer’s disease when she was 37  years old, everyone was shocked. Shannon passed on Easter Sunday in 2017. The head of the Alzheimer’s disease PAG at the time was June Kinoshita. Carl told me how compassionate and smart June was, and I read about her long before I met her. I was delighted when June later joined the FSHD Society and is currently serving as Director of Research and Patient Engagement. June is the author of Chap. 2 of this book, on the rare disease patient perspective. Recognizing the need to expand nationally, and with strong support from June and Dan, the FSHD Society asked me and Meredith to start a chapter of the Society in North Carolina (NC). The group was first formed in 2013 with the idea that folks with FSHD and their loved ones and caregivers could meet and discuss the issues most pressing to them. Later, the meetings

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became more formal, and Meredith was selected as the Chapter Director of the NC Chapter of the FSHD Society in 2018. Meredith and I invited numerous physicians, some from the MDA Care Centers, as well as PhD scientists, MDA representatives, physical therapists, and occupational therapists, to our statewide meetings. Other members also solicited experts to come to our group. When Mark Stone, the current CEO who took over from Dan Perez in 2018, joined, he evolved the focus of the chapter to include fundraising. He also identified a goal – commensurate with the fundraising and consistent with my aspiration identified in my Springer book discussing the muscular dystrophies – of championing advancement of an FSHD disease-modifying drug to the market by 2025. See: • 2015 Huml RA (Editor). Muscular Dystrophy: A Concise Guide; ISBN 978-3-319-17,361-0; 186 pp., Springer, Copyright 2015 When the members of the NC Chapter of the FSHD Society asked Meredith and me to provide an updated overview of FSHD (in addition to the information on the FSHD Society’s website), we collaborated with neurologist Dr. Lucie Undus to publish the following overview: • Huml RA, Undus L, Dean M, Huml ML. Facioscapulohumeral Muscular Dystrophy: Clinical, Therapeutic and Regulatory Updates. Journal for Clinical Studies, Volume 9; Issue 3, pp.  12–14, 16, and 18. Published online June 5, 2017 When the members of the NC Chapter of the FSHD Society asked Meredith and me to summarize the state of the FSHD product pipeline, we again solicited the help of Dr. Undus to publish the following: • Huml RA, Undus L, Smith G, Huml ML, Dean M. Potential Therapies in the R&D Pipeline for Facioscapulohumeral Muscular Dystrophy. Journal for Clinical Studies. Published online September 21, 2017 In 2018, the NC Chapter of the FSHD Society was discussing the lack of animal models to advance FSHD therapies and the increasing role of respiratory issues associated with FSHD. To provide the global community with an update on FSHD, I solicited the help of additional colleagues, including a neurologist and a PhD-­candidate summer intern from North Carolina State University, to publish the following article on FSHD: • Huml RA, Uspenskaya-Cadoz O, Dawson J, Slifer Z. Updating the Clinical Picture of Facioscapulohumeral Muscular Dystrophy: Ramifications for Drug Development with Potential Solutions, DIA’s Therapeutic Innovation & Regulatory Science, 7pp. https://doi.org/10.1007/s43441-019-00038-w. Published online January 10, 2019; published in print January 06, 2020

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Patient Registries Patient registries  – defined by the Agency for Healthcare Research and Quality (AHRQ) as organized systems that use observational research methods to collect data for the scientific assessment of patient outcomes [13] – are potentially important sources of big data. Patient advocacy groups typically enroll the patients they work with in registries that can vary from an MS Excel spreadsheet to a sophisticated registry such as that of the Cystic Fibrosis Foundation (CFF) or the patient databases of the Muscular Dystrophy Association (MDA). Simple registries may be accessible only to a small group of physicians, while highly complex databases covering several diseases may be accessible online across multiple institutions. Currently, however, many registries are only offered in one geographic area or for just a select number of diseases. Ultra rare disease registries may contain only a handful of patients, whereas larger databases, such as the MDA’s MOVR hub (Side Bar 3), ultimately seek to register all neuromuscular disease patients that receive treatment at the 200+ MDA Care Centers. Sometimes misunderstood due to its original moniker, the MDA not only covers all of the types of muscular dystrophies but also currently represents 43 different neuromuscular diseases [14]. Registries can be useful to identify patients for global trials, protect patients’ rights, meet patient expectations, and expedite drug development. They can support efforts to develop treatments not only for the most severe or most common types of rare disease but also, perhaps more importantly, for those diseases that are not supported by well-financed advocacy groups. Rapid identification of patients with rare diseases is important not only for trial recruitment but also for broader engagement with these individuals and their families, to inform and support the overall research effort and empower advocacy activities. New technologies are making it easier to create registry programs that aggregate clinically rich data from electronic health record systems and other sources (e.g., patient-reported outcomes [PROs], mobile devices). However, the complexity of integrating data from numerous hospitals and providers makes it difficult to justify the investment for a single study or rare disease. By banding together, stakeholders can develop a common platform for collecting and managing data while maintaining governance over sponsor-specific needs related to individual diseases of interest. “Big data” are defined by the European Medicines Agency as “extremely large datasets which may be complex, multi-dimensional, unstructured, and heterogeneous, which are accumulating rapidly and which may be analyzed computationally to reveal patterns, trends, and associations [15].” Such data are increasingly helping to identify individuals with rare diseases [16] and improve clinical trial recruitment. In healthcare, sources of big data include electronic health records, medical claims data, prescription data, clinical trial data, and laboratory data – each governed differently in different countries and subject to varying measures to protect individual patient privacy.

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The healthcare sector has historically generated large amounts of data as hard copy, driven by record keeping, compliance, regulatory requirements, and routine patient care [17]. The current trend is toward digitization [18], which can potentially make data sharing easier. Big data requires technological resources and complex analytical approaches for processing and analysis. Yet, the benefits are enormous as these could provide very significant information for the understanding and validation of statistical and medical assumptions about a particular patient population of interest, such as details about the disease characteristics, clinical trial design operational characteristics, dropout rates, response rates, endpoints used, and study duration. For rare disease studies, increasingly sophisticated approaches are needed to track down and select eligible patients – who may be even more elusive if the study is focusing on a specific patient subset such as those with a mutation that qualifies for a gene therapy. The use of master protocols – intended to simultaneously evaluate more than one investigational drug and/or more than one disease subtype within the same overall trial structure  – combined with enriched adaptive designs has proven to be of great value in achieving this goal. Beyond conventional tactics, such as discussions with patient advocacy groups, leading academic centers and specialist centers focused on a particular disease (or set of diseases), a new strategy involves the identification of a “digital footprint” that mirrors or follows the patient. In pursuit of that goal, data analytics could include a review of electronic health records, prescription data, laboratory data, medical imaging data, and ICD-10 codes (if available; and especially if unique [e.g., not clumped with other sets of similar diseases as occurs in the muscular dystrophies: Duchenne and Becker MD are clumped together in one ICD-10 code]). Applying these approaches to a delivery strategy can help to ensure an optimal country mix, identify the most appropriate patients and sites, minimize risks to quality, and increase the chance of meeting project timelines.

Side Bar 3: The MDA’s NeuroMuscular ObserVational Research (MOVR) Data Hub

When Quintiles announced a partnership with the MDA regarding the association’s registry in 2013 [19], I was an employee of Quintiles and volunteered to help with the registry. As I learned more about the potential for a “state-of-the-art” registry, I became increasingly aware of how a registry could be used to help populate a natural history study and how ultimately it could be leveraged to create a “clinical trial-ready network.” To help socialize this idea – and promote the need for a more modernized and unified national registry – Meredith and I wrote the following paper: • Huml RA, Huml ML, Dawson J. The Growing Case for the Rapid Identification of Patients with Muscular Dystrophy for Clinical Trials. Journal for Clinical Studies, Vol 8, Issue 2, pp. 48–51, 2016

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We sent the paper to everyone at the MDA, including the entire Board of Directors (numbering 30 individuals at the time) and President and CEO, Steve Derks (tenure from 2012 to 2017). When Steve and I discussed Quintiles hosting the MDA’s 2017 board meeting, I was delighted, as it would provide me with the opportunity to meet everyone in person. By the time the board meeting was actually held, Steve had stepped down as CEO, and Kristine Welker (Director), a longtime board member, was serving as the interim CEO. I provided a parent/caregiver perspective on the need for a modernized registry to address the growing number of clinical trials in the neuromuscular clinical development pipeline, and a colleague described the information technology that would be required. Without exception, everyone on the board was supportive of expanding and modernizing the MDA neuromuscular disease registry. When several board members asked me how exactly this would be accomplished, I summarized our top-level views and later published the following article, along with the help of several very knowledgeable colleagues: • Huml RA, Campion DM, Lucove J, Blackburn S, Kelly BJ. Accelerating Therapeutic Advancements in Muscular Dystrophies through Shared Registry Platforms. Journal for Clinical Studies, Volume 10 Issue 3, pp. 60–65. Published online May 29, 2018 Unfortunately, the paper was not published until after the MDA announced that they would build MOVR [20], but I was pleased to see that our paper (and the MOVR idea) made the front cover of the Journal for Clinical Studies as an innovative idea. Meredith and I were happy to hear that Lynn O’Connor Vos, who was chosen as the new MDA President and CEO, was highly supportive of the MOVR idea and other innovative initiatives. Since 2018, I have collaborated with the MDA and was invited as a guest to the 2019 MDA Clinical and Scientific Conference in Orlando, Florida. I attended many sessions to learn more about the various neuromuscular diseases, but the highlight of my trip was sharing the stage with John Crowley, CEO of Amicus (Note: John’s family was the basis for the movie entitled “Extraordinary Measures,” starring Brendan Fraser and Harrison Ford). During a special dinner held during the conference, Lynn encouraged us to tell our parent and patient stories. Humbled to share the stage with John, despite my biggest fears about his celebrity status, I found that he was a kind, compassionate, and wicked smart (to borrow a phrase from a British friend) person driven to help folks with rare diseases, not just those with Pompe disease, the rare disease that had afflicted his family. Along with Sir Dennis Gillings, Tom Pike, Pat Furlong, Dan Perez, Dr. Robert Lark, Dr. Edward (“Eddie”) Smith, Dr. Kathryn Wagner, Dr. Jill Dawson, Tara Britt, and Lynn O’Connor Vos, John ranks among my most admired rare disease advocates. Despite his numerous duties as CEO of Amicus, John was gracious enough to provide the Foreword for this book.

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 essons Learned from Muscular Dystrophy Patient L Advocacy Groups Based on my experience in MD drug development and biopharmaceutical investing over the last 18  years, as well as my close association with the MD PAGs, I’ve learned multiple lessons that can accelerate rare disease drug development. Some of these ideas were expressed in the book that my daughter and I wrote on the muscular dystrophies. We felt compelled to get the word out about the muscular dystrophies, especially after witnessing multiple MD diagnoses and accompanying tears – at our local MDA Care Center at Duke University Hospital. We were pleased the MDA advertised the book in one of its Quest Magazine articles (in 2016) [21]. According to the Springer Book Reports, this was one of the “top 25% most downloaded eBooks in the relevant SpringerLink eBook Collection in 2016 (and 2017)” and one of the “top 50% most downloaded eBooks in 2018.” More recently, my son and I started writing rare disease articles together. We first highlighted the use of big data (based partly on us both working at IQVIA in the summer of 2019) to aid rare disease patient recruitment. Later, we highlighted accomplishments of the larger MD PAGS that could be leveraged for advocates/ groups representing nonmuscular dystrophy rare diseases. Two recent articles are: 1. Huml RA, Dawson J, Lipworth K, Rojas L, Warren J, Manaktala C, Huml JR. Use of Big Data to Aid Patient Recruitment for Clinical Trials Involving Biosimilars and Rare Diseases. DIA’s Journal of Therapeutic Innovation & Regulatory Science, 8 pp. Published online December 11, 2019 2. Huml RA, Dawson J, Bailey M, Nakas N, Williams J, Kolochavina M, Huml JR. Accelerating Rare Disease Drug Development: Lessons Learned from Muscular Dystrophy Patient Advocacy Groups. DIA’s Journal for Therapeutic Innovation & Regulatory Science, 8 pp. Published online September 25, 2020 The second article highlighted the coauthorship clan’s experience of working with the MD PAGs and identified a playbook of key strategies that have been successfully employed to advance treatments: –– –– –– ––

Working with other PAGs to better understand and define the patient journey Working with patients to include their voice into all aspects of drug development Creating a national or international registry Better understanding the barriers to identifying patients with certain subtypes of muscular dystrophy to participate in clinical trials –– Partnering with the biopharmaceutical industry –– Collaborating with the regulators –– Incorporating market access and use insights early in clinical development. While clearly helpful within the MD community, these tactics could also be employed by PAGs representing other types of rare diseases. The MD PAGs, in fact, have highlighted a path that others with rare diseases can use to get the word out

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about their particular disease and, most importantly, a path that will allow scientists and drug developers to identify products to treat, and hopefully cure, these diseases. A new muscular dystrophy-oriented book also worth mentioning was written by a healthcare provider of my son, Jon, and a staunch advocate of all families dealing with the muscular dystrophies. Another important note is that she helped write the MD book that I published via Springer in 2015. See Dr. Kathryn R. Wagner’s book entitled: • 100 Questions and Answers About Muscular Dystrophy  – a joint project supported by the MDA, Parent Project Muscular Dystrophy (PPMD), and the FSHD Society, Jones & Bartlett Learning, ISBN: 978-1-284-20,166-6, Copyright 2021 Further elaboration of registries and natural history studies is provided in Chap. 8, a snapshot of the evolving regulatory space is captured in Chap. 18, integrated life cycle management is described in Chap. 23, and a summary of how real-world evidence is utilized in the rare disease space is provided in Chap. 24. Jill Dawson, PhD

I meet Jill almost 10 years ago. Officially, we have coauthored over a half a dozen peer-reviewed articles together. We also worked together on multiple writing projects – some in a consultancy manner and many pro bono – including my books, many white papers, and several chapters in addition to countless emails, biosketches, slide presentations, and phone calls, helping me tackle the rare diseases. She was instrumental in helping Meredith, my daughter, and Jon, my son, get involved in writing. Unofficially, she serves as both my mentor and friend – always willing to help me “get the word” out about rare diseases. A professional medical writing consultant, her calm demeanor and friendly and optimistic attitude have been a great help to me as I continue to try to identify and overcome the innumerable challenges associated with rare disease drug development.

Conclusion Although the number of individuals suffering from a single rare disease is small, the total number of individuals that rare diseases impact is significant, at approximately 400 million people worldwide. Up until the passage of the Orphan Drug Act, only a small number of rare diseases had approved therapies. Since then there has been a marked increase in rare disease research funding and development efforts, thanks to regulatory changes, multiple international initiatives, and development incentives. Today, more than 300 rare diseases have approved therapies. Along with regulatory incentives, there have been major advances in science, helping elucidate many more targets for rare disease therapies. Following a long

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unproductive spell, there have been recent successes in the cell and gene therapy space. For the first time in decades, there is potential for a cure for many of the rare diseases that involve a single-gene mutation. Scientific progress, coupled with regulatory incentives and the promise of favorable pricing, has led to burgeoning of rare disease drug development. Rare diseases represent one of the fastest growing areas for biopharmaceutical R&D investment, with worldwide orphan drug sales forecast to grow at a compound annual growth rate of 12.3% from 2019 to 2024, which is approximately double the rate forecast for the non-orphan drug market. By 2024, orphan drug sales are expected to reach $242bn and capture one-fifth of the worldwide prescription sales. Despite the lure of lucrative markets, inherent challenges pose significant risks that sponsors must anticipate and navigate to be successful, including the extreme heterogeneity among rare diseases, the many unknowns about rare disease pathophysiology and natural history, the very long and often frustrating diagnostic journey many patients and their families must travel, complex and changeable global regulatory frameworks, uncertain reimbursement landscapes, and the limited availability of rare disease research expertise and – by definition – rare disease patients to participate in clinical trials. This book benefits from the efforts of many dedicated patients, caregivers, physicians, scientists, healthcare providers, patient advocates, and patient advocacy groups to allow the reader to better understand the nuances of rare disease drug development. This book would not have been possible without these experts graciously being willing to serve as authors, sharing their time and expertise for the benefit of clinicians, medical experts, caregivers, disease drug developers, pharmaceutical executives, third-party capital providers, and, most importantly, the rare disease patients themselves. It will take a village to address the myriad of challenges facing patients with rare diseases. We need to work together to support each other and apply the many lessons learned. My personal journey has entailed, first, facing my children’s FSHD diagnoses and, later, helping others with muscular dystrophies. I’m fortunate that the muscular dystrophy PAGs are among the most active and influential in the rare disease drug development space. Notable achievements in the last decade include promulgating the first US clinical research guidance, setting up registries and natural history studies, and investing in companies – some of which have brought potentially disease-­ modifying products to the market. While some MD PAGs, such as the MDA, receive >$100m in funding on a yearly basis, enabling them to support larger projects such as the national registry, smaller groups such as the FSHD Society, which receives $10m have risen from six deals in 2009 to 30 in 2018, growing from 4% to 19% of the out-licensing market [6]. The total upfront payments for these deals similarly rose from $300m to $2B, growing from 6% to 22% of the market (Fig. 7.2).

100

J. E. Tunnicliffe and D. T. Rosenthal

100% 90%

39% rare CAGR

28% rare vs 6% nonrare per-deal CAGR

80%

Percent of Total

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59%

60% 50% 40%

34%

30% 20% 10%

13%

6%

0% 2009

2018 Value

2009

2018 Volume

Fig. 7.3  Worldwide emerging company acquisitions. Total potential acquisition amount (left) and number of acquisitions (right) [6]

As with venture funding, growth in the out-licensing market is driven by volume rather than value. The number of rare disease out-licensing deals grew at a 17% CAGR from 2009 to 2018—in sharp contrast to non-rare disease deals over the same period, which held essentially flat. The same trend held true for value growth between the two segments. Out-licensing upfront payment per deal, however, grew at nearly identical rates for the two segments (3% CAGR for rare vs 4% CAGR for non-rare)—thus indicating that deal volume was responsible for the observed differences in total upfront payments.

Acquisitions Unlike venture funding and licensing deals, worldwide acquisitions of non-­oncology rare disease companies have grown primarily as a product of value rather than volume. While rare disease company acquisitions grew approximately twofold from 2009 to 2018 (from 5 to 11), the total potential acquisition amount grew from $1B to $26.7B—a nearly 27-fold increase (Fig. 7.3) [6]! This is particularly impressive when compared to non-rare disease company acquisitions, which grew less than twofold in both number and value during the same period. Even with value clearly leading rare disease acquisition market growth, it bears emphasis that the volume of rare disease M&A deals is also quite strong within the industry, with the total number of rare disease M&A deals from 2015 to 1H 2020 behind only oncology and neurology over the same period [3]. I nitial Public Offerings Even against the backdrop of a healthy overall biopharma IPO market, rare disease IPOs stand out as exceptionally strong performers. From a volume standpoint, rare disease companies consistently rank among the top share of

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biopharma IPOs. In 2019, rare disease companies ranked third among the top indications in terms of number of IPOs, falling behind only oncology and platform companies [2]. Looking more broadly over the last half-decade, rare disease companies have on average constituted ~20% of annual US biopharma IPOs (range 10–32%; [5–9]). Rare disease companies tended to IPO at a later stage than oncology or platform companies with only 38%, being either preclinical or Phase I at IPO versus 52% of oncology and 44% of platform companies in 2019 [3]. As discussed earlier, rare disease products often have a faster path to late-stage development with potentially fewer inflection points along the way, which may account for the bias toward later-­stage rare disease company IPOs (though this connection is admittedly speculative). In line with the observed M&A trends, valuation is where rare disease companies truly differentiate within the IPO market. Rare disease companies had the highest median step-up in value from mezzanine rounds to pre-money IPO in 2019 (1.6X increase in value vs 1.0–1.3X for other top indications) [2]. Intriguingly, this valuation increase is part of a broader trend surrounding IPOs for rare disease companies. In the lead-up to IPO, rare disease companies also lead in valuation increase among their peers as assessed by value change from venture to mezzanine rounds (median 2.6X increase in value vs 1.8–2.0X for other top indications). Post-IPO performance was even stronger, with rare disease companies posting a median 60% year-end increase in 2019 versus 6%–16% for other top indications [2]. Notably, and in contrast to other top indications, rare disease company post-IPO performance was comparable on both median and mean bases (60% median and 48% mean), implying that post-IPO performance was driven by the strength of the class and not exceptional outlier performance. Further to the rare disease public market valuation point, a recent study found that even the simple act of announcing an ODD increases a company’s stock price by 3.36% on average [12].

Opportunity and Considerations for Venture Capital Rare disease in many ways takes the best of what VCs look for in a company and combines that with a range of uniquely favorable exit opportunities. The aforementioned streamlined development pathways, market-signaling regulatory designations, and lower R&D costs give the average early-stage rare disease investment a 2.6X+ higher probability of technical and regulatory success (PTRS) from Phase I (25.3%) than for overall diseases (9.6%) [13]. Importantly, this lower risk does not come with a corresponding decrease in returns; rather, rare disease investments come with the rare combination of decreased risk and increased return potential (as discussed above). Furthermore, given the lower costs for rare disease programs, VC investment into rare disease companies can be further derisked by investing in companies with a portfolio of assets—a strategy employed at large scale by companies like Roivant, BridgeBio, and others but one that is also accessible to, and increasingly utilized by, smaller-scale rare disease companies too. Favorable exit values have already been discussed in detail and apply to VC rare disease opportunities, but an additional element that bears emphasis is the

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availability of non-dilutive funding to enhance and/or derisk VC funding. Grant funding by patient advocacy groups and disease-specific nonprofits can help advance early product development to a point that subsequent VC investment carries less preclinical risk. These funding sources may also supplement existing VC funding and thus extend the runway for an early VC round through to additional value inflection points with no/minimal dilution. Another example is licensing deals, which as described above have significantly increased in number in recent years. With these deals comes a combination of non-dilutive partnership funding as well as added development support and expertise from an established organization—factors that further derisk a VC’s investment. One key consideration for VCs is the recently increased Series A round size for rare disease companies. In looking at the number of rare disease Series A investments and the total they raised, the average rare disease Series A was $46m in 2019—significantly more than the average $14–25m round sizes for the other top 4 indications (oncology, platform technology, neurology, and anti-infectives [2]). Although the overall cost of development and commercialization may be lower for rare disease companies, the larger early financing expectation necessitates either a larger check size from a firm (and consequently more concentrated risk exposure) or the added complication of syndication.

 pportunity and Considerations for Debt O and Alternative Financing The opportunities in rare disease investing for debt and alternative financing fall along similar lines to those for VCs. Just as rare disease companies’ lower R&D costs and enhanced liquidity options improve the return potential for VCs, so too do they improve these companies’ credit risk profile for debt providers and other structured financing One notable difference, however, is that the trend toward higher PTRS for rare diseases is dampened at later stages of development (i.e., stages where debt and alternative financing more frequently occur). The difference between rare disease and overall disease PTRS falls from a 2.6X higher rare disease PTRS at Phase I to only 1.3X at Phase III and a negligible difference at the NDA/BLA stage [13]; thus, when entering at later stages of development, these investors receive less benefit from rare disease status in this regard. The rare disease market is particularly attractive for NovaQuest’s product financing model. NovaQuest provides non-dilutive, product-specific, at-risk structured financing—differentiated from debt in that the financing is truly at risk (i.e., not fully collateralized) and differentiated from equity in that returns are non-dilutive and centered on a specific product. Although NovaQuest traditionally invests in Phase III products, the aforementioned rare disease development derisking combined with the diverse return potential (e.g., sales royalties, licensing revenues, PRV proceeds, etc.) allows for our firm to approach rare disease products using our standard product evaluation criteria but at an earlier stage of development. The commercial opportunity for rare disease products is significant as described earlier—particularly in comparison to the R&D spend required to reach

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commercialization—and thus the potential for servicing debt is enhanced. The caveat to this opportunity, though, is that rare disease product sales are largely driven by pricing, not volume. This fact combined with rising public and political sentiment against current drug pricing practices (discussed in more detail below) increases the risk in forecasting rare disease product sales. PRV program renewal helps mitigate some of this commercial risk either directly (if PRV proceeds are shared as part of the funding agreement) or indirectly by improving company liquidity, but this only applies to companies with RPD programs.

Rare Disease Investment Challenges and Opportunities Challenge: Pricing The high prices of rare disease products have come under increased scrutiny in recent years, threatening the existing reimbursement paradigm that enables rare disease products to generate attractive returns for investors. The pricing debate centers on three main aspects: overall price, time period for reimbursement, and accountability. Rare disease products have traditionally carried high prices due to a combination of small patient populations and, in many cases, significant health improvements for patients with few—if any—other treatment options. As the prevalence of rare disease products has grown, so too have questions around the long-term viability of this pricing strategy and its implications for the overall healthcare system. Enzyme replacement therapies (ERTs), for example, may cost upward of $300,000 per year—but they also effectively cure patients of otherwise debilitating diseases. The added caveat, though, is that ERTs require chronic administration, and so their costs are borne by the healthcare system in perpetuity. The question of justifiable price therefore becomes complicated. Should it be based on the cost to develop the treatment, on the costs of treating patients in the absence of the new treatment, or on the quality-of-life improvements enabled by the treatment? We would argue that a reasonable answer is some combination of these items, but defining what constitutes a reasonable balance—and a reasonable ceiling—is the challenge. The pricing challenge is perhaps best encapsulated by gene therapies, which combine the overall pricing challenge of ERTs with an added duration of benefit and reimbursement challenge. The challenge of tying one-time payment for pricing to long-term benefit is exemplified by the commercial failure of UniQure’s Glybera, the first EMAapproved gene therapy—and the world’s most expensive treatment when it launched. Glybera was approved in 2012 to treat lipoprotein lipase deficiency, an ultrarare and debilitating metabolic disease, and was priced at ~$1 million due as a single upfront payment [14]. European payers objected to the upfront cost and lack of accountability for long-term efficacy, questions about the product’s efficacy mounted (the trials used for approval were small and open label), and the product languished commercially. With these unabating headwinds, UniQure decided to pull the product from the market in 2017 after having treated just a single patient commercially [15]. Novartis’ gene therapy Zolgensma® for spinal muscular atrophy represents one new direction in long-term pricing. In 2019, Zolgensma® became the world’s most

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expensive therapy with a $2.12m price tag; however, unlike Glybera, Zolgensma® is reimbursed in installments over time ($425,000 installments over 5 years). Taking this paradigm a step further, Bluebird Bio’s Zynteglo®, which was approved by the EMA later in 2019 for transfusion-independent beta-thalassemia, carries a total potential cost of €1.6  m similarly paid over five installments; however, only the initial €315,000 payment is guaranteed, with the following four payments made only if patients do not require transfusions during that time [16]. It’s still too early to gauge the success of these approaches in improving market access and commercialization (for instance, NICE appraisals have not yet been issued for either product), but investors, companies, and patients alike will be watching as these new pricing paradigms are put to the test (see Chap. 22 for additional details).

Challenge: Unmet Funding Need A funding disconnect exists for new rare disease companies focused on repurposing drugs with prior clinical data. In these circumstances, the product may technically be preclinical stage for the indication (e.g., requiring preclinical work bridging a formulation change) but is simultaneously Phase III-ready, given the wealth of clinical data carried from other indications. Rare disease company founders often find that this places the company in the nebulous position of being too advanced for seed-stage investors yet perceived as inappropriately early-stage by VCs. The consequences of these disconnects are that seed investors do not know how to properly assess the product development plan as it falls well beyond their standard stage, and VCs devalue the company and thus demand a disproportionately large share in exchange for their investment.

Opportunity: Risk-Sharing Agreements Risk-sharing agreements (RSAs) like that used for Bluebird’s Zynteglo are a relatively new approach to addressing drug pricing. The goal of RSAs is to equitably share drug pricing risk between companies and payers, with risk here referring to the therapy’s ability to deliver on its stated safety and efficacy claims and thus substantiate its pricing. RSAs can fall into any number of different categories, but the underlying concept is that payers should only pay full value if a therapy performs as advertised, while companies should have some minimum level of expected reimbursement. Frequent mechanisms employed to maintain this balance include clawback provisions for upfront reimbursement if a patients does not achieve a certain level or duration of efficacy, contingent reimbursement that is only paid upon achievement of specific benchmarks, and caps on the total number of reimbursed treatments [17, 18]. Although still more the exception than the norm, RSAs have grown in popularity over the past decade as healthcare systems have become increasingly focused on value-based care. Further fueling the move toward RSAs, and of particular relevance to rare disease products, is the increasing number of products approved through breakthrough, accelerated, or other conditional approval pathways. In these

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circumstances, there is a gap between expected product potential and clinical data at the time of approval. This is particularly applicable to rare disease programs due to the frequently smaller numbers of subjects evaluated in clinical trials, potential long-term efficacy beyond that assessed in clinical trials, and related parameters. Understanding and effectively utilizing RSAs can be a boon for rare disease companies and investors alike. Strategic RSA structuring and implementation has the potential to increase and expedite market access—a benefit both in terms of treating patients and maximizing sales returns. A key limitation of RSAs, though, is that the risk itself must be borne by either the company (by potentially delaying receipt of full payment) or the payer (by potentially paying more upfront than will ultimately be substantiated) during the data generation period.

 pportunity: Platform Approaches to Clinical Trials O and Product Approvals An emerging trend in the rare disease space is the use of “platform trials” as a disease-­centric, streamlined approach to drug development. Platform trials are clinical trials that consist of a single master protocol into which multiple investigational agents can be concurrently added or dropped. Sophisticated platform trials include not only a master protocol but also a committed, coordinated network of clinical sites; prior FDA agreement on endpoints and benchmarks for approvability; and engagement with patient advocacy groups and other patient recruitment resources. The opportunity for investors here is an expedited, cost-effective, and operationally derisked pathway to approval—one that addresses upfront a significant portion of any investor’s standard due diligence. Examples of such platform trials include the HEALEY and MAGNET ALS trials [19, 20], and the FDA is currently developing a Rare Disease Cures Accelerator aimed at establishing common rare disease clinical trial practices and platforms [21, 22]. In a similar vein, recent tissue-agnostic drug approvals and orphan drug designations (e.g., larotrectinib and entrectinib for solid tumors with NTRK-fusion proteins) raise potential for both significant opportunities and considerations for rare disease products. The potential to expand genetically defined rare disease findings to a broader set of indications has obvious positive implications for investors and companies, but the regulatory and reimbursement environment is currently very much in development as these first approvals are evaluated and scrutinized [23].

Concluding Remarks Rare diseases carry their own unique set of risks and return opportunities, which with the help of proactive, innovative legislation and regulation have been shaped into an attractive space for drug development—and consequently for investment. The enhanced focus on disease biology connecting to therapeutic mechanism of action, the streamlined pathways to approval, and the pricing and alternative return mechanisms compensating for smaller commercial markets all serve to create investment opportunities wrapped around an area of clear unmet need.

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Importantly, this investment opportunity has been created by virtue of addressing the key principles that guide all investment decisions—hence the attractiveness to investors across the investment spectrum, from early grant providers to public equity markets. Adherence to these key principles, both in the course of advocacy and drug development and in future regulatory and legislative decisions, will help ensure that the investment and advancement seen in the rare disease market over the past three decades continue in perpetuity.

Side Bar 1 Case Study: Mirum Pharmaceuticals

Mirum Pharmaceuticals’ story presents a comprehensive case study of many of the elements discussed here, including development derisking, fundraising, and exit opportunities. Mirum Pharmaceuticals develops products for treating rare, debilitating liver diseases. The company’s lead product is maralixibat, an apical sodium-­ dependent bile acid transporter (ASBT) inhibitor being developed for rare cholestatic liver diseases including pediatric Alagille syndrome (ALGS). Mirum’s journey began within Pfizer, where maralixibat was originally developed as a mass-market cholesterol-lowering drug [24]. Although not successful in this indication, a team recognized the product’s potential for addressing elevated liver bile acid levels typified by ALGS and in-licensed the product as the basis for a new pharma company, Lumena Pharmaceuticals, in 2011. With this newly targeted development pathway and a >1400 subject safety database from Pfizer’s prior development work, Lumena raised a $23 m Series A in May 2013 quickly followed by a $45 m Series B in April 2014, with the intention of going public that summer [24, 25]. This plan quickly changed, however, when Shire acquired the company for $260 m upfront plus clinical development milestones—all based on Lumena’s promising rare disease pipeline. After enduring several mid-stage trial failures, modifications to the trial design and dosage ultimately led to a successful Phase IIb ALGS study, at which point maralixibat was once again out-licensed—to the same team and investor syndicate that originally founded Lumena—in November 2018 following Shire’s acquisition by Takeda [26]. The new company, Mirum Pharmaceuticals, concomitantly raised a massive $120 m Series A and in July 2019 went public at $15 per share, netting a $75 m IPO raise at a $345 m market cap [27, 28]. Mirum reached an agreement with the FDA in December 2019 to use the 31-subject Phase IIb ALGS trial as maralixibat’s single registrational trial and picked up breakthrough therapy and RPD designations (and thus qualified for a PRV) for the product along the way [29]. The company’s stock price soared, enabling a nearly $125 m raise in subsequent public offerings, and it has consistently remained ~30% above its IPO price as of January 2021. Maralixibat’s rolling NDA was initiated in September 2020, with a full submission—and decision—expected in 2021.

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References 1. Office USGA. Drug development: FDA’s priority review voucher programs. 2020 Jan 31 [cited 2021 Jan 28]; (GAO-20-251). Available from: https://www.gao.gov/products/GAO-­20-­251. 2. 2020 Healthcare investments & exits report [Internet]. [cited 2021 Jan 28]. Available from: https://www.svb.com/trends-­insights/reports/healthcare-­investments-­and-­exits/2020-­annual 3. Trends in healthcare investments and exits, Mid Year 2020 | Silicon Valley Bank [Internet]. [cited 2021 Jan 28]. Available from: https://www.svb.com/trends-­insights/reports/ healthcare-­investments-­and-­exits/2020-­mid-­year. 4. How orphan drugs became a highly profitable industry [Internet]. The Scientist Magazine®. [cited 2021 Jan 28]. Available from: https://www.the-­scientist.com/features/ how-­orphan-­drugs-­became-­a-­highly-­profitable-­industry-­64278. 5. Thomas D, Wessel C.  BIO emerging therapeutics company investment and deal trends report 2008–2017.pdf [Internet]. Biotechnology Industry Organization; 2018 [cited 2020 Jan 28]. Available from: http://go.bio.org/rs/490-­EHZ-­999/images/BIO%20Emerging%20 Therapeutics%20Company%20Investment%20and%20Deal%20Trends%20Report%20 2008-­2017.pdf?_ga=2.198662575.1692623197.1611889295-­1576093676.1611889295. 6. Thomas D, Wessel C. 2019 Emerging therapeutic company trend report [Internet]. Biotechnology Industry Organization; 2019 [cited 2021 Jan 28] p. 50. Available from: http:// go.bio.org/rs/490-­EHZ-­999/images/BIO%202019%20Emerging%20Company%20Trend%20 Report.pdf?_ga=2.105771395.1692623197.1611889295-­1576093676.1611889295. 7. Thomas D, Wessel C.  BIO emerging therapeutic company report 2007–2016.pdf [Internet]. Biotechnology Industry Organization; 2017 [cited 2021 Jan 28]. Available from: http://go.bio.org/rs/490-­E HZ-­9 99/images/BIO%20Emerging%20Therapeutic%20 Company%20Report%202007-­2 016.pdf?_ga=2.163909951.1692623197.1611889295­1576093676.1611889295. 8. Thomas D, Wessel C. Emerging therapeutic company investment and deal trends [Internet]. Biotechnology Industry Organization; 2016 [cited 2021 Jan 29] p. 40.Available from: http://go.bio. org/rs/490-­EHZ-­999/images/BIO_Emerging_Therapeutic_Company_Report_2006_2015_ Final.pdf?_ga=2.198047407.1692623197.1611889295-­1576093676.1611889295. 9. Thomas D, Wessel C.  Emerging therapeutic company investment and deal trends [Internet]. Biotechnology Industry Organization; 2015 [cited 2021 Jan 29] p. 54. Available from: http://go.bio.org/rs/490-­E HZ-­9 99/images/BIO%20Emerging%20Therapeutic%20Company%20Report%20June%2011%202015.pdf?_ga=2.165010111.1692623197.1611889295­1576093676.1611889295. 10. Rare disease secondary and debt offerings rise in 2020 [Internet]. Global Genes. 2021 [cited 2021 Jan 29]. Available from: https://globalgenes.org/2021/01/05/ rare-­disease-­secondary-­and-­debt-­offerings-­rise-­in-­2020/. 11. Hughes DA, Poletti-Hughes J.  Profitability and market value of orphan drug companies: a retrospective, propensity-matched case-control study. PLoS ONE [Internet]. 2016 Oct 21 [cited 2021 Jan 29];11(10). Available from: https://www.ncbi.nlm.nih.gov/pmc/articles/ PMC5074462/. 12. Miller KL.  Do investors value the FDA orphan drug designation? Orphanet J Rare Dis. 2017;12(1):114. 13. Thomas D, Burns J, Audette J, Carroll A, Dow-Hygelund C, Hay M.  Clinical development success rates 2006–2015 [Internet]. Biotechnology Industry Organization; 2016 Jun [cited 2021 Jan 29]. Available from: https://www.bio.org/sites/default/files/legacy/bioorg/ docs/Clinical%20Development%20Success%20Rates%202006-­2015%20-­%20BIO,%20 Biomedtracker,%20Amplion%202016.pdf. 14. The world’s most expensive medicine is a bust [Internet]. MIT Technology Review. [cited 2021 Jan 29]. Available from: https://www.technologyreview.com/2016/05/04/245988/ the-­worlds-­most-­expensive-­medicine-­is-­a-­bust/.

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15. The World’s most expensive medicine is being pulled from the market [Internet]. MIT Technology Review. [cited 2021 Jan 29]. Available from: https://www.technologyreview. com/2017/04/21/152385/the-­w orlds-­m ost-­e xpensive-­m edicine-­i s-­b eing-­p ulled-­f rom-­ the-­market/. 16. Bluebird bio launches beta thalassaemia gene therapy Zynteglo in Germany [Internet]. PMLive. PMGroup Worldwide Limited; 2020 [cited 2021 Jan 29]. Available from: http:// www.pmlive.com/pharma_news/bluebird_bio_launches_beta_thalassaemia_gene_therapy_ zynteglo_in_germany_1322226. 17. Bastian A, Dua D, Mirzahossein S. The use of risk-sharing agreements to manage costs, mitigate risk, and improve value for pharmaceutical products. J Clin Pathw. 2016;2(1):43–51. 18. Hampson E, Taylor K, Sanghera A.  Patient access to innovative medicines in Europe | A collaborative and value based approach [Internet]. Deloitte Centre for Health Solutions; 2019. Available from: https://www2.deloitte.com/content/dam/Deloitte/uk/Documents/life-­ sciences-­health-­care/deloitte-­uk-­patient-­access-­to-­innovative-­medicine-­in-­europe.pdf. 19. HEALEY ALS platform trial [Internet]. Massachusetts General Hospital. [cited 2021 Jan 29]. Available from: https://www.massgeneral.org/neurology/als/research/platform-­trial. 20. MAGNET - TRICALS [Internet]. [cited 2021 Jan 29]. Available from: https://www.tricals.org/ trials/magnet/. 21. Center for drug evaluation and research. Rare disease cures accelerator. FDA [Internet]. 2020 Nov 13 [cited 2021 Jan 29]; Available from: https://www.fda.gov/drugs/ regulatory-­science-­research-­and-­education/rare-­disease-­cures-­accelerator. 22. FDA seeks input on rare disease clinical trials network [Internet]. [cited 2021 Jan 29]. Available from: https://www.raps.org/news-­and-­articles/news-­articles/2020/5/fda-­seeks-­input-­on-­ rare-­disease-­clinical-­trials-­ne. 23. Tumor-agnostic therapies: the complex path to commercial viability [Internet]. CBPartners. 2020 [cited 2021 Jan 29]. Available from: https://www.cbpartners.com/ tumor-­agnostic-­therapies-­the-­complex-­path-­to-­commercial-­viability/. 24. Lumena pharmaceuticals. Lumena pharmaceuticals announces $23 million series A financing [Internet]. [cited 2021 Jan 31]. Available from: https://www.prnewswire.com/news-­releases/ lumena-­pharmaceuticals-­announces-­23-­million-­series-­a-­financing-­206565821.html. 25. Shire acquires lumena for $260M+ [Internet]. GEN - Genetic Engineering and Biotechnology News. 2014 [cited 2021 Jan 31]. Available from: https://www.genengnews.com/news/ shire-­acquires-­lumena-­for-­260m/. 26. Shire licenses rare liver disease candidates to $120M startup mirum [Internet]. GEN - Genetic Engineering and Biotechnology News. 2018 [cited 2021 Jan 29]. Available from: https://www. genengnews.com/news/shire-­licenses-­rare-­liver-­disease-­candidates-­to-­120m-­startup-­mirum/. 27. AssetMark financial, mirum pharmaceutical raise $350 million in IPOs [Internet]. Silicon Valley Business Journal. [cited 2021 Jan 29]. Available from: https://www.bizjournals.com/ sanjose/news/2019/07/18/2019-­ipos-­bay-­area-­assetmark-­mirum-­pharmaceutical.html. 28. In $86M IPO pitch, Mirum spells out plans to turn Shire discards into orphan liver drug successes [Internet]. Endpoints News. [cited 2021 Jan 29]. Available from: https://endpts.com/ in-­86m-­ipo-­pitch-­mirum-­spells-­out-­plans-­to-­turn-­shire-­discards-­into-­orphan-­liver-­drug-­ successes/. 29. Mirum pharmaceuticals completes successful pre-NDA meeting with FDA for maralixibat [Internet]. Mirum Pharmaceuticals, Inc. [cited 2021 Jan 29]. Available from: https:// ir.mirumpharma.com/news-­releases/news-­release-­details/mirum-­pharmaceuticals-­completes-­ successful-­pre-­nda-­meeting-­fda.

8

Optimizing Rare Disease Registries and Natural History Studies Sharon Hesterlee

Illustration 8 This illustration is of Erica Hoffmann’s unaffected daughter, Madeleine, fishing with Logan at the lake. According to Erica, this was the first summer with Logan having a diagnosis of idiopathic ketotic hypoglycemia. Logan had the fishing net and asked Madeleine why she did not just let the fish jump in the net. She chuckled and said, “It’s never going to be that easy, buddy.” As we continue on this journey, we have found that Madeleine was right in so many ways. Photo for the illustration courtesy of Logan’s mother. See more about Logan in Chap. 3, Select Patient Narratives. Artwork courtesy of the artist.

S. Hesterlee (*) Muscular Dystrophy Association, New York, NY, USA e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Switzerland AG 2021 R. A. Huml (ed.), Rare Disease Drug Development, https://doi.org/10.1007/978-3-030-78605-2_8

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 are Disease Registries and Natural History: What They Are R and Why They Matter Although the challenges to managing and treating rare disease are many, there is a general agreement that finding the patients in the first place and documenting and understanding normal progression of disease have been key barriers to progress. Fortunately, in today’s interconnected world, patients with rare diseases are able as never before to find one another and nucleate around shared objectives. And in the same way that groups of patients can come together, their demographic and medical data can also be organized from disparate sources to create global repositories in ways that were not possible just 10 years ago. Increasingly, rare disease registries are being used to identify participants and organize the spaces around them, documenting the natural history of the disease, evaluating the efficacy and safety of interventions, improving the quality of care, informing clinical trial design, recruiting for clinical studies, confirming clinical efficacy post-approval, and assessing disparities in care [1–3]. For example, in response to the potential of an interventional trial for Canavan disease, in which the author was involved in 2017, the Canavan Disease Illinois Foundation was able to launch a registry and enroll over 100 participants within 6 weeks of its launch—a feat that would have been inconceivable before the connectedness of the Internet. The platforms and options available to academic, industry, and patient groups to host registries have also increased dramatically in the last 10 years [4]. Similarly, the natural history of rare disease—the progression of the disease in the absence of interventions—has received increased focus as evidenced by the recent release of the FDA Draft Guidance on Natural History for Rare Disease [5]. The concepts of a registry and natural history data are interrelated but not the same as registries may be used, in some cases, to document the natural history of a disease, but they have many other purposes as well, such as determining the feasibility of a trial protocol or documenting the outcomes of interventions. A popular definition for a patient registry is “…an organized system that uses observational study methods to collect uniform data (clinical and other) to evaluate specified outcomes for a population defined by a particular disease, condition, or exposure and that serves one or more predetermined scientific, clinical, or policy purposes” [3]. A registry can also refer to both patient records and the software used to collect and store those records [6]. In short, a patient registry is one way to collect natural history data, but a patient registry can be used for other objectives, while natural history is focused specifically on the progression of the disease process in an individual over time in the absence of treatment or in the presence of standard of care [5, 7]. Understanding the natural history of a disease is important for drug development in any indication for evaluating outcome assessments, identifying biomarkers, and designing externally controlled studies and for a host of fringe benefits, such as identifying disease-specific centers of excellence [7].

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Patient data in the form of registries and natural history studies plays many essential roles in drug development for rare disease—in many cases weightier roles than they play in common disease. For example, rare disease patient data may be used for clinical trial planning and feasibility, for natural history comparators, required post-approval studies, improving clinical care, and collaborating with the rare disease community [8]. Clinical Trial Planning and Feasibility  Patient registries are often used in identifying or selecting clinical trial sites and in feasibility and planning of clinical trials, which can be complicated when patients are few and far between. Registries can be used to estimate the number of patients who meet eligibility criteria for a trial, to help select sites that have the most patients meeting those eligibility criteria, and to adjust criteria in advance of launching the protocol if feasibility studies show that the criteria are too narrow, thus avoiding the dreaded protocol amendment [1, 8]. Registries and natural history data can also be used to select subpopulations of patients who are most likely to benefit from an intervention—for example, the Collaborative Trajectory Analysis Project (cTAP) for Duchenne muscular dystrophy is a collection of aggregate natural history data from multiple sources that has been able to define four distinct disease trajectories for ambulation [9]. Natural History Comparators  In rare disease, there may also be a heavier reliance on natural history comparators due to lack of patients or severity of disease [8, 10]. For example, the orphan drug “Zolgensma®” (onasemnogene abeparvovec) was approved in May of 2019 for the treatment of pediatric patients up to the age of 2 with spinal muscular atrophy (SMA), a rare disease that is the leading genetic cause of death in infants. Efficacy was evaluated in an open-label, singlearm clinical trial with comparisons made to a natural history of disease in a cohort of 34 SMA patients [11]. In a separate comparison to untreated SMA type I patients, 100% of the treated patients survived to 24 months, while only 38% of the untreated participants were surviving at that time [12]. On the strength of this dramatic difference compared to natural history, the drug was approved by US and Japanese regulators and has been granted conditional approval by the European Commission [13–15]. Required Post-Approval Studies  A requirement for post-approval confirmation of efficacy is also more common in orphan disease, likely due to rare disease trials tending to be shorter and underpowered, necessitating the need for follow-up data [16]. Between 2007and 2015, of 17 therapies in Europe and 25 in the USA to receive a conditional approval, the majority were orphan drugs [16]. This need for post-­ approval confirmatory databases typically results in drug-specific registries that

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may be developed with less external input from patient representatives and healthcare professionals and may suffer from limited access and issues with conflicts of interest [16]. In particular, rare disease patient registries may play an important role in gathering post-approval real-world evidence (RWE) for payers. Although a recent survey by Syneos Health showed that payers seem reluctant to embrace the use of RWE in rare disease, this is likely to change with time as comfort with this class of evidence increases due to the 21st Centure Cures Act, which requires that the FDA develop a framework to evaluate RWE [17, 18]. Already the FDA has started to embrace its use as in the recent creation of the Regenerative Medicine Advanced Therapy or “RMAT” designation, which allows that confirmatory studies may rely upon RWE [19]. The potential for value-based contracts for big ticket therapies, like gene therapy, that rely upon following patients for many years posttreatment may also bolster the use of RWE from patient registries [20] (see Chap. 24 for additional details). Improving Clinical Care  Patient registries and natural history data are also used to establish standards of care via clinical practice guidelines (CPGs) in rare disease, which can have a significant impact, since many clinicians who may interact with these patients may have never seen a person with this diagnosis before. CPGs are generally accepted to have the ability to shorten diagnosis and improve quality of care [21]. The Cystic Fibrosis Foundation Registry data is actively used by the Foundation to evaluate outcomes and to determine if registry participants are being managed in ways consistent with existing standards of care and the data have been used to extend and modify those standards based on outcomes [22–24]. In another example, both the Parent Project Muscular Dystrophy Duchenne Connect Registry (now called the Duchenne Registry) and the TREAT-NMD global registry for Duchenne have confirmed the benefits of corticoid steroids in maintaining ambulation in this disease [25, 26]. Registry data can also drive hypotheses for future study—the PPMD Duchenne registry showed hints that commonly prescribed cardiac medications may extend the time of ambulation beyond the benefits of steroids alone [26]. Collaborating with Participants  Finally, patient registries serve the purpose of communicating to participants and allowing participants to be part of research without being in a clinical trial [2]. For instance, in a Global Genes white paper, Megan O’Boyle of the Phelan-McDermid Syndrome Data Network notes that registry findings can help bring families closer together as they compare symptoms and outcomes [27]. Many patient registries, such as the Cystic Fibrosis Foundation Patient Registry, publish an easy-to-understand annual report that describes registry findings in lay terms.

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Types and Sources of Patient Data Both registries and natural history data are typically “fit for purpose,” and just as there are many different uses for these data, they come in many flavors: prospective or retrospective, population-based or selected, product-focused or indication-­ focused, patient-entered or clinic-entered, and protocol-based or real world-based. The lack of availability of patient data for most rare diseases has been a particular limitation in therapy development and disease management, so every piece of data, no matter its origin, is precious in the rare disease space. For this reason, stakeholders in the space are increasingly looking to glean patient data from a variety of sources, including registries, electronic health records, academic silos, placebo arms of interventional trials, smart watches, claims data, patient surveys, and roundtables. Because of the disparate nature of the sources of data, it comes in many different architectures and formatting, from imaging results to genetic data to unstructured text in EHRs [28]. Natural History Studies  Natural history studies are a valuable source of patient information in rare disease as the small numbers of patients makes documenting progression and appreciating variability difficult. A natural history study that is governed by a specific protocol and for which participants are recruited and then followed from that time on is what we think of as a typical prospective study. The advantage is that you can predetermine what data should be collected for a specific purpose and how it should be collected. An example of a prospective natural history study can be seen in the rolling study maintained by the Friedreich’s Ataxia Research Alliance called “Clinical Outcome Measures in Friedreich’s Ataxia: a natural history study,” which has been ongoing since 2003 [29]. Data from this study was recently used to identify predictors of loss of ambulation in Friedreich’s ataxia [30]. Retrospective Vs Prospective Studies  Retrospective data collection, on the other hand, takes many forms and is particularly valuable in the case of rare disease, where every bit of data is precious. With retrospective data, you are filling in information that happened in the past, and you get what is available and cannot dictate how or what is collected. For example, electronic medical records (EMRs) can be helpful in rare disease when formal natural history is lacking and have been used successfully in patient registries, but these data are frequently unstructured, and much of the interesting information may not be collected routinely or may exist as unstructured text [3]. Some companies are focusing on extracting useful information from EMRs, for example, PicnicHealth, which aggregates medical records extracts data for individuals and companies like Roche as a fee-for-service and new start-up AllStripes (formerly RDMD), which is focused on extracting and providing structure for EMR data for rare disease, in particular, and does not charge a fee to patients/participants for its services. Population-Based Vs Selected  Patient data can also be population-based or selected. In the former, an entity like the Centers for Disease Control may conduct

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a surveillance study, often in defined geographic areas, by reviewing data from multiple sources to find cases, and then report out on the characteristics of the data with the goal of identifying all cases in those geographic regions. The advantage to this method is that the dataset is less likely to suffer from ascertainment bias due to the broad nature of the sources. In the latter, registrants are selected because of certain criteria like being in a particular health system, or they hear about the registry and volunteer to participate. Examples of all three types of data exist in the Duchenne muscular dystrophy (DMD) world, as evidenced by the CDC’s MD STARnet project (Muscular Dystrophy Surveillance, Tracking, and Research Network), which identifies as many cases as possible in the states participating in the program, and two DMD registries maintained by the Muscular Dystrophy Association (MDA) and Parent Project Muscular Dystrophy (PPMD), respectively, that each collect data prospectively from consented individuals [31–33]. In the case of the MDA MOVR registry, participants are identified and consented at participating sites, while in the case of the PPMD Duchenne Registry, participants are recruited directly through a variety of mechanisms. Disease-Focused Vs Product-Focused  Another clear distinction is disease-focused vs product-focused. One of the better-known indication-focused rare disease registries is that developed by the Cystic Fibrosis Foundation. Begun in the 1960s as a paper-based system, it migrated to an electronic system in 1994 and to a Web-based system in 2003 with periodic improvements since [34]. The registry currently contains records of about 50,000 individuals with cystic fibrosis, many going back 20 years, and has approved over 100 data requests by a variety of requestors [35]. Although the registry has been used in two regulatory filings for new drugs, it is not an inherently product-focused registry [16]. In many cases, however, companies may set up registries for required post-marketing surveillance that are product-­ based, in whole or in part. For example, in the lysosomal storage diseases, for which there are nine approved drugs, there are 15 existing registries sponsored by the pharmaceutical industry. In addition, of the 88 non-cancer orphan drugs approved between 2000 to 2019, 58 were required to have a follow-on registry and of those 41 were sponsored by industry [16]. Concerns about accessibility of data in these registries and potential conflicts of interest in reporting have led to calls for independent post-approval rare disease registries [16]. Protocol-Based Vs Passive Data Collection  Finally, data collection may be protocol-­based, in which the data to be collected and the schedule on which it is collected are defined, or it may be based on passively collected observational, noninterventional data. For example, a prospective natural history study may require that participates are seen at required intervals and that they complete a predefined list of assessments, such as the ongoing Clinical Outcome Measures in Friedreich’s ataxia, described previously, while other data sources, like many registries, may encourage the standard or minimum data elements but do not require changes to a patient’s routine care. The MDA MOVR database, in which the author is involved, is one such registry that collects data at point of care and has, as one of its goals, the ability to benchmark care delivery.

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Challenges and Solutions Although the need for rare disease patient data has never been greater, there are many challenges to collecting this data, including lack of existence of data, fragmentation of available data, lack of independence for product-based registries and lack of resources to sustain independent registries, low-quality patient data, selection bias in datasets, and the potential for available data to not be fit-for-purpose [2, 6, 16, 28, 36–38]. Lack of Existence of Data  Unlike more common diseases where an interest in medical management and therapy development have driven data collection efforts many years, there is still a paucity of data in many rare disease areas. Standing up a registry like that of the CF Foundation is a major undertaking and can be very expensive if custom-built solutions are used and the data are collected and curated by clinicians and allied health professionals. For smaller rare disease groups, this type of registry can be out of reach but there are other solutions. Early in the rise of patient registries, a company called Research Crossroads began offering an “off-­ the-­shelf” registry solution to patient organizations that was relatively inexpensive but could be customized to include additional bells and whistles. Eventually the platform was sold to diagnostic testing company Invitae and became their free “PIN” or “Patient Insights Network” registries. The company provides the very basic platform for free and makes the data available to industry partners for a fee. Invitae states that PINs for 400 medical conditions have been established [39]. Another option for creating an inexpensive registry platform is the Rare Disease Registry Framework, or RDRF, which is “an open-source tool for the creation of web-based registries.” This platform was used by the Global Angelman Registry [40]. Data Fragmentation  Data fragmentation serves to complicate the issue of scarce resources and is a frustratingly persistent problem in rare disease so will be considered in some detail here. Redundant initiatives or nationalistic approaches, disincentives to share, lack of data element consistency and data system interoperability, and the need to balance the privacy and rights of participants with data sharing all contribute to the issue [2, 6, 28]. Bellgard and McGree point out that data silos can be useful in their role of constantly adding to and assimilating data but that this must be weighed against the potential inaccessibility of that data and that the trick is to leverage the useful aspects of these repositories [6]. As resources and tools for building rare disease registries are made available, the number and type of rare disease registries have proliferated rapidly—leading to many redundant indication-specific registries. In Europe, there are an estimated 793 rare disease registries [4]. Finding the registries has become increasingly difficult, and several efforts have been undertaken to develop “registries of registries.” RD Connect, which is an EU-sponsored project to improve the global infrastructure for rare disease, maintains a “registry and biobank” finder, and in 2012, the USA Agency for Healthcare Research and Quality established the “Registry of Patient

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Registries” (RoRP) to provide a resource for registries to mirror what ClinicalTrials. gov does for trials. Unfortunately, funding for RoPR ended in 2019 [41]. Data silos, due to disincentives to share, also play a significant role in fragmentation of rare disease patient data. These disincentives include the desire by academic investigators to be able to research and publish on what is often years of painstakingly accumulated data and to receive credit for these efforts. The same is true for patient advocacy organizations, who also invest significant time and resources into the establishment of registries and natural history studies and who frequently compete with one another for funding. For advocacy organizations, there is sometimes the added need to receive a return on investment to help support the registry, and this frequently means supplying the data to industry for a fee—sharing data too freely would undermine this valuable source of support. Finally, there is a clear disincentive for for-profit companies to share rare disease data with one another as they are often in stiff competition for very small markets of patients. It is possible that even the Orphan Drug Act, which rewards “first to market” in rare disease, may serve to increase the sense of competition in industry despite the significant benefits the Act has had on stimulating interest in drug development for rare disease. Efforts to reward more clearly collaborative research through publications and tenure considerations may help to alleviate some of the concerns around academic data sharing, such as provision of “badges” by journals to promote data sharing, or data sharing agreements that clearly address fair acknowledgment for contributors in publications [42]. Another clear incentive to share data would be to allow only those who share data to have access to a larger pool of aggregate data, and some data sharing consortia are organized in this “pay to play” fashion. Pressure from donors and patient communities can also encourage sharing of data among patient advocacy organizations and industry. Despite a commitment by industry group PhRMA and a requirement by the EMA that clinical data be made more widely available, according to a 2018 study, only 15% of a sampled group of clinical trials from Clinicaltrials.gov were available for data sharing 2 years after the final study report [43, 44]. On the positive side, the YODA project, Vivli project, and Project Data Sphere are all clinical data sharing initiatives that do include data from pharmaceutical companies [45–47]. Another major cause of fragmentation is incompatibility of datasets due to differing technological platforms, heterogeneity of data types, and/or lack of standards [28]. This challenge is recognized by the 21st Century Cures Act, which mandates several technical facets of interoperability to make the data as useful as possible [48]. Several solutions to this problem have been put forth, and these typically come in two main flavors of data aggregation: a centralized model and a federated model. In the centralized model, registry owners attempt to overcome silos and leverage economies of scale by designing registries from the ground up to house data from multiple indications with shared data architecture and often shared core data elements across indications. One example of the centralized model is the Coordination of Rare Diseases, or “CoRDS” registry, at Sanford Research [49]. It is described as a “centralized international patient registry for all rare diseases.” Because the remit of CoRDS is so broad (including any individual diagnosed with or suspected of

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having a rare disease), it serves more as a clearinghouse to channel individuals into more rigorous studies or registries and does not take the place of these things entirely. The Muscular Dystrophy Association’s (MDA’s) MOVR (neuroMuscular ObserVational Research) registry is an example of a purpose-built, centralized rare disease data base that uses a standard architecture and formatting to house data on Duchenne muscular dystrophy, Becker muscular dystrophy, amyotrophic lateral sclerosis, facioscapulohumeral muscular dystrophy, spinal muscular atrophy, limb-­ girdle muscular dystrophy, and Pompe disease [31]. This registry leverages MDA’s national Care Center Network to collect clinic-entered data at the point of care and contains many standardized data points across all seven indications that can be searched together or separately. Although the economies of scale and control over data content and quality seen in the centralized approach are valuable, challenges to this model include the need to shoehorn many different indications into a single undifferentiated format or limit the total number of different indications included. This model also does not address the redundant registries that may already be in existence and may, in fact, add to this problem. The federated model of registries is exemplified particularly well by the TREAT-­ NMD Global Registries, which rely on the ability to query mandatory core datasets for member registries in four disease areas: Duchenne muscular dystrophy, spinal muscular atrophy, facioscapulohumeral muscular dystrophy, and myotonic dystrophy type 1 [50]. Academic or industry users seeking access to the data are able to query these core elements across all the registries through the global structure. For example, the Duchenne global registry is able to access data from more than 13,500 patients in 31 different countries [51]. Although the data has been used primarily to conduct feasibility studies or to aid in recruitment for clinical trials, a 2017 study that reviewed data from over 5000 participants from 31 countries was able to confirm with high significance that corticoid steroids preserved ambulation for three more years compared to boys who were not treated [25, 52]. One drawback to this approach is that only a relatively small set of core common data elements are queryable across all the registry participants. Another example of a federated model is the Rare Disease Registry and Analytics Platform (RD-RAP) [6]. The authors of this proposed platform recognize that data is often collected in small silos for many valid reasons and suggest that, rather than envisioning a centralized primary repository of data that must meet broad needs, the data from these existing fit-for-purposes databases can be evaluated in situ through a shared analytical platform. Similarly, the idea of “Linked Registries” put forth by Sernadela et al. in 2017 recognizes the need for a distributed method of data sharing [28]. The authors have developed, and propose to use, a “semantic Web layer” that could overlay individual data sources and translate them into existing international medical classifications and phenotype ontologies, allowing them to be queried collectively without requiring either a central repository of data or changes to the participating data sources. Unlike the TREAT-NMD global registries, RD-RAP and the Linked Registry concept do not require any level of a priori data consistency. Although the federated model takes advantage of the capabilities and motivations of

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individual registries to add data and perform upkeep, the quality of the data may be ultimately variable and not under central control. Hybrids between the truly centralized model and the federated model of data sharing also exist. In these examples, data from many different sources are contributed to a centralized data repository, where they are translated into standardized data formats and aggregated in a de-identified manner. The best example of this approach is probably that of the Critical Path Institute (C-Path), a nonprofit, public-­ private partnership with the Food and Drug Administration. C-Path maintains a large number of ongoing programs that aggregate existing data from multiple data sources. In this model, the datasets are typically ingested by C-Path via data sharing agreements and then mapped and standardized into formats defined by the Clinical Data Interchange Standards Consortium (CDISC) to produce a consistent aggregate dataset that can be queried by C-Path and by external users [53]. These aggregate datasets have been used successfully, for example, to develop clinical simulation tools for Alzheimer’s disease endorsed by both the FDA and the EMA (2013) and a prognostic biomarker for polycystic kidney disease qualified by the FDA and EMA in 2015 [54–57]. The C-Path and the FDA are expanding this concept further to develop a planned Rare Disease Cures Accelerator-Data and Analytics Platform (RDCA-DAP), which is an FDA-funded initiative to provide centralized and standardized de-identified patient level data from a variety of sources across all rare diseases [58]. This ambitious project is just beginning to upload its first contributed datasets. These hybrid models maximize the value of existing datasets by aggregating and standardizing them. They also do not run the risk of the data not being maintained by the original source, since they house copies of all the data centrally. On the downside, the hybrid models still run the risk of losing some data information in the process of standardizing it, and the process of mapping and standardizing is a considerable amount of work. This may prove challenging to scale across all 7000 rare diseases as is planned for the RDCA-DAP. Data Quality  In addition to tools to help stand up single indication registries, there are several groups who have developed tools to guide and improve the quality of data in these registries, including the Rare Disease Registry Program (RaDaR), a Web site hosted by the National Institutes of Health, and the Global Genes Registry Toolkit, both of which contain guidelines for best practices and considerations in setting up a registry [27, 59]. In addition, in 2017 the National Center for Rare Diseases in Italy organized a working group of subject matter experts, who ultimately developed a list of 18 recommendations to form the basis of a framework to improve the quality of rare disease registries. Recommendations from this group centered on governance, sources of data, data collection mechanisms, IT infrastructure, data quality, and dissemination of results [38]. For example, one recommendation was to take into account and budget for items like security audits, quality controls, reimbursement for data collection, and data analysis and to be prepared to collect data for many years to maximize its utility. It is worth noting that, in many

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cases, the term “quality” is applied to patient registries when what is really meant is “standardization.” A contact registry that is used only for contacting participants about research opportunities may be very well fit-for-purpose without a lot of additional bells and whistles. Selection Bias  Bias is a problem in any registry and can be introduced in a number of insidious ways. For example, a hospital or clinic entering data for an outcomes database may only enter the patients with the best prognoses [60]. Rare disease patient registries are even more likely to have an issue with selection bias, because many are recruited through voluntary direct-to-patient efforts, in which patients who are well-connected, well-educated, and of higher socioeconomic status than the prevalent population are more likely to learn of the registry and be motivated to participate. The first step in managing selection bias is to be aware that it is occurring. The Duchenne registries maintained by the CDC, MDA, and PPMD, described earlier, offer a unique chance to compare populations between three groups ascertained in very different ways. One might anticipate that the CDC population-based surveillance dataset would represent the least biased data while that from the clinic-­ recruited MDA database is likely to have some bias and the PPMD dataset might have the greatest degree of bias, given that individuals entirely self-select and may represent less diversity than the general population. The three groups are currently working to answer this question in a collaborative manner by comparing the core characteristics of each dataset. One reported way to manage bias when identifying a matching control group based on data from the International Collaborative Gaucher Group Gaucher Registry is described by Cole et al., who relied upon a predetermined series of attributes to match case and control cohorts and successfully eliminate selection bias [37]. Independence and Sustainability  Maintaining these valuable sources of data over time can also be challenging—registries are not hypothesis-driven research but rather are tools or resources over which hypotheses can be laid. Many rare disease registries are funded by patient organizations who struggle to convince donors that a registry or natural history study, although not “sexy” like a high-profile therapy project, is a significant value-add across multiple therapeutic platforms. When data are extensively QC’d or entered by clinicians or other allied health professionals, the time for data entry must often be compensated, adding significant costs to the resource. There are a few models for capturing a return on the investment of a registry, but these typically also involve a tiered system in which academic investigators are allowed free access to data while industry is charged some amount for access. A survey report from FasterCures recommends “…taking a portfolio approach that blends grants, contracts, user fees, charitable donations and sponsorship to support registry costs…,” pointing out that many registry developers have outsized expectations for the ability of the registry to become self-sustaining through user fees [23]. Another way that registries can be paid for and sustained by entities with deeper pockets is through industry, but this runs the risk of restricted access and bias in reporting [16].

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Privacy and Ethical Considerations  Participants in registries have an expectation of privacy around their individual-level data that is subject to different legal considerations around the world. When registry or natural history data is shared, privacy considerations must be taken into account. In the USA, the Health Insurance Accountability and Portability Act of 1996 (HIPAA) governs the use of personally identifiable health information, and in the EU, the General Data Protection Regulation (GDPR), which went into effect in May of 2018, is the “toughest privacy and security law in the world,” according to its own Web site [61, 62]. Its requirements apply to anyone who uses data from EU citizens and the fines for violating it are high. This means that global registries will need to be at least as stringent in privacy protection as the GDPR, which may be a high hurdle for smaller groups or groups from multiple parts of the world seeking to combine data. Challenges in sharing data due to the need to protect patient privacy are not actually challenges unique to rare disease, but they have an outsized impact on rare disease because the scarcity of data necessitates data sharing. One way to protect data privacy while still maintaining the ability to track specific participants when data is shared or combined is through the use of a “unique identifier” that serves to code the identity of the individual. The term “GUID” or “Global Unique Identifier” refers to such a coding system that can be used and recognized worldwide. The GUID generating tool uses a series of predefined attributes like “birth name,” “gender at birth,” and “age” or “birthdate” to uniquely identify an individual and assign a code to that person. For this system to work as intended, ideally, a single GUID system would be in place, and all patients would be coded by this centralized system. In reality, there are many GUID systems in place now (e.g., NeuroGUID, FITBIR GUID, GRDR GUID, other proprietary identifiers), and each may use a slightly different combination of attributes to assign a code [63–66]. If registries or patient databases use different GUIDS, then one group must submit its registrants to the GUID generation platform of the other to try to determine if there are overlapping subjects. If the right data has not been collected for the second GUID to be assigned, it may not be possible to match participants.

Conclusion The interconnectedness of the world is making it feasible to aggregate and share rare disease data in ways never-before possible, despite the frequently cited roadblocks. There is incredible potential in the use of electric health records to break down silos as innovators continue to chip away at the barriers to using them, and numerous creative solutions for combining datasets from centralized to federated are in development. The need for multi-stakeholder collaborations in the development and analysis of rare disease patient data, independently maintained post-­ approval registries, and the recognition that registry requirements may change over time remain key issues [2, 6, 16].

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17. Alexander M, de Renteria J, Macdonald A, Rosner A, McDevitt M, Thompson D.  Real world value: advancing payer understanding of RWE in rare disease. Syneos Health. https://syneoshealthcommunications.com/perspectives/real-­world-­value-­advancing-­payer-­ understanding-­of-­rwe-­in-­rare-­disease. Accessed 1 Feb 2021. 18. Framework for FDA’s Real-World Evidence Program. US Food and Drug Administration. https://www.fda.gov/media/120060/download. Accessed 6 Feb 2021. 19. Expedited programs for regenerative medicine therapies for serious conditions. US Food and Drug Administration. https://www.fda.gov/media/120267/download. Accessed 7 Feb 2021. 20. Waddill K. 2020. https://healthpayerintelligence.com/news/how-­payer-­value-­based-­contracts-­ seek-­to-­cut-­gene-­therapy-­costs. Accessed 6 Feb 2021. 21. Pavan S, Rommel K, Mateo Marquina ME, Ho S, Lanneau V, Rath A. Clinical practice guidelines for rare diseases: the orphanet database. PLoS One. 2017;12(1):e0170365. https://doi. org/10.1371/journal.pone.0170365. 22. 2019 Patient Registry Annual Data Report. Cystic Fibrosis Foundation. https://www.cff. org/Research/Researcher-­Resources/Patient-­Registry/2019-­Patient-­Registry-­Annual-­Data-­ Report.pdf. Accessed 6 Feb 2021. 23. Expanding the science of patient input: building smarter patient registries. Reports. Milken Institute Faster Cures. 2016. https://www.fastercures.org/assets/Uploads/PDF/Patient-­ Registries.pdf/. Accessed 7 Feb 2021. 24. Fink K, Loeffler DR, Marshall BC, Goss CH, Morgan WJ. Data that empower: the success and promise of CF patient registries. Ped Pulmon. 2017;52:S44–51. https://doi.org/10.1002/ ppul.23790. 25. Koeks Z, Bladen CL, Salgado D, van Zwet E, Pogoryelova O, McMacken G, et al. Clinical outcomes in duchenne muscular dystrophy: a study of 5345 patients from the TREAT-NMD DMD global database. J Neuromuscul Dis. 2017;4(4):293–306. https://doi.org/10.3233/ JND-­170280. 26. Wang RT, Nelson SF.  What can Duchenne connect teach us about treating Duchenne muscular dystrophy? Curr Opin Neurol. 2015;28(5):535–41. https://doi.org/10.1097/ WCO000000000000245. 27. Garcia F. Rare disease registries: advancing disease understanding, treatments and cures. Rare Toolkits. Global Genes. 2020. https://globalgenes.happyfox.com/kb/article/23-­rare-­disease-­ registries-­advancing-­disease-­understanding-­treatments-­and-­cures/. Accessed 1 Feb 2021. 28. Sernadela P, González-Castro L, Carta C, van der Horst E, Lopes P, Kaliyaperumal R, et al. Linked registries: connecting rare diseases patient registries through a semantic web layer. Biomed Res Int. 2017; https://doi.org/10.1155/2017/8327980. 29. Clinical outcome measures in Friedreich’s ataxia: a natural history study. Friedreich’s Ataxia Research Alliance. 2019. https://curefa.org/clinical-­trials/clinical-­trials-­active-­enrolling/ clinical-­outcome-­measures-­in-­friedreich-­s-­ataxia-­a-­natural-­history-­study. Accessed 6 Feb 2021. 30. Rummey C, Farmer JM, Lynch DR. Predictors of loss of ambulation in Friedreich’s ataxia. E Clin Med. 2020;18:100213. https://doi.org/10.1016/j.eclinm.2019.11.006. 31. MOVR Data Hub (neuroMuscular ObserVational Research). Muscular dystrophy association. https://www.mda.org/science/movr-­data-­hub-­neuromuscular-­observational-­research. Accessed 7 Feb 2021. 32. Muscular Dystrophy Research and Tracking  – MDSTARnet. Centers for Disease Control. https://www.cdc.gov/ncbddd/musculardystrophy/research.html. Accessed 6 Feb 2021. 33. The Duchenne Registry. Parent project muscular dystrophy. https://www.duchenneregistry. org/. Accessed 7 Feb 2021. 34. Knapp EA, Fink AK, Goss CH, Sewall A, Ostrenga J, Dowd C, et al. The cystic fibrosis foundation patient registry: design and methods of a national observational disease registry. Ann Am Thorac Soc. 2016;13(7):173–1179. https://doi.org/10.1513/AnnalsATS.201511-­781OC. 35. Patient registry data requests. Cystic Fibrosis Foundation. https://www.cff.org/Research/ Researcher-­Resources/Tools-­and-­Resources/Patient-­Registry-­Data-­Requests/. Accessed 7 Feb 2021.

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36. Aitken M, Kleinrock M. Orphan Drugs in the United States. IQVIA Institute for Human Data Science. 2018. https://www.iqvia.com/-­/media/iqvia/pdfs/institute-­reports/orphan-­drugs-­in-­ the-­united-­states-­growth-­trends-­in-­rare-­disease-­treatments.pdf?_=1612762723809. Accessed 7 Feb 2021. 37. Cole AJ, Taylor JS, Hangartner TN, Weinreb NJ, Mistry PK, Khan A. Reducing selection bias in case-control studies from rare disease registries. Orphanet J of Rare Disease. 2011;6:61. https://doi.org/10.1186/1750-­1172-­6-­61. 38. Kodra Y, Weinbach J, Posada-de-la-Paz M, Coi A, Lemonnier SL, van Enckevort D, et  al. Recommendations for improving the quality of rare disease registries. Int J Environ Res Pub Health. 2018;15:1644. https://doi.org/10.3390/ijerph15081644. 39. PIN Infographic Director. Invitae. https://pindirectory.invitae.com/infographic. Accessed 7 Feb 2021. 40. Bellgard MI, Render L, Radochonski M, Hunter A.  Second generation registry framework. Source Code Biol Med. 2014;9:14. https://doi.org/10.1186/1751-­0473-­9-­14. 41. Registry of Patient Registries (RoPR). Agency for healthcare research and quality. https:// effectivehealthcare.ahrq.gov/products/registry-­of-­patient-­registries/abstract. Accessed 2 Feb 2021. 42. Kidwell MC, Lazarević LB, Baranski E, Hardwick TE, Piechowski S, Falkenberg L-S, et al. Badges to acknowledge open practices: a simple, low-cost effective method for increasing transparency. PLoS Biol. 2016; https://doi.org/10.1371/journal.pbio.1002456. 43. Hopkins A, Rowland A, Sorich M.  Data sharing from pharmaceutical industry sponsored clinical studies: audit of data availability. BMC Med. 2018;16:165. https://doi.org/10.1186/ s12916-­018-­1154-­z. 44. PhRMA Principles for clinical data sharing. PhRMA. 2013. https://www.phrma.org/-­/media/ Project/PhRMA/PhRMA-­Org/PhRMA-­Org/PDF/P-­R/PhRMAPrinciplesForResponsibleClinicalTrialDataSharing.pdf. Accessed 7 Feb 2021. 45. Project Data Sphere. CEO Roundtable on Cancer. https://www.projectdatasphere.org/. Accessed 7 Feb 2021. 46. The YODA Project. Yale University. https://yoda.yale.edu/. Accessed 7 Feb 2021. 47. Vivli. Center for global clinical research data. https://vivli.org/. Accessed 7 Feb 2021. 48. 21st Century Cures Act. Public Law 114–255. 114th Congress. https://www.govinfo.gov/content/pkg/PLAW-­114publ255/pdf/PLAW-­114publ255.pdf. Accessed 7 Feb 2021. 49. Coordination of Rare Diseases at Sanford (CoRDS). Sanford Research. research.sanfordhealth.org/rare-­disease-­registry. Accessed 2 Feb 2021. 50. Bladen CL, Rafferty K, Straub V, Monges S, Moresco A, Dawkins H, et al. The TREAT-NMD Duchenne muscular dystrophy registries: conception, design and utilization by industry and academia. Hum Mut. 2013;34(11):1449–57. https://doi.org/10.1002/humu.22390. 51. Global DMD registry statistics. TREAT-NMD. 2013. https://treat-­nmd.org/patient-­registries/ what-­are-­the-­treat-­nmd-­global-­registries/global-­dmd-­registry-­statistics/. Accessed 7 Feb 2021. 52. Global Registry enquires. TREAT-NMD. https://treat-­nmd.org/patient-­registries/what-­are-­the-­ treat-­nmd-­global-­registries/registry-­enquiries/. Accessed 7 Feb 2021. 53. Core Competencies of the Critical Path Institute. Critical Path Institute. https://c-­path.org/ core-­competencies/. Accessed 7 Feb 2021. 54. Qualification opinion of a novel data driven model of disease progression and trial evaluation in mild and moderate Alzheimer’s disease. European Medicines Agency. 2013. https://www.ema. europa.eu/en/documents/regulatory-­procedural-­guideline/draft-­qualification-­opinion-­novel-­ data-­driven-­model-­disease-­progression-­trial-­evaluation-­mild_en.pdf. Accessed 7 Feb 2021. 55. Qualification opinion: Total kidney volume (TKV) as a prognostic biomarker for use in clinical trials evaluating patients with autosomal dominant polycystic kidney disease (ADPKD). 2015. https://www.ema.europa.eu/en/documents/regulatory-­procedural-­guideline/qualification-­ opinion-­total-­kidney-­volume-­tkv-­prognostic-­biomarker-­use-­clinical-­trials-­evaluating_en.pdf. Accessed 7 Feb 2021. 56. Reviews: Qualification of biomarker: Total kidney volume in studies for treatment of autosomal dominant polycystic kidney disease. US Food and Drug Administration. 2015. https://

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9

Novel Approaches to Clinical Trials in Rare Diseases Rui (Sammi) Tang, Robert A. Beckman, Yi Liu, Heng Xu, Mercedeh Ghadessi, Cong Chen, and Zoran Antonijevic

Illustration 9 Illustration from a photo of Susan Beckman (center); Daniel, her son, is on the left; Laura, her daughter, is on the right; wife of Dr. Robert A. Beckman (who coauthored Chaps. 9 and 10). Susan is a retired medical social worker, and the beloved mother of their two children. Later in life, she acquired idiopathic T4 cell lymphopenia. See more about Susan via Bob’s narrative about her in Chap. 3. Artwork courtesy of the artist. R. Tang (*) Drug Information Association Innovative Design Scientific Working Group, Horsham, PA, USA Servier Pharmaceuticals, Boston, MA, USA e-mail: [email protected] R. A. Beckman Drug Information Association Innovative Design Scientific Working Group, Horsham, PA, USA Lombardi Comprehensive Cancer Center and Innovation Center for Biomedical Informatics, Georgetown University Medical Center, Georgetown University, Washington, DC, USA Departments of Oncology and of Biostatistics, Bioinformatics, & Biomathematics, Georgetown University Medical Center, Washington, DC, USA Y. Liu Drug Information Association Innovative Design Scientific Working Group, Horsham, PA, USA Nektar Therapeutics, San Francisco, CA, USA H. Xu Nektar Therapeutics, San Francisco, CA, USA M. Ghadessi Drug Information Association Innovative Design Scientific Working Group, Horsham, PA, USA Bayer U.S. LLC Pharmaceuticals, Whippany, NJ, USA C. Chen Biostatistics and Research Decision Sciences (BARDS), Merck & Co., Inc., Kenilworth, NJ, USA Z. Antonijevic Abond CRO Inc., Allendale, MI, USA

© The Author(s), under exclusive license to Springer Nature Switzerland AG 2021 R. A. Huml (ed.), Rare Disease Drug Development, https://doi.org/10.1007/978-3-030-78605-2_9

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Introduction The Orphan Drug Act defines rare diseases as disorders affecting fewer than 200,000 individuals in the United States [1]. Collectively, there are more than 8000 recognized rare diseases, which affect almost 30 million individuals and their families in the United States [2]. There are many common challenges with therapeutic development in rare diseases, and it is extremely important to provide alternative, more efficient study designs and analytical methods in drug development. The randomized controlled trial is considered the gold standard for establishing treatment efficacy. It minimizes selection bias and the impact of confounders via randomization and blinding. However, this approach also means large trial size, particularly when the treatment effect size is moderate. Enrolling a large number of patients is costly, time-consuming, and ultimately not feasible in rare diseases. Rare diseases are at a dual disadvantage. By necessity, clinical trials in rare disorders can only enroll small number of patients. In combination with the high interindividual variability in the clinical course observed in many rare diseases, this diminishes a study’s power [3]. Thus, alternative trial designs and statistical techniques that maximize information from a small and heterogeneous group of subjects are needed. In this chapter, we will discuss the following alternative design options for rare diseases: (1) use of external data as historical controls; (2) a novel adaptive design, known as the informational designs; and (3) passive and active data collection in rare diseases, utilizing high-end technologies and analytics.

Section 1: Use of External Controls in Rare Diseases In order to conduct efficient trials in rare diseases, it is important to consider utilizing historical control data to fully or partially replace a concurrent control. This is particularly the case when there are ethical concerns in recruiting patients with life-­ threatening diseases with no credible control arm. Secondly, challenges in developing trials for underserved indications may be partially ameliorated by historical controls (HCs), making drug developers more likely to invest, as programs will be more cost-effective. By reducing the required patient numbers, the use of HC can make enrollment of rare disease trials more feasible. HCs can be used for model parameter estimation at the study design phase, to support adaptation within a study or to supplement or completely replace the control arm [4]. In this section, we will discuss important topics related to planning, conducting, analyzing, and reporting of studies using HCs. HCs can come from many data sources with varied structure and quality, resulting in different biases. The main sources of HCs we consider are the following: • Real-world data (RWD): A wide spectrum, ranging from electronic health records of clinical care to observational studies to studies that incorporate planned interventions with or without randomization at the point of care. Examples include:

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–– –– –– ––

Medical charts, published data on off-label use Patient registries Natural history studies Any data relating to patient health status and/or the delivery of healthcare routinely collected from a variety of sources. • Completed clinical trials When we consider the use of HCs in clinical trials, our focus is not limited to confirmatory studies (i.e., studies intended to support drug approval by national health authorities). However, looking at the current use of HCs for approvals by regulatory agencies, we can see that the majority of cases involve rare diseases, where there are no approved therapies for standard of care (SOC). This suggests that HC can be considered in confirmatory studies for rare diseases and that health authorities are aware of their importance, especially when there is no credible SOC. However, the international and US FDA guidelines generally accept the use of external controls as a credible approach only in exceptional situations, in which either the effect of treatment is dramatic, the usual course of the disease is highly predictable, the endpoints are objective, or the impact of baseline and treatment variables on the endpoint are well characterized. There are also some disadvantages in using HCs. Randomized trials reduce the chance of bias and dissimilarity among the arms of a clinical trial. In nonrandomized trials, an external control group potentially could lead to a selection bias, i.e., a systematic difference between the enrolled participants and the external control group that could affect the final result. Outcomes of untreated patients may be worse in HCs than a concurrent control group in a randomized trial, due to more stringent inclusion and exclusion criteria in the trial, subtle selection bias (trial physicians selecting healthier patients for the trial), as well as improvement in medical care and potentially increased access to care over time and more sensitive diagnostic techniques that diagnose patients with less severe disease over time. There are suitable methods to minimize these disadvantages during the process of trial design. Most of the literature is focused on choosing an HC that matches with the current clinical trials and on reducing bias in the analysis. However, our recommendation would be different. We highly recommend, if possible, that researchers start with selection of a high-quality HC for the indication and then design and match the current study population as closely as possible to the selected HC. The recommendations for selecting HCs are consistent with Pocock’s criteria [5]: choosing an HC with similar (a) inclusion/exclusion criteria, (b) type of study design, (c) known prognostic factors, (d) data quality, and (e) treatment for the control group. In case the existing clinical trial cannot be redesigned to match closely the selected HCs, we discuss below methods for selecting HCs for replacement or augmentation of a control arm for an existing clinical trial design. It is of primary importance to improve the quality and usability of the data and consequently increase the feasibility of using HCs in clinical trials. We recommend using uniform standards as much as possible in HC data collections [4].

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Statistical approaches for minimizing bias introduced by HC may be divided into two major classes, based on major schools of clinical trial statistics, frequentist (focuses on control of rates of false-positive and false-negative conclusions), and Bayesian (focuses on using estimates of what is known prior to the clinical study and flexibly updating these estimates based on study results). Both approaches have merit, and which is better should be evaluated on a case-by-case basis, often using computer simulations. One important application of the Bayesian approach is when an HC is used to augment a concurrent randomized control group to reduce the control group’s size. The HC data is combined with the concurrent control data, but the weight given to the HC data is reduced if there are signs that it is dissimilar to the concurrent control. One crucial step in properly using HCs is performing sensitivity analyses by computer simulation. Sensitivity analyses simulate the trial under a variety of assumptions that might cause variation in a clinical trial result, thus interrogating the sensitivity of the trial result to the assumption in question. Figure 9.1 provides a road map of decision-making when using HCs in the design and analysis stages of a clinical trial. With the increasing cost of clinical trials and proliferation of digital data, the FDA is embracing the use of real-world data (RWD) to increase opportunities for resourceful approaches, such as use of external control data in regulatory decision-­ making in orphan and rare diseases. These innovations may increase efficiency, lower trial costs, and speed up lifesaving therapies to market. In this evolving era, pharmaceutical and regulatory bodies may be more open to the use of HCs as a replacement of or supplement to a concurrent control, when a concurrent control is either unethical or impossible or the condition under study is making it difficult to enroll. HCs introduce multiple biases compared to a concurrent randomized control. However, these biases can be minimized by careful choice of HCs and appropriate study design and analysis methodologies. There are many new developments in areas of design, conduct, and analysis of the studies that incorporate HCs. If we pay careful attention to these approaches, we can apply HCs when they are needed – while maximizing scientific validity and increasing the feasibility of rare disease drug development.

 ection 2: Informational Designs and Possible Application S in Rare Diseases The recent FDA draft guidance for rare diseases suggests that a natural history study be conducted as an initial step in rare disease development to address the many uncertainties alluded to in this chapter and allow optimal design of a pivotal study [6]. Such a natural history study would be devoted to understanding the temporal course of the disease in the absence of therapy, identifying prognostic subgroups, exploring clinical endpoints and their temporal relationships and correlation structure, and validating potentially clinically meaningful and sensitive pivotal endpoint(s). While the use of a separate dedicated natural history study is a very

Yes

Search external database

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Historical Control or Concurrent Control

Historical Control (HC)

No

No

Disease without recruitment challenge?

No

Can I design a new study similar to an existing HC?

Ethical and possible to use control arm?

Fig. 9.1  Decision-making road map of using HCs in clinical trials. (Adapted from [5])

HC available and pass the quality check?

Use HC with the best quality

Yes

No

Yes

Yes

Highly recommended

Use appropriate statistical methods to borrown information from HC; Computer simulation supports the decsion of methods.

Design the study with or without CC comparable with HC

Potential use for new drug application?

No

Confirmatory study ?

Concurrent Control (CC)

Yes

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rational recommendation based on the reasons above, it significantly raises the cost and delays the timeline from the drug developers’ perspective, thus making them less likely to sponsor such trials. Sponsors would need to invest in a natural history study prior to even knowing if their drug had a chance to be effective. At the same time, natural history studies might face opposition from patient communities who would face a substantial delay while the natural history study was conducted, without the prospect of a therapeutic benefit. An alternative to a separate natural history study is a pivotal (confirmatory) study that is adaptive, i.e., is capable of adapting its design or analysis based on what is learned about the natural history from within the study itself. The classical adaptive design uses early interim results to inform elements of the second half of the study according to predefined (“prospective”) rules (“interim analysis”). However, early interim results may not provide sufficient information about the development of long-term endpoints or their relationship to earlier short-term measures. Yet, long-­ term endpoints are often the most clinically meaningful and most appropriate for judging whether a therapy should be approved. This is particularly true in oncology, where survival is the key endpoint and where many “rare” diseases are appearing as molecularly defined subsets of common cancers. The informational design [7, 8] is an alternative to the classical adaptive designs in that the adaptation is performed at the end of the trial and is based on the information from a randomly selected subset of patients (called the information cohort) in the pivotal study, as illustrated in Fig. 9.2. Compared with a conventional interim

Patients selected for informational analysis

Interim analysis is conducted mid-trial in all enrolled patients

Informational analysis is conducted at end of the trial in a subgroup of patients

Fig. 9.2  Conventional interim analysis compared with informational analysis: patients are represented by horizontal lines. The informational analysis cohort is indicated by black horizontal lines, and the informational analysis itself is indicated by a black vertical line at the end of the study. The interim analysis is indicated by the dashed vertical line in the middle of the study. (Reproduced from Chen et al. [8] with permission)

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analysis, the informational analysis provides much fuller insight into long-term endpoints and their relationship to earlier measures. The disadvantage of the informational design is that the adaptation cannot occur until the end of the study. Therefore, any adaptive decision that can potentially impact the trial design such as dropping a subgroup of trial participants, who are not benefitting, or adjusting the sample size (number of trial participants) cannot be implemented. However, the study can adapt its analysis plan, and this can often be useful as discussed below. The informational design is ideal for applications in which a decision does not have to be made until the end of the study. For example, if it is unclear whether a therapy benefits the whole population or a biomarker-defined subgroup, the whole population can be enrolled, and questions of benefit in the subgroup and the overall population can both be tested at the same time in parallel. In order to ensure no increase in probability of any false-positive claim, typically, the overall 5% false-­ positive (“type I error” or “alpha”) rate needs to be “split” between the subgroup and overall population, although the sum of the false-positive rates alpha may add up to be more than 5%, depending on the relationship between the subgroup and the overall population. Based on the learnings from the information cohort, an “optimal” alpha allocation can be determined, which maximizes the probability of a successful clinical study in either the subgroup or the overall population. In comparison to using external data sources, such as a natural history trial for optimal alpha allocation, the informational design not only eliminates the need for such studies, but the data source for adaption from the internal information cohort is also more relevant, since it comes from the same study and, therefore, has the same study design, study sites, study population, etc. Another related application is defining the cutoff of a continuous biomarker that optimally separates people who will benefit from those who will not [7]. An intriguing potential application of the informational design is to choose the primary study endpoint in rare diseases, where the most appropriate endpoints may not be known. These rare diseases often are associated with considerable unmet medical need, creating an urgency to complete development. Yet, if the endpoints are not known, health authorities will often suggest a dedicated natural history study with no therapy to determine the best endpoints [9]. Given the difficulty of enrolling patients, this study can delay the availability of desperately needed therapies for years. The informational design could clarify the relationship and correlation between multiple primary endpoints and provide “optimal” alpha allocation to maximize study power or guide selection of a single pivotal endpoint among the candidate set in the overall study, thus allowing the natural history study to be done simultaneously within the pivotal study, saving years of crucial development time. We will present new results concerning the selection of a single primary endpoint from several candidate endpoints in this chapter. For all these applications, the steps for using the informational design are as follows: • Prospectively define a fraction (i.e., 20%, 40%, 60%, or 80%) of the study patients as an information cohort, identify what information will be sought from

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the information cohort, and specify what the rules will be for a prespecified adaptation (any adaptation done must be specified in advance and agreed with health authorities such as the FDA) based on this information. The higher the fraction of patients in the information cohort, the better the information, but the greater the statistical penalty that must be paid if these patients are used in the final analysis. Decide whether the information cohort patients will also be used in the final analysis. If so, a statistical penalty must be paid, and the study must be done at a lower nominal false-positive rate of less than 0.05. The procedures in Chen et al. [7] can be used to calculate the required lower nominal false-positive rate, or a combination test (below) can be used as described in this chapter. Enroll the study with any applicable “stratifications” (subgroups of trial participants that are specifically tracked in each study arm, being enrolled according to prespecified quotas), randomly assigning the predetermined fraction of the patients to the information cohort within each stratum. Extract the needed information from the information cohort, and perform the study adaptation (optimal alpha allocation, continuous biomarker cutoff determination, or choice of a pivotal endpoint). Evaluate the whole study, using the lower nominal false-positive rate, if applicable.

In general, it is worthwhile to pay the statistical penalty to include the information cohort patients in the final study analysis compared to the strategy of excluding these patients. Next, we illustrate the use of the informational design in the rare disease setting for two of the applications described above: determining whether the therapy works in the whole population or only in a subgroup and selecting a single primary endpoint from multiple endpoints.

 ection 2.1: A Case Study Using Informational Design to Optimize S Subgroup Testing in a Phase III Non-Small Cell Lung (NSCLC) Cancer Trial (RADIANT) [7] The informational design permits evaluation of both the full population and a subgroup in a parallel fashion, optimally “splitting” the allowed false-positive rate for each of these groups. Another statistical approach is the “sequential” approach, in which the statistical tests for the two groups occurs in a prespecified order. The first subgroup can be tested at the full 5% false-positive rate, increasing the sensitivity. However, the second statistical test may be considered only if the first one is positive. Sequential testing can be more efficient if the choice of which test should be first is made correctly. But if an incorrect choice of testing order is made, this can be very risky. In the RADIANT study [10], patients with locally advanced NSCLC received erlotinib, a drug directed against a target called EGFR, on the experimental arm.

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The study designers believed the drug would benefit all patients with NSCLC and benefit those who had more EGFR on the surface of their cancer cells more. Thus, the statistical analysis was designed in a sequential fashion in which a benefit in the overall population was required before benefit in subgroups could be considered. This allowed greater power, since the overall population had a larger sample size. Unfortunately, the study failed in the overall population, and even though the study was nominally positive in the subgroup with more EGFR, this result could not be considered based on the study design. In contrast, we assume the optimal order of testing is not known and seek to allocate the false-positive rate α between the full population hypothesis (α1) and the subpopulation hypothesis (α2). There are several statistical methods to do this without using data to find the optimum [11, 12] or by optimizing study power using internal study data [13] or external data from a mature Phase II study (i.e., an earlier study to provide preliminary evidence of effectiveness and safety) of a similar design, the “Phase II+ method” [14]. If one uses the informational design, the allocation will be performed based on data in the information cohort comprising a fraction of the patients to optimize study power [7, 8]. The readout of the RADIANT trial [10] revealed an observed hazard ratio (HR) of 0.90 for the full population and 0.61 for the subpopulation. HR is an indicator of how much the drug reduces the risk to patients with the disease, where a number less than 1 indicates a reduced risk for patients, smaller numbers are better, and 0.90 is too close to 1 to be statistically distinguishable Assuming these are true unknown HRs, we looked at cases where either 17% or 34% of the events came from the subgroup. A hypothetical study with 1:1 randomization required 410 events (worsening of cancer or death) for 83% power (83% chance of the study correctly detecting an effective drug) under an assumed HR of 0.75  in the full population at a false-positive rate of 5%. However, the true HR in the full population was worse than the 0.75 the study designers assumed. A sequential procedure with the first statistical test evaluating the full population hypothesis as in RADIANT would have only 19% power under true HR of 0.9, i.e., a high risk of failing to detect the drug effect, as in fact happened in RADIANT. If the subpopulation hypothesis were primary, the design would have 54% (or 83%) power when 17% (or 34%) of events are from the subpopulation. However, in the case under consideration, one does not know a priori which hypothesis should be primary, and if one made the biomarker hypothesis primary when the putative biomarker was not predictive, that would substantially decrease the power. The informational design can provide ~45% (or ~75%) power when 17% (or 34%) of the events were from the subgroup, nearly as good as could have been achieved by a priori knowledge of the hazard ratios. Indeed, a broad exploration of conditions showed that the informational design, by allocating more or less alpha to the subgroup, depending on the strength of the biomarker effect in the informational cohort, provided stable performance across the different possible versions of truth, and this performance was nearly as good as knowing the truth a priori [7, 8].

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 ection 2.2: A Case Study Using Informational Design for Primary S Endpoint (EP) Adaptation in a Phase III Pompe Disease Trial In this section, we use a hypothetical example to illustrate the benefits of the informational design in cases where the primary endpoints are not known at the time of pivotal Phase III trial planning, without the need of conducting a separate natural history study. The example is based on a randomized, double-blind, placebo-­ controlled study of alglucosidase alfa in patients with Pompe disease [15]. Pompe disease is a rare genetic disease caused by the buildup of a complex sugar called glycogen in the body’s cells. Its symptoms include weak muscles, enlarged liver, and trouble breathing. Suppose a total of 180 patients are planned to be enrolled into the study with 2:1 randomization ratio to receive either alglucosidase alfa or placebo. There are three potential primary endpoints, changes from baseline in %FVC (percentage of the predicted forced vital capacity representing the total amount of air exhaled during a forced breath), 6MWT (6-minute walk test – a test that entails measurement of distance walked over a span of 6 minutes), and %MIP (percentage of the predicted maximum inspiratory pressure when breathing in). However, with limited prior information, it is hard to decide which primary endpoint(s) to include in order to maximize the probability of success of the trial using traditional fixed designs. In our example, the information cohort will be used to pick one single endpoint that maximizes the trial probability of success and designate this as primary among the three candidates. The false-positive rate (Type I error rate) can be strongly controlled with a combination statistical test that considers the trial participants in the information cohort and those outside it separately and combines them into an overall result for the whole study. Within the information cohort, there are still three possible endpoints in play, and different statistical adjustments must be made to account for this fact. Table 9.1 below shows comparisons of study power between informational and fixed designs under six true standardized treatment effect scenarios. The degree of clinical benefit is expressed as the ratio of the improvement to the standard deviation of each endpoint measurement. For example, 0.5 means that the treatment,

Table 9.1  Comparisons of study power between informational design with primary endpoint selection and fixed designs. Standardized treatment effect size is measured in the number of standard deviations of improvement: higher numbers are better True standardized treatment effect scenarios Index %FVC 6MWT %MIP 1 0.5 0 0 2 0.5 0.25 0 3 0.5 0.25 0.17 4 0.5 0.5 0.5 5 0 0.5 0.33 6 0 0.5 0

Informational design 77.4% 78.0% 78.0% 93.0% 79.6% 77.3%

Fixed designs for one chosen EP %FVC 6MWT %MIP 88.5% 2.5% 2.5% 88.5% 35.2% 2.5% 88.5% 35.2% 18.8% 88.5% 88.5% 88.5% 2.5% 88.5% 55.1% 2.5% 88.5% 2.5%

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alglucosidase alfa, improves the endpoint by 0.5 standard deviations compared to the placebo group. Three fixed designs are included, representing options to power each endpoint separately. However, in reality, only one endpoint can be selected for the fixed study design. If the selected endpoint is indeed the most effective endpoint, the study power is maximized, just as in scenario 1, where only %FVC is effective with 88.5% power, and scenario 6, where only 6 MWT is effective with 88.5% power. However, if the other two endpoints are somehow selected, there is almost no chance of having a positive study result. Without much prior information, the chance of picking the wrong primary endpoint is 2 in 3, or 67%. Alternatively, if the informational design is used, the study power is slightly lower, i.e., 77.4% compared to the best case of picking the correct endpoint %FVC in scenario 1. However, this power is ensured regardless of which endpoint is effective, and the slight decrease in power can be easily remedied by increasing the number of trial participants. This is much better than gambling with a high risk of a failed study. Similar trends are observed when we examine other scenarios except for scenario 4, where all endpoints are equally effective. Using the informational design in this case results in even higher power compared to the use of any single endpoint in a fixed design.

 ection 2.3: Benefits of Informational Design in Rare S Disease Applications Rare diseases represent a uniquely challenging area for a variety of reasons, including lack of sufficient prior knowledge to allow design of optimal studies as well as a limited available population for study. The informational design may be very useful for optimally testing both the full population and a subgroup that may benefit more simultaneously, for identifying optimal subgroup boundaries between patients who will benefit and those who will not for continuous biomarkers, and for choosing an optimal primary endpoint for clinical study without prior knowledge and without the added delay of a separate natural history study. These applications may accelerate the availability of life-altering therapies for the many patients suffering from these conditions.

Section 3: Passive and Active Data Collection in Rare Diseases As mentioned before, small sample size and lack of high-quality outcome in rare diseases are an immense hurdle to generating enough evidence and achieving statistically meaningful results to support drug approvals. With rapid advances in technology, a great deal of data from each patient can be captured, which is objective, comprehensive, relevant, and high-quality. Moreover, sponsors can benefit from a low-cost, low-risk, and potentially high-quality clinical trial with much more comfort and convenience for patients with rare diseases. With well-thought-out planning

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and design, recruitment can be accelerated and patients’ adherence to study procedures increased, while the operational procedures can be simplified.

 ection 3.1: Digital Data Collection in Classical Clinical S Trial Settings Real-time data capture involves repeated sampling of people’s behavior and physical states in their natural environment either actively or passively [16]. In the passive mode, patients’ data are transmitted automatically without their involvement. In the active mode, patients directly report and respond to surveys or questionnaires. A combination of both can be used, for example, when patients have an electronic diary or respond to a survey via an app or an accelerometry device indirectly logs their physical activity (PA). In classical clinical trials, self-reported questionnaires subjectively assess the presence and severity of pain relying on the patient’s memory. However, by using a combination of an app and wearables, participants rate their current state rather than their experience in the past, thus minimizing the recall bias, and the generalizability of the results is increased by wearables, capturing patients’ behavior in a natural and real-world environment. Even though wearable data can be massive and noisy, with higher granularity, there is enough data to investigate and identify patterns at the population and individual levels while reducing noise by proper annotation and labeling. Granular phenotyping makes the detection of adverse events, treatment efficacy, and objective assessment of quality of life possible. Novel digital biomarkers can be developed and validated for diagnosis, prognosis, treatment response, or as a surrogate endpoint for early phase trials. Most of all, objectively obtaining real-world data during patients’ daily lives reduces the burden on participants and the barriers to lowering the cost of conducting clinical trials in rare diseases. The Clinical Trials Transformation Initiative (CTTI) has several recommendations relevant to this topic [17]. The recommendations address mobile technology selection, data collection, data analysis and interpretation, protocol design and execution, FDA submission and inspection, existing scientific principles, data quality and standards, and participant engagement, which remain central in implementation and operation of trials using these new technologies. Identifying a proper wearable for collecting data is a challenge that could significantly affect the quality and outcome of a trial. There are decisions to be made before selecting a device for a specific population and indication [18]. Device technical and physical characteristics such as size, convenience to wear, battery life, and impact on daily life activities should be well thought-out in each trial context. Other factors are location of the device, number of devices per participant, duration of observation in each cycle, frequency of sampling, frequency and method of synchronization with the center, device replacement and lifetime, preprocessing and standardization of the data to account for differences between and within participants and devices, device orientation or calibration, and synchronization of multiple

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devices. Creating a checklist to select a suitable device for the purpose of a clinical trial can facilitate decision-making. The next step is a well-designed validation to provide additional scientific evidence beyond what is claimed by vendors, even for regulatory validated devices. The original validation study population and the indication could be drastically different from the study that the device would be used in. The capability of participants in different trials can vary due to many factors such as age or the stage of their illness. The objective of validation in a specific context must be stated, and the device must be proven to be associated with an indication of interest in a targeted population. It is also recommended to conduct a pilot study by recruiting only a small number of patients who are representative of the targeted population, to understand patients’ acceptance and compliance, the nature of the data, data acquisition and storage, preprocessing and analytical aspects of the trial to enable implementation of necessary settings, adjustments, support, and required patient and staff trainings before initiating a larger clinical trial. Handling the digital data volume and complexity alone can be challenging, which consequently makes visualization and interpretation of the results a daunting task, so it is wise to collect only as much as data as is required to satisfy the objective of a trial. The resulting multimodal data (clinical, genetic, wearables, imaging, etc.) require advanced analytic platforms and analytical algorithms that are not available to most pharmaceutical companies and CROs. Recently, a number of mergers, acquisition, and collaborations between CROs and hi-tech companies such as Amazon, Google, and Microsoft, have been reported with the hope of filling this gap [19, 20]. These interdisciplinary tasks require highly skilled people working in a well-defined, collaborative culture. The resulting novel biomarkers need to be understood, trained, validated, replicated, interpreted, and visually and verbally communicated in a simple language. CTTI has listed several recommendations for developing novel endpoints using mobile technology [21]. Currently, patient’s assessments of their functionality or disease burden take place in healthcare settings or rely on subjective outcomes reported by participants or their healthcare providers (HCPs). It is recommended to select a novel endpoint only if it is more informative and meaningful to the patients in comparison to an existing outcome, to choose the technology after selecting an endpoint, to adopt a systematic approach to identify key novel endpoints, to engage with national health authorities, and to define a strategy for optimally positioning novel endpoints in interventional trials. A novel endpoint may be considered as a primary endpoint to address an unmet need in the absence of an existing one or can be most valuable as a complementary endpoint when there is a well-established one. The use of novel endpoints in Phase II could prevent the termination of promising drugs prematurely due to lack of evidence. CTTI has developed an interactive tool for selecting among viable technology-driven novel endpoints that can facilitate decision-making. Industry-wide standards need to be established, in order to promote the scientific, technical, and medical benefits of novel biomarkers and endpoints in clinical

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trials. These standards include capturing and storage of the data, terminology, processing principles, collection and reporting of that data, and transparency requirements for the algorithms used to convert the data into physiologically and medically useful endpoints. During the trial, the quality of data should be proactively checked with patients, and statistical criteria implemented for acceptable measurements and responses, such as range and reasonable variability of measurement to minimize erroneous and missing data. Data sharing should be driven by safety, confidentiality, and trial integrity. Participants should be engaged for safety monitoring signals using the device data. Since clouds and wireless communication form the backbone of these advanced platforms, cyber security is complex but to some extent has already been addressed and is well-managed. Like any new approach in clinical trials, a benefit-risk assessment is crucial. Project planning and training are a crucial step to avoid confusion among the clinical trial study team and patients, when a new technology is added to a well-established process. It is critical to foster collaboration among key stakeholders, such as sponsors, patients, clinicians, regulatory authorities, private payers, CROs, medical device companies, researchers, HCPs, professional medical associations, and technology companies. Responsibilities must be well-defined in the study design and reflect the impact of the new technology on different parts of the study. The data flow from different sources, and a process map should be laid out to evaluate the impact on trial milestones and schedule, study staff, participants, procedures, and processes. Timing of data preprocessing, creating analysis datasets, and data review in real-time prospectively or retrospectively should be clearly defined. Training materials should be clear and simple to understand. Backup plans for device failure and detection of adverse events should be in place. A new concept of compliance and adherence in the light of new data sources should be clarified. There are many benefits using wearable devices in clinical trials with small populations, especially for rare disease indications. Collecting data during the trial in the patient’s natural environment can provide a deeper insight into disease and treatment response variability [22]. Nevertheless, this approach faces several challenges in many areas, including scientific, regulatory, ethical, legal, data management, infrastructure, analysis, and security. It is highly recommended that biostatisticians and data scientists be involved in all decision-making processes during study protocol design, data collection, quality controls, analysis, and interpretation.

 ection 3.2: A Patient-Centric Approach: Decentralized Control S Trials (DCTs) In a specific rare disease, a small number of patients are typically affected and may be dispersed around the world. In traditional clinical trials, participants may have to pay frequent visits to specific sites, which adds to the burden of the disease, especially for children, the elderly, the disabled, and cognitively impaired individuals

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who require transportation or caregiver assistance. Financial costs (e.g., travel, missing work, dependent care), commute time, and site visits that conflict with the patient’s or their family’s obligations may prevent them from participating in clinical trials. In some cases, a language burden prevents these families and their children from clearly communicating their experiences in the trial or understanding what needs to be done. Clinical trial tests and procedures, added to their routine standard tests and procedures, may be burdensome. It has been shown that remotely participating in clinical trials from the comfort of patients’ homes and using apps in their native language can speed up enrollment and increase compliance and retention of patients [23]. Moreover, from the sponsor’s perspective, this reduces operational obstacles and improves data generation and data sharing, in turn increasing the efficiency and reducing the cost of conducting clinical trials in rare diseases. CTTI refers to remote clinical trials as decentralized clinical trials (DCTs), which are defined as those executed through telemedicine and mobile/local HCPs, using procedures that vary from the traditional clinical trial model. These studies often require wearable sensors and mobile health applications that allow the collection of important physiological data, such as respiratory rate, heart rate, temperature, and blood pressure [24]. DCTs also create a possibility for local HCPs to have their patients participate in clinical trials, thereby expanding the pool of clinical trial sites. This in turn leads to a trial population and clinical practices that are more representative of the overall population. This approach is the most valuable in rare disease, where patients are generally limited in number, are highly geographically dispersed, are mostly children, and have progressive diseases that make traveling to sites very challenging. DCTs can range from fully decentralized to varying levels of decentralization. This may include one or more elements of a DCT, such as performing visits via telemedicine or mobile/local HCP, remote data capturing using mobile technologies/apps such as drug administration reminders, portable electrocardiograms (ECGs), in-home lab kits, wearables, shipping of clinical supplies, and more. Given some clinical trial procedures or measurements may require a patient’s physical presence (e.g., surgery or advanced imaging), a hybrid approach can still reduce the amount of disturbance and burden on patients while reducing costs and improving trial efficiency. Various versions of a hybrid DCT have already been applied by different sponsors. For example, Roche used only telemedicine and electronic diaries in a Phase II trial for Parkinson’s disease. In 2020, Bayer in collaboration with Stanford implemented a DCT in patients with Atrial Fibrillation (DeTAP) using apps for data collection, televisit function, and  information and reminders;  along with Bluetooth-connected EKG home devices and blood pressure cuffs. Dendreon has used electronic patient reported outcomes (ePRO) forms and other apps in Phase III for advanced prostate cancer, while Janssen, Novartis, and Sanofi have used more DCT components in their trials. CROs are also moving fast with mergers and alliances to provide appropriate platforms for DCTs. The Decentralized Trials and Research Alliance (DTRA) has been recently launched with the goal of bringing stakeholders together. This is currently made up of more than 50 life sciences and

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healthcare organizations, including AstraZeneca, Pfizer, the US Food and Drug Administration, and others [25]. First,  a  research and feasibility study may be warranted. This approach  can begin with simple steps such as changing the logistics for shipping the investigational drug to patients or sending a cab to their home to take them to a visit if necessary or sending a mobile local healthcare professional to measure their vitals and collect necessary questionnaires. As each part of the process establishes itself in the organization and the platform develops further, more sophisticated and advanced services can be gradually introduced. It is much easier to consider DCTs in early trials as this eliminates concerns about acceptance of novel endpoints in regulatory submissions. This approach provides enough time to develop and validate new digital biomarkers and endpoints and to evaluate performance and third-party vendor support for the selected device on a small scale before applying it to a much larger pivotal trial. This also gives the trial personnel time to adapt and develop their skills with regard to the new technologies and processes. This approach has its own pitfalls and challenges, which may be alleviated or resolved with innovative thinking. For example, if participants are not comfortable with the new technology, it is possible to simplify these apps and make them more user-friendly. If patients have problems remembering their schedule, text messages or notifications can be sent to their phones. If technical problems, changing batteries or synching devices are too complex, mobile technical support or devices equipped with long-lasting batteries can be provided to reduce the loss of data and patient burden. DCTs are patient-centric, by bringing the clinical trial to the patients instead of patients to clinical trial sites. While a major initial investment is required, this approach can be highly cost-effective in the end. Multisource data is a strategic asset for the future. A noncompetitive and collaborative environment is required to move forward efficiently and rapidly. This new approach may expedite the drug development and approval process and put promising drugs or interventions into the hands of patients who have a few or no options to alleviate their debilitating disease.

Conclusion In summary, rare diseases present numerous challenges for drug development [26]. Typically, it is not even known for certain whether the disease is truly rare. The apparent incidence and prevalence of a particular disease may increase with increased awareness in the medical or patient community, often triggered by the availability of new treatment options. There is typically limited information on the underlying biology or pathophysiology of the disease, making the design of therapies and the search for pharmacodynamic and predictive biomarkers especially difficult. Often, little is known about the natural history of the disease, including prognostic subgroups and relevant clinical endpoints. In particular, there may be no validated primary endpoint for pivotal studies and, importantly, the correlation structure among candidate endpoints is unknown. There may be no previous

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precedents for drug development and no standard comparison therapy, and a placebo control may be unethical for severe conditions. Compounding these difficulties is the very small number of available patients, who may be widely distributed among a large number of practitioners, each with relatively limited experience in treating the rare condition. In light of these difficulties, the FDA has shown flexibility in evaluating scientific evidence for approval in these conditions, as reflected in the 230 drug approvals for orphan disease in the last decade. Nonetheless, a review of the rare disease area shows that many of the successful drugs were exceptional in their efficacy [27]. In addition, the approval of 230 drugs is small when set against the 8000 rare diseases. Cost-effective methods to provide reliable evidence suitable for approval in the rare disease area are still urgently needed, especially for drugs that are effective but not exceptional, since there are many more in the former category. In this chapter, we discussed novels ways to design rare disease clinical trials, through using external historical control data, adaptive designs, and decentralized trials with digital wearables. Taken together, these new approaches would expedite drug development and approval and potentially put a promising investigational drug or intervention into the hands of patients who have few or no options to alleviate their debilitating disease.

References 1. Congress US. Public Law 97-414. Orphan Drug Act; 1983. 2. Griggs RC, Batshaw M, Dunkle M, Gopal-Srivastava R, Kaye E, Krischer J, et al. Clinical research for rare disease: opportunities, challenges, and solutions. Mol Genet Metab. 2009;96:20–6. 3. Augustine EF, Adams HR, Mink JW. Clinical trials in rare disease: challenges and opportunities. J Child Neurol. 2013;28:1142–50. https://doi.org/10.1177/0883073813495959. 4. Ghadessi M, Tang R, Zhou J, Liu R, Wang C, Toyoizumi K, et al. A roadmap to using historical controls in clinical trials – by Drug Information Association Adaptive Design Scientific Working Group (DIA-ADSWG). Orphanet J Rare Dis. 2020;15:69. https://doi.org/10.1186/ s13023-­020-­1332-­x. 5. Pocock SJ. The combination of randomized and historical controls in clinical trials. J Chronic Dis. 1976;29(3):175–88. http://linkinghub.elsevier.com/retrieve/pii/0021968176900448. 6. Food and Drug Administration. Rare diseases: common issues in drug development. Washington, DC: US Department of Health and Human Services; 2019. 7. Chen C, Beckman RA. Informational design of confirmatory phase III trials. Biopharm Rep. 2016;23:1–16. 8. Chen C, Li N, Shentu Y, Pang L, Beckman RA. Adaptive informational design of confirmatory phase III trials with an uncertain biomarker effect to improve the probability of success. Stat Biopharm Res. 2016;8:238–47. 9. U.S. Food and Drug Administration. Multiple end points in clinical trials, guidance for industry. 2017. https://www.fda.gov/downloads/Drugs/GuidanceComplianceRegulatoryInformation/ Guidances/UCM536750.pdf. Accessed on 5 Mar 2017. 10. Kelly K, Altorki NK, Eberhardt WE, O’Brien ME, Spigel DR, Crinò L, et.al. Adjuvant erlotinib versus placebo in patients with stage IB-IIIA non-small-cell lung cancer (RADIANT): a randomized, double-blind, phase III trial, J Clin Oncol 2015; 33(34):4007–4014. doi: https:// doi.org/10.1200/JCO.2015.61.8918.

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11. Spiessens B, Debois M. Adjusted significance levels for subgroup analyses in clinical trials. Contemp Clin Trials. 2010;31(6):647–56. https://doi.org/10.1016/j.cct.2010.08.011. 12. Freidlin B, Simon R. Adaptive signature design: an adaptive clinical trial design for generating and prospectively testing a gene expression signature for sensitive patients. Clin Cancer Res. 2005;11(21):7872–8. https://doi.org/10.1158/1078-­0432.CCR-­05-­0605. 13. Chen C, Beckman RA.  Hypothesis testing in a confirmatory phase III trial with a possible subset effect. Stat Biopharm Res. 2009;1(4):431–40. 14. Beckman RA, Clark J, Chen C. Integrating predictive biomarkers and classifiers into oncology clinical development programmes. Nat Rev Drug Discov. 2011;10(10):735–48. https:// doi.org/10.1038/nrd3550. 15. van der Ploeg AT, Clemens PR, Corzo D, Escolar DM, Florence J, Groeneveld GJ, Herson S, et al. A randomized study of alglucosidase alfa in late-onset Pompe’s disease. N Engl J Med. 2010;362(15):1396–406. https://doi.org/10.1056/NEJMoa0909859. 16. Ruwaard J, Kooistra L, Thong M. Ecological momentary assessment in mental health research: a practical introduction, with examples in R. 1st ed. 2018-11-26. 17. Recommendations executive summary: advancing the use of mobile technologies for data capture & improved clinical trials. https://www.ctti-­clinicaltrials.org/sites/www.ctti-­clinicaltrials. org/files/mobile-­technologies-­executive-­summary.pdf. 18. Karas M, et al. Accelerometry data in health research: challenges and opportunities: review and examples. Received: 18 December 2017 / Revised: 24 September 2018 / Accepted: 1 December 2018. Published online: 12 January 2019,© International Chinese Statistical Association 2019. 19. New capabilities from acquisition of snapIoT. https://www.covance.com/snapIoT.html. 20. IQVIA quietly purchased UK-based NLP provider Linguamatics, Melissa Fassbender, Feb 12, 2019. https://www.outsourcing-­pharma.com/Article/2019/02/12/ Iqvia-­acquires-­NLP-­provider-­Linguamatics. 21. CTTI Project: novel endpoints. https://www.ctti-­clinicaltrials.org/projects/novel-­endpoints. 22. Wearable devices in clinical trials: hype and hypothesis. Clin Pharmacol Ther. 2018;104(1):42–52. https://doi.org/10.1002/cpt.966. Epub 2018 Apr 2. PMID: 29205294; PMCID: PMC6032822. 23. National Academies of Sciences, Engineering, and Medicine; Health and Medicine Division; Board on Health Sciences Policy; Forum on Drug Discovery, Development, and Translation; Shore C, Khandekar E, Alper J, editors. Washington, DC: National Academies Press (US); 2019 Jul 23. https://www.ncbi.nlm.nih.gov/books/NBK548971. 24. CTTI Project: decentralized clinical trials. https://www.ctti-­clinicaltrials.org/projects/ decentralized-­clinical-­trials. 25. Alliance forms to accelerate adoption of decentralized clinical trials, Mallory Hackett, December 16,2020. https://www.mobihealthnews.com/news/ alliance-­forms-­accelerate-­adoption-­decentralized-­clinical-­trials. 26. Schwartz J. Research in rare disease: the nature and extent of evidence needed for decision. 51st Annual Meeting of the Drug Information Association, session #318 (track 17), June 2015, Washington, DC. https://issuu.com/postscripts/docs/dia2015_finalprogram. 27. U.S. Food and Drug Administration, Novel drugs 2014 summary. https://www.fda.gov/Drugs/ DevelopmentApprovalProcess/DrugInnovation/ucm429247.htm. Accessed on 5 Mar 2015.

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Zoran Antonijevic, Yi Liu, Rui (Sammi) Tang, Jonathan R. Huml, Robert A. Beckman, Cristiana Mayer, and Gianna McMillan

Illustration 10 Illustration of 12-year-old Yash Krishnan (center) and his mother, Parvathy, and father, Iyer, made from a family photo. Yash provided a patient narrative in Chap. 3, Select Patient Narratives. The editor watched a video of Yash (that was ultimately turned into the patient narrative) and was inspired by Yash’s wisdom, courage, and empathy for others.

The original version of this chapter was revised. The correction to this chapter can be found at https://doi.org/10.1007/978-3-030-78605-2_26 Z. Antonijevic (*) Abond CRO Inc., Allendale, MI, USA e-mail: [email protected] Y. Liu Nektar Therapeutics, San Francisco, CA, USA R. Tang Servier Pharmaceuticals, Boston, MA, USA J. R. Huml Harvard University, Cambridge, MA, USA R. A. Beckman Lombardi Comprehensive Cancer Center and Innovation Center for Biomedical Informatics, Georgetown University Medical Center, Georgetown University, Washington, DC, USA C. Mayer Janssen Research and Development, LLC, Raritan, NJ, USA G. McMillan LMU Bioethics Institute, Los Angeles, CA, USA © The Author(s), under exclusive license to Springer Nature Switzerland AG 2021, corrected publication 2022 R. A. Huml (ed.), Rare Disease Drug Development, https://doi.org/10.1007/978-3-030-78605-2_10

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Introduction The validity of clinical research rests on a regulated series of phased trials that collect and evaluate data about the safety and efficacy of investigational therapeutics. The goal is the development of a new or improved treatment that will successfully address a disease or disorder. Sponsors, investigators, regulators, payers, patients, and research subjects share similar goals of increasing the availability of effective and safer medicines. However, these stakeholders may emphasize different aspects of drug development and even have preferences for different approaches for achieving this ultimate success [1, 2]. For the funding sponsor, economic considerations influence what development pathways will be prioritized and, in some cases, which ones can be realistically followed. Physicians and investigators must provide the best personalized health care to individual patients, follow regulations, advance science, and address the practicalities of achieving enrollment numbers and measuring endpoints. Regulators must serve public health needs, while payers must evaluate the comparative effectiveness of medicinal products entering the market. The perspectives of patients and research participants hinge on access to better treatments and study interventions that offer the most benefit with the fewest risks. The literature seldom addresses the views of patients and research participants on clinical study design. Additionally, these key stakeholders have limited input on development approaches and how various design options might best meet their needs. This chapter focuses on how innovative trial designs can align with the priorities of patients and research participants and on which study objectives and development scenarios best address these priorities. The use of two distinct terms, patients vs. research participants, is intentional to address the important differences in their roles. In this chapter, we first present the ethical context within which a patient interacts with health-care professionals and a research participant interacts with investigators. We then define traditional trial designs and their shortcomings, followed by descriptions of noteworthy innovative approaches. We conclude with how these approaches can benefit patients and research participants, with particular emphasis on rare diseases.

Ethical Context The terms “patient” and “research participant” are not interchangeable. A patient’s well-being is the primary goal of medical intervention, and, together with the physician, he or she decides what course of action is most likely to positively address their individual health need. In clinical research, however, the investigator is interested in a study in order to answer a research question, and the primary goal is to gather data that will contribute to generalizable knowledge. Typically, this knowledge is intended to improve the “average” treatment effect in a patient population. While a research participant might benefit from the study, there is no guarantee of a positive outcome, and there may be unknown risks associated with study interventions. Thus, while the ethical underpinning of a patient’s decision about treatment lies in their confidence that actions will be taken for their personal

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greatest benefit, a research subject’s decision about study participation hinges on the assurance that risks will be minimized with the potential for personal benefit. In this chapter, the key concepts are about what a patient wants from research as well as what a research participant wants from research. The focus on the patient/participant perspective – and what this means in a measurable sense – becomes increasingly important, as drug developers work to incorporate creative data mining and innovation in study designs. A patient’s conceptualization of relative success for a research outcome will include a potential improvement of the treatment benefit/risk profile over existing standards of care, decreased health-care costs, and accelerated time to approval. For a patient, clinical research is a “black box,” that is, while they might have an interest in what goes in one side (knowledge of disease, standard of care, patient goals), they have little to do with what happens inside the box (during the research itself), hoping only that an effective treatment comes out on the other side. For the patient, in this instance, the research goal would have been successfully attained. For a research participant, however, participation includes the risks associated with what happens within “the box” (the actual study procedures) and the knowledge that no benefit is guaranteed for them. Thus, relative success of the research is more ambiguous and more intimately tied to the process. The decision to enroll in a clinical trial is made by potential participants based on their understanding of risks and benefits and their evaluation of existing therapeutic products. Research participants often make this decision with a smaller set of information than that available to the investigators of a given trial. Even if the research participant understands that the purpose of the study does not prioritize his or her individual personal benefit, the expected risk/benefit ratio must be acceptable. The most accurate estimation of this ratio must be shared and understood before the participant consents to participate and be updated, when appropriate, for the duration of the study. In early stages of development, where studies are exploratory, the ability to accurately estimate the risk/benefit ratio will be less than with studies in later stages of development. Research participants with severe, life-threatening, or rare disease – without acceptable treatment options – may be willing to accept more risk for the possibility of benefit.

Traditional Trial Designs In the standard paradigm of drug development, each traditional clinical trial evaluates one single treatment, for one indication and population. Traditionally, all key trial parameters, such as study population, treatment arms, and sample size, must be defined and held fixed during the entire execution of the trial. Such studies are developed in a “learning and confirming” cycle [3], marked by sequential discrete decision milestones that identify distinct phases, I to IV, of drug development. The complete body of evidence of one completed trial constitutes the wealth of information required to inform the design of the subsequent trial(s). In this chapter, traditional trial designs are defined as those that do not allow any adaptations, do not incorporate interim analyses and decision rules, do not use external data, or do not combine different objectives of multiple phases of development, multiple populations, or multiple therapeutic regimens.

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Rare Diseases Rare diseases are particularly affected by the challenges of traditional designs. Not only are sponsors less inclined to invest in a process that will benefit very few patients, but also enrolling a sufficient number of participants to satisfy requirements of traditional randomized clinical trials (RCTs) is often not possible. A much higher level of design sophistication and flexibility is necessary to develop treatments for rare diseases in a timely and cost-effective fashion.

The Patient Perspective Patients are understandably concerned about the quality of treatments that reach the market. Traditional designs lack the flexibility to modify dose(s) and participant populations to explore maximal benefit from the trial drug. As a result, treatments that reach the market may not be optimal or completely meet patient need. Patients also wonder if new therapies will reach the market in a timely manner and possess a better benefit/risk profile than the standard of care. Traditional phased trials are unwieldy. Many clinical trials may end up being underpowered (i.e., treatments may fail to show evidence of efficacy because an insufficient number of research participants were studied) and lead to termination of development of a potentially beneficial drug. On other occasions, trials may be oversized and trial enrollment may take longer than usual, delaying approval of therapies. This traditional, expensive paradigm limits the number of investigations and contributes to the final market cost of the drug. Prior to the era of adaptive trials, the estimated cost to the point of marketing approval per new drug in the late 1990s was US$802m, in year 2000 dollar value [4]. A more recent estimate shows a staggering fourfold cost increase [5]. Such estimates span therapeutic areas and drug classes.

The Perspective of Research Participants Participants in clinical trials are concerned that they will be harmed by an experimental agent or procedure, that this harm will be worse than expected, and that any harm suffered will not produce meaningful data to advance the study question. In other words, they hope the risks are as low as possible but are willing to accept risk if it is perceived to be worthwhile. In traditional RCTs, participants are randomly allocated to treatment arms in prespecified ratios. Given that information at the planning stage of the trial is limited, research participants may be allocated to treatment regimens with a benefit/ risk profile that was a “best guess” but proves to be inadequate. The design itself does not allow any corrections based on the incremental learnings from that trial. Research participants are typically disappointed when they cannot enroll in trials due to restrictive enrollment criteria for matches between biomarker-defined subpopulations and therapies, particularly in oncology. For example, trial eligibility for a traditional biomarker-directed study may be determined by techniques such as immunohistochemistry. If the research participant tests negative, she/he will not be

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eligible for the trial and will have to seek another treatment option. This could waste precious time and limited supply of tumor tissue. Finally, traditional designs may require more patients to reach a conclusion due to lower efficiency as defined above. Best practices in research ethics demand that the fewest number of participants possible be exposed to risk when investigating a novel agent. A research participant should know that his or her participation is essential for the public good, and any assumed risks generate the maximum amount of scientific knowledge possible.

Innovative Designs Clinical trials are based on scientific hypotheses that leverage uncertainties into intelligent predictions of probable outcomes. Transitions between stages require assumptions about the expected magnitude of treatment effect and its variability, the optimal dose, or best responding subpopulation. In traditional designs, these assumptions are made prior to the initiation of the trial and are usually based on limited information. Innovative designs allow these assumptions to be updated using accumulating data in a prespecified manner that respects rigorous scientific principles. Traditional design does not make use of external data, but there are situations – particularly in development of treatments for rare disease – where, with proper methodologies, leveraging external information can broaden the context of the trial and reduce sample sizes [6]. Novel statistical methodologies permit utilization of multiple external data sources, including real-world data (RWD), to augment the information from RCTs. The impact of these additional opportunities on company portfolios and finances has been discussed [7], but not much has been said about the ramifications for patients and research participants, namely, that innovative clinical trial designs can be patient centric and scientifically rigorous while minimizing cost and time. This section highlights several noteworthy categories of innovative design. “Adaptive designs” apply to both exploratory and confirmatory phases. “Master protocols” are more complex infrastructures that undertake adaptive, multi-arm, multidrug, and multi-population and/or disease studies [8, 9]. Other relevant innovative approaches are the use of external data, informational designs, and portfolio optimization. Adaptive designs [10, 11]  The FDA released its final guidance on “Adaptive Designs for Clinical Trials of Drugs and Biologics” in November 2019, which defines an adaptive design as “a clinical trial design that allows for prospectively planned modifications to one or more aspects of the design based on accumulating data from subjects in that trial.” Adaptations are implemented at interim analyses by an independent board of experts not involved with the trial execution and strictly following the prespecified algorithm. Adaptive designs are used in early development studies to learn about dosing, exposure, differential patient dose-response profiles, response modifiers, and biomarkers predictive of response. At this stage, they offer opportunities to accelerate drug development and make correct decisions earlier to maximize the probability of success of future studies. In the confirmatory setting, adaptive designs are used to further alleviate uncertainty, regarding sample size, selected dose, or whether the

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submission should be made with the whole population or a biomarker-­defined subpopulation. All adaptations must be carried out according to prespecified rules. Commonly used adaptive designs include adaptive randomization, group sequential design (GSD), sample size reestimation (SSR), seamless Phase II/III design, and adaptive-enrichment design. Adaptive randomization is usually applied in earlystage trials, while other designs listed are used mainly at the confirmatory stage. Adaptive randomization allows randomization ratios of various study arms to be updated frequently, based on the observed interim efficacy outcome achieved by the current patients already in the trial. This method is usually applied in dose-finding trials but can also be applied in master protocols, which are discussed in the next section. For most adaptive randomization techniques, future subjects entering the trial will have higher probability of allocation to treatment arms that showed a better risk/benefit profile. GSD allows for efficacy or futility stopping at earlier time points of interim analyses, i.e., stopping early if the interim data already show that the drug works or is very unlikely to work. This accelerates progress to the next step in development or allows for early termination of an ineffective drug. SSR design reassesses planningstage assumptions about the treatment effect size and its variability at the planning stage. The treatment effect is reestimated at interim analysis using the data collected within the trial, and the final sample size is adjusted accordingly. Traditional drug development involves Phase IIb trials designed to select the best dose and separate Phase III trials to demonstrate the efficacy of the selected dose over control. Adaptive seamless design integrates Phase IIb and Phase III objectives into one single trial. In this design, dose selection is implemented at the interim analysis, and the trial continues with the selected dose arm and the control arm into the Phase III stage of the same trial. This results in accelerated development and often a reduction in the total sample size. Adaptive design with the goal of patient subpopulation selection is called “adaptive enrichment design.” Here, the focus in drug development is to find predictive biomarkers that select patients who would benefit from higher efficacy, e.g., precision medicine. Adaptive enrichment designs compare the treatment effect in the overall study population vs. the targeted subgroup (the enriched population). Patients are enrolled first from a wider population. At interim analysis, the trial population may be adapted based on the observed treatment effects. The trial continues either in the full study population or in the enriched subpopulation, but only if the latter demonstrates much better efficacy. Master protocols  The adaptive elements described have been extended into higher-­ order structures called “master protocols.” The FDA released a draft guidance “Master Protocols: Efficient Clinical Trial Design Strategies to Expedite Development of Oncology Drugs and Biologics” in September 2018, which defines a master protocol as a “protocol designed with multiple sub-studies, which may have different objectives and involves coordinated efforts to evaluate one or more investigational drugs in one or more disease subtypes within the overall trial structure.” While the focus of the FDA guidance is on oncology, it should be noted that master protocols have application across therapeutic areas and benefits are substantial in rare diseases. Master protocols include umbrella trials, basket trials, and platform trials [9].

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Umbrella trials study multiple therapies to treat the same disease family in a single trial. Research subjects with different biomarker signatures are either directly matched to corresponding therapies (BATTLE-2, Lung-MAP in lung cancer) or adaptive randomization is used to identify subpopulations matching best to each therapy and then to adjust randomization probabilities accordingly (I-SPY in breast cancer). An umbrella study with multiple experimental therapeutic arms may benefit from having them all share a common control arm, reducing cost, time, and the number of patients that need to be enrolled on the control arm. Because of the requirement for multiple drugs, most umbrella trials involve multiple sponsors, and gaining agreement among them requires an initial investment in money and time. Basket trials evaluate a single therapy for multiple diseases or disease subtypes that share a common molecular marker or other pathogenic feature and hinge on a molecular pathway hypothesis of a strong link between disease, molecular target, and targeted therapy. In fact, basket trials may assume that the molecular pathway classification of disease is more fundamental than traditional classification schemes based on histology. Data from disease subtypes may be fully pooled, or partial pooling (“information borrowing”) may occur between cohorts. In the latter case, Bayesian techniques (statistical techniques specifically designed to flexibly update probability estimates with emerging data) may be used to quantify similarity between cohorts, which is used to determine information borrowing [12, 13]. Pooling and/or information borrowing can lead to remarkable increases in efficiency, especially if 3–5 cohorts are included in the trial. However, there is a risk that a single cohort in which the drug is ineffective could dilute a positive efficacy signal from other cohorts in the pooled result, leading to a false negative. Conversely, a strong efficacy signal in one cohort could overshadow ineffectiveness in other cohorts, resulting in an overly broad approval for the drug. These risks, related to unanticipated heterogeneity between cohorts, may be ameliorated to some degree either with Bayesian techniques or with GSDs, in which interim data is used to eliminate indications [12, 14–16]. To date, basket trials have been largely applied in the exploratory development space and have only been used in the confirmatory space in exceptional cases, featuring either extraordinary clinical benefit or extremely strong supporting scientific evidence coupled with severe unmet medical need. Recognizing that a large majority of effective therapies are not exceptional and that the confirmatory phase of development is the most costly and time-consuming phase, a basket trial design that may be suitable for the confirmatory phase of development for any effective therapy has been developed [14, 15]. With this methodology, improvements in development efficiency of at least 40% and up to severalfold can, in principle, be achieved compared to traditional development [14, 17]. Examples of basket trials include one that led to the approval of the exceptional drug, imatinib, in several rare cancers expressing its target [18] and one that resulted in the approval of the immune checkpoint inhibitor, pembrolizumab, in cancers with a DNA repair defect that is expected to make them more susceptible to attack by the immune system [19] (see Chap. 13 for additional examples). Platform trials provide an investigational infrastructure to study multiple treatments, heterogeneous populations, and/or multiple diseases within the same protocol, with an operational setup (a “platform”) prepared in advance, perpetually in operation so that compounds/populations unknown today may enter the platform in

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the future. Compounds also exit when they are found to be effective or ineffective. This assembly line approach leads to standardization, saving time and cost, as operational infrastructure does not have to be recreated for each individual trial as in the traditional setting. The opportunity to share control arms reduces the number of research participants receiving only control therapy. In a platform trial, the control arm performance may improve over time, and the control arm therapy may change if a new standard of care is approved, creating challenges regarding which portion of the control arm to use as comparison for a given experimental therapy. The simplest approach is to compare a given experimental therapy to the concurrently running control arm, but approaches that also utilize prior control therapies using mathematical modeling techniques may be considered. Platform trials offer the opportunity to address a broad range of research questions at once, and this reduces the chance a research participant will be ineligible. For example, a very recent platform trial evaluates different investigational treatments for three different types of chronic pain, osteoarthritis, diabetic peripheral neuropathic pain, and lower back pain [20]. This trial also combines multiple innovative elements presented earlier: sample size adaptation, addition/removal of treatment arms, sharing the placebo data, as well as “borrowing” data on the treatment effect across pain types as the same asset may have been used by different disease subpopulations. Most platform trials involve many different sponsors sharing the platform investigational infrastructure. It can be a challenge to get multiple sponsors to agree on the platform, adding to cost and time at the beginning. However, it is expected that over time an effective platform trial will save more time and money than it costs to set up. One very recent example is a platform trial evaluating compounds from different sponsors to treat Crohn’s disease in children. Use of external data  More recently, techniques have been developed to augment an RCT with external data such as real-world data (RWD) or information from published literature. When applied to augmenting control arms, these techniques can reduce the number of research participants required on these arms while minimizing the loss of rigor compared to concurrent controls [6] (see Chap. 24 for additional examples). Informational design  At times, particularly in the rare disease setting, it is not clear what the primary endpoint for confirmatory trials should be. The primary endpoint is the main indicator of clinical benefit that the FDA and other national regulatory authorities will use to judge whether the therapy is approvable for use. FDA recommendations for rare diseases suggest that data from untreated patients should be used to clarify the study endpoints of interest. However, if natural history data are not available, following this recommendation entails observation of untreated research subjects, who may have debilitating and/or life-threatening diseases. In this situation, the adaptive informational design [21] can be employed. A set of several candidate primary endpoints based on medical/clinical/patient perspectives are predefined at the pivotal trial start including the adaptation algorithms, which can result in the selection of one primary endpoint or the formation of a new primary endpoint by combining several candidate endpoints. A subset of the patients, the informational cohort, is randomly selected at the end of the trial to inform the

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best primary endpoint for the trial according to prespecified adaptation rules. As an alternative to the informational design, interim analyses can be performed during trial conduct to make preplanned trial decisions including the primary endpoint selection. However, that would require that all candidate endpoints mature at the same interim time point, a somewhat uncommon scenario. Decision science and portfolio optimization  Previously, we discussed the concept of efficiency. Clearly, designs that are more efficient would allow studying more treatments under the same budget. Decision analysis is an essential tool for improving efficiency of drug development. Decision analyses can be performed to optimize a single study, the development program for a single drug, or an entire portfolio of drugs subject to a budgetary limit. Considering optimization at the portfolio level is of particular importance, as spending more resources on one drug will likely impede the development of another [17, 22–25]. While we will not discuss decision analysis further, its use is required to optimize any of the innovations discussed herein.

Benefits of Innovative Designs from the Patient’s Perspective This section highlights patient benefit from innovative designs and is summarized in Table 10.1. Table 10.1  Patients awaiting treatment Benefit More appropriate treatment for patients

How More efficient dose/regimen selection More efficient subpopulation selection More efficient treatment selection

Faster access to treatments

Early stopping for efficacy Fewer patients in the control arm Combining stages of development

Start with a smaller trial and increase sample size only if needed Overcoming enrollment challenges in indications Making it possible for Overcoming enrollment challenges in rare diseases and small indications Smaller trial size subgroups of patients to have innovative Validating the primary endpoint within therapies developed the same trial – no need for a separate natural history trial More good treatments Increasing development efficiency, available better decision-making Affordable medicines

Increasing development efficiency and lowering cost

Innovative design Adaptive randomization Adaptive enrichment, basket trials Umbrella trials, GSD with multiple treatment groups GSD Use of historic data, master protocols Seamless Phase II/III design SSR Basket trials Basket trials Use of historical information Informational design Decision analysis, portfolio optimization, master protocols GSD, decision analysis, portfolio optimization, master protocols

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Treatment-related benefits  Umbrella designs can improve the overall treatment selection when multiple treatments are tested in parallel in the same trial. The efficiency of master protocols allows more treatments to be tested within a given budget. Inferior treatments can stem from a suboptimal dose taken to Phase III or from sponsor failure to investigate subpopulations that would most benefit from the treatment. Adaptive dose finding adds efficiency to dose selection, while adaptive enrichment design improves subpopulation selection, allowing better-quality treatments to reach the market. Faster access to medicines  Patients want effective medicines, sooner. Innovation that accelerates drug development without diminishing the ethical and scientific principles of clinical research is a moral imperative. Several innovative designs accelerate access to new therapeutics. GSD allows for early drug approval for highly efficacious drugs, by providing for early stops for efficacy if this becomes clear at interim analysis points. SSR allows sponsors to start with a smaller trial and increase sample size only if interim data suggests that is necessary. Seamless Phase II/III designs reduce the total sample size and the operational gap between phases. Pooling of indications in basket trials will reduce sample size and enrollment time. Use of either a common control arm (platform and umbrella trials) or external control data may reduce the sample size and therefore enrollment time. Affordable medicines  The high cost of medicine contributes to health-care inequities. Large segments of society must choose between medicine and other life necessities. The high cost and low success rate of drug development contribute to this issue. Innovations described in this chapter, such as basket trial designs and portfolio level optimization, can increase the efficiency and lower the cost of drug development, a first step in ameliorating this problem. Enhanced access in rare disease  Patients with rare diseases often wait a particularly long time for effective therapies. It is simply a matter of scale: a limited patient population means a smaller pool of potential research participants. These small numbers increase the cost of development and decrease the potential return on investment for sponsors, resulting in limited research focused on the disease. Basket trials may be particularly helpful here. If enrollment is faster and more feasible via pooling of related diseases or disease subtypes, cost-effective development might be feasible for sponsors. Thus, efficient innovative designs that decrease the required sample size enhance the discovery of new therapies. Insufficient knowledge about rare disease makes it difficult to design a study with optimal endpoints and sample sizes. In the absence of natural history data, informational and other adaptive designs with SSR make development more feasible [21, 26].

 enefits of Innovative Designs from the Research Participant’s B Perspective This section highlights patient benefit from innovative designs and is summarized in Table 10.2

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Table 10.2  Research participants Benefit Receiving more effective therapy while participating in a trial

How Receiving better doses more frequently Receiving tailored treatment Fewer research participants exposed to control

Higher probability to be Inclusion criteria comprising enrolled faster multiple biomarkers profiles/ subpopulations/multiple regimens

Innovative design feature Adaptive randomization, dropping futile doses Adaptive randomization, enrichment designs, master protocols Use of historic data, adaptive randomization, master protocols, seamless Phase II/III design Master protocols

Study procedures and benefit  Research participants must be informed of the risks and benefits associated with the trial before they consent to participate. Although clinical trials prioritize the gathering of data, it is possible for subjects to benefit from the study. Research participants are understandably concerned about exposure to futile investigational products, nonoptimal doses or schedules, or targeted agents that do not match the molecular characteristics of their disease. As discussed above, the use of group sequential designs allows early termination of futile products and nonoptimal doses or schedules. Adaptive randomization facilitates better matching of therapies and disease molecular characteristics, especially within umbrella trials and platform trials. Adaptive enrichment designs provide the opportunity to identify and further study the subpopulations benefitting most from the treatment. Research participants may be reluctant to enroll on an active control arm or a placebo arm, preferring to have access to the experimental therapy. The required fraction of research participants on the control or placebo arm can be reduced by the use of external controls or by control arm sharing, as is potentially available in umbrella trials and platform trials. In the setting of rare diseases, there may be no credible standard of care, and therefore, when these diseases are severe or life-­ threatening, external controls may be essential to minimize or eliminate placebo exposure. Enrollment  Research participants may lose precious time and biopsy tissue during evaluation for trial participation. Umbrella trials and platform trials offer an opportunity to be considered for a larger variety of possible arms and/or sub-studies, enhancing the chances of eligibility. Altruism and respect  Research participants have the right to expect that their participation is essential to increase medical knowledge for the general good. If they participate in an inefficient design, their contribution is not fully leveraged toward this purpose. Whether the participant expects possible benefit or not, any exposure to risk must be justified. All the innovations listed here increase the efficiency and/ or speed of drug development.

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Summary and Conclusions Patients need quality treatments and timely access to life-altering medications that have been rigorously developed and can therefore be trusted to be safe and efficacious. These medicines must also be affordable and available, even to patients with rare diseases. The contributions of research participants are enhanced and respected, by matching them to the best available study or arm of a study and minimizing the chance of their being enrolled on control therapy, a futile investigational therapy, or an inappropriate dose or schedule of therapy. The innovative clinical study designs and innovative use of nonstandard data sources described in this chapter can improve the quality of treatments reaching the market and maximize the value of research participant involvement in development. These designs can also minimize the cost and time involved in drug development, making better medicines available sooner and at lower cost, in both common and rare disease settings. Care must be taken to use proper techniques to maintain scientific rigor when applying these methods. Not all benefits might be available in any one case. For example, if we use the increased efficiency to explore more drugs while keeping the budget the same, the cost may not be reduced. The simplicity of traditional designs may be preferable in selected instances, but when the voice of research participants and patients is considered, there is an ethical imperative to take advantage of the opportunities offered by innovation.

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Central Nervous System Rare Disease Drug Development

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Jane Williams and Nermina Nakas

Illustration 11 Illustration of Jennifer Powell, author of a Select Patient Narrative in Chap. 3 and a volunteer with canine rescue, along with her beloved friend and canine companion – a Golden Retriever named Abby. Artwork courtesy of the artist.

J. Williams (*) Medical Management and Scientific Services, Syneos Health Clinical Solutions, Boston, MA, USA e-mail: [email protected] N. Nakas Medical Management and Scientific Services, Clinical Solutions, Syneos Health, Morrisville, NC, USA © The Author(s), under exclusive license to Springer Nature Switzerland AG 2021 R. A. Huml (ed.), Rare Disease Drug Development, https://doi.org/10.1007/978-3-030-78605-2_11

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Introduction There are approximately 7000 different types of rare diseases with over a thousand nervous system diseases listed by the Genetic and Rare Diseases Information Center [1]. As a reminder of the impact of rare disease, they can be chronic, progressive, as well life-threatening, chronically debilitating (an EU requirement), or fatal. Approximately 50% of rare diseases affect children; 30% of whom will not live to see their fifth birthday [2]. Notably, it takes an average of 5 years to diagnose a child with a rare disease [3]. At a recent EU RD congress (tenth European Conference on Rare Disease & Orphan Products [ECRD] 2020 [14-15MAY2020]), one of the congress themes noted was “When Therapies Meet the Needs: Enabling a patient-centric approach to therapeutic development.” Today’s scientific innovations and clinical research, supported by regulatory agencies in developing rare disease therapies, meet several roadblocks and formidable challenges. The rare disease drug development paradigm includes finding the right patients at the right time to participate in the right clinical trials with the right design and endpoints, taking the right drug at the right dose for regulatory approval and reimbursement at the right price for the right patient. Collaboration and partnership are crucial to achieve meaningful solutions: interaction and information exchange between patients and their families, key stakeholders, scientists, industry, regulatory, payers, and advocates is crucial. Technology, social media, and cultural changes provide opportunities to engage and collaborate with patients and their families early in the drug development process. Clinical outcome assessments (COAs) measure a patient’s symptoms, overall mental state, or the effects of a disease or condition on how the patient functions. One type of COAs is patient-reported outcome (PRO) measures (FDA CDER-COA DDT Qualification Program, updated January 2018). Patient-focused drug development includes PROs being utilized with the patient in mind: when therapy meets the needs by enabling a patient-centric approach to therapeutic development. By having the patient at the core of all policies, the lines between sectors disappears, the journey of a patient living with rare disease is eased: from diagnosis to specialized care, to integration into society, to being not just empowered and engaged, but an equal partner with all the other stakeholders in creating treatments and solutions for a better quality of life. Rare disease patients are being systematically involved into research and development processes from design to execution, including the aspects pertaining to data collection and data sharing. Patients are experts on what it is like to live with their condition: they are our “eye openers” for research and development. The “future of inclusion” builds upon the right balance between the push of science, technology, and business and the pull of patients’ needs. This would be possible by integrating the information collected through real-world evidence (see Chap. 24 for more details) together with well-­designed clinical trials in order to fulfill the scientific and regulatory standards while covering all gaps in knowledge and enhancing the value of medicinal products. The role of patients is crucial in understanding their condition and the clinical relevance and the real value of drug, as well as being owners of their data and full contributors to the research and development programs or to the regulatory assessments. The FDA’s Patient Affairs Staff (PAS), established in 2017, conducts cross-cutting patient engagement listening

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sessions to facilitate expeditious sharing of patient perspectives on disease and burden, impact on daily activities and quality of life, and priorities to consider in medicinal product development. Currently, drug repurposing is a hot topic and an area where rare disease groups are leaning today, directing the drug development pathways for patients’ benefit. Accelerated translation of technologies into available, accessible, and affordable treatment is ongoing today, but the uptake of new technologies remains too slow, and decision-making processes need to be adapted in order to make curative or disease-modifying treatments available. There is a need for mutual effort for mutual benefit, to foster the development of therapies that meet the needs of patients. Collaboration of all stakeholders allows complementarity of skills and resources while respecting the scope and specificities and “how independent we can be all if we support each other and recognize our inter-dependence.” Rare diseases often affect varying parts of the central nervous system (CNS), and with such a large and disseminated organ system, therapies targeting CNS clinical manifestations are complex. Next-generation sequencing has accelerated gene-­based research and development, helping to identify genetic mutations such as CDKL5 epilepsy syndrome. However, even with having a causative gene been identified and with continuing research to identify more specific genetic mutations and phenotypic subtypes, it is apparent that specific therapy for one subtype of a disease may not work or be appropriate for another subtype. Additionally, genetic testing for infantile epilepsies has increased our awareness of the incidence of each rare disease. The US epilepsy overall prevalence is ~1225 per 100,000 and is expected to grow. By the numbers and using the umbrella term of epilepsy, it is not considered a rare disease; however, there are multiple etiologies for epilepsy, and once phenotypic, genotypic, and other factors are taken into account, there are many causes of epilepsy which are, ultimately, rare diseases. In 1909, a group of world-renown scholar epileptologists writing for the journal Epilepsia joined to form the International League Against Epilepsy (ILAE) to, among other goals, “advance and disseminate knowledge about epilepsy”. The ILAE classification of epilepsies is not based on the underlying cause of the epilepsy but provides an operational mechanism to place a type of epilepsy in context [4]. Epilepsy is predominantly treated with antiepileptic drugs (AEDs), now referred to as antiseizure drugs (ASDs), that are mostly selected based on seizure classification and age. Marketed drugs are mostly oral treatments, given once or twice daily, and many have a generic available, such as Vimpat® (lacosamide). Treatment-­ resistant epilepsy is frequently treated with lifestyle and dietary changes, medical devices, and surgical interventions, while a few drugs have been recently approved for specific types of treatment-resistant epilepsy; these come at a high cost. Vigabatrin (e.g., even the generic version) is priced high due to its utilization for refractory patients/infantile spasms, while Epidiolex® (cannabidiol) is priced high due to being a first-in-class cannabinoid drug approved for LGS, DS, and tuberous sclerosis complex. The US epilepsy market is highly saturated with branded and generic ASDs. The market is expected to grow moderately in the next 4 years to reach $3.4 billion by 2024, mostly driven by Epidiolex® uptake, price increases, and competitive label expansions. The epilepsy treatment landscape will likely evolve toward therapies that will target specific treatment-resistant epilepsy types

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with a wide range of modalities; gene therapy and monoclonal antibodies may play a key role in the long term. All epilepsy late-stage assets are small molecules; Phase II and Phase III products include several GABA modulators, representing ~40% products. Most of the early-stage assets developed for epilepsy are targeting ion channels or receptors already known to play a role in epilepsy.

History of Antiepileptic Drugs Epilepsy was treated with various herbal remedies until a chance observation by a physician in the Victorian era found potassium bromide caused sedative effects and impotence, leading to a positive clinical trial of its use stopping seizures. Potassium bromide was widely used for epilepsy until phenobarbital was discovered in 1912, a drug with continued use today. The discovery of phenytoin in 1938 and then trimethadione in 1946 was followed by paramethadione in 1949. Phenytoin works on sodium channels, stabilizing the neuronal action potential. Both trimethadione and paramethadione target thalamic neurons to reduce T-type calcium currents, raising the threshold for seizures. These discoveries were followed by nearly a dozen new ASDs in the 1950s, covering such classes as gamma-aminobutyric acid (GABA) receptor agonists, carbonic anhydrase (CA) inhibitors, and melanocortin-2 (MC2) receptor agonists. Many of these early ASDs were mixed use, with indications such as glaucoma and altitude sickness (acetazolamide sodium) and arthritis (repository corticotropin). These drugs were FDA approved for epilepsy and used worldwide, even when newer drugs came on the market. Through the 1960s, benzodiazepines were found to minimize some seizures; however, in 1975 the Anticonvulsant Screening Program developed tests to screen identify molecules as potential ASDs. There have been more than 32,000 agents screened through this program by government researchers, academicians, and pharmaceutical companies, contributing to the development of topiramate, rufinamide, retigabine, lacosamide, and felbamate [5]. In all, these drugs are targeting the symptom of seizure, and as we move further into the twenty-first century, the goal to identify the underlying cause of an epilepsy syndrome is bolstered by the development of genetic testing to identify the rare disease and guide precision medicine to a treatment and cure.

Disease Overview Epilepsia in Greek means to be shaken, to be seized, or to take hold of. Epilepsy is the third most common neurologic disorder with lifetime prevalence of 8–10% [6]. The diagnosis in epilepsy may be convoluted, because its principal clinical symptom, seizures, may be manifested in very different ways, not always being easy to classify, quantify, and/or track consistently. Epileptic seizures are sudden, involuntary, time-limited alterations in behavior, with changes in motor activity or autonomic function, consciousness, or sensation, accompanied by an abnormal electrical discharge in the brain. The causes of epilepsy include, but are not limited to, brain defects, head trauma, infectious diseases, stroke, brain tumors, and genetic or developmental abnormalities. Unfortunately, about 2/3 of epilepsies are of unknown etiology and are considered idiopathic. Epileptic seizures are recurrent and may be

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generalized or focal (called partial as well) seizures. Approximately a third are generalized seizures. Focal seizures, simple or complex, may progress secondarily to generalized seizures. There are many paroxysmal events similar to epileptic seizures, making the diagnosis even more complicated – such as various movement disorders, panic attacks, migraine, convulsive syncope, and hypoglycemia. In fact, as many as up to 30% of patients with seizure-like symptoms not responsive to medications turn out to be of non-epileptic origin after continuous video-EEG monitoring [7]. On the other hand, seizure over- and/or underreporting is also a factor that needs to be taken into consideration when assessing a patient’s prior epilepsy history. This is especially applicable in the pediatric population since over 60% of patients turn out to have a different seizure frequency seen in clinical observations compared to records kept by their parents or caregivers [8]. In 2017, the International League Against Epilepsy revised its seizure types classification and the main principles, or rather levels of diagnosis, are outlined below [9]: • Level 1. Seizure type (focal, generalized, or unknown seizure types). At this stage, it is assumed that the clinician has already made a definite diagnosis of an epileptic seizure as opposed to non-epileptic events. • Level 2. Epilepsy type (focal, generalized, combined generalized and focal, unknown). Many epilepsies will include multiple types of seizures, and these are further classified into specific types. For example, generalized seizures may include absence, myotonic, atonic, tonic, and tonic-clonic seizures. Further, the generalized epilepsy family also includes idiopathic generalized epilepsies: childhood absence epilepsy, juvenile absence epilepsy, juvenile myoclonic epilepsy, and generalized tonic-clonic seizures alone. The same branching principle applies to other types, too. • At Level 3 (epilepsy syndromes), a specific diagnosis can be made. At this stage, the clinician considers age-dependent features, seizure triggers, diurnal variation, and other factors. Another key factor in classifying epilepsy is its etiology, which can be of structural origin or otherwise (genetic, structural, infectious, metabolic, immune, or unknown). When speaking about genetic etiology, some cases may be associated with a specific gene (e.g., around 80% of patients with Dravet syndrome have underlying sodium channel subunit gene – SCN1A – mutation), but in the majority of other cases, the exact gene can be difficult, if not impossible, to pinpoint. Finally, to make matters worse, there may well be a combination of two or more etiologies in an individual patient, each potentially requiring different treatment approaches. It is also known that patients with epilepsy may experience a new seizure type at any time with or without changes to their current therapy; likewise, an existing seizure type may morph into a new one with increased or decreased severity. Change in seizure type (up to a point of disappearance of a certain seizure) is also likely because even patients with treatment-resistant epilepsy may develop spontaneous long-term remission.

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Epilepsy Centers of Excellence Epilepsy centers provide a comprehensive approach to the diagnosis and treatment of epilepsy. The team typically includes neurologists, including pediatric neurologists and epileptologists, with expertise in treating seizures; neurosurgeons; neuropsychologists; and electroencephalography (EEG) technologists. Patients with seizures not controlled by a second ASD, treatment-resistant adult and pediatric patients, and patients with childhood catastrophic epilepsy such as Dravet syndrome and/or LGS are treated at these centers. Here, ASD therapies are weaned off and/or added based on medication efficacy and side effects. The patient’s lifestyle needs are also taken into account, such as avoiding drugs with significant adverse effects, especially in younger patients. The National Association of Epilepsy Centers (NAEC) is an association of Comprehensive Epilepsy Centers in the United States, providing an accreditation program and promoting quality standards of epilepsy care. The NAEC does advocacy and provides educational resources to its members and the public. In the United States, there are over 230 specialized epilepsy centers affiliated with the NAEC, providing four different levels of care. Level 3 and 4 centers are accredited by the NAEC with Level 4 centers, providing the most comprehensive and complex monitoring and interventions [10].

Epilepsy Presentation Epilepsy is defined when any of the following exist [11]: • At least two unprovoked seizures occurring more than 24 hours apart. • One unprovoked seizure and a probability of further seizures similar to the general recurrence risk after two unprovoked seizures (e.g., ≥60 percent) occurring over the next 10 years. This may be the case with remote structural lesions, such as stroke, central nervous system infection, or certain types of traumatic brain injury. • Diagnosis of an epilepsy syndrome. The second criterion was added by the ILAE working group in 2014 and emphasizes the importance of neuroimaging and EEG in the evaluation of patients with a first-time seizure, as some of these patients will meet criteria for epilepsy at the time of a first seizure.

Epilepsy Evaluation: Initial The initial diagnostic evaluation starts with detailed medical history of a first seizure to confirm the event as a seizure and to rule out any alternative diagnoses, to determine if similar events have happened in the past, and to evaluate for underlying risks for seizures in the past. Family history and concomitant medications are

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reviewed. As much as possible, an accurate description of the seizure event should be gathered, though it may be difficult to obtain from the patient and family: usually, it is necessary to ask pointed questions about the circumstances and/or triggers leading up to the seizure, the ictal behaviors, as well the postictal state. Additionally, timing of the seizure related to sleep is also important to determine, as events that occur during sleep, or in the transition periods between sleep and awake, have implications for both differential diagnosis and the risk of recurrence. After the seizure has ended, there is a transition period from the ictal state back to the pre-­ seizure baseline level of awareness and function, referred to as the postictal period, and this is usually manifested by confusion. Focal neurologic deficits are noted as well, such as Todd paralysis or postictal paresis. Seizure precipitants or triggers are important key elements in the medical history, noted immediately prior the seizure. Some examples of triggers include but are not limited to emotions, exercise, loud music, and flashing lights. However, the majority of patients with epilepsy have no consistent trigger to their seizures, and triggers are the sole cause of epileptic seizures in only a very small percentage of patients. Seizures are a clinical diagnosis made by history, physical and neurologic examinations, and selected additional tests to identify an underlying cause. Testing should include laboratory studies (electrolytes, glucose, calcium, magnesium, complete blood count, renal function tests, liver function tests, urinalysis, and toxicology screens), an electrocardiogram (ECG), an EEG (urgently when impaired sensorium is persistent), and a neuroimaging study.

Treatment Most seizures diminish spontaneously within 2 minutes and rapid administration of a benzodiazepine or ASD may not be required. Notably, antiseizure drugs are not always indicated after a first seizure. The decision whether to start ASD therapy depends on multiple factors, including if the event has represented a seizure, the suspected or confirmed cause/trigger of the seizure based on the initial evaluation, the stability of the patient, and the estimated risk of recurrent seizure. In 2013, the ILAE updated its 2006 review of evidence, supporting the efficacy and use of antiepileptic drugs as first-line monotherapies in epilepsy subpopulations as shown in Table 11.1. Many patients, even when they become seizure free, are not without bothersome side effects from their ASD [12]. Assessing the side effects of ASDs is especially challenging in patients on long-term ASDs as any ‘baseline’ may be many years past and even intelligent adults, parents, and physicians may fail to appreciate chronic adverse effects [13]. Additionally, up to 50% patients relapse with the first ASD treatment, and another ASD replaces or is added to the first. About 30–40% of patients with epilepsy continue to have seizures affecting their quality of life and suffer from some of the common “comorbidities” associated with chronic epilepsy, such as cognitive issues, memory problems, and depression. Those patients who do not respond well to ASDs are also more likely to develop sudden unexpected death (SUDEP) and other forms of epilepsy-related mortality [14].

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Table 11.1  First-line monotherapies in epilepsy subpopulations Adults

Focal seizures Carbamazepine*, phenytoin*

Levetiracetam* Zonisamide*, valproic acid N/A Children Oxcarbazepine*, carbamazepine, Phenytoin Vigabatrin Phenobarbital, valproic acid Topiramate

Generalized onset tonic-clonic seizures Oxcarbazepine, lamotrigine, carbamazepine, phenytoin N/A Phenobarbital, valproic acid Topiramate Carbamazepine, phenytoin Phenobarbital, valproic acid N/A Topiramate

*Level A – antiepileptic drug established as efficacious or effective as initial monotherapy

Pharmacoresistance: A Discussion of Treatment-Resistant Epilepsy Antiseizure therapy is plagued by the pharmacoresistance phenomenon, as up to a third of patients remain non-responsive to any medication administered to alleviate their seizure burden [15]. The pharmacoresistance in epilepsy setting is defined as a failure to achieve sustained seizure freedom after two or more adequately chosen, tolerated, and appropriately-used antiseizure drugs [16]. To note, the development of drug resistance is far more likely in patients with past multiple AED exposure [17]. Several prospective long-term population-based studies have demonstrated that patients can develop pharmacoresistant epilepsy at any time: their very first seizure may be associated with subsequent treatment failure, or, after years of being seizure-­free, patients may have progressively more modest response to anti-epileptic drugs [18].

Focus on Treatment-Resistant Childhood Epilepsy Approximately 1% of children in the United States have epilepsy [19] with the most common causes of childhood being epilepsy fever and/or infections of the CNS. The incidence is highest in the first few months of life, particularly in the immediate postnatal period, falls significantly after the first year of life, is stable during the first decade, and then falls again in adolescence. Treatment-resistant childhood epilepsy accounts for approximately 30% of US childhood epilepsy cases [20, 21]. Seizures in younger children differ significantly from those in older children and adults. Children older than 6 years tend to have seizures that are quite similar to those of adults, whereas younger children and infants have less complex behaviors, particularly with focal seizures with impairment of consciousness. Determination of an alteration of consciousness is difficult in infants and young children. Typical generalized tonic-clonic and absence seizures are extremely uncommon in the first 2 years of life and rarely occur in the newborn. Most childhood seizures (e.g., febrile seizures) are either one-time occurrences that do not result in epilepsy or can be controlled with the same antiepileptic medications over time (e.g., juvenile myoclonic

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epilepsy). Childhood epilepsy may cause changes in the development of the brain, and it affects language development and education. Chronic childhood epilepsy is often associated with reduced language skills. Notably, childhood epilepsy may lead to trouble learning and lower grades, including many children ending up in special education classes. Treatment-resistant childhood epilepsy covers a spectrum of rare disorders, including Dravet syndrome, LGS, CDKL5 deficiency disorder, infantile spasms, and tuberous sclerosis complex.

Epilepsy Prognosis For epileptic patients, prognosis means the probability of further seizures after a single seizure or the likelihood of achieving seizure freedom or terminal remission after a pattern of recurring seizures has been established. Approximately 80% of patients require ongoing treatment to prevent seizures from disrupting their daily activities. Factors that determine prognosis include age, health history, infections, neurological or vascular comorbidities, and genetics. The impact of epilepsy is determined by many factors: patients have psychological, behavioral, cognitive, neurologic, academic, and social problems caused by this chronic neurologic condition. Frequent subclinical or subtle seizures may also have a direct impact. Children with intractable seizures may manifest a decline of cognitive function and memory without an etiology other than the frequent seizures.

Epilepsy Market Landscape The historical term antiepileptic drugs (AED) is currently being replaced by a more fitting definition – antiseizure drug (ASD) as the focus of epilepsy research turns more toward the understanding of what triggers the seizures and the potential means by which that process may be stopped or reversed. Indeed, despite the broad group of available ASDs, no accepted intervention strategy (pharmaceutical or otherwise) has been shown to effectively prevent or halt epileptogenesis in individuals at risk [22]. There is an ever-growing list of newer ASDs and non-pharmacologic therapies available to manage epilepsy. The ASD chosen for initial therapy should be one that is highly effective for a particular seizure type or syndrome and that is safe and well tolerated. In addition, the ASD should have low toxicity and few adverse effects. A long serum half-life allows for relatively smooth serum levels with less frequent daily dosing that can enhance medical compliance. Cost-effectiveness is also desirable; as an example, the World Health Organization recommends phenobarbital as the treatment of choice for partial and tonic-clonic seizures in countries with restricted resources. In younger children, oral suspensions, chewable tablets, and sprinkle formulations may be useful. Some ASDs come in a sustained-release form (phenytoin, carbamazepine, valproic acid, levetiracetam, lamotrigine) or have particularly long half-lives (ethosuximide, phenytoin, phenobarbital, zonisamide), requiring only once- or twice-daily dosing. These ASDs allow for “make-up dosing.”

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First-generation ASDs have been administered for well over a century, and these include carbamazepine (Tegretol®), divalproex sodium (Depakote®), ethosuximide (Zarontin®), phenobarbital (Luminal®), phenytoin (Dilantin®), primidone (Mysoline®), and valproic acid (Depakene®). Second-generation ASDs have only became available in the last two decades. While they may not necessarily be more effective, the overall tolerability has improved [23]. Among these are brivaracetam (Briviact®), ezogabine (Potiga®), felbamate (Felbatol®), gabapentin (Neurontin®), lamotrigine (Lamictal™), levetiracetam (Keppra®), oxcarbazepine (Trileptal®), perampanel (Fycompa®), pregabalin (Lyrica®), rufinamide (Banzel®), tiagabine (Gabitril®), topiramate (Topamax®), vigabatrin (Sabril®), and zonisamide (Zonegran®). Third-generation ASDs entered the market in the last several years, and their number is due to expand quickly. Most notable members of this family are eslicarbazepine (Aptiom®/Zebinix®), lacosamide (Vimpat®), and retigabine (Potiga®/ Trobalt®). See more examples in Table 11.2 below. As noted above, rare epileptic syndromes are finally receiving enough attention with several marketing approvals recently secured. To note, there are dozens of promising molecules in various stages of development.

Table 11.2  Antiseizure drugs currently on the US market

Generic name Brivaracetam Cannabidiol

Trade name Briviact® Epidiolex®

Carbamazepine Carbamazepine Carbamazepine Cenobamate Clobazam Clobazam Clobazam Clorazepate dipotassium Corticotropin

Carbatrol Carnexiv™ Tegretol Xcorpri® Klonopin Onfi® Sympazan™ Tranxene®

Diazepam Diazepam Divalproex sodium Eslicarbazepine Ethosuximide Ethotoin Everolimus Felbamate

HP Acthar Gel® Diastat® Valtoco® Depakote® Aptiom® Zarontin® Peganone Afinitor Disperz® Felbatol

Indication Epilepsy, general Dravet syndrome, Lennox-Gastaut syndrome Epilepsy, general Epilepsy, general Epilepsy, general Partial-onset seizures Epilepsy, general Dravet syndrome Lennox-Gastaut syndrome Generalized anxiety disorder

FDA approval date 2016 2018 1997 2016 1968 2019 1997 2011 2018 1972

Epilepsy and multiple indications

2010

Epilepsy, general Epilepsy, general Migraine, bipolar disorder, Epilepsy Epilepsy, general Absence seizures Epilepsy, general Tuberous sclerosis complex- associated partial onset seizures Lennox-Gastaut syndrome, Epilepsy, traumatic brain injury

1997 2020 1983 2013 1960 1957 2018 1993 (continued)

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Table 11.2 (continued) FDA approval date 2020

Generic name Fenfluramine hydrochloride Fosphenytoin Gabapentin Lacosamide Lacosamide Lamotrigine Lamotrigine

Trade name Fintepla®

Indication Dravet syndrome

Cerebyx® Neurontin® Vimpat® Vimpat® Lamictal® Lamictal XR®

1996 1993 2008 2020 1994 2009

Levetiracetam Levetiracetam Levetiracetam

Elepsia XR™ Keppra XR® Spirtam®

Lorazepam Midazolam Mmidazolam hydrochloride Oxcarbazepine Oxcarbazepine Perampanel Perampanel Phenobarbital Phenytoin Pregabalin

Ativan® Nayzilam® Seizalam®

Epilepsy, general Post-herpetic neuralgia Epilepsy, general Primary generalized tonic-clonic seizures Partial-onset seizures Partial-onset seizures, General Tonic-clonic, Partial-onset seizures Partial-onset seizures, myoclonic Partial-onset seizures, Myoclonic, general tonic-clonic Generalized anxiety Disorder Seizure clusters, acute repetitive seizures Status epilepticus Partial-onset seizures Partial seizures Epilepsy, general Partial onset seizures Epilepsy, general Epilepsy, general Fibromyalgia; post-herpetic neuralgia; epilepsy, general, multiple indications Epilepsy, general Lennox-Gastaut syndrome Dravet syndrome Epilepsy, general

2012 2000 2012 2017

Primidone Rufinamide Stiripentol Tiagabine hydrochloride Topiramate Topiramate Topiramate Valproate Valproic acid Vigabatrin Zonisamide

Oxtellar XR® Trileptal® Fycompa® Fycompa® Dilantin® Lyrica® Mysoline® Banzel® Diacomit® Gabitril® Qudexy XR®

Lennox-Gastaut syndrome; epilepsy, general; migraine Topamax® Lennox-Gastaut syndrome Trokendi XR® Epilepsy, general Depacon® Epilepsy, general Stavzor™ Epilepsy, general Sabril® Complex partial seizures Zonegran® Epilepsy, general

2018 2008 2015 1977 2019 2018

a

1953 2005 1954 2008 2018 1997 2014 1996 2013 1996 2008 2019 2000

Drugs already on the market pre-1938 were allowed to remain on the market without FDA review/ approval

a

Antiseizure Drugs in the Pipeline Epilepsy is one of the most common indications for clinical research in neurology, so it is not unexpected that at any time, well over a hundred international clinical trials in epilepsy are ongoing. Epilepsy development pipeline includes therapeutic strategy evolution with the epilepsy treatment landscape likely evolving toward therapies that will target specific treatment-resistant epilepsy types with a wide range of

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modalities; gene therapy and monoclonal antibodies may play a key role in the long term. Currently, conventional ion channels inhibitors are used as antiepileptic initial monotherapies, including, but not limited to, sodium channel blockers (e.g., carbamazepine, phenytoin, lamotrigine, pregabalin, and oxcarbazepine), calcium channel modulators (e.g., gabapentin), and GABA receptor modulators (e.g., zonisamide).

Cannabis Derivatives With the recent FDA approval for Epidiolex®, research in the cannabis derivatives area continues, as the plant is known to contain over 80 phytocannabinoids, with two of them proven for their antiepileptic activity: tetrahydrocannabiniol (THC) and cannabidiol (CBD) [15]. The transcutaneous delivery via gel or patch (Zygel™) was recently confirmed to be associated with a continuing improvement in seizure control in a heterogenous group of rare and ultrarare epilepsies known as developmental and epileptic encephalopathies (DEE).

Ganaxolone Despite the drug failing Phase III trial in adult focal-onset seizures back in 2016, the sponsor company is currently advancing the candidate in status epilepticus and pediatric orphan indications. In September 2017, the company announced top-line data from a Phase III study in patients with CDKL5 disorder (a severe, rare epilepsy type with no effective treatment available). Orphan drug designation already received for this indication should speed up the availability of ganaxolone on the market, if the results from subsequent trials confirm its favorable safety and efficacy profile. On the horizon for general epilepsy treatment are small molecules aimed at different targets beyond ion channels, such as ERK inhibitor and cholesterol 24-­hydroxylase inhibitor. Additional modalities with monoclonal antibodies such as natalizumab, targeting α4-integrin, are to be noted as well. More drugs are targeting specific epilepsy types: soticlestat and EPX-300 are developed for both general epilepsy and Dravet syndrome, and Translarna™ (ataluren) is an RNA therapy solely being developed for Dravet syndrome. There are additional targets available (e.g., beyond ion channels) for drug-resistant patients and targeted treatments for specific syndromes. The future brings possibilities of new targets and modalities, with better outcomes for specific syndromes and potential cure: ASO and non-ASO gene therapy and monoclonal antibodies; non-ASO gene therapy such as neuropeptide galanin, NPY and Y2 gene transfer, GDNF, p38γ, and SCN1A RNA; and therapies which may be “one shot” treatments. Preclinical studies have demonstrated the potential for AAV-based gene therapy for epilepsy, from modulating classic neurotransmitter systems to targeting or overexpressing of neuropeptide receptors in seizure-specific brain regions. Additional small molecules preventing seizures and targeting specific epilepsy types for Dravet syndrome include an LIMK1 inhibitor (general epilepsy as well) and a prokineticin receptor modulator.

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Table 11.3  Current treatments aimed at minimizing symptoms Symptom Hypertension or hypertensive crisis Hypotension Gastroesophageal reflux Decrease tear production Sleep apnea

Treatment Alpha2 agonist, benzodiazepine Mineralocorticoid, alpha1 agonist H2 blocker, proton pump inhibitor Artificial tears CPAP or BiPAP

Familial Dysautonomia Familial dysautonomia (FD) is a rare autosomal recessive disease affecting the development of sensory and autonomic neurons and the survivability of these neurons, mainly seen in children of Ashkenazi Jewish decent [24]. Also known as Riley-Day syndrome or hereditary sensory and autonomic neuropathy type III, FD is a congenital disease caused by a mutation in the IkB kinase-associated protein gene (IKBKAP) gene, resulting in lack of functional ELP-1 protein occurring in 1 in 10,000 births [25, 26]. Sensory and sympathetic neuronal and neural plexus deficits and loss in the dorsal root ganglia, spinal cord, superior cervical ganglia, dermis and epidermis, cardiac neural plexus, blood vessels, optic nerve, and brain stem result in a decreased pain and temperature sensitivity, swallowing difficulty, gastrointestinal dysfunction, gait dysfunction, and cardiovascular dysfunction, among other symptoms. Episodes of uncontrollable nausea, retching, and vomiting, combined with swallowing dysfunction lead to aspiration pneumonia, which may lead to death (Rubin et al. 2019). A failure in the chemoreceptor reflex added to sleep-disordered breathing, subsequent hypercapnia, hypoxia, hypotension, and bradycardia place these patients at the risk of sudden unexpected death during sleep (SUDS). Available treatment is directed at alleviating or minimizing symptoms (Table 11.3, Rubin et al 2017). Care must be taken for patients with FD to avoid serotonergic medications as these patients have monoamine oxidase A deficiency. For patients on a mineralocorticoid, it is important to add potassium replacement to avoid cardiac dysrhythmias from hypokalemia [25]. Research includes use of generic drugs to target symptoms as well as cognitive behavioral therapy to assist with minimizing catecholamine release after a stressor. Clinical trials have looked into producing a functional ELP-1 gene, initially searching for compounds that would reverse the neuronal dysgenesis, and several were identified. Tocotrienol has been studied, and ongoing development of mouse models to assist with R&D continues. At the time of this writing, there is one natural history study (NCT03920774) ongoing for FD.

CDKL5 Deficiency Disorder CDKL5 deficiency disorder (CDD) is an X-linked rare developmental and epileptic encephalopathy (DEE) caused by a mutation in cyclin-dependent kinase-like 5, an integral protein in brain development [27, 28]. The incidence is 1:40,000–60,000 live births affecting more females than males [29]. Within the first months of life, these infants, often presenting with hypotonia, can have multiple seizure types,

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which are initially amenable to treatment but eventually become treatment resistant. Developmental delay with gross motor impairment and intellectual disability are common, as is as visual impairment. CDD was previously thought to be an early-­ onset subtype of Rett syndrome [30]. Treatment for CDD is based on the seizure type, which initially presents within the first 6 weeks of life as infantile spasms, progressing to mixed focal or generalized seizures, including generalized tonic-clonic, clonic, and spasm. The initial treatment of CDD with ASD may help up to a quarter of infants become seizure free; however, after a period of time, these seizures return. Current research involves the use of molecular pathways to eliminate the seizures. Another study is using a molecule to target nonsense mutations in the CDKL5 gene. Clinical trials are ongoing for CDD, including an expanded access for the use of ganaxolone (NCT04678479), a GABA-A receptor modulator; an expanded access study for refractory epilepsies using lorcaserin (NCT04457687), a selective serotonin agonist; an interventional trial using fenfluramine (NCT03861871), a sympathomimetic stimulant; a Phase II interventional study using TAK-935/ OV935 (NCT03635073), a cholesterol 24-hydroxylase inhibitor; and a Phase II interventional study using ataluren (NCT02758626), a drug targeting nonsense mutations [31–34, 35].

Spinal Muscular Atrophy Spinal muscular atrophy (SMA) is a severe autosomal recessive disease with an incidence of 1:11,000 live births and is the result of a deletion in the survival motor neuron 1 (SMN1) gene, leading to premature death or severe disability [36]. This gene, working in tandem with SMN2, is responsible for making a full-length intracellular protein (spliceosomal small nuclear ribonucleoproteins (snRNPs)) essential for the assembly of other proteins in most cells and at higher levels in spinal motor neurons [37, 38]. The result is muscle weakness and wasting of muscle. SMA has several types, 0–IV, classified by age of onset, severity, and level of motor function. Infants and children with SMA have low muscle tone, weakness in the upper limbs more than the lower limbs, and weakness in the intercostal muscles, affecting breathing. SMA is being considered the most common monogenic cause of infant mortality [39]. The recent functional classification groups patients as non-sitters, sitters, and walkers [40]. The most common SMA is Type I, where patients have no copies of the SMN1 gene and two copies of the SMN2 gene. Other types of SMA have three to six copies of the SMN2 gene, and the more copies of this gene, the less severe the disease (see Chap. 21 for more details). There are three treatments approved by the FDA for SMA.  Nusinersen (Spinraza®) acts to modify mRNA splicing of the SMN2 gene. It is delivered intrathecally (into the fluid surrounding the spinal cord) as a series of loading doses, followed by doses three times each year. The second approved treatment for SMA is onasemnogene abeparvovec (Zolgensma®), formerly known as AVXS-101. Risdiplam (Evrysdi™) works to modify mRNA splicing of the SMN2 gene and is taken daily by mouth. It is approved for patients age 2 months and older. Symptomatic treatment may include speech, occupational, and physical therapy. With disease progression, patients often need assistance with nighttime breathing: noninvasive for sleep apnea or invasive assistance for full ventilation support.

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With effective treatments available, SMA was added to the list of core conditions in the Uniform Screening Panel for newborns in the United States and other countries, including Australia, Belgium, and Germany. ClinicalsTrials.gov has over 160 clinical trials listed for SMA. Investigative therapies include a human anti-proMyostatin monoclonal antibody (mAb) (NCT03921528) and a potassium channel blocker, amifampridine phosphate (NCT03819660). The currently approved therapies are being studied for treating other subpopulations of SMA.

Conclusion The highest unmet needs in epilepsy have remained consistent over the past several years and include targeted treatments for treatment-resistant epilepsy and specific epilepsy syndromes as well as better treatments for epilepsy comorbidities. Despite significant progress toward better understanding of epileptogenesis, ongoing research, and recent breakthroughs in therapy options, a single effective medication to treat and/or prevent epilepsy is yet to be found. As there are a large number of seizure triggers, the current focus in epilepsy research is to find out how to prevent seizures as early as possible before irreversible changes in the brain have occurred. Even though many cases of epilepsy may remain treatment-resistant, the goal of the future is to reduce the economic burden and improve the quality of life in this patient population. Epilepsy treatment should focus on the underlying disease and attempt to prevent the seizures rather than on symptomatic seizure treatment. Additionally, using precision medicine to find cures for these epilepsy syndromes and other CNS rare diseases, such as SMA, HD, and CDD, continues. The ongoing researches in targeted therapies have provided major breakthroughs, and with each discovery, new avenues of research are opened with providing hope for many.

References 1. GARD [Internet] US Department of Health and Human Services, National Institutes of Health, National Center for Advancing Translational Research, Genetics and Rare Diseases Information Center. https://rarediseases.info.nih.gov/diseases/diseases-­by-­category/17/ nervous-­system-­diseases (2021). Accessed 04 Feb 2021. 2. National Organization of Rare Disorders. Rare disease facts. https://rarediseases.org/wp-­ content/uploads/2019/02/nord-­rareinsights-­rd-­facts-­2019.pdf. 2019. 3. Global Commission to End the Diagnostic Odyssey for Children with a Rare Disease. Global Commission Year One Report. https://globalrarediseasecommission.com/Report/assets/static/ documents/GlobalCommission-­print-­021919-­a68c8ce2a5.pdf. 4. Fisher RS, Cross JH, French JA, Higurashi N, Hirsch E, Jansen FE, Lagae L, Moshé SL, Peltola J, Roulet Perez E, Scheffer IE, Zuberi SM. Operational classification of seizure types by the International League Against Epilepsy: Position Paper of the ILAE Commission for Classification and Terminology. Epilepsia. 2017;58:522–30. https://doi.org/10.1111/epi.13670. 5. Rho JM, White HS.  Brief history of anti-seizure drug development. Epilepsia open. 2018;3(Suppl Suppl 2):114–9. https://doi.org/10.1002/epi4.12268. 6. Annegers JF, Hauser WA, Lee JR, Rocca WA.  Incidence of acute symptomatic seizures in Rochester, Minnesota, 1935-1984. Epilepsia. 1995;36(4):327.

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7. Benbadis SR, O’Neill E, Tarum WO, Heriaud L. Outcome of prolonged video-EEG monitoring at a typical referral epilepsy center. Epilepsia. 2004;45(9):1150–3. 8. Akman CI, Montenegro MA, Jacob S, Eck K, Chiriboga C, Gilliam F.  Seizure frequency in children with epilepsy: factors influencing accuracy and parental awareness. Seizure. 2009;18(7):524–9. 9. Scheffer IE, Berkovic S, Capovilla G, Connolly MB, French J, Guilhoto L, et al. ILAE classification of the epilepsies: position paper of the ILAE Commission for Classification and Terminology. Epilepsia. 2017;58(4):512–21. https://onlinelibrary.wiley.com/doi/full/10.1111/epi.13709 10. National Association of Epilepsy Centers: What is an Epilepsy Center https://www.naec-­ epilepsy.org/about-­epilepsy-­centers/what-­is-­an-­epilepsy-­center/ (2021). Accessed 05 Feb 2021. 11. Fisher RS, Acevedo C, Arzimanoglou A, Bogacz A, Cross JH, Elger CE, et al. ILAE official report: a practical clinical definition of epilepsy. Epilepsia. 2014;55(4):475. 12. Schmidt D, Friedman D, Dichter MA.  Anti-epileptogenic clinical trial designs in epilepsy: issues and options. Neurotherapeutics. 2014;11(2):401–11. 13. Rosenberg EC, Tsien RW, Whalley BJ, Devinsky O.  Cannabinoids and epilepsy. Neurotherapeutics. 2015;12(4):747–68. 14. Walczak TS, Leppik IE, D’Amelio M, Rarick J, So E, Ahman P, et  al. Incidence and risk factors in sudden unexpected death in epilepsy: a prospective cohort study. Neurology. 2001;56(4):519–25. 15. Devinsky O, Cross H, Laux L, Marsh E, Miller I, Nabbout R, et al. Trial of cannabidiol for drug-resistant seizures in the dravet syndrome. N Engl J Med. 2017;376:2011–20. https:// www.nejm.org/doi/full/10.1056/NEJMoa1611618 16. Kwan P, Schachter SC, Brodie MJ.  Drug-resistant epilepsy. N Engl J Med. 2011 Sep 8;365(10):919–26. 17. Brodie MJ, Barry SJ, Bamagous GA, Norrie JD, Kwan P. Patterns of treatment response in newly diagnosed epilepsy. Neurology. 2012;78(20):1548–54. Available from: https://www. ncbi.nlm.nih.gov/pubmed/22573629/ 18. Sillanpää M, Schmidt D.  Natural history of treated childhood-onset epilepsy: prospective, long-term population-based study. Brain. 2006;129(Pt 3):617–24. https://www.ncbi.nlm.nih. gov/pubmed/16401617/ 19. Russ SA, et  al. A national profile of childhood epilepsy and seizure disorder. Pediatrics. 2012;129(2):256–64. 20. Kwan P, Arzimanoglou A, Berg AT, Brodie MJ, Hauser WA, Mathern G, et al. Definition of drug resistant epilepsy: consensus proposal by the ad hoc task force of the ILAE commission on therapeutic strategies. Epilepsia. 2010;51(6):1069–77. 21. Minardi C, Minacapelli R, Valastro P, Vasile F, Pitino S, Pavone P, et al. Epilepsy in children: from diagnosis to treatment with focus on emergency. J Clin Med. 2019;8(1):39. 22. Clossen BL, Reddy DS. Novel therapeutic approaches for disease-modification of epileptogenesis for curing epilepsy. Biochim Biophys Acta. 2017 Jun;1863(6):1519–38. 23. Löscher W, Klitgaard H, Twyman RE, Schmidt D. New avenues for anti-epileptic drug discovery and development. Nat Rev Drug Discov. 2013;12(10):757–76. 24. Shohat M, Halpern GJ. Familial dysautonomia. GeneReviews. www.ncbi.nlm.nih.gov/books/ NBK1180/. Accessed on 8 Dec 2011. 25. Palma JA, Gileles-Hillel A, Norcliffe-Kaufmann L, Kaufmann H.  Chemoreflex failure and sleep-disordered breathing in familial dysautonomia: Implications for sudden death during sleep. Autonomic Neurosci Basic Clin. 2019;218:10–5. https://doi.org/10.1016/j. autneu.2019.02.003. 26. Rubin BY, Anderson SL. IKBKAP/ELP1 gene mutations: mechanisms of familial dysautonomia and gene-targeting therapies. Appl Clin Genet. 2017;10:95–103. https://doi.org/10.2147/ TACG.S129638. 27. Bahi-Buisson N, Kaminska A, Boddaert N, Rio M, Afenjar A, Gérard M, Giuliano F, Motte J, Héron D, Morel MA, Plouin P, Richelme C, des Portes V, Dulac O, Philippe C, Chiron C, Nabbout R, Bienvenu T. The three stages of epilepsy in patients with CDKL5 mutations. Epilepsia. 2008 Jun;49(6):1027–37. https://doi.org/10.1111/j.1528-­1167.2007.01520.x. Epub 2008 Feb 7

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28. Kilstrup-Nielsen C, Rusconi L, La Montanara P, Ciceri D, Bergo A, Bedogni F, Landsberger N. What we know and would like to know about CDKL5 and its involvement in epileptic encephalopathy. Neural Plast. 2012. Article ID 728267, 11 pages. https://doi. org/10.1155/2012/728267. 29. Olson HE, Demarest ST, Pestana-Knight EM, Swanson LC, Iqbal S, Lal D, Leonard H, Cross JH, Devinsky O, Benke TA.  Cyclin-dependent kinase-Like 5 deficiency disorder: clinical review. Pediatr Neurol. 2019;97:18–25. https://doi.org/10.1016/j.pediatrneurol.2019.02.015. 30. Fehr S, Wilson M, Downs J, Williams S, Murgia A, Sartori S, Vecchi M, Ho G, Polli R, Psoni S, Bao X, de Klerk N, Leonard H, Christodoulou J.  The CDKL5 disorder is an independent clinical entity associated with early-onset encephalopathy. Eur J Hum Genet. 2013;EJHG,21(3):266–73. https://doi.org/10.1038/ejhg.2012.156. 31. National Center for Biotechnology Information [Internet]. PubChem Compound Summary for CID 6918305, Ganaxolone. 2021a. Retrieved 31 Jan 2021 from https://pubchem.ncbi.nlm.nih. gov/compound/Ganaxolone. 32. National Center for Biotechnology Information [Internet]. PubChem Compound Summary for CID 11658860, Lorcaserin. 2021b. Retrieved 31 Jan 2021 from https://pubchem.ncbi.nlm.nih. gov/compound/Lorcaserin. 33. National Center for Biotechnology Information [Internet] (2021c). PubChem Compound Summary for CID 3337, Fenfluramine. Retrieved January 31, 2021 from https://pubchem. ncbi.nlm.nih.gov/compound/Fenfluramine. 34. National Center for Biotechnology Information [Internet]. PubChem Compound Summary for CID 11219835, Ataluren. 2021d. Retrieved 1 Feb 2021 from https://pubchem.ncbi.nlm.nih. gov/compound/Ataluren. 35. Takeda [Internet]. Newsroom, News Releases, Phase 2 ELEKTRA Study of Soticlestat (TAK-935/OV935) Meets Primary Endpoint Reducing Seizure Frequency in Children with Dravet Syndrome or Lennox-Gastaut Syndrome. https://www.takeda.com/newsroom/newsreleases/2020/phase-­2-­elektra-­study-­of-­soticlestat-­tak-­935ov935-­meets-­primary-­endpoint-­ reducing-­seizure-­frequency-­in-­children-­with-­dravet-­syndrome-­or-­lennox-­gastaut-­syndrome/ Accessed 04 Feb 2021. 36. Kolb SJ, Kissel JT.  Spinal muscular atrophy. Neurol Clin. 2015;33(4):831–46. https://doi. org/10.1016/j.ncl.2015.07.004. 37. Singh RN, Singh NN.  Mechanism of splicing regulation of spinal muscular atrophy genes. Adv Neurobiol. 2018;20:31–61. https://doi.org/10.1007/978-­3-­319-­89689-­2_2. 38. Workman E, Kolb SJ, Battle DJ.  Spliceosomal small nuclear ribonucleoprotein biogenesis defects and motor neuron selectivity in spinal muscular atrophy. Brain Res. 2012;1462:93–9. https://doi.org/10.1016/j.brainres.2012.02.051. 39. Darras BT. Spinal muscular atrophies. Pediatr Clin North Am. 2015;62(3):743. 40. Mercuri E, Finkel RS, Muntoni F, Wirth B, Montes J, Main M, Mazzone ES, Vitale M, Snyder B, Quijano-Roy S, Bertini E, Davis RH, Meyer OH, Simonds AK, Schroth MK, Graham RJ, Kirschner J, Iannaccone ST, Crawford TO, Woods S, Qian Y, Sejersen T, SMA Care Group. Diagnosis and management of spinal muscular atrophy: Part 1: Recommendations for diagnosis, rehabilitation, orthopedic and nutritional care. Neuromuscul Disord. 2018;28(2):103–15. https://doi.org/10.1016/j.nmd.2017.11.005. Epub 2017 Nov 23.

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Keren Moss and Jozsef Palatka

Illustration 12 Illustration of a photo of Gabrielle “Gabby” Carlo, whose mother, Shelly Carlo, provided a select patient narrative about Gabby’s journey with CHARGE in Chap. 3, because Gabby cannot speak. Gabby’s beloved canine friend, Chesney, is also pictured. Artwork courtesy of the artist.

K. Moss (*) Syneos Health, Tel Aviv, Israel e-mail: [email protected] J. Palatka Syneos Health, Budapest, Hungary © The Author(s), under exclusive license to Springer Nature Switzerland AG 2021 R. A. Huml (ed.), Rare Disease Drug Development, https://doi.org/10.1007/978-3-030-78605-2_12

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Outline Oncology drug development, focusing on the provision of safe and effective cancer treatments, has seen unprecedented growth and achievements over a period that spans no more than half a century. Oncology is currently the largest and quickest growing segment of ongoing clinical research, with a considerable number of drugs developed under the provisions of the Orphan Drug Act (ODA). Approximately 40% of all new orphan drug designations target rare cancers. Oncology drugs accounted for a third (35%) of all new orphan drug approvals in the period 1998–2007 and for almost half (48%) of new orphan approvals between 2008 and 2020 [1]. In 2020, from a total of 53 FDA approvals, 33 were in orphan disease indications, of which 15 were drugs for cancer treatment (Fig. 12.1), most of these [2] being precision medicines linking biomarkers to drug action. This chapter focuses on solid tumor oncology; hematologic oncology is discussed in Chap. 13. In this chapter, we examine some of the driving forces for these trends and identify ones that have shaped oncology drug development pathways. • We start by explaining the segmentation of oncology into multiple rare indications, which has been augmented by the scientific advances of the past few decades. Additionally, we examine how discoveries of the cellular mechanisms that promote cancer development (oncogenesis) have allowed scientists to identify cancer biomarkers. • The same progress that has rendered oncology the leader in personalized medicine has also allowed oncology to drive advancement of rare disease drug development regulatory pathways. We illustrate this process using an example of a drug developed for a rare biomarker-driven lung cancer population. • Beyond the science, we review the unique financial opportunities in oncology drug development. This is followed by an example of drug developed in an ultrarare indication and the success of this strategy in what may have otherwise been considered a saturated market. This example highlights the use of frequent regulatory interaction and the novel use of real-world retrospective data for rare disease drug development. • Finally, we explore research relating to the probability of success for oncology drugs obtaining orphan drug designation and examine an example of a drug whose development for a rare oncology indication has been unsuccessful. Careful assessment of the rare indication at hand, study design, and choice of study endpoints are critical components in a successful registration study. Fig. 12.1  Drugs approved for cancer treatment in rare oncology indications in 2020 [3]

Ayvakit™, Blenrep™, Danyelza®, Gavreto™, Inqovi®, Monjuvi®, Pemazyre®, Qinlock™, Retevmo™, Sarclisa ®, Tabrecta™, Tazverik®, Trodelvy®, Tukysa®, Zepzelca™

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By reviewing the scientific and financial trends in oncology rare disease development and illustrative examples of drugs developed in this space, we provide the reader with fundamental concepts on which to build a deeper understanding of this landscape.

Segmenting Oncology into Multiple Rare Diseases It is perhaps surprising that cancer, the second leading cause of mortality globally [4], is in fact approximately 150 distinctly defined diseases (and the number is growing), the majority of which have a prevalence that meets the definition of orphan disease, as defined in the USA, Europe, and Japan. Historically, cancers were evaluated based on their consistency and anatomical location, often considering in a similar manner any pathological process that led to tissue swelling. The early nineteenth century brought with it the initial histological understanding of cancer as a collective name for tumors comprised of distinct cells based on the tissue of origin [5]. Theodor Schwann and Thomas Muller in Germany, followed by pathologists in England, Italy, and France, began the painstaking task of microscopic classification of various tumor types – a task continued by pathologists over the next few decades. This led to the histopathological understanding of cancers as a group of distinct diseases determined predominantly by tissue of origin and further classified by histological features. Each disease or indication has a unique biological behavior, prognosis, and treatment, with several common features that are shared by different cancers. This segmented the cancer patient population and resulted in many cancers being classified as rare indications, each of which constitutes an orphan disease. Several reviews have been produced to systematically document orphan disease drug approvals in oncology [6]. An example of such an indication is epithelioid sarcoma [7]. Described initially in 1970, this tumor accounts for no more than 1% of all soft tissue sarcomas, an incidence of 0.1–0.4 cases per million in the USA. The year 2020 saw the first drug specifically developed for this rare cancer, with the FDA approval of tazemetostat (Tazverik®) for patients with locally advanced or metastatic epithelioid sarcoma who are not eligible for complete surgical resection.

Sub-segmentation and Biomarker-Driven Drug Development Recent decades have seen an exponential increase in scientific discoveries relating to the disruptions of various cellular processes associated with cancer – tumorigenesis, proliferation, invasion and metastases, immune response modulation and evasion, and microenvironment manipulation [8]. Major advances in DNA sequencing techniques have increased our ability to obtain genetic data from tumor tissue samples and to interrogate these data – further accelerating discoveries in this field. Traditional anticancer therapies focused on the development of drugs for histopathologically defined tumor indications. The enhanced understanding of cancer

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biology led efforts to develop highly specific (targeted) therapies, the so-called “magic bullets.” These therapies target aberrant cellular pathways and require an identifiable tumor biomarker to determine which patients have cancers that are susceptible to treatment. A cancer biomarker refers to a tumor feature that can be measured or evaluated, such as the qualitative or quantitative expression of a certain protein or the identification of a specific genomic alteration. Biomarkers can support therapeutic decisions: predictive biomarkers provide information on the probability a patient will respond to a treatment; other biomarkers may indicate resistance to treatment or even predict which patient will suffer more severe toxicity (see more details in Chap. 24). In modern oncology, the discoveries related to disease biology, the identification of druggable targets, drug development, and biomarker research are often done simultaneously. Navigating safely through these complexities is a major challenge for drug makers. Oncology personalized medicine seeks to profile the cancer and identify actionable aberration(s) with biomarkers and treat accordingly. Biomarker-selected patient populations have enhanced patient segmentation and promoted the ability of drugs to obtain orphan drug designation and are central to oncology drug development today. For example, eight of the nine FDA approvals for non-small cell lung cancer (NSCLC) in 2020 [9] were in biomarker-selected patient populations (Table 12.1). Below, we review some of the techniques used in biomarker determination. Immunohistochemistry (IHC) profiling is the most established method for classifying tumors based on a cellular biomarker. It evaluates the expression of protein markers using cellular staining methods and is a routine component of histopathological diagnoses. IHC evaluates the presence/absence and relative quantity of the protein marker expressed in/on tumor cells. One of the earliest and best-known examples of IHC assessment is the evaluation of the expression of estrogen and progesterone receptors (ER and PR, respectively) on breast cancer cells [10]. This became part of the routine diagnosis of breast cancer in the 1970s and is used as a predictive marker of response to endocrine therapy, as well as a prognostic marker. Genetic markers are tumor-specific biomarkers. Hundreds of genetic alterations (gene mutations, deletions, insertions, etc.) are known today to be associated with Table 12.1  NSCLC biomarker-specific drug approvals in 2020 Trade name Tagrisso®

Drug Osimertinib

Gavreto™ Cyramza® and Tarceva® Alunbrig® Tecentriq® Opdivo® and Yervoy® Retevmo™ Tabrecta™

Pralsetinib Ramucirumab and erlotinib Brigantinib Atezolizumab Nivolumab and ipilimumab Selpercatinib Capmatinib

Biomarker EGFR exon 19 deletions or exon 21 L858R mutations RET fusion EGFR exon 19 deletions or exon 21 L858R mutations Anaplastic lymphoma kinase (ALK) PD-L1 PD-L1 RET fusion Mesenchymal-epithelial transition (MET) exon 14 skipping

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Fig. 12.2  Tests used for several common NSCLC biomarkers

Markers Tested

Tissue-Based NGS or Gene Panel

IHC

FISH/PCR

Liquid Biopsy Gene Panel

EGFR, ALK, ROS1, NTRK, KRAS, BRAF, etc.

PD-L1, cMET, ROS1, ALK EGFR, ALK, ROS1, BRAF

EGFR, ALK, ROS1, NTRK, KRAS, BRAF, etc.

cancer. Various techniques are used to analyze a patient’s tumor sample and to identify select cohorts of patients whose tumor is positive for the relevant genetic alteration. If a therapeutic decision is relevant to the identified genetic biomarker, this is known as an actionable alteration (Fig.  12.2). One such example is epidermal growth factor receptor (EGFR). This is a transmembrane protein, which binds to epidermal growth factors, playing a critical role in the regulation of cellular proliferation. Mutation and overexpression of EGFR are associated with multiple cancers and are present in 30–40% of NSCLC patients. Evaluating for mutations that lead to constant activation of the EGFR has allowed for the identification of a cohort of patients that benefit from EGFR-directed therapy. Several EGFR targeting therapies are available today, and multiple additional agents are in ongoing clinical trials. The second most common actionable genomic alteration in lung cancer is the one associated with anaplastic lymphoma kinase (ALK), and the story of success in developing a drug specific to ALK-positive patients will be described in the subsequent sections. Gene expression profiling is a method employed to deconstruct a single tumor type into subsets, based on profiling multiple genes within the cell. In contrast to a genetic marker, gene profiling evaluates multiple genes simultaneously. Clustering of gene expressions or genetic “fingerprinting” is observed, accounting for tumor subsets with similar biological features, such as incidence, response to treatment, and prognosis. The identification of numerous genes is accomplished using microarrays constructed with the use of DNA probes. Twenty years ago, this allowed for the identification of breast cancer “subtypes”: luminal A, luminal B, HER2 enriched, basal like, and normal breast like [11]. These were later endorsed officially by St.

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Gallen International Expert Consensus on breast cancer, determining diagnosis and treatment guidelines based on the defined subsets, and marking the first formal use of genetic profiling for tumor subtype classification. Several additional biomarker-profiling methodologies are currently being investigated. Epigenetic profiling evaluates gene activity, which further to the genetic sequencing provides information on the regulation of gene expression at the DNA level. This is predominantly performed by the analysis of DNA methylation on either a genome-wide analysis or a more limited/focused analysis. Proteomic profiling or secretome is an emerging field evaluating the differential protein expression within a biological sample – the tumor tissue or biological fluid – using mass spectrometry for protein separation and identification. Arrays can be constructed and used to evaluate the expression of multiple proteins simultaneously. Finally, microbiome profiling has stemmed from recent research on the role the community of microbial organisms on and within the human body plays on cancer development, prognosis, and response to treatment. This involves the identification and classification of the microbial composition of the human microbiome, predominantly within the gut. Biomarker-driven drug development focuses investigational drug exposure on patients who might respond better from a safety or efficacy perspective, potentially enabling time and cost saving in clinical trials – as well as improving the odds in the quest for “right drug for the right patient” on the journey to personalized medicine . There are currently more than 40 targeted anticancer therapies approved by the FDA in biomarker-selected patients across nearly 20 different oncology indications [12]. However, very few of these targets address a substantial percentage of patients. The following example demonstrates the development of a drug for a very small subset of NSCLC patients:

Example: Crizotinib – A Rare Biomarker Targeting Drug The development of ALK (anaplastic lymphoma kinase)-targeted therapy for NSCLC is one of the best examples of rapid biomarker-driven drug development in oncology. In only 4 years, this agent moved from biomarker discovery to FDA drug approval. ALK was known to be involved in the control of cellular proliferation, survival, and differentiation and thus associated with the development of several cancers. In 2007, Japanese researchers described a chromosomal translocation, leading to constitutive activation of the ALK gene, and oncogenic activity of ALK [13]. This was followed by the description of PF-2341066, a new molecular entity (NME) with ALK kinase inhibition activity [2], in an experimental model of ALK-­positive anaplastic large-cell lymphoma (ALCL). PF-2341006, later renamed crizotinib (Xalkori®) by Pfizer, is an orally available ALK inhibitor as well as an inhibitor of two other oncogenic kinases – MET and ROS1. Initial in-vitro screening of crizotinib activity was performed in more than 600 cell lines, which revealed a correlation between ALK gene rearrangements and sensitivity to treatment in NSCLC, ALCL, and neuroblastoma [14]. The Phase I study was conducted in two parts [15, 16]. The initial part was a dose escalation in the typical oncology Phase I population of unselected advanced solid

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tumors for whom no standard of care is available. Once safety and a recommended Phase II dose were determined, the second part targeted an enriched patient population, including patients with tumors that met one of the several criteria relating to the presence of ALK, MET, or ROS1 genetic aberrations. The identification of antitumor activity in two NSCLC patients with ALK gene rearrangements led to an expansion of ALK-positive NSCLC patients in the study. Over an 18-month period, 1,500 patients with NSCLC were screened for ALK gene rearrangements, identifying 82 patients for the study. ALK genetic aberrations are present in in 3–7% of NSCLC tumors, typically in younger patients and those that have never smoked or are light smokers. Overall response rate (measured by tumor shrinkage) neared 60%, with more than 30% additional patients demonstrating stable disease – a significant result in a cohort of heavily pretreated NSCLC patients. Survival data, which was not mature at the time but was later published, showed significant prolongation of patient survival [17]. Crizotinib was granted FDA orphan drug status in September 2010 and fast track in December 2010. Phase II was a single-arm study demonstrating similar results to the earlier phase study [17]. These remarkable results led to FDA accelerated approval of crizotinib for ALK-positive NSCLC in August 2011, followed by full approval in November 2013 based on the results of the confirmatory randomized Phase III study. Since crizotinib, several newer-generation ALK inhibitors have been identified, evaluated, and approved. The extraordinary success of crizotinib development highlights some of the obstacles and mitigation strategies for the development of agents in a rare biomarker-­ determined oncology patient population. Features of this case study include: • Robust preclinical models and cell-line activity screening demonstrated clear correlation between the biomarker and antitumor activity and pointed to several potential tumor types. • Early Phase I dose escalation was conducted in an unselected patient population  – allowing for rapid safety evaluation and recommended Phase II dose (rather than conducting the study in rare biomarker-select patients only). • An ALK-positive NSCLC population was selected for Phase I expansion, representing a rare subset of a common cancer rather than very rare cancers, such as ALCL or neuroblastoma. Early efficacy signals in two NSCLC patients supported this decision. • Rare biomarker studies were conducted that required the screening of a very large population to identify a handful of patients. This strategy resulted in a very high screen failure rate and may not have been the most efficient or beneficial to patients. An alternative strategy would be to combine several biomarker targeting studies into one “umbrella” study, which allows for simultaneous development of multiple biomarker-driven therapeutics and offering patients multiple possibilities for clinical trial enrollment. • Approval of crizotinib in 2011 was achieved with rapid FDA review via the accelerated approval pathway and based on the remarkable response data observed in the single-arm Phase I expansion and Phase II studies. Conditional

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approval was modified to full approval upon the results of the randomized Phase III study in pretreated NSCLC patients. Results have since been confirmed, and approval expanded to untreated ALK-positive NSCLC, ROS1-positive NSCLC, and ALK-positive ALCL.

Opportunities in Rare Disease Oncology Drug Development In order to examine why oncology accounts for nearly half of all orphan drug applications, it is necessary to understand the financial incentive associated with oncology drug development. Niche cancer indications are eligible for the benefits of orphan drug incentives. These incentives are similar across regions and are enforced in the USA under the Orphan Drug Act (ODA) issued in 1983, in Japan under the amended Pharmaceutical Affairs Law of 1993, and in Europe by the EMA under Regulation (EC) No 141/2000 of 2000. They include fiscal and economic incentives, 7 years of marketing exclusivity, protocol assistance, and others, as described in other chapters. However, beyond this benefit, research has demonstrated that the mere announcement of orphan drug designation/status for an oncology drug is highly valued by investors. An analysis of 1085 FDA orphan designation announcements, made by publicly traded companies between 1985 and 2015 [18], reveals a 3.36% average increase in the company’s stock price. On further analysis by therapeutic area, oncology announcements resulted in a statistically significant 3.78% increase, compared to a 2.91% non-statistically significant increase for non-­ oncology announcements. By comparison, positive Phase III results increase stock valuations by 1.56% and new drug approvals by 0.35–1.56%. Thus, announcing orphan drug designation in itself is an important and tangible financial milestone, long before drug approval. A further trend for oncology drugs, as demonstrated by the approval of avelumab described later in this chapter, is the potential for oncology drugs to obtain approvals for more than one orphan indication. This is due to shared etiological pathways across different tumors, rendering the drug effective across multiple indications. In fact, secondary and tertiary oncology orphan drug approvals are becoming more prevalent, with a reduced number of NMEs. As an example, Genentech’s (Roche) cancer treatment Avastin® (bevacizumab) achieved 11 different orphan designations, with approval achieved for eight of these indications. A review of all 667 FDA orphan drug approvals from 1983 to 2017 [19] indicated that oncology product approvals increased over the entire period but spiked in the last 5 years of the analysis (2013–2017). This surge in approvals was comprised predominantly of secondary indications (53%), with NMEs accounting for only 40% in this same time period. While the trend of reduced NMEs was demonstrated across all therapeutic areas, it was most pronounced in oncology. Beyond this, it is important to remember that incentives for rare disease drug development were established to help offset the financial cost of developing a drug intended for a small patient population. The prime philosophy was to enhance patient access to drugs that are unlikely to generate sufficient revenue to absorb the

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costs related to their development and marketing. In oncology, the aim was to promote drug development for very rare types of cancer, by making them more financially viable. Prasad and Mailankody published in 2017 [20] a cost analysis of developing a single oncology drug and evaluated the average cost for developing oncology drugs approved between 2006 and 2015, by manufacturers that, at the time of FDA approval, had no other drugs on the market. This cost was evaluated at $648 million, a figure significantly lower than previous estimates. Of interest, nine of 10 drugs they evaluated had FDA orphan drug designation. With a median of 4 years following approval, nine of the 10 drugs had higher revenue than development cost, amounting to a median revenue of $1,658.4 million and a mean revenue of $6,699.1 million, demonstrating significant profitability, which is expected to increase over time (see more details in Chap. 22). The enhanced use of orphan drug regulatory pathways for oncology drug approvals does not directly translate to better patient access. Oncology drug costs increased more than fivefold between 2006 and 2015 [21]. It is in fact the years of drug exclusivity under the ODA that prevent the development of generics, impacting the ability to increase competition and lower the price of these drugs. As an example, capmatinib (Tabrecta™), recently approved to treat MET mutation-positive NSCLC, has a price tag of $215,000 per year per patient [22] and has market exclusivity on a market of $1.3bn in the USA only. The high drug prices and clever drug positioning strategies, such as repeat orphan indication approvals, may go against the original intent of ODA and in fact prove detrimental to patient access. As we demonstrate in the example below, rare disease regulatory pathways, such as ODA, may promote rapid drug development for niche populations.

 xample: Avelumab – An Initial Rare Indication and Use E of Synthetic Control Arms As already explained, certain oncogenic pathways are similar across several cancer indications; as such, the drugs targeting these might be effective in multiple types of cancer. Targeting a rare cancer indication rather than a more prevalent cancer may initially pursue a much smaller market size; however, it may give the manufacturer a regulatory advantage. Developing the drug in this niche indication could prove quicker and cheaper and ultimately allows for strategies, such as initial approval for an orphan indication, followed by a mass indication, or expansion to additional orphan indications. This is a well-known and widely adopted strategy in oncology drug development often referred to as either “indication sequencing” or “niche entry and sequencing.” By the time EMD Serono’s checkpoint inhibitor avelumab (Bavencio®) reached the initial stages of clinical development, two other same-in-class blockbusters from Merck & Co. (pembrolizumab) and Bristol-Myers Squibb (nivolumab) were already approved and were advancing fast to become the new standard of care in major oncology indications, such as malignant melanoma and NSCLC. Additionally, several contenders from Roche (atezolizumab), AstraZeneca (durvalumab), and other

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companies were in advanced development. Entering this crowded space would have meant a lengthy registration study for avelumab, without specific support from the regulators and with strong competition for patients. Following a Phase I study in solid tumor all-comers [23], EMD Serono opted for an alternative regulatory strategy and focused on a niche indication, Merkel cell carcinoma (MCC). MCC is a rare and aggressive form of skin cancer with limited treatment options, considered at the time an unmet medical need. With only 1,600 new MCC patients diagnosed each year [24], the market is small, yet this route proved to be sufficiently fast and efficient to support avelumab’s approval. EMD Serono employed all of the tools available to the company during the drug development process. Orphan drug designation was granted by the FDA and EMA in 2015. The company was able to obtain both fast track and breakthrough therapy designation (BTD). Based on BTD, a preliminary advice meeting was held with the FDA to discuss an accelerated approval approach, a very advantageous step that avoided a number of risks and obstacles to future approval. The marketing application utilized a rolling review process, with the nonclinical sections of the application submitted ahead of the clinical data. By the end of 2016, the FDA granted the company Priority Review status, which meant the application was reviewed in a reduced 6-month time frame. All of this was accomplished without the need for an FDA advisory committee meeting.

“The clinical trials system is “broken” and there need to be new ways to collect and utilize patient data. FDA will work with its stakeholders to understand how RWE can best be used to increase the efficiency of clinical research and answer questions that may not have been answered in the trials that led to the drug approval, for example how a drug works in populations that weren't studied prior to approval.” - Dr Janet Woodcock, Director of the US FDA, 2017 [25]

In response to the challenge posed by the sparsity of MCC patients and short survival time, EMD Serono adopted a novel approach in its pivotal registration trial. JAVELIN Merkel 200 included two arms: (1) an active agent arm, in which 88 MCC patients were treated prospectively with avelumab after failure of a prior treatment [26], and (2) a “synthetic” comparator arm, which used a retrospective analysis of data from patients with MCC at similar stage of disease [27]. The comparator arm used evidence extracted from electronic health records (EHRs) (see more details in Chap. 24). The retrospective analysis included data for 67 patients with MCC, who qualified for the study and were treated with standard chemotherapy after failure of prior treatment at community oncology practices across the USA.

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“The FDA recognizes how important RWD and RWE are. This is a top strategic priority for the FDA. We’re committed to realizing the full potential of these tools in advancing the development of novel therapeutic products and strengthening our regulatory oversight of medical products across the life-­ cycle continuum.” - Dr. Scott Gottlieb, FDA Commissioner, 2018 [28]

In the historical analysis of the control-arm patients, the overall response rate (ORR) was 28.6%, and the median duration of response (DOR) was 1.7 months. In contrast, in patients treated with avelumab, the ORR was 33%, with a median follow­up of 16.4 months – but the median DOR had not yet been reached and was later determined to be 40.5 months. This was the first time EHR data was used for a firstline approval in oncology, a true turning point in the use of real-world data for drug approvals. The fact that the FDA embraced this concept with enthusiasm transpires from their statements published during those days, called out in the text boxes nearby. The use of a rare indication could therefore be the winning strategy for a drug to gain fast market access, especially if combined with contemporary statistical and data solutions. Avelumab has since been approved for two other orphan indications – urothelial cancer and renal cell cancer.

Success of Assigned Oncology Orphan Drug Status The probability of success (POS) for any new drug entering clinical development is a key question for the drug manufacturer and its private or public investors. Obtaining orphan disease status, as we have seen, is becoming easier with oncology segmentation into so many discrete indications. However, does orphan status increase POS? Several published reviews analyzed longitudinal data from oncology drug development from Phase I through to successful FDA drug registration [29, 30, 31], if achieved. Even if methods of analysis vary, these reviews have arrived at similar conclusions. Hay et  al. [29] reviewed the success of clinical trial phase transitions from January 1, 2003, to December 31, 2011, in a dataset of 4,451 drugs with 7,372 independent clinical development paths. Oncology drugs were the largest therapeutic area (31% of all transitions). However, they had the lowest likelihood of transitioning from Phase I to approval – 10.4% across the entire dataset and 6.7% for oncology. Orphan oncology success rates are much higher than for oncology overall but are still considerably lower than for other orphan therapeutic areas. Giannuzzi et al. [30] evaluated 788 EMA orphan drug designations between 2000 and 2012. Lack of efficacy or safety issues were the dominant reason for drug failure, with the majority of drugs discontinued for this reason being oncology drugs (42.5%). The second reason for failure was company inactivity/bankruptcy, and about 51% of the abandoned drugs were in the oncology area.

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Wong et al. [31] evaluated nearly 186,000 unique clinical trials for over 21,000 compounds, listed in publicly available databases, between the years 2000 and 2015. Across all therapeutic areas, 13.8% of development programs eventually led to approval. This figure decreased to 3.4% for all oncology development programs. Orphan oncology drug success is marginally higher at 6.2% but still considerably lower than the 13.6% achieved when considering non-oncology orphan drugs. In trials that included a biomarker to stratify patients, POS almost doubled, and this success was most significant in Phase I and II stages of development. The vast majority (94%) of biomarker trials evaluated were in oncology. Of note, oncology drugs spent the longest median time in clinical trials compared to other therapeutic areas, a parameter that inversely correlated with drug success. Oncology also had the lowest percentage of studies that were conducted to completion – 73.9% of trials. The reasons for failure are multiple. Cancer biology is extremely complex, and many aspects are still at the discovery stage. Preclinical models have limited predictive value for human pharmacodynamics. Ultimately, proof-of-concept results obtained in Phase II trials often cannot be reproduced in Phase III confirmatory trials. Furthermore, the unknowns of disease biology, mode of action, and the role of biomarkers are studied almost in parallel and under tremendous time pressure (see Chap. 23 for more details). Most drugs fail in development for two main reasons: either the expectations for efficacy and safety are not correctly set – i.e., the drug is not good enough  – or the patient population for treatment is not selected appropriately. The challenge of correctly assembling this complex puzzle of pieces during drug development is enormous. This is further compounded by the fact that pieces are shifting as the data emerges and matures, unknowns are answered, new voids are created, etc. Beyond efficacy and safety issues, insufficient data, quality issues, regulatory issues, and commercial and financial issues are flagged as reasons for failure. Although orphan drug status slightly increases the probability of success for oncology drug development (vs. the oncology drugs developed in non-orphan segments), the overall probability of success remains low. Other factors, such as longer average time in clinical trials and higher early termination rates, are a reflection of the considerable crowding experienced by oncology drug development and the hurdles that lie ahead for manufacturers planning studies in this area.

 xample: Palifosfamide – Unsuccessful Development for Soft E Tissue Sarcoma Palifosfamide was developed in attempt to reduce toxicity associated with ifosfamide – a chemotherapy typically used in combination with other agents to treat various cancers, including soft tissue sarcoma (STS). Use of palifosfamide, the active metabolite of ifosfamide, was intended to avoid exposure to metabolic by-­products responsible for some of the toxicities observed with ifosfamide. A Phase I/II singleagent trial was followed by a small Phase I trial of palifosfamide in combination with doxorubicin predominantly in patients with STS [32]. This trial demonstrated the combination to be well tolerated and indicated an early efficacy signal. The FDA

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granted orphan drug designation in 2009. Ziopharm Oncology then moved ahead rapidly with PICASSO, a small randomized, non-blinded Phase II study, evaluating palifosfamide with doxorubicin versus doxorubicin alone. This was followed by PICASSO III [33], a large randomized, placebo-controlled Phase III trial, aimed at demonstrating superior survival for doxorubicin/placebo versus doxorubicin/palifosfamide. The primary endpoint was revised to progression-free survival (PFS) in 2013, after all patients had been enrolled, without amending the sample size. This was based on discussions between Ziopharm and the FDA, and to enable regulatory approval based on PFS benefit only, provided PFS improvement magnitude was large and overall survival (OS) endpoint positive or at least showing no negative impact. Ultimately, no significant difference in PFS or OS was found between the two study arms. Despite a slightly higher response rate in patients treated with palifosfamide, increased severe toxicity was also demonstrated in this arm. Why did palifosfamide fail at Phase III? There are a number of factors to consider [34, 35]. STS is a rare oncology indication, which is in fact a heterogeneous group of more than 70 subtypes, with varying biology and unclear/variable standards of care and sensitivity to treatment. Due to the rarity of STS patients overall – less than 1% of all new cancers  – many sponsors will consider it not feasible to conduct a study for just one of the subtypes. A mix of STS patients in what appears to be a well-powered Phase III may result in failure to meet a survival endpoint due to biological heterogeneity. This heterogeneity impacts patients’ responses to study treatment, as well as influencing the OS endpoint due to the high variability in patient management after the study. Recent STS approvals have considered subtype populations and, while requiring a very big international effort to recruit the patients, have resulted in positive Phase III outcomes. Finally, it is important to note that the Phase II study was conducted in previously treated STS patients, whereas the Phase III was conducted in the frontline setting. Other items cited as design flaws are the controversial use of PFS as a surrogate endpoint for treatment benefit in STS trials, the high number of patients experiencing early disease progression and dropping out before first evaluation, as well as the shifting standard of care for STS, including a more aggressive surgical approaches to metastatic disease. The story of palifosfamide is not unique [36]. Out of 344 oncology drugs that entered Phase II drug development between 1991 and 2014 and were later discontinued, 62 reached Phase III or preregistration studies. Obtaining orphan drug status increases the probability of success but is certainly no guarantee of the drug obtaining regulatory approval and reaching the market.

 onclusions and Future Directions in Oncology Rare Disease C Drug Development The focus on well-defined niche cancer indications has resulted in substantial advancement of oncology drug development over the past few decades. The enactment of the ODA enhanced drug development efforts in the rare cancer disease

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space. This occurred at about the same time that larger cancer indications began to be subdivided into hundreds of rare diseases as a result of biomarker discovery and improved knowledge of disease etiology and molecular drivers of disease. As a result, both opportunities and challenges were seen as related to the discovery and use of predictive biomarkers, as well as operational delivery of the requisite clinical trials, in addition to business issues and incentives. While the benefits of turning to a more personalized approach to oncotherapy are clear, finding the right biomarkers (if they exist) remains the key for most drugs to succeed in this space. However, this is easier said than done. Finding the right biomarkers needs to be achieved as early as possible in the drug development process. An incorrect biomarker strategy is one of the main reasons for drug failure in the final stages of development. Possible solutions for better biomarker selection include longer and more in-depth investigations at discovery (preclinical and clinical Phase I and II stages), investing in intensive translational medicine work, or shifting the drug development and biomarker search into an integrated, parallel approach (see Chap. 23 for more details). For example, this may be achieved by using molecular stratification of patients in clinical trials, to qualify the biomarkers together with the drug efficacy and safety in a clinical setting [37]. Not all oncology drugs for rare indications become blockbusters, but the few that are successful achieve astronomical sales figures. Once approved, the price of these drugs is very high – likely also encouraged by the 7-year commercial exclusivity – making their accessibility for patients a pipe dream. However, actual revenues and patient access appear to violate the objectives of the ODA. The related figures give rise to reservations about the investments made in these drugs as well as the contributions made by the public under the ODA. The optimal use of funding and regulatory resources as this relates to maximizing access to new oncology therapeutics drugs is a sensitive yet necessary debate that needs to be tackled in the future. Where do we go from here? The future of rare oncology drug development is most certainly destined to continue down the path of biomarker-driven therapies. With a sound scientific rationale, promoting the development of such therapeutics across multiple indications, rather than just one, can enhance development timelines. This will accelerate time to market, as well as preventing repeat orphan disease indication approvals and allowing for more competition and better pricing. The industry and regulators should continue to push the boundaries of clinical trial design to allow for smarter and more efficient use of patient data via rare patient registries, use of real-world evidence, and many of the innovative methodologies outlined in other chapters of this book. Finally, the optimal use of funding and regulatory resources probably needs fine-­ tuning to bring oncology drug development back to the core concept of the ODA: to keep encouraging activity in the space and to truly maximize access to new oncology therapeutics for patients with rare cancers. We can then ensure that the trajectory of improvements in outcomes seen in patients with rare cancers, as we have experienced over the past few decades, will continue to soar.

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21. Saluja R, Arciero VS, Cheng S, McDonald E, Wong WWL, Cheung MC, Chan KKW.  Examining trends in cost and clinical benefit of novel anticancer drugs over time. J Oncol Pract. 2018;14(5):e280–94. https://doi.org/10.1200/JOP.17.00058. 22. Novartis beats Merck KGaA to U.S. finish line with targeted lung cancer drug Tabrecta. https:// www.fiercepharma.com/pharma/novartis-­beats-­merck-­kgaa-­to-­finish-­line-­targeted-­lung-­ cancer-­drug-­capmatinib. Accessed 27 Jan 2021. 23. Arkenau HT, Kelly K, Patel MR, Neuteboom B, Speit I, Chin K, et al. Phase I JAVELIN solid tumor trial of avelumab (MSB0010718C), an anti-PD-L1 antibody: safety and pharmacokinetics. Ann Oncol. 2015;25(s8):VIII1. https://doi.org/10.1093/annonc/mdv513.08. 24. Hughes MP, Hardee ME, Cornelius LA, Hutchins LF, Becker JC, Gao L. Merkel cell carcinoma: epidemiology, target, and therapy. Curr Dermatol Rep. 2014;3(1):46–53. https://doi. org/10.1007/s13671-­014-­0068-­z. 25. Endpoints. FDA’s Janet Woodcock: the clinical trials system is ‘broken’. https://endpts.com/ fdas-­janet-­woodcock-­the-­clinical-­trials-­system-­is-­broken/. Accessed 27 Jan 2021. 26. Kaufman HL, Russell J, Hamid O, Bhatia S, Terheyden P, D’Angelo SP, et al. Avelumab in patients with chemotherapy-refractory metastatic Merkel cell carcinoma: a multicentre, single-­ group, open-label, phase 2 trial. Lancet Oncol. 2016;17(10):1374–85. https://doi.org/10.1016/ S1470-­2045(16)30364-­3. 27. Cowey CL, Mahnke L, Espirito J, Helwig C, Oksen D, Bharmal M.  Real-world treatment outcomes in patients with metastatic Merkel cell carcinoma treated with chemotherapy in the USA. Future Oncol. 2017;13(19):1699–710. https://doi.org/10.2217/fon-­2017-­0187. 28. Statement from FDA Commissioner Scott Gottlieb, M.D., on FDA’s new strategic framework to advance use of real-world evidence to support development of drugs and biologics. U.S. FDA, https://www.fda.gov/news-­events/press-­announcements/statement-­fda-­commissioner-­scott-­ gottlieb-­md-­fdas-­new-­strategic-­framework-­advance-­use-­real-­world. Accessed 27 Jan 2021. 29. Hay M, Thomas D, Craighead J, et al. Clinical development success rates for investigational drugs. Nat Biotechnol. 2014;32:40–51. https://doi.org/10.1038/nbt.2786. 30. Giannuzzi V, Landi A, Bosone E, Giznnuzzi F, Nirotri S, Torrent-Farnell J, et al. Failures to further developing orphan medicinal products after designation granted in Europe: an analysis of marketing authorisation failures and abandoned drugs. BMJ Open. 2017;7:e017358. https:// doi.org/10.1136/bmjopen-­2017-­017358. 31. Wong CH, Siah KW, Lo AW. Estimation of clinical trial success rates and related parameters. Biostatistics. 2019;20(2):273–86. https://doi.org/10.1093/biostatistics/kxx069. 32. Camacho LH, Chawla SP, Chua V, Abbadessa G, Komarnitsky PB, Lewis J. A phase I study of palifosfamide in combination with doxorubicin: safety and preliminary efficacy. J Clin Oncol. 2009;27(15_suppl):10577. https://doi.org/10.1200/jco.2009.27.15_suppl. 33. Ryan CW, Merimsky O, Agulnik M, Blay JY, Schuetze SM, Van Tine BA, et al. PICASSO III: a phase III, placebo-controlled study of doxorubicin with or without palifosfamide in patients with metastatic soft tissue sarcoma. J Clin Oncol. 2016;34(32):3898–905. https://doi. org/10.1200/JCO.2016.67.6684. 34. Lee AT, Pollack SM, Huang P, Jones RL. Phase III soft tissue sarcoma trials: success or failure? Curr Treat Options Oncol. 2017;18(3):19. https://doi.org/10.1007/s11864-­017-­0457-­1. 35. Constantinidou A, van der Graaf WTA.  The fate of new fosfamides in phase III studies in advanced soft tissue sarcoma. Eur J Cancer. 2017;84:257–61. https://doi.org/10.1016/j. ejca.2017.07.043. 36. Jardim DL, Groves ES, Breitfeld PP, Kurzrock R. Factors associated with failure of oncology drugs in late-stage clinical development: a systematic review. Cancer Treat Rev. 2017;52:12–21. https://doi.org/10.1016/j.ctrv.2016.10.009. 37. Garcia VM, Cassier PA, de Bono J. Parallel anticancer drug development and molecular stratification to qualify predictive biomarkers: dealing with obstacles hindering progress. Cancer Discov. 2011;1(3):207–12. https://doi.org/10.1158/2159-­8290.CD-­11-­0161.

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Daniel Mazzolenis and Liat Vidal

Illustration 13. Illustration from a photo of Shellye Horowitz, who provided a select patient narrative in Chap. 3, shown here self-administering a blood-clotting protein treatment for her hemophilia. Artwork courtesy of the artist.

D. Mazzolenis Oncology Hematology, Syneos Health, Morrisville, NC, USA L. Vidal (*) Syneos Health, Tel Aviv, Israel e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Switzerland AG 2021 R. A. Huml (ed.), Rare Disease Drug Development, https://doi.org/10.1007/978-3-030-78605-2_13

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Introduction Randomized controlled trials (RCTs) with clinically relevant outcomes are the “gold standard” for the evaluation of investigational new drugs. Such trials often require a large sample size, making them challenging in the case of rare diseases. The challenges differ between (a) rare hematological diseases, in which the main aim is usually improving quality of life or reducing chronic, debilitating complications, and (b) hematological-oncological diseases, in which prolongation of survival is the main goal, even in indolent diseases. The respective outcome measures for benign disease are often patient-reported outcomes, assessing quality of life or disease complications, whereas for malignant hematological diseases, overall survival (OS) is frequently used. The Orphan Drug Act [1], and the introduction of the orphan designation in the EU regulatory pathway by the European Medicines Agency (EMA) [2], led to major changes in drug development for rare diseases. Based on these, some justifiable divergence from the “gold standard” of study design may be accepted. In addition, due to the scarcity of patients, more data should be captured from each enrolled patient, including pharmacokinetics, detailed labs, and medical history, to evaluate possible treatment effects, and adverse events – to gain a complete picture of all aspects of drug effects. The path of drug development for patients with rare hematological diseases includes alternative trial designs and involves input from collaborative groups. We discuss some of alternative trial designs and illustrate these with examples from benign and malignant hematological diseases.

 he Case of Marginal Zone Lymphoma: Historical Control T and Surrogate Endpoints The case of marginal zone lymphoma (MZL) illustrates several elements used in studies of rare diseases. In the case of indolent (slow-growing) lymphomas, it is not uncommon to include various common and rare lymphoma subtypes under one study protocol, defining the primary outcome based on the intention to treat population and performing subgroup analysis per lymphoma subtype. MZL is a rare B-cell lymphoma, including subtypes, nodal, extranodal, and splenic, and with a heterogeneous disease course [3]. Treatment of MZL patients includes chemotherapy, rituximab, radiation, and, in specific cases, antibiotics. Most trials of MZL include all subtypes, and, not infrequently, trials include patients with MZL and other rare and more common indolent lymphomas. The path of drug development for MZL typically includes alternative designs to RCT and surrogate endpoints for the primary outcome (e.g., overall response rate (ORR)), as demonstrated in the examples below. Ibrutinib was the first FDA-approved drug for MZL, receiving an orphan drug designation (ODD) in 2015. Approval was based on a Phase 2 single-arm study, including 63 patients with MZL showing an ORR of 43% [4]. No randomized trial was required to support these results. Ibrutinib is not approved by the EMA for MZL. In 2013, idelalisib became the first phosphoinositide 3-kinase (PI3k) inhibitor to obtain ODD. It was evaluated in a single-arm study in 125 patients with indolent

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Table 13.1  Example of drug approvals originating from a study of indolent B-cell lymphomas: study of idelalisib in indolent lymphoma

Subgroup Subgroup Subgroup Subgroup Total

Disease type of subtype Follicular lymphoma Small lymphocytic lymphoma Marginal zone lymphoma Lymphoplasmacytic lymphoma Indolent lymphoma

Number of patients 72 28 15 10 125

Overall response rate 54% 61% 47% 1% 57%

FDA approved Yes Yes No No

lymphoma, including follicular lymphoma (FL), small lymphocytic lymphoma, MZL, and lymphoplasmacytic lymphoma [5]. The primary outcome of ORR among all patients was 57%, and 47% among 15 MZL patients. Based on the results of this study, idelalisib was granted accelerated approval for FL and SLL but was not approved for MZL (Table 13.1). As with idelalisib, copanlisib, a PI3k inhibitor, was evaluated in a single-arm study, including indolent lymphomas. The drug received accelerated approval for FL patients based on an ORR of 59% in 104 patients with FL [6]. Twenty-three patients with MZL had an ORR of 70%, and at a follow-up analysis at 18 months, the ORR was 78%. Copanlisib was approved for treatment of FL and was granted breakthrough designation (but not yet approval) by the FDA. Confirmatory randomized studies are ongoing. Lenalidomide, an immunomodulatory agent, was approved for MZL by the FDA in 2019 after receiving ODD from the FDA and EMA. Lenalidomide was evaluated in combination with rituximab for MZL in two trials: a single-arm treatment trial and a RCT [7, 8]. Again, these two trials included patients with different lymphoma subtypes, the rare malignancy MZL and a the more common lymphoma, FL. In the single-arm trial, the ORR was 51% (23/45) for patients with MZL.  In the RCT, lenalidomide-rituximab improved progression-free survival (PFS) compared to rituximab in the overall population of 358 patients (hazard ratio (HR) 0.46, 95% confidence interval (CI) 0.34 to 0.62), yet there was no evidence of PFS difference in 63 patients with MZL. Furthermore, there was no statistically significant difference in ORR (65% vs. 44%, respectively) or other secondary efficacy endpoints in this subgroup. FDA approved lenalidomide for the treatment of patients with MZL based on these studies. The EMA did not approve lenalidomide for MZL due to lack of statistically significant PFS benefit within this subgroup. The case of MZL exemplifies how drugs may be approved by the FDA based on non-comparative trials and surrogate endpoints in order to expedite evaluations in rare patient groups, ensuring a practical clinical evaluation and efficient drug development. It should be noted that, in many settings, the EMA approach to drug approval differs from that of the FDA, and developers must ensure clear dialogue and plans with each regulatory agency. Drug approval based on non-RCT requires several conditions: an unmet need, a predictable clinical course that can be objectively measured, and a drug effect that is large, self-evident, and temporally associated with the intervention. It also demonstrates that the evolving requirements for drug approval may have a significant impact on the future of the drug’s label for rare indications.

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 rentuximab Vedotin for CD30+ (Positive) Lymphoma: B From Non-randomized to Randomized Trials on the Path to Approval and Basket Trials While non-comparative trials may be sufficient for patients who previously received several treatment lines and have exhausted approved treatment options, a well-­ controlled comparison with the standard of care treatment – where one is available – is warranted when introducing an investigational drug earlier in the treatment course (i.e., first treatment). CD30 is a cell surface receptor expressed in certain lymphomas, including classical Hodgkin’s lymphoma and anaplastic large cell lymphoma (ALCL), a subtype of peripheral T-cell lymphoma (PTCL). Given the restriction of CD30 to those tumor types, this receptor was suggested as a possible therapeutic target. Brentuximab vedotin (BV) is an antibody-drug conjugate targeting CD30, designed to use the CD30 target to deliver a toxic payload to the cancer cell. The evaluation of BV for these indications begun with a non-comparative basket trial (a clinical trial that tests a drug in patients with different types of cancers, which share a common mutation or biomarker [9] (Fig.  13.1)). Both Hodgkin’s lymphoma and ALCL are rare diseases, and, at the time BV was granted ODD, there was no standard therapy for patients who had relapsed after autologous stem cell transplantation (ASCT) or those ineligible for ASCT.  BV was evaluated in a single-arm trial in patients with relapsed or refractory Hodgkin’s lymphoma and ALCL [10]. The ORRs were impressive: 75% in Hodgkin’s lymphoma (33% complete response (CR)) and 86% in ALCL (59% CR). Lack of other effective therapies, and the large effect size based on objective endpoint (ORR) for a disease that is otherwise fatal, led to approval by both the EMA and FDA of BV for Hodgkin’s lymphoma and ALCL after at least two previous treatment lines. To bring BV into earlier treatment lines for Hodgkin’s lymphoma and PTCL, RCTs were required. The high response rates demonstrated in the non-comparative trials in relapsed patients facilitated the enrollment of a large number of patients to these RCTs.

Umbrella Trial

Basket Trial Disease A

Single Disease (e.g. AML)

Disease B

Screening for specific target for drug X

Screening for molecular targets

Target Present Target A Study of drug targeting A

Target B Study of drug targeting B

Fig. 13.1  Umbrella and basket trials

Target C Study of drug targeting C

Trial of drug X

Disease C

Target Absent (screen failure)

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A RCT including 452 patients with newly diagnosed CD30+ PTCL assigned to BV, cyclophosphamide, doxorubicin, and prednisone (BV  +  CHP) or cyclophosphamide, doxorubicin, vincristine, and prednisone (CHOP), demonstrated improved PFS (HR 0.71, 95% CI 0.54 to 0.93) and OS (HR 0.66, 95% CI 0.46 to 0.95) with BV + CHP compared to CHOP [11]. In the Hodgkin’s lymphoma population, BV was evaluated in two RCTs. One study was carried out in the first-line setting randomizing 1334 advanced Hodgkin’s lymphoma patients to receive BV-chemotherapy or standard chemotherapy [12]. BV-chemotherapy led to improved PFS (HR 0.77, 95% CI 0.60 to 0.98). In a second study in the relapsed/refractory setting for unfavorable-risk patients, BV was assessed as consolidation therapy after the standard ASCT. BV improved PFS when given as early consolidation after ASCT [13]. These randomized trials led to approval of BV in each of these settings for earlier treatment lines in Hodgkin’s lymphoma and for patients with newly diagnosed PTCL. Non-randomized trials were sufficient for drug approval in scenario with an unmet need and with a clinically significant effect of the investigational drug (due to lack of an alternative, standard, effective treatment). RCTs were required for drug approval for earlier treatment lines, in which these minimum requirements were not met.

 ML and the Power of Many: Collaborative Groups, Historical A Control, and Umbrella Trials Acute myeloid leukemia (AML) is a rare cancer of the myeloid progenitor cells in the bone marrow. It originates from a dominant mutation, which then acquires additional mutations that lead to the rapid growth of immature cells. The different genetic changes account for biological and clinical heterogeneity of AML, represented by more than ten subtypes of AML in the WHO classification [14]. Survival can vary, ranging from 35–40% in adults aged 60 and below to as low as 5–15% in older patients, who are often not fit enough for intensive chemotherapy. The classification of AML based on molecular and cytogenetic abnormalities further divides this rare disease into even rarer disease entities. For over 4 decades, the treatment of AML was intensive chemotherapy, with or without allogeneic stem cell transplantation. Very little changed in the management and prognosis of AML patients, until the introduction of drugs targeting specific AML mutations, such as midostaurin targeting FMS-like tyrosine kinase 3 (FLT-3)-mutated AML. Midostaurin improved OS and changed the standard of care for these patients [15]. Another difficulty in AML studies, besides the rarity of the indication is the need to initiate treatment very rapidly at the time of diagnosis. Therefore, the clinical development of a targeted drug requiring specialized lab tests to identify the specific AML subtype or genetic abnormality is extremely challenging due to the time and patient number constraints. The Alliance for Clinical Trials in Oncology study of a cohort of 589 AML patients demonstrated the feasibility of applying advanced genomic technology (next-generation sequencing) to define genomic groups in the

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majority (88%) of AML patients and paved the way for the BEAT AML® Master Clinical Trial (see below). The Leukemia & Lymphoma Society (LLS) is a nonprofit organization dedicated to funding blood cancer research. The LLS and leading investigators in the field have initiated a collaboration around the BEAT AML Master Clinical Trial, which uses next-generation sequencing to rapidly identify AML mutations, and match the patient to the targeted therapy clinical study according to the genomic classification [9, 16, 17]. In that trial, instead of studying one drug for one extremely rare AML subtype at a time, multiple different therapies for one disease, i.e., AML, are evaluated under one “umbrella” trial (a trial testing different therapies in patients who have the same type of cancer but different gene mutations, where patients receive treatment based on their specific mutation) [9] (Fig. 13.1). Multiple pharmaceutical companies have joined this master clinical trial to evaluate their orphan drug for patients with AML.  Each targeted therapy is evaluated in a substudy targeting a specific AML subtype. This master clinical trial can be a model for future precision medicine studies in cancer. The innovative trial design based on genomic profiling was not only feasible but also shown to result in improved patient outcomes compared to standard of care [16]. Master trials such as BEAT AML require the alignment of different stakeholders including industry, investigators, genomic tests vendors, and regulators. Such a large collaboration can be handled under the governance of a large well-established body as a government-funded group, a medical association, and, as in the case of BEAT AML, a patient advocacy group. The BEAT AML trial is a success story of true innovation that allows for significant time and cost savings in the development of targeted drugs in small patient populations. The LLS and BEAT AML study demonstrate the power of patients and investigator groups. Collaborations around observational and interventional studies of rare hematological diseases are crucial in conducting research that would be otherwise clinically, logistically, and financially impossible. These are essential tools in expanding the knowledge of rare diseases and their treatment. BEAT AML includes several substudies, each for a single therapy, with no control treatment group. The Alliance study mentioned above [18] also provided prospective data on outcomes in older AML patients according to the genomic group that can be used as historical control for BEAT AML.

I mmune Thrombocytopenia (ITP): Variable Disease Course Requires a Controlled Trial Criteria for accepting historical control data are that the disease should have a predictable clinical course and a stable, broadly accepted, standard of care. When assessing investigational interventions for diseases with a heterogeneous course, with unpredictable outcomes, RCTs are expected in order to control for selection bias.

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ITP is an acquired immune-mediated disorder characterized by isolated thrombocytopenia due to platelet destruction and inefficient platelet production. The incidence of ITP is 1.6 to 3.9 cases per 100,000 adults per year with a prevalence of 9.5 per 100,000 adults [19–21]. Most patients with ITP have either no symptoms or minimal bruising, whereas some experience serious bleeding and, rarely, intracranial hemorrhage. Although severe bleeding is rare, it is the most important clinical endpoint, causing physicians’ concern and patients’ anxiety [22]. Bleeding risk in ITP is increased at platelet counts