Precision Medicine (Handbook of Experimental Pharmacology, 280) 3031400461, 9783031400469

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Handbook of Experimental Pharmacology  280

Ingolf Cascorbi Matthias Schwab   Editors

Precision Medicine

Handbook of Experimental Pharmacology Volume 280 Editor-in-Chief Martin C. Michel, Dept of Pharmacology, Johannes Gutenberg Universität, Mainz, Germany Editorial Board Members James E. Barrett, Center for Substance Abuse Research, Lewis Katz School of Medicine at Temple University, Philadelphia, PA, USA David Centurión, Dept. of Pharmabiology, Center for Research and Advanced Studies, Col. Granjas-Coapa, Mexico Veit Flockerzi, Institute for Experimental and Clinical Pharmacology and Toxicology, Saarland University, Homburg, Germany Pierangelo Geppetti, Headache Center, University of Florence, Florence, Italy Franz B. Hofmann, Forschergruppe 923 Carvas, Technical University, München, Germany Kathryn Elaine Meier, Dept. of Pharmaceutical Sciences, Washington State University Spokane, Spokane, USA Clive P. Page, SIPP, Kings College London, London, UK KeWei Wang, School of Pharmacy, Qingdao University, Qingdao, China

The Handbook of Experimental Pharmacology is one of the most authoritative and influential book series in pharmacology. It provides critical and comprehensive discussions of the most significant areas of pharmacological research, written by leading international authorities. Each volume in the series represents the most informative and contemporary account of its subject available, making it an unrivalled reference source. HEP is indexed in PubMed and Scopus.

Ingolf Cascorbi • Matthias Schwab Editors

Precision Medicine

Editors Ingolf Cascorbi Institute of Experimental and Clinical Pharmacology University Hospital Schleswig-Holstein Kiel, Germany

Matthias Schwab Dr. Margarete Fischer-Bosch-Institute of Clinical Pharmacology Stuttgart, Germany

ISSN 0171-2004 ISSN 1865-0325 (electronic) Handbook of Experimental Pharmacology ISBN 978-3-031-40046-9 ISBN 978-3-031-40047-6 (eBook) https://doi.org/10.1007/978-3-031-40047-6 # The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 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 Paper in this product is recyclable.

Preface

In recent years, precision medicine (also often considered as personalized or individualized medicine) has emerged as a groundbreaking approach to healthcare, promising tailored treatments and therapies based on an individual’s unique genetic makeup, lifestyle, and environment [1]. By combining advanced technologies, such as genomics, big data analytics, and artificial intelligence, precision medicine has the potential to revolutionize medical practice, moving away from the one-size-fits-all approach and toward personalized and more effective treatments. With respect to pharmacology, the deciphering of the human genome has contributed a lot to the understanding of interindividual variability of drug response and technological advancements, such as high-throughput DNA sequencing, have made it possible to rapidly and cost-effectively decode an individual’s entire genome. Based on increasing evidence, guidelines considering hereditary variants have been developed by international consortia and pharmacogenomic diagnostics is recommended for a number of prescribed drugs. Based on detailed genetic profiles, medical professionals may thus tailor drug selection and dosage in order to maximize therapeutic efficacy while reducing the risk of adverse drug reactions. In this issue, McDermott et al. report on the current implementation of pharmacogenetics in the United Kingdom [2]. In their comprehensive article, the authors address the big challenges of implementation such as development of evidence-based guidelines, technical tools of diagnostics and translation into the practice including clinical decision support systems, and implementation into existing IT infrastructures. Moreover, health care systems are excellently addressed and give a figure of the large complexity of this special part of Precision Medicine. This approach holds great promise for diseases such as cancer, where targeted therapies can be designed to attack specific molecular alterations, but is increasingly considered for a wide range of therapeutic areas. A definition on Precision Medicine with focus on specific features of the tumor is given by Schmidts et al. in this issue [3]. The authors not only include the identification of targetable lesions and tumor vulnerabilities, but also consider the molecular and cellular interactions. The striking development for a successful targeted anti-cancer therapy exemplifying the role model chronic myeloid leukemia is presented by Kaehler and Cascorbi [4].

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Improved diagnostics, such as the impressing rapid progress in imaging techniques (see Freidel et al. [5]), disease differentiation, and stratification, has been established in almost all medical fields aiming to improve the individual clinical outcome. These processes require critical reflection of evidence building as well as consideration of economic consequences. The example of asthma nicely demonstrates a much better differentiation of a disease phenotype leading to stratified therapeutic regimens [6], where technologies like genomics, proteomics, epigenomics, and even microbiomics led to a much deeper understanding on the underlying mechanisms of the disease and opportunities to develop new therapies. Better understanding of the genomics background and/or distinct phenotype of metabolic diseases like diabetes mellitus (see Dawed et al. [7]) or neurological diseases like neuropathic pain exemplified by Sachau and Baron [8] allows progress on development of personalized treatment options. This becomes particularly clear with the example of major depression, one of the early fields where interindividual differences in the pharmacokinetics of drugs were observed, although implementation into practice is still a challenge [9]. Another crucial aspect of precision medicine is the integration of big data analytics and artificial intelligence (AI). The analysis of vast amounts of patient data, including clinical records, lifestyle factors, and treatment outcomes, but also genomic information, can unveil patterns, correlations, and predictive models that were previously impossible to detect. AI algorithms can identify hidden relationships, discover biomarkers, and develop decision support tools for clinicians. New technologies like Next Generation Sequencing allowed the identification of novel markers, requiring AI tools to predict its functionality as excellently described by Zou and Lauschke [10] in this issue. By leveraging these technologies, precision medicine is transforming medical research, diagnosis, and treatment, driving advancements in disease prevention and management. While precision medicine holds immense potential, several challenges must be overcome to realize its full benefits. One of the key obstacles is the integration of genomic data into routine clinical practice and ensuring its accessibility and affordability. Regarding individualized pharmacological treatment, there is increasing evidence on cost effectiveness, as outlined by Payne and Gavan [11]. Privacy and ethical concerns regarding the handling of sensitive genetic information also need to be addressed. Additionally, there is a need for extensive collaboration between researchers, healthcare providers, policymakers, and industry stakeholders to advance precision medicine initiatives. Looking ahead, precision medicine has the potential to transform medical practice, improve patient outcomes, and lead to a more efficient and effective healthcare system. While challenges remain, continued research, technological advancements, and collaboration are essential to unlocking the full potential of precision medicine and shaping the future of personalized healthcare. Dealing with AI and its implementation in clinical routine decisions is one of the most critical aspects, which requires multidisciplinary interactions.

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In this issue of the Handbook of Experimental Pharmacology on Precision Medicine highly renowned experts have contributed to scientifically outstanding chapters exploring principles, applications, and future prospects of precision medicine and highlighting its transformative impact on healthcare. Kiel, Germany Stuttgart, Germany

Ingolf Cascorbi Matthias Schwab

References 1. Zeggini E, Gloyn AL, Barton AC, Wain LV (2019) Translational genomics and precision medicine: moving from the lab to the clinic. Science 365:1409–1413 2. McDermott JH, Sharma V, Keen J, Newman WG, Pirmohamed M (2023) The implementation of pharmacogenetics in the United Kingdom. Handb Exp Pharmacol 3. Schmidts I, Haferlach T, Hoermann G (2022) Precision medicine in therapy of non-solid cancer. Handb Exp Pharmacol 4. Kaehler M, Cascorbi I (2023) Molecular mechanisms of tyrosine kinase inhibitor resistance in chronic myeloid leukemia. Handb Exp Pharmacol 5. Freidel L, Li S, Choffart A, Kuebler L, Martins AF (2023) Imaging techniques in pharmacological precision medicine. Handb Exp Pharmacol 6. Principe S, Vijverberg SJH, Abdel-Aziz MI, Scichilone N, Maitland-van der Zee AH (2022) Precision medicine in asthma therapy. Handb Exp Pharmacol 7. Dawed AY, Haider E, Pearson ER (2022) Precision medicine in diabetes. Handb Exp Pharmacol 8. Sachau J, Baron R (2023) Precision medicine in neuropathic pain. Handb Exp Pharmacol 9. Tsermpini EE, Serretti A, Dolzan V (2023) Precision medicine in antidepressants treatment. Handb Exp Pharmacol 10. Zhou Y, Lauschke VM (2022) Challenges related to the use of next-generation sequencing for the optimization of drug therapy. Handb Exp Pharmacol 11. Payne K, Gavan SP (2022) Economics and precision medicine. Handb Exp Pharmacol

Contents

Part I

Pharmacogenomics

The Implementation of Pharmacogenetics in the United Kingdom . . . . . John H. McDermott, Videha Sharma, Jessica Keen, William G. Newman, and Munir Pirmohamed Part II

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Precision Medicine in Clinical Entities

Precision Medicine in Therapy of Non-solid Cancer . . . . . . . . . . . . . . . . Ines Schmidts, Torsten Haferlach, and Gregor Hoermann Molecular Mechanisms of Tyrosine Kinase Inhibitor Resistance in Chronic Myeloid Leukemia . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Meike Kaehler and Ingolf Cascorbi Precision Medicine in Asthma Therapy . . . . . . . . . . . . . . . . . . . . . . . . . Stefania Principe, Susanne J. H. Vijverberg, Mahmoud I. Abdel-Aziz, Nicola Scichilone, and Anke H. Maitland-van der Zee

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Precision Medicine in Diabetes . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 107 Adem Y. Dawed, Eram Haider, and Ewan R. Pearson Precision Medicine in Antidepressants Treatment . . . . . . . . . . . . . . . . . 131 Evangelia Eirini Tsermpini, Alessandro Serretti, and Vita Dolžan Precision Medicine in Neuropathic Pain . . . . . . . . . . . . . . . . . . . . . . . . . 187 Juliane Sachau and Ralf Baron Part III

Techniques in Precision Medicine

Imaging Techniques in Pharmacological Precision Medicine . . . . . . . . . 213 Lucas Freidel, Sixing Li, Anais Choffart, Laura Kuebler, and André F. Martins Challenges Related to the Use of Next-Generation Sequencing for the Optimization of Drug Therapy . . . . . . . . . . . . . . . . . . . . . . . . . . 237 Yitian Zhou and Volker M. Lauschke ix

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Part IV

Contents

Economics

Economics and Precision Medicine . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 263 Katherine Payne and Sean P. Gavan Correction to: Precision Medicine in Antidepressants Treatment . . . . . . 283 Evangelia Eirini Tsermpini, Alessandro Serretti, and Vita Dolžan

Part I Pharmacogenomics

The Implementation of Pharmacogenetics in the United Kingdom John H. McDermott, Videha Sharma, Jessica Keen, William G. Newman, and Munir Pirmohamed

Contents 1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2 The Evidence for Pharmacogenetics . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.1 A Historical Context . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.2 Adverse Drug Reactions (ADRs) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.2.1 Gene–Drug Pair 1: Aminoglycosides and RNR1 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.2.2 Gene–Drug Pair 2: DPYD and Fluoropyrimidines . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.3 Medicines’ Effectiveness . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.3.1 Gene–Drug Pair 3: Clopidogrel and CYP2C19 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.3.2 Gene–Drug Pair 4: Codeine and CYP2CD6 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.4 Pharmacogenetic Guidelines and Consortia . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.5 Models of Pharmacogenetic Testing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.5.1 Pre-Emptive Pharmacogenetic Testing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.6 Potential for Implementation of Pharmacogenetic Testing in the UK NHS . . . . . . . . . .

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J. H. McDermott · W. G. Newman Manchester Centre for Genomic Medicine, St Mary’s Hospital, Manchester University NHS Foundation Trust, Manchester, UK Division of Evolution and Genomic Sciences, School of Biological Sciences, University of Manchester, Manchester, UK V. Sharma Division of Informatics, Imaging and Data Science, Centre for Health Informatics, The University of Manchester, Manchester, UK J. Keen Manchester Centre for Genomic Medicine, St Mary’s Hospital, Manchester University NHS Foundation Trust, Manchester, UK M. Pirmohamed (✉) Department of Pharmacology and Therapeutics, Wolfson Centre for Personalised Medicine, University of Liverpool, Liverpool, UK Liverpool University Hospital Foundation NHS Trust, Liverpool, UK e-mail: [email protected] # The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 I. Cascorbi, M. Schwab (eds.), Precision Medicine, Handbook of Experimental Pharmacology 280, https://doi.org/10.1007/164_2023_658

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3 Testing Approaches . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.1 Single-Gene Testing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.2 Array-Based Panels . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.3 Sequencing Data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4 The Role of Information Technology in the Implementation of Pharmacogenetics in the NHS . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.1 Digital Transformation and User-Centred Design . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.2 Clinical Decision Support Systems . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.3 Interoperability and Data Standards . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5 Workforce Considerations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5.1 Professional Competence . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5.2 Educational Approaches . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

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Abstract

There is considerable inter-individual variability in the effectiveness and safety of pharmaceutical interventions. This phenomenon can be attributed to a multitude of factors; however, it is widely acknowledged that common genetic variation affecting drug absorption or metabolism play a substantial contributory role. This is a concept known as pharmacogenetics. Understanding how common genetic variants influence responses to medications, and using this knowledge to inform prescribing practice, could yield significant advantages for both patients and healthcare systems. Some health services around the world have introduced pharmacogenetics into routine practice, whereas others are less advanced along the implementation pathway. This chapter introduces the field of pharmacogenetics, the existing body of evidence, and discusses barriers to implementation. The chapter will specifically focus on efforts to introduce pharmacogenetics in the NHS, highlighting key challenges related to scale, informatics, and education. Keywords

Genetics · Pharmacogenetics · Prescribing · Personalised Medicine · Health Informatics

1

Introduction

Variability in the effectiveness and safety of commonly prescribed medicines is a frequent and often frustrating clinical phenomenon. Such variation is regularly attributed to the chosen dosing strategy, the accuracy of the initial diagnosis or the so-called individual factors, such as medical co-morbidities, the impact of polypharmacy or adherence issues. However, there is compelling evidence that the effectiveness and safety of many medicines is also influenced by an individual’s genetic make-up, an area known as pharmacogenetics or pharmacogenomics.

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Fig. 1 Absorption and metabolisation of clopidogrel to form an active metabolite. Clopidogrel is not an active metabolite, it first needs to be absorbed into the intestinal cells before being converted into its active metabolite by the hepatic P450 Cytochrome system enzymes. Figure adapted from PharmGKB and shows the two sequential oxidative steps required to form its active metabolite. Additional pathways leading to inactive metabolites are not shown. CYP2C19 (shown in orange) is the major contributor to both oxidative steps. GSH Glutathione

Genetic variability can affect either the pharmacokinetics and/or the pharmacodynamics of drugs. Pharmacokinetics is the study of drug absorption, distribution, metabolism, and excretion (Fan and de Lannoy 2014), while pharmacodynamics refers to the process whereby a drug interacts with macromolecules (enzymes, receptors, etc.) to produce its therapeutic or toxic effect. The synergistic functioning of these processes ensures that an appropriate concentration of the medicine and/or its active metabolite is in the appropriate body space and in the appropriate timeframe to interact with its target, to deliver a clinically relevant effect, which in some cases, unfortunately also leads to toxicity. As an example, consider the commonly prescribed antiplatelet medicine clopidogrel, used in the treatment of coronary artery disease (CAD), ischaemic stroke (IS), and peripheral arterial disease (PAD). Clopidogrel is a pro-drug and requires metabolism to its active metabolite before it can irreversibly inhibit the P2Y12 subtype of the ADP receptor, conferring antiplatelet activity (Fig. 1) (Scott et al. 2013). The absorption of clopidogrel is mediated, at least partly, by the ABCB1 transporter on the apical surface of intestinal cells. It then undergoes metabolism by various cytochrome P450 enzymes, CYP2C19 being the most important. Non-P450 enzymes such as carboxylesterase and paraoxonase are also involved in clopidogrel metabolism. Disrupted activity of all of these proteins (transporters, enzymes, receptors) through genetic variability could all, theoretically, disturb the action of clopidogrel. However, the most important determinant of the efficacy of clopidogrel identified to date has been polymorphisms in the CYP2C19 gene (a pharmacokinetic process), while P2Y12 variability (a pharmacodynamic target) does not play a role.

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By contrast, with a drug such as phenytoin, predisposition to toxicity has been shown to be due to variation in both pharmacokinetic (CYP2C9) and pharmacodynamic (HLA-B*15:02) genes (Su et al. 2019). Most of the genes in the human genome are highly polymorphic, i.e. there are many alleles (variants) which can occupy the same genomic position within a given population. Much of this variation will have very little impact on protein function, but some genetic variation can disrupt the activity of the final protein product. In some cases, just one sequence difference, termed a single nucleotide variant (SNV), in a gene is capable of rendering the medicine ineffective or toxic (Daly 2017). These pharmacogenetic variants are common in the population (greater than 99% of us carry at least one), can contribute significantly to differences observed in medicine effectiveness and safety (McInnes et al. 2020; Pirmohamed 2023). The United States (US) Food and Drug Administration (FDA) defines pharmacogenetics as “the study of variation in DNA sequence as related to drug response” whilst pharmacogenomics, a relatively more modern term, refers to “the study of variations in DNA and RNA characteristics as related to drug response” (Ventola 2011). In reality, the distinction between these two definitions is somewhat arbitrary and they are often used interchangeably. In addition, there are a group of rare inherited conditions, including acute porphyria and long QT syndrome where knowledge of the diagnosis allows certain drugs to be avoided to prevent triggering of adverse reactions. In several countries there is increasing interest in integrating pre-emptive pharmacogenetic testing into routine practice (van der Wouden et al. 2017; Keeling et al. 2019). This represents a complex healthcare intervention comprising many interacting components and, as such, several factors influence the likelihood of successful implementation (Skivington et al. 2021). Implementation is defined as the process of integrating evidence-based interventions within a setting (Rabin et al. 2008). Pharmacogenetics represents one such evidence-based intervention, and the “implementation of pharmacogenetics” can be defined as the process by which genedrug prescribing guidelines (the evidence base) are realised to guide prescribing for patients. Pharmacogenetic programmes represent the organisational structures which aim to implement pharmacogenetics in practice (McDermott et al. 2022a). At present, there is no international consensus around how pre-emptive pharmacogenetic programmes should be designed. It is highly probable that programmes designed to deliver pre-emptive pharmacogenetics will differ in their optimal design depending on the country, institutional and clinical context. Despite this, there are certain design decisions, methodological challenges, and logistical hurdles which are common across programmes (Turner et al. 2020). This chapter discusses the common themes which emerge from pharmacogenetic implementation programmes, based on extensive review of the literature and experience of establishing pharmacogenetic services in the NHS (McDermott et al. 2022a). We consider the evidence for pharmacogenetic testing, the testing approaches available in the NHS, the associated informatic challenges of delivering results in a clinically relevant timeframe, and the workforce implications associated with the delivery of pharmacogenetics in the NHS.

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The Evidence for Pharmacogenetics

2.1

A Historical Context

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The observation that individuals respond differently to different compounds is by no means a recent one. Some authors argue that there was an awareness of this phenomenon as early as the sixth century BC (Pirmohamed 2001). In the archives of the National Gallery of Art in Washington DC, USA, there exists an early sixteenth century French folio which contains a pen and brown ink drawing of the Ionian Greek Philosopher, Pythagoras. The drawing shows Pythagoras withdrawing from a field of fava beans, a depiction of the circumstances of the philosopher’s untimely death (Fig. 2). Legend states that Pythagoras observed that the consumption of fava beans, in some unfortunate individuals, lead to a severe and often fatal illness in the hours or

Fig. 2 “Do Not Eat Beans” [fol. 25 recto]. Drawing from a sixteenth century French manuscript, stored at the US National Gallery of Art, depicting Pythagoras’s revulsion of fava beans. [Published under the National Gallery of Art’s Open Access Policy]

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days following ingestion. It is speculated that it was this observation that led to the avoidance of beans within the Pythagorean diet, strictly followed by philosophers in the Pythagorean brotherhood (Cappellini and Fiorelli 2008). Such was Pythagoras’ aversion to fava beans that one account of his death states that he was fleeing from attackers and had almost escaped when he came across a field of beans, as depicted in the French folio. On seeing the beans, he stopped, refused to cross, and was cut down by his attackers. Although likely anachronistic, it is an interesting hypothesis that the origins of these traditions began after Pythagoras, or one of his contemporaries, witnessed someone with a deficiency of the enzyme glucose-6-phosphate dehydrogenase (G6PD) exposed to fava beans. G6PD deficiency, historically referred to as favism, is an X-linked disorder resulting from predominantly missense variants in the G6PD gene. G6PD forms part of the pentose phosphate pathway, catalysing nicotinamide adenine dinucleotide phosphate (NADP) to its reduced form, NADPH (Frank 2005). NADPH protects cells from oxidative damage but, under normal circumstances, erythrocytes only require around 2% of G6PD activity to function. Therefore, even in individuals who have severe G6PD deficiency, without a stressor they often display no clinical symptoms. However, exposure to substances which promote oxidative stress can precipitate haemolysis, leading to a severe and occasionally life-threatening reaction. Substances which promote oxidative stress not only include fava beans, but also drugs such as primaquine, dapsone and rasburicase (Gammal et al. 2023).

2.2

Adverse Drug Reactions (ADRs)

A majority of pharmacogenetic gene–drug interactions are associated with variability in medicines’ effectiveness but some confer an increased risk of developing severe ADRs, such as haemolytic attacks in those with G6PD deficiency, as discussed above. When attempting to highlight the relevance of pharmacogenetics in clinical practice, it is often these gene–drug pairs which authors choose to emphasise. Striking examples include the relationship between HLA-B*15:02 and Stevens– Johnson syndrome with carbamazepine and HLA-B*57:01 and serious hypersensitivity reactions with abacavir (Karnes et al. 2021; Martin et al. 2014). The following sections will begin to outline the strong evidence base for pharmacogenetics by highlighting two increasingly important gene–drug pairs associated with ADRs; RNR1 genotype and deafness associated with aminoglycoside antibiotics (Sect. 2.2.1) and the relationship between DPYD variation and fluoropyrimidine chemotherapy agents (Sect. 2.2.2).

2.2.1 Gene–Drug Pair 1: Aminoglycosides and RNR1 Aminoglycosides are used for the treatment of a wide range of infections. Their safety profile is well understood, they have proven effectiveness and can be used synergistically with other antibiotics (McDermott et al. 2021a). Streptomycin was the first aminoglycoside antibiotic, isolated in 1943 by the American biochemist

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Selman Waksman (Woodruff and Selman 2014). Since then, the class has grown significantly to include many natural and semi-synthetic agents. They are typically administered by intravenous or intramuscular injection for treatment of serious gram-negative bacterial infections or as synergistic treatment for serious grampositive bacterial infections, and topically for other purposes (Krause et al. 2016). Therapeutic drug monitoring is required because pharmacokinetics (largely renal excretion) varies between individuals and high plasma concentrations increase the risk of nephrotoxicity and ototoxicity (McDermott et al. 2021a). Sensorineural hearing loss (cochleotoxicity) and vestibulotoxicity, wellrecognised harmful effects of aminoglycoside antibiotics, are dose-dependent and usually observed in patients who receive high doses of aminoglycosides for a protracted period (McDermott et al. 2021a). However, certain individuals appear to have a predisposition towards aminoglycoside-induced hearing loss (AIHL), with reports of single doses causing profound bilateral sensorineural hearing loss. This predisposition towards AIHL appeared to be inherited down the maternal lineage, in an extra-nuclear (mitochondrial) inheritance pattern. In 1993, genetic analysis of four families with AIHL identified the m.1555A > G variant in affected individuals in each family (Prezant et al. 1993). This variant, present in approximately 1 in 500 individuals, lies within a highly conserved region of the 12 s rRNA subunit, which has two single-stranded regions separated by two stem-loops (Ryu and Rando 2002; Qian and Guan 2009). In the bacterial homologue, this region is where mRNAs are decoded, and it is where aminoglycosides bind to confer their therapeutic, bactericidal effect. Variants in MT-RNR1 which predispose to AIHL appear to cause the 12 s rRNA subunit to resemble more closely the bacterial 16 s rRNA subunit, thus allowing aminoglycosides to bind more readily (Hamasaki and Rando 1997). A large number of variants have been reported in the RNR1 gene, but a recent international review of the literature concluded that, other than m.1555A > G, the only additional variants with sufficient evidence to support a drug-variant interaction were m.1095 T > C and m.1494C > T (McDermott et al. 2021a). As such, the Clinical Pharmacogenetics Implementation Consortium (CPIC) recommends that anyone carrying one of these three variants should avoid aminoglycosides unless the risk of AIHL is outweighed by the severity of infection and lack of safe or effective alternative therapies (McDermott et al. 2021a). In the NHS, testing for the m.1555A > G variant is available, but most frequently undertaken retrospectively to investigate the aetiology of hearing loss, rather than prospectively to avoid AIHL. This testing paradigm seems unsatisfactory, but adequately exemplifies some of the key issues associated with the implementation of preventative medicine, specifically pharmacogenetics. Sequencing to prevent AIHL would require a patient’s genotype to be available prior to the prescription of aminoglycosides. Currently, this is not a common practice due to a series of technical challenges, including genotyping strategies not being sufficiently rapid. With this in mind, the Pharmacogenetics to Avoid Loss of Hearing (PALOH) trial was developed to assess whether a rapid point-of-care diagnostic platform for the m.1555A > G variant could be integrated

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into routine clinical practice in neonatal intensive care units (NICUs) (McDermott 2020; McDermott et al. 2021b, 2022b).

2.2.2 Gene–Drug Pair 2: DPYD and Fluoropyrimidines Fluoropyrimidines, such as 5-fluorouracil and its pro-drug capecitabine, are chemotherapy agents used in the treatment of various cancers, prescribed to approximately 46,000 patients in England annually (MHRA 2020). DPYD is the rate-limiting enzyme in the catabolism of fluoropyrimidine chemotherapy agents and genetic variants within the DYPD gene can impact enzyme function. Over 20 of these alleles, conferring either reduced or no activity, have been documented with strong evidence. Four of these variants make up the great majority of loss of function (LoF) variation in the Europid population, with 7% of Europeans carrying at least one of these variants (Amstutz et al. 2018). Most other DPYD variants of phenotypic consequence are extremely rare, but may still predispose to severe toxicity. Fluoropyrimidines have a narrow therapeutic window, resulting in a small difference between minimum effectiveness and maximum tolerable dose. Only 1 to 3% of the administered drug is metabolised to cytotoxic agents, the rest is degraded and excreted in the urine (Amstutz et al. 2018). Over 80% is catabolised to dihydrofluorouracil (DHFU) by dihydropyrimidine dehydrogenase (DPYD), which is the first and rate-limiting enzyme in this catabolic process. Reduced or absent DPYD activity reduces fluoropyrimidines clearance and can predispose to severe or life-threatening side effects, such as neutropenia, nausea, vomiting, severe diarrhoea, stomatitis, and mucositis (Froehlich et al. 2015; Lee et al. 2014). In 2020, in the UK, the Medicines Health Regulatory Authority (MHRA) published a drug safety update stating, unambiguously, that patients with complete or partial DPYD deficiency are at increased risk of severe and fatal toxicity during treatment with fluoropyrimidine agents (MHRA 2020). Because of this, they stated that all patients should be tested for DPD deficiency before initiation to minimise the risk of these reactions. Within the same document they outlined that the number of patients needed to test to avoid 1 ADR was approximately 53. Similar recommendations were also made by the European Medicines Agency. Metrics such as these can facilitate health economic analyses, which serve to assess whether the introduction of a pharmacogenetic test realises sufficient health gain to warrant implementation, given the expected costs to the health care system when compared with relevant alternative uses of the healthcare budget. Indeed, formal costeffectiveness studies have shown that screening for the DPYD genotype is costeffective (Brooks et al. 2022). At present, in the UK and EU, four variants in the DPYD gene are genotyped. If an individual carries one of these variants in heterozygous state, they are an intermediate metaboliser and at risk of severe drug toxicity, necessitating a 25–50% dose reduction. Anyone homozygous for these variants are poor metabolisers and should avoid fluoropyrimidines due to the risk of fatal toxicity. Importantly however, it should be noted that these four variants predict just 20–30% of early onset life-threatening fluoropyrimidines toxicity. Part of the reason for this is because these four variants are more common within white populations of European

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ancestry. In other ethnic groups, other less well characterised variants may be more common, and may have the same functional effects as the four tested variants but are currently not genotyped. This highlights a fundamental issue about implementation of pharmacogenomics – since most studies have been conducted in European ancestry populations, implementation may not necessarily capture variants in other ethnic groups, and this therefore has the potential to exacerbate health and race inequalities.

2.3

Medicines’ Effectiveness

Both of the drug–gene pairs discussed thus far are associated with readily observable and severe adverse drug reactions. There is also a clear temporal relationship between the administration of the drug and the reaction. Aminoglycosides are given and the child fails their newborn hearing screen. Fluoropyrimidines are prescribed and neutropenia follows. ADRs also have tangible costs, such as the tariff associated with cochlear implantation or the utilisation of bed space to treat a patient with neutropenia. Pharmacogenetic relationships associated with medicines’ effectiveness are arguably less tangible. A medicine not working as hoped is a common clinical phenomenon and, in current practice, may not be attributed to genetic variation. This offers one explanation for why this type of drug–gene association has received less focus in clinical practice. Difficulties in defining in what constitutes an efficacious endpoint and difficulties in measuring adherence may also have impacted on efficacy pharmacogenetic evaluation (Lonergan et al. 2017). Whereas DPYD and RNR1 testing are starting to be undertaken as part of clinical practice in the UK, gene–drug pairs related to medicines effectiveness, as discussed here, are not.

2.3.1 Gene–Drug Pair 3: Clopidogrel and CYP2C19 Clopidogrel is a thienopyridine pro-drug which requires hepatic biotransformation to form an active metabolite that selectively and irreversibly inhibits platelet aggregation (Scott et al. 2013). As discussed above, conversion of clopidogrel to its active form requires two sequential oxidative steps in the liver involving several cytochrome P450 enzymes, including CYP2C19. Like many other members of the CYP450 superfamily, the CYP2C19 gene is highly polymorphic with more than 25 known alleles. The combination alleles, in diplotype, determines one’s metaboliser status, and a significant proportion of the population are considered to have “poor” or “intermediate” CYP2C19 function (Scott et al. 2013; Karnes et al. 2021). In pharmacogenetics the star (*) allele format is the nomenclature of choice, allowing for easier notation of diplotype status. In the case of CYP2C19, *1 is the normal or “wild-type” allele with a Western-European allele frequency of 0.63. *2 and *3 are the most common LOF alleles and carriers of these (i.e. *1/*2 or *1/*3) are labelled as “intermediate” metabolisers. Meanwhile, any individual who is biallelic for two LOF alleles is a “poor” metaboliser. Retrospective analyses have repeatedly demonstrated that LOF allele carriers have worse outcomes after primary coronary intervention (PCI) when treated with

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clopidogrel. This same pattern has been seen after ischaemic strokes (IS) and peripheral vascular disease (PVD) (Sun et al. 2015; Tornio et al. 2018; Wang et al. 2021a). However, despite strong evidence to support genotyping following PCI, there have been no concerted efforts to implement these findings in the clinical setting in the UK. The main reason for this is because, in 2009, the PLATO trial was published demonstrating the superiority of ticagrelor over clopidogrel after PCI in acute myocardial infarction (AMI) (James et al. 2011). As such, national and international guidelines swiftly changed to recommend ticagrelor as the first-line agent in AMI, despite being more expensive, with an increased risk of bleeding in treated individuals. Unlike clopidogrel, ticagrelor does not require activation by CYP2C19. However, given the impact of CYP2C19 genotype on the effectiveness of clopidogrel and the frequency of these variants in the population, one might ask whether clopidogrel is as effective as ticagrelor in those individuals who metabolise the drug properly? This question was answered in a 2019 landmark randomised, assessor-blinded trial which compared genotype-guided dosing strategy for clopidogrel against ticagrelor in patients undergoing primary PCI (Claassens et al. 2019). In the genotype-guided cohort, patients with LOF alleles were prescribed ticagrelor in lieu of clopidogrel. The trial demonstrated that genotype-guided therapy was non-inferior to ticagrelor and, importantly, the risk of bleeding was lower. In summary, this work demonstrated that genetics could be applied acutely to guide prescribing, improve safety, and potentially save money for the healthcare system. International CPIC guidance recommends that CYP2C19 poor or intermediate metabolisers should receive an alternative antiplatelet agent due to the reduced effectiveness of clopidogrel. However, the strength of these recommendations varies depending on clinical indication and there is variability in how routinely these tests are undertaken in clinical practice. In the UK, clopidogrel genotyping has been introduced in Tayside, a region in Scotland, but not in other parts of the UK (Wilkinson 2023). At the time of writing, no major clinical consensus guidance pertaining to the prescription of clopidogrel recommends CYP2C19 testing as part of a standard practice. It is likely that this will change over time as evidence continues to emerge and clinical decisions should always take into account contemporaneous guidance.

2.3.2 Gene–Drug Pair 4: Codeine and CYP2CD6 Codeine is a pro-drug which has a 200-fold weaker affinity for the mu opioid receptor compared to morphine. As such, codeine elicits the majority of its opioid effect once converted to morphine following O-demethylation by CYP2D6. Typically, this accounts for only 5–10% of codeine clearance in the majority of individuals, with around 80% being inactivated via glucuronidation to codeine-6glucuronide by uridine 5′-diphosphate glucuronosyltransferase-2B7 (UGT2B7) and via N-demethylation to norcodeine via CYP3A4 (Crews et al. 2012). In white European populations, approximately 80% of patients are extensive CYP2D6 metabolisers, meaning that they carry two normal function alleles and convert codeine to morphine at an expected rate and no prescribing adjustments are

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indicated. There is some conjecture surrounding how carrying one LoF CYP2D6 allele affects codeine metabolism (Crews et al. 2012). Although there is certainly lower CYP2D6 activity in these individuals, the clinical relevance of this is uncertain. Individuals with biallelic LoF alleles, about 5–10% of the Caucasian population, are poor metabolisers and get little to no analgesic benefit from codeine and an alternative agent should be used. If there was an awareness of an individual’s poor metaboliser status prior to prescription, analgesia could be better tailored to the individual promoting better outcomes. Codeine is usually the strongest opioid that emergency departments (EDs) in the NHS will dispense, the rationale being that the ED is not an appropriate environment to monitor an individual on stronger opioids and that the availability of stronger opioids may encourage drug-seeking behaviour in the ED. This is an entirely reasonable policy decision; however, there is a risk it may well lead to those individuals reattending with chronic pain and being unfairly labelled as having drug-seeking tendencies. In the white European population, the allele frequency of the wild-type *1 CYP2D6 allele is 0.54, whilst in other populations the frequency is far lower, just 0.39 and 0.34 in the African and East Asian populations, respectively (Crews et al. 2012; Bradford 2002). This means that individuals from these populations are less likely to respond to codeine. This is particularly important when considering that individuals from these ethnic groups often have higher incidences of chronic pain and poorer access to healthcare (Hoffman et al. 2016; Campbell and Edwards 2012). Having a knowledge that these patients might not respond to codeine might allow for more tailored prescribing, resulting in better pain control. Interestingly, about 2% of the population are ultra-rapid CYP2D6 metabolisers because they carry more than two copies of the CYP2D6 gene on the same chromosome. These individuals form greater amounts of morphine from codeine, and this has been shown to predispose to respiratory depression, particularly in newborn babies and in children with upper airway obstruction caused by enlarged adenoids or tonsils. This has led to regulatory warnings about the use of codeine in children (Fortenberry et al. 2019; Koren et al. 2006). CYP2D6 is not amenable to rapid genotyping strategies, due to the presence of multiple variants, pseudogenes, and copy number variation. Furthermore, as codeine is so widely prescribed, it is not clear whether it would be feasible or cost-effective to genotype all individuals prior to the use of codeine. However, CYP2D6 per se is involved in the metabolism of about 25% of drugs, and therefore is an important candidate to consider for implementation.

2.4

Pharmacogenetic Guidelines and Consortia

The gene–drug pairs discussed thus far represent just a small proportion of the known pharmacogenetic interactions. Of the pairs outlined above, all of which have strong evidence to support implementation, only DPYD testing has reached mainstream clinical practice in the UK. The slow adoption of pharmacogenetics,

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despite robust evidence, has been a long-standing and well-recognised global phenomenon which has led to various initiatives to investigate and rectify poor uptake (Roden 2019). In the late 2000s, it was perceived that one major barrier to clinical implementation was the lack of curated guidelines which could provide clear peerreviewed consensus recommendations for the use of pharmacogenetic variation. To address this, in 2009, experts from the US based Pharmacogenetics Research Network (PGRN) and the Pharmacogenetics Knowledge Based (PharmGKB) launched the Clinical Pharmacogenetics Implementation Consortium (CPIC) (Relling and Klein 2011). Over the past decade, CPIC and a European counterpart, the Dutch Pharmacogenetic Working Group (DPWG), have developed structured methodologies to iteratively review the pharmacogenetic literature and produce standardised clinical guidelines. These documents appraise the available evidence to provide guidance on variant nomenclature, metaboliser classification, testing approaches and, critically, guidelines on dosing and drug choice (for individuals with known genotype). At the time of writing, CPIC have produced guidelines for 26 gene–drug class pairs (Table 1). These guidelines cover frequently prescribed medicines including antibiotics, antiplatelets, opioids, non-steroidal anti-inflammatory drugs (NSAIDs), proton pump inhibitors (PPIs), and antidepressants. The existence of initiatives such as CPIC and the DPWG has provided a muchneeded structure to organise the pharmacogenetic literature. These guidelines represent a robust evidence base to support the implementation of pharmacogenetics within a healthcare system and they provide a curated list of gene–drug pairs which are deemed clinically important enough for testing. Furthermore, CPIC also provides an informatic interface which healthcare systems can use to develop pharmacogenetic prescribing systems within their electronic patient records (EPRs). The CPIC and DPWG guidelines summarise, based on expert review of the literature, how prescribing practice should be modified in the presence of clinically relevant genetic variation. What they explicitly do not say is when pharmacogenetic testing should take place. For example, the guidelines for RNR1 and aminoglycoside antibiotics state that, in the presence of clinically relevant RNR1 variation, aminoglycoside antibiotics should be avoided unless the high risk of permanent hearing loss is outweighed by the severity of infection and lack of safe or effective alternative therapies (McDermott et al. 2021a). The guideline does not state when, how, or even if testing should take place. This same approach is consistent throughout international pharmacogenetic guidelines, reflecting an awareness that models for pharmacogenetic delivery will differ between countries and institutions depending on testing capacity, access to expertise, and funding availability.

2.5

Models of Pharmacogenetic Testing

Overly prescriptive guidelines stating how and when testing should be undertaken would be of little value in a system where pharmacogenetics is still in its infancy. As such, the ambition of organisations such as CPIC and the DPWG is that individual

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Table 1 Drug–gene pairs covered in CPIC guidelines Medicine(s) Abacavir Allopurinol

Gene(s) HLA-B HLA-B

ADR/ effectiveness ADR ADR

Aminoglycosides

MT-RNR1

ADR

Atazanavir Atomoxetine

UGT1A1 CYP2D6

Carbamazepine and oxcarbazepine Clopidogrel Efavirenz

HLA-A, HLA-B CYP2C19 CYP2B6

ADR Effectiveness and ADR ADR

Fluoropyrimidines

DPYD

ADR

Ivacaftor NSAIDs

CFTR CYP2C9

Effectiveness ADR

Ondansetron and Tropisetron Opioid therapy

CYP2D6

Effectiveness

CYP2D6, OPRM1, COMT IFNL3

Effectiveness and ADR

Nausea and drowsiness

Effectiveness

N/A

CYP2C9, HLA-B CYP2C19

ADR

Stevens–Johnson syndrome

Effectiveness

N/A

G6PD CYP2D6, CYP2C19

ADR Effectiveness and ADR

SLCO1B1 CYP3A5 CYP2D6 TPMT, NUDT15 CYP2D6, CYP2C19 RYR1, CACNA1S

ADR Effectiveness Effectiveness ADR

Haemolysis Insomnia, headache, gastrointestinal dysfunction, and sexual dysfunction Myopathy N/A N/A Neutropenia and myelosuppression Anticholinergic, central nervous system, and cardiac effects Malignant hyperthermia

CYP2C19

Effectiveness and ADR

Peginterferon-alphabased regimens Phenytoin Proton pump inhibitors Rasburicase Selective serotonin reuptake inhibitors Simvastatin Tacrolimus Tamoxifen Thiopurines Tricyclic antidepressants Volatile anaesthetic agents and succinylcholine Voriconazole

Effectiveness ADR

Effectiveness and ADR ADR

Reaction Abacavir hypersensitivity Severe cutaneous adverse reaction Aminoglycoside induced hearing loss Bilirubin-related adverse events Dry mouth, blurred vision, sleep, disturbances, decreased weight Stevens–Johnson syndrome N/A Central nervous system side effects Nausea, headaches, neutropenia, myelosuppression. N/A Gastrointestinal, renal, and cardiovascular events N/A

Hepatotoxicity and neurotoxicity (continued)

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Table 1 (continued) Medicine(s) Warfarin

Gene(s) CYP2C9, VKORC1, CYP4F2

ADR/ effectiveness Effectiveness (Fan and de Lannoy 2014)

Reaction N/A

A summary of the drug–gene pairs with existing guidelines from the Clinical Pharmacogenetics Implementation Consortium (www.cpicpgx.org) (1). The pharmacogenetics of warfarin is associated with optimal dosing strategy which, if not tailored appropriately, can lead to reduced effectiveness or bleeding

institutes, or broader healthcare systems, can integrate pharmacogenetic guidelines into their infrastructures as they grow. One example of this adaptive approach can be seen at the Mayo Clinic in Minnesota, USA. The Mayo Clinic is a large, not-for-profit, academic medical centre based in Rochester, Minnesota, with other major sites in Jacksonville, Florida, and Phoenix– Scottsdale, Arizona. In 2011, the Mayo’s Centre for Individualised Medicine (CIM) launched a translational pharmacogenetics programme. In response to early CPIC guidelines, by 2013, the Institute was offering single-gene testing for TPMT, HLAB*57:01, HLA-B*15:02, and IL28B after the prescription of thiopurines, abacavir, carbamazepine, and ribavirin–pegylated interferon, respectively (Farrugia and Weinshilboum 2013). Around this time, the Institute established a “Pharmacogenomics Task Force” under the auspices of the Pharmacy department to oversee the development of a pharmacogenetic service. The Task Force had multidisciplinary representation from clinical and institutional stakeholders. Representatives from genomic medicine, primary and specialitycare clinics, pharmacy, laboratory, education, research, informatics, and hospital leadership sat on the committee (Caraballo et al. 2017). Over the course of several years, the Mayo steadily introduced more pharmacogenetic tests. By 2017, the Institute was offering pharmacogenetic testing for 21 single gene–drug pairs. Critically, these tests were developed and offered using a reactive model of delivery. This is where a test is undertaken as and when a drug was being prescribed. For example, genetic testing for SLCO1B1 variants, which predispose to statin induced myopathy, would be performed as and when simvastatin was being prescribed for the first time in a cardiology clinic. As well as establishing a pharmacogenetic testing infrastructure and strategy, the Mayo Clinic developed educational programmes, to up-skill the workforce, and built bespoke pharmacogenetic clinical decision support (CDS) tools, to facilitate implementation (Formea et al. 2015; Hicks et al. 2016). An active research environment was also cultivated, leading to several manuscripts describing clinical trials and implementation initiatives related to pharmacogenetics at the clinic (Hicks et al. 2017; Matey et al. 2021; Bielinski et al. 2020; St Sauver et al. 2016). In many of their early publications, the Mayo team regularly cited the potential value of transitioning to a pre-emptive model of clinical delivery.

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2.5.1 Pre-Emptive Pharmacogenetic Testing Genetic variants which influence an individual’s response to medicine occur with a high frequency in populations of all ancestries. Large biobank studies, both in the USA and Europe, have served to demonstrate just how common pharmacogenetic variation is. An analysis of US veterans found that the projected prevalence of actionable pharmacogenetic variants was 99%, whilst recent analysis of the UK-Biobank identified 99.5% of participants carried an actionable pharmacogenetic variant (McInnes et al. 2020; Chanfreau-Coffinier et al. 2019). Within the UK-Biobank study, 24% of patients had also been prescribed a drug for which they were predicted to have an atypical response. Although studies such as these have limitations, specifically they may not be representative of the general population and there can be evidence of a “healthy volunteer” selection bias, they nevertheless demonstrate the almost ubiquitous presence of actionable pharmacogenetic variation. Evidence from local and national biobank studies demonstrating the high frequency of clinically relevant pharmacogenetic variation led authors and opinion leaders to consider the relevance of pre-emptive pharmacogenetic testing (Weitzel et al. 2017; Bielinski et al. 2014). This approach, in comparison to reactive testing, is where a genetic test is performed prior to the decision to prescribe a medicine. The test would typically involve a panel of different pharmacogenetic variants within several genes. To use the scenario described above, if a patient presented to the cardiology clinic and a statin was indicated, the patient’s SLCO1B1 genotype would already be available within the patient’s medical records. Furthermore, if the physician chose to prescribe another medicine for which pharmacogenetic guidelines exist, such as clopidogrel, the relevant genotype could be queried for that medicine as well. If implemented correctly, a pre-emptive strategy could be advantageous as genotyping would only need to be done once and the data could then be used to inform prescribing throughout the patient’s life, rather than undertaking number of single-gene tests as required with a reactive testing strategy. In 2023, the PREPARE study from the Ubiquitous Pharmacogenomics Consortium (UPGx) was published showing that a 12-gene panel covering 50 germlinevariants was able to reduce adverse drug reactions in the genotyped group by 30% when compared with patients randomised to standard care (Swen et al. 2023). This further adds to the already strong evidence base for the clinical utility of panel-based pharmacogenomic testing. Understanding how successful systems throughout the world have developed and are structured is imperative as, at best, they provide a model to adopt for use in other healthcare systems and, at least, they serve to highlight the barriers and enablers to implementation which can be considered when designing strategies.

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Potential for Implementation of Pharmacogenetic Testing in the UK NHS

The UK NHS was founded on 5 July 1948 with the underlying principle that it should provide care at the point of need from cradle to grave. Aneurin Bevan, who founded the NHS, at the time said “Illness is neither an indulgence for which people have to pay, nor an offence for which they should be penalised, but a misfortune the cost of which should be shared by the community”. Although the NHS has evolved over the last 75 years and is now administered separately within the four nations that make up the United Kingdom, it is the largest integrated healthcare system in the world. As such it has the opportunity, not afforded in other healthcare systems, to implement new innovations for populations at scale. In relation to genomics, an example of this was the 100,000 Genomes Project which enabled whole genome sequencing of over 100,000 genomes from patients with rare diseases and cancer and make their data available to clinicians within the NHS. Implementation of pharmacogenomics into the NHS could thus represent the first example of whole population pharmacogenomic testing. In order to scope the implementation of pharmacogenomics, the NHS Genomics Unit in England held a workshop in collaboration with the UK Pharmacogenetics and Stratified Medicine Network (Turner et al. 2020). This highlighted some of the opportunities, challenges, and potential mitigation to enable implementation of pharmacogenomics into the NHS, favouring a pre-emptive genotyping approach. More recently, the Royal College of Physicians of London, which represents 26 medical specialities, and the British Pharmacological Society, jointly published a report, “Personalised Prescribing”, which called for the implementation of pharmacogenomic testing in the NHS (British Pharmacological Society, Royal College of Physicians 2022). The recommendations, summarised from the report, were as follows: • Clinical implementation should follow a pre-emptive genotyping approach and should be available to primary, secondary, and tertiary care. The testing panel should be adaptable so that it can be expanded or contracted with the availability of new evidence. Furthermore, the clinical effectiveness of the pharmacogenomics implementation service should be subject to continuous and iterative evaluation with the aim of ensuring that the service has maximum utility and efficiency. • Funding for pharmacogenomic testing should be made available from central, rather than local, funds to avoid a postcode lottery to prevent exacerbating health inequalities. • The implementation of pharmacogenomics should be accompanied by a comprehensive education and training package. It will be important to ensure support is available for clinicians as testing is rolled out – this can include a combination of clinical decision support systems, an advice service, as well the possibility of referrals to experts in complicated situations.

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• Funding for pharmacogenomics research should be made available in order to ensure that the panel is continually refined and updated. The research should also evaluate ethical, legal, and social issues. • Pharmacogenomics implementation should be accompanied by clear lines of communication with clinicians, patients, and the public. Patients should be involved in the design of the service.

3

Testing Approaches

An important component of any pharmacogenetic programme is the choice of genetic technology – specifically the laboratory test which is able to identify whether the patient carries clinically relevant genetic variants, or alternative forms of assay that directly measure enzyme or metabolite levels. Examples of the latter type of test adopted in the UK include testing of pseudocholinesterase to determine susceptibility to prolonged paralysis from suxamethonium and TPMT activity for thiopurine toxicity. There is notable variation between testing programmes, both in relation to the genes and variants tested (hereafter “targets”), and also with regard to the genetic technology used. Broadly, the technical approaches to pharmacogenetic testing can be split into three categories, each of which might utilise a range of genetic technologies.

3.1

Single-Gene Testing

The traditional approach to pharmacogenetic testing involves genotyping, or sequencing, of a single gene. This is typically undertaken in the context of a reactive pharmacogenetic test following the prescription, or anticipated prescription, of a given medicine where there is evidence for pharmacogenetic guided prescribing. This approach represents the way the vast majority of pharmacogenetic testing has taken place in the NHS to date and reflects the historical approach to genetic testing generally. At present, there are several single gene–drug tests widely available within the NHS (Table 2). DPYD testing in the context of fluoropyrimidine chemotherapy agents (Sect. 2.2.2) and MT-RNR1 testing to avoid aminoglycoside-induced ototoxicity (Sect. 2.2.1). HLA typing is also available in the NHS and is relevant for the prescription of several medicines including abacavir, carbamazepine, allopurinol, and phenytoin (Table 2). However, in practice, HLA typing is rarely undertaken on a routine basis for patients receiving allopurinol and phenytoin. Conversely, for HLA-B*57:01 to prevent abacavir hypersensitivity, HIV physicians in the UK now order the test at the time the person is first diagnosed as HIV-positive rather than when they are due to receive abacavir, with the result stored in their health record. The reasons that some drug–gene pairs have been implemented in clinical practice, whilst some have not, are manifold and complex. Major drivers include regulatory guidance from the MHRA for DPYD and fluoropyrimidines, HLA testing for abacavir and carbamazepine, professional guidelines for TPMT testing for

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Table 2 Gene–drug pairs where pharmacogenetic testing is currently available within the NHS testing ecosystem (accurate as of January 2023) (1). CYP2C9 testing, although included in the CPIC Phenytoin guideline, is not currently available in the NHS. Variants across CYP2C9 can be tested to assign a metaboliser state, which can then be used to guide dosing Medicine Fluoropyrimidines

Gene(s) DPYD

Aminoglycosides Thiopurines

MT-RNR1 TPMT NUDT15

Abacavir Carbamazepine

HLA-A HLA-A HLA-B HLA-B HLA-B CYP2C9 (Fan and de Lannoy 2014)

Allopurinol Phenytoin

Variant/haplotype testing available in the NHS • c.1905 + 1G > A (rs3918290) DPYD*2A • c. 2846A > T (rs67376798) • c.1679 T > G (rs55886062) DYPD*13 • c.1236G > A/HapB3DPYD (rs56038477) • m.1555A > G (rs267606617) • c.238G > C (TPMT*2) (rs1800462) • c.460G > A (rs1800460) • c.719A > G (TPMT*3A) (rs1142345) Typically undertaken for individuals with TPMT deficiency measured by activity assay or following recent blood transfusion • c.415C > T (NUDT15*3) (rs1166855232) HLA-B*57:01 • HLA-A*31:01 • HLA-B*15:02 • HLA-B*58:01 • HLA-B*15:02

thiopurines; the availability for testing; and the complexity and turnaround time for certain tests and the health economic evidence. There are several technological approaches which might be used to perform single-gene testing and will vary depending on the testing laboratory, the molecular target, and the clinical context. MT-RNR1 testing (Sect. 2.2.2) is available via the NHS Genomic Test Directory and is eligible as a pre-emptive test in patients who might be exposed to aminoglycosides, but also to investigate the cause of hearing loss. Although the CPIC guidance includes three variants where there is sufficient evidence to recommend avoiding aminoglycosides, at present testing is only available for the most common m.1555A > G variant. This might be performed via a process such as Sanger sequencing or Pyrosequencing, with turnaround times of 4 weeks. Meanwhile, DPYD testing requires a more rapid turnaround time (5 days from sample collection to result) as the result will be used to determine the choice of chemotherapy which represents a time critical decision. This has necessitated the adoption of genetic testing approaches which are both relatively rapid and high throughput, given that all patients receiving fluoropyrimidines should undergo this testing. There are several assay systems commercially available which typically implement an amplification stage followed by a detection method such as fluorescence or capillary electrophoresis.

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Array-Based Panels

Where pharmacogenetic programmes have been established, the vast majority have migrated from using single-gene tests and have adopted array-based platforms (Thornley et al. 2021; Hoffman et al. 2014; Gordon et al. 2016; Manzi et al. 2017; Keller et al. 2010; Kitzmiller et al. 2012; Niedrig et al. 2021; Patel et al. 2021). These range from extremely broad systems, with the capacity to flexibly test for many thousands of variants across hundreds of genes, to more targeted gene panels (Aquilante et al. 2020). This represents a relatively agnostic approach to gene/variant selection, however where a service was designed around a specific indication, such as mental health or hypertension, panels were occasionally restricted to include genes and variants associated with medicines related to those diseases only (Dunbar et al. 2012; Ramsey et al. 2019; Wang et al. 2021b). Array platforms from several commercial vendors are used by pharmacogenetic programmes, and most systems allow for an element of customisation.

3.3

Sequencing Data

Despite variability in the choice of test provider and target selection, previous analysis has found that the vast majority of pharmacogenetic programmes make use of genotyping rather than sequencing approaches, with two notable exceptions. Firstly, the Hospital for Sick Children in Toronto, as part of a clinical trial, compared pre-emptive against point-of-care (reactive) pharmacogenetic testing (Cohn et al. 2021). The pre-emptive pharmacogenetic testing cohort involved the reanalysis of whole genome sequencing (WGS) data undertaken as part of separate investigations for congenital cardiac malformations. Pharmacogenetic variants were extracted from this sequence data and an assessment was made regarding its potential utility in clinical practice. The cohort who underwent WGS analysis were found to have an increased number of dosing recommendations advising deviation from standard dosing regimens, suggesting this approach provided more comprehensive pharmacogenetic testing than a reactive strategy. The second example of a programme utilising a sequencing rather than genotyping strategy is at the Mayo Clinic which, in 2012, launched the Right Drug, Right Dose, Right Time: Using Genomic Data to Individualize Treatment Protocol (RIGHT) Study (Bielinski et al. 2014). For this project, the team developed PGRNseq, a Targeted Capture Sequencing Panel for over 250 genes (Bielinski et al. 2020; Gordon et al. 2016; Wang et al. 2022). This cohort study recruited over 10,000 patients who were enrolled in a local biobank. The trial found that over 99% of participants carried a clinically actionable pharmacogenetic variant and 79% of participants carried clinically actionable variants in three or more genes. Critically, DNA sequencing identified an average of 3.3 additional, conservatively predicted, deleterious variants that would not have been evident using genotyping (Bielinski et al. 2020; Wang et al. 2022). Further detail from the study is awaited to assess

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whether the integration of pharmacogenetics into routine clinical practice measurably improved outcomes. For some medicines there is a lag time for clinical provision of pharmacogenetic testing following licencing approval. For example, siponimod, a sphingosine-1phosphate receptor modulator, has been licenced for the treatment of multiple sclerosis. Currently, the drug manufacturer will organise CYP2C9*3 genotyping as the drug is contra-indicated in individuals homozygous for this variant (DíazVillamarín et al. 2022). Likewise, the chaperone treatment, eliglustat, for the rare inherited metabolic condition, Gaucher disease, is not recommended for patients in whom a CYP2D6 genotype cannot be determined or in ultra-rapid metabolisers, with multiple gene copies (Kane and Dean 2012). Ensuring that there is a rapidly responsive service that is able to react to regulatory guidance and new drugs coming onto market will be key as pharmacogenetics becomes routine in clinical practice. Any technology utilised must be able to flexibly respond to new pharmacogenetic targets and also scale as testing becomes increasingly common.

4

The Role of Information Technology in the Implementation of Pharmacogenetics in the NHS

Most aspects of healthcare delivery today are supported by information technology (IT). However, healthcare systems remain far from utilising the benefits of IT to improve care and outcomes (Cresswell et al. 2017). Given the rising demand on clinical services, the application of well-designed digital solutions has the potential to give healthcare professionals the time and capacity needed to better care for patients and ultimately improve outcomes. In order to realise this potential in the context of implementing pharmacogenetics, the role of IT must be recognised from the outset and solutions developed with a digital-first mindset.

4.1

Digital Transformation and User-Centred Design

The mantra of digital transformation is people, process, and technology. Historically, there has been a focus on technical innovation. As a result, there remains a need to better understand the social, cultural, and organisational barriers to the adoption of new technologies in healthcare (Krasuska et al. 2021). Reflections on the National Programme for IT (NPfIT) in the UK and Health Information Technology for Economic and Clinical Health (HITECH) Act in the USA highlight how digitisation without end-user input is likely to lead to failure and wasted effort (Wachter 2016). At the heart of the informatics challenge for pharmacogenomics is the need to share data across organisational boundaries; pharmacogenetic data are produced in specialist genomic laboratories, but results must be available across the health system in order for care professionals to use them (Fig. 3). These results must add value to clinical decision-making and thus be presented in a meaningful way to ensure they are adopted and acted upon.

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Fig. 3 One of the key informatics challenges for implementing pharmacogenetics is the sharing of data across organisational boundaries

In order to understand the technical specifications required to develop an informatics solution that allows the implementation of pharmacogenetics in the NHS it is critical to start with the end-users. Design that is focused on the needs and wants of humans interacting with a given product or service is known as “user-centred design”. This approach is well recognised as key driver of innovation that leads to usable and desirable solutions across industries with designers being embedded within most successful enterprises. The healthcare sector is yet to fully embrace user-centred design, though its value is increasingly recognised. In the context of pharmacogenetics in the NHS, Rafi et al. undertook an extensive qualitative study of primary care professionals exploring the barriers, challenges, and opportunities in UK general practice. They highlight how general practitioners highlighted the importance of integrating pharmacogenomic results within electronic health records in order for them to make better informed prescribing choices for patients. In particular, it was felt important that implementing pharmacogenetics should not increase the workload of front-line staff who are already under significant pressures (Rafi et al. 2020). Additionally, a national survey of over 10,000 doctors in the USA revealed that though nearly 98% of respondents recognised the role of genetic variations on drug prescribing, only 10% felt adequately informed about pharmacogenetic testing (Stanek et al. 2012). This suggests that any implementation of pharmacogenetics in the NHS must ensure that the information delivered healthcare professionals is clear and understandable to all. Additionally, an associated educational programme is critical to ensure prescribers’ confidence in their knowledge base is synchronously improved. Finally, prototyping is an appropriate method to better understand the needs of healthcare professionals using pharmacogenetics in routine practice. This involves designing interactive screens that demonstrate potential solutions using design software such as Adobe XD#. Such prototypes allow end-users to visualise the proposed intervention and provide feedback at an early stage of the implementation process. Prototyping in healthcare has been shown to better inform the technical specifications of health IT solutions as well as support staff engagement and lower barriers future to adoption (Sharma et al. 2022).

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Clinical Decision Support Systems

Clinical decision support (CDS) systems are digital tools that empower clinicians to make better decisions for patients by supplying them with relevant, accurate information at the right time. As is clear from the qualitative work highlighted above, pharmacogenetic clinical decision support systems must be integrated into existing electronic health records and clinical workflows. Poor integration of CDS systems with existing IT systems and EHRs has been shown to be a key barrier to adoption (Sharma et al. 2021). This is reflected in the large body of evidence on the development, calibration, and validation of CDS systems, but a gap in the evidence on real-world implementation. To ensure adoption and minimise solutions being lost in the so-called valley of death, an understanding of the socio-technical barriers is critical (Clark et al. 2020). Considering the clinical workflow of prescribing medicines, pharmacogenetic clinical decision support should only be triggered if a patient is being prescribed a drug for which they have a relevant genetic variant, which precludes a change to the prescription. This means that clinicians are not burdened by irrelevant triggers and minimises alert fatigue (Ancker et al. 2017). An international review of programmes described how the use of pharmacogenetic data is largely limited to non-integrated portable document format (PDF) files, which are transmitted via e-mail or uploaded as images to electronic health records (McDermott et al. 2022a). Similarly the multinational U-PGx study used physical cards with a QR code, which were given to patients as a way of providing access to pharmacogenetic results at the point of prescribing. These approaches are archaic, out-of-context, and asynchronous. It is primarily caused by a lack of agreed data standards to harmonise results produced by different laboratories. Consequently, it is not possible to store pharmacogenomic results as structured data and share them effectively to power clinical decision support. As highlighted in the concluding remarks of the U-PGx study, developing data standards and promoting those through recognised institutions is critical to overcoming the system fragmentation for the implementation of pharmacogenetics at scale (Blagec et al. 2022).

4.3

Interoperability and Data Standards

Mature health systems recognise the need for healthcare data to be shared across organisational boundaries for the delivery of care. This requires an IT infrastructure that supports the exchange of data between systems and make it available at the point of care. This is known as interoperability and critically relies on a common machinereadable description of clinical data, allowing a semantic understanding between systems exchanging information. To achieve semantic interoperability, it is critical to define clinical concepts into technical artefacts that use international standards for health data, such as openEHR, fast healthcare interoperability resources (FHIR), and

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SNOMED-CT. The challenge extends to committing to the use of a set of standards and ensuring adoption and compliance by the health IT sector. As the maturity of thinking around health IT has increased and technology continues to advance, there is a shift away from organisation solutions that provide both data management and clinical workflow support as one product. Contemporary data architectures focus on a data-centric approach where data and application are separated, encouraging an innovative ecosystem where health IT suppliers compete on features and functionalities, rather than isolating health data. Reflecting on the needs for pharmacogenetics implementation in the NHS to be equitable and fair across organisations, services, and specialities, it is clear that a single IT supplier is unlikely to manage and integrate pharmacogenomic data in clinical practice at scale. Thus, a vendor-neutral data platform approach, which is based on standards will allow implementation of pharmacogenomics by providing access to pharmacogenetic data as and when required through application programming interfaces (APIs).

5

Workforce Considerations

Taking into consideration the almost ubiquitous pharmacogenetic variation in the population, combined with the fact that prescribing is the most common therapeutic intervention in the NHS, it is clear that a pre-emptive pharmacogenetic testing model necessitates an appropriately trained workforce in order to realise the benefits of testing. Since pharmacogenetic variation is generally inconsequential in the absence of a relevant medicine being prescribed, it is perhaps not surprising that pharmacists have been suggested as natural proponents of pharmacogenetics, and indeed have led pharmacogenetic implementation programmes in many major healthcare institutions (Matey et al. 2021; Weitzel et al. 2014, 2017; Hoffman et al. 2014). Through the NHS Genomic Medicine Service (GMS), seven regional pharmacists have been appointed to engage the pharmacy workforce and link with wider medicines optimisation structures across England. Through combined committee structures which also incorporate Scotland, Wales, and Northern Ireland, the aim is to embed genomic medicine into routine healthcare across the UK, with the pharmacy profession leading pharmacogenetic implementation across the NHS.

5.1

Professional Competence

In reactive pharmacogenetic testing models, the clinician incorporating the pharmacogenetic result into their prescribing will frequently have been involved in the decision to request the test for a given patient and is therefore likely to understand the implications for the individual patient in the context of their prescribing decision. By contrast, a truly pre-emptive pharmacogenetic model where an individual’s genotype for several pharmacogenes is held within their electronic health record would enable the use of this data at multiple timepoints and by multiple healthcare

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professionals over the course of their interaction with the healthcare system. Consequently, healthcare professionals need to have sufficient knowledge and understanding of pharmacogenetics to be able to use an individual’s pharmacogenetic variation wherever results are available. The essential skills and competence required to carry out a particular role must be defined in order to deliver a pharmacogenetics service in the NHS (and any healthcare system), and roles may be considered core or advanced/ specialist. At least for doctors and pharmacists, some fundamental competence in relation to pharmacogenomics is already expected in the UK: in 2021, The Academy of Medical Royal Colleges published the Genomics Generic Syllabus 2021 outlining that “integration of pharmacogenomics into patient care” is a core skill for doctors (Genomics Generic Syllabus [Internet] 2021). Similarly, the General Pharmaceutical Council (GPhC) and Pharmaceutical Society of Northern Ireland (PSNI) have published standards for the initial education and training of pharmacists including a requirement to apply the principles of genomics to make effective use of medicines. Further, the multi-professional prescribing competency framework produced by the Royal Pharmaceutical Society notes that “individual patient genetic factors should be considered along with their potential impact on the choice and formulation of medicines when prescribing”. (Prescribing Competency Framework | RPS [Internet] 2023) Expectations around which activities are considered core or specialist must be clear in order to ensure staff are competent in the delivery of a pharmacogenetic service for patients within the NHS. Examples of specialist services that have been implemented outside the UK (at Duke, Mayo, etc.) include the incorporation of pharmacogenetic results within polypharmacy reviews and consideration of drug– drug–gene interactions (Elliott et al. 2017).

5.2

Educational Approaches

It is clear that the successful implementation of pharmacogenetics requires defined education and training for staff relevant to the role(s) they will perform and a range of approaches may be considered. Despite the inclusion of pharmacogenomics within undergraduate pharmacy curricula, a 2020 survey of pharmacists led the authors to conclude that existing training may be inadequate for pharmacists to engage in delivery of pharmacogenomic services, and that further training is required and under- and post-graduate levels (Qureshi et al. 2020). Similarly, for doctors, the amount of training received in pharmacogenomics in the undergraduate curriculum is extremely limited, and there is a need to expand this. After qualification (and for currently qualified staff), access to pharmacogenomics educational content is available, for example through Health Education England, but this needs reviewing and expanding. Pharmacogenetics programmes globally have used a series of different educations strategies with varying success. Since 2013, pharmacogenomic education has been mandated for all pharmacy staff at the Mayo Clinic, initially delivered through a

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series of compulsory and optional lectures; later evolving into a comprehensive multi-professional framework comprising online modules aligned to their institutional clinical decision support systems (Formea et al. 2015; Giri et al. 2018). They acknowledged that brief, targeted modules closely integrated with routine workflow were associated with higher educational value and improved acceptance with the workforce when compared with lecture-style teaching (Formea et al. 2018). Commonly, a “train the trainer” approach is used, where pharmacogenetic experts educate generalist pharmacists who can then educate and support the wider workforce (Lee et al. 2012). The PRIME PGx programme in Canada involves participation in online modules, training workshops, and finally development of individualised care plans for simulated patient cases comprising a formative assessment (Crown et al. 2020). In Europe, the U-PGx group introduced a combination of didactic teaching with case-based workshops in support of their multi-national pre-emptive pharmacogenetics study, recognising again the importance of combining experiential and didactic teaching approaches (Just et al. 2019). Crucially, in order to support medicines optimisation based on pharmacogenetics, it will be necessary to support NHS healthcare professionals with both background knowledge and point-of-care resources, such as clinical decision support systems, specific to the individual patient and clinical scenario. Furthermore, training and upskilling in pharmacogenomics is important for all healthcare professionals involved in medicines supply, prescribing, and administration – without this, implementation will be slow and patchy to the detriment of patient care. On the positive side, we need to acknowledge that healthcare professionals are a highly skilled group of individuals, who have the capacity to increase their professional competence and expertise when new innovations are brought into any healthcare system.

6

Conclusion

The development of a pharmacogenetic service within the NHS could improve medicines optimisation on both an individual and a societal level. In 2016/17, the estimated total spend on medicines in England was £17.4 billion per year, with over 1.1 billion items prescribed (Turner et al. 2020; Ewbank et al. 2018). Given this, even small improvements in effectiveness and safety could have significant health benefits at the individual and population level. Currently, there is no pharmacogenetics service in the UK, and testing is limited to a small number of gene–drug pairs. These are all undertaken reactively, when a medicine is being prescribed or considered, rather than forming part of a pre-emptive approach. As pharmacogenetic testing is at such an embryonic stage in the UK, the design of any future programme remains uncertain. Questions around the genotyping strategy, the process to request the test, how results will be returned, and who will have access to the data all need to be considered. Who, though, should make those choices? Most pharmacogenetics programmes are led by Clinical Geneticists or Clinical Pharmacologists. Although both these specialities will serve important roles in the operation of any programme, they will not be the primary users of a pharmacogenetic system. Primary care is the area where the majority of

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prescribing is undertaken, and it is therefore likely that the greatest need for pharmacogenetics will be in this sector. Irrespective of where drug prescribing is initiated, multidisciplinary team involvement (doctors, pharmacists, nurses, information technologists, software engineers, etc.) will be crucial for success. Most importantly, however, we need to make sure patients and the public are involved in, and informed of, developments in this area to ensure buy-in and ultimate success of any pharmacogenetic implementation programme.

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Part II Precision Medicine in Clinical Entities

Precision Medicine in Therapy of Non-solid Cancer Ines Schmidts, Torsten Haferlach, and Gregor Hoermann

Contents 1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2 Targeted Therapies . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.1 Small Molecule Inhibitors . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.1.1 Kinase Inhibitors . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.1.2 IDH Inhibitors . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.1.3 Hedgehog Inhibitor: Glasdegib . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.1.4 Histone Deacetylase Inhibitors . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.1.5 Proteasome Inhibitors . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.1.6 Exportin 1 Inhibitor: Selinexor . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.1.7 BCL-2 Inhibitor: Venetoclax . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.2 Retinoids/Rexinoids . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.3 Antibody-Based Therapies . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.3.1 Monoclonal Antibodies . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.3.2 Conjugated Antibodies . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.3.3 Checkpoint Inhibitors . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.3.4 Bispecific T Cell Engager: Blinatumomab . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.4 CAR-T Cells . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3 Precision Medicine in Context . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.1 Biomarkers Provide Guidance in Precision Medicine . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.2 Therapy Selection, Monitoring and Management in Precision Medicine: Three Examples . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.2.1 Acute Myeloid Leukemia (AML), Other Than APL . . . . . . . . . . . . . . . . . . . . . . . . . . 3.2.2 Chronic Lymphocytic Leukemia (CLL) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.2.3 Chronic Myeloid Leukemia (CML) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

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I. Schmidts · T. Haferlach · G. Hoermann (*) MLL Munich Leukemia Laboratory, Munich, Germany e-mail: [email protected] # The Author(s), under exclusive license to Springer Nature Switzerland AG 2022 I. Cascorbi, M. Schwab (eds.), Precision Medicine, Handbook of Experimental Pharmacology 280, https://doi.org/10.1007/164_2022_608

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Abstract

The development and approval of the tyrosine kinase inhibitor imatinib in 2001 has heralded the advance of directed therapy options. Today, an armamentarium of targeted therapeutics is available and enables the use of precision medicine in non-solid cancer. Precision medicine is guided by the detection of tumor-specific and targetable characteristics. These include pathogenic fusions and/or mutations, dependency on specific signaling pathways, and the expression of certain cell surface markers. Within the first part, we review approved targeted therapies for the compound classes of small molecule inhibitors, antibody-based therapies and cellular therapies. Particular consideration is given to the underlying pathobiology and the respective mechanism of action. The second part emphasizes on how biomarkers, whether they are of diagnostic, prognostic, or predictive relevance, are indispensable tools to guide therapy choice and management in precision medicine. Finally, the examples of acute myeloid leukemia, chronic lymphocytic leukemia, and chronic myeloid leukemia illustrate how integration of these biomarkers helps to tailor therapy. Keywords

Biomarkers · Hematooncology · Leukemia · Lymphoma · Precision medicine · Predictive markers · Targeted therapy

1

Introduction

In the narrowest sense of the term, precision medicine could possibly be reduced to the identification of targets and matching these targets with targeted therapy (Jäger et al. 2020). However, we argue for a broader understanding of the term, in accordance with a recent proposal on precision and personalized medicine concepts (Valent et al. 2021). Precision medicine, by this definition, focuses on specific features of the tumor. This not only includes the identification of targetable lesions and tumor vulnerabilities, but also takes into account molecular and cellular interactions (Döhner et al. 2021; Valent et al. 2021). The findings of a comprehensive tumor characterization inform not only therapy, but also classification, diagnosis, and prognostication. Although often used interchangeably, we would like to distinguish the term personalized medicine from precision medicine. In personalized medicine, patient-specific characteristics are taken into consideration in addition to tumor-specific features (Valent et al. 2021). The concept of precision medicine is deeply rooted in the field of hematooncology. The fact that there are hematologic neoplasms with well-defined disease drivers as well as a comparatively homogeneous genetic landscape was a major contributing factor (Wästerlid et al. 2022). The recognition of the druggability of acute promyelocytic leukemia (APL) with all-trans retinoic acid in the 1980s as well as the approval of the tyrosine kinase inhibitor imatinib in 2001 paved the way

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for precision medicine (Cohen et al. 2021; Thomas 2019). In the ensuing two decades, technical advances, most notably in sequencing, the elucidation of pathogenesis and pathophysiology, and the development of targeted therapeutics have gone hand in hand. Thanks to these efforts, the number of approved therapeutics and the importance of precision medicine are ever increasing. Herein, we first review a selection of targeted drugs approved by regulatory bodies (FDA and/or EMA) for treatment of non-solid tumors, i.e., leukemia, lymphoma, or multiple myeloma (National Cancer Institute 2022; Staber 2022). The focus will lie on the underlying (patho-)physiology and the mechanism of action of the respective targeted therapy. Subsequently, we will also discuss precision medicine in the clinical context and show how biomarkers serve as powerful tools that guide therapy choice and management. This is further emphasized and illustrated by three examples on how precision medicine is implemented in leukemia therapy.

2

Targeted Therapies

2.1

Small Molecule Inhibitors

2.1.1 Kinase Inhibitors Kinases are central mediators of signal transduction and through their key role in cell signaling they participate in regulating intra- and extracellular processes, such as cell metabolism, transcription, cell cycle progression, the cytoskeleton and cell movement, apoptosis, differentiation as well as in development, homeostasis, and the nervous and immune system (Manning et al. 2002). Kinase dysregulation can be observed in a myriad of disorders, including cancer – making kinases an ideal drug target. Kinases catalyze phosphorylation of a substrate hydroxyl group and can be categorized by their respective substrate, such as protein kinases and lipid kinases. Protein kinases are further classified with respect to the type of substrate amino acid they phosphorylate, i.e., tyrosine kinases and serine/threonine kinases (Roskoski 2016). Table 1 summarizes the targets and the indications of FDA approved kinase inhibitors. Tyrosine Kinase Inhibitors (TKI) The class of tyrosine kinases plays a prominent role in the pathogenesis and pathobiology of leukemia and lymphoma. Tyrosine kinases can be further subdivided with respect to their cellular localization into receptor tyrosine kinases (see also Fig. 1) and non-receptor tyrosine kinases (compare Fig. 2a–c). Inhibitors of Receptor Tyrosine Kinases

FLT3 Inhibitors (Patho-)Physiology: FLT3 is activated by binding its ligand, the cytokine FLT3LG. In addition to its role in the MAPK pathway (Fig. 1), FLT3 also influences signaling through PI3K and STAT5. FLT3 mutations are observed in approximately one third of patients with acute myeloid leukemia. Mutations are either present as internal

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Table 1 FDA approved kinase inhibitorsa Drug name Medically relevant targets Tyrosine kinase inhibitors BCR::ABL1 inhibitors Asciminib BCR::ABL1 Bosutinib BCR::ABL1 Dasatinib BCR::ABL1 Imatinib BCR::ABL1, PDGFR Nilotinib Ponatinib BTK inhibitors Acalabrutinib Ibrutinib Zanubrutinib FLT3 inhibitors Gilteritinib Midostaurin Sorafenib KIT inhibitors Avapritinib Midostaurin JAK inhibitors Fedratinib Pacritinib Ruxolitinib BRAF inhibitors Dabrafenib Vemurafenib PI3K inhibitors Copanlisib Duvelisib Idelalisib

BCR::ABL1 BCR::ABL1 (including T315I)

Indications in hematooncology

CML CML CML, Ph + ALL CML, Ph + ALL, HES and/or CEL, MDS/MPN, off label: Ph + AML, Ph + MPAL CML CML, Ph + ALL

BTK BTK BTK

MCL, CLL MCL, CLL, WM MCL, WM, MZL

FLT3 FLT3, KIT (see below) FLT3

AML AML Off-label: AML (post ASCT maintenance therapy in FLT3 mutated AML)

KIT D816V KIT (including D816V)

ASM ASM, SM-AHN, mast cell leukemia

JAK2 JAK2 JAK1/2

MF MF MF, PV

BRAF BRAF

Off-label: HCL Off-label: HCL

Pan-PI3K, predominant activity: PI3Kα and PI3Kδ PI3Kδ and PI3Kγ PI3Kδ

FL CLL, FL CLL, FL

AML acute myeloid leukemia, ASM advanced systemic mastocytosis, CEL chronic eosinophilic leukemia, CLL chronic lymphocytic leukemia, CML: chronic myeloid leukemia, FL follicular lymphoma, HCL hairy cell leukemia, HES hypereosinophilic syndrome, MCL mantle cell lymphoma, MZL marginal zone lymphoma, MF myelofibrosis, Ph+ ALL Philadelphia chromosome positive acute lymphoblastic leukemia, WM Waldenström’s macroglobulinemia, PV polycythemia vera, SM-AHN systemic mastocytosis associated with a hematological neoplasm a The reader is also referred to a continuously updated online tool that lists FDA approved therapies and can be filtered by cancer type: https://www.cancer.gov/about-cancer/treatment/drugs/cancertype

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Fig. 1 The mitogen-activated protein kinase (MAPK) pathway (simplified). Receptor tyrosine kinases (RTK) are activated upon binding of a growth factor ligand. Targetable RTKs in hematooncology include: fms-like tyrosine kinase 3 (FLT3), protein kinase KIT, platelet-derived growth factor receptor (PDGFR). The signal is transduced through the small G-coupled protein RAS and a series of MAP kinases (RAF, MEK, ERK). Activated ERK induces a transcriptional response that favors proliferation and differentiation. Modeled after KEGG pathway and (Falini et al. 2022). Depicted in red are targetable factors

tandem duplications (FLT3-ITD) or affect the tyrosine kinase domain (FLT3-TKD). Both abnormalities result in a constitutive and ligand-independent FLT3 activation and thus uncontrolled proliferation and cell survival signaling (Daver et al. 2019). Mechanism of action: FLT3 inhibitors can be categorized by the FLT3 conformation they bind to. Gilteritinib and midostaurin inhibit FLT3 in its active conformation and thus are classified as type I inhibitors. FLT3-TKD mutations favor the active enzyme conformation. In contrast to this, FLT3-ITD can adapt both active and inactive conformation. Sorafenib as a type II inhibitor binds to the inactive conformation and thus specifically inhibits FLT3-ITD (Daver et al. 2019). KIT Inhibitors (Patho-)Physiology: KIT encodes the receptor for stem cell factor (SCF) and is expressed in a variety of cell types, including hematopoietic precursors. While KIT expression is downregulated in the course of maturation of cells of the blood cell lineage, high expression is retained in mast cells. Under physiological conditions, KIT functions in the maturation, proliferation, survival, and chemotaxis of mast cells (Pardanani 2021). Mast cell neoplasms are strongly associated with the KIT D816V mutation, e.g., this mutation is detectable in >90% of patients with systemic

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Fig. 2 Non-receptor tyrosine kinases in cellular signaling (simplified pathways). (a) JAK-STAT pathway; the Janus kinases (JAK) are physiologically activated by cytokines binding to their respective receptors. Downstream of JAK, signal transducer and activator of transcription (STAT) proteins are activated. In addition, signaling via phosphatidylinositol-30 -kinase (PI3K)AKT pathway and the mitogen-activated protein kinase (MAPK) pathways are also induced. (b) Signaling in CML; downstream to BCR::ABL1, signaling occurs via STAT5 and PI3K-AKT and the MAPK pathway. (c) B cell receptor (BCR) signaling; upon antigen binding, the kinases LYN and SYK phosphorylate CD79a/CD79b, which results in recruitment and activation of Bruton’s tyrosine kinase (BTK) (Woyach et al. 2012). Additionally, the lipase kinase PI3K is activated and mediates signaling through the PI3K-AKT pathway. Downstream to both BTK and PI3K-AKT, the NFкB pathway is triggered. Furthermore, the MAPK pathway is induced downstream to BTK. (a– c) Modeled after information from KEGG pathway (Sochacka-Ćwikła et al. 2022) and (Skånland and Mato 2021). Depicted in red are targetable factors

mastocytosis. This gain-of-function mutation affects the activation loop region within the tyrosine kinase domain. It increases KIT’s activity and affinity for ATP and alters its substrate specificity (Heinrich et al. 2002). The biological consequence is a ligand-independent, constitutive activation of KIT (Reiter et al. 2020). Mechanism of action: While wild-type KIT or rare mutations outside the tyrosine kinase domain demonstrate sensitivity to imatinib (Reiter et al. 2020; Valent et al. 2021), the D816V mutation introduces a conformational change, preventing imatinib binding and resulting in a primary resistance of KIT D816V against imatinib (Piris-Villaespesa and Alvarez-Twose 2020). Avapritinib and midostaurin (see also above) are targeted therapeutics with action against KIT D816V. While avapritinib is selective for KIT D816V, midostaurin inhibits both wild-type and D816V-mutated KIT (Reiter et al. 2020; Sochacka-Ćwikła et al. 2022; Valent et al. 2021). Inhibitors of Non-receptor Tyrosine Kinases

JAK Inhibitors (Patho-)Physiology: Members of the Janus kinase (JAK) family are mediators of hematopoietic and immunological cytokine triggered cell signaling (compare

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Fig. 2a)). Cytokine binding to its designated receptor leads to JAK activation, which in turn phosphorylates the respective receptor, thereby creating a scaffold for STAT proteins as well as factors of the PI3K and MAPK pathway (compare Fig. 2c)). In BCR::ABL1 negative myeloproliferative neoplasms (MPN), the most common mechanism for JAK-STAT dysregulation is a JAK2 mutation (JAK2 V617F or exon 12), which results in constitutive JAK2 activation. Other common mutations in BCR::ABL1 negative MPNs affect the genes MPL and CALR. MPL encodes the thrombopoietin receptor, which associates with JAK2. In contrast to wild-type CALR, mutated CALR can associate to MPL and also activate JAK2 signaling (Vainchenker et al. 2018). Thus, activation of the JAK2 pathway is common hallmark of all MPN patients irrespective of the individual driver mutation. Mechanism of action: All three FDA approved JAK inhibitors (ruxolitinib, fedratinib, pacritinib) bind to JAK2 in its active conformation at the ATP binding site and target both wild-type and mutated JAK2. While ruxolitinib inhibits JAK1 and JAK2, fedratinib and pacritinib are relatively specific for JAK2, with pacritinib also inhibiting FLT3. None of these inhibitors is specific for JAK2 V617F. On the one hand, the JAK inhibitors are effective in MPN irrespective of the driver mutation (JAK2 V617F, JAK2 exon 12, MPL, or CALR). On the other hand, the survival benefit and reduction of spleen size and general symptoms observed in patients treated with JAK inhibitors are thought to be mediated through anti-inflammatory effect rather than through clearance of malignant cells (Vainchenker et al. 2018). BCR::ABL1 Inhibitors (Patho-)Physiology: The genetic fusion between BCR (breakpoint cluster region) and ABL1 (Abelson tyrosine-protein kinase 1) leads to the constitutive activation of the ABL1 kinase, which is tightly regulated under physiological conditions. The constitutive activation promotes cell proliferation and increased cell survival (see also Fig. 2 b). The BCR::ABL1 fusion is the central driver and defining feature of chronic myeloid leukemia (CML). The BCR::ABL1 fusion is also detectable in a subset of acute lymphoblastic leukemia (ALL), acute myeloid leukemia (AML), and mixed phenotype acute leukemia (MPAL) (Swerdlow et al. 2017). Mechanism of action: BCR::ABL1 inhibitors of the first (imatinib), second (nilotinib, dasatinib, bosutinib), and third (ponatinib) generation competitively bind to the ATP binding site of the ABL1 kinase domain. Asciminib is the first approved allosteric TKI inhibitor and binds to the ABL1 myristoyl pocket (Lee et al. 2021; Rossari et al. 2018). BTK Inhibitors (Patho-)Physiology: BTK is an essential transducer in B cell receptor (BCR) signaling (compare Fig. 2c). In addition to ligand binding, BTK can also be activated through the toll like receptor pathway and chemokine receptor signaling (Wen et al. 2021). BTK is strongly expressed in chronic lymphocytic leukemia (CLL) and mantle cell lymphoma (MCL). In Waldenström’s macroglobulinemia, BTK might contribute to MYD88 dependent aberrant signaling (Hendriks et al. 2014).

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Mechanism of action: All approved BTK inhibitors (ibrutinib, acalabrutinib, and zanubrutinib) irreversibly bind to cysteine 481, which is located in the ATP binding pocket of the enzyme (Sochacka-Ćwikła et al. 2022; Wen et al. 2021). BTK inhibition leads to inhibition of downstream signaling and induces cell apoptosis (Sun et al. 2018). Inhibitors of the Serine/Threonine Kinase BRAF (Patho-)Physiology: Under physiological conditions, activation of the MAPK pathway (also termed RAS-RAF-MEK-ERK pathway) is dependent on an extracellular signal (compare Fig. 1), which is transduced by RAS, followed by the sequentially connected serine/threonine kinases RAF, MEK, and ERK. ERK has a broad spectrum of cytoplasmic and nuclear substrate proteins and induces a transcriptional response that promotes proliferation, growth, survival, and motility (Falini et al. 2022). In hairy cell leukemia, the BRAF V600E mutation renders the pathway constitutively active – independent from ligand binding. This mutation, detectable in >90% of patients, represents the key event in the pathogenesis of hairy cell leukemia (Tiacci et al. 2011). Mechanism of action: As of yet, the BRAF inhibitors vemurafenib and dabrafenib are approved for treatment of melanoma. They competitively bind to the ATP binding pocket (Proietti et al. 2020). Vemurafenib and dabrafenib are type I inhibitors, i.e., they bind preferentially the active conformation, which might enhance the specificity toward V600E mutated BRAF (Proietti et al. 2020). For hairy cell leukemia, BRAF inhibitors might provide a therapeutic option in the relapsed/refractory setting. They are currently under clinical investigation for this indication or used off-label (Falini et al. 2022; Liebers et al. 2020; Tiacci et al. 2021). Inhibitors of the Lipid Kinase PI3K (Patho-)Physiology: Phosphoinositides (PI) lipids play important roles in cellular signaling. The inositol group of PI can be phosphorylated at three different positions, PI3 kinase (PI3K) catalyzes PI phosphorylation at the 3-hydroxyl group (Tariq and Luikart 2021). Trisphosphorylated PI (PIP3) plays a crucial role within the PI3KAKT-pathway (compare also Fig. 2c)), as it recruits the kinase AKT to the cell membrane, where it can be activated (Jethwa et al. 2015). Signaling through the PI3K-AKT pathway promotes cell proliferation and cell growth. Dysregulation, e.g., due to aberrant activation of this pathway, is a common finding in hematological neoplasms (Arora and Portell 2018; Sochacka-Ćwikła et al. 2022). Mechanism of action: Four different isoforms of the PI3K class I catalytically active subunit exist, i.e., α, β, γ, δ. While α and β are ubiquitously expressed, expression of the γ and δ isoforms is predominantly found in leukocytes (Lucas et al. 2016). The three PI3K inhibitors approved so far differ in their specificity for the different PI3K class I isoforms. Idelalisib inhibits PI3K δ, while duvelisib is a dual PI3K inhibitor and copanlisib a pan-PI3K inhibitor (Arora and Portell 2018; Sochacka-Ćwikła et al. 2022).

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2.1.2 IDH Inhibitors (Patho-)Physiology: The isocitrate dehydrogenases IDH1 and IDH2 play a key role in the tricarboxylic acid cycle, during which they catalyze the oxidative decarboxylation of isocitrate to 2-oxoglutarate. IDH1 and IDH2 mutations are associated with myeloid neoplasms and show a gain-of-function phenotype. Instead of 2-oxoglutarate, isocitrate is converted to 2-hydroxygluturate by mutated IDH1/ IDH2 (Dang et al. 2009). 2-hydroxygluturate acts as competitive inhibitor to 2-oxoglutarate-dependent enzymes. TET2, which promotes DNA demethylation belongs to this enzyme class (Heuser et al. 2018; Rose et al. 2011; Xu et al. 2011), resulting in a hypermethylation phenotype in the background of IDH1/IDH2 mutations (Figueroa et al. 2010). In addition to aberrant epigenetic regulation, IDH mutations also cause a differentiation block (McMurry et al. 2021). Mechanism of action: Ivosidenib (IDH1 inhibitor) and enasidenib (IDH2 inhibitor) are allosteric inhibitors approved for treatment of AML. Both inhibit 2-hydroxglutarate production and suppress the differentiation block associated with IDH1/2 mutations. By competing with magnesium, the cofactor of IDH1, ivosidenib brings the formation of a catalytically active site to a halt. Enasidenib, on the other hand, prevents IDH2 from transitioning into the closed, catalytically active, conformation (McMurry et al. 2021). 2.1.3 Hedgehog Inhibitor: Glasdegib (Patho-)Physiology: The signal protein Hedgehog, which serves as ligand for the receptor patched (PTCH), is the namesake for the hedgehog pathway. The central effectors of the pathway are GLI zinc finger proteins that can act either as transcriptional repressor (GLIR) or as transcriptional activator (GLIA), depending on the absence or presence of ligand, respectively. A central element of the pathway regulation is the protein smoothened (SMO, compare Fig. 3). While hedgehog signaling is essential in embryogenesis, its exact function and relevance in hematopoiesis and hematopoietic disease is yet unclear, however there are observations that link AML and aberrant hedgehog signaling. For example, GLI3 was found epigenetically silenced in the majority of AML samples, possibly enabling ligandindependent hedgehog pathway activation through loss of GLI3R (Feld et al. 2021; Lainez-González et al. 2021). Mechanism of action: Glasdegib acts as an inhibitor to the hedgehog pathway by binding to the protein SMO and preventing its translocation and thus downstream activation of GLI proteins. While it is hypothesized that glasdegib might increase sensitivity of leukemic stem cells to chemotherapy, the exact mechanism of action is yet elusive (Feld et al. 2021; Lainez-González et al. 2021). Glasdegib is approved for use in AML therapy. 2.1.4 Histone Deacetylase Inhibitors (Patho-)Physiology: Despite its enzyme family name, histone deacetylases (HDACs) are not restricted to histones, but can remove acetyl groups from Nacetyl-lysine residues of non-histone proteins as well. Given the variety of HDAC substrates, HDACs affect a myriad of cellular processes. With respect to epigenetics,

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Fig. 3 Hedgehog signaling (simplified). In the absence of the ligand Hedgehog (Hh), the Hh receptor Patched (PTCH) inhibits the protein smoothened (SMO). In consequence, the zinc finger protein GLI (Glioma-associated oncogene) acts as transcriptional repressor. Upon binding of Hh (gray circle) to PTCH, SMO is released and translocates within the cell. This triggers GLI activation and the transcription of target genes implicated in cell proliferation, cell fate, and cell death. Modified adapted from Yang et al. (2010)

histone deacetylation generally results in chromatin compaction, which is associated with transcriptional silencing (Wang et al. 2020). Among the non-histone substrates are proteins with roles in cell growth and differentiation (Moskowitz and Horwitz 2017). During T cell development, HDACs play roles in maturation and development, CD4 and CD8 lineage commitment, homeostasis of regulatory T cells as well as regulation of T cell effector phenotype and function. During B cell development, HDACs are likewise implicated in cell differentiation and lineage commitment (Wang et al. 2020). HDACs are grouped into four different classes. HDACs belonging to the classes I, II, and IV are zinc dependent, sirtuins (class III HDACs) are NAD dependent. Mechanism of action: HDAC inhibitors chelate the zinc ion within the catalytically active site of HDAC enzymes. The four HDAC inhibitors approved so far are all pan-HDACs inhibitors (i.e., they inhibit the zinc-depending HDAC classes I, II, and IV). HDAC inhibitors seem to influence a myriad of cellular processes that (may) contribute to anti-tumor activity; these include cell cycle arrest, production of reactive oxygenic species, DNA damage repair, angiogenesis, autophagy, and apoptosis (Cappellacci et al. 2020). Indications for approved HDAC inhibitors are given in Table 2.

2.1.5 Proteasome Inhibitors (Patho-)Physiology: Cells are dependent on a functional protein degradation system in order to maintain homeostasis or respond to cellular stresses, this might be

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Table 2 FDA approved HDAC inhibitors Drug name Belinostat Panobinostat Romidepsin Vorinostat

Indications in hematooncology Peripheral T cell lymphoma Multiple myeloma Cutaneous T cell lymphoma, peripheral T cell lymphoma Cutaneous T cell lymphoma

especially the case in cancer, which often exhibits cellular stress, e.g., due to hyperactivation of signaling pathways. The main degradation pathway for unfolded, damaged, and short-lived proteins is the ubiquitin-proteasome-pathway. Proteins are “tagged” for degradation by polyubiquitination. Polyubiquitinylated substrates are recognized and degraded by the proteasome. The catalytic core of this multiprotein complex is comprised of four stacked “rings,” with each ring made out of seven α- or β-subunits, stacked in the order αββα. The β-subunits β1 (caspase-like), β2 (trypsinlike), and β5 (chymotrypsin-like) exert catalytic activity (Leonardo-Sousa et al. 2022). Among the substrates for proteasomal degradation are key factors of cell cycle regulation (e.g., cyclins, the CDK inhibitors p21 and p27) as well as oncoproteins (e.g., the inhibitor of NFкB (IкB)). A functioning protein degradation system also prevents the endoplasmic reticulum (ER) stress response, which is triggered by ER accumulation of unfolded or misfolded proteins and ultimately can result in cell death (Hambley et al. 2016; Ito 2020). Mechanism of action: Constitutive NFкB signaling is a hallmark of both multiple myeloma and mantle cell lymphoma. Moreover, in multiple myeloma, tumor cells overproduce antibodies, rendering them especially susceptible to ER stress upon proteasome inhibition. Proteasome inhibitors are thought to affect a myriad of other cellular processes, including NFкB signaling, cell cycle, DNA repair, and apoptosis, all of which contribute to the anti-tumoral activity (Paradzik et al. 2021). While bortezomib and ixazomib are reversible inhibitors of the β5 subunit, carfilzomib binds covalently to the β5 subunit (Leonardo-Sousa et al. 2022). All three inhibitors are approved for use in multiple myeloma; bortezomib is additionally approved for use in mantle cell lymphoma.

2.1.6 Exportin 1 Inhibitor: Selinexor (Patho-)Physiology: In this case, the name already describes the function – exportin 1 (XPO1) plays a key role in the nuclear export. Among its cargo are >200 proteins as well as messenger RNAs (Azmi et al. 2021). XPO1’s cargo proteins include tumor-suppressor proteins (e.g., p53), signaling pathways factors (e.g., IкB), as well as proto-oncogenes (e.g., BCR::ABL1). XPO1 is overexpressed in many cancer types, including multiple myeloma. Biological consequences include the nuclear exclusion of p53 and an increase in NFкB signaling (Nachmias and Schimmer 2020). Mechanism of action: Selinexor targets the cargo binding pocket of XPO1 by forming a covalent bond with a cysteine on position p.528. This inhibits XPO1mediated export, leading to the nuclear retention of both tumor-suppressor proteins

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and mRNA. The nuclear accumulation and activation of tumor-suppressor proteins and the translation stop, which also affects translation of oncogenes, ultimately promote cell cycle arrest and apoptosis (Chari et al. 2019; Sochacka-Ćwikła et al. 2022).

2.1.7 BCL-2 Inhibitor: Venetoclax (Patho-)Physiology: The intrinsic (or mitochondrial) pathway of apoptosis can be triggered in response to intracellular stresses, such as DNA damage. Whether apoptosis is initiated depends on the balance between pro- and anti-apoptotic proteins. Pro-apoptosis proteins can be further categorized into sensors of cellular stress and effectors of apoptosis. The latter are pore-forming proteins, and their activation represents a point of no return in the apoptosis cascade. The resulting permeabilization of the outer mitochondrial membrane leads to release of cytochrome c and the recruitment of the apoptosome. Anti-apoptotic proteins, of which BCL-2 is the most prominent representative, antagonize and sequester pro-apoptotic proteins. If the balance is shifted toward antiapoptotic proteins, initiation of apoptosis is prevented. Lymphoid neoplasms frequently overexpress BCL-2, which shifts the balance toward cell survival and contributes to therapy resistance (Lasica and Anderson 2021). Mechanism of action: BCL-2 binds to the BH3 (BCL-2 homology 3) domain of pro-apoptotic proteins via a dedicated BH3-binding groove. Venetoclax is a BH3 mimetic that targets this groove. In result, unbound pro-apoptotic proteins can exert their function. Venetoclax is highly specific for BCL-2 and only shows lower affinity for other anti-apoptotic proteins. It is approved for use in CLL and AML (Lasica and Anderson 2021; Sochacka-Ćwikła et al. 2022).

2.2

Retinoids/Rexinoids

(Patho-)Physiology: The retinoid receptor subtypes RAR (retinoic acid receptors) and RXR (retinoid X receptors) are the target of retinoids and rexinoids, respectively. RAR mediate their function as transcription factor upon forming a heterodimer with RXR. The RAR/RXR dimer binds to retinoic response elements and its transcriptional activity is regulated by binding to a co-repressor (in absence of a ligand) or to a co-activator (in presence of a ligand) (le Maire et al. 2012). Target genes of RAR are implicated in cell differentiation and proliferation. In acute promyelocytic leukemia (APL), the gene fusion of PML (a nuclear regulatory factor protein) to retinoic acid receptor alpha (RARA) results in impaired RARA function and promyelocyte differentiation block (Swerdlow et al. 2017). RXRs are more promiscuous; aside to RAR/RXR heterodimers, homodimerization as well as heterodimerization with a variety of other nuclear receptors has been described (Mangelsdorf and Evans 1995). Through the versatility of RXR-nuclear receptor complexes, RXRs impact not only differentiation, but also cell growth and apoptosis (de Almeida and Conda-Sheridan 2019).

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Mechanism of action: All-trans-retinoic acid (ATRA), together with arsenic trioxide, used in the therapy of APL, reverses the differentiation block associated with the PML-RARA fusion (Altucci et al. 2007). Bexarotene is an RXR ligand and approved for treatment of refractory cutaneous T cell lymphoma. In the cell culture model, bexarotene led to apoptosis of the malignant T cell population (de Almeida and Conda-Sheridan 2019).

2.3

Antibody-Based Therapies

The advance of antibody-based therapies brought about important improvements in the field of hematooncology, especially in lymphoid neoplasms. Different types of antibody-based therapies can be distinguished: “naked” monoclonal antibodies, conjugated antibodies (immunotoxins, antibody-drug conjugates, and radioimmunotherapy), and antibody derivatives (T cell engager). All of the different classes will be discussed in greater detail below. A common feature of antibody-based therapies is that the target antigens are chosen predominantly for their expression on cancer cells, rather than for their (potential) involvement in (patho-)physiology. In light of this, we will provide an overview over the targets of approved monoclonal and conjugated antibodies (see also Tables 3 and 4), before we discuss their respective mechanism of action below. Targets in B cell lymphoma include CD19, CD20, CD22, and CD79b. CD19 is a modulator of B cell signaling and represents a reliable B cell marker. It is expressed from early on in B cell maturation. Accordingly, the majority of B cell neoplasms are CD19 positive (Wang et al. 2012; Zinzani and Minotti 2022). From the late pre-B state on, CD20 is expressed throughout B cell maturation, but not present in terminally differentiated plasmablasts and mature plasma cells. CD20 expression is also a hallmark of B cell lymphomas, although expression levels vary between different entities (Pavlasova and Mraz 2020). CD22 is maximally expressed only in the mature B cell stage, while pre- and immature B cells exhibit lower levels. CD22 is implicated in activation, survival, and migration of B cells (Sochacka-Ćwikła et al. 2022; Walker and Smith 2008). CD79b is an essential component of the B cell

Table 3 FDA approved monoclonal antibodies Drug name Daratumumab Isatuximab Elotuzumab Mogamulizumab Obinutuzumab Ofatumumab Rituximab Tafasitamab

Targets CD38 CD38 SLAMF7 CCR4 CD20 CD20 CD20 CD19

Indications in hematooncology Multiple myeloma Multiple myeloma Multiple myeloma Mycosis fungoides, Sézary syndrome Chronic lymphocytic leukemia, follicular lymphoma Chronic lymphocytic leukemia B-cell non-Hodgkin lymphoma, chronic lymphocytic leukemia Diffuse large B cell lymphoma

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Table 4 FDA approved conjugated antibodies Drug name Targets Antibody-drug conjugates BelantamabBCMA Mafodotin BrentuximabCD30 Vedotin GemtuzumabCD33 Ozogamicin InotuzumabCD22 Ozogamicin LoncastuximabCD19 Tesirine PolatuzumabCD79b Vedotin Immunotoxins Denileukin-Diftitox CD25 MoxetumomabCD22 Pasudotox Tagraxofusp CD123 Radioimmunotherapeutic IbritumomabCD20 Tiuxetan

Conjugated agents

Indications in hematooncology

Monomethyl auristatin F Monomethyl auristatin E Calicheamicin

MM

Calicheamicin

ALL

Tesirine

FL

Monomethyl auristatin E

DLBCL

Diphtheria toxin PE38

CTCL HCL

Diphtheria toxin

BPDCN

90 yttrium (β-radiation)

FL

HL, sALCL or other CD30-positive PTCL, CTCL AML

ALL acute lymphoblastic leukemia, AML acute myeloid leukemia, BPDCN blastic plasmacytoid dendritic cell neoplasm, CTCL cutaneous T cell lymphoma, DLBCL diffuse large B cell lymphoma, FL follicular lymphoma, HL Hodgkin lymphoma, HCL hairy cell leukemia, MM multiple myeloma, PTCL peripheral T cell lymphoma, sALCL systemic anaplastic large cell lymphoma

receptor complex and central to B cell receptor signaling. It is expressed in mature B cells and B cell neoplasms (Hashmi et al. 2021; Sochacka-Ćwikła et al. 2022). In multiple myeloma, the approved antibodies target the cell surface markers CD38 and SLAMF7 (CD319). CD38 and SLAMF7 are expressed by normal and malignant plasma cells (Campbell et al. 2018; van de Donk and Usmani 2018). B cell maturation antigen A (BCMA) is overexpressed specifically in plasma cells and expression is retained in multiple myeloma, making it a suitable molecular target (Sochacka-Ćwikła et al. 2022). CD30, also known as TNFRSF8, belongs to the tumor necrosis factor receptor superfamily and is involved in activation of the NFкB pathway (Aizawa et al. 1997; Smith et al. 1993). Under physiological conditions, only a small subset of B and T lymphocytes shows CD30 expression (van der Weyden et al. 2017). In lymphoma, CD30 expression is a reliable marker for Hodgkin lymphoma, but can also be found in anaplastic large cell lymphoma (ALCL) and a subset of diffuse large B cell lymphoma (DLBCL) cases (Hashmi et al. 2021; Sochacka-Ćwikła et al. 2022). Increased expression of the C-C chemokine receptor 4 (CCR4) and its primary ligands CCL17 and CCL22 is a hallmark of the (cutaneous) T cell lymphomas, mycosis fungoides and Sézary syndrome. The concurrent overexpression of receptor

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and ligands results in homing of malignant T cells to the skin. Moreover, CCR4 is physiologically expressed in the regulatory T cell (Treg) subset. Treg recruitment might also contribute to pathogenesis, given the ability of Tregs to dampen antitumor activity (Nicolay et al. 2021). Physiologically, CD33 is expressed only within a narrow time frame of differentiation of cells of the myeloid lineage, i.e., early multi-lineage hematopoietic progenitors. It is also present in 85–90% of AML cells (Molica et al. 2021). Further targets are the α-subunits of the interleukin 2 receptor CD25 and the interleukin 3 receptor CD123. CD25 is highly expressed in activated circulating T lymphocytes as well as regulatory T cells, but high expression can also be found in a subset of lymphoma, including T cell lymphoma (Flynn and Hartley 2017). While CD123 shows low expression in myeloid progenitor cells (Muñoz et al. 2001; Swerdlow et al. 2017), its overexpression can be detected in various hematological neoplasm, including blastic plasmacytoid dendritic cell neoplasm (BPDCN) (El Achi et al. 2020).

2.3.1 Monoclonal Antibodies Mechanism of action: The monoclonal antibodies mentioned in Table 3 all “tag” target cells for the immune system, which is able to induce cell death through different mechanisms: antibody-dependent phagocytosis (ADPC), antibodydependent cytotoxicity (ADCC), and complement-dependent cytotoxicity (CDC). While the target antigen is bound by the antigen-binding fragment (Fab) of the antibody, the constant Fragment (Fc) can be bound by the complement protein complex C1q or by Fc receptors on immune effector cells, such as macrophages and natural killer cells (Peschke et al. 2017). In case of C1q binding, the complement cascade is triggered and results in the formation of the membrane attack complex, disrupting the target cell’s membrane (Charmsaz et al. 2017). In ADCP and ADCC, the monoclonal antibody links the target cell to the immune effector cell. In ADCP, this results in phagocytosis and is mediated mainly by macrophages (Gül and van Egmond 2015). In ADCC, immune effector cells (mainly natural killer cells) mediate target cell death through cytotoxic granules, death receptor (FAS) signaling, and reactive oxygen species (Romano et al. 2021; Zahavi and Weiner 2020). Isatuximab might additionally trigger cell death directly through lysosomal-associated and apoptotic pathways (Jiang et al. 2016). 2.3.2

Conjugated Antibodies

Antibody-Drug Conjugates Mechanism of action: Antibody-drug conjugates are composed of a monoclonal antibody and a cytotoxic drug bound via a linker. The monoclonal antibody component enables specific delivery of the cytotoxic “payload” to the target cell. Internalization of the antibody-drug conjugate occurs via endocytosis. The drug is released during lysosomal degradation. Types of “payloads” used in hematooncology are the calicheamicins “ozogamicin” and “tesirine” and the microtubule inhibitors monomethyl auristatin E (“vedotin”) and monomethyl auristatin F (“mafodotin”).

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Calicheamicin derivatives introduce DNA breaks leading to apoptosis, tesirine yields a DNA cross-linking agent upon cleavage (Hartley et al. 2018), and microtubule inhibitors inhibit tubulin polymerization, leading to a cell cycle arrest and ultimately cell death. In addition, indirect, immune-mediated effects might also contribute to the anti-tumor activity (see Sect. 2.3.1) (Abramson et al. 2020; Sochacka-Ćwikła et al. 2022). Immunotoxins Mechanism of action: Immunotoxins are fusion molecules comprised of an antigenbinding component (see above) and a toxin that inhibits target cell protein synthesis. As for antibody-drug conjugates, the release of the respective toxin is dependent on endosomal internalization and lysosomal degradation. The toxins in use are diphtheria toxin and PE38, a fragment of Pseudomonas exotoxin A. In both cases, the biological consequence is ADP-ribosylation of the elongation factor eEF-2 and a resulting block of protein synthesis. This ultimately leads to cell death (Antignani and Fitzgerald 2013; Sochacka-Ćwikła et al. 2022). Radioimmunotherapy: Ibritumomab-Tiuxetan Mechanism of action: Ibritumomab is conjugated with a chelator that can be coupled to a radiometal (90 Yttrium). 90 Yttrium is a β-emitter with a mean radiation length of 5 mm (Witzig et al. 2002). Radiation results in the generation of free radicals, which damage DNA, and ultimately triggers cell death. As a β-emitter, ibritumomabtiuxetan not only causes self-irradiation (i.e., the radiation of targeted cells), but also affects adjacent cells due to crossfire-irradiation (White et al. 2021).

2.3.3 Checkpoint Inhibitors (Patho-)Physiology: Immune escape is a common motif in cancer biology. One mechanism is the exploitation of the PD-1/PD-L immune checkpoint. While the receptor PD-1 (programmed death) is expressed in T cells, target cells within the tissue express PD-ligand (PD-L1 or PD-L2). The receptor-ligand binding leads to exhaustion of T cells and a decrease in T cell effector functions. Under physiological conditions, the PD-1/PD-L axis protects tissue against autoimmunogenic damage, however, if cancer cells express PD-ligand, they likewise dampen the anti-tumor T cell response. In Hodgkin lymphoma, overexpression of PD-ligand by the tumor cells is in almost all cases the result of a copy gain or amplification of 9p24.1, where both PD-ligand genes are encoded. Moreover, the microenvironment plays an important role in the pathobiology of Hodgkin lymphoma, and tumor-associated macrophages also express PD-ligand (Moy and Younes 2018). Mechanism of action: The checkpoint inhibitors nivolumab and pembrolizumab are monoclonal antibodies against the receptor PD-1. Blockage of the PD-1/PD-L mediated immune evasion (predominantly) results in activation of cytotoxic T cells and induces cancer cell death. In Hodgkin lymphoma, an additional role of the tumor microenvironment has been described as major mechanism of action. In treatmentnaïve Hodgkin lymphoma, a decrease in PD-L1 expressing tumor cells, tumorassociated macrophages as well as a decrease in regulatory T cells was observed,

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while no considerable cytotoxic T cell response was detected (Reinke et al. 2020). In the relapsed/refractory setting, for which both checkpoint inhibitors have their approval, an increase in the diversity of T helper cells was observed after PD-1 inhibition (Cader et al. 2020).

2.3.4 Bispecific T Cell Engager: Blinatumomab (Patho-)Physiology: The pan-B cell marker CD19 is expressed in the vast majority (80–90%) of B lineage ALL cells (Wang et al. 2012; Wei et al. 2017), making it a suitable target in this disease. Mechanism of action: The bispecific T cell engager (“BiTE”) is comprised of two single chain variable fragments that are connected via a linker. For blinatumomab, the target antigens are CD19 (B cell marker) and CD3 (T cell marker). Binding of both malignant B cell and T cell forces the formation of an immune synapse and triggers ADCC as well as T cell activation through CD3 cross-linking. Interestingly, CD3 cross-linking by blinatumomab also circumvents the need for a costimulatory signal in T cell activation (Abramson et al. 2020).

2.4

CAR-T Cells

For this targeted therapeutic strategy, T cells from a patient are isolated and genetically modified to express a chimeric antigen receptor (CAR). Subsequently, the patient receives the genetically engineered autologous CAR-T cells. Different domains constitute the CAR: (1) an extracellular, antigen-targeting domain, (2) a spacer (CD4, CD8, or IgG4 Fc), (3) a transmembrane domain, (4) an intracellular signaling domain (Marofi et al. 2021; Sochacka-Ćwikła et al. 2022). (Patho-)Physiology: As for antibody (derived) agents, robust markers of B cell neoplasms are chosen as targets. This currently includes the B cell markers CD19 and CD20 and the plasma cell marker BCMA (Table 5). The target antigen is bound by the antigen-targeting domain of a given CAR. For five out of the six approved CAR-T cell therapies, this domain consists of a single variable chain fragment (Lakshman and Kumar 2022; Marofi et al. 2021; Sochacka-Ćwikła et al. 2022). For ciltacabtagene autoleucel, the antigen-targeting domain consists of two singledomain llama antibodies that target different BCMA epitopes (Lakshman and Kumar 2022). Mechanism of action: The binding of antigen triggers T cell signaling through the T cell receptor factor CD3ζ and a costimulatory receptor (for the approved CAR-T cells CD28 or 4-1BB). This results in T cell activation, proliferation, and differentiation. Target cell death is ultimately induced through release of cytokines and cytotoxic granules by the activated T cells (Marofi et al. 2021; Sochacka-Ćwikła et al. 2022).

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Table 5 FDA approved CAR-T cells Drug name Axicabtagene Ciloleucel Brexucabtagene Autoleucel Tisagenlecleucel Lisocabtagene Maraleucel Ciltacabtagene Autoleucel Idecabtagene Vicleucel

Target CD19

Costimulatory receptor CD28

CD19

CD28

CD19

4-1BB

CD20

4-1BB

Mantle cell lymphoma, acute lymphoblastic leukemia Acute lymphoblastic leukemia, diffuse large B cell lymphoma, follicular lymphoma Large B cell lymphoma

BCMA

4-1BB

Multiple myeloma

BCMA

4-1BB

Multiple myeloma

Indications in hematooncology Large B cell lymphoma

Fig. 4 In precision medicine, a comprehensive tumor characterization provides the basis for diagnosis, risk evaluation, therapy planning, and monitoring

3

Precision Medicine in Context

3.1

Biomarkers Provide Guidance in Precision Medicine

Biomarkers represent important tools in precision medicine and provide guidance throughout the disease course. At diagnosis, a comprehensive characterization that includes histology, cytomorphology, immunophenotyping, cytogenetics, and molecular genetics identifies disease-specific features that can serve as biomarkers in diagnosis, prognosis, therapy planning and therapy choice, and determination of residual disease (compare Fig. 4).

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Diagnostic markers aid in establishing a diagnosis and assist in differential diagnostics. All diagnostic disciplines can contribute to identification of diagnostic markers, and in many instances and entities, the interdisciplinary approach is key in verifying the diagnosis and differentiating the disease from other entities. While it is self-evident that a correct diagnosis is indispensable for planning effective treatment, subclassification also plays a role for an increasing number of entities. One example we will discuss in more detail below is AML. Prognostic markers are essential in risk assessment. It is predominantly cytogenetic and molecular genetic markers, whose influence on patient outcome is well established. Consideration of prognostic markers accompanied by established clinical risk factors and risk scores represents a main pillar of precision medicine, as they allow risk-adapted therapy planning. As detailed in Sect. 3.2.1, AML is again an example, which also illustrates the importance of prognostic markers. Biomarkers that predict response – or lack thereof – to a given therapy or therapeutic regimen guide treatment choice. In the era of targeted therapy, companion diagnostics are needed to identify predictive markers. The diagnostic disciplines that predominantly contribute to this are immunophenotyping (to detect surface expression of the respective antigen target for antibody-based therapies) and (molecular) genetics (to detect targetable fusions or other lesions targetable by small molecule inhibitors). Moreover, predictive markers can be used to identify subgroups that show poor response to a given therapy (e.g., chemotherapy) and might especially benefit from targeted therapy. An example illustrating the latter scenario is chronic lymphocytic leukemia (see Sect. 3.2.2). In the refractory/relapsed (r/r) setting, immunophenotyping and molecular genetics play the key role in detecting resistance mechanisms, e.g., the loss of target expression or the presence of resistance mutations. Another aspect is the potentially altered genetic landscape and/or clonal composition at relapse due to clonal evolution. A comprehensive tumor re-characterization needs to be performed at relapse as it may lead to the identification of novel or alternative targets. In disease monitoring, the evaluation of measurable residual disease (MRD) plays an ever-increasing role in hematooncology. Disease-specific immunophenotypic or genetic MRD markers allow identification of one malignant cell in 104 to 106 cells (Heuser et al. 2021). This is not only a sensitive measure for assessing therapeutic response, but has also been shown to be an important prognostic factor in various hematologic neoplasms. Across different entities, MRD positivity is generally associated with an increased relapse risk and reduced progression free and/or overall survival. Today, MRD monitoring in CML is the single most important factor for therapy management. This will be discussed in further detail in Sect. 3.2.3.

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Therapy Selection, Monitoring and Management in Precision Medicine: Three Examples

3.2.1 Acute Myeloid Leukemia (AML), Other Than APL AML is genetically and prognostically heterogeneous disease that still represents a major therapeutic challenge in hematology. Both (genetic) biomarkers and the patient’s fitness/treatment eligibility need to be considered for AML therapy. Achievement of complete remission (CR) under induction chemotherapy differs depending on genetic abnormalities and ranges from >80–90% for patients with favorable alterations to less than 30% for patients with unfavorable abnormalities (Schlenk and Döhner 2013). Therefore, prognostic markers and the MRD status guide treatment planning in post-remission therapy. In particular, the decision for or against allogeneic stem cell transplantation (allo-SCT) cannot be made without knowledge of the prognostically relevant changes. Allo-SCT should be considered in patients with poor/intermediate-risk cytogenetic- and/or molecular genetic abnormalities as well as in patients with a positive MRD status. Allo-SCT is preferred in high-risk AML (Pollyea et al. 2022). In induction therapy eligible cases, prognostic markers enable patient stratification into different risk groups (Döhner et al. 2017). Patients of the favorable-risk group receive the 7 + 3 chemotherapy regimen (consistent of 7 days of cytarabine and 3 days of an anthracycline, most often daunorubicin) which can be supplemented with gemtuzumab-ozogamicin in CD33 positive AML. In the intermediate- to poor-risk group, the mutation status of FLT3 is taken into account, with midostaurin addition to the 7 + 3 regimen in FLT3-mutated AML. Alternatively, gemtuzumab-ozogamicin can also be added to treatment in intermediate- to high-risk group. In the presence of unfavorable-risk cytogenetics and TP53 mutation alternative induction strategies in clinical trials should be considered, due to the poor response to conventional chemotherapy (Pollyea et al. 2022). Likewise, diagnostic markers aid in identifying the subgroups of AML with MDS-related changes, therapy-associated AML or secondary AML. Patients with these subtypes benefit from treatment with CPX-351, a liposomal formulation of cytarabine and daunorubicin at a fixed ratio of 5:1. Furthermore, predictive markers play an especially important role in patients who are not eligible for induction chemotherapy. While treatment with azacitidine has long been considered standard of care, the use of targeted therapies extends the therapeutic armamentarium. The combination with the BCL-2 inhibitor venetoclax is an effective therapeutic option for all subgroups of AML. Mutated IDH can be targeted with the IDH inhibitors ivosidenib (IDH1) and enasidenib (IDH2), and mutated FLT3 with the FLT3 inhibitor sorafenib. In the absence of targetable mutations, the hedgehog inhibitor glasdegib also provides an additional therapeutic option. In addition, rare patients with BCR::ABL1 positive AML might benefit from addition of a TKI to induction chemotherapy (Pollyea et al. 2022).

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3.2.2 Chronic Lymphocytic Leukemia (CLL) For a long time, the treatment of CLL has relied mainly on chemo(immuno)therapy. While the use of anti-CD20 monoclonal antibodies (rituximab, obinutuzumab, and ofatumumab) still represents the backbone of CLL therapy, the characterization of CLL pathobiology has revealed novel tumor-specific vulnerabilities replacing chemotherapy in more and more patients. In particular, proliferation of malignant cells is promoted by antigen-independent constitutive activation of the B cell receptor and overstimulation of cellular signaling pathways. Central factors in these signaling pathways include Bruton’s tyrosine kinase (BTK) and phosphatidylinositol 3-kinase (PI3K) (Woyach et al. 2012), both of which can be therapeutically targeted by BTK inhibitors (ibrutinib and acalabrutinib) and PI3K inhibitors (idelalisib and duvelisib), respectively. Moreover, increased cell survival mediated by BCL-2 can be therapeutically antagonized with venetoclax. Resistance mutations can occur under inhibitor treatment (Skånland and Mato 2021); for example, BTK and PLCG2 mutations mediate resistance to ibrutinib (Furman et al. 2014; Quinquenel et al. 2019; Woyach et al. 2014), while BCL-2 mutations confer resistance to venetoclax (Blombery et al. 2020; Lucas et al. 2020; Tausch et al. 2019). Today, targeted therapies provide chemotherapy-free treatment strategies in symptomatic CLL (with or without CD20 targeting immunotherapy). This benefits especially patients that carry one or more of the following genetic risk factors: • unmutated IGHV • TP53 deletion and/or TP53 mutation • complex karyotype Unmutated IGHV and TP53 abnormalities are predictive for a reduced response to chemo(immuno)therapy and associated with an inferior survival following treatment with such regimen (Eichhorst et al. 2016; Oscier et al. 2010; Rossi et al. 2014; Stilgenbauer et al. 2014). Such cases warrant the use of targeted approaches (Wierda et al. 2022). In addition, a complex karyotype also serves as predictive marker. While there is some evidence that a complex karyotype is a negative predictor for chemo(immuno)therapy, the issue has been more systematically studied for ibrutinib treatment. Here, a complex karyotype is associated with an inferior outcome (reviewed in Chatzikonstantinou et al. (2021)).

3.2.3 Chronic Myeloid Leukemia (CML) CML is the ideal example of how advances in the understanding of pathogenesis and tumor biology can be translated into therapy optimization. In the case of CML, so much so that the life expectancy of CML patients today is on par with that of the normal age-matched population (Hehlmann 2016). Due to its unambiguous driver, CML is considered a genetically homogenous disease. Given the dependency of CML tumor cells on BCR::ABL1, this lesion also represents the ideal therapeutic target. Since the introduction of tyrosine kinase inhibitors, the BCR::ABL1 fusion not only represents a diagnostic but also a predictive marker (Hochhaus et al. 2020).

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Moreover, BCR::ABL1 serves as MRD marker. The therapeutic success and CML’s vulnerability to TKIs have led to the diagnostic challenge of being able to measure residual disease as sensitively as possible. To this end, BCR::ABL1 transcript levels are quantified relative to a reference gene. For standardization and comparability, results are reported as % BCR::ABL1/ABL1 according to International Scale (IS). This also allows the definition of molecular response criteria, which decisively guide CML therapy (Cross et al. 2015). A therapy failure or loss of response warrants BCR::ABL1 mutational analysis as additionally acquired point mutations within the tyrosine kinase domain of BCR::ABL1 may cause resistance to a particular TKI. This does not only ascertain the cause of therapy resistance, but also guides therapy in the following line, as some BCR::ABL1 targeting TKI may be effective in the presence of the given mutation, while others may not (Zabriskie et al. 2014). One example is the BCR::ABL1 T315I mutation. This mutation confers resistance to the majority of TKIs, but is still sensitive to ponatinib and the novel allosteric inhibitor asciminib (Hochhaus et al. 2020; Shah et al. 2022). In CML therapy, we are also seeing a transition from precision to personalized medicine. For treatment in first line, imatinib, bosutinib, dasatinib, and nilotinib are approved. In the presence of high-risk features, i.e., additional high-risk cytogenetics and/or a clinical high-risk score, second generation TKIs are to be favored. Therapy choice is additionally influenced by the individual patient’s comorbidities and preferences. Moreover, the toxicity profile and possible drug interactions should be considered. A TKI change is mandatory in the case of treatment failure or resistance and optional in case of suboptimal response. In addition, patient individual factors, including intolerance and treatment-related complications may also necessitate a change. In the presence of a detectable BCR::ABL1 kinase domain mutation, the sensitivity profile should drive therapy choice in second and subsequent lines. In case of suboptimal response to two or more consecutive TKIs, allo-SCT should be considered (Hochhaus et al. 2020). In blast phase CML, the phenotype determines the recommended regimen (AML- or ALL-like induction therapy + TKI) (Hochhaus et al. 2020; Shah et al. 2022). Another aspect in the success story of TKIs in CML therapy is the possibility of a “functional cure.” Studies have demonstrated that approximately 50% of patients remain in molecular remission after TKI stop. Requisite for treatment discontinuation is achievement of a long-lasting deep molecular response. Subsequent to TKI stop, it is crucial to monitor patients closely in order to identify molecular relapses early. Molecular relapses can be therapeutically intercepted by TKI re-initiation (Hochhaus et al. 2020). In summary, CML exemplifies how comprehensive diagnostic methods and targeted treatments work together in precision and personalized medicine of non-solid cancer.

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Conclusion

CML therapy has been essential in paving the way for precision medicine and is continuing to set the goal in the treatment of non-solid cancers. Within the last two decades, precision medicine has also been adopted in other entities and can now be considered state-of-the art. For the precise use of these valuable therapeutic tools, an in-depth characterization of the tumor is mandatory. It includes understanding of tumor-specific biology, vulnerability, and biomarkers, which can only be achieved through an interdisciplinary diagnostic approach. Comprehensive diagnostics and characterization are therefore key to the use, but importantly also the development of precision medicine. We envision that technical advances, in particular the increasing use of genome and transcriptome as well as single-cell sequencing accompanied and complemented by functional precision medicine assays (in vitro drug screens), will provide an even deeper insight into pathogenesis and pathophysiology of non-solid cancers, further drive the development of new drug classes and targeted therapeutics, and promote their personalized selection for individual patients.

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Molecular Mechanisms of Tyrosine Kinase Inhibitor Resistance in Chronic Myeloid Leukemia Meike Kaehler and Ingolf Cascorbi

Contents 1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2 Chronic Myeloid Leukemia . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3 Tyrosine Kinase Inhibitors: From Imatinib to Asciminib . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4 Molecular Mechanisms of TKI Resistance . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5 BCR-ABL1-Dependent Mechanisms . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6 BCR-ABL1-Independent Mechanisms . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7 TKI Metabolism . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8 TKI Transmembranal Transport . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9 Alternative Signaling Pathways . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10 Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

66 66 67 69 70 71 72 73 75 76 77

Abstract

The hematopoietic neoplasm chronic myeloid leukemia (CML) is a rare disease caused by chromosomal reciprocal translocation t(9;22)(q34:q11) with subsequent formation of the BCR-ABL1 fusion gene. This fusion gene encodes a constitutively active tyrosine kinase, which results in malignant transformation of the cells. Since 2001, CML can be effectively treated using tyrosine kinase inhibitors (TKIs) such as imatinib, which prevent phosphorylation of downstream targets by blockade of the BCR-ABL kinase. Due to its tremendous success, this treatment became the role model of targeted therapy in precision oncology. Here, we review the mechanisms of TKI resistance focusing on BCR-ABL1-dependent

M. Kaehler · I. Cascorbi (✉) Institute of Experimental and Clinical Pharmacology, University Hospital Schleswig-Holstein, Kiel, Germany e-mail: [email protected] # The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 I. Cascorbi, M. Schwab (eds.), Precision Medicine, Handbook of Experimental Pharmacology 280, https://doi.org/10.1007/164_2023_639

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and -independent mechanisms. These include the genomics of the BCR-ABL1, TKI metabolism and transport and alternative signaling pathways. Keywords

BCR/ABL · Chronic myeloid leukemia · Drug resistance · Imatinib · Tyrosine kinase inhibitor

1

Introduction

Targeted therapy of the malignant disease chronic myeloid leukemia is the role model for a successful anti-cancer therapy. Since application of tyrosine kinase inhibitors (TKIs) impressively demonstrated that specifically blocking one target protein is able to stop tumor cell proliferation, a whole panel of novel treatment compounds, options, and strategies evolved resulting in increased patient survival rates reduces side effects and improved outcome. However, there are also limitations in the treatment with TKIs as therapy resistances may occur. Here, we discuss the molecular mechanisms of TKI resistance focusing on BCR-ABL1-dependent and -independent mechanisms. These include the genomics of BCR-ABL1, TKI metabolisms, and transport, as well as alternative mechanisms of resistance, i.e. mutations and gene expression changes.

2

Chronic Myeloid Leukemia

The hematopoietic neoplasm chronic myelogenous leukemia (CML) is a rare disorder predominantly caused by chromosomal reciprocal translocation t(9;22)(q34; q11). The emerging so-called Philadelphia chromosome (Ph), a shortened chromosome 22 with parts of the q-arm of chromosome 9, leads to formation of the BCRABL1 fusion oncogene (Nowell and Hungerford 1960; Rowley 1973; Heisterkamp et al. 1983), which can be detected in 95% of all CML, as well as 20% of Ph-positive acute lymphatic leukemia (ALL) cases (Radich 2001; Soverini et al. 2019). The oncogene consists of two proteins: While BCR encodes the breakpoint cluster region protein, a phosphoprotein with widely unknown function, ABL1 encodes the cytosolic Abelson tyrosine kinase. The fusion to the BCR promoter leads to constitutive transcription and subsequently activity of the resulting BCR-ABL1 kinase (Bixby and Talpaz 2011). As ABL1 positively regulates Ras-MAP-kinase, Jak/STAT, and PI3K/Akt signaling (McCubrey et al. 2008), the presence of BCR-ABL1 leads to malignant transformation of the cells. CML develops in three phases: An initial chronic phase, an accelerated phase, and a terminal blast crisis. The perennial chronic phase is asymptomatic in 50% of patients or accompanied by anemia, weight loss, fever, or night sweat (Sawyers 1999). Thus, diagnosis is often a coincidence. The accelerated phase occurs for several months with splenomegaly and associated pain in upper abdomen,

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leukocytosis, and accumulation of immature blood cells (Savage et al. 1997). In the terminal blast crisis, the symptoms are similar to an acute leukemia with >30% immature blasts in the blood and bone marrow, improper hematopoiesis, anemia, bleeding, and infections (Kantarjian et al. 1987). Overall, accelerated phases and blast crisis became quite rare due to excellent therapy response rates. CML treatment aims at inhibition of tumor cell proliferation in the blood. To quantify the success, remission rates after three to 12 months are levied: Hematologic remission shows the normalization of the blood, in particular the number of leucocytes and the absence of splenomegaly (O'Hare et al. 2012). The presence of Ph + cells in the blood quantifies the cytogenetic response, while the number of BCR-ABL1-transcripts is used for molecular remission (Baccarani et al. 2013). A value of 50% of variations were found only in a single individual (Ingelman-Sundberg et al. 2018; Wright et al. 2018). It was estimated that those uncharacterized variations could account for approximately 30% of the genetically encoded variability in drug disposition (Kozyra et al. 2017). Similar patterns were observed in drug target genes. Targeted sequencing of 202 drug targets

Million Veteran Program Tohoku Medical

Name of project or organization The Human Genome Diversity Project Iceland deCODE genetics UK Biobank The 1000 Genomes Project The NHLBI Exome Sequencing Project Genome of the Netherlands Project UK10K

2009

2010

U.S.

Netherlands

U.K.

2011

2009

U.K. International

Japan

2006 2008

Iceland

2011

1996

Country U.S.

U.S.

Initiate Year 1990s

Japanese

Multiple

>250,000

1,070

British

Dutch

European and African

British Multiple

Icelander

Ethnicity Multiple

8,963

769

7,034

454,787 2,504

2,636

Cohort size 929

Table 1 Overview of major national and international sequencing projects

WES and WGS WES and WGS WGS

WGS

WES WES and WGS WES

WGS

NGS method used WGS

21.2 million SNPs, 3.4 million indels and 25,923 CNVs

20.4 million SNPs, 1.2 million indels and 27,500 larger deletions >42 million SNPs, 3.5 million indels and 18,739 large deletions Unknown

(continued)

Nagasaki et al. (2015)

Gaziano et al. (2016)

UK10K Consortium (2015)

Francioli et al. (2014)

Backman et al. (2021) 1000 Genomes Project Consortium (2015), Sudmant et al. (2015) http://evs.gs. washington.edu/EVS/

>12 million variants 84.7 million SNPs, 3.6 million small indels and 60,000 SVs 677,000 missense, 411,000 synonymous, 17,000 nonsense and 7,000 splice variants

Gudbjartsson et al. (2015)

Reference Almarri et al. (2020), Bergström et al. (2020)

19.7 million SNPs and 1.4 indels

Variants identified 67.3 million SNPs, 8.8 million small indels and 126,018 SVs

Challenges Related to the Use of Next-Generation Sequencing for. . . 239

GenomeAsia 100K Project PGG.Han

Megabank Project Alzheimer’s Disease Sequencing Project The NHLBI TOPMed Program Sequencing Initiative Suomi Project Estonia Biobank

Name of project or organization

2019

2018

Estonia

China

2014

International

2019

2014

U.S.

International

2012

Initiate Year

U.S.

Country

Table 1 (continued)

319 high coverage data and 11,878 low coverage data

Pilot phase: 1,739

5,500

1,463

53,831

10,836

Cohort size

Chinese

Asian

Estonian

Finnish

European and Caribbean Hispanic Multiple

Ethnicity

WGS

WES and WGS WGS

WGS

WGS

WES

NGS method used

Taliun et al. (2021)

Chheda et al. (2017)

Reisberg et al. (2019)

Genome Asia 100K Consortium (2019) Gao et al. (2020)

>10 million SNPs >29 million novel variants >3 million SNPs and 250,000 indels per individual 25.1 million variants

Bis et al. (2020)

Reference

381 million SNPs and 29 million indels

1.5 million SNPs or short indels

Variants identified

240 Y. Zhou and V. M. Lauschke

Challenges Related to the Use of Next-Generation Sequencing for. . .

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in 14,000 individuals revealed that rare variants occur on average once every 17 bases (Nelson et al. 2012). In agreement with these findings, analyses of publicly available sequencing data from up to 130,000 individuals identified 800,000 variants across 606 genes encoding the targets of FDA-approved medications, of which rare variants account for >97% (Schärfe et al. 2017; Zhou et al. 2021a). While the aforementioned studies have already drastically extended our understanding of pharmacogenomic variability, major frontiers remain. The landscape of pharmacogenomic variability is highly population-specific. One example is CYP2C19*3, a loss-of-function allele that impacts the response to the antiplatelet medication clopidogrel, as well as to various antidepressants and anxiolytics. Notably, while CYP2C19*3 is globally less frequent (MAF ¼ 0.5%), the allele is common in East Asian populations (MAF ¼ 6.3%) and, thus, population-specific testing could provide population-specific benefits. Similarly, HLA-B*15:02, a risk allele predisposing to severe cutaneous adverse reactions upon treatment with the antiepileptic carbamazepine, is almost exclusively found in Southeast Asia (with MAFs up to 22% compared to 5%) as the neutral set for model training. However, many pharmacogenetic reduced function variants, such as CYP2C9*3, CYP2C19*2, CYP2D6*10, and CYP2D6*41 are highly frequent with MAFs >5%, and, thus, these models are trained to classify these variants as neutral. Consequently, the results of computational prediction methods that analyze variant pathogenicity should be interpreted with caution when applied on pharmacogenetic variations. The currently most widely used methods for missense variant interpretation are SIFT and Polyphen-2. SIFT is one of the earliest developed tools that use sequence homology to predict phenotypic effects of missense variants. It utilizes sequence conservation to evaluate genomic regions of interest and outperformed three substitution scoring matrices when tested on variants from protein-specific studies (Ng and

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Henikoff 2001). While SIFT as well as almost all other algorithms can only predict deleterious effects, i.e., variants resulting in reduced activity, more recently, a modified version of SIFT called Bi-directional SIFT (B-SIFT), was presented that can also assess whether a variant increases protein function (Lee et al. 2009). Polyphen-2 constitutes a machine learning algorithm that is trained to predict damaging missense variants based on both sequence and structure-based information from UniProt (Adzhubei et al. 2010). Using a naïve Bayes classifier, this tool generates two sets of predictions with sensitivity up to 92% across multiple test sets. Besides SIFT and Polyphen-2, other promising algorithms for missense variant assessments include, MutationAssessor (Reva et al. 2011), PROVEAN (Choi et al. 2012), and REVEL (Ioannidis et al. 2016), as well as newly developed tools, such as MVP (Qi et al. 2021) and VARITY (Wu et al. 2021); however, all these models focus on the identification of pathogenic variants. In recent years, there is increasing interest in the functional interpretation of variant categories beyond missense variants. Variants affecting splicing contribute substantially to the repertoire of human loss-of-function variants, either by affecting splice donor and acceptor sites, branch point sequences or intronic or exonic splicing enhancer and silencer regions (Anna and Monika 2018). To predict variant effects on splicing, a variety of algorithms have been developed; these include MaxEntScan (Yeo and Burge 2004), CryptSplice (Lee et al. 2017) and SpliceAI (Jaganathan et al. 2019) to predict the spliceogenicity of variants located directly in splice sites, Skippy (Woolfe et al. 2010) and MutPred Splice (Mort et al. 2014) for exonic splice variant predictions, and MMSplice (Cheng et al. 2019), SPANR (Xiong et al. 2015) and SQUIRLS (Danis et al. 2021) that predict splice variants in both splice sites and regulatory sequences. SpliceAI and SQUIRLS are particularly promising for variant prioritization. SpliceAI employs a deep neutral network trained on pre-mRNA transcript sequences from ENCODE to predict whether a given base in pre-mRNA constitutes a splice acceptor or splice donor. SpliceAI significantly outperformed other splicing variant prediction methods in both validation rate and sensitivity at different cutoff scores, resulting in accurate flagging of splice-altering mutations (Jaganathan et al. 2019). On the other hand, SQUIRLS used random forest model for training on a set of over 8,000 manually curated disease-causing splice variants in donor, acceptor and regulatory sites from over 1,000 genes and 70,000 presumably benign variants from ClinVar that are in or close to splice sites. Impressively, on an independent dataset with 808 splice variants and 10,068 neutral variants, both SQUIRLS and SpliceAI achieved the best areas under the receiver operating characteristic curves (AUCROC) and significantly outperformed other methods (Danis et al. 2021). Besides splice site variations, other non-coding variants localize to regulatory regions, such as promoters, enhancers, silencers, and insulators, resulting in altered binding affinity to transcription factors, chromatin structure remodeling and differential gene activity (Zhang and Lupski 2015; Deplancke et al. 2016). By training on large-scale data from projects that mapped regulatory elements along human genome, such as ENCODE (ENCODE Project Consortium 2012) and the Roadmap Epigenomes Project (Roadmap Epigenomics Consortium 2015), computational

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models such as gkm-SVM (Lee et al. 2015), DeepSEA (Zhou and Troyanskaya 2015), FUN-LDA (Backenroth et al. 2018), GenoNet (He et al. 2018), cepip (Li et al. 2017) and TURF (Dong and Boyle 2021) allow to predict the regulatory effects of non-coding variations. Importantly, these tools are calibrated to assess the tissueand cell-type specific effects of noncoding genetic variants, which can facilitate the fine-mapping of causal GWAS hits and can contribute to the discovery of novel genotype-phenotype associations. Besides the aforementioned variant class-specific algorithms, there are general tools, such as CADD (Kircher et al. 2014) and Eigen (Ionita-Laza et al. 2016) that are applicable to both coding and non-coding variants. CADD integrates diverse genomic annotations, including conservation data, chromatin accessibility, transcription factor binding, and variant frequency (Kircher et al. 2014). Furthermore, the splicing variant predictors MMSplice and SpliceAI were recently integrated into CADD to build a refined model for splice variants interpretation (Rentzsch et al. 2021). Eigen was trained on more than 70 million variants by weighing 29 different variant annotations, including protein function score, evolutionary conservation, and allele frequency. By using unsupervised training, the model reduced the dependency on the quality of training data and the calculated scores can be used complementary to other component annotations (Ionita-Laza et al. 2016). For more details about variant prediction algorithms we refer readers to recently published reviews (Rojano et al. 2019; McInnes et al. 2021; Zhou and Lauschke 2021). Importantly, while the reported predictive accuracies of these methods are excellent, there is a significant disconnect between the statistical modeling and their performance in practical applications, such as the fine mapping of causal variants and the prioritization of variants for genetic risk assessment, likely at least in part due to multi-dimensional circularity between training and test data (Grimm et al. 2015; Liu et al. 2019). Recently, we developed a computational ensemble score termed ADMEprediction framework (APF), specifically designed for the analysis of pharmacogenetic variations (Zhou et al. 2019). This algorithm was developed by recalibration and integration of existing prediction tools using 337 variants with high-quality quantitative experimental characterization data across 44 pharmacogenes, primarily from CYP genes and drug transporter. Using fivefold cross-validations, APF achieved high sensitivity and specificity for loss-of-function and functionally neutral pharmacogenetic variants. Notably, while computational evaluation of novel pharmacogenetic variations identified by NGS showed very poor alignment between experimental evaluations and computational predictions by SIFT and Polyphen-2 (Devarajan et al. 2019), in vitro and in silico data were in good agreement when using APF (Siamoglou et al. 2022). These results further corroborate that standard pathogenicity prediction methods perform relatively poorly on rare pharmacogenetic variations while the performance of specifically developed pharmacogenetic predictors is substantially higher. Furthermore, the tool is the first to provide prediction scores that correlate with in vitro protein activity (R2 ¼ 0.9; p ¼ 2.9  105), thus allowing to quantitatively assess variant effects. While no DPYD variants were utilized for APF training, the algorithm correctly predicted 91.4% of DPYD variants with known functions in vivo and showed a

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similar performance compared to DPYD-Varifier, an algorithm developed exclusively for the analysis of DPYD variability (Shrestha et al. 2018; Zhou et al. 2020). These results suggest that APF can be useful also for other genes and gene families with limited evolutionary constraints. Of note, comprehensive pharmacogenetic deep mutational scanning data, e.g., of TPMT (Matreyek et al. 2018) and CYP2C9 (Amorosi et al. 2021), promise to drastically increase the available training data for computational prediction tools and are expected to drastically improve predictive accuracy. Besides ADME genes, polymorphisms in drug target genes constitute another important determinant of variability in drug response. While individual polymorphisms in drug target genes, such as ADRB2, have been widely studied (Bleecker et al. 2008; Ortega et al. 2014), there is only little systematic information about the functional effects of human drug target variability (Nelson et al. 2012; Schärfe et al. 2017; Hauser et al. 2018). In an interesting study about G-proteincoupled receptors in GPCR pharmacogenomics the authors identified a total of 14,192 variants across 108 GPCR drug targets from 60,706 individuals and could demonstrate experimentally that naturally occurring missense variants in the ligand or G-protein binding interface impacted G-protein coupling, signaling bias and, putatively, drug response (Hauser et al. 2018). Furthermore, naturally occurring variants that affect amino acids within 6 Å of the crystallographically determined drug binding sites were shown to impact the pharmacological response in a variantand drug-specific manner (Zhou et al. 2021a). In addition, this study emphasized that the mapping of genetic data on high-resolution crystal structures can faithfully identify variants that alter drug action, which cannot be accurately predicted by current computational algorithms.

3

Locus-Specific Issues Related to Short-Read Sequencing

Sequencing technologies underwent major developments over the past three decades (Heather and Chain 2016; Shendure et al. 2017). Conventional Sanger sequencing, often referred to as “first-generation sequencing,” is based on DNA polymerasemediated extension of a labeled primer using four reactions each with trace amounts of one chain-terminating nucleotide, resulting in fragment sizes of different lengths that could be resolved by polyacrylamide gel electrophoresis. This chemical sequencing method is highly accurate and could decode around 1 kb per day. As such, it is still used today for the profiling of short target sequences (few kb) in small sets of samples. However, its high workload and low scalability renders it timeconsuming and exceedingly expensive for major efforts, such as the sequencing of complex whole genomes. In an attempt to overcome the limitations of Sanger sequencing, massively parallel sequencing, also termed “second-generation sequencing,” was developed with early exploratory studies dating back to the late 1980s (Hyman 1988; Ronaghi et al. 1998). This new set of methods did not use electrophoresis, radioactivity, or fluorescence but rather detected the production of pyrophosphate released upon

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extension of the nascent DNA strand as the template DNA affixed to a solid phase was exposed to different nucleotides. Alternatively, stepwise incorporation of reversibly terminating, reversibly fluorescent nucleotides was developed so that each cycle consists of three phases, i) the incorporation of a photocleavable fluorescent nucleotide, ii) detection of the fluorescent signal, and iii) removal of the fluorophore so that the next incorporation cycle can start (Seo et al. 2005). Individual reads of these original platforms were short (around 35 bp); however, mass parallelization of sequencing with millions to billions of immobilized densely packed templates nevertheless increased sequencing throughput by multiple orders of magnitude and gave rise to the first commercially successful NGS platforms in 2005. Since then, read lengths have increased to typically 100–600 bp and around 1 Tb of sequence can be generated in 1 day on a single state-of-the-art instrument. Importantly, these advancements in short-read sequencing were paired with major developments in analytical tools and pipelines for read mapping (Langmead et al. 2009; Li and Durbin 2009), quality control (Guo et al. 2014), variant calling and post-processing (Li et al. 2009; McKenna et al. 2010), which remain in wide use today. While NGS has been a major catalyst for our increased understanding of human genomics in the past 15 years, the use of short reads results in important limitations for its use in the analysis of complex or repetitive genetic loci even if high-quality reference genomes are available. Short reads are often difficult to unambiguously map, which hinders the analysis of structural variations, such as copy number variations or genomic inversions (Russell and Schwarz 2020). Moreover, short reads can be difficult to assemble into contigs resulting in difficulties pertaining to variant phasing. Importantly, multiple pharmacogenetically relevant loci such as CYP2D6, CYP2B6, SLC22A1, and the HLA gene cluster contain nearby highly homologous pseudogenes and are prone to segmental duplications or variable number tandem repeats. As a result, short-read sequencing using read lengths 10 kb (Van Dijk et al. 2018; Logsdon et al. 2020). Two major platforms are available for long-read sequencing, Pacific Biosciences (PacBio) singlemolecule real-time sequencing (SMRT Seq) and nanopore sequencing offered by Oxford Nanopore Technologies. SMRT Seq is based on the optical monitoring of the activity of an immobilized bioengineered DNA polymerase using zero-mode waveguides that spatially limit the excitation to the fluorescently labeled nucleotide being integrated into the replicated strand at a given time (Eid et al. 2009). While error rates are as typically 8–15%, the error distribution is remarkably stochastic and can be controlled using computational error correction tools (Zhang et al. 2020a). In

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contrast to SMRT Seq, nanopore sequencing relies on the motor protein-mediated translocation of negatively charged DNA through nanopores within a synthetic membrane (Feng et al. 2015; Wang et al. 2021a). Upon application of an electric current, the passing of nucleotides through the pores causes characteristic fluctuations of the current that are then decoded into the respective DNA sequence. Long-read sequencing has been successfully used for the sequencing of complex pharmacogenomic loci and offers promising opportunities for haplotype phasing (Van der Lee et al. 2021). CYP2D6 is a highly complex gene with more than >130 distinct CYP2D6 haplotypes and further suballeles. Moreover, CYP2D6 has two nearby pseudogenes, CYP2D7 and CYP2D8, which are >97% sequence identical, and the locus is subject to a multitude of structural variations, including copy number variations, inversions, and gene fusions (Kimura et al. 1989; Nofziger et al. 2020). Both SMRT Seq (Qiao et al. 2016; Buermans et al. 2017; Fukunaga et al. 2021) and Nanopore sequencing (Liau et al. 2019) have been successfully used to sequence CYP2D6 and have been shown to facilitate diplotype refinement from short-read sequencing data and contribute to the discovery of novel suballeles. A current limitation is that commonly used CYP2D6 haplotype callers, such as Aldy (Numanagić et al. 2018), Stargazer (Lee et al. 2019), and Cyrius (Chen et al. 2021), are not compatible with long-read sequencing data formats, requiring manual expert-driven haplotype calling or laborious conversions of input data. Long-read sequencing has also been successfully applied to HLA typing and the authors conclude that it has sufficient accuracy and might be cost-effective for routine diagnostics (Chang et al. 2014; Matern et al. 2020; Liu et al. 2021). Furthermore, long-reads can be used in the research setting to identify novel genetic associations, for example between HLA-C*07:01 and clozapine-induced myocarditis in patients with schizophrenia (Lacaze et al. 2020). Combined, the available data demonstrate that short-read sequencing remains the most cost-effective sequencing modality for the vast majority of pharmacogenes; however, long-read sequencing offers important advantages for complex loci and allows the unambiguous determination of pharmacogene haplotypes, which can provide major improvements for phenotype inference. As such, both second and third-generation sequencing currently fill different niches in sequencing-based pharmacogenetic profiling. With decreasing per-sample costs and increasing throughput, population-scale long-read sequencing is soon coming within reach and promises to revolutionize human genomics once again (De Coster et al. 2021).

4

Cost-Effectiveness

The implementation of pharmacogenomics in clinical practice not only relies on robust scientific evidence and demonstrated clinical utility, but also needs to be economically viable. Such assessments of cost-effectiveness require that the costs of preemptive genetic testing and change in therapeutic strategy for risk allele carriers need to be outweighed by the added value for the patient and/or the reduction in healthcare costs due to decreased number or severity of treatment-associated adverse

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events. Such cost-effectiveness evaluations are impacted by many factors including risk allele frequencies, positive and negative predictive values of the risk variants, costs and effectiveness of first-line and alternative treatments, as well as the cost of genetic testing. Notably, cost-effectiveness studies are usually conducted from the perspective of the national health care scheme and its results can change if any of the country-specific parameters change. Cost-effectiveness of preemptive pharmacogenetic testing has been studied for a multitude of gene-drug pairs across various therapeutic areas. Of 44 health economic evaluations of genotype-guided treatment, more than half were found to be in favor of preemptive pharmacogenomic testing (Verbelen et al. 2017). Cost-effectiveness increases even further with the simultaneous genotyping of multiple variations, as the test cost scales underlinearly with the number of interrogated polymorphisms in a test panel (Van Driest et al. 2014; Plumpton et al. 2019; Zhu et al. 2021). While all aforementioned studies pertained to the profiling of patients by genotyping of candidate SNPs, cost-effectiveness evaluations of NGS-based strategies have only been conducted for oncology or genetic disease diagnostics. For patients with advanced adenocarcinoma, targeted gene sequencing was found to increase diagnostic accuracy and patient outcomes compared to conventional companion diagnostic tests but was not found to be cost-effective from the perspective of the Brazilian supplementary health system (Schluckebier et al. 2020). This result is consistent with a previous review of the literature on the cost-effectiveness of target sequencing to improve cancer care, which reported that NGS is not cost-effective for the guidance of targeted therapy but might produce cost-effective clinical benefits in cancer screening programs (Tan et al. 2018). Furthermore, a health economic evaluation of whole-exome sequencing (WES) and whole-genome sequencing (WGS) approaches for a variety of genetic conditions in clinical practice showed that only five of 36 included studies reported cost-effective benefits (Schwarze et al. 2018). Importantly, the cost of sequencing varied widely for different diagnostic purposes and was found to largely depend on sample size, suggesting that the costeffectiveness of sequencing is itself a function of its clinical adoption. In addition, NGS requires extensive downstream analytics, clinical interpretation and data storage, for which the cost is difficult to evaluate (Schwarze et al. 2020). Interestingly, while sequencing costs are widely believed to decrease continuously, these developments have mostly stalled in recent years and costs of targeted sequencing, WES and WGS remained at $240–297, $604–1932 and $2006–3347, respectively (Gordon et al. 2020). In recent years, the use of NGS in pharmacogenomics has been mostly limited to method comparison and novel variant discovery (Tafazoli et al. 2021). While the design of optimized NGS-based pharmacogenomic sequencing panels was suggested to be more cost-effective than conventional testing approaches (Katragadda et al. 2021), evaluations of such models in the form of prospective controlled trials are lacking. However, in light of the evidence for other applications, the utilization of NGS for preemptive pharmacogenomic testing in clinical settings is, at present, not likely to be cost-effective. However, we anticipate that with decreasing test costs driven by technological developments and economies-of-

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scale due to increased accessibility and adoption of genetic testing, as well as increasing accuracy of variant interpretation that allows to better leverage the identified variations, NGS-based pharmacogenomics might become cost-effective in the near future.

5

Concerns Regarding Incidental Findings

The comprehensive sequencing of whole exomes or whole genomes, be it by second- or third-generation sequencing technologies, can result in the discovery of incidental findings which, while not related to the primary purpose for ordering the genetic test, have direct medical relevance for patient care. In this context, it is important to consider the difference between medically indicated testing, e.g., in the context of oncology, carrier screening or perinatal diagnostics, and direct-to-consumer testing of healthy individuals, as in the latter, all findings can be considered incidental (Berg et al. 2011). The decision of whether or not to report incidental findings is multifaceted and complex. Reasons against disclosure include the potential psychological harm, lack of actionability and the difficulty in following up on potential findings, which can divert attention and resources away from more pressing measures (Lohn et al. 2014). The counterarguments are primarily based on patient autonomy and data ownership (Foster et al. 2009; Christenhusz et al. 2013). While there is increasing consensus in principle favor of disclosure, which incidental findings should be reported depends on both its clinical significance, including pathogenicity and medical actionability, as well as patient-related factors, such as the patient’s preference to know (Saelaert et al. 2019). As such, only those findings should be reported that affect treatable or preventable conditions and information about which might reduce patient morbidity in order to not overwhelm neither patient nor genetic counseling staff. Among 20 studies using genome and exome sequencing technologies in differentclinical and research settings, the frequency of incidental findings defined as pathogenic or likely pathogenic variations according to the American College of Medical Genetics and Genomics (ACMG) was reported to be between 0.59% and 17% (Elfatih et al. 2021). Current guidelines recommend that actionable variants are reported in heterozygous individuals for autosomal dominant conditions, in homozygous and compound heterozygous for autosomal recessive conditions and in homozygous or hemizygous individuals for allosomal conditions (Green et al. 2013). Thus, how to manage incidental findings is a crucial step in the development of comprehensive sequencing-based genomic profiling toward routine clinical use irrespective of the initial indication for genetic testing. For pharmacogenomic sequencing this entails that test providers should consider to include also the evaluation of established analytically valid and medically actionable variations to maximize the personal benefit from genomic information beyond pharmacogenes.

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Toward the Integration of Pharmacogenomic Sequencing into Clinical Decision-Making

While rare pharmacogenetic variants are likely to contribute to the observed interindividual variability in drug disposition and response, their integration into clinical decision-making in a real-world setting is challenging. Currently, most pharmacogenetic biomarkers are not tested to guide prescribing and/or dosing and from the thousands of markers discussed in the academic literature, the probing of only three genes is required before the initiation of the respective therapy (HLAB*57:01 for abacavir, DPYD*2A, *13 and HapB3 for fluoropyrimidines and HLAB*15:02 for carbamazepine in Asians). Barriers for the adoption of pharmacogenetic biomarkers include the high hurdles that are in place for evaluating the utility of preemptive testing, lack of acceptance on the part of healthcare providers, as well as high cost and uncertainty of reimbursement. Moreover, various ethical, legal, and social considerations, such as assuring confidentiality and informed consent, equitable access, as well as avoiding discrimination and stigmatization, need to be addressed. As these important issues are outside of the scope of this review, we refer the interested reader to recent comprehensive reviews on these matters (Lauschke and Ingelman-Sundberg 2016; Klein et al. 2017; Russell et al. 2021). In addition to prospective testing, NGS offers exciting opportunities for retrospective identification of gene-drug associations using electronic medical records (EMRs). One interesting example are genetic associations between CTNNA3 variations and oxicam-induced myopathies that was identified by associating genetic information of 2,240 Estonian Biobank participants with patient-specific medical histories and longitudinal drug prescription data (Tasa et al. 2019). Similarly, leveraging EMR data from 281,104 participants of the UK Biobank for a phenome-wide association studies (PheWAS) resulted in the identification of >1,700 significant gene–phenotype associations that were enriched in drug targets (Wang et al. 2021b). Combined, these data suggest that the integration of genetic data with dense population-scale phenotypic annotations is sufficiently powered to drive hypothesis-free identification of novel gene-drug associations. From a technical standpoint, we believe that clinical implementation of pharmacogenetic knowledge can be two-pronged (Fig. 2). On one side, information about population-specific prevalence of well-characterized biomarkers with clear functional effects can provide statistical guidance for clinical decision-making without the need to genotype the individual patient. Such precision public health strategies have the advantage of being less resource-intensive and relatively easy to implement. One current example would be the mandate to genotype Asian individuals for HLA-B*15:02 before initiation of carbamazepine, whereas other ethnogeographic groups do not require preemptive testing. An opposite case is the implementation of efavirenz as first-line treatment of HIV in Zimbabwe (Lauschke et al. 2017). While this regimen was globally established, it resulted in an unexpectedly high number of overdose cases due to the high local frequency of CYP2B6 reduced function alleles and, eventually, required a national modification of the clinical treatment algorithm.

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All patients

Populations

Individual patient

SNP panel

NGS technology

Annotated variants (mostly common)

Annotated variants (mostly common)

Not annotated variants (mostly rare) Computational prediction

Allele scoring

Allele scoring

Population-based drug response prediction

Personalized drug response prediction

Standard prescription

Population-based drug prescription

Personalized drug prescription

Standard-of-care

Precision public health

Personalized medicine

Cost Information Drug efficacy ADR

Fig. 2 Schematic roadmap for the clinical implementation of pharmacogenomics. Implementation of pharmacogenomics might be easiest accomplished following a precision public health strategy, in which variant and allele frequencies of populations are considered to guide pharmacological therapy. The advantage of this approach is that it does not require the genotyping of each individual patient, thus reducing costs and avoiding practical and ethical issues pertaining to the implementation of genetic profiling. However, recommendations are only based on population statistics and have to remain limited to well-characterized common candidate variations. In the future, the application of NGS-based profiling of each individual patient promises to result in the true personalization of treatment. In this paradigm, allele scores are derived based on all functionally annotated variations found in the personal genome. Furthermore, once the accuracy of computational prediction algorithms is deemed sufficient, also experimentally uncharacterized variants can be integrated into phenotypic predictions. While more costly, this approach promises to make maximal use of genomic data; while it is intuitive to assume that this will increase treatment efficacy and decrease adverse drug reactions (ADRs), it is important to note that the feasibility and added value will need to be determined in stringent clinical trials. Figure modified with permission from (Zhou 2021)

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While the pharmacogenetic strategy described above aims at public health guidance at the population level, a second strategy aims at genotype-optimized therapy at the level of the individual patient. In this paradigm, the patient is genotyped either for established variants using SNP panels or more comprehensively using NGS (targeted sequencing or whole genome sequencing). Functionally characterized variants can then be assigned with established functionality scores while novel and uncharacterized variations can be interpreted using pharmacogenetically tailored computational algorithms. All individual variant scores can be aggregated at the gene and pathway level, putatively resulting in more accurate personalized phenotype prediction. Linking those inferred phenotypes with pharmacological information and drug labels then enables to optimize personalized drug selection and dosing. Critically, the implementation of NGS-based prescribing requires that the added clinical value and cost-effectiveness of this strategy compared to candidate genotyping and standard-of-care is demonstrated in robust prospective controlled trials, ideally across genes, drugs and healthcare systems.

7

Conclusions

While sequencing technologies have made substantial advancements over the last decades, the application of NGS for the guidance of pharmacogenetic treatment decisions still faces multiple challenges that hinder more widespread clinical implementation. These include issues pertaining to the interpretation of rare or novel pharmacogenomic variants, technical challenges for the sequencing of complex pharmacogenetic loci, ethical considerations regarding incidental findings, as well as a paucity of cost-effectiveness studies that allow to conclude about the added value of preemptive sequencing. We envision that in the upcoming decade, longread sequencing will replace conventional short-read technologies as the predominant sequencing paradigm, which will solve the current technical limitations and facilitate pharmacogenetic haplotype calling. In addition, fueled by advancements in machine learning and an exponential increase in available training data, the predictive power of advanced computational methods promises to increase substantially, which might enable the integration of automatic computational variant interpretation into clinical sequencing workflows to unlock the true power of NGS-based phenotype predictions. In this context, the increasing awareness and availability of directto-consumer genotyping might be a major driving force for demonstrating the potential value of genotype-considerate care. Based on these developments, we are thus confident and hopeful that pharmacogenomic information will be increasingly utilized to guide drug prescriptions in both personalized medicine and precision public health. Acknowledgments The work in the authors’ laboratory is funded by the Swedish Research Council [grant agreement numbers: 2016-01153, 2016-01154, and 2019-01837], by the EU/EFPIA/OICR/McGill/KTH/Diamond Innovative Medicines Initiative 2 Joint Undertaking

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(EUbOPEN grant number 875510), and by the European Union’s Horizon 2020 research and innovation program Ubiquitous Pharmacogenomics (grant agreement number 668353). Competing Interests YZ and VML are co-founders and shareholders of PersoMedix AB. In addition, VML is the CEO and shareholder of HepaPredict AB and discloses consultancy work for Enginzyme AB.

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Part IV Economics

Economics and Precision Medicine Katherine Payne and Sean P. Gavan

Contents 1 2 3 4

Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Healthcare Budgets and Making Choices . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . The Budget Impact of Precision Medicine . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Economic Evaluation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.1 Design and Conduct of Cost-Effectiveness Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.2 Study Perspective . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.3 Study Time Horizon . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.4 Identifying and Measuring Costs . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.5 Identifying, Measuring and Valuing Health Consequences . . . . . . . . . . . . . . . . . . . . . . . . . . 5 Using the Results of CEA . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6 Appraising the Quality of CEA . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7 Beyond CEA: Implementing Precision Medicine . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8 The Role of Preferences in Precision Medicine . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

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Abstract

Introducing precision medicine strategies into routine practice will require robust economic evidence. Decision-makers need to understand the value of a precision medicine strategy compared with alternative ways to treat patients. This chapter describes health economic analysis techniques that are needed to generate this evidence. The value of any precision medicine strategy can be demonstrated early to inform evidence generation and improve the likelihood of translation into

K. Payne (*) · S. P. Gavan Manchester Centre for Health Economics, Division of Population Health, Health Services Research and Primary Care, School of Health Sciences, Faculty of Biology, Medicine and Health, The University of Manchester, Manchester, UK e-mail: [email protected] # The Author(s), under exclusive license to Springer Nature Switzerland AG 2022 I. Cascorbi, M. Schwab (eds.), Precision Medicine, Handbook of Experimental Pharmacology 280, https://doi.org/10.1007/164_2022_591

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routine practice. Advances in health economic analysis techniques are also explained and their relevance to precision medicine is highlighted. Ensuring that constraints on delivery are resolved to increase uptake and implementation will improve the value of a new precision medicine strategy. Empirical methods to quantify stakeholders’ preferences can be effective to inform the design of a precision medicine intervention or service delivery model. A range of techniques to generate relevant economic evidence are now available to support the development and translation of precision medicine into routine practice. This economic evidence is essential to inform resource allocation decisions and will enable patients to benefit from cost-effective precision medicine strategies in the future. Keywords

Budget impact analysis · Cost-effectiveness analysis · Decision-analytic model · Discrete choice experiment · Economic evaluation · Microcosting · Pharmacoeconomics · Stated preferences · Value of implementation · Value of information

1

Introduction

There are numerous and diverse examples of precision medicine (also known as stratified or personalised medicine) being developed for use in clinical practice. The overarching concept of precision medicine is that it is possible to identify known heterogeneity in a patient population (e.g., by using a single test, multiple tests, algorithms, or imaging). A better understanding of this heterogeneity can then guide patient care to improve health and well-being (UKRI 2022). There is no unified definition of precision medicine. In practice, the most common definition refers to some test-and-treatment combination to target an intervention (UKRI 2022). A variety of technologies have been used, and are undergoing continued development, to identify and quantify heterogeneity in the outcomes and the progression of disease in patient populations informed by genomic, proteomic, transcriptomic and metabolomic strategies (Pearson 2016). In parallel to these ‘omic’-based approaches, new technologies are now available such as liquid biopsy (measurement of circulating tumour cells) and automated (machine learning-based) approaches to read X-rays, CT scans or using imaging to target screening. Precision medicine as an approach to treatment has many potential disease applications with cancer leading the way and with emerging applications across a range of diseases. Determining which patients might be more likely to benefit from a treatment, avoid harmful side effects, or experience more severe disease has driven claims that precision medicine is a cost-effective use of healthcare resources (Gavan et al. 2018; Phillips et al. 2013). A fundamental challenge is how to generate the required economic evidence so that decision-makers in charge of healthcare budgets can make informed choices about which examples of precision medicine should be funded from the myriad of potential applications. Key to this fundamental challenge

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is understanding the precise nature of the precision medicine strategy (Faulkner et al. 2020) and its potential impact on existing pathways of care, healthcare costs and health consequences (both benefits and harms) to patient populations. The method of cost-effectiveness analysis is used widely and has become synonymous with the term ‘economic evidence’ (Eichler et al. 2004). Economic evidence can also be generated using other methods: budget impact analysis; value of information analysis; value of implementation analysis; and the valuation of preferences. This chapter will describe each of these methods and show how they can be used to inform the development and implementation of precision medicine into clinical practice to realise patient benefits.

2

Healthcare Budgets and Making Choices

The demand for health economics has been driven by the existence of fixed healthcare budgets, finite healthcare resources, and the continued development of new approaches to healthcare or modifications of existing healthcare services. Health economics involves the application of economics to health and healthcare problems (Maynard and Kanavos 2000). Introducing any change to the healthcare system, or the interventions available within the system, has an opportunity cost (Drummond et al. 2015). The decision to reallocate resources towards a new approach to managing patients (e.g., a screening programme, diagnosis, or monitoring strategy) excludes those resources from alternative possible uses within a healthcare system. The proposed introduction of precision medicine into a healthcare system raises some specific challenges. The relevant budget may be spread across different sectors of a healthcare system. For example, the budget to pay for diagnostic tests is often overseen by a discrete set of decision-makers to those responsible for managing the budget for medicines (Payne et al. 2018). There are also likely to be constraints in addition to the finite budget, such as bottlenecks in the healthcare system, that will affect the uptake (implementation) of precision medicine into practice. The precision medicine intervention is best characterised as a ‘complex intervention’ (Skivington et al. 2021) with multiple interacting components and numerous possible relevant outcomes that can occur in the short, medium or long-term (Faulkner et al. 2020). In addition to these complexities, any precision medicine intervention will be developed in a stepwise (iterative) approach moving from basic science to translational research. The generation of economic evidence needs to move in parallel with this iterative approach and take account of what the potential end-users of the precision medicine intervention want. Viewed collectively, these specific challenges mean that a considered approach must be taken by analysts tasked with generating economic evidence to support the introduction of precision medicine into healthcare systems. The fundamental goal of this evidence is to understand if precision medicine offers added value, in terms of the relative costs and consequences (including benefits and harms), compared with established current practice.

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The Budget Impact of Precision Medicine

Budget impact analysis is a descriptive method that tells a decision-maker about the quantity of healthcare resources required to deliver a precision medicine intervention and how much this will cost the healthcare system over a defined timeframe (Sullivan et al. 2014). Sullivan and colleagues explain the steps needed to conduct a budget impact analysis of a healthcare intervention. Budget impact analysis compares resources required by the new intervention with the current pathways of care. The three key requirements for a budget impact analysis are to define: the relevant study perspective (e.g., the healthcare system); the relevant time horizon to start and follow up the pathway of interest (e.g., over the short-term starting with a patient presenting until the test result to select a medicine); and a specific description of the intervention. There are very few examples of budget impact analyses relevant to precision medicine. A recent systematic review identified 14 studies that aimed to quantify the budget impact of targeted treatments for lung cancer, and concluded that the use of different methods, study perspectives, and time horizons limited the comparability of the results from the identified studies (Han et al. 2020). Microcosting is a suite of methods that enable analysts to collect detailed (‘bottom-up’) data about resources consumed and the cost of those resources in a specific healthcare system (Frick 2009). Microcosting can use a number of different designs in isolation or in combination including the use of: administrative databases at single facilities; insurer administrative data; bespoke data collection forms applied across multiple settings; expert opinion from providers; surveys or interviews with one or more types of provider; review of patient medical charts; direct observation; personal digital assistants; operation logs; and diary data (Frick 2009). Such study designs are useful for estimating the cost of new interventions (including complex interventions), nonmarket goods, and for studying within-procedure cost variation. Microcosting is relevant to precision medicine because national tariffs for many components of the ‘precision’ element (such as a single or panel test, statistical algorithm, or machine learning approach) are usually not available (Payne et al. 2018). This lack of a national tariff has been characterised by other testing strategies such as genetic tests (Payne 2009) and is often driven by, for example, practical challenges with agreeing a national price for an intervention offered by different laboratories; lack of clear regulatory processes for the intervention; and a lack of standardisation in the delivery of the intervention. Jani and colleagues provide a good example of the steps required in a microcosting study using expert opinion to inform the resource pathways required to deliver immunogenicity and tumour necrosis factor alpha inhibitor drug level tests for therapeutic drug monitoring in rheumatology (Jani et al. 2016). Budget impact analyses and microcosting studies may sometimes have a useful role in describing the impact of precision medicine because they highlight the potential and current situation at a single point in time (a positive statement). Their role as informative methods to decide if, and how, to invest in precision medicine is generally very limited. Formal evaluative methods are required for this purpose.

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Economic Evaluation

Economic evaluation is one method to generate evidence to inform decision-making and plays a crucial role in deciding whether to divert funds to precision medicine. Economic evaluation is defined as ‘the comparative analysis of alternative courses of action in terms of both their costs and consequences’ (Drummond et al. 2015). The role of economic evaluation can start early in the development of a precision medicine and continue along the development pathway up to the point immediately prior to full implementation. The economic evaluation framework enables an assessment of an intervention’s incremental costs, benefits and harms over existing treatment options, or, if appropriate, the alternative of doing nothing. Economists require explicit principles to make clear statements about what decision-makers should do (a normative statement). These normative principles guide the technical application of the different methods of economic evaluation. Table 1 provides an overview of the different types of economic evaluation with some examples relevant to precision medicine. The key feature that distinguishes the types of economic evaluation is how the consequences (benefits and harms) are measured, valued and analysed. Cost-minimisation analyses aim to identify the intervention with the lowest cost from two (or more) alternatives thought to be equally effective. CMA has been used in the literature but is no longer considered useful for decision-making because of challenges in establishing whether alternative interventions are equally effective (Briggs and O'Brien 2001). Cost-consequences analysis (CCA) is another potentially useful method, particularly in the context of precision medicine which, as a complex intervention, may have more than one relevant outcome of interest (Coast 2004). CCA compares the costs and benefits of different interventions in a disaggregated form. The key challenge for CCA to inform decision-making is how to trade off the range of competing outcomes that are measured in different units. Cost-benefit analysis (CBA) potentially addresses one of the limitations of using CCA for precision medicine by providing a method to attach a monetary value to all consequences, including health benefits, non-health benefits including the process of providing an intervention, and the associated harms (McIntosh et al. 2010). The method used to estimate a monetary value is called contingent valuation and is applied most often using willingness to pay (WTP) techniques. Eden and colleagues published a pilot study to show how WTP could be applied to value the consequences attached to a genomic-based diagnosis of an inherited eye condition when compared with genetic counselling (Eden et al. 2013). There are very few published examples of CBA for health technologies in general and relevant to precision medicine specifically. There are also debates amongst health economists about whether CBA should be used to inform resource allocation decisions, especially in healthcare systems that are publically funded and have a ringfenced budget for healthcare (Drummond et al. 2015). These debates centre on the relevance of the normative principle that underpins the use of the contingent valuation method; welfarism (Brouwer et al. 2008; Brouwer and Koopmanschap 2000). Welfarism assumes that individuals are the best judge of their own welfare

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Table 1 Types of economic evaluation Economic evaluation Costminimisation analysis

Definition of Consequences Assumed to be the equivalent. This assumption must be based on robust evidence

Costeffectiveness analysis

Measured using a single natural unit such as clinical response to treatment or life-years gained. Treatments that improve the same endpoint can be compared Measured using health utility values to estimate quality-adjusted lifeyears (QALYs) which comprise the effect of treatment on mortality and morbidity. It is possible to compare treatments for different conditions Measured using monetary units to reflect individuals’ preferences for the interventions compared. Contingent valuation methods estimate this monetary unit as the willingness to pay All relevant consequences are quantified and presented in a disaggregated form

Cost-utility analysis

Cost-benefit analysis

Costconsequences analysis

Main limitation Establishing equivalent effectiveness can be difficult to justify

Inability to compare different outcomes expressed in natural units; choice of outcome may not capture all relevant domains of benefit or harm Domains of healthrelated quality of life used to construct a QALY may not represent some conditions well

Individual preferences to inform resource allocation decisions may be rejected on normative grounds; limitations of the contingent valuation method in applied analyses Trade-offs between changes in competing outcomes (costs and consequences) are not explicit

Example relevant to precision medicine Pipitprapat et al. (2021) report a costminimisation analysis comparing sequential genetic testing and next-generation sequencing panels for pheochromocytoma and paraganglioma in Thailand Ladabaum et al. (2011) report a costeffectiveness analysis of different strategies to identify Lynch syndrome in the United States of America Thompson et al. (2014) report a cost-utility analysis of TPMT genotyping to target azathioprine prescribing decisions in the United Kingdom

Ezennia et al. (2017) report a contingent valuation and costbenefit analysis of a test-and-treatment strategy for malaria in Nigeria

Neyt et al. (2014) report a cost-consequences analysis of non-invasive prenatal testing for trisomy 21 in Belgium

(or utility) and that welfare is taken to be the satisfaction of an individual’s preferences. Decision-makers should only enact policies that improve the welfare of some within society whilst harming no others, but very few policy changes can meet this goal. Most potential policies will typically have costs and consequences

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that are spread between individuals and groups in society. Therefore, a weaker assumption is needed that says interventions should be introduced so that individuals who gain from a policy could compensate the losers and everyone overall is better off than if the policy had not been introduced (Ng 1983). The predominant view in many jurisdictions, however, is that CBA is not a relevant method to inform how to spend a healthcare budget because of concerns about the theoretical underpinnings and application of the method by analysts (Smith 2003; Smith and Sach 2009, 2010). For these reasons, cost-effectiveness (CEA) and cost-utility analysis (CUA) have become the predominant methods used by analysts to inform resource allocation decisions in jurisdictions across the world (ISPOR 2022b). The terms CEA and CUA are often interchanged and in some settings, for example, the United Kingdom, the term CEA is used to also include CUA (Gray et al. 2011). For simplicity, we will assume this notation here. Using this simplification is consistent with theory as both CEA and CUA are underpinned by the same normative principle; extra-welfarism. This normative principle provides the theoretical foundation for CEA and CUA by recognising that decision-makers, seeking to promote the best use of resources from a defined budget, can use non-utility information in their decision-making (Brouwer et al. 2008; Culyer 1989). For interventions being funded by a healthcare budget, it is argued that the appropriate measure of consequence is ‘health’ or health status. The section on valuing health consequences explains how this is done for the purpose of CEA. Alternative interpretations of consequences, such as capability, that are broader than health status but also consistent with extra-welfarism are being used in CEA of complex interventions (Coast et al. 2008a, b). Using capability as an outcome measure is potentially useful in the context of CEA for precision medicine but there are no known published examples to date.

4.1

Design and Conduct of Cost-Effectiveness Analysis

Two predominant vehicles are available to collect evidence for a CEA: trial-based or model-based designs (Sculpher et al. 2006). Prospective or trial-based CEA collect individual patient-level data on the resource use, cost, clinical outcomes, adverse events and health status (Glick et al. 2015). The collection of data necessary for the CEA should be an integral component of the clinical trial protocol and have an associated health economic analysis plan (Thorn et al. 2021). Prospective studies can be observational or randomised but it is vital for a CEA to be able to compare the new intervention with a relevant comparator. The statistical issues when designing a trial-based CEA are the same as those that should be considered for a robust clinical trial, such as consideration of selection bias, clustering, sample size calculations and appropriate methods of data analysis. There are additional issues that must be also considered in the design of a prospective economic study specific to the analysis of economic data including resource use and health status (Mihaylova et al. 2011). Trial-based studies are generally time and resource intensive in terms of required research funding because of the need for extensive researcher input. Most importantly the results are often not sufficiently timely for decision-making in practice and

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will only generate results for the specified exemplar of precision medicine and comparator as set out in the trial protocol. This is the key limitation of using trialbased CEA in decision-making and the reason that model-based CEA is preferred by some decision-making bodies as the appropriate vehicle to inform decision-making. Model-based CEA can incorporate all the available evidence, the appropriate time horizon for the follow-up of the patient sample, and all relevant comparators (Briggs et al. 2006). The mathematical models used in the context of CEA are referred to as decision-analytic models because they are used to address a specific problem that decision-makers want to answer (the decision problem). An example of a decision problem relevant to precision medicine is: what are the incremental costs and health consequences of using a new gene-panel to target the use of medicines for treating a patient with lung cancer compared with current practice? There are many different types of decision-analytic model, and they may be sub-divided into those that are cohort-based or individual-patient level based. Brennan and colleagues provide a useful taxonomy and potential uses of the different types of decision-analytic models commonly applied in CEA for health care (Brennan et al. 2006). The conceptualisation of a decision-analytic model’s structure is a key component of any model-based CEA (Roberts et al. 2012). This process can improve the face validity of the model making it fit for the purpose of addressing the stated decision problem. In the context of precision medicine, conceptualisation generally involves thinking about the place of the ‘precision’ (test or algorithm) component in the patient pathway, the specific nature of the medicine (or intervention) to be used, and crucially what alternative management options are available if a patient is identified to be a poor responder. Each of these elements can affect the value of a precision medicine strategy through their impact on health consequences and costs to the healthcare system. The next step is to select the appropriate software to build the decision-analytic model. The chosen software is often driven by the preferences and technical skills of the analyst (Briggs et al. 2006). The decision-analytic model must then be populated with relevant evidence and many different sources (such as data from the literature identified using systematic reviews, trial-based effectiveness data, observational studies, microcosting studies and expert opinion) should be compiled in a structured manner (Kaltenthaler et al. 2011). The advantage of model-based CEA is that they are generally less time and resource intensive than trials. Most importantly using a decision-analytic model allows for extrapolation of data for a lifetime horizon, and exploration of the sources and impact of uncertainty in the data. Uncertainty in a model-based CEA can arise for a number of reasons (Philips et al. 2006). Sensitivity analysis is a key component of any model-based CEA and involves looking at how changing the assumed input parameter values impacts the results. Probabilistic analysis is a required component of model-based CEA which is used to explore the impact of uncertainty in all the relevant input parameters at the same time (Claxton et al. 2005). Both trial and model-based CEA have important roles within different stages of the iterative process of evidence gathering. The use of model-based CEA in the early phases of developing a new precision medicine intervention (called early Health

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Technology Assessment) is an informative step (Annemans et al. 2013; IJzerman et al. 2017). This step allows an analyst to identify the key drivers which affect the cost-effectiveness of a precision medicine intervention and decide, if, and how, to collect further evidence. This iterative process fits alongside the framework for evaluating complex interventions that suggests early model-based CEA can be used to inform the need for subsequent studies and the design of an appropriately sized definitive trial. Model-based CEA are useful when assessing the indicative costs and consequences of an intervention in the development phases. Incorporating a value of information analysis in the model-based CEA means this process can be used to inform the value of further research. Value of information analysis is a suite of methods to quantify the impact of resolving uncertainty in the parameters used to populate the model-based CEA compared with the cost of resolving this uncertainty to indicate the value of further research (Wilson 2015). Methods for value of information analysis include those used to understand: the need for further research (expected value of perfect information); the specific parameters driving uncertainty (expected value of partial perfect information); and the required sample size and study design for future data collection (expected value of sample information and the expected net benefit of sampling) (Fenwick et al. 2020). This iterative approach of using early model-based CEA to inform subsequent data collection activities, and then updating the model-based CEA when new evidence becomes available (Sculpher et al. 1997), is a common practice in the context of precision medicine (Abel et al. 2019; Grutters et al. 2019).

4.2

Study Perspective

The study perspective refers to the viewpoint taken by the analyst and affects the types of costs and consequences included in the CEA. A healthcare system perspective is used most often to evaluate direct costs that fall on the budget for healthcare. However, other study perspectives are possible. For example, a societal perspective could include all possible costs that fall on different government-level budget constraints and aim to maximise population well-being. Similarly, a patient-level perspective could be concerned only with costs that fall on an individual’s own income constraint (such as out-of-pocket expenditures). The study perspective used in CEA will be specific to a decision-making jurisdiction. For example, in jurisdictions such as The Netherlands, the societal perspective is recommended (Zorginstituut Nederland 2016). In England, guided by the National Institute for Health and Care Excellence (NICE), the relevant study perspective is that of the National Health Service (NHS) England and Social Services (National Institute for Health and Care Excellence 2013).

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Study Time Horizon

The time horizon in a CEA refers to the duration over which the relevant patient population is followed. The process of defining the relevant time horizon is driven by the needs of the decision-maker and the decision problem. A ‘short’ time horizon, for example, could start from when a patient is prescribed a medicine for analgesia until pain relief is achieved. This short time horizon might be most relevant when conducting a CEA of CYP2D6 testing for response to codeine (Kirchheiner et al. 2006). A ‘long’ time horizon, for example, could start from when a patient first receives treatment until they die. This longer lifetime horizon might be more relevant when conducting a CEA of EGFR testing for gefitinib (Herbst et al. 2004). The required time horizon for a CEA is specified by national guidelines relevant to the jurisdiction for decision-making.

4.4

Identifying and Measuring Costs

The relevant costs to identify and measure in a CEA are specified to be consistent with the chosen study perspective and time horizon. The relevant costs are described by the resources consumed in the delivery of the intervention under evaluation and the relevant alternative use of the budget (for the comparator intervention(s)). The steps and methods to identify the different types of resource use and costs are described in many textbooks and not reproduced here (Drummond et al. 2015; Elliott and Payne 2005). Identifying the items of resource use in the context of precision medicine is often challenging because the use of resources may be associated with (i) providing the precision component, which can involve multiple steps, members of staff or interacting components, and (ii) subsequent care pathways, which may be numerous and diverse. Resource use data, once identified, should be combined with published unit cost data to generate the total costs of the care pathways for the precision medicine intervention and its comparator(s).

4.5

Identifying, Measuring and Valuing Health Consequences

Health consequences to include in a CEA should quantify how an intervention affects patients (Brazier et al. 2017). The patient of interest is generally the individual eligible for the intervention. However, in some cases, it may also be relevant to consider the consequences for family members and carers, which are termed ‘spill over’ effects (Basu and Meltzer 2005). For example, a clinical genetics service for inherited cancers could generate consequences for the person presenting to the service for a diagnosis of hereditary breast cancer and also other female family members. In the context of CEA, using health as the consequence to be identified, measured and valued implies there must be a reliable way of quantifying the impact on health status, which can be described using generic or disease-specific measures (Bowling

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2004). Health economists have diverted substantial efforts to define health and have developed multi-attribute measures of health status. The multi-attribute measure of health status used most widely is the EQ-5D instrument (EuroQol Group 1990). It is available in different versions including two for adults (EQ-5D-3 level and EQ-5D-5 level) and for children (EQ-5D-youth). The EQ-5D instrument is used to measure the health of a population before and after an intervention. A set of preference weights are then used to transform the EQ-5D health state profile into a single score (also sometimes called a utility value). Preference weights for the EQ-5D-5L are available for decision-making jurisdictions around the world (EuroQol Group 2021). There is currently no agreed social tariff for the EQ-5D-5L in the UK and work is ongoing to produce this evidence (EuroQol Group 2020). At the time of writing, a crosswalk method is used in the UK to transform an EQ-5D-5L descriptive profile to an EQ-5D-3L utility value (van Hout et al. 2012). Alternatively, the EQ-5D-3L can be used directly with its published social tariffs to estimate utility scores (Dolan 1997). The health consequences are then estimated by multiplying the utility score for the health state measured using the EQ-5D by the duration of time in that health state. This composite of utility score and duration of time is called a quality adjusted-life year (QALY). The QALY is used as the health consequence of relevance in most CEA. Using the QALY as the relevant measure is not always viewed as ideal and there are numerous debates in the literature focussing on issues of theoretical validity, measurement methodology, and the ethics of using them to inform health policy decisions (Coast et al. 2008a; Dolan 2008). In the context of precision medicine, it is generally appropriate for CEA to measure health consequences as QALYs. However, this approach measures the impact of the selected treatment resulting from the ‘precision’ component. The value of the precision strategy is not valued per se but some analysts suggest this is an important omission (Garrison et al. 2016).

5

Using the Results of CEA

The use of model-based CEA has become an integral component of health technology assessment used in appraisal processes to inform national guidance and guidelines. The output of a model-based CEA is the expected healthcare costs and health consequences (measured using QALYs) of at least two competing interventions, one of which is generally current practice. The results of a CEA must be presented as a comparison of the difference in costs and consequences between the intervention of interest and relevant comparators (ie. an incremental analysis). In instances when more QALYs are gained but at an increased cost, an incremental cost-effectiveness ratio (ICER) is calculated to show the incremental cost per additional QALY gained (Drummond et al. 2015). Once an ICER has been calculated, it needs to be compared with a threshold (for example: £20,000 per QALY gained) to gauge whether the health technology represents a ‘cost-effective’

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use of limited health care resources. An alternative approach to presenting the results from a CEA is to use net monetary benefit or net health benefit (Paulden 2020).

6

Appraising the Quality of CEA

The discrete steps and elements to consider in the design and conduct of a CEA are clear with published guidance for specific jurisdictions. These recommendations generally take the form of a ‘reference case’ that sets out the ideal scenario for presenting evidence (Drummond et al. 2015). Analysts should keep to this ideal scenario where possible. But if the decision problem means that a specific element of the reference case is not relevant or feasible, then analysts are free to explain why and omit or replace this aspect of the design from the CEA. However, the design and conduct of a CEA is not a simple cookbook approach and requires considerable knowledge and technical capabilities on the part of the analyst. The need to appraise the quality of CEA has driven the development of a number of assessment tools (Walker et al. 2012). Generally, these tools focus on the reporting quality of the study design and conduct rather than a critique of the individual methods used. These reporting guidelines are useful tools for authors wishing to submit manuscripts for publication in journals but also provide a useful template for thinking about the key elements of a CEA. The Consolidated Health Economic Evaluation Reporting Standards (CHEERS) has become the generally recommended set of reporting criteria for CEA (Husereau et al. 2022). These reporting criteria, coupled with the ability for analysts to use electronic supplementary appendices, have improved the overall quality of reporting in published CEA. The methodological quality of CEA is on a moving trajectory driven by the continued efforts of health economists to improve existing methods and develop new ones. This trajectory means that all health economists must strive to keep their knowledge and skills up-to-date by assimilating a substantial body of literature. To enable this process, organisations such as the International Society for Pharmacoeconomics and Outcomes Research have published methodological guidance in the form of ‘good practices reports’ produced by experts in the field (ISPOR 2022a). Of relevance to precision medicine, there is an active Special Interest Group in Precision Medicine and Advanced Therapies most recently producing a publication describing the evolving paradigm of precision medicine with examples, perspectives on the value of precision medicine, and core factors that should be considered in a framework to assess the value of precision medicine (Faulkner et al. 2020).

7

Beyond CEA: Implementing Precision Medicine

There are numerous examples of precision medicine being shown to be a costeffective use of health care resources (Hatz et al. 2014; Payne et al. 2018; Phillips et al. 2014). One of the earlier examples is the case of TPMT testing to inform the

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prescription of azathioprine in which model-based CEAs indicated the pharmacogenetic test was a cost-effective use of resources (Payne et al. 2009). However, a subsequent trial-based CEA suggested that the intervention was found to be less expensive than current practice but generated fewer QALYs because clinicians did not modify their prescribing behaviour after receiving the TPMT test result (Thompson et al. 2014). There are also numerous examples of precision medicine where the uptake of the ‘precision’ component into health systems has been slower than anticipated (Wright et al. 2019). There is some empirical evidence of factors limiting the uptake of precision medicine such as significant delays in providing genetic mutation testing to patients with lung cancer (Cancer Research UK 2015). Factors limiting the uptake of precision medicine can include constraints in the system such as the: quality of the testing process; whether testing is easy to use in clinical practice; and the need for more economic evidence to reduce uncertainty about whether the intervention is a good use of resources (Wright et al. 2018). There is evidence that taking account of a healthcare system’s capacity constraints can have an impact on the estimated cost-effectiveness and net monetary benefit of an intervention (Fenwick et al. 2008). However, there are very few examples of CEA for precision medicine that have accounted for the impact of capacity constraints (Wright et al. 2019). This paucity of examples is mainly because of the need to apply ‘value of implementation’ methods in the context of precision medicine. There are some early examples of these methods to show how capacity constraints impact the cost-effectiveness of a precision medicine strategy if it were implemented into practice, and the value of investing in specific strategies to reduce the impact of these constraints (Wright et al. 2020, 2021).

8

The Role of Preferences in Precision Medicine

The use of economic evaluation, in general, and CEA, specifically is most appropriately applied to inform population-level decision-making (Drummond et al. 2015). Rogowski and colleagues introduced the concept that two distinct, but linked, interpretations of precision medicine exist (personalisation by ‘physiology’ or ‘preferences’) (Rogowski et al. 2015). Introducing the concept of preferences means that valuation methods are required to measure the views of individuals within a population (Brazier et al. 2017). Broadly, there are two main types of valuation methods: revealed preference and stated preference. Revealed preference methods require that a market is observable, and data are available, to capture the use of a good in practice (such as precision medicine). There are two challenges with using revealed preference methods. The market for health care is imperfect and the consumer (the patient) is not the sole agent responsible for making the decision to purchase the good (healthcare) (Morris et al. 2007). There are often no data available to reveal preferences if the new intervention is being developed. These two reasons have stimulated the development of stated preference methods (Brazier et al. 2017). Discrete choice experiments now have widespread application as a method to

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measure stated preferences (Ryan et al. 2008). The advantage of using discrete choice experiments is that they are underpinned by economic theory that can provide decision-makers allocating healthcare budgets with quantitative information about the preferences of relevant stakeholders. Relevant stakeholders can be the public (comprising both current and future patients), patients with experience of a specific condition, healthcare professionals and policymakers. The role of a discrete choice experiment is to help identify which characteristics (termed ‘attributes’) of an intervention are liked, the balance between these different attributes, and the relative value of each attribute. In terms of precision medicine, there are three clear potential uses of a discrete choice experiment. Payne and colleagues used a discrete choice experiment to inform the key design elements of a service to offer TMPT testing in the UK setting. This study compared the views of patients with healthcare professionals and suggested different views about what is important about a pharmacogenetic testing service (Payne et al. 2011). A discrete choice experiment can also be used to predict the uptake of a future intervention. Regier and colleagues explored the elements of a precision medicine that may influence the uptake of a 21-gene recurrence score assay for breast cancer (Regier et al. 2020). This study also found close agreement between the predicted uptake estimated by the discrete choice experiment and observed uptake. Gorantis and colleagues predicted the uptake of genomic sequencing in exemplars of paediatric and adult conditions (Goranitis et al. 2020). For example, they identified that for paediatric conditions, the predicted uptake of genomic sequencing was 60% for complex neurological conditions if testing shortened the diagnostic odyssey. For retinoblastoma, where there is also an opportunity to prevent or cure the condition, the predicted uptake was 81%. A more unusual application of discrete choice experiments is to inform the development of a precision medicine intervention (Dalal et al. 2021; Vass et al. 2022). For example, Vass and colleagues used a discrete choice experiment to understand the key elements needed to design an algorithm-guided approach to prescribing biologics (termed ‘biologic calculator’) for people with rheumatoid arthritis (Vass et al. 2022). This study showed that, as anticipated, positive predictive value (ability to correctly predict who will respond to a certain dose of a biologic) was a strong driver of preferences. Of note, however, was that the negative predictive value (ability to correctly predict who will not respond to a certain dose of a biologic) was also viewed as a key aspect for researchers to take account of when designing a precision medicine intervention.

9

Summary

Decision-makers charged with allocating finite healthcare budgets require timely, informative, and robust economic evidence on precision medicine. This chapter has introduced several economic methods available to understand the budget impact of precision medicine, its potential value to health care systems, the potential need to generate evidence iteratively, the impact of imperfect implementation, and the role

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of preferences. Collectively, these techniques can help to bring examples of precision medicine into routine practice, improving patient health outcomes and the allocation of resources for healthcare.

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Correction to: Precision Medicine in Antidepressants Treatment Evangelia Eirini Tsermpini, Alessandro Serretti, and Vita Dolžan

Correction to: Chapter “Precision Medicine in Antidepressants Treatment” in: I. Cascorbi, M. Schwab (eds.), Handbook of Experimental Pharmacology, https://doi.org/10.1007/164_2023_654 The original version of the chapter was inadvertently published with an error in the name of author: Alessandro Seretti. However, the author name has now been corrected as per the author request. The incorrect author name: Alessandro Seretti is now corrected as Alessandro Serretti

The updated original version for this chapter can be found at https://doi.org/10.1007/164_2023_654 # The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 I. Cascorbi, M. Schwab (eds.), Precision Medicine, Handbook of Experimental Pharmacology 280, https://doi.org/10.1007/164_2023_661

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