Pharmacogenomics in the age of personalized medicine

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Editors-in-Chief Kelvin Lam – Simplex Pharma Advisors, Inc., Boston, MA, USA Henk Timmerman – Vrije Universiteit, The Netherlands DRUG DISCOVERY

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TECHNOLOGIES

Pharmacogenomics in the age of personalized medicine Leslie J. Dickmann*, Joseph A. Ware Department of Clinical Pharmacology, Genentech, 1 DNA Way, South San Francisco, CA 94080, United States

The aim of personalized medicine is to offer the right treatment to the right person at the right dose, thus maximizing efficacy and minimizing toxicity for each individual patient. Pharmacogenomic approaches at-

Section editor: Saileta Prabhu – Preclinical and Translational PKPD, Genentech, 1 DNA Way, South San Francisco, CA 94080, United States.

tempt to refine the aim of personalized medicine by utilizing an individual’s germline and somatic DNA signatures to guide treatment. In this review, we highlight the current use of pharmacogenomic based bio-

beliefs). However, the introduction of pharmacogenomics/ pharmacogenetics into clinical care has added an entirely new dimension to the term ‘personalized medicine.’

marker information in drug labeling. We also present several case studies on the implementation of pharma-

Pharmacogenomic based biomarkers in drug labeling

cogenomic strategies in drug discovery and develop-

Pharmacogenetics and pharmacogenomics are broad terms that tend to be used interchangeably, and there remains debate as to the most appropriate definition and use of each term. Historically, pharmacogenetics has been used to refer to the effect of genetic variation in one gene on drug metabolism and disposition [1]. Pharmacogenomics, on the other hand, generally refers to how the entire genome can influence the response to drugs. In this article, we will not debate the correct terminology but rather point out that both terms refer to the ability of genetic-based testing to give the correct drug at the correct dose to the correct patient, thus maximizing efficacy and minimizing toxicity. A summary of pharmacogenomic based biomarker information included in drug labeling is shown in Table 1 (http:// www.fda.gov). It should be noted that this table is based on information from the Food and Drug Administration (FDA) and does not necessarily translate precisely to drug labels approved by other health authorities such as Health Canada (HCSC) and the European Medicines Agency (EMA). For instance, although genetic testing for HLA-B*5701 status is

ment. Lastly, we comment on current challenges to implementing pharmacogenomic based testing in the clinic. Introduction In the 2014 USA State of the Union address, President Barack Obama launched a Precision Medicine Initiative which would ‘give all of us access to the personalized information we need to keep ourselves and our families healthier (http:// www.whitehouse.gov).’ Indeed, precision medicine is currently a keen area of basic and medical research across both academia and industry. The idea of precision or personalized medicine is not new. For decades health practitioners have tried to tailor medical treatment to specific patients by taking into account both biological (e.g. concurrent disease and medication, previous health history, age) and socio-economic factors (e.g. support network, medical coverage, cultural *Corresponding author: L.J. Dickmann ([email protected]) 1740-6749/$ ß 2016 Elsevier Ltd. All rights reserved.

http://dx.doi.org/10.1016/j.ddtec.2016.11.003

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Table 1. Current listing of FDA pharmacogenomic biomarkers in drug labeling. The number in parentheses is the number of biomarkers represented in that particular category. Note that some drug labels contain information on more than one biomarker. Data was compiled using information at http://www.fda.gov/Drugs/ScienceResearch/ResearchAreas/ Pharmacogenetics/ucm083378.htm. Therapeutic area (no. of biomarkers)

Main concern (no. of biomarkers)

ADME related (%)

PharmGKB criteria labeling advicea (no. of biomarkers)

Anesthesiology and Analgesics (3)

Safety

100

Actionable

Cardiology (11)

Efficacy (2) Safety (9)

100

Actionable (2) Informative (8) Recommended (1)

Dental (1)

Safety

100

Actionable

Dermatology (2)

Safety

50

Actionable

Endocrinology (7)

Efficacy (3) Safety (4)

0

Actionable (4) Informative (3)

Gastroenterology (7)

Efficacy (1) Safety (6)

71

Actionable (5) Informative (2)

Genitourinary (3)

Safety

100

Actionable

Gynecology (1)

Safety

100

Informative

Hematology (9)

Safety

11

Actionable (5) Informative (4)

Inborn Errors of Metabolism (4)

Efficacy (3) Safety (1)

25

Informative (1) Required (3)

Infectious Diseases (20)

Efficacy (5) Safety (15)

20

Actionable (12) Informative (7) Recommended (1)

Neurology (13)

Safety

54

Actionable (4) Informative (3) Recommended (1) Required (5)

Oncology (57)

Efficacy (42) Safety (15)

16

Actionable (11) Informative (8) Recommended (1) Required (37)

Psychiatry (26)

Efficacy (1) Safety (25)

100

Actionable (19) Informative (6) Required (1)

Pulmonary (4)

Efficacy (1) Safety (3)

75

Informative (3) Required (1)

Rheumatology (6)

Safety

67

Actionable (4) Informative (1) Recommended (1)

Toxicology (1)

Safety

0

Actionable

Transplantation (1)

Safety

0

Actionable

a Actionable: The label does not discuss genetic or other testing for gene/protein/chromosomal variants, but does contain information about changes in efficacy, dosage or toxicity due to such variants. Informative: The label mentions a gene or protein is involved in the metabolism or pharmacodynamics of the drug, but there is no information to suggest that variation in these genes/proteins leads to different response. Recommended: The label states or implies that some sort of gene, protein or chromosomal testing, including genetic testing, functional protein assays, cytogenetic studies, etc., is recommended before using this drug. Required: The label states or implies that some sort of gene, protein or chromosomal testing, including genetic testing, functional protein assays, cytogenetic studies, etc., should be conducted before using this drug. Please refer to http://www. pharmgkb.org/view/drug-labels.do for further information.

required by the EMA before initiation of abacvir therapy, it is only recommended by both the FDA and HCSC. The EMA Summary of Product Characteristics (SmPC) also contains information to better facilitate pharmacogenomics-guided drug treatment. According to a recent publication by the 12

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EMA, approximately 15% of EMA evaluated medications have pharmacogenomic information in their drug label that ‘directly impacts patient treatment.’ [2] Similar to FDA label guidance, a SmPC contains pharmacogenomic information that is either mandatory for drug use, important for drug use,

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or purely informative. Although the number of genes related to pharmacogenomic biomarkers found in EU product labels was similar to that found in FDA labels (approximately 50), the composition of this gene list varied [2] (http://www.fda. gov). This could be due to several reasons including differences in marketed drug between and EU and the US along with different methodologies to decide if genetic information in the drug label is warranted. In terms of therapeutic area, oncology has the most biomarker information in FDA drug labels, followed by psychiatry and infectious diseases. However, as shown in Table 1, not all biomarker information included in drug labeling leads to a required or recommended action. Thus it is up to the discretion of the health care providers and payers to determine whether or not testing is warranted. In certain instances, pharmacogenomic information is purely informative. An example of this is for nefazodone. The drug label states that the pharmacokinetics of the drug are not altered in CYP2D6 poor metabolizers. Although not actionable information in terms of testing or dosing, it can reassure a health care worker who may have concern for a patient who is a known CYP2D6 poor metabolizer. CYP2D6 is the most prominent pharmacogenomic biomarker observed in FDA drug labeling. This is not surprising given the impact that CYP2D6 gene variants, deletions, and duplications have on drug efficacy and safety [3,4]. However, of the 41 drug labels containing information about CYP2D6 pharmacogenetics, only 3 labels require testing prior to initiation of therapy. Thus, having pharmacogenomic language in the drug label will not necessarily prompt prior testing on the part of the health care provider. The case of CYP2D6 highlights the intricacies of whether to test or not to test. There is no precise formula when it comes to translating pharmacogenomic discoveries into clinical practice. Some of the reasons for this have been highlighted previously and include inadequate study size, poor clinical phenotyping, inadequate study design including an overreliance on retrospective data, lack of collaboration between groups, and a deficiency in our understanding of the interplay between genetics and environment [5]. As more and more clinical drug studies begin to prospectively incorporate pharmacogenomic strategies and hypotheses in a careful manner, there will hopefully be less confusion around which testing is required in the clinical setting.

Pharmacogenomic strategies in drug discovery and development The ultimate goal behind pharmacogenomic approaches in drug discovery and development is to treat an appropriate subpopulation of patients in order to maximize efficacy and minimize toxicity. Strategies can be numerous and many times are based on whether or not prior knowledge exists. A ‘one size fits all’ strategy to pharmacogenomics is

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nonexistent and any strategy must be fit for purpose. Many pharmacogenomic strategies begin and end solely as exploratory studies. However, many move further into confirmatory studies and ultimately into a clinically useful and validated pharmacogenomic test. Below we give several case examples of how pharmacogenomics was implemented in drug discovery and development.

Target discovery to exploratory biomarker in macular degeneration Age-related macular degeneration (AMD) is a progressive and degenerate disease of the retina. In AMD, the macula, which is responsible for the perception of color and for visual acuity, is slowly destroyed. Epidemiological studies in the early to mid-1990s demonstrated a strong genetic component to AMD risk. It was estimated that genetic factors can account for 50–70% of the total variability in the disease risk, and the lifetime risk of late AMD was 50% for an individual with an affected family member while only 12% for individuals who had no affected family members [6–8]. Subsequent GWAS studies have implicated multiple members of the complement system in AMD. Although the exact mechanisms have not been fully elucidated, it is hypothesized that the dysregulation of the complement system leads to both local and systemic inflammation observed in AMD [9]. Using this human genetic information, many drug companies are developing potent inhibitors to block the complement pathway in the eye. Novartis is developing a monoclonal antibody (LFG316) to inhibit activation of complement C5 which is currently being evaluated in a phase 2 study in patients with geographic atrophy (GA). Appellis is developing a peptide (APL-2) to prevent all three major pathways of complement activation and is currently recruiting patients for a phase 2 study. Genentech is currently conducting a phase 3 trial with Lampalizumab, a monoclonal antibody fragment that inhibits complement factor D. Of special interest is the application of exploratory pharmacogenomics to the phase 2 Lampalizumab study (MAHALO). In a specific sub-population of GA patients identified using an exploratory pharmacogenomic biomarker, the GA progression rate was decreased by 44% at 18 months compared to 20.4% for all comers (Roche news release, http://www.roche.com). Whether or not this exploratory biomarker finding holds true will surely be further scrutinized in the ongoing phase 3 trials and illustrates how early discovery pharmacogenomic strategies can facilitate development strategies with the potential for a clinically meaningful biomarker.

Impact of pharmacogenomics on drug safety The case of abacavir hypersensitivity highlights the pharmacogenomic challenges going from an initial clinical safety finding to the development of a clinically useful test to screen for susceptible individuals. Abacavir is a small molecule reverse transcriptase inhibitor which was approved in 1998 www.drugdiscoverytoday.com

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for the treatment of HIV. During clinical development, approximately 5–8% of patients developed hypersensitivity reactions. This can lead to respiratory distress and severe rash and can be life-threatening, especially upon re-exposure to the drug [10]. Two independent groups, one of which was the drug sponsor, identified HLA-B*5701 as a marker associated with abacavir hypersensitivity reaction [11,12]. In the US, the robustness of the findings was not considered evidence enough to encourage routine genetic testing. However, HLA testing was routinely undertaken in the UK, France, and Australia by 2006, highlighting differences among countries as to what and how much information is necessary to establish burden of proof. One key issue for US regulators was the imprecise definition of the hypersensitivity phenotype, which was difficult to diagnose due to the variety of clinical symptoms. Another key issue was the homogeneous nature of the original study cohorts which included almost exclusively males of European ancestry. Whether results could be extrapolated to women and other ethnic groups was unknown. The subsequent development of a skin patch test allowed an accurate assessment of abacavir hypersensitivity phenotype without exposing individuals to drug rechallenge and thus allowed for a clinically useful measure of the hypersensitivity reaction [13]. In 2006, the drug sponsor conducted an additional prospective trial (PREDICT-1) to investigate whether pharmacogenetic testing and exclusion of HLA-B*5701 patients would reduce the incidence of abacavir hypersensitivity reactions. The study results indicated a reduction in the incidence of both clinically suspected (from 7.8% to 3.4%) and immunologically confirmed (from 2.7% to 0) hypersensitivity reactions [14]. Based on these data and other investigator sponsored trials, most health authorities and HIV treatment guidelines now recommend, require, or endorse prospective genetic screening before the initiation of abacavir therapy. The abacavir example poses an intriguing question, how early in the development pipeline should we consider implementing a drug safety pharmacogenomic strategy? Any likely strategy depends on the prevalence and severity of safety signals in Phase 1 and 2 studies, but if recognized and addressed early, a cohesive, prospective strategy could be implemented for Phase 3 trials.

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contemporary treatment of non-small cell lung cancer. Of squamous cell adenocarcinomas, approximately 50% are thought to be driven by mutations in EGFR, ALK, or KRAS genes, while 80% squamous cell carcinomas are thought to be driven by mutations or amplifications in PIK3CA, FGFR1, and PTEN [15]. Over the past decade, several genetically targeted therapies have been developed, including erlotinib, afatinib, gefitinib, and osimertinib to treat EGFR mutation specific cancers, and crizotinib, ceritinib, and alectinib to treat cancers positive for ALK gene rearrangement. A recent prospective study by the French Cooperative Thoracic Intergroup investigated the routine molecular profiling of almost 20,000 patients with advanced non-small cell lung cancer (NSCLC) [16]. The median time between tissue collection and the initiation of testing was 8 days and the median time to final written report was 11 days which was deemed an acceptable turnaround time for these results. Almost 50% of patients had at least one actionable genetic alteration which affected physician’s treatment decisions in 51% of patients. Importantly, when a genetic alteration was detected, the median overall survival was 4.7 months longer than if a genetic alteration was absent suggesting that molecular profiling of NSCLC patients provides clinical benefit. Interpreting clinical trial data for targeted cancer therapies can be misleading if a full characterization of a patient’s tumor has not been assessed. Thus, a new paradigm is to enroll patients onto clinical trials based on the molecular and genetic characterization of their tumor instead of relying solely on tumor type. This is being achieved through advances such as a FoundationOne report which provides whole exome sequencing of known cancer genes to identify drivers and prognostic markers of cancer, provide insight into the complex biology of why certain therapies work for some individuals and not others and define which patients are most appropriate for a particular clinical trial [17–19]. Foundation Medicine recently launched FoundationACCESSTM Trial Navigator, a service to health care providers to refer patients to appropriate clinical trials based on their clinical and genomic profiles (http://www.foundationone.com). This holds much promise for patients in that if they choose to enroll in a clinical trial, it will be correctly tailored to their disease and will increase the chances of remission and survival.

Genetic profiling in oncology Perhaps the most promising area of personalized medicine is the ability to tailor cancer treatments to the molecular profile of an individual’s cancerous tumor. Knowledge of genetic mutations, rearrangements, and fusions which drive cancer cell expansion can be exploited to develop targeted therapies which would bypass the toxicities observed with conventional chemotherapies. Screening tumors for a range of predictive and prognostic genetic biomarkers is now a hallmark of many cancer therapy regimens. A prime example of this is with 14

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Current challenges in the application of pharmacogenomics There are several current challenges to the seamless implementation of pharmacogenomics testing into everyday clinical practice. These include lack of clear guidelines for health care practitioners, incorporation of test results into electronic medical records, uncertainty over reimbursement, and ambiguity about clinical utility to name but a few. Although an indepth discussion on the current challenges in the application

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of pharmacogenomics is beyond this review, below we discuss two current challenges in which the pharmacogenomics community is making steady progress.

Education for health care professionals Recent studies have indicated that health care practitioners believe in the potential of pharmacogenomics to improve patient care, but they also believe they lack the knowledge necessary to implement pharmacogenomic information into daily practice [20–23]. In one recent survey of US primary care physicians, cardiologists, and psychiatrists, only 12.6% were extremely or very familiar with pharmacogenomics and only 11% had any formal training in pharmacogenomics. However, 37% either strongly or somewhat agreed that they were confident in their knowledge about the influence of genetics on drug therapy [21]. Given this information, many professional associations and academic institutions are working diligently to incorporate pharmacogenomic education in pharmacy and medical schools [23–25]. As an example, the medical curriculum at the University of Maryland offers students the opportunity to genotype themselves (or an unidentified individual) using the Affymetrix DMET array and to use their own data for interpretation exercises throughout the course. In a recent survey, 80% of students felt that this was an appropriate educational exercise and 65% felt that using their own DNA sample was an educational benefit [23]. Courses such as these also expose students to valuable pharmacogenomic resources such as the Clinical Pharmacogenetics Implementation Consortium (CPIC) guidelines. As more pharmacogenomic training is implemented in the early phases of a health care worker’s career, their comfort with and ability to implement pharmacogenomic data into patients’ health care will become routine.

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are not completely understood, previous studies which failed to show that genotyping improved anticoagulation control may have had limitations of samples size, dosing algorithms, and/or genotyping strategy. Whether data from randomized controlled trials (RCT) is needed for implementation of routine pharmacogenomic testing is another challenge to be addressed. RTC are considered the gold standard to show the safety and effectiveness of treatments and therapies because many of the possible sources of bias are removed from the process. Unfortunately, RTC are not always amenable to pharmacogenomics due to either safety concerns or the vast number of individuals needed to power the study accordingly. Several publications have highlighted these concerns and suggested rethinking clinical trial design and subsequent analysis of pharmacogenomic data [28–30]. Ultimately, pharmacogenomic trial design will likely be driven by several factors including a priori knowledge about potential genetic effects, how far along a program is in clinical development or whether it is postmarket, and the viewpoint of health authorities on alternative trial design and analysis. Groups such as the Evaluation of Genomic Applications in Practice and Prevention (EGAPP) are currently working to develop systematic processes to evaluate the validity and utility of genetic tests for clinical practice [31]. Additionally, pharmacoeconomic models, such as the ACCE model and variations thereof, hold much promise in calculating the clinical and financial utility of pharmacogenomic testing [32]. Such models take into consideration the disorder, analytical and clinical validity of testing, clinical utility, and ethical, legal, and societal issues associated with testing. Ultimately, the cost-effectiveness of pharmacogenomic testing has to be weighed against the benefit to public health.

Burden of proof Questions still remain as to whether many pharmacogenomic tests are actually cost-effective and clinically useful. Although the value and benefit of genetic testing can be rather straight-forward in certain instances (such as for abacavir and hypersensitivity reactions or Herceptin and efficacy), many others remain debatable. Genetic testing for warfarin is a perfect example. Although testing for CYP2C9 and VKORC1 is distinctly called out in the FDA drug label, many pharmacoeconomic studies have called into question the financial and clinical utility of this testing [26]. However, a recent multicenter, randomized, controlled trial by the European Pharmacogenetics of Anticoagulant Therapy (EUPACT) showed a clear benefit of genotype-guided dosing for warfarin [27]. In this trial, the genotype-guided therapy group had significantly fewer incidences of excessive anti-coagulation, an eight day shorter median time to reach therapeutic benefit, and increased time in the therapeutic range. Although the discrepancies between different warfarin studies

Conclusion Barriers remain to the implementation of pharmacogenomic based precision medicine, but many advances have been made in the past decade. Although many pharmacogenomic hypotheses do not proceed beyond the discovery or exploratory phase, it is still worth the effort and investment considering the progress made in areas such as NSCLS and our ability to avoid drug related safety events, as in the case of abacavir. As noted in the case examples above, pharmacogenomics has advanced our understanding of disease biology, drug action, and the development of targeted therapeutics. With the recent technological advances, such as whole genome sequencing, in analyzing the relationship between genomes, disease, and clinical outcomes, pharmacogenomic strategies can become commonplace, routine, and relatively inexpensive. Whether clinical implementation of a specific pharmacogenomic based biomarker test is appropriate will depend on several factors including the robustness of the www.drugdiscoverytoday.com

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science, cost–benefit analysis, and the overall benefit to public health.

Conflict of interest The authors declare no conflict of interest.

Acknowledgements The authors thank the reviewers for their thoughtful comments and suggested edits to this review.

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