Pharmacogenomic biomarkers in drug labels: what do they tell us?

Current pharmacogenomic recommendations in chronic respiratory diseases: Is there a biomarker ready for clinical implementation?

INTRODUCTION

The study of genetic variants in response to different drugs has predominated in fields of medicine such as oncology and infectious diseases. In chronic respiratory diseases, the available pharmacogenomic information is scarce but not less relevant.

AREAS COVERED

We searched the pharmacogenomic recommendations for respiratory diseases in the Table of Pharmacogenomic Biomarkers in Drug Labeling (U.S. Food and Drug Administration), the Clinical Pharmacogenomics Implementation Consortium (CPIC), and PharmGKB. The main pharmacogenomics recommendation in this field is to assess CFTR variants for using ivacaftor and its combination. The drugs' labels for arformoterol, indacaterol, and umeclidinium indicate a lack of influence of genetic variants in the pharmacokinetics of these drugs. Further studies should evaluate the contribution of CYP2D6 and CYP2C19 variants for formoterol. In addition, there are reports of potential pharmacogenetic variants in the treatment with acetylcysteine (TOLLIP rs3750920) and captopril (ACE rs1799752). The genetic variations for warfarin also are presented in PharmGKB and CPIC for patients with pulmonary hypertension.

EXPERT OPINION

The pharmacogenomics recommendations for lung diseases are limited. The clinical implementation of pharmacogenomics in treating respiratory diseases will contribute to the quality of life of patients with chronic respiratory diseases.

Artificial intelligence and machine learning in precision medicine: A paradigm shift in big data analysis

The integration of artificial intelligence in precision medicine has revolutionized healthcare delivery. Precision medicine identifies the phenotype of particular patients with less-common responses to treatment. Recent studies have demonstrated that translational research exploring the convergence between artificial intelligence and precision medicine will help solve the most difficult challenges facing precision medicine. Here, we discuss different aspects of artificial intelligence in precision medicine that improve healthcare delivery. First, we discuss how artificial intelligence changes the landscape of precision medicine and the evolution of artificial intelligence in precision medicine. Second, we highlight the synergies between artificial intelligence and precision medicine and promises of artificial intelligence and precision medicine in healthcare delivery. Third, we briefly explain the promise of big data analytics and the integration of nanomaterials in precision medicine. Last, we highlight the challenges and opportunities of artificial intelligence in precision medicine.

Will the Use of Pharmacogenetics Improve Treatment Efficiency in COVID-19?

The COVID-19 pandemic is associated with a global health crisis and the greatest challenge for scientists and doctors. The virus causes severe acute respiratory syndrome with an outcome that is fatal in more vulnerable populations. Due to the need to find an efficient treatment in a short time, there were several drugs that were repurposed or repositioned for COVID-19. There are many types of available COVID-19 therapies, including antiviral agents (remdesivir, lopinavir/ritonavir, oseltamivir), antibiotics (azithromycin), antiparasitics (chloroquine, hydroxychloroquine, ivermectin), and corticosteroids (dexamethasone). A combination of antivirals with various mechanisms of action may be more efficient. However, the use of some of these medicines can be related to the occurrence of adverse effects. Some promising drug candidates have been found to be ineffective in clinical trials. The knowledge of pharmacogenetic issues, which translate into variability in drug conversion from prodrug into drug, metabolism as well as transport, could help to predict treatment efficiency and the occurrence of adverse effects in patients. However, many drugs used for the treatment of COVID-19 have not undergone pharmacogenetic studies, perhaps as a result of the lack of time.

Future of Perioperative Precision Medicine: Integration of Molecular Science, Dynamic Health Care Informatics, and Implementation of Predictive Pathways in Real Time

Conceptually, precision medicine is a deep dive to discover disease origin at the molecular or genetic level, thus providing insights that allow clinicians to design corresponding individualized patient therapies. We know that a disease state is created by not only certain molecular derangements but also a biologic milieu promoting the expression of such derangements. These factors together lead to manifested symptoms. At the level of molecular definition, every average, "similar" individual stands to be "dissimilar." Hence, there is the need for customized therapy, moving away from therapy based on aggregate statistics. The perioperative state is a mix of several, simultaneously active molecular mechanisms, surgical insult, drugs, severe inflammatory response, and the body's continuous adaptation to maintain a state of homeostasis. Postoperative outcomes are a net result of several of those rapid genetic and molecular transformations that do or do not ensue. With the advent and advances of artificial intelligence, the translation from identifying these intricate mechanisms to implementing them in clinical practice has made a huge leap. Precision medicine is gaining ground with the help of personalized health recorders and personal devices that identify disease mechanics, patient-reported outcomes, adverse drug reactions, and drug-drug interaction at the individual level in a closed-loop feedback system. This phenomenon is especially true given increasing surgeries in older adults, many of whom are on multiple medications and varyingly frail. In this era of precision medicine, to provide a comprehensive remedy, the perioperative surgical home must expand, incorporating not only clinicians but also basic science experts and data scientists.

A retrospective analysis of actionable pharmacogenetic/genomic biomarker language in FDA labels

The primary goal of precision medicine is to maximize the benefit‐risk relationships for individual patients by delivering the right drug to the right patients at the right dose. To achieve this goal, it has become increasingly important to assess gene‐drug interactions (GDIs) in clinical settings. The US Food and Drug Administration (FDA) periodically updates the table of pharmacogenetic/genomic (PGx) biomarkers in drug labeling on their website. As described herein, an effort was made to categorize various PGx biomarkers covered by the FDA‐PGx table into certain groups. There were 2 major groups, oncology molecular targets (OMT) and drug‐metabolizing enzymes and transporters (DMETs), which constitute ~70% of all biomarkers (~33% and ~35%, respectively). These biomarkers were further classified whether their labeling languages could be actionable in clinical practice. For OMT biomarkers, ~70% of biomarkers are considered actionable in clinical practice as they are critical for the selection of appropriate drugs to individual patients. In contrast, ~30% of DMET biomarkers are considered actionable for the dose adjustments or alternative therapies in specific populations, such as CYP2C19 and CYP2D6 poor metabolizers. In addition, the GDI results related to some of the other OMT and DMET biomarkers are considered to provide valuable information to clinicians. However, clinical GDI results on the other DMET biomarkers can possibly be used more effectively for dose recommendation. As the labels of some drugs already recommend the precise doses in specific populations, it will be desirable to have clear language for dose recommendation of other (or new) drugs if appropriate.

Pharmacogenetics Approach for the Improvement of COVID-19 Treatment

The treatment of coronavirus disease 2019 (COVID-19) has been a challenge. The efficacy of several drugs has been evaluated and variability in drug response has been observed. Pharmacogenetics could explain this variation and improve patients' outcomes with this complex disease; nevertheless, several disease-related issues must be carefully reviewed in the pharmacogenetic study of COVID-19 treatment. We aimed to describe the pharmacogenetic variants reported for drugs used for COVID-19 treatment (remdesivir, oseltamivir, lopinavir, ritonavir, azithromycin, chloroquine, hydroxychloroquine, ivermectin, and dexamethasone). In addition, other factors relevant to the design of pharmacogenetic studies were mentioned. Variants in CYP3A4, CYP3A5, CYP2C8, CY2D6, ABCB1, ABCC2, and SLCO1B1, among other variants, could be included in pharmacogenetic studies of COVID-19 treatment. Besides, nongenetic factors such as drug-drug interactions and inflammation should be considered in the search for personalized therapy of COVID-19.

Pharmacogenomic Biomarkers in US FDA-Approved Drug Labels (2000-2020)

Pharmacogenomics (PGx) is a key subset of precision medicine that relates genomic variation to individual response to pharmacotherapy. We assessed longitudinal trends in US FDA approval of new drugs labeled with PGx information. Drug labels containing PGx information were obtained from Drugs@FDA and guidelines from PharmGKB were used to compare the actionability of PGx information in drug labels across therapeutic areas. The annual proportion of new drug approvals with PGx labeling has increased by nearly threefold from 10.3% (n = 3) in 2000 to 28.2% (n = 11) in 2020. Inclusion of PGx information in drug labels has increased for all clinical areas over the last two decades but most prominently for cancer therapies, which comprise the largest proportion (75.5%) of biomarker–drug pairs for which PGx testing is required. Clinically actionable information was more frequently observed in biomarker–drug pairs associated with cancer drugs compared to those for other therapeutic areas (n = 92 (59.7%) vs. n = 62 (40.3%), p < 0.0051). These results suggest that further evidence is needed to support the clinical adoption of pharmacogenomics in non-cancer therapeutic areas.

Combination of Genome-Wide Polymorphisms and Copy Number Variations of Pharmacogenes in Koreans

For predicting phenotypes and executing precision medicine, combination analysis of single nucleotide variants (SNVs) genotyping with copy number variations (CNVs) is required. The aim of this study was to discover SNVs or common copy CNVs and examine the combined frequencies of SNVs and CNVs in pharmacogenes using the Korean genome and epidemiology study (KoGES), a consortium project. The genotypes (N = 72,299) and CNV data (N = 1000) were provided by the Korean National Institute of Health, Korea Centers for Disease Control and Prevention. The allele frequencies of SNVs, CNVs, and combined SNVs with CNVs were calculated and haplotype analysis was performed. CYP2D6 rs1065852 (c.100C>T, p.P34S) was the most common variant allele (48.23%). A total of 8454 haplotype blocks in 18 pharmacogenes were estimated. DMD ranked the highest in frequency for gene gain (64.52%), while TPMT ranked the highest in frequency for gene loss (51.80%). Copy number gain of CYP4F2 was observed in 22 subjects; 13 of those subjects were carriers with CYP4F2*3 gain. In the case of TPMT, approximately one-half of the participants (N = 308) had loss of the TPMT*1*1 diplotype. The frequencies of SNVs and CNVs in pharmacogenes were determined using the Korean cohort-based genome-wide association study.

Characterization of Pharmacogenetic Information in Food and Drug Administration Drug Labeling and the Table of Pharmacogenetic Associations

BACKGROUND

The US Food and Drug Administration (FDA) recommends using only FDA-reviewed pharmacogenetic information to make prescribing decisions based on genetic test results. Such information is available in drug labeling and in the Table of Pharmacogenetic Associations ("Associations table").

OBJECTIVE

To compile a list of drug-gene pairs from drug labeling and the Associations table and categorize the pharmacogenetic information and clinical outcome associated with each drug-gene pair.

METHODS

This was a cross-sectional analysis of pharmacogenetic information in the Associations table and individual drug labeling in March 2020. We used the Table of Pharmacogenomic Biomarkers in Drug Labeling to identify drug labels to review. We categorized the pharmacogenetic information for each drug-gene pair according to whether the purpose was to describe (1) polymorphisms affecting drug disposition (metabolism or transport), (2) polymorphisms affecting a direct drug target, (3) variants associated with adverse drug reaction (ADR) susceptibility, (4) variants associated with therapeutic failure, (5) a biomarker-defined indication, or (6) a biomarker-defined ADR. We also categorized the clinical outcome-efficacy, safety, or unknown-associated with each drug-gene pair. We reported counts and proportions of drug-gene pairs in each pharmacogenetic information and clinical outcome category.

RESULTS

We identified 308 drug-gene pairs, of which 36% were associated with a biomarker-defined drug indication, 33% with polymorphic drug metabolism, and 28% with ADR susceptibility. Most drug-gene pairs (n = 267, 87%) were associated with an efficacy or safety-related outcome.

CONCLUSION AND RELEVANCE

FDA-reviewed pharmacogenetic information is available for more than 300 drug-gene pairs and can help guide prescribing decisions.

Linking Pharmacogenomic Information on Drug Safety and Efficacy with Ethnic Minority Populations

Numerous prescription drugs’ labeling contains pharmacogenomic (PGx) information to aid health providers and patients in the safe and effective use of drugs. However, clinical studies for such PGx biomarkers and related drug doses are generally not conducted in diverse ethnic populations. Thus, it is urgently important to incorporate PGx information with genetic characteristics of racial and ethnic minority populations and utilize it to promote minority health. In this project a bioinformatics approach was developed to enhance the collection of PGx information related to ethnic minorities to pave the way toward understanding the population-wide utility of PGx information. To address this challenge, we first gathered PGx information from drug labels. Second, we extracted data on the allele frequency information of genetic variants in ethnic minority groups from public resources. Then, we collected published research articles on PGx biomarkers and related drugs for reference. Finally, the data were integrated and formatted to build a new PGx database containing information on known drugs and biomarkers for ethnic minority groups. This database provides scientific information needed to evaluate available PGx information to enhance drug dose selection and drug safety for ethnic minority populations.

A Web-Based Pharmacogenomics Search Tool for Precision Medicine in Perioperative Care

Background: Precision medicine represents an evolving approach to improve treatment efficacy by modifying it to individual patient's gene variation. Pharmacogenetics, an applicable branch of precision medicine, identifies patient's predisposing genotypes that alter the clinical outcome of the drug, hence preventing serious adverse drug reactions. Pharmacogenetics has been extensively applied to various fields of medicine, but in the field of anesthesiology and preoperative medicine, it has been unexploited. Although the US Food and Drug Administration (FDA) has a table of pharmacogenomics biomarkers and pharmacogenetics, this table only includes general side effects of the included drugs. Thus, the existing FDA table offers limited information on genetic variations that may increase drug side effects. Aims: The purpose of this paper is to provide a web-based pharmacogenomics search tool composed of a comprehensive list of medications that have pharmacogenetic relevance to perioperative medicine that might also have application in other fields of medicine. Method: For this investigation, the FDA table of pharmacogenomics biomarkers in drug labeling was utilized as an in-depth of drugs to construct our pharmacogenetics drug table. We performed a literature search for drug-gene interactions using the unique list of drugs in the FDA table. Publications containing the drug-gene interactions were identified and reviewed. Additional drugs and extracted gene-interactions in the identified publications were added to the constructed drug table. Result: Our tool provides a comprehensive pharmacogenetic drug table including 258 drugs with a total of 461 drug-gene interactions and their corresponding gene variations that might cause modifications in drug efficacy, pharmacokinetics, pharmacodynamics and adverse reactions. This tool is freely accessible online and can be applied as a web-based search instrument for drug-gene interactions in different fields of medicine, including perioperative medicine. Conclusion: In this research, we collected drug-gene interactions in a web-based searchable tool that could be used by physicians to expand their field knowledge in pharmacogenetics and facilitate their clinical decision making. This precision medicine tool could further serve in establishing a comprehensive perioperative pharmacogenomics database that also includes different fields of medicine that could influence the outcome of perioperative medicine.

Study of pharmacogenomic information in FDA-approved drug labeling to facilitate application of precision medicine

Pharmacogenomics (PGx), studying the relationship between drug response and genetic makeup of an individual, is accelerating advances in precision medicine. The FDA includes PGx information in the labeling of approved drugs to better inform on their safety and effectiveness. We herein present a summary of PGx information found in 261 prescription drug labeling documents by querying the publicly available FDALabel database. A total of 362 drug-biomarker pairs (DBPs) were identified. We profiled DBPs using frequency of the biomarkers and their therapeutic classes. Four categories of applications (indication, safety, dosing and information) were discussed according to information in labeling. This analysis facilitates better understanding, utilization and translation of PGx information in drug labeling among researchers, healthcare professionals and the public.

Pharmacogenetics and drug metabolism: historical perspective and appraisal

The events leading up to the discovery of genetically controlled polymorphic metabolism of xenobiotics and pharmaceutical chemicals are briefly summarised with the salient historical features being emphasised. Especial attention has been given to seminal works in the then emerging field.The evolving knowledge of such polymorphic metabolism and its role in the quest for personalised medicine and the individualisation of patient drug therapy are appraised. Opinion is offered as to whether or not the full potential has been exploited and if the practical application of this information may be regarded as a success or failure within the present clinical arena.

Cytochrome P450: genotype to phenotype

The cytochromes P450 comprise a family of enzymes that are responsible for around three-quarters of all drug metabolism reactions that occur in human populations. Many isoforms of cytochrome P450 exist but most reactions are undertaken by CYP2C9, CYP2C19, CYP2D6 and CYP3A4. This brief review focusses on the first three isozymes which exhibit polymorphism of phenotype.If there is a wide variation in drug metabolising capacity within the population, this may precipitate clinical consequences and influence the drug treatment of patients. Such problems range from a lack of efficacy to unanticipated toxicity. In order to minimise untoward events and "personalise" a patient's treatment, efforts have been made to discover an individual's drug metabolism status. This requires knowledge of the subject's phenotype at the time of clinical treatment. Since such testing is difficult, time-consuming and costly, the simpler approach of genotyping has been advocated.However, the correlation between genotype and phenotype is not good, with values of up to 50% misprediction being reported. Genotype-assisted forecasts cannot therefore be used with confidence to replace actual phenotype measurements. Obfuscating factors discussed include gene splicing, single nucleotide polymorphisms, epigenetics and microRNA, transcription regulation and multiple gene copies.

The cytochrome P450 isoenzyme and some new opportunities for the prediction of negative drug interaction in vivo

Cytochrome (CYP) 450 isoenzymes are the basic enzymes involved in Phase I biotransformation. The most important role in biotransformation belongs to CYP3A4, CYP2D6, CYP2C9, CYP2C19 and CYP1A2. Inhibition and induction of CYP isoenzymes caused by drugs are important and clinically relevant pharmacokinetic mechanisms of drug interaction. Investigation of the activity of CYP isoenzymes by using phenotyping methods (such as the determination of the concentration of specific substrates and metabolites in biological fluids) during drug administration provides the prediction of negative side effects caused by drug interaction. In clinical practice, the process of phenotyping of CYP isoenzymes and some endogenous substrates in the ratio of cortisol to 6β-hydroxycortisol in urine for the evaluation of CYP3A4 activity has been deemed to be a quite promising, safe and minimally invasive method for patients nowadays.

Integrating pharmacogenetic testing into primary care

Introduction

Pharmacogenetic (PGx) testing has greatly expanded due to enhanced understanding of the role of genes in drug response and advances in DNA-based testing technology development. As many primary care visits result in a prescription, the use of PGx testing may be particularly beneficial in this setting. However, integration of PGx testing may be limited as no uniform approach to delivery of tests has been established and providers are ill-prepared to integrate PGx testing into routine care.

Areas covered

In this paper, the readiness of primary care practitioners are reviewed as well as strategies to address these barriers based on published research and ongoing activities on education and implementation of PGx testing.

Expert Commentary

Widespread integration of PGx testing will warrant continued education and point-of-care decisional support. Primary care providers may also benefit from consultation services or team-based care with laboratory medicine specialists, pharmacists, and genetic counselors.

Precision Medicine at the University of Alabama at Birmingham: Laying the Foundational Processes Through Implementation of Genotype-Guided Antiplatelet Therapy

Precision medicine entails tailoring treatment based on patients' unique characteristics. As drug therapy constitutes the cornerstone of treatment for most chronic diseases, pharmacogenomics (PGx), the study of genetic variation influencing individual response to drugs, is an important component of precision medicine. Over the past decade investigations have identified genes and single-nucleotide polymorphisms (SNPs) and quantified their effect on drug response. Parallel development of point-of-care (POC) genotyping platforms has enabled the interrogation of the genes/SNPs within a timeline conducive to the provision of care. Despite these advances, the pace of integration of genotype-guided drug therapy (GGTx) into practice has faced significant challenges. These include difficulty in identifying SNPs with sufficiently robust evidence to guide clinical decision making, lack of clinician training on how to order and use genotype data, lack of clinical decision support (CDS) to guide treatment, and limited reimbursement. The University of Alabama at Birmingham's (UAB) efforts in precision medicine were initiated to address these challenges and improve the health of the racially diverse patients we treat.

Precision medicine: from pharmacogenomics to pharmacoproteomics

Disease progression and drug response may vary significantly from patient to patient. Fortunately, the rapid development of high-throughput ‘omics’ technologies has allowed for the identification of potential biomarkers that may aid in the understanding of the heterogeneities in disease development and treatment outcomes. However, mechanistic gaps remain when the genome or the proteome are investigated independently in response to drug treatment. In this article, we discuss the current status of pharmacogenomics in precision medicine and highlight the needs for concordant analysis at the proteome and metabolome levels via the more recently-evolved fields of pharmacoproteomics, toxicoproteomics, and pharmacometabolomics. Integrated ‘omics’ investigations will be critical in piecing together targetable mechanisms of action for both drug development and monitoring of therapy in order to fully apply precision medicine to the clinic.

Pharmacogenomics in the clinic

After decades of discovery, inherited variations have been identified in approximately 20 genes that affect about 80 medications and are actionable in the clinic. And some somatically acquired genetic variants direct the choice of 'targeted' anticancer drugs for individual patients. Current efforts that focus on the processes required to appropriately act on pharmacogenomic variability in the clinic are moving away from discovery and towards implementation of an evidenced-based strategy for improving the use of medications, thereby providing a cornerstone for precision medicine.

Overcoming regulatory challenges in the development of companion diagnostics for monitoring and safety

Concurrent development and co-approval of a companion diagnostic (CDx) with a corresponding drug is ideal, but often unfeasible. Because of limited exposure to a drug in clinical trials, crucial information on safety is sometimes revealed only after approval. Therefore, a CDx for monitoring/safety is often developed after approval of a corresponding drug. However, regulatory guidance is insufficient if contemporaneous development is not possible, thereby leaving plenty of opportunities for improvement with respect to pharmacovigilance and retrospective validation of the CDx. Furthermore, global harmonization of guidance on how to incorporate new scientific information from retrospective analyses of biomarkers should lead to the establishment of more evidence for the development of CDx for approved drugs.

The IGNITE network: a model for genomic medicine implementation and research

BACKGROUND

Patients, clinicians, researchers and payers are seeking to understand the value of using genomic information (as reflected by genotyping, sequencing, family history or other data) to inform clinical decision-making. However, challenges exist to widespread clinical implementation of genomic medicine, a prerequisite for developing evidence of its real-world utility.

METHODS

To address these challenges, the National Institutes of Health-funded IGNITE (Implementing GeNomics In pracTicE; www.ignite-genomics.org ) Network, comprised of six projects and a coordinating center, was established in 2013 to support the development, investigation and dissemination of genomic medicine practice models that seamlessly integrate genomic data into the electronic health record and that deploy tools for point of care decision making. IGNITE site projects are aligned in their purpose of testing these models, but individual projects vary in scope and design, including exploring genetic markers for disease risk prediction and prevention, developing tools for using family history data, incorporating pharmacogenomic data into clinical care, refining disease diagnosis using sequence-based mutation discovery, and creating novel educational approaches.

RESULTS

This paper describes the IGNITE Network and member projects, including network structure, collaborative initiatives, clinical decision support strategies, methods for return of genomic test results, and educational initiatives for patients and providers. Clinical and outcomes data from individual sites and network-wide projects are anticipated to begin being published over the next few years.

CONCLUSIONS

The IGNITE Network is an innovative series of projects and pilot demonstrations aiming to enhance translation of validated actionable genomic information into clinical settings and develop and use measures of outcome in response to genome-based clinical interventions using a pragmatic framework to provide early data and proofs of concept on the utility of these interventions. Through these efforts and collaboration with other stakeholders, IGNITE is poised to have a significant impact on the acceleration of genomic information into medical practice.

The 3-I framework: a framework for developing public policies regarding pharmacogenomics (PGx) testing in Canada

The 3-I framework of analyzing the ideas, interests, and institutions around a topic has been used by political scientists to guide public policy development. In Canada, there is a lack of policy governing pharmacogenomics (PGx) testing compared to other developed nations. The goal of this study was to use the 3-I framework, a policy development tool, and apply it to PGx testing to identify and analyze areas where current policy is limited and challenges exist in bringing PGx testing into wide-spread clinical practice in Canada. A scoping review of the literature was conducted to determine the extent and challenges of PGx policy implementation at federal and provincial levels. Based on the 3-I analysis, contentious ideas related to PGx are (i) genetic discrimination, (ii) informed consent, (iii) the lack of knowledge about PGx in health care, (iv) the value of PGx testing, (v) the roles of health care workers in the coordination of PGx services, and (vi) confidentiality and privacy. The 3-I framework is a useful tool for policy makers, and applying it to PGx policy development is a new approach in Canadian genomics. Policy makers at every organizational level can use this analysis to help develop targeted PGx policies.

Ethical and public policy challenges for pharmacogenomics

It is timely to consider the ethical and social questions raised by progress in pharmacogenomics, based on the current importance of pharmacogenomics for avoidance of predictable side effects of drugs, and for correct choice of medications in certain cancers. It has been proposed that the entire population be genotyped for drug-metabolizing enzyme polymorphisms, as a measure that would prevent many untoward and dangerous drug reactions. Pharmacologic treatment targeting based on genomics of disease can be expected to increase greatly in the coming years. Policy and ethical issues exist on consent for large-scale genomic pharmacogenomic data collection, public vs corporate ownership of genomic research results, testing efficacy and safety of drugs used for rare genomic indications, and accessibility of treatments based on costly research that is applicable to relatively few patients. In major psychiatric disorders and intellectual deficiency, rare and de novo deletion or duplication of chromosomal segments (copy number variation), in the aggregate, are common causes of increased risk. This implies that the policy problems of pharmacogenomics will be particularly important for the psychiatric disorders.

Guidance for pharmacogenomic biomarker testing in labels of FDA-approved drugs

PURPOSE

The aim of this study was to compare guidance for genetic testing in US Food and Drug Administration (FDA)-approved drug labels in oncology to those of drugs for other therapeutic areas.

METHODS

We reviewed labels of all the FDA-approved drugs with labels containing pharmacogenomic information. We assessed whether genetic testing was required or recommended before prescription and, if not, the reason for pharmacogenomic labeling.

RESULTS

We included 140 drugs corresponding to 158 drug-biomarker pairs. Overall, 46 (29%) of 158 pairs stated a requirement or recommendation for genetic biomarker testing in the label. This proportion was higher in oncology than in other areas (62 vs. 12%; P < 0.001). For the 112 drug-biomarker pairs (including 20 in oncology) without recommendation or requirement for genetic testing, the main reasons for pharmacogenomic labeling were change in pharmacologic end points (32%) and higher risk of toxicity (30%). For 11 (10%) pairs (including 1 in oncology), a genetic biomarker was mentioned only to inform that it was not relevant. In oncology, the main reasons for pharmacogenomic labeling were higher risk of toxicity (55%) and definition of the mechanism of action (25%).

CONCLUSION

Inclusion of biomarkers in drug labels does not always correspond to required or recommended genetic testing, especially outside oncology.Genet Med 17 9, 733-738.

Analytical strategies for discovery and replication of genetic effects in pharmacogenomic studies

In the past decade, the pharmaceutical industry and biomedical research sector have devoted considerable resources to pharmacogenomics (PGx) with the hope that understanding genetic variation in patients would deliver on the promise of personalized medicine. With the advent of new technologies and the improved collection of DNA samples, the roadblock to advancements in PGx discovery is no longer the lack of high-density genetic information captured on patient populations, but rather the development, adaptation, and tailoring of analytical strategies to effectively harness this wealth of information. The current analytical paradigm in PGx considers the single-nucleotide polymorphism (SNP) as the genomic feature of interest and performs single SNP association tests to discover PGx effects – ie, genetic effects impacting drug response. While it can be straightforward to process single SNP results and to consider how this information may be extended for use in downstream patient stratification, the rate of replication for single SNP associations has been low and the desired success of producing clinically and commercially viable biomarkers has not been realized. This may be due to the fact that single SNP association testing is suboptimal given the complexities of PGx discovery in the clinical trial setting, including: 1) relatively small sample sizes; 2) diverse clinical cohorts within and across trials due to genetic ancestry (potentially impacting the ability to replicate findings); and 3) the potential polygenic nature of a drug response. Subsequently, a shift in the current paradigm is proposed: to consider the gene as the genomic feature of interest in PGx discovery. The proof-of-concept study presented in this manuscript demonstrates that genomic region-based association testing has the potential to improve the power of detecting single SNP or complex PGx effects in the discovery stage (by leveraging the underlying genetic architecture and reducing the multiplicity burden), and it can also improve power in the replication stage.