Learning from product labels and label changes: how to build pharmacogenomics into drug-development programs

Health regulatory communications of well-established safety-related pharmacogenomics associations in six developed countries: an evaluation of alignment

Recommendations on genetic testing are typically conveyed by drug regulatory authorities through drug labels, which are legal requirements for market authorization of drugs. We conducted a cross-sectional study of drug labels focusing on three crucial aspects of regulatory pharmacogenomics communications: (i) intent; (ii) interpretation in the local context; and (iii) implications of the genetic information. Labels of drugs associated with well-established safety-related genetic markers for adverse drug reactions across six developed countries of United States, Canada, United Kingdom, Australia, New Zealand and Singapore were reviewed. We found differing medical advice for genotype-positive HLA-B*15:02, HLA-A*31:01, UGT1A1*28 and CYP2D6 ultra-rapid metabolisers in breastfeeding women. This raises questions on implications to clinical practice between these countries. Varying ways of presenting at-risk population and allele frequencies also raises question in incorporating such information in drug labels. An international guidance addressing these crucial aspects of regulatory pharmacogenomic communications in drug labels is long overdue.

Improving the Efficiency and Quality of the Value Assessment Process for Companion Diagnostic Tests: The Companion test Assessment Tool (CAT)

BACKGROUND

Companion diagnostic tests (CDTs) have emerged as a vital technology in the effective use of an increasing number of targeted drug therapies. Although CDTs can offer a multitude of potential benefits, assessing their value within a health technology appraisal process can be challenging because of a complex array of factors that influence clinical and economic outcomes.

OBJECTIVE

To develop a user-friendly tool to assist managed care and other health care decision makers in screening companion tests and determining whether an intensive technology review is necessary and, if so, where the review should be focused to improve efficiency.

METHODS

First, we conducted a systematic literature review of CDT cost-effectiveness studies to identify value drivers. Second, we conducted key informant interviews with a diverse group of stakeholders to elicit feedback and solicit any additional value drivers and identify desirable attributes for an evidence review tool. A draft tool was developed based on this information that captured value drivers, usability features, and had a particular focus on practical use by nonexperts. Finally, the tool was pilot tested with test developers and managed care evidence evaluators to assess face-validity and usability. The tool was also evaluated using several diverse examples of existing companion diagnostics and refined accordingly.

RESULTS

We identified 65 cost-effectiveness studies of companion diagnostic technologies. The following factors were most commonly identified as value drivers from our literature review: clinical validity of testing; efficacy, safety, and cost of baseline and alternative treatments; cost and mortality of health states; and biomarker prevalence and testing cost. Stakeholders identified the following additional factors that they believed influenced the overall value of a companion test: regulatory status, actionability, utility, and market penetration. These factors were used to maximize the efficiency of the evidence review process. Stakeholders also stated that a tool should be easy to use and time efficient. Cognitive interviews with stakeholders led to minor changes in the draft tool to improve usability and relevance. The final tool consisted of 4 sections: (1) eligibility for review (2 questions), (2) prioritization of review (3 questions), (3) clinical review (3 questions), and (4) economic review (5 questions).

CONCLUSIONS

Although the evaluation of CDTs can be challenging because of limited evidence and the added complexity of incorporating a diagnostic test into drug treatment decisions, using a pragmatic tool to identify tests that do not need extensive evaluation may improve the efficiency and effectiveness of CDT value assessments.

An inter-professional approach to personalized medicine education: one institution's experience

Personalized medicine offers the promise of better diagnoses, targeted therapies and individualized treatment plans. Pharmacogenomics is an integral component of personalized medicine; it aids in the prediction of an individual's response to medications. Despite growing public acceptance and emerging clinical evidence, this rapidly expanding field of medicine is slow to be adopted and utilized by healthcare providers, although many believe that they should be knowledgeable and able to apply pharmacogenomics in clinical practice. Institutional infrastructure must be built to support pharmacogenomic implementation. Multidisciplinary education for healthcare providers is a critical component for pharmacogenomics to achieve its full potential to optimize patient care. We describe our recent experience at the Mayo Clinic implementing pharmacogenomics education in a large, academic healthcare system facilitated by the Mayo Clinic Center for Individualized Medicine.

Polymorphism in CYP2D6 and CYP2C19, members of the cytochrome P450 mixed-function oxidase system, in the metabolism of psychotropic drugs

Numerous studies in the field of psychopharmacological treatment have investigated the possible contribution of genetic variability between individuals to differences in drug efficacy and safety, motivated by the wide individual variation in treatment response. Genomewide analyses have been conducted in several large-scale studies on antidepressant drug response. However, no consistent findings have emerged from these studies. In a recent meta-analysis of genomewide data from the three studies capturing common variation for association with symptomatic improvement and remission revealed the absence of any strong genetic association and failed to replicate results of individual studies in the pooled data. However, there are good reasons to consider the possible importance of pharmacogenetic variants separately. These variants explain a large portion of the manifold variability in individual drug metabolism. More than 20 psychotropic drugs have now been relabelled by the FDA adding information on polymorphic drug metabolism and therapeutic recommendations. Furthermore, dose recommendations for polymorphisms in drug metabolizing enzymes, first and foremost CYP2D6 and CYP2C19, have been issued with the advice to reduce the dosage in poor metabolizers to 50% or less (in eight cases), or to choose an alternative treatment. Beside the well-described role in hepatic drug metabolism, these enzymes are also expressed in the brain and play a role in biotransformation of endogenous substrates. These polymorphisms may therefore modulate brain metabolism and affect the function of the neural substrates of cognition and emotion.

Pharmacogenomics in early-phase clinical development

Pharmacogenomics (PGx) offers the promise of utilizing genetic fingerprints to predict individual responses to drugs in terms of safety, efficacy and pharmacokinetics. Early-phase clinical trial PGx applications can identify human genome variations that are meaningful to study design, selection of participants, allocation of resources and clinical research ethics. Results can inform later-phase study design and pipeline developmental decisions. Nevertheless, our review of the clinicaltrials.gov database demonstrates that PGx is rarely used by drug developers. Of the total 323 trials that included PGx as an outcome, 80% have been conducted by academic institutions after initial regulatory approval. Barriers for the application of PGx are discussed. We propose a framework for the role of PGx in early-phase drug development and recommend PGx be universally considered in study design, result interpretation and hypothesis generation for later-phase studies, but PGx results from underpowered studies should not be used by themselves to terminate drug-development programs.

Implications of pharmacogenetics for the therapeutic use of antiepileptic drugs

INTRODUCTION

Epilepsy is a chronic neurological disease manifesting as recurrent seizures. Despite the availability of numerous antiepileptic drugs (AEDs), one-third of the patients are not responsive to treatment. Such inter-individual variability in the response to AEDs may be partly explained by genetic differences. This review summarizes the pharmacogenetics (PGx) of AEDs. In addition, a model-based approach is presented that enables the integration of PGx data with other relevant sources of variability, such as demographic characteristics and co-medications.

AREAS COVERED

A comprehensive overview is provided of the data available in the literature on the evidence for correlations between genetic mutations and pharmacokinetic (PK) and/or pharmacodynamics (PD) of AEDs. This information is then used in an integrated manner in the second part, where PGx differences are parameterized as covariates in PK and PKPD models.

EXPERT OPINION

Polymorphisms are profuse in the PK and PD of AEDs. However, understanding of their clinical implication remains limited due to the lack of methodologies that discriminate the contribution of other sources of variability in CNS exposure to drugs. A model-based approach, in which other intrinsic (e.g., demographic covariates) and extrinsic (e.g., drug-drug interactions) factors are evaluated concurrently is needed to ensure optimization and individualization of treatment in epileptic patients.

Scientific challenges and implementation barriers to translation of pharmacogenomics in clinical practice

The mapping of the human genome and subsequent advancements in genetic technology had provided clinicians and scientists an understanding of the genetic basis of altered drug pharmacokinetics and pharmacodynamics, as well as some examples of applying genomic data in clinical practice. This has raised the public expectation that predicting patients' responses to drug therapy is now possible in every therapeutic area, and personalized drug therapy would come sooner than later. However, debate continues among most stakeholders involved in drug development and clinical decision-making on whether pharmacogenomic biomarkers should be used in patient assessment, as well as when and in whom to use the biomarker-based diagnostic tests. Currently, most would agree that achieving the goal of personalized therapy remains years, if not decades, away. Realistic application of genomic findings and technologies in clinical practice and drug development require addressing multiple logistics and challenges that go beyond discovery of gene variants and/or completion of prospective controlled clinical trials. The goal of personalized medicine can only be achieved when all stakeholders in the field work together, with willingness to accept occasional paradigm change in their current approach.

Chapter 7: Pharmacogenomics

There is great variation in drug-response phenotypes, and a “one size fits all” paradigm for drug delivery is flawed. Pharmacogenomics is the study of how human genetic information impacts drug response, and it aims to improve efficacy and reduced side effects. In this article, we provide an overview of pharmacogenetics, including pharmacokinetics (PK), pharmacodynamics (PD), gene and pathway interactions, and off-target effects. We describe methods for discovering genetic factors in drug response, including genome-wide association studies (GWAS), expression analysis, and other methods such as chemoinformatics and natural language processing (NLP). We cover the practical applications of pharmacogenomics both in the pharmaceutical industry and in a clinical setting. In drug discovery, pharmacogenomics can be used to aid lead identification, anticipate adverse events, and assist in drug repurposing efforts. Moreover, pharmacogenomic discoveries show promise as important elements of physician decision support. Finally, we consider the ethical, regulatory, and reimbursement challenges that remain for the clinical implementation of pharmacogenomics.

Generating and weighing evidence in drug development and regulatory decision making: 5th US FDA-DIA workshop on pharmacogenomics

The 5th US FDA-Drug Industry Association (DIA) workshop in a series on pharmacogenomics entitled: 'Generating and Weighing Evidence in Drug Development and Regulatory Decision Making', contained four major topics (tracks): 'Learning from Labels and Label Changes: How to Build Pharmacogenomics into Drug Development Programs'; 'Enabling Pharmacogenomic Clinical Trials Through Sampling'; 'Designing Pharmacogenomics Studies to be Fit for Purpose'; and 'Co-Development of Drugs and Diagnostics'. The meeting was attended by approximately 200 professionals, primarily involved in drug development and healthcare delivery. Several critical elements drove the success of the meeting: it was recognized that the enriched conversation at this workshop between regulators and drug developers was driven with less inhibition than before and with a greater scientific focus on the issues. Multiple examples in the field and broader collective experience helped more in-depth thinking of the pros and cons of implementing pharmacogenetic/genetic approaches during drug development, in the current environment. It was also noted that this field is still developing and nascent as illustrated by the paucity of actual diagnostic-drug co-development examples. Furthermore, the complexities of conducting pharmacogenetic research in global drug-development programs was acknowledged as was the need for rigorous research designs and methodologies despite these challenges.