Designing pharmacogenetic projects in industry: practical design perspectives from the Industry Pharmacogenomics Working Group

Eight pharmacokinetic genetic variants are not associated with the risk of bleeding from direct oral anticoagulants in non-valvular atrial fibrillation patients

Background: Atrial fibrillation (AF) is the leading cause of ischemic stroke and treatment has focused on reducing this risk through anticoagulation. Direct Oral Anticoagulants (DOACs) are the first-line guideline-recommended therapy since they are as effective and overall safer than warfarin in preventing AF-related stroke. Although patients bleed less from DOACs compared to warfarin, bleeding remains the primary safety concern with this therapy. Hypothesis: Genetic variants known to modify the function of metabolic enzymes or transporters involved in the pharmacokinetics (PK) of DOACs could increase the risk of bleeding. Aim: To assess the association of eight, functional PK-related single nucleotide variants (SNVs) in five genes (ABCB1, ABCG2, CYP2J2, CYP3A4, CYP3A5) with the risk of bleeding from DOACs in non-valvular AF patients. Methods: A retrospective cohort study was carried out with 2,364 self-identified white non-valvular AF patients treated with either rivaroxaban or apixaban. Genotyping was performed with Illumina Infinium CoreExome v12.1 bead arrays by the Michigan Genomics Initiative biobank. The primary endpoint was a composite of major and clinically relevant non-major bleeding. Cox proportional hazards regression with time-varying analysis assessed the association of the eight PK-related SNVs with the risk of bleeding from DOACs in unadjusted and covariate-adjusted models. The pre-specified primary analysis was the covariate-adjusted, additive genetic models. Six tests were performed in the primary analysis as three SNVs are in the same haplotype, and thus p-values below the Bonferroni-corrected level of 8.33e-3 were considered statistically significant. Results: In the primary analysis, none of the SNVs met the Bonferroni-corrected level of statistical significance (all p > 0.1). In exploratory analyses with other genetic models, the ABCB1 (rs4148732) GG genotype tended to be associated with the risk of bleeding from rivaroxaban [HR: 1.391 (95%CI: 1.019-1.900); p = 0.038] but not from apixaban (p = 0.487). Conclusion: Eight functional PK-related genetic variants were not significantly associated with bleeding from either rivaroxaban or apixaban in more than 2,000 AF self-identified white outpatients.

Dosage optimization of clopidogrel via a precision medicine approach: the way forward

Clopidogrel is a prodrug chiefly metabolized by the hepatic isoenzyme CYP2C19 to its active metabolite that inhibits the platelet aggregation. It has been proven in many populations that the genetic polymorphism of CYP2C19 has influence on the pharmacokinetic and or pharmacodynamics of this drug and resulting in high inter-individual variability in the treatment outcomes. As CYP2C19 genetic polymorphism is highly prevalent among the Asian population, the influence of the same on the pharmacokinetics and; thereby, the pharmacodynamics of clopidogrel needs more attention. Using the pharmacogenetic information for drug therapy could help overcome these issues and to optimize the dosage regimen of clopidogrel, this review advocates the precision medicine approach for reducing the clopidogrel resistance and adverse cardiovascular events.

A critical appraisal of pharmacogenetic inference

In essence, pharmacogenetic research is aimed at discovering variants of importance to gene-treatment interaction. However, epidemiological studies are rarely set up with this goal in mind. It is therefore of great importance that researchers clearly communicate which assumptions they have had to make, and which inherent limitations apply to the interpretation of their results. This review discusses considerations of, and the underlying assumptions for, utilizing different response phenotypes and study designs popular in pharmacogenetic research to infer gene-treatment interaction effects, with a special focus on those dealing with of clinical effects of drug treatment.

Combined Analysis of Phase I and Phase II Data to Enhance the Power of Pharmacogenetic Tests

We show through a simulation study how the joint analysis of data from phase I and phase II studies enhances the power of pharmacogenetic tests in pharmacokinetic (PK) studies. PK profiles were simulated under different designs along with 176 genetic markers. The null scenarios assumed no genetic effect, while under the alternative scenarios, drug clearance was associated with six genetic markers randomly sampled in each simulated dataset. We compared penalized regression Lasso and stepwise procedures to detect the associations between empirical Bayes estimates of clearance, estimated by nonlinear mixed effects models, and genetic variants. Combining data from phase I and phase II studies, even if sparse, increases the power to identify the associations between genetics and PK due to the larger sample size. Design optimization brings a further improvement, and we highlight a direct relationship between η‐shrinkage and loss of genetic signal.

Promises and challenges of pharmacogenetics: an overview of study design, methodological and statistical issues

Pharmacogenetics is the study of inherited variation in drug response. The goal of pharmacogenetics is to develop novel ways of maximizing drug efficacy and minimizing toxicity for individual patients. Personalized medicine has the potential to allow for a patient's genetic information to predict optimal dosage for a drug with a narrow therapeutic index, to select the most appropriate pharmacological agent for a given patient and to develop cost-effective treatments. Although there is supporting evidence in favour of pharmacogenetics, its adoption in clinical practice has been slow because of sometimes conflicting findings among studies. This failure to replicate findings may result from a lack of high-quality pharmacogenetic studies, as well as unresolved methodological and statistical issues. The objective of this review is to discuss the benefits of incorporating pharmacogenetics into clinical practice. We will also address outstanding methodological and statistical issues that may lead to heterogeneity among reported pharmacogenetic studies and how they may be addressed.

Genetic variants in CYP (-1A2, -2C9, -2C19, -3A4 and -3A5), VKORC1 and ABCB1 genes in a black South African population: a window into diversity

AIM

The frequencies of variants of pharmacogenetic importance differ across populations. African populations exhibit the greatest genetic heterogeneity, cautioning against extrapolating results among African groups. The aim of this study was to genotype pharmacogenetically relevant variants in black South Africans, to expand the limited data set available for indigenous African populations.

SUBJECTS & METHODS

A total of 14 SNPs associated with seven genes known to influence drug metabolism or transport (CYP1A2, CYP2C19, CYP2C9, CYP3A4, CYP3A5, VKORC1 and ABCB1) were investigated in a South African black (SAB) population (n = 993) and allele frequencies were compared with populations of African, Asian and European origin.

RESULTS

The majority of SNPs in the SAB demonstrated significant allele frequency differences when compared with both Europeans and Asians. There was greater similarity between the SAB and the Luhya (Kenya) and the Yoruba (Nigeria), than with Maasai (Kenya) individuals. The CYP2C9 SNP (rs1799853) was not polymorphic in the SAB and two VKORC1 SNPs (rs17708472 and rs9934438) had low variant allele frequencies, limiting their relevance to warfarin dose in this population. Population differences are emphasized by the significant differences in ABCB1 and the CYP3A gene family allele frequencies, with implications for drug metabolism and transport.

CONCLUSION

This study highlights the importance of investigating and documenting genetic variation at loci of pharmacogenetic relevance among different populations since this information could be used to inform drug efficacy, safety and recommended dosage.

Pharmacogenetics in randomized controlled trials: considerations for trial design

Pharmacogenetic analyses of clinical trials aim to either detect whether a subgroup of patients identified by genetic characteristics responds differently to the treatment or to verify whether a proposed genotype-guided treatment is beneficial over standard care. This article describes three different trial designs, differing in the timing of randomization and genotyping. Each design has its own advantages, and the objectives and conditions under which each one is most suited are discussed.

Pharmacogenetics: practices and opportunities for study design and data analysis

Pharmacogenetics (PGx) is increasingly used as a way to target treatment to patients who are most likely to benefit. To date, PGx has shown clinical significance across a few applications but widespread use has been limited by the need for further technical, methodological and practical advances and for educating clinical researchers on the value of PGx. Here, I describe the current scope of PGx research, including recent contributions to prospective study design. A case study is included to demonstrate the limitations of current practice and to describe some practical steps for improving the chances of identifying genetic effects. Additionally, I describe some opportunities for the integration and application of disparate data sources in exploratory PGx research.

Fulfilling the promise of personalized medicine? Systematic review and field synopsis of pharmacogenetic studies

BACKGROUND

Studies of the genetic basis of drug response could help clarify mechanisms of drug action/metabolism, and facilitate development of genotype-based predictive tests of efficacy or toxicity (pharmacogenetics).

OBJECTIVES

We conducted a systematic review and field synopsis of pharmacogenetic studies to quantify the scope and quality of available evidence in this field in order to inform future research.

DATA SOURCES

Original research articles were identified in Medline, reference lists from 24 meta-analyses/systematic reviews/review articles and U.S. Food and Drug Administration website of approved pharmacogenetic tests. STUDY ELIGIBILITY CRITERIA, PARTICIPANTS, AND INTERVENTION CRITERIA: We included any study in which either intended or adverse response to drug therapy was examined in relation to genetic variation in the germline or cancer cells in humans.

STUDY APPRAISAL AND SYNTHESIS METHODS

Study characteristics and data reported in abstracts were recorded. We further analysed full text from a random 10% subset of articles spanning the different subclasses of study.

RESULTS

From 102,264 Medline hits and 1,641 articles from other sources, we identified 1,668 primary research articles (1987 to 2007, inclusive). A high proportion of remaining articles were reviews/commentaries (ratio of reviews to primary research approximately 25 ratio 1). The majority of studies (81.8%) were set in Europe and North America focussing on cancer, cardiovascular disease and neurology/psychiatry. There was predominantly a candidate gene approach using common alleles, which despite small sample sizes (median 93 [IQR 40-222]) with no trend to an increase over time, generated a high proportion (74.5%) of nominally significant (p<0.05) reported associations suggesting the possibility of significance-chasing bias. Despite 136 examples of gene/drug interventions being the subject of >or=4 studies, only 31 meta-analyses were identified. The majority (69.4%) of end-points were continuous and likely surrogate rather than hard (binary) clinical end-points.

CONCLUSIONS AND IMPLICATIONS OF KEY FINDINGS

The high expectation but limited translation of pharmacogenetic research thus far may be explained by the preponderance of reviews over primary research, small sample sizes, a mainly candidate gene approach, surrogate markers, an excess of nominally positive to truly positive associations and paucity of meta-analyses. Recommendations based on these findings should inform future study design to help realise the goal of personalised medicines. SYSTEMATIC REVIEW REGISTRATION NUMBER: Not Registered.