The need to shift pharmacogenetic research from candidate gene to genome-wide association studies

Pharmacogenomic markers associated with drug-induced QT prolongation: a systematic review

AIM

To systematically assess clinical studies involving patients undergoing drug therapy, comparing different genotypes to assess the relationship with changes in QT intervals, with no limitations on study design, setting, population, dosing regimens, or duration.

METHODS

This systematic review followed PRISMA guidelines and a pre-registered protocol. Clinical human studies on PGx markers of diQTP were identified, assessed using standardized tools, and categorized by design. Gene associations were classified as pharmacokinetic or pharmacodynamic. Identified genes underwent pathway enrichment analyses. Drugs were classified by third-level Anatomical Therapeutic Chemical (ATC) codes. Descriptive statistics were computed by study category and drug classes.

RESULTS

Of 4,493 reports, 84 studies were included, identifying 213 unique variants across 42 drug classes, of which 10% were replicated. KCNE1-Asp85Asn was the most consistent variant. Most findings (82%) were derived from candidate gene studies, suggesting bias toward known markers. The diQTP-associated genes were mainly linked to "cardiac conduction" and "muscle contraction" pathways (false discovery rate = 4.71 × 10-14). We also found an overlap between diQTP-associated genes and congenital long QT syndrome genes.

CONCLUSION

Key genes, drugs, and pathways were identified, but few consistent PGx markers emerged. Extensive, unbiased studies with diverse populations are crucial to advancing the field.

REGISTRATION

A protocol was pre-registered at PROSPERO under registration number CRD42022296097.

DATA DEPOSITION

Data sets generated by this review are available at figshare: DOI: 10.6084/m9.figshare.27959616.

Characterizing the genetic architecture of drug response using gene-context interaction methods

Identifying factors that affect treatment response is a central objective of clinical research, yet the role of common genetic variation remains largely unknown. Here, we develop a framework to study the genetic architecture of response to commonly prescribed drugs in large biobanks. We quantify treatment response heritability for statins, metformin, warfarin, and methotrexate in the UK Biobank. We find that genetic variation modifies the primary effect of statins on LDL cholesterol (9% heritable) as well as their side effects on hemoglobin A1c and blood glucose (10% and 11% heritable, respectively). We identify dozens of genes that modify drug response, which we replicate in a retrospective pharmacogenomic study. Finally, we find that polygenic score (PGS) accuracy varies up to 2-fold depending on treatment status, showing that standard PGSs are likely to underperform in clinical contexts.

Receptor Pharmacogenomics: Deciphering Genetic Influence on Drug Response

The paradigm "one drug fits all" or "one dose fits all" will soon be challenged by pharmacogenetics research and application. Drug response-efficacy or safety-depends on interindividual variability. The current clinical practice does not include genetic screening as a routine procedure and does not account for genetic variation. Patients with the same illness receive the same treatment, yielding different responses. Integrating pharmacogenomics in therapy would provide critical information about how a patient will respond to a certain drug. Worldwide, great efforts are being made to achieve a personalized therapy-based approach. Nevertheless, a global harmonized guideline is still needed. Plasma membrane proteins, like receptor tyrosine kinase (RTK) and G protein-coupled receptors (GPCRs), are ubiquitously expressed, being involved in a diverse array of physiopathological processes. Over 30% of drugs approved by the FDA target GPCRs, reflecting the importance of assessing the genetic variability among individuals who are treated with these drugs. Pharmacogenomics of transmembrane protein receptors is a dynamic field with profound implications for precision medicine. Understanding genetic variations in these receptors provides a framework for optimizing drug therapies, minimizing adverse reactions, and advancing the paradigm of personalized healthcare.

Cardiovascular Pharmacogenetics: From Discovery of Genetic Association to Clinical Adoption of Derived Test

Recent breakthroughs in human genetics and in information technologies have markedly expanded our understanding at the molecular level of the response to drugs, i.e., pharmacogenetics (PGx), across therapy areas. This review is restricted to PGx for cardiovascular (CV) drugs. First, we examined the PGx information in the labels approved by regulatory agencies in Europe, Japan, and North America and related recommendations from expert panels. Out of 221 marketed CV drugs, 36 had PGx information in their labels approved by one or more agencies. The level of annotations and recommendations varied markedly between agencies and expert panels. Clopidogrel is the only CV drug with consistent PGx recommendation (i.e., "actionable"). This situation prompted us to dissect the steps from discovery of a PGx association to clinical translation. We found 101 genome-wide association studies that investigated the response to CV drugs or drug classes. These studies reported significant associations for 48 PGx traits mapping to 306 genes. Six of these 306 genes are mentioned in the corresponding PGx labels or recommendations for CV drugs. Genomic analyses also highlighted the wide between-population differences in risk allele frequencies and the individual load of actionable PGx variants. Given the high attrition rate and the long road to clinical translation, additional work is warranted to identify and validate PGx variants for more CV drugs across diverse populations and to demonstrate the utility of PGx testing. To that end, pre-emptive PGx combining genomic profiling with electronic medical records opens unprecedented opportunities to improve healthcare, for CV diseases and beyond. SIGNIFICANCE STATEMENT: Despite spectacular breakthroughs in human molecular genetics and information technologies, consistent evidence supporting PGx testing in the cardiovascular area is limited to a few drugs. Additional work is warranted to discover and validate new PGx markers and demonstrate their utility. Pre-emptive PGx combining genomic profiling with electronic medical records opens unprecedented opportunities to improve healthcare, for CV diseases and beyond.

Prediction models of persistent taxane-induced peripheral neuropathy among breast cancer survivors using whole-exome sequencing

Persistent taxane-induced peripheral neuropathy (TIPN) is highly prevalent among early-stage breast cancer survivors (ESBCS) and has detrimental effect on quality of life. We leveraged logistic regression models to develop and validate polygenic prediction models to estimate the risk of persistent PN symptoms in a training cohort and validation cohort taking clinical risk factors into account. Based on 337 whole-exome sequenced ESBCS two of five prediction models for individual PN symptoms obtained AUC results above 60% when validated. Using the model for numbness in feet (35 SNVs) in the test cohort, 73% survivors were correctly predicted. For tingling in feet (55 SNVs) 70% were correctly predicted. Both models included SNVs from the ADAMTS20, APT6V0A2, CCDC88C, CYP2C8, EPHA5, NR1H3, PSKH2/APTV0D2, and SCN10A genes. For cramps in feet, difficulty climbing stairs and difficulty opening a jar the validation was unsuccessful. Polygenic prediction models including clinical risk factors can estimate the risk of persistent taxane-induced numbness in feet and tingling in feet in ESBCS.

Food Administration and Not Genetic Variants Causes Pharmacokinetic Variability of Tadalafil and Finasteride

Tadalafil and finasteride are used in combination for the management of benign prostatic hyperplasia (BPH). Genetic variations in genes involved in the metabolism and transport of tadalafil or finasteride (i.e., pharmacogenes) could affect their pharmacokinetic processes altering their drug exposure, efficacy, and toxicity. The main objective of this study was to investigate the effects of variants in pharmacogenes on the pharmacokinetics of tadalafil and finasteride. An exploratory candidate gene study involving 120 variants in 33 genes was performed with 66 male healthy volunteers from two bioequivalence clinical trials after administration of tadalafil/finasteride 5 mg/5 mg under fed or fasting conditions. Afterwards, a confirmatory study was conducted with 189 male and female volunteers receiving tadalafil 20 mg formulations in seven additional bioequivalence clinical trials. Regarding tadalafil, fed volunteers showed higher area in the time-concentration curve (AUC∞), maximum plasma concentration (Cmax), and time to reach Cmax (tmax) compared to fasting volunteers; male volunteers also showed higher AUC∞ and Cmax compared to female volunteers. Furthermore, fed volunteers presented higher finasteride AUC∞, Cmax and tmax compared to fasting individuals. Variants in ABCC3, CYP1A2, CES1, NUDT15, SLC22A1/A2 and UGT2B10 were nominally associated with pharmacokinetic variation in tadalafil and/or finasteride but did not remain significant after correction for multiple comparisons. Genetic variation did not demonstrate to clinically impact on the pharmacokinetics of finasteride and tadalafil; however, additional studies with larger sample sizes are needed to assess the effect of rare variants, such as CYP3A4*20 or *22, on tadalafil and finasteride pharmacokinetics.

Pharmacogenetics of Antiplatelet Therapy

Antiplatelet therapy is used in the treatment of patients with acute coronary syndromes, stroke, and those undergoing percutaneous coronary intervention. Clopidogrel is the most widely used antiplatelet P2Y12 inhibitor in clinical practice. Genetic variation in CYP2C19 may influence its enzymatic activity, resulting in individuals who are carriers of loss-of-function CYP2C19 alleles and thus have reduced active clopidogrel metabolites, high on-treatment platelet reactivity, and increased ischemic risk. Prospective studies have examined the utility of CYP2C19 genetic testing to guide antiplatelet therapy, and more recently published meta-analyses suggest that pharmacogenetics represents a key treatment strategy to individualize antiplatelet therapy. Rapid genetic tests, including bedside genotyping platforms that are validated and have high reproducibility, are available to guide selection of P2Y12 inhibitors in clinical practice. The aim of this review is to provide an overview of the background and rationale for the role of a guided antiplatelet approach to enhance patient care.

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.

A Systematic Review of Polygenic Models for Predicting Drug Outcomes

Polygenic models have emerged as promising prediction tools for the prediction of complex traits. Currently, the majority of polygenic models are developed in the context of predicting disease risk, but polygenic models may also prove useful in predicting drug outcomes. This study sought to understand how polygenic models incorporating pharmacogenetic variants are being used in the prediction of drug outcomes. A systematic review was conducted with the aim of gaining insights into the methods used to construct polygenic models, as well as their performance in drug outcome prediction. The search uncovered 89 papers that incorporated pharmacogenetic variants in the development of polygenic models. It was found that the most common polygenic models were constructed for drug dosing predictions in anticoagulant therapies (n = 27). While nearly all studies found a significant association with their polygenic model and the investigated drug outcome (93.3%), less than half (47.2%) compared the performance of the polygenic model against clinical predictors, and even fewer (40.4%) sought to validate model predictions in an independent cohort. Additionally, the heterogeneity of reported performance measures makes the comparison of models across studies challenging. These findings highlight key considerations for future work in developing polygenic models in pharmacogenomic research.

The genetics of drug-induced QT prolongation: evaluating the evidence for pharmacodynamic variants

Drug-induced long QT syndrome (diLQTS) is an adverse effect of many commonly prescribed drugs, and it can increase the risk for lethal ventricular arrhythmias. Genetic variants in pharmacodynamic genes have been associated with diLQTS, but the strength of the evidence for each of those variants has not yet been evaluated. Therefore, the purpose of this review was to evaluate the strength of the evidence for pharmacodynamic genetic variants associated with diLQTS using a novel, semiquantitative scoring system modified from the approach used for congenital LQTS. KCNE1-D85N and KCNE2-T8A had definitive and strong evidence for diLQTS, respectively. The high level of evidence for these variants supports current consideration as risk factors for patients that will be prescribed a QT-prolonging drug.