Variant Interpretation in Current Pharmacogenetic Testing

The pharmacogenetics of the new-generation antipsychotics - A scoping review focused on patients with severe psychiatric disorders

Exploring the possible correlations between gene variations and the clinical effects of the new-generation antipsychotics is considered essential in the framework of personalized medicine. It is expected that pharmacogenetic data will be useful for increasing the treatment efficacy, tolerability, therapeutic adherence, functional recovery, and quality of life in patients with severe psychiatric disorders (SPD). This scoping review investigated the available evidence about the pharmacokinetics, pharmacodynamics, and pharmacogenetics of five new-generation antipsychotics, i.e., cariprazine, brexpiprazole, aripiprazole, lumateperone, and pimavanserin. Based on the analysis of 25 primary and secondary sources and the review of these agents' summaries of product characteristics, aripiprazole benefits from the most relevant data about the impact of gene variability on its pharmacokinetics and pharmacodynamics, with significant consequences on this antipsychotic's efficacy and tolerability. The determination of the CYP2D6 metabolizer status is important when administering aripiprazole, either as monotherapy or associated with other pharmacological agents. Allelic variability in genes encoding dopamine D2, D3, and serotonin, 5HT2A, 5HT2C receptors, COMT, BDNF, and dopamine transporter DAT1 was also associated with different adverse events or variations in the clinical efficacy of aripiprazole. Brexpiprazole also benefits from specific recommendations regarding the CYP2D6 metabolizer status and the risks of associating this antipsychotic with strong/moderate CYP2D6 or CYP3A4 inhibitors. US Food and Drug Administration (FDA) and European Medicines Agency (EMA) recommendations about cariprazine refer to possible pharmacokinetic interactions with strong CYP3A4 inhibitors or inducers. Pharmacogenetic data about cariprazine is sparse, and relevant information regarding gene-drug interactions for lumateperone and pimavanserin is yet lacking. In conclusion, more studies are needed to detect the influence of gene variations on the pharmacokinetics and pharmacodynamics of new-generation antipsychotics. This type of research could increase the ability of clinicians to predict favorable responses to specific antipsychotics and to improve the tolerability of the treatment regimen in patients with SPD.

A clinician’s guide for counseling patients on results of a multigene pharmacogenomic panel

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Purpose

This article explores approaches to pharmacogenomic counseling for patients who have undergone multigene panel testing by describing the collective experience of 5 institutions.

Summary

Multigene panel pharmacogenomic testing has the potential to unlock a myriad of information about a patient’s past, present, and future drug response. The multifaceted nature of drug response coupled with the complexity of genetic results necessitates some form of patient education through pharmacogenomic counseling. Published literature regarding disclosure of pharmacogenomic test results is limited. This article compares the counseling practices of pharmacists from 5 different institutions with pharmacogenomics clinics whose experience represents perspectives ranging from academia to community clinical environments. Overarching counseling themes discussed during result disclosure center around (1) pharmacogenomic results, (2) gene-drug interactions, (3) gene-drug-drug interactions, (4) drug changes (5) future, familial, or disease-risk implications, (6) updates in the interpretation and application of pharmacogenomic results, (7) gauging patient comprehension, and (8) sharing results and supplemental information.

Conclusion

Dedicating time to counseling patients on the results of a multigene pharmacogenomic panel is important given the lifelong applications of a test that is generally performed only once. The content and methods of disclosing test results shared by the experiences of pharmacists at 5 different institutions serve as guide to be further refined as research addresses effective communication strategies that enhance patient comprehension of pharmacogenomic results.

Keeping pace with CYP2D6 haplotype discovery: innovative methods to assign function

The discovery of haplotypes with unknown or uncertain function in the CYP2D6 pharmacogene is outpacing the capabilities of traditional in vitro and in vivo approaches to characterize their function. This challenge will undoubtedly grow as pharmacogenomic research becomes more inclusive of globally diverse populations. As accurate phenotypic assignment is paramount to the utility of pharmacogenomics, high-throughput technologies are needed for this complex pharmacogene. We describe the evolving landscape of innovative approaches to assign function to CYP2D6 haplotypes and possibilities for adopting these technologies into cohesive processes. Promising approaches include ADME-optimized prediction frameworks, machine learning algorithms, deep mutational scanning and phenoconversion predictions. Implementing these approaches will lead to improved personalization of treatment for patients.

Computational Tools to Assess the Functional Consequences of Rare and Noncoding Pharmacogenetic Variability

Interindividual differences in drug response are a common concern in both drug development and across layers of care. While genetics clearly influences drug response and toxicity of many drugs, a substantial fraction of the heritable pharmacological and toxicological variability remains unexplained by known genetic polymorphisms. In recent years, population-scale sequencing projects have unveiled tens of thousands of coding and noncoding pharmacogenetic variants with unclear functional effects that might explain at least part of this missing heritability. However, translating these personalized variant signatures into drug response predictions and actionable advice remains challenging and constitutes one of the most important frontiers of contemporary pharmacogenomics. Conventional prediction methods are primarily based on evolutionary conservation, which drastically reduces their predictive accuracy when applied to poorly conserved pharmacogenes. Here, we review the current state-of-the-art of computational variant effect predictors across variant classes and critically discuss their utility for pharmacogenomics. Besides missense variants, we discuss recent progress in the evaluation of synonymous, splice, and noncoding variations. Furthermore, we discuss emerging possibilities to assess haplotypes and structural variations. We advocate for the development of algorithms trained on pharmacogenomic instead of pathogenic data sets to improve the predictive accuracy in order to facilitate the utilization of next-generation sequencing data for personalized clinical decision support and precision pharmacogenomics.