Pharmacogenomics in the era of next generation sequencing – from byte to bedside

Abstract

Pharmacogenetic research has resulted in the identification of a multitude of genetic variants that impact drug response or toxicity. These polymorphisms are mostly common and have been included as actionable information in the labels of numerous drugs. In addition to common variants, recent advances in Next Generation Sequencing (NGS) technologies have resulted in the identification of a plethora of rare and population-specific pharmacogenetic variations with unclear functional consequences that are not accessible by conventional forward genetics strategies. In this review, we discuss how comprehensive sequencing information can be translated into personalized pharmacogenomic advice in the age of NGS. Specifically, we provide an update of the functional impacts of rare pharmacogenetic variability and how this information can be leveraged to improve pharmacogenetic guidance. Furthermore, we critically discuss the current status of implementation of pharmacogenetic testing across drug development and layers of care. We identify major gaps and provide perspectives on how these can be minimized to optimize the utilization of NGS data for personalized clinical decision-support.

Characterization of ADME Gene Variation in Colombian Population by Exome Sequencing

“…Despite the advantages of incorporating WES in pharmacogenomics, identification of variants in promoter and deep intronic regions is limited and should be considered. For instance, polymorphisms such as CYP2C19*17 , VKORC1 -1639 G > A o UGT1A1 (TA)n, related to the response to clopidogrel, warfarin and glucuronidation of many drugs, are not identified by WES ( Russell et al, 2021 ). These actionable variants are responsible for adverse reactions to drugs in standard dosages and, therefore, their genotyping is recommended by The Clinical Pharmacogenetics Implementation Consortium (CPIC) ( Johnson et al, 2011 ; Scott et al, 2011 ).…”

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

“…As a consequence, a considerable fraction of genetic interindividual variability remains unexplained, which complicates the clinical implementation of pharmacogenetic data. 95 Current trends go toward the development of refined algorithms trained for specific variant classes, genes, or applications, which promises to improve predictive accuracy. Importantly though, the repertoire of methods that have been developed for or, at least, have been tested on poorly conserved pharmacogenes remains limited, and, while methodological improvements for pathogenic assessment algorithms are actively discussed in the literature, 96,97 these developments are only starting to catch on in pharmacogenomics.…”

“…It is becoming increasingly clear that the most commonly used methods that are developed for the genome-wide detection of pathogenic variants perform relatively poorly when applied on specific data sets, such as pharmacogenetic variations (Figure 3). As a consequence, a considerable fraction of genetic inter-individual variability remains unexplained, which complicates the clinical implementation of pharmacogenetic data (94). Current trends go towards the development of refined algorithms trained for specific variant classes, genes or applications, which promises to improve predictive accuracy.…”

Unraveling Drug Response from Pharmacogenomic Data to Advance Systems Pharmacology Decisions in Tumor Therapeutics

“…One of the reasons is that the clinical benefit of anti-cancer agents is limited by the presence of genomic alterations at functionally key loci that block the cascading molecular events triggered by the administered drug and cause adverse drug reactions or aberrant responses. Next-generation sequencing enabled the development of predictive models that incorporate genomic and gene expression data in order to assess the therapeutic potential of anti-cancer drugs [5]. These pharmacogenomic models have proven highly accurate in several cases, implying the prevalence of strong interactions between genomic profiles and drug sensitivity in the pre-clinical setting.…”