Research over the past 50 years has demonstrated that interindividual variability in genes encoding drug metabolizing enzymes, transporters, receptors and HLA antigens influences a patient’s susceptibility to adverse drug reactions as well as the probability of pharmacological treatment success. Since then, the field of pharmacogenomics has developed rapidly fuelled by major advances in omics profiling methods, big data analytics and biobank initiatives. These developments are paralleled by major advances in machine learning that enable to parse this immense wealth of data and identify novel complex associations with genetic factors that may guide drug selection and dosing. While most genetic associations were identified between individual single nucleotide variations (SNVs) and drug-related phenotypes, recent research started to unravel effects beyond single variations.
In this Collection, we invite original research articles, case reports, meta-analyses, and reviews that focus on associations between genetic variability with drug metabolism, response or toxicity. We particularly welcome reports that identify combinatorial variant effects in cis or trans, epigenetic variations, regulation of gene expression by non-coding RNAs as well as polygenic models for drug response and safety predictions. Pharmacogenetic and -genomic studies of both the germline and somatic genome will be considered. The Collection is furthermore open to implementation studies that demonstrate feasibility or outcomes of point-of-care genetic profiling.