Wellcome Open Research · 2021 · 286 citations · 57 references
Life SciencesGeneticsPharmacologyMedical GeneticsPharmacogenomicsStatistical GeneticsMendelian RandomizationPharmacogenomic Data InterpretationConfoundingDrug Target AlterationsDrug Target ValidationDrug Target VariantsMolecular Genetic AssociationNatural Variation AnalysisSummary-data Mendelian RandomizationVariant-response Associations
Genetic evidence supporting drug target efficacy and safety increases approval likelihood, but Mendelian randomization faces limitations such as genetic confounding, lifelong effect assumptions, and tissue‑ and population‑specific heterogeneity. The paper reviews how natural genetic variation in drug target genes can be leveraged in Mendelian randomization to reveal mechanism‑based efficacy and adverse effects. The approach uses large summary‑level genetic association databases to identify and validate variants that proxy drug target perturbation, enabling Mendelian randomization analyses. When properly applied, Mendelian randomization offers an empirical framework that can enhance drug development success rates by utilizing population‑level data.
Drugs whose targets have genetic evidence to support efficacy and safety are more likely to be approved after clinical development. In this paper, we provide an overview of how natural sequence variation in the genes that encode drug targets can be used in Mendelian randomization analyses to offer insight into mechanism-based efficacy and adverse effects. Large databases of summary level genetic association data are increasingly available and can be leveraged to identify and validate variants that serve as proxies for drug target perturbation. As with all empirical research, Mendelian randomization has limitations including genetic confounding, its consideration of lifelong effects, and issues related to heterogeneity across different tissues and populations. When appropriately applied, Mendelian randomization provides a useful empirical framework for using population level data to improve the success rates of the drug development pipeline.
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