Mendelian randomization as a tool to inform drug development using human genetics

Abstract

Drug development is essential to the advancement of human health, however the process is slow, costly, and at high risk of failure at all stages. A promising strategy for expediting and improving the probability of success in the drug development process is the use of naturally randomized human genetic variation for drug target identification and validation. These data can be harnessed using the Mendelian randomization (MR) analytic paradigm to proxy the lifelong consequences of genetic perturbations of drug targets. In this review we discuss the myriad applications of the Mendelian randomization (MR) paradigm for human drug target identification and validation. We review the methodology and applications of MR, key limitations of MR, and potential future opportunities for research. Throughout the Review, we refer to illustrative examples of MR analyses investigating consequences of genetic inhibition of interleukin 6 signaling which, in some cases, have anticipated results from randomized controlled trials. As human genetic data become more widely available, we predict that MR will serve as a key pillar of support for drug development efforts.

Mendelian Randomization Applied to Neurology

“…Second, drug-target MR can inform on drug repurposing opportunities by leveraging the genetic predictors of targets of medications with published safety profiles to identify novel indications for the drug. 15 Finally, with individual-level data, it is possible to test for the interactions between 2 drug targets using MR methods, as in a 2 × 2 factorial RCT. 16 …”

Integrative Mendelian Randomization approaches for therapeutic target prioritisation in immune-mediated diseases

“…In the intermediate trait MR, exposure variants are extracted from GWAS for relevant disease risk factors or biomarkers with available lead variant(s) located in the drug target gene of interest. This approach offers confirmation that the genetic variant indeed influences the clinical outcome of interest[14]. Previously employed intermediate traits in MR include HbA1c for proxying effects of GLP-1 agonists[18], a class of antidiabetic medication, CRP for mimicking the effect of IL-6 signalling[19] and height for the effect of NPR2 and NPR3 signalling on cardiovascular disease[20].…”

“…We can identify two main approaches in MR analyses looking at causal support for a given drug target against a disease indication. While the outcome GWAS used involve primarily disease incidence, the exposure GWAS can instrument a direct measure of gene expression (either messenger RNA or protein) in disease-relevant tissue or a downstream biomarker or clinical risk factor (here referred to as "intermediate trait") [14]. In the first approach, variants related to two molecular phenotypes: protein and mRNA abundance are known as protein and expression quantitative trait loci (QTL), respectively.…”

Robust use of phenotypic heterogeneity at drug target genes for mechanistic insights: application ofcis-multivariable Mendelian randomization toGLP1Rgene region

“…Genes code for proteins, which make up the majority of drug targets, particularly for small molecule and biologic compounds. Previous work has demonstrated that cis-Mendelian randomization focused on genes coding for drug target proteins can be used to anticipate the effects of their pharmacological perturbation Holmes et al, 2021;Daghlas and Gill, 2023). The human relevance of such insights offers considerable advantages over animal models, which can be difficult to translate to patient populations (Hingorani et al, 2019).…”