# Moving Mendelian Randomization From Traditional Risk Factors to Molecular Targets for Drug Development and Clinical Trials in Nephrology

## TL;DR

This paper reviews how Mendelian randomization can help identify drug targets and improve drug development in kidney disease by using genetic data.

## Contribution

The paper highlights the shift from traditional risk factors to molecular targets in Mendelian randomization for drug development in nephrology.

## Key findings

- Mendelian randomization can identify new therapeutic targets and predict drug efficacy before clinical trials.
- Genetic evidence aligns with clinical trial outcomes for drugs like statins and SGLT2 inhibitors.
- Integrating multiomics and phenome-wide approaches can enhance drug repurposing and development.

## Abstract

Mendelian randomization leverages the random assortment of alleles at conception to investigate how genetically mediated changes in an exposure affect an outcome while minimizing concerns related to reverse causation and unmeasured confounding. Initially applied to assess the causal impact of modifiable traditional risk factors as mediators of disease risk, Mendelian randomization studies now incorporate large-scale multiomic datasets providing valuable insights for drug target discovery. By analyzing cis genetic changes that affect gene activity or protein levels—using advancing techniques like single-cell sequencing and proteomics—Mendelian randomization can identify new therapeutic targets, predict drug target efficacy and effect size before trial development, anticipate adverse effects, reduce late-stage trial failures, and identify opportunities for drug repurposing. This review explains the basic principles, broad applications, and inherent limitations of Mendelian randomization in drug-target identification, validation, and repurposing within the context of kidney disease. Many retrospective examples of concordant conclusions from clinical trials and Mendelian randomization studies have been reported including statins, allopurinol, sodium-glucose cotransporter-2 (SGLT2) inhibitors, and glucagon-like peptide-1 (GLP-1) receptor agonists. Genetic evidence should now be prospectively evaluated for all drugs attempting to traverse the “translational valley of death” in drug development. We summarize current examples, spotlight emerging analytic methodologies such as phenome-wide Mendelian randomization and integrated multiomics, and discuss future directions to accelerate drug development in nephrology.