A genomic-led strategy to anticipate drug safety effects

Authors

  • Brian R. Ferolito Author
  • Andrea R. V. R. Horimoto Author
  • Kai Gravel-Pucillo Author
  • Daniel J. Golden Author
  • Hesam Dashti Author
  • Claudia Giambartolomei Author
  • Danielle Rasooly Author
  • Rachael Matty Author
  • Liam Gaziano Author
  • Yakov Tsepilov Author
  • Lauren Costa Author
  • Nicole Kosik Author
  • Harris Ioannidis Author
  • Mohd Karim Author
  • Giovanna Winicki Author
  • Fiona Hunter Author
  • Claudia Langenberg Author
  • John C. Whittaker Author
  • Million Veteran Program Author
  • Tianxi Cai Author
  • Gina M. Peloso Author
  • Barbara Zdrazil Author
  • Maya Ghoussaini Author
  • Andrew R. Leach Author
  • Sumitra Muralidhar Author
  • Ines A. Smit Author
  • Juan P. Casas Author
  • J. Michael Gaziano Author
  • Kelly Cho Author
  • Alexandre C. Pereira Author

Keywords:

Clinical Medicine and Drug Research, Genetics, Phenotypes, Gene mapping, Safety studies, Adverse reactions, Drug research and development, Drug discovery, Adverse events

Abstract

Safety-related issues account for approximately 25% of failures in new drug discovery programs. On top of that, many are discovered during post-marketing surveillance, significantly limiting drug utility and application. To proactively address these concerns, we developed a genetics-led strategy leveraging Mendelian Randomization (MR) across large-scale genetic datasets from the Million Veteran Program, FinnGen, and UK Biobank. By mapping genetic variants associated with gene expression and protein abundance to 1,449 harmonized human phenotypes, we systematically identified potential adverse drug reactions (ADR). Our extensive MR analysis, encompassing 16,915 protein-coding genes, demonstrated the capacity to predict hundreds of known ADR for approved medications, with approximately 40% corroborated by FDA Adverse Event Reporting System (FAERS) data. Additionally, we found significant enrichment of identified gene-mechanism pairs in clinical trials terminated early due to safety concerns, highlighting the clinical utility of genetics-informed safety prediction. Notably, immune-related pathways were prominently associated with ADR, indicating particular sensitivity within immune modulation targets. Our comprehensive atlas, integrating genetic evidence with pharmacological mechanisms, provides a robust predictive framework for anticipating drug safety, potentially enhancing decision-making in drug development and pharmacovigilance. An interactive web interface allowing filtering by gene, phenotype, drug phase, and mechanism of action is available at https://shiny.parse-health.org/safety/. Author summary: Safety-related failures remain a substantial challenge in drug development, often emerging late in clinical trials or post-marketing. To address this, we developed a genomic-led framework that anticipates adverse drug reactions (ADRs) by leveraging Two-sample Mendelian Randomization (MR) across large-scale genetic datasets from the Million Veteran Program, UK Biobank, and FinnGen. Our approach systematically mapped genetic proxies for gene expression and protein abundance to 1,449 human phenotypes, generating an atlas of 8,495 gene–mechanism of action pairs linked to potential safety concerns. This resource recapitulates hundreds of known ADRs for approved drugs, with ~40% confirmed in FDA Adverse Event Reporting System data. Immune-related pathways were disproportionately associated with predicted ADRs, highlighting the sensitivity of immune modulation targets. We also demonstrate a liver-focused application that describes hepatotoxicity into mechanistic axes, improving interpretability and prediction. An interactive web visualization tool enables researchers to explore gene-level safety signals by drug phase and mechanism of action.

Original publication: PLOS Genetics (2026-07-16). Source. Source DOI: 10.1371/journal.pgen.1012211.

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Published

2026-07-16

Issue

Section

Research Articles