A large-scale human toxicogenomics resource for drug-induced liver injury prediction

Volker Bergen (), Konstantia Kodella, Sreenath Srikrishnan, Ornella Barrandon, Sara Anderson, Max Rogers-Grazado, Casey Fowler, Hirit Beyene, Nicole Robichaud, Timothy Fulton, Nina Lapchyk, Mauricio Cortes, Nick Plugis, Matthew Goddeeris and Mahdi Zamanighomi ()
Additional contact information
Volker Bergen: Cellarity Inc.
Konstantia Kodella: Cellarity Inc.
Sreenath Srikrishnan: Cellarity Inc.
Ornella Barrandon: Cellarity Inc.
Sara Anderson: Cellarity Inc.
Max Rogers-Grazado: Cellarity Inc.
Casey Fowler: Cellarity Inc.
Hirit Beyene: Cellarity Inc.
Nicole Robichaud: Cellarity Inc.
Timothy Fulton: Cellarity Inc.
Nina Lapchyk: Cellarity Inc.
Mauricio Cortes: Cellarity Inc.
Nick Plugis: Cellarity Inc.
Matthew Goddeeris: Cellarity Inc.
Mahdi Zamanighomi: Cellarity Inc.

Nature Communications, 2025, vol. 16, issue 1, 1-15

Abstract: Abstract Drug-Induced Liver Injury (DILI) remains one of the most critical challenges in drug development, causing patient safety concerns, clinical trial failures and drug withdrawals. We introduce ToxPredictor, a toxicogenomics framework combining RNA-seq data from primary human hepatocytes with pharmacokinetic data to predict dose-resolved DILI risks and safety margins. At its core is DILImap, an RNA-seq library tailored for DILI research, comprising 300 compounds at multiple concentrations. ToxPredictor achieves 88% sensitivity at 100% specificity in blind validation, outperforming state-of-the-art methods. It flagged recent phase III clinical failures, including Evobrutinib, TAK-875, and BMS-986142, overlooked by animal studies. Beyond prediction, ToxPredictor provides mechanistic insights into hepatotoxic pathways, enabling early de-risking and actionable safety decisions. Unlike single-endpoint readouts—even from 3D models—transcriptomics offers a multi-dimensional system-level view of hepatocyte responses, capable of detecting diverse DILI mechanisms not captured by conventional assays. Scalable, actionable, and integrated into a broader AI/ML drug discovery platform, this work establishes toxicogenomics as a promising tool for developing safer therapeutics and addressing one of the most pressing challenges in toxicology.

Date: 2025
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DOI: 10.1038/s41467-025-65690-3

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