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17 September 2026

Divergent Mechanisms of Gefitinib Hepatotoxicity in Metabolically and Genetically Diverse Hepatocyte Models

Abstract

This white paper reports early findings from the Ignota Labs project Understanding and Mitigating Drug Toxicity Risks in Diverse Populations through AI-Enabled Genomics and Pharmacogenomics. Building on our previous work, in which the causal and explainable AI platform SAFEPATH predicted and experimentally validated a novel PRKD1/PRKD3-sphingolipid mechanism of gefitinib hepatotoxicity, we asked why the clinical toxicity response to gefitinib is so heterogeneous between patients. Pharmacogenomic analysis of a non-small-cell lung cancer cohort identified CYP3A4 activity as a candidate modifier, and we therefore profiled two donor-diverse, female iPSC-derived hepatocyte lines with contrasting CYP3A4 activity - i18F (low activity, predominantly Native American ancestry) and i30F (high activity, predominantly European ancestry) across five gefitinib concentrations by RNA-seq. Both lines converge on suppression of globo sphingolipid metabolism, independently reproducing the mechanism identified in our earlier case study. Beyond that shared core, the two lines diverge sharply: i18F shows early stress signalling, collapse of phase I/II biotransformation and loss of hepatocyte identity transcription factors, consistent with parent-compound accumulation; i30F mounts a high-output metabolic response with strong NRF2-ARE, oxidative stress, p53 and genotoxicity enrichment, consistent with a burden of reactive metabolites. The NRF2 signature in the higher-European-ancestry line aligns with population-level genomics, in which KEAP1-NRF2 alterations are far more frequent in Western than in East Asian NSCLC cohorts, offering a mechanistic rationale for population-specific differences in gefitinib toxicity and resistance. These findings are hypothesis-generating and define a set of specific experimental validations.

Read more:
Divergent Mechanisms of Gefitinib Hepatotoxicity in Metabolically and Genetically Diverse Hepatocyte Models can be read in full at ignotalabs.ai.

Authors: Sara Masarone, Katie V. Beckwith, Philip S. Lewis, Kate Cameron, Layla Hosseini-Gerami

Published: 17 September 2026