ArticleCommunications medicine2025
Apoptotic signatures allow early and rapid screening of drug-induced liver injury to accelerate drug discovery.
Article in Communications medicine, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
What it found
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Who cites it
1 citing paper in PubMed.
- Evolution of artificial intelligence and machine learning in DILI toxicogenomics: from descriptive profiling to mechanistic insights.Frontiers in pharmacology · 2026Review
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Authors and funding
17 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundEarly detection of drug-induced liver injury (DILI) during drug development is crucial for reducing drug attrition and ensuring the safety of patients. A versatile, biologically interpretable, and dose-dependent screening approach is therefore needed to inform early stop/go decisions and therapeutic margins.
methodsWe have developed AEGIS (Apoptotic Effector Genes In Safety), a preclinical DILI risk screening and prioritization tool that quantifies dose dependent perturbation of apoptosis-regulating transcription factors from transcriptomics data. We profiled transcriptomic responses after short exposures across primary human hepatocytes (PHH), HepG2/C3A cells, RAW 264.7 cells, and an acute Balb/c mouse study. From these profiles, AEGIS provides quantitative risk scores to rank and prioritize compounds and exposures.
resultsHere we show that AEGIS distinguishes compounds with different degree of DILI concern, achieving 86% specificity, 75% sensitivity and 90% precision in PHHs. We demonstrate versatility in data type usage and clinical translation of AEGIS with accurate predictions across species, in vitro and in vivo models, and therapeutic modalities. In addition, we apply AEGIS in a precision medicine context during drug-development within the pharmaceutical industry and investigate the contribution of underlying liver disease on DILI severity. Our findings indicate that cells from patients with metabolic dysfunction-associated steatotic liver disease (MASLD) develop more severe DILI from treatment with troglitazone, aligning with preclinical observations.
conclusionsUsing AEGIS early in drug discovery exemplifies a more efficient approach to identify and mitigate potential safety concerns. This can reduce the need for animal testing, and accelerates drug discovery, ultimately providing the right medicines to patients more quickly.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.