Drug-Induced Hepatotoxicity and Protection

Using SAFEPATH to Uncover a Novel Mechanism of Hepatotoxicity for Gefitinib

This study uses SAFEPATH to identify a novel sphingolipid metabolism mechanism of Gefitinib toxicity and validate it experimentally, while recovering the known UGT1A1-mediated toxicity of Erlotinib.

AI-driven Discovery of Morphomolecular Signatures in Toxicology

This paper introduces GEESE, an AI model that imputes morphomolecular signatures in toxicology data, linking gene expression changes to drug-induced liver injuries.

Improved Detection of Drug-Induced Liver Injury by Integrating Predicted in vivo and in vitro Data

This paper develops a machine learning model to predict drug-induced liver injury by integrating in vitro and in vivo data with chemical features, achieving improved detection and human-specific sensitivity.

BML-257, a small molecule that protects against drug induced liver injury in zebrafish

The paper identifies BML-257, a small molecule AKT inhibitor, that protects against drug-induced liver injury in zebrafish models.

DILIc: An AI-based classifier to search for Drug-Induced Liver Injury literature

This paper introduces DILIc, an AI classifier that identifies Drug-Induced Liver Injury literature using NLP and ML, achieving high accuracy in cross-validation and external validation.

Deriving time-concordant event cascades from gene expression data: A case study for Drug-Induced Liver Injury (DILI)

The paper develops a framework to identify time-concordant gene expression events in DILI using time-resolved transcriptomics and applies it to rat liver data from TG-GATEs to derive mechanistic hypotheses.

An in vivo and in silico evaluation of the magical hepatoprotective potentialities of Gynura procumbens: a promising agent for combating hepatotoxicity

This paper evaluates the hepatoprotective effects of Gynura procumbens through in vivo and in silico methods, showing reduced liver damage markers and antioxidant activity compared to a control.

Contextualization of causal regulatory networks from toxicogenomics data applied to drug-induced liver injury

The paper applies a causal network tool to toxicogenomics data to infer regulatory networks in drug-induced liver injury, identifying conserved pathways between rat and human models.

Machine characterization of rat toxin responses identifies disease states, tolerance mechanisms and organ to whole-body communication

The paper uses machine learning to analyze toxicogenomic data and identifies toxin-induced disease states, tolerance mechanisms, and organ-to-whole-body communication in rats.