AI and Machine Learning Reshape Drug-Induced Liver Injury Toxicogenomics
Artificial intelligence helps predict liver damage from certain medications
AI and machine learning are moving DILI toxicogenomics from descriptive profiling toward mechanism-driven predictive toxicology.
New tools use artificial intelligence to identify the specific biological signals that cause liver injury from drugs.
Risk prediction models for anti-tuberculosis drug-induced liver injury show pooled AUC of 0.81
Risk Prediction Models Help Identify Liver Damage from Tuberculosis Drugs
This meta-analysis of 26 studies involving 41,734 cases evaluated risk prediction models for anti-tuberculosis drug-induced liver injury (AT…
New risk models can help identify patients at risk for liver injury when taking tuberculosis medications, especially those with specific hea…
Narrative review of phytochemicals in zebrafish models for metabolic dysfunction-associated steatotic liver disease and related conditions
Tiny fish help find new safe liver disease drugs
This narrative review examines preclinical data on phytochemicals using zebrafish models for metabolic dysfunction-associated steatotic live…
Tiny zebrafish are helping scientists test plant-based medicines for liver disease faster than ever before while checking safety and toxicit…
In silico modeling shows acetaminophen overdose levels affect liver damage sensitivity and metabolic parameters
New computer models predict who is most at risk from acetaminophen overdose
This in silico modeling study evaluated a representative patient cohort exposed to acetaminophen overdose at lower and higher levels.
This research helps us understand why some people suffer worse liver damage from acetaminophen overdose, paving the way for safer drug use.
CAM products associated with ACLF in 39.6% of patients with liver injury in retrospective cohort
CAM products linked to liver failure and heavy metal contamination in patients
A retrospective cohort study of 91 consecutive patients with CAM-related adverse events at a South Indian tertiary center found that 39.6% d…
Many complementary medicine products tested contained dangerous heavy metals like cadmium linked to severe liver failure and death in patien…
In Silico Modeling Predicts Fezolinetant Hepatotoxicity Risk in MAFLD Population
Could a menopause drug harm the liver? A computer model offers clues
Quantitative systems toxicology modeling simulated fezolinetant hepatotoxicity in virtual populations.
A computer model predicts a menopause drug might cause rare liver injury in people with fatty liver disease, but higher doses could be safer…
High-dose melatonin linked to hepatotoxicity in small PP-MS trial, prompting early halt
High-dose melatonin trial halted after liver safety concerns in MS patients
A phase I/II RCT of 8 patients with PP-MS on stable ocrelizumab found that adjunctive high-dose oral melatonin (300 mg/day) was associated w…
A high-dose melatonin trial for multiple sclerosis stopped early after three of four patients developed mild liver injury, though the issue …
Questions about drug-induced liver injury
How is AI used to predict drug-induced liver injury in toxicogenomics?
AI and machine learning use toxicogenomic data, such as gene expression profiles and protein interaction networks, to predict which drugs may cause liver injury, with models reaching about 79% accuracy in one 2025 study [8].
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