AI Medical Compendium Journal:
The Journal of toxicological sciences

Showing 1 to 9 of 9 articles

Reported liver toxicity of food chemicals in rats extrapolated to humans using virtual human-to-rat hepatic concentration ratios generated by pharmacokinetic modeling with machine learning-derived parameters.

The Journal of toxicological sciences
Pharmacokinetic data are not generally available for evaluating the toxicological potential of food chemicals. A simplified physiologically based pharmacokinetic (PBPK) model has been established to evaluate internal exposures to chemicals in rats or...

Novel predictive approaches for drug-induced convulsions in non-human primates using machine learning and heart rate variability analysis.

The Journal of toxicological sciences
Drug-induced convulsions are a major challenge to drug development because of the lack of reliable biomarkers. Using machine learning, our previous research indicated the potential use of an index derived from heart rate variability (HRV) analysis in...

Developing a GNN-based AI model to predict mitochondrial toxicity using the bagging method.

The Journal of toxicological sciences
Mitochondrial toxicity has been implicated in the development of various toxicities, including hepatotoxicity. Therefore, mitochondrial toxicity has become a major screening factor in the early discovery phase of drug development. Several models have...

Development of a GCN-based model to predict in vitro phototoxicity from the chemical structure and HOMO-LUMO gap.

The Journal of toxicological sciences
The interaction between sunlight and drugs can lead to phototoxicity in patients who have received such drugs. Phototoxicity assessment is a regulatory requirement globally and one of the main toxicity screening steps in the early stages of drug disc...

Evaluation of genotoxicity after acute and chronic exposure to 2,4-dichlorophenoxyacetic acid herbicide (2,4-D) in rodents using machine learning algorithms.

The Journal of toxicological sciences
2,4-Dichlorophenoxyacetic acid (2,4-D) is one of the most widely used herbicides in the world, but its mutagenic and carcinogenic potential is still controversial. We simulated environmental exposure to 2,4-D, with the objective of evaluating the gen...

Current status and future perspective of computational toxicology in drug safety assessment under ontological intellection.

The Journal of toxicological sciences
For the safety assessment of pharmaceuticals, initial data management requires accurate toxicological data acquisition, which is based on regulatory safety studies according to guidelines, and computational systems have been developed under the appli...

Artificial neural network analysis for predicting human percutaneous absorption taking account of vehicle properties.

The Journal of toxicological sciences
An in silico method for predicting percutaneous absorption of cosmetic ingredients was developed by using artificial neural network (ANN) analysis to predict the human skin permeability coefficient (log Kp), taking account of the physicochemical prop...

In silico risk assessment for skin sensitization using artificial neural network analysis.

The Journal of toxicological sciences
The sensitizing potential of chemicals is usually identified and characterized using in vivo methods such as the murine local lymph node assay (LLNA). Due to regulatory constraints and ethical concerns, alternatives to animal testing are needed to pr...

Development of QSAR models using artificial neural network analysis for risk assessment of repeated-dose, reproductive, and developmental toxicities of cosmetic ingredients.

The Journal of toxicological sciences
Use of laboratory animals for systemic toxicity testing is subject to strong ethical and regulatory constraints, but few alternatives are yet available. One possible approach to predict systemic toxicity of chemicals in the absence of experimental da...