Prediction of long term toxic effects by genome based network models
Large-scale physiological models comprising cellular metabolic and regulatory networks are suitable to the application of the prediction of long term toxicity of chemicals. The simulation with mechanistic models coupled to pharmacokinetic or physiologically-based pharmacokinetic (PBPK) models link the route and dose of administration to local effective toxic concentration. Thus, the models support the elucidation of the mechanisms of toxic action.
Here, we present an in silico genome based network model of valproic acid distribution connected to models of hepatic valproic acid metabolism and mechanisms of toxic action. The mechanistic mode of action steps attenuate cell viability via toxic metabolites and by disturbance of lipid metabolism. In particular, the following factors are comprised in this model: (i) long-term up-regulation of fatty acid synthesis; (ii) down-regulation of fatty-acid oxidation; (iii) direct short-term competitive inhibition of beta-oxidation by valproic acid metabolites; and (iv) oxidative damage by reactive valproic acid metabolites.
Coupling of this valproic acid toxic mode of action model with Insilico’s human PBPK model enables in-vitro-in-vivo extrapolation and the prediction of the oral equivalent dose.
Model identification and validation was conducted by experimental data from the NOTOX case study on VPA using HepaRG culture including measured metabolite, transcript and protein data.
A model capturing valproic acid toxic mode of action integrated in a whole-body PBPK model was successfully verified against the conducted VPA multi-scale experiment on HepaRG.