Key points are not available for this paper at this time.
New approach methodologies (NAMs) are advancing the reduction of animal testing by promoting human-relevant safety assessments across diverse applications, including cosmetics, pharmaceuticals, and environmental chemicals. Among these methodologies, computational models like quantitative systems toxicology (QST) have emerged as powerful tools, enabling the simulation of mechanisms underlying drug (or chemical) induced toxicity to predict potential adverse outcomes. Valproic acid (VPA), a treatment for epilepsy, convulsions and bipolar disorder, is associated with a risk of drug-induced liver injury. The mechanism of hepatotoxicity is not fully understood, although VPA's competitive inhibition of fatty acid metabolism via carnitine palmitoyl transferase 1 (CPT1) in the liver is a leading hypothesis. In this study, we employed a QST approach by integrating a physiologically-based pharmacokinetic model of VPA with the large-scale liver metabolism model HEPATOKIN1 to evaluate whether simulated VPA dosing with increasing CPT1 inhibition predicts clinically relevant markers of lipid metabolism disruption and hepatic triacylgyceride (TAG) accumulation, an early maker for hepatic steatosis. The integrated model predicted a dose-dependent increase in hepatic TAG as a result of the competitive inhibition of CPT1 by VPA, qualitatively consistent with clinical observations. Notably, explicit inclusion of PPARα-mediated regulatory effects on key liver enzymes was essential to ensure biologically consistent outcomes in liver metabolism. These results highlight the necessity of including relevant regulatory pathways in QST applications to achieve credible and physiologically relevant safety predictions.
Gupta et al. (Fri,) studied this question.