PBPK modeling of impact of nonalcoholic fatty liver disease on toxicokinetics of perchloroethylene in mice Academic Article uri icon

abstract

  • BACKGROUND: Nonalcoholic fatty liver disease (NAFLD), a major cause of chronic liver disease in the Western countries with increasing prevalence worldwide, may substantially affect chemical toxicokinetics and thereby modulate chemical toxicity. OBJECTIVES: This study aims to use physiologically-based pharmacokinetic (PBPK) modeling to characterize the impact of NAFLD on toxicokinetics of perchloroethylene (perc). METHODS: Quantitative measures of physiological and biochemical changes associated with the presence of NAFLD induced by high-fat or methionine/choline-deficient diets in C57B1/6┬áJ mice are incorporated into a previously developed PBPK model for perc and its oxidative and conjugative metabolites. Impacts on liver fat and volume, as well as blood:air and liver:air partition coefficients, are incorporated into the model. Hierarchical Bayesian population analysis using Markov chain Monte Carlo simulation is conducted to characterize uncertainty, as well as disease-induced variability in toxicokinetics. RESULTS: NAFLD has a major effect on toxicokinetics of perc, with greater oxidative and lower conjugative metabolism as compared to healthy mice. The NAFLD-updated PBPK model accurately predicts in vivo metabolism of perc through oxidative and conjugative pathways in all tissues across disease states and strains, but underestimated parent compound concentrations in blood and liver of NAFLD mice. CONCLUSIONS: We demonstrate the application of PBPK modeling to predict the effects of pre-existing disease conditions as a variability factor in perc metabolism. These results suggest that non-genetic factors such as diet and pre-existing disease can be as influential as genetic factors in altering toxicokinetics of perc, and thus are likely contribute substantially to population variation in its adverse effects.

altmetric score

  • 3.7

author list (cited authors)

  • Dalaijamts, C., Cichocki, J. A., Luo, Y., Rusyn, I., & Chiu, W. A.

citation count

  • 0

publication date

  • May 2020