A Mechanistic Modeling Framework for Predicting Metabolic Interactions in Complex Mixtures

Background: Computational modeling of the absorption, distribution, metabolism, and excretion of chemicals is now theoretically able to describe metabolic interactions in realistic mixtures of tens to hundreds of substances. That framework awaits validation. Objectives: Our objectives were to a) evaluate the conditions of application of such a framework, b) confront the predictions of a physiologically integrated model of benzene, toluene, ethylbenzene, and m-xylene (BTEX) interactions with observed kinetics data on these substances in mixtures and, c) assess whether improving the mechanistic description has the potential to lead to better predictions of interactions. Methods: We developed three joint models of BTEX toxicokinetics and metabolism and calibrated them using Markov chain Monte Carlo simulations and single-substance exposure data. We then checked their predictive capabilities for metabolic interactions by comparison with mixture kinetic data. Results: The simplest joint model (BTEX interacting competitively for cytochrome P450 2E1 access) gives qualitatively correct and quantitatively acceptable predictions (with at most 50% deviations from the data). More complex models with two pathways or back-competition with metabolites have the potential to further improve predictions for BTEX mixtures. Conclusions: A systems biology approach to large-scale prediction of metabolic interactions is advantageous on several counts and technically feasible. However, ways to obtain the required parameters need to be further explored.