Publication: Bridging the Data Gap from in vitro Toxicity Testing to Chemical Safety Assessment through Computational Modeling

Chemical toxicity testing is moving steadily toward using a human cell and organoid-based in vitro approach for reasons including scientific relevancy, efficiency, cost, and ethical rightfulness. Inferring human health risk from chemical exposure based on in vitro testing data is a challenging task, with various data gaps along the way. This review identifies these gaps and makes a case for the in silico approach of using computational dose-response and extrapolation modeling to address many of the challenges. Mathematical models mechanistically describing chemical toxicokinetics (TK) and toxicodynamics (TD), for both in vitro and in vivo conditions, are the founding pieces of tools in this regard. Identifying toxicity pathways and in vitro point of departure (PoD) associated with adverse health outcomes requires an understanding of the molecular key events in the interacting transcriptome, proteome, and metabolome, which in turn determine the types of sensitive biomarkers to be measured in vitro and the scope of toxicity pathways to be modelled mathematically. In vitro data reporting both pathway perturbation and chemical biokinetics in the culture medium serve to calibrate the toxicity pathways and virtual tissues models, which can then predict PoDs in response to chemical kinetics experienced by cells in vivo. Two types of in vitro to in vivo extrapolation (IVIVE) are needed. (1) For toxic effects involving systemic regulations, such as endocrine disruption, organism-level adverse outcome pathway (AOP) models are needed to extrapolate in vitro toxicity pathway perturbation to in vivo PoD. (2) Physiological-based toxicokinetic (PBTK) modeling is needed to extrapolate in vitro PoD chemical concentrations into external doses depending on the exposure scenarios expected. Linked PBTK and TD models can explore parameter distributions to recapitulate the variability of human population responses to chemical insults. While challenges remain for applying these modeling approaches to support in vitro toxicity testing, they open the door toward population-stratified and personalized risk assessment.

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