Abstract
Chemical toxicity testing is moving steadily toward 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, facing various data gaps along the way. This review identifies these gaps and makes a case for the in silico approach of computational dose-response and extrapolation modeling to address many of the challenges. Mathematical models that can mechanistically describe chemical toxicokinetics (TK) and toxicodynamics (TD), for both in vitro and in vivo conditions, are the founding pieces 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. Such an understanding will in turn help determine the sets of sensitive biomarkers to be measured in vitro and the scope of toxicity pathways to be modeled in silico. In vitro data reporting both pathway perturbation and chemical biokinetics in the culture medium serve to calibrate the toxicity pathway and virtual tissue models, which can then help predict PoDs in response to chemical dosimetry 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) Physiologically-based toxicokinetic (PBTK) modeling is needed to extrapolate in vitro PoD dose metrics into external doses for expected exposure scenarios. Linked PBTK and TD models can explore the parameter space to recapitulate human population variability in response to chemical insults. While challenges remain for applying these modeling tools to support in vitro toxicity testing, they open the door toward population-stratified and personalized risk assessment.
Highlights
Next-Generation Risk AssessmentHuman health risk assessment of chemical exposures is a complex monitoring, experimental testing, and modeling task, facing uncertainties and challenges at nearly every step of the way along the exposure-to-outcome continuum [1]
This article aims to provide a glimpse at the computational modeling tools that would help enable in vitro assay-based risk assessment, with a primary focus on toxicodynamic modeling of toxicity pathway perturbations
One of the radical changes after switching to cell or organoidbased testing is that some combinations of in vitro measurements will be used as biomarkers to define the point of departure (PoD) instead of the apical, organism-level endpoints normally screened for in animal testing, such as cancer, liver pathology, and reproductive functional disruption
Summary
Human health risk assessment of chemical exposures is a complex monitoring, experimental testing, and modeling task, facing uncertainties and challenges at nearly every step of the way along the exposure-to-outcome continuum [1]. Data streams coming from all these disciplines, at a growing personalized resolution, are converging to revolutionize the way chemical safety assessment is conducted for the betterment of public health Among all these ongoing changes, a paradigm shift has been under way since the publication of the influential NRC report, “Toxicity Testing in the 21st Century (TT21C): A Vision and a Strategy” in 2007 [7]. The animal-based approach examining gross apical endpoints has been in place for nearly half a century and its ability to predict human health risk under environmental exposure conditions is very limited with a wide range of uncertainties [8] This adherence to a failing tradition without much progression in decades is in stark contrast to the exponential growth of our knowledge in the basic biological science where the revolutionary advancement of molecular interrogation and manipulation tools is enabling us to examine biological perturbations at unprecedented resolutions and scales. Inter-agency collaborations such as the Tox project develop, standardize and use high-throughput, high-content cell assays to generate highly reproducible screening data [14]
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
