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In Vitro to In Vivo Extrapolation

A workflow for conducting in vitro to in vivo extrapolation (IVIVE) analyses is available in the Integrated Chemical Environment (ICE). Updates to ICE in March 2020 (ICE 3.0) and July 2020 (ICE 3.1) improved the In Vitro to In Vivo Extrapolation (IVIVE) tool. ICE now allows users to upload their own data for IVIVE analyses. Other improvements included a new physiologically based toxicokinetic model, improved output graphics, and selection of assays based on mode of action.

A key issue with high-throughput in vitro testing methods is how to accurately relate concentrations of substances that induce in vitro responses to in vivo exposure levels that could result in human or animal adverse effects. This relationship is established through IVIVE, the focus of a webinar series and following workshop presented by NICEATM and EPA during 2015 and 2016. Scientists interested in the use of IVIVE for substance screening and risk decision-making met at the 2016 workshop to develop best practices and identify areas for further research (Bell et al. 2018).

NICEATM's computational toxicologists developed methods for conducting IVIVE analyses (Chang et al. 2014). Subsequent work focused on understanding the impact of various parameters, such as using free plasma concentration as a surrogate for total plasma concentration, and comparing multiple modeling approaches (Casey et al. 2018).

Application of these IVIVE approaches to predict the potential of substances to cause developmental toxicity and to interact with the endocrine system was described in a poster presented at the 2017 SOT Annual Meeting (Chang et al.). Current work on developmental toxicity is evaluating the impact of pharmacokinetics and different modeling approaches on predicting relevant external exposure. Preliminary results using data from a specific in vitro stem cell-based assay as input for IVIVE suggest that these approaches could quantitatively predict in vivo developmental toxicity potential of valproate analogues. An abstract describing this work (Chang et al.) was accepted for presentation at the 2020 SOT annual meeting.