Abstract

As a key step in next-generation risk assessment (NGRA), in vitro to in vivo extrapolation (IVIVE) aims to mobilize a mechanism-based understanding of toxicology to translate bioactive chemical concentrations obtained from in vitro assays to corresponding exposures likely to induce bioactivity in vivo. This conversion can be achieved via physiologically-based toxicokinetic (PBTK) models and machine learning (ML) algorithms. The last 5 years have witnessed a period of rapid development in IVIVE, with the number of IVIVE-related publications increasing annually. This Review aims to (1) provide a comprehensive overview of the origin of IVIVE and initiatives undertaken by multiple national agencies to promote its development; (2) compile and sort out IVIVE-related publications and perform a clustering analysis of their high-frequency keywords to capture key research hotspots; (3) comprehensively review PBTK and ML model-based IVIVE studies published in the last 5 years to understand the research directions and methodology developments; and (4) propose future perspectives for IVIVE from two aspects: expanding the scope of application and integrating new technologies. The former includes focusing on metabolite toxicity, conducting IVIVE studies on susceptible populations, advancing ML-based quantitative IVIVE models, and extending research to ecological effects. The latter includes combining systems biology, multiomics, and adverse outcome networks with IVIVE, aiming at a more microscopic, mechanistic, and comprehensive toxicity prediction. This Review highlights the important value of IVIVE in NGRA, with the goal of providing confidence for its routine use in chemical prioritization, hazard assessment, and regulatory decision making.

By Peiling Han, Xuehua Li, Jingyuan Yang, Yuxuan Zhang, and Jingwen Chen