Advancing Toxicity Predictions:A Review on in Vitro to in Vivo Extrapolation in Next-Generation Risk Assessment  

作　　者：Peiling Han Xuehua Li Jingyuan Yang Yuxuan Zhang Jingwen Chen 

机构地区：[1]Key Laboratory of Industrial Ecology and Environmental Engineering,School of Environmental Science and Technology,Dalian University of Technology,Liaoning,Dalian 116024,China

出　　处：《Environment & Health》2024年第7期499-513,共15页环境与健康（英文）

基　　金：National Natural Science Foundation of China(grant no.2217060631);National Key Research and Development Program of China(grant no.2022YFC3902104).

摘　　要：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.

关 键 词：in vitro to in vivo extrapolation physiologically-based toxicokinetic model big data machine learning in vivo toxicity 

分 类 号：X592[环境科学与工程—环境工程]