期刊
TOXICOLOGY IN VITRO
卷 73, 期 -, 页码 -出版社
PERGAMON-ELSEVIER SCIENCE LTD
DOI: 10.1016/j.tiv.2021.105133
关键词
in vitro assays; mass balance; pharmacokinetics; partitioning; QIVIVE; free concentration
类别
资金
- Marie Sklodowska-Curie Action-Innovative Training Network-project [721975]
- CEFIC Long-Range Research Initiative (LRI)
- EU Horizon 2020 EUToxRisk project [681002]
Nominal effect concentrations from in vitro toxicity assays can lead to inaccurate estimations of in vivo toxic doses. Chemicals distribute differently between in vitro assay compartments, affecting representative concentrations, while free concentrations and cell-associated concentrations can better represent the biologically effective dose.
Nominal effect concentrations from in vitro toxicity assays may lead to inaccurate estimations of in vivo toxic doses because the nominal concentration poorly reflects the concentration at the molecular target in cells in vitro, which is responsible for initiating effects and can be referred to as the biologically effective dose. Chemicals can differentially distribute between in vitro assay compartments, including serum constituents in exposure medium, microtitre plate plastic, headspace and extracellular matrices. The partitioning of test chemicals to these extracellular compartments reduces the concentration at the molecular target. Free concentrations in medium and cell-associated concentrations are considered better proxies of the biologically effective dose. This paper reviews the mechanisms by which test chemicals distribute between in vitro assay compartments, and also lists the physicochemical properties driving the extent of this distribution. The mechanisms and physicochemical properties driving the distribution of test chemical in vitro help explain the makeup of mass balance models that estimate free concentrations and cell-associated concentrations in in vitro toxicity assays. A thorough understanding of the distribution processes and assumptions underlying these mass balance models helps define chemical and biological applicability domains of individual models, as well as provide a perspective on how to improve model predictivity and quantitative in vitro-in vivo extrapolations.
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