A generic Bayesian hierarchical model for the meta-analysis of human population variability in kinetics and its applications in chemical risk assessment
作者： Witold Wiecek , Jean-Lou Dorne , Nadia Quignot , Camille Bechaux , Billy Amzal
DOI: 10.1016/J.COMTOX.2019.100106
关键词:
摘要: Abstract Traditionally, chemical risk assessment in humans aims to derive safe levels of exposure chemicals using toxicological data test species while applying a default 100-fold uncertainty factor (UF) allow for both interspecies differences (10) and human variability toxicokinetic (TK) toxicodynamic (TD) processes. Over the last two decades, meta-analysis methods have allowed perform quantitative analysis population kinetics dynamics replace these UFs with data-based UFs. Pathway-related can now be derived known metabolic pathways. This study presents new hierarchical Bayesian model TK parameters, along its application derivation Data standardisation gaps are also discussed, as well implementation, selection validation. Applications generic illustrated through case quantifying inter-phenotypic substances metabolised via CYP2D6 polymorphic enzyme. Model implementations open-source software provided future refinements applications proposed.
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