Human variability in glutathione-S-transferase activities, tissue distribution and major polymorphic variants: Meta-analysis and implication for chemical risk assessment

The input into the QIVIVE and Physiologically-Based kinetic and dynamic models of drug metabolising enzymes performance and their inter-individual differences significantly improve the modelling performance, supporting the development and integration of alternative approaches to animal testing. Bayesian meta-analyses allow generating and integrating statistical distributions with human in vitro metabolism data for quantitative in vitro-in vivo extrapolation. Such data are lacking on glutathione-S-transferases (GSTs). This paper reports for the first time results on the human variability of GST activities in healthy individuals, their tissue localisation and the frequencies of their major polymorphic variants by means of extensive literature search, data collection, data base creation and meta-analysis. A limited number of papers focussed on in vivo GST inter-individual differences in humans. Ex-vivo total GST activity without discriminating amongst isozymes is generally reported, resulting in a high inter-individual variability. The highest levels of cytosolic GSTs in humans are measured in the kidney, liver, adrenal glands and blood. The frequencies of GST polymorphisms for cytosolic isozymes in populations of different geographical ancestry were also presented. Bayesian meta-analyses to derive GST-related uncertainty factors provided uncertain estimates, due to the limited database. Considering the relevance of GST activities and their pivotal role in cellular adaptive response mechanisms to chemical stressors, further studies are needed to identify GST probe substrates for specific isozymes and quantify inter-individual differences. (C) 2020 Published by Elsevier B.V.

Automated summary

Inputting data on drug metabolizing enzyme performance and inter-individual differences into models significantly enhances modeling performance, supporting the development of alternative testing methods. Bayesian meta-analyses can be used to integrate statistical distributions with human in vitro metabolism data for in vitro-in vivo extrapolation, though data on GSTs is currently lacking. This study provides new insights into human variability of GST activities and their tissue localization in healthy individuals.