TY - CHAP
T1 - Accounting for in vitro and in vivo kinetics in quantitative in vitro to in vivo extrapolations of organophosphate pesticide toxicity
AU - Kramer, N.I.
PY - 2023/8
Y1 - 2023/8
N2 - The toxicity of organophosphate pesticides has been widely studied in animal models. However, next generation risk assessment requires the characterization of the risk of pesticide exposure using non-animal test methods. In this study, we developed physiologically based kinetic (PBK) models parameterised using in silico and in vitro-derived absorption, distribution, metabolism and excretion (ADME) data to extrapolate in vitro neurotoxic effect concentrations to rat and human bioequivalent oral doses, a process referred to as quantitative in vitro-in vivo extrapolation (QIVIVE). The PBK model was developed for chlorpyrifos, diazinon, fenitrothion, profenofos, chlorfenvinfos, and their respective bioactive metabolites and evaluated using available toxicokinetic data obtained from rat studies. The model was subsequently translated to simulate human tissue concentrations of the bioactive metabolites using a population-based model. In vitro acetyl choline esterase (AChE) inhibition data from blood from different donors as well as human neuroprogenitor test (hNPT) data were subsequently used to extrapolate in vitro effect concentrations to in vivo bioequivalent doses. Tissue simulations and predicted points of departure (PODs) were generally within a 10-fold of observed in vivo plasma concentrations and regulatory PODs, respectively. Even though free concentrations in vitro varied significantly between chemicals and in vitro toxicity assays, accounting for in vitro kinetics (e.g., serum constituent and well plate plastic binding), using partition models, had only a minor effect on QIVIVE outcome. This PBK-based QIVIVE approach allowed us to define chemical specific assessment factors to account for interspecies and interindividual differences in risk assessment.
AB - The toxicity of organophosphate pesticides has been widely studied in animal models. However, next generation risk assessment requires the characterization of the risk of pesticide exposure using non-animal test methods. In this study, we developed physiologically based kinetic (PBK) models parameterised using in silico and in vitro-derived absorption, distribution, metabolism and excretion (ADME) data to extrapolate in vitro neurotoxic effect concentrations to rat and human bioequivalent oral doses, a process referred to as quantitative in vitro-in vivo extrapolation (QIVIVE). The PBK model was developed for chlorpyrifos, diazinon, fenitrothion, profenofos, chlorfenvinfos, and their respective bioactive metabolites and evaluated using available toxicokinetic data obtained from rat studies. The model was subsequently translated to simulate human tissue concentrations of the bioactive metabolites using a population-based model. In vitro acetyl choline esterase (AChE) inhibition data from blood from different donors as well as human neuroprogenitor test (hNPT) data were subsequently used to extrapolate in vitro effect concentrations to in vivo bioequivalent doses. Tissue simulations and predicted points of departure (PODs) were generally within a 10-fold of observed in vivo plasma concentrations and regulatory PODs, respectively. Even though free concentrations in vitro varied significantly between chemicals and in vitro toxicity assays, accounting for in vitro kinetics (e.g., serum constituent and well plate plastic binding), using partition models, had only a minor effect on QIVIVE outcome. This PBK-based QIVIVE approach allowed us to define chemical specific assessment factors to account for interspecies and interindividual differences in risk assessment.
M3 - Abstract
T3 - ALTEX Proceedings
SP - 161
EP - 161
BT - Abstracts of the 12th World Congress on Alternatives and Animal Use in the Life Sciences, Niagara Falls, 2023
A2 - van Aulock, S.
PB - Springer
T2 - 12th World Congress on Alternatives and Animal Use in the Life Sciences (2023)
Y2 - 27 August 2023 through 31 August 2023
ER -