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Abstract
In vitro data on metabolism and partitioning may be integrated within physiologically-based pharmacokinetic (PBPK) models to provide simulations of the kinetics and bioaccumulation of chemicals in intact organisms. Quantitative structure–property relationship (QSPR) modelling of available in vitro data may be performed to predict metabolism rates and partition coefficients (PCs) for developing in vivo PBPK models. The objective of the present study was to develop an integrated QSPR–PBPK modelling approach for the conduct of in vitro to in vivo extrapolation. For this purpose, data on rat blood:air (P b) and fat:air (P f) PCs, as well as intrinsic metabolic clearance (CLint) obtained using rat liver slices for some C5–C10 volatile organic compounds (VOCs) were compiled from the literature. Multilinear additive QSPR models for P f, P b and CLint were developed based on the number and nature of molecular fragments in these VOCs (CH3, CH2, CH, C, C=C, H, benzene ring and H in benzene ring structure). The mean estimated/experimental (est/exp) ratios (±SD; range) were 1.0 (±0.04; 0.93 − 1.06) for log P f, 1.08 (±0.26; 0.70 − 1.62) for log P b, and 1.07 (± 0.21; 0.80 − 1.44) for CLint. By accounting for the difference in the content of neutral lipids in fat and other tissues, the liver : air and muscle : air PCs of the compounds investigated in this study, with the excerption of n-decane, were adequately predicted from P f. Integrating the QSPRs for P f, P b and CLint within a rat PBPK model, simulations of inhalation pharmacokinetics of several VOCs were generated on the basis of molecular structure, for a given exposure scenario. The integrated QSPR–PBPK model developed in this study is a potentially useful tool for predicting in vivo kinetics and bioaccumulation of chemicals in rats under poor data situations.
†Presented at the 13th International Workshop on QSARs in the Environmental Sciences (QSAR 2008), 8–12 June 2008, Syracuse, USA.
Acknowledgements
The authors wish to thank Natural Sciences and Engineering Research Council of Canada (NSERC) as well as ExxonMobil for support, and Dr Thomas F. Parkerton for fruitful discussions leading to this work.
Notes
†Presented at the 13th International Workshop on QSARs in the Environmental Sciences (QSAR 2008), 8–12 June 2008, Syracuse, USA.
