An integrated QSPR–PBPK modelling approach for in vitro–in vivo extrapolation of pharmacokinetics in rats

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 (CL(int)) obtained using rat liver slices for some C(5)-C(10) volatile organic compounds (VOCs) were compiled from the literature. Multilinear additive QSPR models for P(f), P(b) and CL(int) were developed based on the number and nature of molecular fragments in these VOCs (CH(3), CH(2), 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 CL(int). 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 CL(int) 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.

Real-time breath gas analysis for pharmacokinetics: monitoring exhaled breath by on-line proton-transfer-reaction mass spectrometry after ingestion of eucalyptol-containing capsules

“…With respect to blood:lung or lung:air partitioning studies, most so far have been conducted from a theoretical approach, with developments of kinetic models to predict behaviour of compounds in the body based on their physico-chemical properties (e.g. Connell et al 1993, Meulenberg and Vijverberg 2000, Beliveau and Krishnan 2005, Beliveau et al 2005, Abraham et al 2008, Kamgang et al 2008, Basak et al 2009. In contrast, there is a scarcity in correlation studies of blood:breath ratios.…”

Quantitative Property-Property Relationship for Screening-Level Prediction of Intrinsic Clearance of Volatile Organic Chemicals in Rats and Its Integration within PBPK Models to Predict Inhalation Pharmacokinetics in Humans

“…Accordingly, these publications demonstrated the feasibility of developing structure-property relationships for the metabolism rates. The group contribution method was successfully used to develop quantitative structure-property relationships (QSPRs) for the tissue : air partition coefficients as well as intrinsic ( CL int⁡ ) and hepatic clearance ( CL h for a group of low-molecular-weight volatile organic chemicals (VOCs) in rats [41, 42]. These QSPR models, in turn, were incorporated within PBPK models to predict reasonably well the blood kinetics of inhaled VOCs in rats.…”

Quantitative Structure–Activity Relationships for Organophosphates Binding to Trypsin and Chymotrypsin

“…Examples of QSAR models predicting biochemical parameters such as skin permeability coefficients, skin/water and tissue/blood partition coefficients, bimolecular rate constants, and metabolic parameters such as V max and K m , for the development of BBDR models and/or for human health risk assessment are not a new endeavor (Hansch and Deutsch, 1966;Kamgang et al, 2008;Knaak et al, 2004;Lowe et al, 2006;Poulin and Krishnan, 1995;Raevskii et al, 1990;Yang et al, 1998). Specifically, AMPAC and CODESSA methodology has shown promising results in medicinal chemistry applications including predicting BBDR specific parameters (Katritzky et al, 1998(Katritzky et al, , 2005.…”