Biomedical subjects

Martin Béliveau

Publications and source records attributed to Martin Béliveau.

3 recordsLinked to original sources

PubMed2005

Quantitative structure-property relationships for interspecies extrapolation of the inhalation pharmacokinetics of organic chemicals.

The objectives of this study were to (i) develop quantitative structure-property relationships (QSPRs) for blood:air partition coefficients (Pb:a), tissue:air partition coefficients (Pt:a), and hepatic clearance (CLh) and (ii) conduct interspecies extrapolations of the pharmacokinetics of low molecular weight volatile organic chemicals (VOCs) by incorporating the above QSPRs within a physiologically based pharmacokinetic (PBPK) modeling framework. Pb:a and Pt:a were predicted using the following algorithm: FnlxPo:a+FwxPw:a+f(b)xFpxPp:a, where Fnl=content of neutral lipid equivalents in biological matrix, Fw=content of water equivalents in biological matrix, Fp=protein content of blood and tissues, Po:a=vegetable oil:air partition coefficient, Pw:a=water:air partition coefficient, f(b)=fraction of total protein involved in the partitioning process, and Pp:a=protein:air partition coefficient. CLh was estimated as follows: Qlx[(CLintxC(P4502E1)xVl)/(Ql+CLintxC(P4502E1)xVl)], where CLint=intrinsic clearance normalized for P450 2E1 content, Ql=blood flow to the liver, C(P4502E1)=hepatic concentration of P450 2E1 in the species of interest, and Vl=volume of liver. QSPRs relating molecular fragments of 46 VOCs and parameters required for estimating Pb:a, Pt:a, and CLh (namely, Po:a, Pw:a, Pp:a, and CLint) were established using a group contribution method (f(i)xCi, where f=frequency of occurrence of the group i in a given molecule and Ci=contribution of the group i to Po:a, Pw:a, Pp:a, or CLint). Values of group contributions were determined by multilinear regression of experimental data. The species specific parameters required for solving the above algorithms were obtained from the literature. These algorithms, once incorporated into a multispecies PBPK modeling framework, enabled extrapolation of the kinetics of chemicals across species. The inhalation pharmacokinetics of dichloromethane and toluene as well as two de novo compounds (1,2,4-trimethyl benzene and ethyl benzene) were extrapolated from rat to human, using the present modeling methodology. This study has demonstrated that it is possible to extrapolate the pharmacokinetic behavior of chemicals from rats to humans on the basis of QSPRs and species specific physiological information.

Administration, Inhalation↗

PubMed2003

Quantitative structure-property relationships for physiologically based pharmacokinetic modeling of volatile organic chemicals in rats.

The objective of present study was to develop quantitative structure-property relationships (QSPRs) for the chemical-specific input parameters of rat physiologically based pharmacokinetic (PBPK) models (i.e., blood:air partition coefficient (P(b)), liver:air partition coefficient (P(l)), muscle:air partition coefficient (P(m)), fat:air partition coefficient (P(f)), and hepatic clearance (CL(h))), for simulating the inhalation pharmacokinetics of volatile organic chemicals (VOCs). The literature data on P(b), P(l), P(f), and P(m) for 46 low-molecular-weight VOCs as well as CL(h) for 25 such VOCs primarily metabolized by CYP2E1 (alkanes, haloalkanes, haloethylenes, and aromatic hydrocarbons) were analysed to develop QSPRs. The QSPRs developed in this study were essentially multilinear additive models, which imply that each fragment in the molecular structure has an additive and constant contribution to partition coefficients and hepatic clearance. Most of the values in the calibration set could be reproduced adequately with the QSPR approach, which involved the calculation of the sum of the frequency of occurrence of fragments (CH(3), CH(2), CH, C, C=C, H, Cl, Br, F, benzene ring, and H in benzene ring structure) times the fragment-specific contributions determined in this study. The QSPRs for P(b), P(l), P(m), P(f), and CL(h) were then included within a PBPK model, which only required the specification of the frequency of occurrence of fragments in a molecule along with exposure concentration and duration as input for conducting pharmacokinetic simulations. This QSPR-PBPK model framework facilitated the prediction of the inhalation pharmacokinetics of four VOCs present in the calibration dataset (toluene, dichloromethane, trichloroethylene, and 1,1,1-trichloroethane) and four VOCs that were not part of the calibration set (1,2,4-trimethyl benzene, ethyl benzene, 1,3-dichloropropene, and 2,2-dichloro-1,1,1-trifluoroethane) but that could be described using the molecular fragments investigated in the present study. The QSPRs developed in this study should be potentially useful for providing a first-cut evaluation of the inhalation pharmacokinetics of VOCs prior to experimentation, as long as the number and nature of the fragments do not exceed the ones in the calibration dataset used in this study.

Animals↗

PubMed2002

Physiological modeling and extrapolation of pharmacokinetic interactions from binary to more complex chemical mixtures.

The available data on binary interactions are yet to be considered within the context of mixture risk assessment because of our inability to predict the effect of a third or a fourth chemical in the mixture on the interacting binary pairs. Physiologically based pharmacokinetic (PBPK) models represent a potentially useful framework for predicting the consequences of interactions in mixtures of increasing complexity. This article highlights the conceptual basis and validity of PBPK models for extrapolating the occurrence and magnitude of interactions from binary to more complex chemical mixtures. The methodology involves the development of PBPK models for all mixture components and interconnecting them at the level of the tissue where the interaction is occurring. Once all component models are interconnected at the binary level, the PBPK framework simulates the kinetics of all mixture components, accounting for the interactions occurring at various levels in more complex mixtures. This aspect was validated by comparing the simulations of a binary interaction-based PBPK model with experimental data on the inhalation kinetics of m-xylene, toluene, ethyl benzene, dichloromethane, and benzene in mixtures of varying composition and complexity. The ability to predict the kinetics of chemicals in complex mixtures by accounting for binary interactions alone within a PBPK model is a significant step toward the development of interaction-based risk assessment for chemical mixtures.

Animals↗