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Biotransformation model of neutral and weakly polar organic compounds in fish incorporating internal partitioning



A model for whole-body in vivo biotransformation of neutral and weakly polar organic chemicals in fish is presented. It considers internal chemical partitioning and uses Abraham solvation parameters as reactivity descriptors. It assumes that only chemicals freely dissolved in the body fluid may bind with enzymes and subsequently undergo biotransformation reactions. Consequently, the whole-body biotransformation rate of a chemical is retarded by the extent of its distribution in different biological compartments. Using a randomly generated training set (n = 64), the biotransformation model is found to be: log (HLφfish) = 2.2 (±0.3)B − 2.1 (±0.2)V − 0.6 (±0.3) (root mean square error of prediction [RMSE] = 0.71), where HL is the whole-body biotransformation half-life in days, φfish is the freely dissolved fraction in body fluid, and B and V are the chemical's H-bond acceptance capacity and molecular volume. Abraham-type linear free energy equations were also developed for lipid–water (Klipidw) and protein–water (Kprotw) partition coefficients needed for the computation of φfish from independent determinations. These were found to be 1) log Klipidw = 0.77E − 1.10S − 0.47A − 3.52B + 3.37V + 0.84 (in Lwat/kglipid; n = 248, RMSE = 0.57) and 2) log Kprotw = 0.74E − 0.37S − 0.13A − 1.37B + 1.06V − 0.88 (in Lwat/kgprot; n = 69, RMSE = 0.38), where E, S, and A quantify dispersive/polarization, dipolar, and H-bond-donating interactions, respectively. The biotransformation model performs well in the validation of HL (n = 424, RMSE = 0.71). The predicted rate constants do not exceed the transport limit due to circulatory flow. Furthermore, the model adequately captures variation in biotransformation rate between chemicals with varying log octanol–water partitioning coefficient, B, and V and exhibits high degree of independence from the choice of training chemicals. The present study suggests a new framework for modeling chemical reactivity in biological systems. Environ Toxicol Chem 2013;32:1873–1881. © 2013 SETAC

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