Development of QSARs for parameterizing Physiology Based ToxicoKinetic models

• Published in: Food and chemical toxicology Vol. 106; no. Pt A; pp. 114 - 124
• Main Authors: Sarigiannis, Dimosthenis Α., Papadaki, Krystalia, Kontoroupis, Periklis, Karakitsios, Spyros P.
• Format: Journal Article
• Language: English
• Published: England Elsevier Ltd 01.08.2017
• Subjects: Abraham's solvation equation; Artificial neural networks; Kinetics; Metabolic constants; Models, Biological; Neural Networks (Computer); Organic Chemicals - chemistry; Organic Chemicals - toxicity; QSARs; Quantitative Structure-Activity Relationship; Tissue/ blood partition coefficients; Toxicokinetics; Metabolic constants; Abraham's solvation equation; Tissue/ blood partition coefficients; QSARs; Artificial neural networks
• ISSN: 0278-6915; 1873-6351
• DOI: 10.1016/j.fct.2017.05.029

A Quantitative Structure Activity Relationship (QSAR) model was developed in order to predict physicochemical and biochemical properties of industrial chemicals of various groups. This model was based on the solvation equation, originally proposed by Abraham. In this work Abraham's solvation model got parameterized using artificial intelligence techniques such as artificial neural networks (ANNs) for the prediction of partitioning into kidney, heart, adipose, liver, muscle, brain and lung for the estimation of the bodyweight-normalized maximal metabolic velocity (Vmax) and the Michaelis – Menten constant (Km). Model parameterization using ANNs was compared to the use of non-linear regression (NLR) for organic chemicals. The coupling of ANNs with Abraham's solvation equation resulted in a model with strong predictive power (R2 up to 0.95) for both partitioning and biokinetic parameters. The proposed model outperformed other QSAR models found in the literature, especially with regard to the estimation and prediction of key biokinetic parameters such as Km. The results show that the physicochemical descriptors used in the model successfully describe the complex interactions of the micro-processes governing chemical distribution and metabolism in human tissues. Moreover, ANNs provide a flexible mathematical framework to capture the non-linear biochemical and biological interactions compared to less flexible regression techniques. •Abraham solvation equation predicts with high accuracy physicochemical and biochemical constants.•ANN models outperform the regression based techniques.•“Non-linear” interactions occurring in biological systems are captured.•The most critical descriptor was found to be the McGowan volume for most of the parameters.