Sarigiannis Dimosthenis Α, Papadaki Krystalia, Kontoroupis Periklis, Karakitsios Spyros P
Aristotle University of Thessaloniki, Department of Chemical Engineering, Environmental Engineering Laboratory, 54124 Thessaloniki, Greece; Centre for Research and Technology Hellas, Chemical Process and Energy Resources Institute, Natural and Renewable Resource Exploitation Laboratory, 57001 Thessaloniki, Greece; Institute for Advanced Study (IUSS), Environmental Health Engineering, Piazza della Vittoria 15, 27100 Pavia, Italy.
Aristotle University of Thessaloniki, Department of Chemical Engineering, Environmental Engineering Laboratory, 54124 Thessaloniki, Greece.
Food Chem Toxicol. 2017 Aug;106(Pt A):114-124. doi: 10.1016/j.fct.2017.05.029. Epub 2017 May 15.
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 (V) and the Michaelis - Menten constant (K). 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 (R 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 K. 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.
