Development of QSARs for parameterizing Physiology Based ToxicoKinetic models

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 microprocesses
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.