Multiple Pharmacokinetic Parameter Prediction for a Series of Cephalosporins

Abstract

The goal of quantitative structure–pharmacokinetic relationship analyses is to develop useful models that can predict one or more pharmacokinetic properties of a particular compound. In the present study, a multiple-output artificial neural network model was constructed to predict human half-life, renal and total body clearance, fraction excreted in urine, volume of distribution, and fraction bound to plasma proteins for a series of cephalosporins. Descriptors generated solely from drug structure were used as inputs for the model, and the six pharmacokinetic parameters were simultaneously predicted as outputs. The final 10 descriptor model contained sufficient information for successful predictions using both internal and external test compounds. Descriptors were found to contribute to individual pharmacokinetic parameters to differing extents, such that descriptor importance was independent of the relationships between pharmacokinetic parameters. This technique provides the advantage of simultaneous prediction of multiple parameters using information obtained by nonexperimental means, with the potential for use during the early stages of drug development.