Quantitative Structure-Retention-Pharmacokinetic Relationship Studies

Snezana      Agatonovic-Kustrin; Joseph   V.   Turner; Beverley   D.   Glass

Abstract

Since the majority of lead compounds identified for drug clinical trials fail to reach the market due to poor efficacy in humans or poor pharmacokinetics (PKs), the prediction of PK properties in humans plays an important role in selection of potential drug candidates. The aim of the present study was to develop novel models for the prediction of separate PK parameters for a diverse set of drugs. Prediction would be based on the retention of each drug using micellar liquid chromatography (MLC) and selected theoretically-derived descriptors. Retention time, half life (t1/2), and volume of distribution (Vd) for each of the 26 training drugs were extracted from literature while molecular descriptors were generated using Molecular Modeling Pro. A total of 35 molecular descriptors describing molecular size, shape and solubility were calculated from the 3D molecular structure of each compound. Artificial neural network (ANN) modeling was used to correlate the calculated descriptors and retention time with half life and volume of distribution. A sensitivity analysis procedure was used to refine the models. The final predictive models showed significant correlations with literature values of t1/2 and Vd: 0.854 and 0.855 respectively for the internal testing data and 0.720 and 0.827 respectively for the external validation set of compounds. Absolute predicted values were in good agreement with literature values. Analysis of descriptors in the optimum models revealed a large degree of overlap. Solubility characteristics, hydrogen bonding, and molecular size and shape were shown to play important roles in determining drug t1/2 and Vd. The reciprocal of retention time was also included in both optimum models attesting to the significance of this particular physicochemical parameter and the complexity of the models developed. This novel combination of theoretical and experimental data for pharmacokinetic modeling may lead to further progress in drug development.

Keywords: Theoretical descriptors, generalized ANN, QSPR, physicochemical parameter

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