Development of quantitative structure-metabolism (QSMR) relationships for substituted anilines based on computational chemistry

Abstract

A novel stepwise classification approach for predicting the metabolic fate of substituted anilines, based on calculated physicochemical parameters of the parent anilines, was developed. Based on multivariate pattern recognition methods (PLS-DA or soft independent modelling of class analogy [SIMCA]), these models allowed prediction of N-acetylation and subsequent N-oxanilic acid formation. These classification methods provided an improved classification success when compared with existing quantitative structure-metabolism relationship models for substituted anilines. Modelling the physicochemical properties of the N-acetylated compounds was considered as an addition to the stepwise model. Inclusion of parameters describing the N-acetyl moiety had little effect on the predictive ability of a stepwise parent to N-acetyl to N-oxanilic acid PLS-DA model, and had a negative impact on that of SIMCA models. This was attributed to the relatively small contribution to the total parameter variance caused by differences arising as a result of N-acetylation compared to the contribution made by the substituent effects. Calculation of physicochemical properties incorporating the effect of solvation using ab initio methods improved the classification model in terms of both the visual separation in multivariate projections and prediction accuracy.