Structure-metabolism relationships of substituted anilines: prediction of N-acetylation and N-oxanilic acid formation using computational chemistry

Abstract

1. The relationship between the in vivo metabolism of substituted anilines, in particular N-acetylation and subsequent formation of oxanilic acids, and their molecular physico-chemical properties has been investigated using computational chemistry and pattern-recognition methods. The methods revealed that the physico-chemical properties most important for N-acetylation and subsequent oxanilic acid formation were electronic descriptors based on partial atomic charges and the susceptibility of the molecules to nucleophilic attack at certain ring positions. 2. The calculated partial atom charge on the amine nitrogen was the parameter most important for predicting that an aniline would be N-acetylated. The calculated nucleophilic susceptibility of the aromatic carbon para to the amino group (NS4) was the most significant parameter for determining oxanilic acid formation following N-acetylation. Thus, highly electron-withdrawing groups substituted at this position gave higher nucleophilic susceptibilities that were related to the presence of an oxanilic acid metabolite. 3. If the parameters relating to N-acetylation were modified by other electron-withdrawing groups in the ring (particularly at the position ortho to the amino group), then acetylation and subsequent oxanilic acid formation did not occur. The introduction of groups that allow the possibility of competing oxidative metabolic pathways elsewhere in the molecule (e.g. CH₃) also affected the production of oxanilic acids. 4. Using chemometric analysis of the computed physico-chemical properties, the result has been the generation of a model that classifies the metabolism of a number of anilines. This could be used to predict the acetylation and oxanilic formation propensity of a number of substituted anilines whose metabolism was unknown to the system, demonstrating that such techniques may be of use for predicting metabolism and hence could provide support for rational drug design.