Quantitative molecular analysis predicts 5-hydroxytryptamine3 receptor binding affinity.

Abstract

A quantitative molecular model was derived to predict drug affinities for 5-hydroxytryptamine3 (5-HT3) receptors. The model was based on the molecular characteristics of a "learning set" of 40 pharmacological agents that had been analyzed previously in radioligand binding studies. Molecules were analyzed for various structural features, i.e., the presence of a benzenoid ring and nitrogen atom, substitutions on the benzenoid ring, the location of the substitutions on the nitrogen, and the molecular characteristics of the most direct pathway from the benzenoid ring to the nitrogen. Weighting factors, based on published 5-HT3 receptor affinity data, were then assigned to each of 10 molecular characteristics. The derived computational model predicts accurately the affinities of the learning set for the 5-HT3 receptor (r = 0.98; p less than 0.001). The computational model was then used to predict the receptor affinities of a "test set" of 40 pharmacological agents. The predicted values for these agents also correlate significantly (r = 0.83; p less than 0.001) with drug affinities for the 5-HT3 receptor, as determined by radioligand binding assays. This first line screening approach allows for the accurate prediction of drug affinities based on molecular characteristics with minimal dependence upon animal tissues or radioactivity.