<i>In silico</i> estimation of basic activity-relevant parameters for a set of drug absorption promoters
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Finding a balance between a desired drug’s potency and its physicochemical properties that are important for its molecule pharmacokinetic or pharmacodynamics profile is still a challenging issue in rational drug discovery. Quantitative assessment of the lipophilic characteristics of potential drug molecules is indispensable for efficient development of Absorption, Distribution, Metabolism, Excretion, Toxicity-tailored structure–activity models; therefore reliable procedures for deriving log <i>P</i> from molecular structure are desirable. In the current work a range of various software log <i>P</i> predictors for estimation of the numerical lipophilic values for a set of cholic acid derivatives were employed and subsequently cross-compared with the experimental parameters. Thus, the empirical lipophilicity (<i>R</i><sub><i>M</i></sub>) was compared with the corresponding log <i>P</i> characteristics calculated using alternative methods for deducing the lipophilic features. The mean values of the selected molecular descriptors that were averaged over the chosen calculation methods (consensus clog <i>P</i>) were subsequently correlated with the <i>R</i><sub><i>M</i></sub> parameter. As an additional experiment, the iterative variable elimination partial least squares (IVE-PLS) methodology for an ensemble of descriptors retrieved from Dragon 6.0 software was applied for a set of drug transporters. To investigate the variations within the ensemble of cholic acid derivatives principal component analysis (PCA) and self-organizing neural network (SOM) procedures were used to visualize the major differences in the performance of drug promoters with respect to their lipophilic profile.
