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Abstract
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 P from molecular structure are desirable. In the current work a range of various software log P 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 (RM) was compared with the corresponding log P 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 P) were subsequently correlated with the RM 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.
Acknowledgements
The authors thank Professor Johann Gasteiger for facilitating access to the SONNIA program. We would like to acknowledge OpenEye and OpenBabel Scientific Software for the free academic license. Dr Andrzej Bak thanks the Foundation for Polish Science for his individual grant. This study was also partially supported by the Slovak Research and Development Agency (Grant No. APVV-0516-12).