Successful identification of key chemical structure modifications that lead to improved ADME profiles

Abstract

The results of a new method developed to identify well defined structural transformations that are key to improve a certain ADME profile are presented in this work. In particular Naïve Bayesian statistics and SciTegic FCFP_6 molecular fingerprints have been used to extract, from a dataset of 1,169 compounds with known in vitro UGT glucuronidation clearance, those changes in chemical structure that lead to a significant increase in this property. The effectiveness in achieving that goal of the thus found 55,987 transformations has been quantified and compared to classical medicinal chemistry transformations. The conclusion is that on average the new transformations found via in silico methods induce increases of UGT clearance by twofold, whilst the classical transformations are on average unable to alter that endpoint significantly in any direction. When both types of transformations are combined via substructural searches (SSS) the average twofold increase in glucuronidation is maintained. The implications of these findings for the drug design process are also discussed, in particular when compared to other methods previously described in the literature to address the question 'Which compound do I make next?'