Bioinformatics: A rational combine approach used for the identification and in-vitro activity evaluation of potent β-Glucuronidase inhibitors.

Identification of hotspot drug-receptor interactions through in-silico prediction methods (Pharmacophore mapping, virtual screening, 3DQSAR, etc), is considered as a key approach in drug designing and development process. In the current design study, advanced in-silico based computational techniques were used for the identification of lead-like molecules against the targeted receptor β-glucuronidase. The binding pattern of a potent inhibitor in the ligand-receptor X-ray co-crystallize complex was used to identify and extract the structure-base Pharmacophore features. Based on these observations; five structure-based pharmacophore models were derived to conduct the virtual screening of ICCBS in-house data-base. Top-ranked identified Hits (33 compounds) were selected to subject for in-vitro biological activity evaluation against β-glucuronidase enzyme; out of them, twenty compounds (61% of screened compounds) evaluated as actives, however eleven compounds were found to have significantly higher inhibitory activity, including compounds 1, 5-8, 10, 12-13, and 17-19 with IC50 values ranging from 1.2 μM to 34.9 μM. Out of the eleven potent inhibitors, seven compounds 1, 5, 6, 7, 8, 13, and 19 were found new, and evaluated first time for the β-glucuronidase inhibitory activity. Compounds 1, 5 and 19 exhibited a highly potent inhibition in uM of β-glucuronidase enzyme with non-cytotoxic behavior against the mouse fibroblast (3T3) cell line. Our combined in-silico and in-vitro results revealed that the binding pattern analysis of the eleven potent inhibitors, showed almost similar non-covalent interactions, as observed in case of our validated pharmacophore model. The obtained results thus demonstrated that the virtual screening minimizes false positives, and provide a template for the identification and development of new and more potent β-glucuronidase inhibitors with non-toxic effects.