Abstract
Inhibition of cytochrome P450 (CYP) is a major cause of herb–drug interactions. The CYP1A2 enzyme plays a major role in the metabolism of drugs in humans. Its broad substrate specificity, as well as its inhibition by a vast array of structurally diverse herbal active ingredients, has indicated the possibility of metabolic herb–drug interactions. Therefore nowadays searching inhibitors for CYP1A2 from herbal medicines are drawing much more attention by biological, chemical and pharmological scientists. In our work, a pharmacophore model as well as the docking technology is proposed to screen inhibitors from herbal ingredients data. Firstly different pharmaphore models were constructed and then validated and modified by 202 herbal ingredients. Secondly the best pharmaphore model was chosen to virtually screen the herbal data (a curated database of 989 herbal compounds). Then the hits (147 herbal compounds) were continued to be filtered by a docking process, and were tested in vitrosuccessively. Finally, five of eighteen candidate compounds (272, 284, 300, 616 and 817) were found to have inhibition of CYP1A2 activity. The model developed in our study is efficient for in silico screening of large herbal databases in the identification of CYP1A2 inhibitors. It will play an important role to prevent the risk of herb–drug interactions at an early stage of the drug development process.
Conclusions
In this study, we have selected several template molecules to generate pharmacophores. 202 different herb compounds were used as test data to test their inhibitory activity against CYP1A2 in vitro and used to identify the best pharmacophore with the highest external predictive power. A highly predictive pharmacophore model was generated with three bifonazole (TMI) in different conformations with sophisticated optimization. A rigorously validated pharmacophore model was then used to screen our in-house database collection of a total of over one thousand herb compounds. 147 hits were filtered out by the selected pharmacophore model and were docked into the active site of CYP1A2. Finally, 18 hits were further filtered and experimentally validated. Five of them were confirmed to have inhibitory activities to CYP1A2, and the accuracy of the model was 27.8%. This study illustrates that the model developed here is efficient for virtual screening of large databases in the identification of CYP1A2 inhibitors or non-inhibitors. Accordingly, the models can play an important role to prevent the risk of e.g., herb–drug interactions through metabolism at an early stage of the drug development process.
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