Machine Learning Enabled Structure-Based Drug Repurposing Approach to Identify Potential CYP1B1 Inhibitors.

Content Provider	Europe PMC
Author	Raju, Baddipadige Narendra, Gera Verma, Himanshu Kumar, Manoj Sapra, Bharti Kaur, Gurleen jain, Subheet Kumar Silakari, Om
Copyright Year	2022
Abstract	Drug-metabolizing enzyme (DME)-mediated pharmacokineticresistanceof some clinically approved anticancer agents is one of the main reasonsfor cancer treatment failure. In particular, some commonly used anticancermedicines, including docetaxel, tamoxifen, imatinib, cisplatin, andpaclitaxel, are inactivated by CYP1B1. Currently, no approved drugsare available to treat this CYP1B1-mediated inactivation, making thepharmaceutical industries strive to discover new anticancer agents.Because of the extreme complexity and high risk in drug discoveryand development, it is worthwhile to come up with a drug repurposingstrategy that may solve the resistance problem of existing chemotherapeutics.Therefore, in the current study, a drug repurposing strategy was implementedto find the possible CYP1B1 inhibitors using machine learning (ML)and structure-based virtual screening (SB-VS) approaches. Initially,three different ML models were developed such as support vector machines(SVMs), random forest (RF), and artificial neural network (ANN); subsequently,the best-selected ML model was employed for virtual screening of theselleckchem database to identify potential CYP1B1 inhibitors. Theinhibition potency of the obtained hits was judged by analyzing thecrucial active site amino acid interactions against CYP1B1. Aftera thorough assessment of docking scores, binding affinities, as wellas binding modes, four compounds were selected and further subjectedto in vitro analysis. From the in vitro analysis, it was observed that chlorprothixene, nadifloxacin, andticagrelor showed promising inhibitory activity toward CYP1B1 in theIC50 range of 0.07–3.00 μM. These new chemicalscaffolds can be explored as adjuvant therapies to address CYP1B1-mediateddrug-resistance problems.
Journal	ACS Omega
Volume Number	7
PubMed Central reference number	PMC9476183
Issue Number	36
PubMed reference number	36120033
e-ISSN	24701343
DOI	10.1021/acsomega.2c02983
Language	English
Publisher	American Chemical Society
Publisher Date	2022-08-31
Access Restriction	Open
Rights License	Permits non-commercial access and re-use, provided that author attribution and integrity are maintained; but does not permit creation of adaptations or other derivative works (https://creativecommons.org/licenses/by-nc-nd/4.0/). © 2022 The Authors. Published by American Chemical Society
Content Type	Text
Resource Type	Article
Subject	Chemistry Chemical Engineering