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

Drug-metabolizing enzyme (DME)-mediated pharmacokinetic resistance of some clinically approved anticancer agents is one of the main reasons for cancer treatment failure. In particular, some commonly used anticancer medicines, including docetaxel, tamoxifen, imatinib, cisplatin, and paclitaxel, are inactivated by CYP1B1. Currently, no approved drugs are available to treat this CYP1B1-mediated inactivation, making the pharmaceutical industries strive to discover new anticancer agents. Because of the extreme complexity and high risk in drug discovery and development, it is worthwhile to come up with a drug repurposing strategy that may solve the resistance problem of existing chemotherapeutics. Therefore, in the current study, a drug repurposing strategy was implemented to 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 the selleckchem database to identify potential CYP1B1 inhibitors. The inhibition potency of the obtained hits was judged by analyzing the crucial active site amino acid interactions against CYP1B1. After a thorough assessment of docking scores, binding affinities, as well as binding modes, four compounds were selected and further subjected to in vitro analysis. From the in vitro analysis, it was observed that chlorprothixene, nadifloxacin, and ticagrelor showed promising inhibitory activity toward CYP1B1 in the IC50 range of 0.07–3.00 μM. These new chemical scaffolds can be explored as adjuvant therapies to address CYP1B1-mediated drug-resistance problems

Scaffold hopping for designing of potent and selective CYP1B1 inhibitors to overcome docetaxel resistance: synthesis and evaluation

Cytochrome P450 1B1, a tumor-specific overexpressed enzyme, significantly impairs the pharmacokinetics of several commonly used anticancer drugs including docetaxel, paclitaxel and cisplatin, leading to the problem of resistance to these drugs. Currently, there is no CYP1B1 inhibition-based adjuvant therapy available to treat this resistance problem. Hence, in the current study, exhaustive in-silico studies including scaffold hopping followed by molecular docking, three-dimensional quantitative structure-activity relationships (3D-QSAR), molecular dynamics and free energy perturbation studies were carried out to identify potent and selective CYP1B1 inhibitors. Initially, scaffold hopping analysis was performed against a well-reported potent and selective CYP1B1 inhibitor (i.e. compound 3n). A total of 200 scaffolds were identified along with their shape and field similarity scores. The top three scaffolds were further selected on the basis of these scores and their synthesis feasibility to design some potent and selective CYP1B1 inhibitors using the aforementioned in-silico techniques. Designed molecules were further synthesized to evaluate their CYP1B1 inhibitory activity and docetaxel resistance reversal potential against CYP1B1 overexpressed drug resistance MCF-7 cell line. In-vitro results indicated that compounds 2a, 2c and 2d manifested IC50 values for CYP1B1 ranging from 0.075, 0.092 to 0.088 μM with at least 10-fold selectivity. At low micromolar concentrations, compounds 1e, 1f, 2a and 2d exhibited promising cytotoxic effects in the docetaxel-resistant CYP1B1 overexpressed MCF-7 cell line. In particular, compound 2a is most effective in reversing the resistance with IC50 of 29.0 ± 3.6 μM. All of these discoveries could pave the way for the development of adjuvant therapy capable of overcoming CYP1B1-mediated resistance. Communicated by Ramaswamy H. Sarma</p