Prospective computational design and in vitro bio-analytical tests of new chemical entities as potential selective CYP17A1 lyase inhibitors

Secondary Binding Site of CYP17A1 in Enhanced Sampling Simulations

Androgens like testosterone and dihydrotestosterone play a key role in prostate cancer progression, making the enzyme CYP17A1, essential for androgen synthesis, a crucial therapeutic target. Recent studies have revealed electron density at the substrate entry channel, suggesting the presence of a secondary binding site. In this study, we calculated the binding free energy landscape of known ligands at this site using Funnel Metadynamics. Our results characterize this binding site and indicate that nonheme-interacting ligands could effectively bind to CYP17A1, providing a novel approach to the design of CYP17A1 inhibitors.

In silico and in vitro assessment of drugs potentially causing adverse effects by inhibiting CYP17A1

Cytochrome P450 enzymes (CYPs) play a crucial role in the metabolism and synthesis of various compound classes. While drug-metabolizing CYP enzymes are frequently investigated as anti-targets, the inhibition of CYP enzymes involved in adrenal steroidogenesis is not well studied. The steroidogenic enzyme CYP17A1 is a dual-function enzyme catalyzing hydroxylase and lyase reactions relevant for the biosynthesis of adrenal glucocorticoids and androgens. Inhibition of CYP17A1-hydroxylase leads to pseudohyperaldosteronism with subsequent excessive mineralocorticoid receptor activation, hypertension and hypokalemia. In contrast, specific inhibition of the lyase function might be beneficial for the treatment of prostate cancer by decreasing adrenal androgen levels. This study combined in silico and in vitro methods to identify drugs inhibiting CYP17A1. The most potent CYP17A1 inhibitors identified are serdemetan, mocetinostat, nolatrexed, liarozole, and talarozole. While some of these drugs are currently under investigation for the treatment of various cancers, their potential for the treatment of prostate cancer is yet to be explored. The DrugBank database was screened for CYP17A1 inhibitors, to increase the awareness for the risk of drug-induced pseudohyperaldosteronism and to highlight drugs so far unknown for their potential to cause side effects resulting from CYP17A1 inhibition.

Exploring the Chemical Space of CYP17A1 Inhibitors Using Cheminformatics and Machine Learning

Cytochrome P450 17A1 (CYP17A1) is one of the key enzymes in steroidogenesis that produces dehydroepiandrosterone (DHEA) from cholesterol. Abnormal DHEA production may lead to the progression of severe diseases, such as prostatic and breast cancers. Thus, CYP17A1 is a druggable target for anti-cancer molecule development. In this study, cheminformatic analyses and quantitative structure-activity relationship (QSAR) modeling were applied on a set of 962 CYP17A1 inhibitors (i.e., consisting of 279 steroidal and 683 nonsteroidal inhibitors) compiled from the ChEMBL database. For steroidal inhibitors, a QSAR classification model built using the PubChem fingerprint along with the extra trees algorithm achieved the best performance, reflected by the accuracy values of 0.933, 0.818, and 0.833 for the training, cross-validation, and test sets, respectively. For nonsteroidal inhibitors, a systematic cheminformatic analysis was applied for exploring the chemical space, Murcko scaffolds, and structure-activity relationships (SARs) for visualizing distributions, patterns, and representative scaffolds for drug discoveries. Furthermore, seven total QSAR classification models were established based on the nonsteroidal scaffolds, and two activity cliff (AC) generators were identified. The best performing model out of these seven was model VIII, which is built upon the PubChem fingerprint along with the random forest algorithm. It achieved a robust accuracy across the training set, the cross-validation set, and the test set, i.e., 0.96, 0.92, and 0.913, respectively. It is anticipated that the results presented herein would be instrumental for further CYP17A1 inhibitor drug discovery efforts.

Pathfinder-Driven Chemical Space Exploration and Multiparameter Optimization in Tandem with Glide/IFD and QSAR-Based Active Learning Approach to Prioritize Design Ideas for FEP+ Calculations of SARS-CoV-2 PLpro Inhibitors

A global pandemic caused by the SARS-CoV-2 virus that started in 2020 and has wreaked havoc on humanity still ravages up until now. As a result, the negative impact of travel restrictions and lockdowns has underscored the importance of our preparedness for future pandemics. The main thrust of this work was based on addressing this need by traversing chemical space to design inhibitors that target the SARS-CoV-2 papain-like protease (PLpro). Pathfinder-based retrosynthesis analysis was used to generate analogs of GRL-0617 using commercially available building blocks by replacing the naphthalene moiety. A total of 10 models were built using active learning QSAR, which achieved good statistical results such as an R2 > 0.70, Q2 > 0.64, STD Dev < 0.30, and RMSE < 0.31, on average for all models. A total of 35 ideas were further prioritized for FEP+ calculations. The FEP+ results revealed that compound 45 was the most active compound in this series with a ΔG of −7.28 ± 0.96 kcal/mol. Compound 5 exhibited a ΔG of −6.78 ± 1.30 kcal/mol. The inactive compounds in this series were compound 91 and compound 23 with a ΔG of −5.74 ± 1.06 and −3.11 ± 1.45 kcal/mol. The combined strategy employed here is envisaged to be of great utility in multiparameter lead optimization efforts, to traverse chemical space, maintaining and/or improving the potency as well as the property space of synthetically aware design ideas.

The Discovery of BMS-737 as a Potent, CYP17 Lyase-Selective Inhibitor for the Treatment of Castration-Resistant Prostate Cancer

We report herein, the discovery of BMS-737 (compound 33) as a potent, non-steroidal, reversible small molecule inhibitor demonstrating 11-fold selectivity for CYP17 lyase over CYP17 hydroxylase, as well as a clean xenobiotic CYP profile for the treatment of castration-resistant prostate cancer (CRPC). Extensive SAR studies on the initial lead 1 at three different regions of the molecule resulted in the identification of BMS-737, which demonstrated a robust 83% lowering of testosterone without any significant perturbation of the mineralocorticoid and glucocorticoid levels in cynomologous monkeys in a 1-day PK/PD study.