Integrated Virtual Screening Approach Identifies New CYP19A1 Inhibitors.

Area of Science:

• Biochemistry
• Medicinal Chemistry
• Computational Biology

Background:

• Human cytochrome P450 19A1 (CYP19A1), also known as aromatase, is crucial for synthesizing the steroid hormone 17β-estradiol.
• CYP19A1 is a primary therapeutic target for sex-hormone-dependent conditions due to its role in converting androgens to estrogens.

Purpose of the Study:

• To develop and apply a virtual screening workflow integrating machine learning and structure-based modeling for identifying novel CYP19A1 inhibitors.
• To discover potent and structurally diverse inhibitors of CYP19A1 for potential therapeutic applications.

Main Methods:

• Machine learning models were constructed using extensive CYP19A1 data from ChEMBL and PubChem Bioassay databases, followed by rigorous validation.
• A virtual screening campaign was executed, and promising hits were experimentally evaluated using an enzymatic assay with heterologously expressed human CYP19A1 in yeast.

Main Results:

• The virtual screening workflow successfully identified seven structurally distinct novel CYP19A1 inhibitors from ten tested compounds.
• Compound 9, a novel noncovalent inhibitor featuring coumarin and imidazole moieties, demonstrated high potency with an IC50 value of 271 ± 51 nM.

Conclusions:

• The developed computational approach is effective for discovering novel CYP19A1 inhibitors.
• Compound 9 represents a promising lead compound for developing new therapeutics targeting CYP19A1-mediated pathways in hormone-related disorders.