Curcumin-pyrazole a molecular hybrids as potential androgen receptor inhibitors for prostate cancer treatment through molecular docking, ADMET and cytotoxicity prediction, pharmacophore modeling, MDS, and DFT approaches
Prostate cancer is a significant cause of cancer-related mortality in men globally. The androgen receptor is a crucial target for drug development. This study investigates curcumin-pyrazole hybrids, which aim to enhance the bioavailability, stability, and anticancer activity of curcumin, a natural compound with limited therapeutic potential due to poor bioavailability. To investigate the binding affinities of 47 curcumin-pyrazole hybrids to androgen receptors using in silico methods and assess their pharmacokinetic profiles and cytotoxicity against prostate cancer cell lines. A total of 47 curcumin-pyrazole hybrids were evaluated through comprehensive in silico approaches. Molecular docking was utilized to investigate the binding affinities of these hybrids to androgen receptors. ADMET (Absorption, Distribution, Metabolism, Excretion, and Toxicity) and cytotoxicity assays were performed to assess their pharmacokinetic profiles and toxicity levels. The lead molecule, CP47 , was further investigated using pharmacophore modeling, molecular dynamics simulations (MDS), and density functional theory (DFT). Among the evaluated hybrids, CP47 displayed the best binding affinity of −9.9 kcal/mol as an effective androgen receptor antagonist. ADMET and cytotoxicity assays confirmed its efficacy, indicating a favorable pharmacokinetic profile and low toxicity, with significant effectiveness against prostate cancer cell lines. CP47 was identified as a promising lead molecule for further investigation. The integration of computational methods highlights the potential of curcumin-pyrazole hybrids in prostate cancer therapy, suggesting pathways for future clinical applications.
Authors
- Narmin Hama amin Hussen
Institutions
- University of Sulaimani (IQ)
Publication Details
- Journal
- Letters in Drug Design & Discovery
- Published
- 2025-04-01
- DOI
- https://doi.org/10.1016/j.lddd.2025.100047
- Citations
- 2
- Primary Topic
- Computational Drug Discovery Methods
- Type
- article
- Field-Weighted Citation Impact
- 3.01