In Silico Design of Novel Bioactive Molecules to Treat Breast Cancer with Uvaria Chamae Derivatives: A Computational Approach
Year: 2025
Type: article
Abstract: Breast cancer, which causes abnormal growth in breast tissue and can spread to other parts of the body, is difficult to treat due to the detrimental effects of traditional treatments such as radiation, chemotherapy, and surgery. Uvaria chamae a plant from the Annonaceae family, has antioxidant and antiproliferative characteristics, making it an alternative herbal treatment for cancer. The study aims to assess the bioactivity of Uvaria chamae leaf chemicals against the human placental aromatase in the breast cancer signaling pathway using a protein-ligand networking approach and molecular docking analysis. Bioactive Uvaria chamae compounds were downloaded from PubChem, examined for similarity using SWISS ADME, anticancer potential using PASSOnline, docked using PyRX, visualized with Biovia Discovery Studio, and tested for ADMET using pkCSM. Uvaria chamae leaves contain bioactive substances such as Apigenin, Diphenylcarbazide, Enamine, Naringin, Obovatin, Rutin trihydrate and Quercetin which have been shown to inhibit human placental aromatase proteins linked to breast cancer. Rutin trihydrate is a bioactive molecule that has a higher binding affinity than Exemestane which is used as reference drugs. The In silico analysis, drug likeness analysis, absorption, distribution, metabolism, excretion, and toxicity (ADMET) evaluation suggest that Rutin trihydrate, Quercetin and Apigenin derivatives from Uvaria chamae is a promising human placental aromatase inhibitor for breast cancer, with a binding affinity of -9.2, -8.0, and -8.0 kcal/mol compared to Exemestane (-7.5 kcal/mol). Considering the results of molecular docking, drug likeness analysis, absorption, distribution, metabolism, excretion, and toxicity (ADMET) evaluation, it can be concluded that Rutin trihydrate Quercetin and Apigenin derivatives hold promise as potent inhibitors for the treatment of breast cancer.
Author: Omobolaji Odunowo
Institution: University of North Texas
Language: English
Subfield: Computational Theory and Mathematics
Field: Computer Science
Domain: Physical Sciences
Open Access status: diamond
OpenAlex ID: W4410215136