Integration of Network Pharmacology and Molecular Docking with Computational Toxicology to Investigate the Predicted Anti-Prostate Cancer Potential of Ficus exasperata Leaf Phytochemicals

Abstract

Background: Ficus exasperata (F. exasperata) is used in African traditional medicine for inflammatory and urinary disorders, but its phytochemical interactions with prostate cancer targets remain insufficiently characterized computationally. Objective: This study used network pharmacology, molecular docking, pharmacokinetic prediction, and computational toxicology to explore the predicted anti-prostate cancer potential of F. exasperata leaf ethanol extract. Methods: GC-MS characterized phytochemicals. SwissADME assessed drug-likeness and pharmacokinetics. SwissTargetPrediction identified targets; prostate cancer genes were retrieved from GeneCards, Open Targets, and DisGeNET. GO and KEGG enrichment were performed. Docking and MM-GBSA were done against the androgen receptor (PDB: 2AX6). PASS, ProTox-3.0, and StopTox predicted activities and toxicity. Results: GC-MS identified eight phytoconstituents; two had favorable pharmacokinetics. Network pharmacology located 174 phytochemical targets, 3,388 prostate cancer genes, and 97 common targets. Docking yielded XP Glide scores of −1.866 kcal/mol for hexadecanoic acid, ethyl ester and −2.609 kcal/mol for enzalutamide; XP MM-GBSA values were −38.29 and −31.99 kcal/mol, respectively. PASS predicted testosterone 17β-dehydrogenase inhibition (Pa=0.781), TP53 enhancement (Pa=0.719), and alpha-methylacyl-CoA racemase inhibition (Pa=0.665) for the phytochemical. Toxicity prediction indicated a higher LD50 but possible skin sensitization. Conclusions: Computational analyses suggest F. exasperata phytochemicals may interact with prostate cancer networks and AR. Hexadecanoic acid, ethyl ester warrants further study. These are predictions, not experimental evidence; AR-binding studies, molecular dynamics, cellular assays, and in vivo validation are needed.