Integration of Network Pharmacology and Molecular Docking with Computational Toxicology to Investigate the Predicted Anti-Prostate Cancer Potential of Ficus exasperata Leaf Phytochemicals
DOI:
https://doi.org/10.23851/mjs.v37i3.1932Keywords:
Ficus exasperata, Prostate cancer, Network pharmacology, Molecular docking, Androgen receptor, SwissADME, Computational toxicologyAbstract
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.
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The data supporting the findings of this study are included within the article. All computational data, including phytochemical, pharmacokinetic, network pharmacology, molecular docking, and toxicity prediction results, are available from the corresponding author upon reasonable request.
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Copyright (c) 2026 Mutiu A. Alabi, Habeebullah A. Jamiu, Dauda S. Adebara, Akeem O. Salawu, AbdulJeleel A. Yusuph, Abdulhameed O. Alli, Fatai A. Kareem, Emmanuel O. Ajani
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