In Silico Screening of Soursop Leaf (Annona muricata L.) Secondary Metabolites as Potential Estrogen Receptor Alpha Inhibitors

Fachrizal Dwi Kautsar; John Ebenezer Sianipar; Ulaya Warda Haiba; Neysa Zahra Fathinabila; Rizky Prasiska Wulandari; Halwa Aulia Nurdin; Rina Fajri Nuwarda

doi:10.24198/ijpst.v12i3.70710

In Silico Screening of Soursop Leaf (Annona muricata L.) Secondary Metabolites as Potential Estrogen Receptor Alpha Inhibitors

Fachrizal Dwi Kautsar, John Ebenezer Sianipar, Ulaya Warda Haiba, Neysa Zahra Fathinabila, Rizky Prasiska Wulandari, Halwa Aulia Nurdin, Rina Fajri Nuwarda

Abstrak

Breast cancer is the most prevalent malignancy among women worldwide. Estrogen receptor alpha (ERα) plays a critical role in regulating cancer cell proliferation, differentiation, and survival, making it an important therapeutic target. Annona muricata Linn. has attracted considerable attention because its leaves contain diverse secondary metabolites with reported anticancer activities, making them a promising source of potential ERα inhibitors. Therefore, this study aimed to evaluate the inhibitory potential of ten selected secondary metabolites from A. muricata leaves against ERα using an in silico approach. Drug-likeness and pharmacokinetic properties were assessed using Lipinski’s Rule of Five and ADMET prediction. Ligand-based pharmacophore modeling was performed, followed by pharmacophore-based virtual screening and molecular docking. Four compounds, namely chlorogenic acid, luteolin, quercetin, and daidzein, fulfilled the pharmacophore criteria with fit scores of 47.17; 47.17; 47.17; and 46.97, respectively, and were subsequently evaluated by molecular docking. Among them, daidzein exhibited the strongest inhibitory potential, with a binding free energy of −8.56 kcal/mol and an inhibition constant of 513.26 nM. Overall, these findings suggest that daidzein is the most promising secondary metabolite of A. muricata leaves for ERα inhibition and may serve as a lead compound for further computational and experimental validation.

Kata Kunci

In-silico, breast cancer, estrogen alpha, soursop leaf.

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Referensi

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DOI: https://doi.org/10.24198/ijpst.v12i3.70710