Document Type : Research Articles
Authors
1
Master in Pharmaceutical Sciences, Faculty of Pharmacy, Gadjah Mada University, Yogyakarta 55281, Indonesia.
2
Laboratory of Advanced Pharmaceutical Sciences, Faculty of Pharmacy, Universitas Gadjah Mada, Sekip Utara II, 55281, Yogyakarta, Indonesia.
3
Laboratory of Medicinal Chemistry, Department of Pharmaceutical Chemistry, Faculty of Pharmacy, Universitas Gadjah Mada, Sekip Utara II, 55281, Yogyakarta, Indonesia.
4
Research Centre for Computation, Research Organization for Electronics and Informatics, National Research and Innovation Agency (BRIN), Jl. Tamansari 71, Bandung City, 40132, Indonesia.
5
Doctoral Program of Computational Science, Division of Mathematical & Physical Sciences, Graduate School of Science & Technology, Kanazawa University, Kakuma, Kanazawa, Ishikawa, 920-1192, Japan.
6
Research Centre for Computing, National Research and Innovation Agency (BRIN), Jl. Raya Jakarta-Bogor KM 46 Cibinong, 16911, Indonesia.
7
Faculty of Pharmacy, Sanata Dharma University, Paingan, Maguwoharjo, Depok, Sleman, Yogyakarta, 55282, Indonesia.
8
Laboratory of Macromolecular Engineering, Department of Pharmaceutical Chemistry, Faculty of Pharmacy, Universitas Gadjah Mada Sekip Utara II, 55281, Yogyakarta, Indonesia.
Abstract
Introduction: Breast cancer stem cells (BCSCs) promote chemoresistance and metastasis by activating aberrant signaling pathways such as phosphatidylinositol 3-kinase/protein kinase B (PI3K/Akt). α-Chaconine (CHA) is a steroidal glycoalkaloid that has demonstrated cytotoxic and antiproliferative activity in various cancer models, possibly through modulation of the PI3K/Akt pathway. However, its direct interaction with PI3Kα and related alterations has not yet been elucidated. This study aimed to assess CHA as a potential BCSC-targeting agent, particularly as a PI3K inhibitor targeting both wild-type and mutant PI3Kα, using an in silico approach. Methods: Mutation data were retrieved from cBioPortal and analyzed using MutPred2. SWISS-MODEL was used to generate homology models of the wild-type and mutant PI3Kα structures. Structural validation was performed using PROCHECK and ERRAT. Molecular docking was employed to evaluate the binding affinity of the PI3Kα-CHA complex. Molecular dynamics simulations were conducted to assess the stability of the interaction between CHA and PI3Kα. Results: Three PIK3CA mutations were identified as pathogenic: E542K, E545K, and H1047R. Molecular docking revealed a strong binding affinity of alpelisib to both wild-type and mutant (E542K, E545K, and H1047R) forms of p110 α. CHA exhibited the lowest binding energy with the H1047R mutant, with a docking score of −9.93 kcal/mol. Molecular dynamics simulations demonstrated stable interactions of alpelisib with wild-type, E542K, and E545K mutants, whereas CHA showed stable interaction only with the H1047R mutant. MM-PBSA calculations confirmed the lowest binding free energies for alpelisib-wild-type p110α and CHA-H1047R complexes, with values of −29.21 kcal/mol and −25.88 kcal/mol, respectively. Conclusion: These findings provide a basis for further in vitro and in vivo studies to evaluate CHA as a selective PI3K inhibitor targeting the H1047R PIK3CA mutation in breast cancer stem cells.
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