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Identification of Novel PI3Kα Inhibitor Against Gastric Cancer: QSAR-, Molecular Docking–, and Molecular Dynamics Simulation–Based Analysis

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Abstract

Gastric cancer (GC) is a malignant tumor with global incidence and death ranking fifth and fourth, respectively. GC patients nevertheless have a poor prognosis despite the effectiveness of more advanced chemotherapy and surgical treatment options. The second most frequently mutated gene in GC is PI3Kalpha, a confirmed oncogene that results in abnormal PI3K/AKT/mTOR signaling, causing enhanced translation, proliferation, and survival, and is mutated in 7–25% of GC patients. The protein PI3Kalpha was targeted in the present study by utilizing machine learning (ML), molecular docking, and simulation. A total of 9214 molecules from the DrugBank database were chosen for the first screening. A training set for 6770 compounds tested against PI3Kalpha was assessed to create a quantitative structure-activity relationship-based machine learning model using five different classification algorithms: random forest, random tree, J48 pruned tree, decision stump, and REPTree. Furthermore, consideration was given to the random forest classifier for screening based on its performance index (Kappa statistics, ROC, and MCC). Overall, 1539 of the 9214 drug bank compounds were predicted to be active. Thereafter, three pharmacological filters, Lipinski’s rule, Ghose filter, and Veber rule, were applied to test the drug-like properties of the screened compounds. Twenty-six of 1593 compounds showed excellent drug-like properties and were further considered for molecular docking. Thereafter, two compounds were screened as hits because they possessed the molecular docked position with the lowest binding energy and an excellent bonding profile. The binding stability of the selected compounds was further assessed through molecular dynamics simulations for up to 100 ns. Furthermore, compound 1-(3-(2,4-dimethylthiazol-5-YL)-4-oxo-2,4-dihydroindeno[1,2-C]pyrazol-5-YL)-3-(4-methylpiperazin-1-YL) urea was selected as a potential hit in the final screening by analyzing a number of parameters, including the Rg, RMSD, RMSF, H bonding, and SASA profile. Therefore, we conclude that compound 1-(3-(2, 4-dimethylthiazol-5-YL)-4-oxo-2,4-dihydroindeno[1,2-C]pyrazol-5-YL)-3-(4-methylpiperazin-1-YL) urea has efficient inhibitory potential against PI3Kalpha protein and could be utilized for the development of effective drugs against GC.

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Data Availability

Initial X-ray structures are available at the Protein Data Bank (https://www.rcsb.org/). Datasets used for different model developments are available at ChEMBL (https://www.ebi.ac.uk/chembl/) and the DrugBank database (https://go.drugbank.com/). All other data are available in the main text.

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Acknowledgements

We extend our appreciation to our research team for their contribution to this study.

Funding

This study was financially supported by The Natural Science Foundation of Shandong Province under Grant Number ZR2020MH355, which funded the project “To explore the mechanism of Kunshen granules in the treatment of advanced gastric cancer based on MIR-92B via the PI3K/Akt pathway.”

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Authors and Affiliations

  1. First Clinical College of Shandong, University of Traditional Chinese Medicine, No. 16369 Jingshi Road, Lixia District, Jinan City, 250014, Shandong Province, China

    Fang Yuan, Ting Chen & Zhiqun Cao

  2. The First Department of Digestion, Affiliated Hospital of Shandong University of Traditional Chinese Medicine, No. 42 Wenhuaxi Road, Jinan City, 250011, Shandong Province, China

    Fang Yuan, Ting Chen & Zhiqun Cao

  3. Department of the Cancer Center, Shandong Provincial Third Hospital, Shandong University, No. 11, Wuyingshan Road, Jinan City, 250000, Shandong Province, China

    Ting Li

  4. TCM Department, Second Affiliated Hospital of Shandong First Medical University, No. 366 Taishan Street, Taian, 271000, China

    Xinjie Xu

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Contributions

F.Y and T.L. conducted the machine learning and molecular docking experiments and planned outline of this research, Z.C. generated the idea and wrote the manuscript, and X.X., T.C., and critically reviewed and guided in the idea generation.

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Correspondence to Zhiqun Cao.

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Fang Yuan and Ting Li are Co-first Authors and equally contributed to this work.

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Yuan, F., Li, T., Xu, X. et al. Identification of Novel PI3Kα Inhibitor Against Gastric Cancer: QSAR-, Molecular Docking–, and Molecular Dynamics Simulation–Based Analysis. Appl Biochem Biotechnol (2024). https://doi.org/10.1007/s12010-024-04898-3

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