AUTHOR(S): Sneha N. Patil*, Ananya R. Sangar, Sanika S. Kokane, Rohini S. Desai , Avadhut S. Mane.
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Abstract:
Imatinib effectively targets the constitutive activation of the c-KIT receptor tyrosine kinase, which is the primary cause of gastrointestinal stromal tumours (GISTs). However, the development of new inhibitors is required due to developed medication resistance and ongoing, lifelong administration with related side effects. Using a hybrid in silico drug repurposing approach, our study quickly identified possible c-KIT antagonists that could overcome imatinib resistance by utilising the established safety of FDA-approved medications. The methodology combines structure-based blind molecular docking against the human c-KIT kinase domain (PDB ID: 8S16) using AutoDock VINA with ligand-based screening via SwissSimilarity, utilising Imatinib as a reference to locate structurally similar FDA-approved medicines (Tanimoto coefficient ≥0.988). This method investigated possible allosteric sites while screening for both strong binding affinity and great structural similarity. Several of the top 10 compounds with high anticipated binding affinities were identified via molecular docking. Interestingly, the chemical CHEMBL475796 performed better than the reference standard, imatinib (−11.2 kcal/mol), with a Vina score of −12.0 kcal/mol. Because they are currently FDA-approved, the identified lead compounds—in particular, CHEMBL475796—are well-positioned for quick translational development. To verify c-KIT inhibitory effectiveness and efficacy against imatinib-resistant GIST phenotypes, a thorough in vitro and in vivo validation process is the next essential step.
Keywords:
Gastrointestinal Stromal Tumours (GISTs), c-KIT, Imatinib, Drug Repurposing, In Silico Drug Discovery, Virtual Screening, Molecular Docking, Blind Docking, SwissSimilarity, Tyrosine Kinase Inhibitor, AutoDock VINA, Tanimoto Coefficient, FDA-approved drugs.
Introduction:
Gain-of-function mutations in the gene encoding the receptor tyrosine kinase, c-KIT, which result in its constitutive, ligand-independent activation, are usually the pathogenesis of gastrointestinal stromal tumours (GISTs), the most prevalent mesenchymal malignancy of the gastrointestinal tract [1]. In most cases of GIST, the primary oncogenic mechanism that promotes unchecked cell proliferation and survival is this hyperactivated signalling. By specifically targeting the mutant c-KIT protein, imatinib, a tyrosine kinase inhibitor, revolutionised the treatment of GIST [2–3]. The nearly unavoidable emergence of acquired drug resistance, frequently brought on by secondary c-KIT mutations, severely limits the benefits of imatinib despite its early clinical success and causes disease progression in the majority of treated patients [4]. Furthermore, continuous, life-long administration is required and is frequently associated with dose-limiting side effects, including periorbital edema, fatigue, and gastrointestinal issues.
By utilising the established safety and pharmacokinetic information of currently approved FDA drugs, drug repurposing offers a quicker and more affordable discovery pathway for the search for new therapeutic compounds that can overcome imatinib resistance and provide a safer profile [5–6]. For this, computational techniques—in particular, molecular docking and virtual screening—have been crucial in quickly sorting through enormous chemical libraries to find potential candidate compounds. Potential repurposed medications that show efficacy against GIST cell lines, including imatinib-resistant phenotypes, have been effectively identified by previous in silico research [7-8].
The current study uses a mixed computational approach to more fully investigate the landscape of putative c-KIT inhibitors. [9–10] This work combines structure-guided blind molecular docking to evaluate the pharmaceuticals' binding affinity and interactions with the c-KIT kinase domain with ligand-based screening, which uses the molecular structure of the well-known inhibitor imatinib to find structurally similar FDA-approved medications.[11–13] In order to quickly identify and rank high-potential drug candidates for repurposing in the treatment of GIST, this hybrid approach prioritises approved medications that are both structurally similar to the established first-line agent and anticipated to exhibit favourable receptor binding. It focusses on mechanisms that may circumvent acquired resistance and minimise side effects.[14–15]
References
1. Lau S, Tam K, Kam CK, Lui CY, Siu CW, Lam HS, Mak KL. Imaging of gastrointestinal stromal tumour (GIST). Clinical radiology. 2004 Jun 1;59(6):487-98. https://doi.org/10.1016/j.crad.2003.10.018
2. Demetri GD. Identification and treatment of chemoresistant inoperable or metastatic GIST: experience with the selective tyrosine kinase inhibitor imatinib mesylate (STI571). European journal of cancer. 2002 Sep 1;38:S52-9. https://doi.org/10.1016/S0959-8049(02)80603-7
3. Demetri GD. Targeting c-kit mutations in solid tumors: scientific rationale and novel therapeutic options. InSeminars in oncology 2001 Oct 1 (Vol. 28, pp. 19-26). WB Saunders. https://doi.org/10.1016/S0093-7754(01)90099-5
4. Tamborini, E., Pricl, S., Negri, T. et al. Functional analyses and molecular modeling of two c-Kit mutations responsible for imatinib secondary resistance in GIST patients. Oncogene 25, 6140–6146 (2006). https://doi.org/10.1038/sj.onc.1209639
5. Stegmeier F, Warmuth M, Sellers WR, Dorsch M. Targeted cancer therapies in the twenty‐first century: lessons from imatinib. Clinical Pharmacology & Therapeutics. 2010 May;87(5):543-52. https://doi.org/10.1038/clpt.2009.297
6. He, C., Wang, Z., Yu, J. et al. Current Drug Resistance Mechanisms and Treatment Options in Gastrointestinal Stromal Tumors: Summary and Update. Curr. Treat. Options in Oncol. 25, 1390–1405 (2024).https://doi.org/10.1007/s11864-024-01272-7
7. Pierotti, M., Tamborini, E., Negri, T. et al. Targeted therapy in GIST: in silico modeling for prediction of resistance. Nat Rev Clin Oncol 8, 161–170 (2011). https://doi.org/10.1038/nrclinonc.2011.3
8. Keretsu S, Ghosh S, Cho SJ. Molecular modeling study of c-KIT/PDGFRα dual inhibitors for the treatment of gastrointestinal stromal tumors. International Journal of Molecular Sciences. 2020 Nov 3;21(21):8232. https://doi.org/10.3390/ijms21218232
9. Elasbali AM, Al-Soud WA, Elfaki EM, Alanazi HH, Alharbi B, Alharethi SH, Anwer K, Mohammad T, Hassan MI. Identification of novel c-Kit inhibitors from natural sources using virtual screening and molecular dynamics simulations. Journal of Biomolecular Structure and Dynamics. 2024 Jul 23;42(11):5982-94. https://doi.org/10.1080/07391102.2023.2231547
10. Kharkar PS. Computational approaches for the design of (mutant-) selective tyrosine kinase inhibitors: state-of-the-art and future prospects. Current Topics in Medicinal Chemistry. 2020 Jul 1;20(17):1564-75. https://doi.org/10.2174/1568026620666200502005853
11. Pratap Reddy Gajulapalli V. Development of Kinase‐Centric Drugs: A Computational Perspective. ChemMedChem. 2023 Oct 4;18(19):e202200693. https://doi.org/10.1002/cmdc.202200693
12. Li J, Gong C, Zhou H, Liu J, Xia X, Ha W, Jiang Y, Liu Q, Xiong H. Kinase inhibitors and kinase-targeted cancer therapies: recent advances and future perspectives. International Journal of Molecular Sciences. 2024 May 17;25(10):5489. https://doi.org/10.3390/ijms25105489
13. Sarkar B, Chakraborty S, Rakshit G, Singh RP. Fundamental approaches of drug discovery. InBiochemical and molecular pharmacology in drug discovery 2024 Jan 1 (pp. 251-282). Elsevier. https://doi.org/10.1016/B978-0-443-16013-4.00012-9
14. Patel S. Managing progressive disease in patients with GIST: factors to consider besides acquired secondary tyrosine kinase inhibitor resistance. Cancer treatment reviews. 2012 Aug 1;38(5):467-72. https://doi.org/10.1016/j.ctrv.2011.10.001Get rights and content
15. Blay JY, Kang YK, Nishida T, von Mehren M. Gastrointestinal stromal tumours. Nature reviews Disease primers. 2021 Mar 18;7(1):22. https://doi.org/10.1038/s41572-021-00254-5
16. Ali S, Ali S. Role of c-kit/SCF in cause and treatment of gastrointestinal stromal tumors (GIST). Gene. 2007 Oct 15;401(1-2):38-45. https://doi.org/10.1016/j.gene.2007.06.017
17. Ashman LK, Griffith R. Therapeutic targeting of c-KIT in cancer. Expert opinion on investigational drugs. 2013 Jan 1;22(1):103-15. https://doi.org/10.1517/13543784.2013.740010
18. Galanis A, Levis M. Inhibition of c-Kit by tyrosine kinase inhibitors. Haematologica. 2015 Mar;100(3):e77. 10.3324/haematol.2014.117028
19. Siehl J, Thiel E. C-kit, GIST, and imatinib. Targeted Therapies in Cancer. 2007 Jan 1:145-51. https://doi.org/10.1007/978-3-540-46091-6
20. Gagic Z, Ruzic D, Djokovic N, Djikic T, Nikolic K. In silico methods for design of kinase inhibitors as anticancer drugs. Frontiers in chemistry. 2020 Jan 8;7:873. https://doi.org/10.3389/fchem.2019.00873
21. Madhavi Sastry G, Adzhigirey M, Day T, Annabhimoju R, Sherman W. Protein and ligand preparation: parameters, protocols, and influence on virtual screening enrichments. Journal of computer-aided molecular design. 2013 Mar;27(3):221-34. https://doi.org/10.1007/s10822-013-9644-8
22. Roche DB, Brackenridge DA, McGuffin LJ. Proteins and their interacting partners: An introduction to protein–ligand binding site prediction methods. International journal of molecular sciences. 2015 Dec 15;16(12):298242. https://doi.org/10.3390/ijms161226202
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