Exploring PI3K Pathway Inhibitors for Acute Myeloid Leukemia: A Drug-Repurposing Approach

Yıl 2023, Cilt: 13 Sayı: 3, 205 - 212, 28.12.2023

Cansu Ergun Buse Zeren Kiremitci Gizem Arslantaş Busenur Bozkurt Gizem Ayna Duran Yağmur Kiraz

https://doi.org/10.26650/experimed.1358971

Öz

Objective: Acute myeloid leukemia (AML) is a malignant disease characterized by the uncontrolled growth, differentiation, and proliferation of immature hematopoietic cells. Patients with AML often have poor survival rates, which are associated with specific gene mutations in FLT3, CEBPA, and NPM1. The phosphatidylinositol 3-kinase (PI3K) pathway, a lipase pathway, is activated in many malignancies, including AML. Given the low survival rates in AML, this study identified candidate drugs that could inhibit the PI3K pathway, thereby offering a potential treatment for AML, by using a drug-repurposing approach.
Materials and Methods: Online bioinformatics tools were utilized to identify pathway-related genes and FDA-approved drugs. Subsequently, molecular docking was performed to determine the binding affinity values. Important genes were identified by evaluating their impact on survival and their aberrant expression in the tumor. In this study, genes such as VAV1, GSK3B, MTOR, PDPK1, PRR5, TSC2, AKT3, and CREB1 were determined and docked with their potential inhibitors. Particular attention was paid to VAV1 because there were no known potential VAV1 inhibitors used in AML.
Results: The docking results were ranked, and the proposed gene–drug pairs were identified as tideglusib and fostamatinib for the inhibition of GSK3B, pimecrolimus and fostamatinib for the inhibition of MTOR, and fostamatinib for the inhibition of PDPK1. Furthermore, nebivolol, darifenacin, dihydroergotamine, libanserin and entereg were identified as potential inhibitors of VAV1 in AML.
Conclusion: To sum up, most effective gene–drug pairs according to binding affinities were proposed as candidate inhibitor drugs for AML.

Anahtar Kelimeler

AML, repurposing, molecular docking, survival, PI3K pathway, VAV1

Etik Beyan

Here we declare that the manuscript including the original research ar4cle entitled Novel Drug Candidates Targeting PI3K Pathway in Acute Myeloid Leukemia Treatment does not require ethicscommitee approval.

Proje Numarası

2209-A

Teşekkür

İzmir University of Economics

Kaynakça

• 1. Short NJ, Rytting ME, Cortes JE. Acute myeloid leukaemia. Lancet 2018; 392(10147): 593-606. google scholar
• 2. Hasserjian RP. Acute myeloid leukemia: advances in diagnosis and classification. Int J Lab Hematol 2013; 35(3): 358-66. google scholar
• 3. Kayser S, Levis MJ. Clinical implications of molecular markers in acute myeloid leukemia. Eur J Haematol 2019; 102(1): 20-35. google scholar
• 4. Short NJ, Konopleva M, Kadia TM, Borthakur G, Ravandi F, DiNardo CD, et al. Advances in the treatment of acute myeloid leukemia: new drugs and new challenges. Cancer Discov 2020; 10(4): 506-25. google scholar
• 5. Jiang N, Dai Q, Su X, Fu J, Feng X, Peng J. Role of PI3K/AKT pathway in cancer: the framework of malignant behavior. Mol Biol Rep 2020; 47(6): 4587-629. google scholar
• 6. The relation between PI3K/AKT signaling pathway and cancer. Gene 2019; 698: 120-8. google scholar
• 7. Nepstad I, Hatfield KJ, Grannincgs^ter IS, Reikvam H. The PI3K-Akt-mTOR signaling pathway in human acute myeloid leukemia (AML) Cells. Int J Mol Sci 2020; 21(8): 2907. google scholar
• 8. Kantarjian HM, Short NJ, Fathi AT, Marcucci G, Ravandi F, Tallman M, et al. Acute myeloid leukemia: historical perspective and progress in research and therapy over 5 decades [Internet]. Vol. 21, Clinical Lymphoma Myeloma and Leukemia. Elsevier BV; 2021. p. 580-97. google scholar
• 9. Fleischmann M, Schnetzke U, Hochhaus A, Scholl S. Management of acute myeloid leukemia: current treatment options and future perspectives. Cancers 2021; 13(22): 5722. google scholar
• 10. Totiger TM, Ghoshal A, Zabroski J, Sondhi A, Bucha S, Jahn J, et al. Targeted therapy development in acute myeloid leukemia. Biomedicines 2023; 11(2): 641. google scholar
• 11. Turabi KS, Deshmukh A, Paul S, Swami D, Siddiqui S, Kumar U, et al. Drug repurposing—an emerging strategy in cancer therapeutics. Naunyn Schmiedebergs Arch Pharmacol 2022; 395(10): 1139-58. google scholar
• 12. KEGG pathway Database [Internet]. [cited 2023 Aug 25]. Available from: https://www.genome.jp/kegg/pathway.html google scholar
• 13. DAVID Functional Annotation Bioinformatics Microarray Analysis [Internet]. [cited 2023 Aug 25]. Available from: https://david. ncifcrf.gov/home.jsp google scholar
• 14. The Comparative Toxicogenomics Database [Internet]. [cited 2023 Aug 25]. Available from: https://ctdbase.org/ google scholar
• 15. GEPIA (Gene Expression Profiling Interactive Analysis) [Internet]. [cited 2023 Aug 25]. Available from: http://gepia.cancer-pku.cn/ google scholar
• 16. DrugBank Online [Internet]. [cited 2023 Aug 25]. Available from: https://go.drugbank.com/ google scholar
• 17. ZINC [Internet]. [cited 2023 Aug 25]. Available from: https:// zinc15.docking.org/ google scholar
• 18. Bank RPD. RCSB PDB: Homepage [Internet]. [cited 2023 Aug 25]. Available from: https://www.rcsb.org/ google scholar
• 19. Database APS. AlphaFold Protein Structure Database [Internet]. [cited 2023 Aug 25]. Available from: https://alphafold.ebi.ac.uk/ google scholar
• 20. PubChem. PubChem [Internet]. [cited 2023 Aug 25]. Available from: https://pubchem.ncbi.nlm.nih.gov/ google scholar
• 21. BIOVIA Discovery Studio - BIOVIA - Dassault Systemes® [Internet]. [cited 2023 Aug 25]. Available from: https://www.3ds.com/ products-services/biovia/products/molecular-modeling-simulation/biovia-discovery-studio/ google scholar
• 22. Collins BJ, Deak M, Arthur JSC, Armit LJ, Alessi DR. In vivo role of the PIF-binding docking site of PDK1 defined by knock-in mutation. EMBO J 2003; 22(16): 4202-11. google scholar
• 23. Ogunleye AJ, Olanrewaju AJ, Arowosegbe M, Omotuyi OI. Molecular docking based screening analysis of GSK3B. Bioinformation 2019; 15(3): 201-8. google scholar
• 24. PyMOL [Internet]. [cited 2023 Aug 25]. Available from: https:// pymol.org/2/ google scholar
• 25. Welcome to the PyRx Website [Internet]. [cited 2023 Aug 25]. Available from: https://pyrx.sourceforge.io/ google scholar
• 26. ProTox-II - Prediction of TOXicity of chemicals [Internet]. [cited 2023 Aug 25]. Available from: https://tox-new.charite.de/protox_ II/ google scholar
• 27. Rodrigues ACBDC, Costa RGA, Silva SLR, Dias IRSB, Dias RB, Bezerra DP. Cell signaling pathways as molecular targets to eliminate AML stem cells. Crit Rev Oncol Hematol 2021; 160: 103277. google scholar
• 28. Abohassan M, Alshahrani M, Alshahrani MY, Rajagopalan P. Insilco and Invitro approaches identify novel dual PI3K/AKT pathway inhibitors to control acute myeloid leukemia cell proliferations. Med Oncol 2022; 39(12): 1-10. google scholar
• 29. Alom MM, Faruqe MO, Molla MKI, Rahman MM. Exploring prognostic biomarkers of acute myeloid leukemia to determine its most effective drugs from the FDA-approved list through molecular docking and dynamic simulation. Biomed Res Int 2023; 2023: 1946703. google scholar
• 30. Mu D, Long S, Guo L, Liu W. High Expression of VAV gene family predicts poor prognosis of acute myeloid leukemia. Technol Cancer Res Treat 2021: 20: 15330338211065877. google scholar