COMPUTATIONAL SCREENING OF NATURAL COMPOUNDS AS POTENTIAL AURORA A KINASE INHIBITORS FOR LEUKEMIA CANCER TREATMENT: A MULTI-STEP IN SILICO APPROACH

JOHRA KHAN

doi:10.22159/ijap.2025v17i6.55338

Authors

JOHRA KHAN Department of Medical Laboratory Sciences, College of Applied Medical Sciences, Majmaah University, Majmaah-11952, Saudi Arabia https://orcid.org/0000-0002-0044-4758

DOI:

https://doi.org/10.22159/ijap.2025v17i6.55338

Keywords:

Aurora a kinase, Molecular docking, Free energy calculations, Acute myeloid leukaemia, Therapeutic target

Abstract

Objective: The overexpression of aurora a kinase is associated with leukaemia as it is an essential regulator of mitotic progression, rendering it a promising therapeutic target. This study employed a multi-step in silico strategy that incorporated computational screening, molecular docking, Density Functional Theory (DFT) analysis, free energy calculations (MM-GBSA), and Molecular Dynamics (MD) simulations to evaluate the potential of natural compounds as Aurora A kinase inhibitors.

Methods: An initial virtual screening of 3,093 natural compounds from the Selleckchem library was performed. Drug-likeness was assessed using Lipinski’s Rule of Five, which filters compounds based on key pharmacokinetic parameters: molecular weight (≤ 500 Da), LogP (≤ 5), hydrogen bond donors (≤ 5), and hydrogen bond acceptors (≤ 10). A total of 2,045 compounds that satisfied all these criteria were selected for further analysis and bioactivity evaluation.

Results: After ML-based QSAR screening, molecular docking identified 683 ligands with binding affinities of ≤-6.0 kcal/mol. Out of all natural compounds, PubChem CID: 94381 (Coumarin 7) and PubChem CID: 1830 (7-Iodo-7-deaza-D-guanosine) showed minimal RMSD (0.1-0.3 nm), and high hydrogen bonding (2-6 stable contacts) during the 100 ns MD simulations, suggesting stable binding within the aurora A kinase active site. The strong binding affinity of MM-GBSA was further validated by the binding free energy calculations, which showed favourable values of ΔG =-27.93 kcal/mol (94381) and -27.50 kcal/mol (1830).

Conclusion: Further, with a dipole moment of 2.80 D, 1830 had great electrical stability, and the lowest HOMO-LUMO gap (-0.1580 eV/-0.1311 eV) was observed in 94381, according to DFT calculations, which indicated significant reactivity. Additionally, synthetic accessibility scores of 3.19 and 4.00, respectively, and iLOGP (integrative octanol–water partition coefficient) values of 2.46 (94381) and 1.57 (1830), both compounds showed ideal drug-like characteristics and may be developed further.

References

1. Key Statistics for Acute Myeloid Leukemia (AML). Available from: https://www.cancer.org/cancer/types/acute-myeloid-leukemia/about/key-statistics.html. [Last accessed on 12 Feb 2025].

2. Tang A, Gao K, Chu L, Zhang R, Yang J, Zheng J. Aurora kinases: novel therapy targets in cancers. Oncotarget. 2017;8(14):23937-54. doi: 10.18632/oncotarget.14893, PMID 28147341.

3. Damodaran AP, Vaufrey L, Gavard O, Prigent C. Aurora a kinase is a priority pharmaceutical target for the treatment of cancers. Trends Pharmacol Sci. 2017;38(8):687-700. doi: 10.1016/j.tips.2017.05.003, PMID 28601256.

4. Howard S, Berdini V, Boulstridge JA, Carr MG, Cross DM, Curry J. Fragment-based discovery of the Pyrazol-4-Yl urea (AT9283), a multitargeted kinase inhibitor with potent aurora kinase activity. J Med Chem. 2009;52(2):379-88. doi: 10.1021/jm800984v, PMID 19143567.

5. Goldenson B, Crispino JD. The aurora kinases in cell cycle and leukemia. Oncogene. 2015;34(5):537-45. doi: 10.1038/onc.2014.14, PMID 24632603.

6. Kollareddy M, Zheleva D, Dzubak P, Brahmkshatriya PS, Lepsik M, Hajduch M. Aurora kinase inhibitors: progress towards the clinic. Investig New Drugs. 2012;30(6):2411-32. doi: 10.1007/s10637-012-9798-6, PMID 22350019.

7. Kim SJ, Jang JE, Cheong JW, Eom JI, Jeung HK, Kim Y. Aurora a kinase expression is increased in leukemia stem cells and a selective aurora a kinase inhibitor enhances ara c induced apoptosis in acute myeloid leukemia stem cells. Korean J Hematol. 2012;47(3):178-85. doi: 10.5045/kjh.2012.47.3.178, PMID 23071472.

8. Siudem P, Szeleszczuk L, Paradowska K. Searching for natural aurora a kinase inhibitors from peppers using molecular docking and molecular dynamics. Pharmaceuticals (Basel). 2023;16(11):1539. doi: 10.3390/ph16111539, PMID 38004405.

9. Mirgany TO, Asiri HH, Rahman AF, Alanazi MM. Discovery of 1H-benzo[d]Imidazole-(halogenated) benzylidenebenzohydrazide hybrids as potential multi-kinase inhibitors. Pharmaceuticals (Basel). 2024;17(7):839. doi: 10.3390/ph17070839, PMID 39065690.

10. Ion GN, Nitulescu GM, Mihai DP. Machine learning assisted drug repurposing framework for discovery of Aurora Kinase B inhibitors. Pharmaceuticals (Basel). 2024;18(1):13. doi: 10.3390/ph18010013, PMID 39861075.

11. Danapur V, Sringeswara A. Pharmacognostic studies on Camellia sinensis (L.) O. Kuntze. Int J Pharm Pharm Sci. 2019;1(1):13-5. doi: 10.33545/26647222.2019.v1.i1a.2.

12. Moore AS, Blagg J, Linardopoulos S, Pearson AD. Aurora kinase inhibitors: novel small molecules with promising activity in acute myeloid and Philadelphia-positive leukemias. Leukemia. 2010;24(4):671-8. doi: 10.1038/leu.2010.15, PMID 20147976.

13. Berman HM, Westbrook J, Feng Z, Gilliland G, Bhat TN, Weissig H. The Protein Data Bank. Nucleic Acids Res. 2000;28(1):235-42. doi: 10.1093/nar/28.1.235, PMID 10592235.

14. Bavetsias V, Large JM, Sun C, Bouloc N, Kosmopoulou M, Matteucci M. Imidazo[4,5-b]pyridine derivatives as inhibitors of aurora kinases: lead optimization studies toward the identification of an orally bioavailable preclinical development candidate. J Med Chem. 2010;53(14):5213-28. doi: 10.1021/jm100262j, PMID 20565112.

15. Lipinski CA, Lombardo F, Dominy BW, Feeney PJ. Experimental and computational approaches to estimate solubility and permeability in drug discovery and development settings. Adv Drug Deliv Rev. 2001;46(1-3):3-26. doi: 10.1016/s0169-409x(00)00129-0, PMID 11259830.

16. Lipinski CA. Drug-like properties and the causes of poor solubility and poor permeability. J Pharmacol Toxicol Methods. 2000;44(1):235-49. doi: 10.1016/S1056-8719(00)00107-6, PMID 11274893.

17. Mao J, Akhtar J, Zhang X, Sun L, Guan S, Li X. Comprehensive strategies of machine learning based quantitative structure-activity relationship models. iScience. 2021;24(9):103052. doi: 10.1016/j.isci.2021.103052, PMID 34553136.

18. Davies M, Nowotka M, Papadatos G, Dedman N, Gaulton A, Atkinson F. ChEMBL Web services: streamlining access to drug discovery data and utilities. Nucleic Acids Res. 2015;43(W1):W612-20. doi: 10.1093/nar/gkv352, PMID 25883136.

19. Landrum G. RDKit: open source cheminformatics. Release. 2014;3:1. doi: 10.5281/zenodo.10398.

20. Rogers D, Hahn M. Extended connectivity fingerprints. J Chem Inf Model. 2010;50(5):742-54. doi: 10.1021/ci100050t, PMID 20426451.

21. Kim S, Chen J, Cheng T, Gindulyte A, He J, He S. PubChem 2023 update. Nucleic Acids Res. 2023;51(D1):D1373-80. doi: 10.1093/nar/gkac956, PMID 36305812.

22. O Boyle NM, Banck M, James CA, Morley C, Vandermeersch T, Hutchison GR. Open babel: an open chemical toolbox. J Cheminform. 2011;3:33. doi: 10.1186/1758-2946-3-33, PMID 21982300.

23. Morris GM, Huey R, Lindstrom W, Sanner MF, Belew RK, Goodsell DS. AutoDock4 and AutoDockTools4: automated docking with selective receptor flexibility. J Comput Chem. 2009;30(16):2785-91. doi: 10.1002/jcc.21256, PMID 19399780.

24. Trott O, Olson AJ. AutoDock vina: improving the speed and accuracy of docking with a new scoring function efficient optimization and multithreading. J Comp Chem. 2010;31(2):455-61. doi: 10.1002/jcc.21334, PMID 19499576.

25. Koh HY, Nguyen AT, Pan S, May LT, Webb GI. Physicochemical graph neural network for learning protein ligand interaction fingerprints from sequence data. Nat Mach Intell. 2024;6(6):673-87. doi: 10.1038/s42256-024-00847-1.

26. Argaman N, Makov G. Density functional theory: an introduction. Am J Phys. 2000;68(1):69-79. doi: 10.1119/1.19375.

27. Rozhenko AB. Density functional theory calculations of enzyme inhibitor interactions in medicinal chemistry and drug design. In: Gorb L, Kuz’min V, Muratov E, editors. Application of computational techniques in pharmacy and medicine. Dordrecht: Springer Netherlands 2014. p. 207-40. doi: 10.1007/978-94-017-9257-8_7.

28. Sun Q, Berkelbach TC, Blunt NS, Booth GH, Guo S, Li Z. PySCF: the Python-based simulations of chemistry framework. WIREs Comp Mol Sci. 2018;8(1):e1340. doi: 10.1002/wcms.1340.

29. Tirado Rives J, Jorgensen WL. Performance of B3LYP density functional methods for a large set of organic molecules. J Chem Theory Comput. 2008;4(2):297-306. doi: 10.1021/ct700248k, PMID 26620661.

30. Nedelec JM, Hench LL. Effect of basis set and of electronic correlation on ab initio calculations on silica rings. J Non-Crystalline Solids. 2000;277(2-3):106-13. doi: 10.1016/S0022-3093(00)00306-9.

31. Daina A, Michielin O, Zoete V. SwissADME: a free web tool to evaluate pharmacokinetics drug likeness and medicinal chemistry friendliness of small molecules. Sci Rep. 2017;7:42717. doi: 10.1038/srep42717, PMID 28256516.

32. Banerjee P, Eckert AO, Schrey AK, Preissner R. ProTox-II: a webserver for the prediction of toxicity of chemicals. Nucleic Acids Res. 2018;46(W1):W257-63. doi: 10.1093/nar/gky318, PMID 29718510.

33. Bauer P, Hess B, Lindahl E. GROMACS 2022.4 manual; 2022 Nov 16. doi: 10.5281/ZENODO.7323409.

34. Huang J, MacKerell AD. CHARMM36 all-atom additive protein force field: validation based on comparison to NMR data. J Comput Chem. 2013;34(25):2135-45. doi: 10.1002/jcc.23354, PMID 23832629.

35. Vanommeslaeghe K, Hatcher E, Acharya C, Kundu S, Zhong S, Shim J. CHARMM general force field: a force field for drug-like molecules compatible with the CHARMM all-atom additive biological force fields. J Comput Chem. 2010;31(4):671-90. doi: 10.1002/jcc.21367, PMID 19575467.

36. Darden T, York D, Pedersen L. Particle mesh Ewald: an N log(N) method for Ewald sums in large systems. J Chem Phys. 1993;98(12):10089-92. doi: 10.1063/1.464397.

37. Harrach MF, Drossel B. Structure and dynamics of TIP3P, TIP4P, and TIP5P water near smooth and atomistic walls of different hydroaffinity. J Chem Phys. 2014;140(17):174501. doi: 10.1063/1.4872239, PMID 24811640.

38. Hess B, Bekker H, Berendsen HJ, Fraaije JG. Lincs: a linear constraint solver for molecular simulations. J Comp Chem. 1997;18(12):1463-72. doi: 10.1002/(SICI)1096-987X(199709)18:12<1463::AID-JCC4>3.0.CO;2-H.

39. Bussi G, Donadio D, Parrinello M. Canonical sampling through velocity rescaling. J Chem Phys. 2007;126(1):014101. doi: 10.1063/1.2408420, PMID 17212484.

40. Martonak R, Laio A, Parrinello M. Predicting crystal structures: the Parrinello-Rahman method revisited. Phys Rev Lett. 2003;90(7):075503. doi: 10.1103/PhysRevLett.90.075503, PMID 12633242.

41. Lipinski CA. Lead and drug-like compounds: the rule of five revolution. Drug Discov Today Technol. 2004;1(4):337-41. doi: 10.1016/j.ddtec.2004.11.007, PMID 24981612.

42. Nogara PA, Saraiva R De A, Caeran Bueno D, Lissner LJ, Lenz Dalla Corte C, Braga MM. Virtual screening of acetylcholinesterase inhibitors using the lipinski’s rule of five and zinc databank. BioMed Res Int. 2015;2015:870389. doi: 10.1155/2015/870389, PMID 25685814.

43. PD, SS, MR, VL. Ligand-based virtual screening on natural compounds for discovering active ligands. Pharm Chem. 2011;3(3):51-7.

44. Nath A, Kumer A, Khan MW. Synthesis, computational and molecular docking study of some 2,3-dihydrobenzofuran and its derivatives. J Mol Struct. 2021;1224:129225. doi: 10.1016/j.molstruc.2020.129225.

45. Cosconati S, Forli S, Perryman AL, Harris R, Goodsell DS, Olson AJ. Virtual screening with AutoDock: theory and practice. Expert Opin Drug Discov. 2010;5(6):597-607. doi: 10.1517/17460441.2010.484460, PMID 21532931.

46. Nath A, Kumer A, Zaben F, Khan MW. Investigating the binding affinity molecular dynamics and admet properties of 2,3-dihydrobenzofuran derivatives as an inhibitor of fungi, bacteria and virus protein. Beni Suef Univ J Basic Appl Sci. 2021;10(1):36. doi: 10.1186/s43088-021-00117-8.

47. Md Mominur Rahman, Md Rezaul Islam, Shopnil Akash, Sadia Afsana Mim, Md Saidur Rahaman, Talha Bin Emran, Esra Kupeli Akkol, Rohit Sharma, Fahad A Alhumaydhi, Sherouk Hussein Sweilam, Md Emon Hossain. In silico investigation and potential therapeutic approaches of natural products for COVID-19 computer-aided drug design perspective. Frontiers in Cellular and Infection Microbiology. 2022 Aug 22;12:929430. doi: 10.3389/fcimb.2022.929430.

48. Chakrabarti T, Allen GM, Gubens MA, Mulvey C, Velazquez Manana AI, Wu W. A phase I/IB study of the aurora kinase a inhibitor alisertib in combination with osimertinib in advanced osimertinib-resistant EGFR-mutated lung cancer. JCO. 2024;42(16Suppl):8572. doi: 10.1200/JCO.2024.42.16_suppl.8572.

49. Lin X, Xiang X, Hao L, Wang T, Lai Y, Abudoureyimu M. The role of aurora a in human cancers and future therapeutics. Am J Cancer Res. 2020;10(9):2705-29. PMID 33042612.

50. Luo Y, Deng YQ, Wang J, Long ZJ, Tu ZC, Peng W. Design synthesis and bioevaluation of N-trisubstituted pyrimidine derivatives as potent aurora a kinase inhibitors. Eur J Med Chem. 2014;78:65-71. doi: 10.1016/j.ejmech.2014.03.027, PMID 24681066.

51. Pradhan T, Gupta O, Singh G, Monga V. Aurora kinase inhibitors as potential anticancer agents: recent advances. Eur J Med Chem. 2021;221:113495. doi: 10.1016/j.ejmech.2021.113495, PMID 34020340.

52. Ma YZ, Tang ZB, Sang CY, Qi ZY, Hui L, Chen SW. Synthesis and biological evaluation of nitroxide-labeled pyrimidines as aurora kinase inhibitors. Bioorg Med Chem Lett. 2019;29(5):694-9. doi: 10.1016/j.bmcl.2019.01.034, PMID 30728112.

53. Qin WW, Sang CY, Zhang LL, Wei W, Tian HZ, Liu HX. Synthesis and biological evaluation of 2,4-diaminopyrimidines as selective aurora a kinase inhibitors. Eur J Med Chem. 2015;95:174-84. doi: 10.1016/j.ejmech.2015.03.044, PMID 25812967.

54. Sang CY, Qin WW, Zhang XJ, Xu Y, Ma YZ, Wang XR. Synthesis and identification of 2,4-Bisanilinopyrimidines bearing 2,2,6,6-tetramethylpiperidine-N-oxyl as potential aurora a inhibitors. Bioorg Med Chem. 2019;27(1):65-78. doi: 10.1016/j.bmc.2018.11.006, PMID 30502115.

55. Long L, Luo Y, Hou ZJ, Ma HJ, Long ZJ, Tu ZC. Synthesis and biological evaluation of Aurora kinases inhibitors based on N-trisubstituted pyrimidine scaffold. Eur J Med Chem. 2018;145:805-12. doi: 10.1016/j.ejmech.2017.12.082, PMID 29358147.

56. Moreira Nunes CA, Mesquita FP, Portilho AJ, Mello Junior FA, Maues JH, Pantoja LD. Targeting aurora kinases as a potential prognostic and therapeutical biomarkers in pediatric acute lymphoblastic leukaemia. Sci Rep. 2020;10(1):21272. doi: 10.1038/s41598-020-78024-8, PMID 33277547.

57. Almilaibary A. Targeting aurora kinase a (AURKA) in cancer: molecular docking and dynamic simulations of potential AURKA inhibitors. Med Oncol. 2022;39(12):246. doi: 10.1007/s12032-022-01852-3, PMID 36180808.

58. Jan R, Khan M, Asaf S, Lubna A, Asif S, Kim KM. Bioactivity and therapeutic potential of kaempferol and quercetin: new insights for plant and human health. Plants (Basel). 2022;11(19):2623. doi: 10.3390/plants11192623, PMID 36235488.