Computational Evaluation Of Phytochemicals In Multi-Targeted Therapy Against AML And CML: A Molecular Docking And Drug-Likeness Study
DOI:
https://doi.org/10.48165/aabr.2026.3.1.05Keywords:
AML (Acute Myeloid Leukemia), CML (Chronic Myeloid Leukemia), phytochemicals, molecular docking, drug-likeness, ADMET, multi-target therapy, computational pharmacology.Abstract
Background and Rationale: Acute Myeloid Leukemia (AML) and Chronic Myeloid Leukemia (CML) are different blood cancers. They face critical treatment challenges, mainly drug resistance, such as the BCR-ABL1 T315I mutation, and the presence of Leukemic Stem Cells (LSCs). In India, CML occurs at a much younger age, creating an urgent need for cost-effective and safe alternatives to standard Tyrosine Kinase Inhibitors (TKIs). Research Aim: This study aims to use detailed computational (in-silico) methods to find and assess effective phytochemicals that can serve as multi-target inhibitors. The main goal is to discover natural compounds that can bypass known resistance mechanisms in CML and AML while effectively targeting pathways crucial for LSC survival, such as PP2A and HIF-1. Methodology: The research uses a high-throughput virtual screening strategy. Molecular docking simulations evaluate the binding strength and suitability of phytochemical libraries against key resistance targets (BCR-ABL1 T315I, FLT3) and LSC regulators. At the same time, we conduct ADMET (Absorption, Distribution, Metabolism, Excretion, and Toxicity) profiling and drug-likeness assessment (Lipinski’s Rule of Five) to prioritize compounds with good oral bioavailability and safety profiles suitable for long-term treatment. Conclusion: This study applies polypharmacology to demonstrate how phytochemicals can function as effective multi-target therapeutic leads. By moving beyond the traditional “one-drug-one target” approach, these natural bioactive compounds provide a strategic way to bypass drug resistance and eliminate the resilient leukemic stem cell populations that drive relapse in AML and CML.
Downloads
References
Apperley, J. F. (2015). Chronic myeloid leukaemia: Chronic myeloid leukaemia. The Lancet, 385(9976), 1447–1459. https://doi.org/10.1016/S0140-6736(13)62120-0
Kantarjian, H., Jabbour, E., Grimley, G., et al. (2024). Chronic myeloid leukemia: 2024 update on diagnosis, therapy, and monitoring. American Journal of Hematology, 99(3), 473–496. https://doi.org/10.1002/ajh.27215
Short, N. J., Rytting, M. E., & Cortes, J. E. (2018). Acute myeloid leukemia: A review. Cancer, 124(1), 31–42. https://doi.org/10.1002/cncr.31039
Gorre, M. E., Mohammed, M., Ellwood, K., et al. (2001). Clinical resistance to STI-571 cancer therapy caused by BCR-ABL gene mutation or amplification. Science, 293(5531), 876–880. https://doi.org/10.1126/science.1062538
O’Hare, T., Zabriskie, M. S., Eide, C. A., et al. (2011). The BCR-ABL T315I mutation: A review of the mechanism of resistance and therapeutic strategies. Blood, 118(11), 2941–2950. https://doi.org/10.1182/blood-2010-11-267823
Jordan, C. T. (2021). The leukemic stem cell: The “root” of resistance in AML. Journal of Clinical Oncology, 39(15), 1611–1619. https://doi.org/10.1200/JCO.20.03382
Guzman, M. L., Rossi, R. M., Neelakantan, S., et al. (2007). An orally bioavailable parthenolide analog selectively eradicates acute myelogenous leukemia stem and progenitor cells. Blood, 110(13), 4427–4435. https://doi.org/10.1182/blood-2007-04-084038
Bredel, M., & Jacoby, E. (2024). Computational biology and the future of drug screening in oncology. Nature Reviews Cancer, 24(1), 45–60. https://doi.org/10.1038/s41568-023-00639-4
Lipinski, C. A. (2004). Lead- and drug-like compounds: The rule-of-five revolution. Drug Discovery Today, 9(22), 937–939. https://doi.org/10.1016/S1359-6446(04)03211-0
Daina, A., Michielin, O., & Zoete, V. (2017). SwissADME: A free web tool to evaluate pharmacokinetics, drug-likeness and medicinal chemistry friendliness of small molecules. Scientific Reports, 7, 42717. https://doi.org/10.1038/srep42717
Trott, O., & Olson, A. J. (2010). AutoDock Vina: Improving the speed and accuracy of docking with a new scoring function, efficient optimization, and multithreading. Journal of Computational Chemistry, 31(2), 455–461. https://doi.org/10.1002/jcc.21334
Morris, G. M., Huey, R., Lindstrom, W., et al. (2009). AutoDock4 and AutoDockTools4: Automated docking with selective receptor flexibility. Journal of Computational Chemistry, 30(16), 2785–2791. https://doi.org/10.1002/jcc.21256
Kitchen, D. B., Decornez, H., Furr, J. R., et al. (2004). Docking and scoring in virtual screening for drug discovery: Methods and applications. Nature Reviews Drug Discovery, 3(11), 935–949. https://doi.org/10.1038/nrd1549
Shannon, P., Markiel, A., Ozier, O., et al. (2003). Cytoscape: A software environment for integrated models of biomolecular interaction networks. Genome Research, 13(11), 2498–2504. https://doi.org/10.1101/gr.1239303
Abraham, M. J., Murtola, T., Schulz, R., et al. (2015). GROMACS: High performance molecular simulations through multi-level parallelism from laptops to supercomputers. SoftwareX, 1, 19–25. https://doi.org/10.1016/j.softx.2015.06.001
Kumar, S., Stecher, G., Li, M., et al. (2018). MEGA X: Molecular evolutionary genetics analysis across computing platforms. Molecular Biology and Evolution, 35(6), 1547–1549. https://doi.org/10.1093/molbev/msy096
Hopkins, A. L. (2008). Network pharmacology: The next paradigm in drug discovery. Nature Chemical Biology, 4(11), 682–690. https://doi.org/10.1038/nchembio.118
Zhu, M., Xiao, J., Lu, J., et al. (2023). Multi-target drugs: The next generation of anticancer therapeutics. Drug Discovery Today, 28(5), 103565. https://doi.org/10.1016/j.drudis.2023.103565
Gupta, S. C., Patchva, S., & Aggarwal, B. B. (2013). Therapeutic roles of curcumin: Lessons learned from clinical trials. The AAPS Journal, 15(1), 195–218. https://doi.org/10.1208/s12248-012-9432-8
Ravindran, J., Prasad, S., & Aggarwal, B. B. (2009). Curcumin and cancer cells: How many ways can curry kill tumor cells? The AAPS Journal, 11(3), 495–510. https://doi.org/10.1208/s12248-009-9128-x
Bauer, M. A., Kellner, J., & Chalasani, N. (2022). Flavonoids in cancer prevention: A review of the evidence for quercetin. Oncology Reviews, 16(1), 542. https://doi.org/10.4081/oncol.2022.542
Vogelstein, B., Papadopoulos, N., Velculescu, V. E., et al. (2013). Cancer genome landscapes. Science, 339(6127), 1546–1558. https://doi.org/10.1126/science.1235122
Touw, I. P., & van de Geijn, G. J. (2007). FLT3 receptors in acute myeloid leukemia: Biological and clinical perspectives. Blood, 109(4), 1353–1362. https://doi.org/10.1182/blood-2006-07-039362
Westermarck, J., & Neel, B. G. (2020). Piecing together the PP2A phosphatase puzzle in cancer. Trends in Cancer, 6(12), 1043–1055. https://doi.org/10.1016/j.trecan.2020.08.010
Semenza, G. L. (2012). Hypoxia-inducible factors in physiology and medicine. Cell, 148(3), 399–408. https://doi.org/10.1016/j.cell.2012.01.021
Berman, H. M., Westbrook, J., Feng, Z., et al. (2000). The Protein Data Bank. Nucleic Acids Research, 28(1), 235–242. https://doi.org/10.1093/nar/28.1.235
Kim, S., Chen, J., Cheng, T., et al. (2023). PubChem 2023 update. Nucleic Acids Research, 51(D1), D1373–D1380. https://doi.org/10.1093/nar/gkac956
Wang, J., Wolf, R. M., Caldwell, J. W., et al. (2004). Development and testing of a general amber force field. Journal of Computational Chemistry, 25(9), 1157–1174. https://doi.org/10.1002/jcc.20035
Phillips, J. C., Braun, R., Wang, W., et al. (2005). Scalable molecular dynamics with NAMD. Journal of Computational Chemistry, 26(16), 1781–1802. https://doi.org/10.1002/jcc.20289
Wishart, D. S., Feunang, Y. D., Guo, A. C., et al. (2018). DrugBank 5.0: A major update to the DrugBank database for 2018. Nucleic Acids Research, 46(D1), D1074–D1082. https://doi.org/10.1093/nar/gkx1037
