440
Views
3
CrossRef citations to date
0
Altmetric
Research Articles

Pharmacophore-based virtual screening, molecular docking, and molecular dynamics studies for the discovery of novel FLT3 inhibitors

ORCID Icon, ORCID Icon, ORCID Icon, ORCID Icon, ORCID Icon & ORCID Icon
Pages 7712-7724 | Received 27 Jun 2022, Accepted 06 Sep 2022, Published online: 15 Sep 2022

References

  • Al-Jumaili, M. H. A., Siddique, F., Abul Qais, F., Hashem, H. E., Chtita, S., Rani, A., Uzair, M., & Almzaien, K. A. (2021). Analysis and prediction pathways of natural products and their cytotoxicity against HeLa cell line protein using docking, molecular dynamics and ADMET. Journal of Biomolecular Structure and Dynamics, 1–13. https://doi.org/10.1080/07391102.2021.2011785
  • Baell, J. B., & Holloway, G. A. (2010). New substructure filters for removal of pan assay interference compounds (PAINS) from screening libraries and for their exclusion in bioassays. Journal of Medicinal Chemistry, 53(7), 2719–2740. https://doi.org/10.1021/jm901137j
  • Chandrasekaran, B., Agrawal, N., & Kaushik, S. (2019). Pharmacophore development. In S. Ranganathan, M. Gribskov, K. Nakai, & C. Schönbach (Eds.), Encyclopedia of bioinformatics and computational biology (pp. 677–687). Academic Press. https://doi.org/10.1016/B978-0-12-809633-8.20276-8
  • Cheng, T., Li, Q., Zhou, Z., Wang, Y., & Bryant, S. H. (2012). Structure-based virtual screening for drug discovery: a problem-centric review. The AAPS Journal, 14(1), 133–141. https://doi.org/10.1208/s12248-012-9322-0
  • Choudhary, M. I., Shaikh, M., tul-Wahab, A., & ur-Rahman, A. (2020). In silico identification of potential inhibitors of key SARS-CoV-2 3CL hydrolase (Mpro) via molecular docking, MMGBSA predictive binding energy calculations, and molecular dynamics simulation. PLoS One, 15(7), e0235030. https://doi.org/10.1371/journal.pone.0235030
  • Choudhury, C., & Narahari Sastry, G. (2019). Pharmacophore modelling and screening: concepts, recent developments and applications in rational drug design. In C. G. Mohan (Ed.), Structural bioinformatics: Applications in preclinical drug discovery process, challenges and advances in computational chemistry and physics (pp. 25–53). Springer International Publishing. https://doi.org/10.1007/978-3-030-05282-9_2
  • 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(1), 42717. https://doi.org/10.1038/srep42717
  • Daina, A., & Zoete, V. (2016). A BOILED-egg to predict gastrointestinal absorption and brain penetration of small molecules. ChemMedChem, 11(11), 1117–1121. https://doi.org/10.1002/cmdc.201600182
  • Daoui, O., Elkhattabi, S., & Chtita, S. (2022a). Design and prediction of ADME/tox properties of novel magnolol derivatives as anticancer agents for NSCLC using 3D-QSAR, molecular docking, MOLCAD and MM-GBSA studies. Letters in Drug Design & Discovery, 19, 1–25. https://doi.org/10.2174/1570180819666220510141710
  • Daoui, O., Elkhattabi, S., & Chtita, S. (2022b). Rational identification of small molecules derived from 9,10-dihydrophenanthrene as potential inhibitors of 3CLpro enzyme for COVID-19 therapy: A computer-aided drug design approach. Structural Chemistry, 33(5), 1667–1690. https://doi.org/10.1007/s11224-022-02004-z
  • Daoui, O., Elkhattabi, S., & Chtita, S. (2022c). Rational design of novel pyridine-based drugs candidates for lymphoma therapy. Journal of Molecular Structure, 1270, 133964. https://doi.org/10.1016/j.molstruc.2022.133964
  • Daver, N., Schlenk, R. F., Russell, N. H., & Levis, M. J. (2019). Targeting FLT3 mutations in AML: Review of current knowledge and evidence. Leukemia, 33(2), 299–312. https://doi.org/10.1038/s41375-018-0357-9
  • Fathi, A., & Levis, M. (2011). FLT3 inhibitors: A story of the old and the new. Current Opinion in Hematology, 18(2), 71–76. https://doi.org/10.1097/MOH.0b013e3283439a03
  • Friesner, R. A., Banks, J. L., Murphy, R. B., Halgren, T. A., Klicic, J. J., Mainz, D. T., Repasky, M. P., Knoll, E. H., Shelley, M., Perry, J. K., Shaw, D. E., Francis, P., & Shenkin, P. S. (2004). Glide: A new approach for rapid, accurate docking and scoring. 1. Method and assessment of docking accuracy. Journal of Medicinal Chemistry, 47(7), 1739–1749. https://doi.org/10.1021/jm0306430
  • Gokhale, P., Chauhan, A. P. S., Arora, A., Khandekar, N., Nayarisseri, A., & Singh, S. K. (2019). FLT3 inhibitor design using molecular docking based virtual screening for acute myeloid leukemia. Bioinformation, 15(2), 104–115. https://doi.org/10.6026/97320630015104
  • Gopinath, P., & Kathiravan, K. M. (2021). Docking studies and molecular dynamics simulation of triazole benzene sulfonamide derivatives with human carbonic anhydrase IX inhibition activity. RSC Advances, 11(60), 38079–38093. https://doi.org/10.1039/D1RA07377J
  • Grafone, T., Palmisano, M., Nicci, C., & Storti, S. (2012). An overview on the role of FLT3-tyrosine kinase receptor in acute myeloid leukemia: biology and treatment. Oncology Reviews, 6(1), e8. https://doi.org/10.4081/oncol.2012.e8
  • Greim, H., Kaden, D. A., Larson, R. A., Palermo, C. M., Rice, J. M., Ross, D., & Snyder, R. (2014). The bone marrow niche, stem cells, and leukemia: impact of drugs, chemicals, and the environment. Annals of the New York Academy of Sciences, 1310(1), 7–31. https://doi.org/10.1111/nyas.12362
  • Hajian-Tilaki, K. (2013). Receiver operating characteristic (ROC) curve analysis for medical diagnostic test evaluation. Caspian Journal of Internal Medicine, 4(2), 627–635.
  • Halgren, T. A., Murphy, R. B., Friesner, R. A., Beard, H. S., Frye, L. L., Pollard, W. T., & Banks, J. L. (2004). Glide: a new approach for rapid, accurate docking and scoring. 2. Enrichment factors in database screening. Journal of Medicinal Chemistry, 47(7), 1750–1759. https://doi.org/10.1021/jm030644s
  • Jin, Z., Wang, Y., Yu, X. F., Tan, Q. Q., Liang, S.-S., Li, T., Zhang, H., Shaw, P.-C., Wang, J., & Hu, C. (2020). Structure-based virtual screening of influenza virus RNA polymerase inhibitors from natural compounds: Molecular dynamics simulation and MM-GBSA calculation. Computational Biology and Chemistry, 85, 107241. https://doi.org/10.1016/j.compbiolchem.2020.107241
  • Jorgensen, W. L., & Tirado-Rives, J. (1988). The OPLS [optimized potentials for liquid simulations] potential functions for proteins, energy minimizations for crystals of cyclic peptides and crambin. Journal of the American Chemical Society, 110(6), 1657–1666. https://doi.org/10.1021/ja00214a001
  • Kaminski, G. A., Friesner, R. A., Tirado-Rives, J., & Jorgensen, W. L. (2001). Evaluation and reparametrization of the opls-aa force field for proteins via comparison with accurate quantum chemical calculations on peptides. The Journal of Physical Chemistry B, 105(28), 6474–6487. https://doi.org/10.1021/jp003919d
  • Kaserer, T., Beck, K. R., Akram, M., Odermatt, A., & Schuster, D. (2015). Pharmacophore models and pharmacophore-based virtual screening: Concepts and applications exemplified on hydroxysteroid dehydrogenases. Molecules (Basel, Switzerland), 20(12), 22799–22832. https://doi.org/10.3390/molecules201219880
  • Kawase, T., Nakazawa, T., Eguchi, T., Tsuzuki, H., Ueno, Y., Amano, Y., Suzuki, T., Mori, M., & Yoshida, T. (2019). Effect of Fms-like tyrosine kinase 3 (FLT3) ligand (FL) on antitumor activity of gilteritinib, a FLT3 inhibitor, in mice xenografted with FL-overexpressing cells. Oncotarget, 10(58), 6111–6123. https://doi.org/10.18632/oncotarget.27222
  • Kiyoi, H. (2015). Flt3 inhibitors: Recent advances and problems for clinical application. Nagoya Journal of Medical Science, 77(1-2), 7–17.
  • Martyna, G. J., Klein, M. L., & Tuckerman, M. (1992). Nosé–Hoover chains: The canonical ensemble via continuous dynamics. The Journal of Chemical Physics, 97(4), 2635–2643. https://doi.org/10.1063/1.463940
  • Mashkani, B., Tanipour, M. H., Saadatmandzadeh, M., Ashman, L. K., & Griffith, R. (2016). FMS-like tyrosine kinase 3 (FLT3) inhibitors: Molecular docking and experimental studies. European Journal of Pharmacology, 776(13), 156–166. https://doi.org/10.1016/j.ejphar.2016.02.048
  • Meshinchi, S., & Appelbaum, F. R. (2009). Structural and functional Alterations of FLT3 in Acute Myeloid Leukemia. Clinical Cancer Research, 15(13), 4263–4269. https://doi.org/10.1158/1078-0432.CCR-08-1123
  • Rajagopal, K., Varakumar, P., Aparna, B., Byran, G., & Jupudi, S. (2021). Identification of some novel oxazine substituted 9-anilinoacridines as SARS-CoV-2 inhibitors for COVID-19 by molecular docking, free energy calculation and molecular dynamics studies. Journal of Biomolecular Structure & Dynamics, 39(15), 5551–5562. https://doi.org/10.1080/07391102.2020.1798285
  • Reville, P. K., & Kadia, T. M. (2020). Maintenance therapy in AML. Frontiers in Oncology, 10, 619085. https://doi.org/10.3389/fonc.2020.619085
  • Saultz, J. N., & Garzon, R. (2016). Acute myeloid leukemia: A concise review. Journal of Clinical Medicine, 5(3), 33. https://doi.org/10.3390/jcm5030033
  • Schneider, G. (2013). Prediction of drug-like properties. Madame Curie Bioscience Database Landes Bioscience,
  • Schrödinger Release. (2021a). Schrödinger Release 2021-2:2021. Maestro. Schrödinger, LLC New York, NY.
  • Schrödinger Release. (2021b). Schrödinger Release 2021-2:2021. Phase. Schrödinger, LLC New York, NY.
  • Schrödinger Release. (2021c). Schrödinger Release 2021-2:2021. LigPrep. Schrödinger, LLC New York, NY.
  • Schrödinger Release. (2021e). Schrödinger Release 2021-2:2021. Glide. Schrödinger, LLC New York, NY.
  • Schrödinger Release. (2021f). Schrödinger Release 2021-2:2021. QikProp. Schrödinger, LLC New York, NY.
  • Schrödinger Release. (2021g). Schrödinger Release 2021-2:2021. Desmond Molecular Dynamics. Schro ¨dinger, LLC New York, NY.
  • Shivakumar, D., Harder, E., Damm, W., Friesner, R. A., & Sherman, W. (2012). Improving the prediction of absolute solvation free energies using the next generation opls force field. Journal of Chemical Theory and Computation, 8(8), 2553–2558. https://doi.org/10.1021/ct300203w
  • Smith, C. C. (2019). The growing landscape of FLT3 inhibition in AML. Hematology. American Society of Hematology. Education Program, 2019(1), 539–547. https://doi.org/10.1182/hematology.2019000058
  • Stanchina, M., Soong, D., Zheng-Lin, B., Watts, J. M., & Taylor, J. (2020). Advances in acute myeloid leukemia: Recently approved therapies and drugs in development. Cancers, 12(11), 3225. https://doi.org/10.3390/cancers12113225
  • Sutamtewagul, G., & Vigil, C. E. (2018). Clinical use of FLT3 inhibitors in acute myeloid leukemia. OncoTargets and Therapy, 11, 7041–7052. https://doi.org/10.2147/OTT.S171640
  • Tariq, M. U., Furqan, M., Parveen, H., Ullah, R., Muddassar, M., Saleem, R. S. Z., Bavetsias, V., Linardopoulos, S., & Faisal, A. (2021). CCT245718, a dual FLT3/Aurora A inhibitor overcomes D835Y-mediated resistance to FLT3 inhibitors in acute myeloid leukaemia cells. British Journal of Cancer, 125(7), 966–974. https://doi.org/10.1038/s41416-021-01527-2
  • Triballeau, N., Acher, F., Brabet, I., Pin, J. P., & Bertrand, H. O. (2005). Virtual screening workflow development guided by the “receiver operating characteristic” curve approach. Application to high-throughput docking on metabotropic glutamate receptor subtype 4. Journal of Medicinal Chemistry, 48(7), 2534–2547. https://doi.org/10.1021/jm049092j
  • Truchon, J. F., & Bayly, C. I. (2007). Evaluating virtual screening methods: good and bad metrics for the “early recognition” problem. Journal of Chemical Information and Modeling, 47(2), 488–508. https://doi.org/10.1021/ci600426e
  • Wang, Y., Feng, S., Gao, H., & Wang, J. (2020). Computational investigations of gram-negative bacteria phosphopantetheine adenylyltransferase inhibitors using 3D-QSAR, molecular docking and molecular dynamic simulations. Journal of Biomolecular Structure & Dynamics, 38(5), 1435–1447. https://doi.org/10.1080/07391102.2019.1608305
  • Wilson, G. L., & Lill, M. A. (2011). Integrating structure-based and ligand-based approaches for computational drug design. Future Medicinal Chemistry, 3(6), 735–750. https://doi.org/10.4155/fmc.11.18
  • Wu, M., Li, C., & Zhu, X. (2018). FLT3 inhibitors in acute myeloid leukemia. Journal of Hematology & Oncology, 11(1), 133. https://doi.org/10.1186/s13045-018-0675-4

Reprints and Corporate Permissions

Please note: Selecting permissions does not provide access to the full text of the article, please see our help page How do I view content?

To request a reprint or corporate permissions for this article, please click on the relevant link below:

Academic Permissions

Please note: Selecting permissions does not provide access to the full text of the article, please see our help page How do I view content?

Obtain permissions instantly via Rightslink by clicking on the button below:

If you are unable to obtain permissions via Rightslink, please complete and submit this Permissions form. For more information, please visit our Permissions help page.