Mitoxantrone 2HCl’s adroit activity against cervical cancer replication and maintenance proteins: a multitargeted approach

References

• Ahmad, S., & Raza, K. (2023). Identification of 5-nitroindazole as a multitargeted inhibitor for CDK and transferase kinase in lung cancer: A multisampling algorithm-based structural study. Molecular Diversity, 1–14. https://doi.org/10.1007/s11030-023-10648-0
   Web of Science ®Google Scholar
• Ahmad, S., Bano, N., Qazi, S., Yadav, M. K., Ahmad, N., & Raza, K. (2022). Multitargeted molecular dynamic understanding of butoxypheser against SARS-CoV-2: An in silico study. Natural Product Communications, 17(7), 1934578X2211154. https://doi.org/10.1177/1934578X221115499
   Web of Science ®Google Scholar
• Ahmad, S., Sheikh, K., Bano, N., Rafeeq, M. M., Mohammed, M. R. S., Yadav, M. K., & Raza, K. (2022). Illustrious implications of nature-inspired computing methods in therapeutics and computer-aided drug design. In Nature-inspired intelligent computing techniques in bioinformatics (pp. 293–308). Springer Nature Singapore Singapore.
   Google Scholar
• Ahmad, S., Pasha Km, M., Raza, K., Rafeeq, M. M., Habib, A. H., Eswaran, M., & Yadav, M. K. (2023). Reporting dinaciclib and theodrenaline as a multitargeted inhibitor against SARS-CoV-2: An in-silico study. Journal of Biomolecular Structure & Dynamics, 41(9), 4013–4023. https://doi.org/10.1080/07391102.2022.2060308
   PubMed Web of Science ®Google Scholar
• Ahmad, S., Sayeed, S., Bano, N., Sheikh, K., & Raza, K. (2022). In-silico analysis reveals Quinic acid as a multitargeted inhibitor against cervical cancer. Journal of Biomolecular Structure & Dynamics, 41(19), 9770–9786. https://doi.org/10.1080/07391102.2022.2146202
   Web of Science ®Google Scholar
• Ahmad, S., Singh, V., Gautam, H. K., & Raza, K. (2023). Multisampling-based docking reveals Imidazolidinyl urea as a multitargeted inhibitor for lung cancer: An optimisation followed multi-simulation and in-vitro study. Journal of Biomolecular Structure & Dynamics, 42(5), 2494–2511. https://doi.org/10.1080/07391102.2023.2209673
   Web of Science ®Google Scholar
• Alghamdi, S., Baeissa, H. M., Azhar Kamal, M., Rafeeq, M. M., Al Zahrani, A., Maslum, A. A., Hakeem, I. J., Alazragi, R. S., & Alam, Q. (2023). Unveiling the multitargeted potency of Sodium Danshensu against cervical cancer: A multitargeted docking-based, structural fingerprinting and molecular dynamics simulation study. Journal of Biomolecular Structure & Dynamics, 1–13. https://doi.org/10.1080/07391102.2023.2248260
   Web of Science ®Google Scholar
• Alghamdi, Y. S., Mashraqi, M. M., Alzamami, A., Alturki, N. A., Ahmad, S., Alharthi, A. A., Alshamrani, S., & Asiri, S. A. (2023). Unveiling the multitargeted potential of N-(4-Aminobutanoyl)-S-(4-methoxybenzyl)-L-cysteinylglycine (NSL-CG) against SARS CoV-2: A virtual screening and molecular dynamics simulation study. Journal of Biomolecular Structure & Dynamics, 41(14), 6633–6642. https://doi.org/10.1080/07391102.2022.2110158
   PubMed Web of Science ®Google Scholar
• Ali, C., Makata, N., & Ezenduka, P. (2016). Cervical cancer: A health limiting condition. Gynecol Obstet (Sunnyvale), 6(378), 2161–0932.10003.
   Google Scholar
• Almasoudi, H. H., Hakami, M. A., Alhazmi, A. Y., Makkawi, M., Alasmari, S., Alghamdi, Y. S., & Mashraqi, M. M. (2023). Unveiling the multitargeted repurposing potential of taxifolin (dihydroquercetin) in cervical cancer: An extensive MM\GBSA-based screening, and MD simulation study. Medical Oncology (Northwood, London, England), 40(8), 218. https://doi.org/10.1007/s12032-023-02094-7
   Web of Science ®Google Scholar
• Alotaibi, B. S., Ajmal, A., Hakami, M. A., Mahmood, A., Wadood, A., & Hu, J. (2023). New drug target identification in Vibrio vulnificus by subtractive genome analysis and their inhibitors through molecular docking and molecular dynamics simulations. Heliyon, 9(7), e17650. https://doi.org/10.1016/j.heliyon.2023.e17650
   PubMed Web of Science ®Google Scholar
• Alsolais, A. M., Althafar, Z. M., Hasan, M. R., Hakami, M. A., Omar, B. I. A., Hessien, K. B. G., Alkhorayef, N., & Khan, F. R. (2023). Ranitidine contaminated with N-Nitrosodimethylamine (Ndma) link to carcinoma: A systematic review and meta-analysis. Journal of Pharmaceutical Negative Results, 14(2), 2031–2043.
   Google Scholar
• Alturki, N. A., Mashraqi, M. M., Alzamami, A., Alghamdi, Y. S., Alharthi, A. A., Asiri, S. A., Ahmad, S., & Alshamrani, S. (2022). In-silico screening and molecular dynamics simulation of drug bank experimental compounds against SARS-CoV-2. Molecules (Basel, Switzerland), 27(14), 4391. https://doi.org/10.3390/molecules27144391
   PubMed Web of Science ®Google Scholar
• Alzamami, A., Alturki, N. A., Alghamdi, Y. S., Ahmad, S., Alshamrani, S., Asiri, S. A., & Mashraqi, M. M. (2022). Hemi-Babim and fenoterol as potential inhibitors of MPro and papain-like protease against SARS-CoV-2: An in-silico study. Medicina, 58(4), 515. https://doi.org/10.3390/medicina58040515
   PubMedGoogle Scholar
• Bochevarov, A. D., Harder, E., Hughes, T. F., Greenwood, J. R., Braden, D. A., Philipp, D. M., Rinaldo, D., Halls, M. D., Zhang, J., & Friesner, R. A. (2013). Jaguar: A high‐performance quantum chemistry software program with strengths in life and materials sciences. International Journal of Quantum Chemistry, 113(18), 2110–2142. https://doi.org/10.1002/qua.24481
   Web of Science ®Google Scholar
• Bowers, K. J., Chow, E., Xu, H., Dror, R. O., Eastwood, M. P., Gregersen, B. A., Klepeis, J. L., Kolossváry, I., Moraes, M. A., Sacerdoti, F. D., Salmon, J. K., Shan, Y., and Shaw, D. E. (2006). Scalable algorithms for molecular dynamics simulations on commodity clusters. In Proceedings of the 2006 ACM/IEEE Conference on Supercomputing. ACM. https://doi.org/10.1145/1188455.1188544
   Google Scholar
• Dieci, M. V., Arnedos, M., Andre, F., & Soria, J. C. (2013). Fibroblast growth factor receptor inhibitors as a cancer treatment: From a biologic rationale to medical perspectives. Cancer Discovery, 3(3), 264–279. https://doi.org/10.1158/2159-8290.CD-12-0362
   PubMed Web of Science ®Google Scholar
• Doghish, A. S., Ali, M. A., Elyan, S. S., Elrebehy, M. A., Mohamed, H. H., Mansour, R. M., Elgohary, A., Ghanem, A., Faraag, A. H. I., Abdelmaksoud, N. M., & Moustafa, H. A. M. (2023). miRNAs role in cervical cancer pathogenesis and targeted therapy: Signaling pathways interplay. Pathology, Research and Practice, 244, 154386. https://doi.org/10.1016/j.prp.2023.154386
   Web of Science ®Google Scholar
• Genheden, S., & Ryde, U. (2015). The MM/PBSA and MM/GBSA methods to estimate ligand-binding affinities. Expert Opinion on Drug Discovery, 10(5), 449–461. https://doi.org/10.1517/17460441.2015.1032936
   PubMed Web of Science ®Google Scholar
• Giesecke, A., & Stewart, M. (2010). Novel binding of the mitotic regulator TPX2 (target protein for Xenopus kinesin-like protein 2) to importin-α. The Journal of Biological Chemistry, 285(23), 17628–17635. https://doi.org/10.1074/jbc.M110.102343
   PubMed Web of Science ®Google Scholar
• Jacobson, M. P., Pincus, D. L., Rapp, C. S., Day, T. J. F., Honig, B., Shaw, D. E., & Friesner, R. A. (2004). A hierarchical approach to all‐atom protein loop prediction. Proteins, 55(2), 351–367. https://doi.org/10.1002/prot.10613
   PubMed Web of Science ®Google Scholar
• Jorgensen, W. L., & Tirado-Rives, J. (1988). The OPLS [optimised potentials for liquid simulations] potential functions for proteins, energy minimisations for crystals of cyclic peptides and crambin. Journal of the American Chemical Society, 110(6), 1657–1666. https://doi.org/10.1021/ja00214a001
   PubMed Web of Science ®Google Scholar
• Karwasra, R., Ahmad, S., Bano, N., Qazi, S., Raza, K., Singh, S., & Varma, S. (2022). Macrophage-targeted punicalagin nanoengineering to alleviate methotrexate-induced neutropenia: A molecular docking, DFT, and MD simulation analysis. Molecules (Basel, Switzerland), 27(18), 6034. https://doi.org/10.3390/molecules27186034
   PubMed Web of Science ®Google Scholar
• Kaul, T., Eswaran, M., Ahmad, S., Thangaraj, A., Jain, R., Kaul, R., Raman, N. M., & Bharti, J. (2019). Probing the effect of a plus 1bp frameshift mutation in protein-DNA interface of domestication gene, NAMB1, in wheat. Journal of Biomolecular Structure & Dynamics, 38(12), 3633–3647. https://doi.org/10.1080/07391102.2019.1680435
   Web of Science ®Google Scholar
• Lipinski, C. A. (2004). Lead-and drug-like compounds: The rule-of-five revolution. Drug Discovery Today. Technologies, 1(4), 337–341. https://doi.org/10.1016/j.ddtec.2004.11.007
   PubMedGoogle Scholar
• Maestro, S. (2020). Maestro. Schrödinger, LLC.
   Google Scholar
• Mark, P., & Nilsson, L. (2001). Structure and dynamics of the TIP3P, SPC, and SPC/E water models at 298 K. The Journal of Physical Chemistry A, 105(43), 9954–9960. https://doi.org/10.1021/jp003020w
   Web of Science ®Google Scholar
• McDonald, I. (1972). NpT-ensemble Monte Carlo calculations for binary liquid mixtures. Molecular Physics, 23(1), 41–58. https://doi.org/10.1080/00268977200100031
   Web of Science ®Google Scholar
• Okunade, K. S. (2020). Human papillomavirus and cervical cancer. Journal of Obstetrics and Gynaecology: The Journal of the Institute of Obstetrics and Gynaecology, 40(5), 602–608. https://doi.org/10.1080/01443615.2019.1634030
   PubMedGoogle Scholar
• QikProp, S. (2017). Schrödinger Release 2017. Maestro LLC.
   Google Scholar
• Ramlal, A., Ahmad, S., Kumar, L., Khan, F. N., & Chongtham, R. (2021). From molecules to patients: The clinical applications of biological databases and electronic health records. In Translational bioinformatics in healthcare and medicine (pp. 107–125). Academic Press.
   Google Scholar
• Release, S. (2017). LigPrep. Schrödinger, LLC.
   Google Scholar
• Release, S. (2019). Receptor grid generation. Schrödinger, LLC.
   Google Scholar
• Richet, N., Liu, D., Legrand, P., Velours, C., Corpet, A., Gaubert, A., Bakail, M., Moal-Raisin, G., Guerois, R., Compper, C., Besle, A., Guichard, B., Almouzni, G., & Ochsenbein, F. (2015). Structural insight into how the human helicase subunit MCM2 may act as a histone chaperone together with ASF1 at the replication fork. Nucleic Acids Research, 43(3), 1905–1917. https://doi.org/10.1093/nar/gkv021
   Web of Science ®Google Scholar
• Rose, P. W., Prlić, A., Altunkaya, A., Bi, C., Bradley, A. R., Christie, C. H., Costanzo, L. D., Duarte, J. M., Dutta, S., Feng, Z., Green, R. K., Goodsell, D. S., Hudson, B., Kalro, T., Lowe, R., Peisach, E., Randle, C., Rose, A. S., Shao, C., … Burley, S. K. (2016). The RCSB protein data bank: Integrative view of protein, gene and 3D structural information. Nucleic Acids Research, 45(D1), D271–D281.
   PubMed Web of Science ®Google Scholar
• Sackton, K. L., Dimova, N., Zeng, X., Tian, W., Zhang, M., Sackton, T. B., Meaders, J., Pfaff, K. L., Sigoillot, F., Yu, H., Luo, X., & King, R. W. (2014). Synergistic blockade of mitotic exit by two chemical inhibitors of the APC/C. Nature, 514(7524), 646–649. https://doi.org/10.1038/nature13660
   PubMed Web of Science ®Google Scholar
• Savaraj, N., Lu, K., Manuel, V., & Loo, T. L. (1982). Pharmacology of mitoxantrone in cancer patients. Cancer Chemotherapy and Pharmacology, 8(1), 113–117. https://doi.org/10.1007/BF00292881
   Web of Science ®Google Scholar
• Schrödinger Release. (2017). Schrödinger Suite 2017-1 Protein Preparation Wizard. Epik, Schrödinger, LLC.
   Google Scholar
• Schrödinger Release. (2020). Glide: Schrödinger. LLC.
   Google Scholar
• Shelley, J. C., Cholleti, A., Frye, L. L., Greenwood, J. R., Timlin, M. R., & Uchimaya, M. (2007). Epik: A software program for pK a prediction and protonation state generation for drug-like molecules. Journal of Computer-Aided Molecular Design, 21(12), 681–691. https://doi.org/10.1007/s10822-007-9133-z
   PubMed Web of Science ®Google Scholar
• Shi, K., Wang, G., Pei, J., Zhang, J., Wang, J., Ouyang, L., Wang, Y., & Li, W. (2022). Emerging strategies to overcome resistance to third-generation EGFR inhibitors. Journal of Hematology & Oncology, 15(1), 94. https://doi.org/10.1186/s13045-022-01311-6
   PubMed Web of Science ®Google Scholar
• Shivakumar, D., Williams, J., Wu, Y., Damm, W., Shelley, J., & Sherman, W. (2010). Prediction of absolute solvation free energies using molecular dynamics free energy perturbation and the OPLS force field. Journal of Chemical Theory and Computation, 6(5), 1509–1519. https://doi.org/10.1021/ct900587b
   PubMed Web of Science ®Google Scholar
• Singh, A. P., Ahmad, S., Raza, K., & Gautam, H. K. (2023). Computational screening and MM/GBSA-based MD simulation studies reveal the high binding potential of FDA-approved drugs against Cutibacterium acnes sialidase. Journal of Biomolecular Structure & Dynamics, 1–11. https://doi.org/10.1080/07391102.2023.2242950
   Google Scholar
• Singh, D., Vignat, J., Lorenzoni, V., Eslahi, M., Ginsburg, O., Lauby-Secretan, B., Arbyn, M., Basu, P., Bray, F., & Vaccarella, S. (2023). Global estimates of incidence and mortality of cervical cancer in 2020: A baseline analysis of the WHO Global Cervical Cancer Elimination Initiative. The Lancet. Global Health, 11(2), e197–e206. https://doi.org/10.1016/S2214-109X(22)00501-0
   PubMed Web of Science ®Google Scholar
• Tarique, M., Ahmad, S., Malik, A., Ahmad, I., Saeed, M., Almatroudi, A., Qadah, T., Murad, M. A., Mashraqi, M., Alam, Q., & Al-Saleh, Y. (2021). Novel severe acute respiratory syndrome coronavirus 2 (SARS-CoV2) and other coronaviruses: A genome-wide comparative annotation and analysis. Molecular and Cellular Biochemistry, 476(5), 2203–2217. https://doi.org/10.1007/s11010-020-04027-8
   PubMed Web of Science ®Google Scholar
• Tripathi, M. K., et al. (2022). Fundamentals of molecular modeling in drug design. In Computer Aided Drug Design (CADD): From ligand-based methods to structure-based approaches (pp. 125–155). Elsevier.
   Google Scholar
• Wei, Z., Liu, C., Wu, X., Xu, N., Zhou, B., Liang, C., & Zhu, G. (2010). Characterisation and structure determination of the Cdt1 binding domain of human minichromosome maintenance (Mcm) 6. The Journal of Biological Chemistry, 285(17), 12469–12473. https://doi.org/10.1074/jbc.C109.094599
   PubMed Web of Science ®Google Scholar
• Yadav, M. K., Ahmad, S., Raza, K., Kumar, S., Eswaran, M., & Pasha Km, M. (2022). Predictive modeling and therapeutic repurposing of natural compounds against the receptor-binding domain of SARS-CoV-2. Journal of Biomolecular Structure & Dynamics, 41(5), 1527–1539. https://doi.org/10.1080/07391102.2021.2021993
   Web of Science ®Google Scholar