Author: admin

World Health Organization. Global cancer burden growing amidst mounting need for services. WHO. (2024). Available from: https://www.who.int/news/item/01-02-2024-global-cancer-burden-growing–amidst-mounting-need-for-services

American Cancer Society. Cancer Facts & Figs. 2024. ACS. (2024). Available from: https://www.cancer.org/research/cancer-facts-statistics/all-cancer-facts-figures/2024-cancer-facts-figures.html

Housman, G. et al. Drug resistance in cancer: an overview. Cancers (Basel). 6 (3), 1769–1792. https://doi.org/10.3390/cancers6031769 (2014). PMID: 25198391; PMCID: PMC4190567.

Google Scholar 

Gottesman, M. M. Mechanisms of Cancer Drug Resistance. Annual Review of Medicine, 53(Volume 53, 2002), 615–627. https://doi.org/https://doi.org/ (2002). https://doi.org/10.1146/annurev.med.53.082901.103929

Ward, R. A. et al. Challenges and opportunities in cancer drug resistance. Chem. Rev. 121 (6), 3297–3351. https://doi.org/10.1021/acs.chemrev.0c00383 (2021).

Google Scholar 

Asma, S. T. et al. Natural Products/Bioactive Compounds as a Source of Anticancer Drugs. Cancers https://doi.org/10.3390/cancers14246203 (2022).

Google Scholar 

Khan, M. I. et al. Anticancer properties of medicinal plants and their bioactive compounds against breast cancer: a review on recent investigations. Environ. Sci. Pollut. Res. 29 (17), 24411–24444. https://doi.org/10.1007/s11356-021-17795-7 (2022).

Google Scholar 

Yuan, M. et al. The role of bioactive compounds in natural products extracted from plants in cancer treatment and their mechanisms related to anticancer effects. Oxidative Med. Cell. Longev. 2022 (1), 1429869. https://doi.org/10.1155/2022/1429869 (2022).

Google Scholar 

Aydoğmuş-Öztürk, F., Jahan, H., Beyazit, N., Günaydın, K. & Choudhary, M. I. The anticancer activity of visnagin, isolated from Ammi Visnaga L., against the human malignant melanoma cell lines, HT 144. Mol. Biol. Rep. 46 (2), 1709–1714. https://doi.org/10.1007/s11033-019-04620-1 (2019).

Google Scholar 

Sharma, R. et al. Khellinoflavanone, a semisynthetic derivative of Khellin, overcomes Benzo[a]pyrene toxicity in human normal and cancer cells that express CYP1A1. ACS Omega. 3 (8), 8553–8566. https://doi.org/10.1021/acsomega.8b01088 (2018).

Google Scholar 

Ragab, F. A., Yahya, T. A. A., El-Naa, M. M. & Arafa, R. K. Design, synthesis and structure–activity relationship of novel semi-synthetic flavonoids as antiproliferative agents. Eur. J. Med. Chem. 82, 506–520. https://doi.org/10.1016/j.ejmech.2014.06.007 (2014).

Google Scholar 

Abdelhafez, O. M., Ali, H. I., Amin, K. M., Abdalla, M. M. & Ahmed, E. Y. Design, synthesis and anticancer activity of Furochromone and Benzofuran derivatives targeting VEGFR-2 tyrosine kinase. RSC Adv. 5 (32), 25312–25324. https://doi.org/10.1039/C4RA16228E (2015).

Google Scholar 

Amin, K. M. et al. Synthesis and molecular Docking study of new Benzofuran and furo[3,2-g]chromone-based cytotoxic agents against breast cancer and p38α MAP kinase inhibitors. Bioorg. Chem. 76, 487–500. https://doi.org/10.1016/j.bioorg.2017.12.029 (2018).

Google Scholar 

Abdelhafez, O. M. et al. Design and molecular modeling of novel P38α MAPK inhibitors targeting breast cancer, synthesized from oxygen heterocyclic natural compounds. Bioorg. Med. Chem. 27 (7), 1308–1319. https://doi.org/10.1016/j.bmc.2019.02.027 (2019).

Google Scholar 

Montassier, E. et al. Chemotherapy-driven dysbiosis in the intestinal Microbiome. Aliment. Pharmacol. Ther. 42 (5), 515–528. https://doi.org/10.1111/apt.13302 (2015).

Google Scholar 

Derosa, L. et al. Negative association of antibiotics on clinical activity of immune checkpoint inhibitors in patients with advanced renal cell and non-small-cell lung cancer. Ann. Oncol. 29 (6), 1437–1444. https://doi.org/10.1093/annonc/mdy103 (2018).

Google Scholar 

Gopalakrishnan, V. et al. Gut Microbiome modulates response to anti–PD-1 immunotherapy in melanoma patients. Science 359 (6371), 97–103. https://doi.org/10.1126/science.aan4236 (2018).

Google Scholar 

Kossakowski, J. & Zawadowski, T. SYNTHESIS OF AMINOALKANOL AND AMINOETHYL DERIVATIVES OF 4,9-DIHYDROXY-7-ETHYL-5H-FURO[3,2-g][1]BENZOPYRAN-5-ONE. Pol. J. Chem. 61, 77–83 (1987).

Google Scholar 

Kossakowski, J. & Zawadowski, T. Synthesis of 4-(3-amino-2-hydroxypropoxy)furobenzopyrans. Acta Pol. Pharm. 43 (6), 539–542 (1986).

Google Scholar 

Sazonova, E. V., Chesnokov, M. S., Zhivotovsky, B. & Kopeina, G. S. Drug toxicity assessment: cell proliferation versus cell death. Cell. Death Discovery. 8 (1), 417. https://doi.org/10.1038/s41420-022-01207-x (2022).

Google Scholar 

Decker, T. & Lohmann-Matthes, M. L. A quick and simple method for the quantitation of lactate dehydrogenase release in measurements of cellular cytotoxicity and tumor necrosis factor (TNF) activity. J. Immunol. Methods. 115 (1), 61–69. https://doi.org/10.1016/0022-1759(88)90310-9 (1988).

Google Scholar 

Mustafa, M. et al. Apoptosis: A Comprehensive Overview of Signaling Pathways, Morphological Changes, and Physiological Significance and Therapeutic Implications. Cells https://doi.org/10.3390/cells13221838 (2024).

Google Scholar 

Huang, G. et al. Synthesis and biological evaluation of sulfur-containing Shikonin oxime derivatives as potential antineoplastic agents. Eur. J. Med. Chem. 143, 166–181. https://doi.org/10.1016/j.ejmech.2017.11.031 (2018).

Google Scholar 

Huang, G. et al. Discovery and synthesis of sulfur-containing 6-substituted 5,8-dimethoxy-1,4-naphthoquinone oxime derivatives as new and potential anti-MDR cancer agents. Eur. J. Med. Chem. 165, 160–171. https://doi.org/10.1016/j.ejmech.2019.01.005 (2019).

Google Scholar 

Cui, J. et al. DMAKO-20 as a new multitarget anticancer prodrug activated by the tumor specific CYP1B1 enzyme. Mol. Pharm. 16 (1), 409–421. https://doi.org/10.1021/acs.molpharmaceut.8b01062 (2019).

Google Scholar 

Hirano, T. IL-6 in inflammation, autoimmunity and cancer. Int. Immunol. 33 (3), 127–148. https://doi.org/10.1093/intimm/dxaa078 (2021).

Google Scholar 

Ashkenazi, A., Fairbrother, W. J., Leverson, J. D. & Souers, A. J. From basic apoptosis discoveries to advanced selective BCL-2 family inhibitors. Nat. Rev. Drug Discovery. 16 (4), 273–284. https://doi.org/10.1038/nrd.2016.253 (2017).

Google Scholar 

Levantini, E., Maroni, G., del Re, M. & Tenen, D. G. EGFR signaling pathway as therapeutic target in human cancers. Sem. Cancer Biol. 85, 253–275. https://doi.org/10.1016/j.semcancer.2022.04.002 (2022).

Google Scholar 

Sun, X., Shan, X., Zunhua, Y., Pengwu, Z., Zhu, W. & and Epidermal growth factor receptor (EGFR) tyrosine kinase inhibitors for the treatment of non-small cell lung cancer: a patent review (2014-present). Expert Opin. Ther. Pat. 31 (3), 223–238. https://doi.org/10.1080/13543776.2021.1860210 (2021).

Google Scholar 

Birkinshaw, R. W. et al. Structures of BCL-2 in complex with venetoclax reveal the molecular basis of resistance mutations. Nat. Commun. 10 (1), 2385. https://doi.org/10.1038/s41467-019-10363-1 (2019).

Google Scholar 

Oltersdorf, T. et al. An inhibitor of Bcl-2 family proteins induces regression of solid tumours. Nature 435 (7042), 677–681. https://doi.org/10.1038/nature03579 (2005).

Google Scholar 

Lee, E. F. et al. Crystal structure of ABT-737 complexed with Bcl-xL: implications for selectivity of antagonists of the Bcl-2 family. Cell. Death Differ. 14 (9), 1711–1713. https://doi.org/10.1038/sj.cdd.4402178 (2007).

Google Scholar 

Hargreaves, D. et al. Design of rigid protein–protein interaction inhibitors enables targeting of undruggable Mcl-1. Proceedings of the National Academy of Sciences, 120(21), e2221967120. (2023). https://doi.org/10.1073/pnas.2221967120

Stamos, J., Sliwkowski, M. X. & Eigenbrot, C. Epidermal growth factor receptor tyrosine kinase domain with 4-anilinoquinazoline inhibitor erlotinib (2002). https://doi.org/10.2210/pdb1M17/pdb

Yun, C. H. et al. Crystal structure of EGFR kinase domain G719S mutation in complex with Iressa (2006). https://doi.org/10.2210/pdb2ITO/pdb

Gajiwala, K. S. et al. Crystal structure of the wild-type EGFR kinase domain in complex with Dacomitinib (soaked) (2012). https://doi.org/10.2210/pdb4I23/pdb

Yun, C. H. & Eck, M. J. EGFR L858R in complex with PD168393 (2013). https://doi.org/10.2210/pdb4LQM/pdb

Hargreaves, D., Studies, Reversible & EGFR C797S Triple Mutant Inhibitor Series. Towards a (2020). https://doi.org/10.2210/pdb7AEM/pdb

Kitchen, D. B., Decornez, H., Furr, J. R. & Bajorath, J. Docking and scoring in virtual screening for drug discovery: methods and applications. Nat. Rev. Drug Discovery. 3 (11), 935–949. https://doi.org/10.1038/nrd1549 (2004).

Google Scholar 

Ting, N. L., Lau, H. C. & Yu, J. Cancer pharmacomicrobiomics: targeting microbiota to optimise cancer therapy outcomes. Gut 71 (7), 1412–1425. https://doi.org/10.1136/gutjnl-2021-326264 (2022).

Google Scholar 

Berman, H. M. et al. The Protein Data Bank. Nucleic Acids Res., 28(1), 235–242. https://doi.org/10.1093/nar/28.1.235 (2000).

Eastman, P. et al. OpenMM 4: A reusable, extensible, hardware independent library for high performance molecular simulation. J. Chem. Theory Comput. 9 (1), 461–469. https://doi.org/10.1021/ct300857j (2013).

Google Scholar 

Morris, G. M. et al. AutoDock4 and AutoDockTools4: automated Docking with selective receptor flexibility. J. Comput. Chem. 30 (16), 2785–2791. https://doi.org/10.1002/jcc.21256 (2009).

Google Scholar 

O’Boyle, N. M. et al. Open babel: an open chemical toolbox. J. Cheminform. 3, 33. https://doi.org/10.1186/1758-2946-3-33 (2011).

Google Scholar 

Trott, O. & Olson, A. J. AutoDock vina: improving the speed and accuracy of Docking with a new scoring function, efficient optimization, and multithreading. J. Comput. Chem. 31 (2), 455–461. https://doi.org/10.1002/jcc.21334 (2010).

Google Scholar 

Koes, D. R., Baumgartner, M. P. & Camacho, C. J. Lessons learned in empirical scoring with Smina from the CSAR 2011 benchmarking exercise. J. Chem. Inf. Model. 53 (8), 1893–1904. https://doi.org/10.1021/ci300604z (2013).

Google Scholar 

Pagadala, N. S. et al. Software for molecular docking: a review. Biophys. Rev. 9 (2), 91–102. https://doi.org/10.1007/s12551-016-0247-1 (2017).

Google Scholar 

Salentin, S. et al. PLIP: fully automated protein–ligand interaction profiler. Nucleic Acids Res. https://doi.org/10.1093/nar/gkv315 (2015).

Google Scholar 

Rego, N. & Koes, D. 3Dmol.js: molecular visualization with WebGL. Bioinformatics 31 (8), 1322–1324. https://doi.org/10.1093/bioinformatics/btu829 (2015).

Google Scholar 

BIOVIA & Systèmes, D. Discovery Studio Visualizer, Version 20.1.0.19295 (Dassault Systèmes, 2020).

Szulczyk, D. et al. Design and synthesis of novel 1H-tetrazol-5-amine based potent antimicrobial agents: DNA topoisomerase IV and gyrase affinity evaluation supported by molecular Docking studies. Eur. J. Med. Chem. 156, 631–640. https://doi.org/10.1016/j.ejmech.2018.07.041 (2018).

Google Scholar 

Woods, G. L. et al. Susceptibility Testing of Mycobacteria, Nocardiae, and Other Aerobic Actinomycetes [Internet]. 2nd edition. Wayne (PA): Clinical and Laboratory Standards Institute; 2011 Mar. (CLSI publication / Clinical and Laboratory Standards Institute, No. 31.5.) Available from: https://www.ncbi.nlm.nih.gov/books/NBK544374/

Franzblau, S. G. et al. Rapid, Low-Technology MIC Determination with Clinical Mycobacterium tuberculosis Isolates by Using the Microplate Alamar Blue Assay. J. Clin. Microbiology. 3654, 362–366. https://doi.org/10.1128/JCM.36.2.362-366 (1998).