Skip to content
2000
image of Prediction and Validation of Novel BRAF Inhibitor as a Potential Drug Candidate for the Treatment of Colorectal Cancer

Abstract

Background

Colorectal cancer (CRC), the world's third leading cause of death, can be caused by a variety of reasons, one of which is a valine-to-glutamate mutation at position 600 in the BRAF gene. Nonetheless, the prognosis of patients with BRAF mutations remains poor, necessitating additional research in this field.

Objective

This work aims to recognize and validate innovative and effective BRAF inhibitor.

Methods

A merged-featured ligand-based pharmacophore model was validated and screened against various external databases. The pharmacokinetic and toxicological characteristics of the 102 hits were analyzed, and the appropriate ligands were docked against BRAF protein. The top four protein-ligand complexes with the lowest binding energies were chosen, and their Molecular Dynamic (MD) simulation studies were accomplished.

Results

The finest complex selected has a Root Mean Square Deviation (RMSD) value of 2.229A0 and a Radius of Gyration (RoG) value of 25.770A0. The LC of the best ligand was experimentally calculated to be 102.83 µg/ml. The ligand was found to destroy CRC cells, but it did not affect normal non-cancerous cells much.

Conclusion

This work thus proposes 3-(6,7-dimethoxy-3,4-dihydroisoquinoline-2-carbonyl)-N-(2-methoxyphenyl)benzenesulphonamide as a potential BRAF V600E inhibitor for the CRC treatment.

© 2025 Bentham Science Publishers
Loading

Article metrics loading...

/content/journals/ctmc/10.2174/0115680266337334250320231553
2025-04-11
2025-09-14

  • From This Site

    /content/journals/ctmc/10.2174/0115680266337334250320231553
    dcterms_title,dcterms_subject,pub_keyword
    -contentType:Contributor -contentType:Concept -contentType:Institution
    10
    5
Loading full text...

Full text loading...

References

  1. Favoriti P. Carbone G. Greco M. Pirozzi F. Pirozzi R.E.M. Corcione F. Worldwide burden of colorectal cancer: A review. Updates Surg. 2016 68 1 7 11 10.1007/s13304‑016‑0359‑y 27067591
    [Google Scholar]
  2. Kuipers E.J. Grady W.M. Lieberman D. Seufferlein T. Sung J.J. Boelens P.G. van de Velde C.J.H. Watanabe T. Colorectal cancer. Nat. Rev. Dis. Primers 2015 1 1 15065 10.1038/nrdp.2015.65 27189416
    [Google Scholar]
  3. Moghimi-Dehkordi B. Safaee A. An overview of colorectal cancer survival rates and prognosis in Asia. World J. Gastrointest. Oncol. 2012 4 4 71 75 10.4251/wjgo.v4.i4.71 22532879
    [Google Scholar]
  4. Kolch W. Meaningful relationships: The regulation of the Ras/Raf/MEK/ERK pathway by protein interactions. Biochem. J. 2000 351 2 289 305 10.1042/bj3510289 11023813
    [Google Scholar]
  5. Sanz-Garcia E. Argiles G. Elez E. Tabernero J. BRAF mutant colorectal cancer: Prognosis, treatment, and new perspectives. Ann. Oncol. 2017 28 11 2648 2657 10.1093/annonc/mdx401 29045527
    [Google Scholar]
  6. Luu L-J. Price J. BRAF Mutation and Its Importance in Colorectal Cancer. Advances in the Molecular Understanding of Colorectal Cancer Intechopen 2019
    [Google Scholar]
  7. Michaloglou C. Vredeveld L.C.W. Mooi W.J. Peeper D.S. BRAFE600 in benign and malignant human tumours. Oncogene 2008 27 7 877 895 10.1038/sj.onc.1210704 17724477
    [Google Scholar]
  8. BRAF Gene. COSMIC. 2018 Available from: https://cancer.sanger.ac.uk/cosmic/gene/analysis?ln=BRAF
  9. Karoulia Z. Gavathiotis E. Poulikakos P.I. New perspectives for targeting RAF kinase in human cancer. Nat. Rev. Cancer 2017 17 11 676 691 10.1038/nrc.2017.79 28984291
    [Google Scholar]
  10. Agianian B. Gavathiotis E. Current insights of BRAF inhibitors in cancer. J. Med. Chem. 2018 61 14 5775 5793 10.1021/acs.jmedchem.7b01306 29461827
    [Google Scholar]
  11. Kumar A. Gautam V. Sandhu A. Rawat K. Sharma A. Saha L. Current and emerging therapeutic approaches for colorectal cancer: A comprehensive review. World J. Gastrointest. Surg. 2023 15 4 495 519 10.4240/wjgs.v15.i4.495 37206081
    [Google Scholar]
  12. National Cancer Institute Advances in Colorectal Cancer Research. 2024 Available from: https://www.cancer.gov/types/colorectal/research
    [Google Scholar]
  13. Ettrich T.J. Seufferlein T. Regorafenib. Recent Results Cancer Res. 2018 211 45 56 10.1007/978‑3‑319‑91442‑8_3 30069758
    [Google Scholar]
  14. Cook F.A. Cook S.J. Inhibition of RAF dimers: It takes two to tango. Biochem. Soc. Trans. 2021 49 1 237 251 10.1042/BST20200485 33367512
    [Google Scholar]
  15. Cotto-Rios X.M. Agianian B. Gitego N. Zacharioudakis E. Giricz O. Wu Y. Zou Y. Verma A. Poulikakos P.I. Gavathiotis E. Inhibitors of BRAF dimers using an allosteric site. Nat. Commun. 2020 11 1 4370 10.1038/s41467‑020‑18123‑2 32873792
    [Google Scholar]
  16. Peng S.B. Henry J.R. Kaufman M.D. Lu W.P. Smith B.D. Vogeti S. Rutkoski T.J. Wise S. Chun L. Zhang Y. Van Horn R.D. Yin T. Zhang X. Yadav V. Chen S.H. Gong X. Ma X. Webster Y. Buchanan S. Mochalkin I. Huber L. Kays L. Donoho G.P. Walgren J. McCann D. Patel P. Conti I. Plowman G.D. Starling J.J. Flynn D.L. Inhibition of RAF isoforms and active dimers by LY3009120 leads to anti-tumor activities in RAS or BRAF mutant cancers. Cancer Cell 2015 28 3 384 398 10.1016/j.ccell.2015.08.002 26343583
    [Google Scholar]
  17. Krishnan K.A. Identification of novel EGFR inhibitors for the targeted therapy of colorectal cancer using pharmacophore modelling, docking, molecular dynamic simulation and biological activity prediction. Anticancer Agents Med Chem. 2024 24 4 263 279 10.2174/0118715206275566231206094645
    [Google Scholar]
  18. Kim S. Chen J. Cheng T. Gindulyte A. He J. He S. Li Q. Shoemaker B.A. Thiessen P.A. Yu B. Zaslavsky L. Zhang J. Bolton E.E. PubChem 2023 update. Nucleic Acids Res. 2023 51 D1 D1373 D1380 10.1093/nar/gkac956 36305812
    [Google Scholar]
  19. Mysinger M.M. Carchia M. Irwin J.J. Shoichet B.K. Directory of useful decoys, enhanced (DUD-E): Better ligands and decoys for better benchmarking. J. Med. Chem. 2012 55 14 6582 6594 10.1021/jm300687e 22716043
    [Google Scholar]
  20. O’Boyle N.M. Banck M. James C.A. Morley C. Vandermeersch T. Hutchison G.R. Open Babel: An open chemical toolbox. J. Cheminform. 2011 3 1 33 10.1186/1758‑2946‑3‑33 21982300
    [Google Scholar]
  21. Golestanian S. Sharifi A. Popowicz G.M. Azizian H. Foroumadi A. Szwagierczak A. Holak T.A. Amanlou M. Discovery of novel dual inhibitors against Mdm2 and Mdmx proteins by in silico approaches and binding assay. Life Sci. 2016 145 240 246 10.1016/j.lfs.2015.12.047 26746660
    [Google Scholar]
  22. Koes D.R. Camacho C.J. ZINCPharmer: pharmacophore search of the ZINC database. Nucleic Acids Res. 2012 40 W1 W409 W414 10.1093/nar/gks378 22553363
    [Google Scholar]
  23. Wishart D.S. Knox C. Guo A.C. Shrivastava S. Hassanali M. Stothard P. Chang Z. Woolsey J. DrugBank: a comprehensive resource for in silico drug discovery and exploration. Nucleic Acids Res. 2006 34 90001 D668 D672 10.1093/nar/gkj067 16381955
    [Google Scholar]
  24. 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 1 42717 10.1038/srep42717 28256516
    [Google Scholar]
  25. Lipinski C.A. Lombardo F. Dominy B.W. Feeney P.J. Experimental and computational approaches to estimate solubility and permeability in drug discovery and development settings 1PII of original article: S0169-409X(96)00423-1. The article was originally published in Advanced Drug Delivery Reviews 23 (1997) 3–25. 1. Adv. Drug Deliv. Rev. 2001 46 1-3 3 26 10.1016/S0169‑409X(00)00129‑0 11259830
    [Google Scholar]
  26. Prabha B. Ezhilarasi M.R. Synthesis, spectral characterization, in vitro and in silico studies of benzodioxin pyrazoline derivatives. Biointerface Res. Appl. Chem. 2020 11 2 9126 9138 10.33263/BRIAC112.91269138
    [Google Scholar]
  27. Banerjee P. Eckert A.O. Schrey A.K. Preissner R. ProTox-II: A webserver for the prediction of toxicity of chemicals. Nucleic Acids Res. 2018 46 W1 W257 W263 10.1093/nar/gky318 29718510
    [Google Scholar]
  28. Bollag G. Hirth P. Tsai J. Zhang J. Ibrahim P.N. Cho H. Spevak W. Zhang C. Zhang Y. Habets G. Burton E.A. Wong B. Tsang G. West B.L. Powell B. Shellooe R. Marimuthu A. Nguyen H. Zhang K.Y.J. Artis D.R. Schlessinger J. Su F. Higgins B. Iyer R. D’Andrea K. Koehler A. Stumm M. Lin P.S. Lee R.J. Grippo J. Puzanov I. Kim K.B. Ribas A. McArthur G.A. Sosman J.A. Chapman P.B. Flaherty K.T. Xu X. Nathanson K.L. Nolop K. Clinical efficacy of a RAF inhibitor needs broad target blockade in BRAF-mutant melanoma. Nature 2010 467 7315 596 599 10.1038/nature09454 20823850
    [Google Scholar]
  29. Berman H.M. Westbrook J. Feng Z. Gilliland G. Bhat T.N. Weissig H. Shindyalov I.N. Bourne P.E. The Protein Data Bank. Nucleic Acids Res. 2000 28 1 235 242 10.1093/nar/28.1.235 10592235
    [Google Scholar]
  30. Sobolev O.V. Afonine P.V. Moriarty N.W. Hekkelman M.L. Joosten R.P. Perrakis A. Adams P.D. A global ramachandran score identifies protein structures with unlikely stereochemistry. Structure 2020 28 11 1249 1258.e2 10.1016/j.str.2020.08.005 32857966
    [Google Scholar]
  31. 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. 2010 31 2 455 461 10.1002/jcc.21334 19499576
    [Google Scholar]
  32. Martínez-Rosell G. Giorgino T. De Fabritiis G. PlayMolecule ProteinPrepare: A web application for protein preparation for molecular dynamics simulations. J. Chem. Inf. Model. 2017 57 7 1511 1516 10.1021/acs.jcim.7b00190 28594549
    [Google Scholar]
  33. Dassault Systèmes B. Discovery Studio Modeling Environment. San Diego, CA, USA Dassault Systèmes 2021
    [Google Scholar]
  34. Samdani A. Vetrivel U. POAP: A GNU parallel based multithreaded pipeline of open babel and AutoDock suite for boosted high throughput virtual screening. Comput. Biol. Chem. 2018 74 39 48 10.1016/j.compbiolchem.2018.02.012 29533817
    [Google Scholar]
  35. Bowers K.J. Chow E. Xu H. Dror R.O. Eastwood M.P. Gregersen B.A. Klepeis J.L. Kolossvary I. Moraes M.A. Sacerdoti F.D. Salmon J.K. Shan Y. Shaw D.E. Scalable algorithms for molecular dynamics simulations on commodity clusters. Proceedings of the ACM/IEEE Conference on Supercomputing (SC06) Tampa, Florida 2006 10.1109/SC.2006.54
    [Google Scholar]
  36. Chow E. Rendleman C.A. Bowers K.J. Dror R.O. Hughes D.H. Gullingsrud J. Desmond performance on a cluster of multicore processors. 2008 Available from: https://www.deshawresearch.com/publications/Desmond_Performance_Cluster.pdf
    [Google Scholar]
  37. Talarico L.B. Zibetti R.G.M. Faria P.C.S. Scolaro L.A. Duarte M.E.R. Noseda M.D. Pujol C.A. Damonte E.B. Anti-herpes simplex virus activity of sulfated galactans from the red seaweeds Gymnogongrus griffithsiae and Cryptonemia crenulata. Int. J. Biol. Macromol. 2004 34 1-2 63 71 10.1016/j.ijbiomac.2004.03.002 15178011
    [Google Scholar]
  38. Schisterman E.F. Faraggi D. Reiser B. Adjusting the generalized ROC curve for covariates. Stat. Med. 2004 23 21 3319 3331 10.1002/sim.1908 15490426
    [Google Scholar]
  39. Selick H.E. Beresford A.P. Tarbit M.H. The emerging importance of predictive ADME simulation in drug discovery. Drug Discov. Today 2002 7 2 109 116 10.1016/S1359‑6446(01)02100‑6 11790621
    [Google Scholar]
  40. Veber D.F. Johnson S.R. Cheng H.Y. Smith B.R. Ward K.W. Kopple K.D. Molecular properties that influence the oral bioavailability of drug candidates. J. Med. Chem. 2002 45 12 2615 2623 10.1021/jm020017n 12036371
    [Google Scholar]
  41. Drug lipophilicity and absorption: The continuous challenge in drug discovery. Emery Pharma 2017 Available from: https://emerypharma.com/blog/drug-lipophilicity-and-absorption-a-continuous-challenge-toward-the-goal-of-drug-discovery/
    [Google Scholar]
  42. Arnott J.A. Planey S.L. The influence of lipophilicity in drug discovery and design. Expert Opin. Drug Discov. 2012 7 10 863 875 10.1517/17460441.2012.714363 22992175
    [Google Scholar]
  43. Vemula V.R. Lagishetty V. Lingala S. Solubility enhancement techniques. Int. J. Pharm. Sci. Rev. Res. 2010 5 1 41 51
    [Google Scholar]
  44. Savjani K.T. Gajjar A.K. Savjani J.K. Drug solubility: Importance and enhancement techniques. ISRN Pharm. 2012 2012 1 10 10.5402/2012/195727 22830056
    [Google Scholar]
  45. Manikandan P. Nagini S. Cytochrome P450 structure, function and clinical significance: A review. Curr. Drug Targets 2018 19 1 38 54 10.2174/1389450118666170125144557 28124606
    [Google Scholar]
  46. Ogu C.C. Maxa J.L. Drug interactions due to cytochrome P450. Proc. Bayl. Univ. Med. Cent. 2000 13 4 421 423 10.1080/08998280.2000.11927719 16389357
    [Google Scholar]
  47. Finch A. Pillans P. P-glycoprotein and its role in drug-drug interactions. Aust. Prescr. 2014 37 4 137 139 10.18773/austprescr.2014.050
    [Google Scholar]
  48. Egan W.J. Merz K.M. Jr Baldwin J.J. Prediction of drug absorption using multivariate statistics. J. Med. Chem. 2000 43 21 3867 3877 10.1021/jm000292e 11052792
    [Google Scholar]
  49. Muegge I. Selection criteria for drug‐like compounds. Med. Res. Rev. 2003 23 3 302 321 10.1002/med.10041 12647312
    [Google Scholar]
  50. Drwal M.N. Banerjee P. Dunkel M. Wettig M.R. Preissner R. ProTox: a web server for the in silico prediction of rodent oral toxicity. Nucleic Acids Res. 2014 42 W1 W53 W58 10.1093/nar/gku401 24838562
    [Google Scholar]
  51. Wallace A.C. Laskowski R.A. Thornton J.M. LIGPLOT: a program to generate schematic diagrams of protein-ligand interactions. Protein Eng. Des. Sel. 1995 8 2 127 134 10.1093/protein/8.2.127 7630882
    [Google Scholar]
  52. Iqbal D. Alsaweed M. Jamal Q.M.S. Asad M.R. Rizvi S.M.D. Rizvi M.R. Albadrani H.M. Hamed M. Jahan S. Alyenbaawi H. Pharmacophore-Based screening, molecular docking, and dynamic simulation of fungal metabolites as inhibitors of Multi-Targets in neurodegenerative disorders. Biomolecules 2023 13 11 1613 10.3390/biom13111613 38002295
    [Google Scholar]
  53. Kirchmair J. Distinto S. Markt P. Schuster D. Spitzer G.M. Liedl K.R. Wolber G. How to optimize shape-based virtual screening: choosing the right query and including chemical information. J. Chem. Inf. Model. 2009 49 3 678 692 10.1021/ci8004226 19434901
    [Google Scholar]
/content/journals/ctmc/10.2174/0115680266337334250320231553
Loading
Prediction and Validation of Novel BRAF Inhibitor as a Potential Drug Candidate for the Treatment of Colorectal Cancer
Bentham Science Publishers ; https://doi.org/10.2174/0115680266337334250320231553
/content/journals/ctmc/10.2174/0115680266337334250320231553
/content/journals/ctmc/10.2174/0115680266337334250320231553
Loading

Data & Media loading...


  • Article Type:     Research Article
Keywords: anti-cancer activity ; molecular dynamic simulation studies ; MTT assay ; molecular docking ; toxicity studies ; ADME prediction ; cytotoxicity ; 3D pharmacophore model

Most Cited Most Cited RSS feed

This is a required field
Please enter a valid email address
Approval was a Success
Invalid data
An Error Occurred
Approval was partially successful, following selected items could not be processed due to error
Please enter a valid_number test