medigraphic.com
ISSN 2448-8747 (Print)
Publicación cuatrimestral editada por la Secretaría de Salud Jalisco

2020, Number 1

<< Back Next >>

Sal Jal 2020; 7 (1)

Reposicionamiento de fármacos identificados por métodos computacionales (SVBS), para su uso como terapias contra el cáncer

Carranza-Aranda AS, Segura-Cabrera A, Cárdenas-Vargas A, Herrera-Rodríguez SE

Language: Spanish
References: 40
Page: 48-57
PDF size: 494.89 Kb.


ABSTRACT

Introduction: Since the 80s high performance screening (HTS) is the standard method for the development and discovery of new drugs, it is carried out by experimental tests to evaluate the action they exert on living systems. However, it requires a long, expensive process and in the end a low index of molecules are approved for clinical use. To date, there is an alternative to HTS, it uses bioinformatics tools, virtual screening based on protein structure (SBVS) and virtual repositioning of drugs. Virtual repositioning has a great impact because it allows the identification of new uses of an already approved medicine, which significantly reduces the costs and time of research, thus allowing new treatments to be found for relevant diseases such as cancer. Objective: To demonstrate the applicability of the use of bioinformatics tools in conjunction with the methodology of virtual repositioning, to identify and thus propose new potential anti-tumor treatments. Results and conclusion: Since the current treatments against different types of cancer have low efficiency or may cause resistance in the tumor, analyzes have been carried out to reposition cancer drugs such as: prostate, breast, colon, glioma and cervical. The SBVS methodology has shown advantages such as astemzol in breast cancer and resperidoena in prostate cancer as antitumor treatments have been proposed. Therefore, in this work the importance and use of computational technologies based on protein structure (SBVS) for the repositioning of drugs with potential for new use as therapeutic anti-tumor agents, with a practical approach for their possible use in the Health sector.


REFERENCES

  1. Secretaria de Salud. Salud: México 2002: Información para la rendición de cuentas. Segunda edición. 2002. ISBN 970-721-148- 2.

  2. Lill M. Chapter 1: Virtual Screening in Drug Desing. En: Kortagere S. (eds) In silico Models for Drug Discovery and Methods in Molecular Biology. Springer Science+Business Media. 2013. ISBN 978-1-62703-342-8

  3. Nordberg G, Fowler B, Nordberg M. Handbook on the toxicology of Metals. 4ª Edición. Academic Press. pp 1542. eBook ISBN: 9780123973399

  4. Plewczynski D, Laźniewski M, Augustyniak R, Krzysztof G. Can We Trust Docking results? Evaluation of Seven comonly used program on PDBdatabase. Journal of Computational Chemistry. 2010; 00:000-000.

  5. Saldivar-González F, Prieto-Martínez F, Medina-Franco JL. Descubrimiento y desarrollo de fármacos: un enfoque computacional. Educación Química. 2016; 28:51-58.

  6. Medina-Franco J, Fernández-de Gortari E, Naveja J. Avances en el diseño de fármacos asistidos por computadora. Educación Química. 2015; 26:180-186.

  7. Ashburn T, Th or K. Drugs repositioning indentifying and developing new use for existing drugs. Nature Review: Drug Discovery. 2004; 3:673-683.

  8. Cronk D. Chapter 8: High-throughput screening. En: Hill RG., Rang HP (eds). Drug Discovery and Development. 2da Edición. Churchill Livingstone. 2012. ISBN: 978-0-7020-4299-7.

  9. Cosconati S, Forli S, Perryman A, Harris R, Goodsell D, Olson A. Virtual screening with Autodock: Th eory and practice. Technology Evaluation. 2010; 5(6): 597-607.

  10. Segura-Cabrera A, Singh N, Komurov K. An integrated network platform for contextual prioritization of drugs and pathways. Molecular BioSystems. 2015; 11(11): 2850-2859.

  11. March-Vila E, Pinzi L, Sturm N, Tinivella A, Engkvist O, Chen H, et al. On the integration of in silico drug desing methods for drug repurposing. Frontier in Pharmacology. 2017; 8:298.

  12. Segura-Cabrera A, Tripathi R, Zhang X, Gui L, Tsui-Fechau C, Komurov K. A structure-and chemical genomics-base approach for repositioning of drugs against VCP/p97 ATPase. Scientifi c Reports. 2017; 7:44912.1-10

  13. Park K. A review of computational drug repurposing. Translational and Clinical Pharmacology. 2019; 27(2): 59-63.

  14. Naveja J, Dueñas-González A, Medina-Franco J. Drug reporposing for epigenetic targets guided by computational methods. En: Medina-Franco J. (ed) Epi-Informatics: Discovery and Development of small molecule epigenteic drug and probe. Elsevier Inc. 2016.

  15. Cheng T, Li Q, Zhou Z, Wang Y, Bryant SH. Structure-based virtual screening for drug discovery: a problema-centric review. Th e AAPS Journal. 2012; 14(1): 133-141.

  16. Padilla A, Rojo A. Simulación del reconocimiento entre proteínas y moléculas orgánicas o docking aplicación al diseño de fármacos. Mensaje Bioquímico. 2002; Vol. XXVI:129-145.

  17. Jain AN. Surfl ex: Fully automatic fl exible molecular docking a molecular similarity-based search engine. Journal of Medicine Chemistry. 2003; 46(4):499-511.

  18. Venkatchalam CM, Jiang X, Oldfi eld T, Waldman M. LigandFit: a novel method for the shape-directed rapid docking of ligand of ligands to protein active sites. Journal of Molecular Graphics y modelling. 2003; 4:289-307.

  19. Friesner RA, Bank JL, Murphy RB, Halgren TA, Klicic JJ, Mainz DT, et al. 2004. Glide: a new approach for rapid acurate docking and scoring 1. Method and assessment of docking accuracy. Journal of Medicine Chemistry. 2004; 47(7):1739-1749.

  20. Jones G, Willett P, Glen RC, Leach AR, Taylor R. Development and validation of a Genetic algorithm for fl exible docking. Journal of Molecular Biology. 1997; 267(3):727-248.

  21. Rarey M, Kramer B, Lengauer T, Kleve G. A fast fl exible docking method using an incremental construction algorithmm. Journal of Molecular Biology. 1996; 261(3):470-489.

  22. Zsoldos Z, Reid D, Simon A, Sabjad SB, Johnson AP. eHiTs: a new, exhaustive fl exible lingand docking system. Journal of Molecular Graphics & modelling. 2007; 26(1):198-2012.

  23. Morris GM, Goodsell DS, Halliday RS, Huey R, Hart WE, Belew RK, et al. Automated docking using a Lamarckian Genetic algorith and empirical binding free energy y funtion. Journal of Computational Chemistry. 1998; 19:1639-1662.

  24. Bollell M, Gepi-Attee S, Gingell JC, Allen MJ. Sildenafi l, a novel efctive oral therapy for male erectile dysfunction. British Journal of Urology. 1996; 78:257-261

  25. Singhal S, Mehta J, Desikan R, Ayers D, Roberson P, Eddlemon P, et al. Antitumor activity of thalidomide in refractory multiple myeloma. Th e New England Journal of Medicine. 1999; 341(2):1565-1571.

  26. Walker SL, Waters MF, Lockwood DN. Th e role of thalidomide in the management of erythema nodusum leprosum. Leprosy review. 2007; 78(3):197-215.

  27. GNS H, GR S, Murahari M, Krishnamurthy M. An update on drugs repurposing: re written saga of the drug’s fate. Biomedicine and Pharmacotherapy. 2019; 110( 2019): 700-716.

  28. Sleire L, Førde HE, Netland IA, Leiss L, Skeie BS, Enger PØ. Drug repurposing in cancer. Pharmacological Research. 2017; 124(2017):74-91.

  29. Kola I, Landis J. Can the pharmaceutical industry reduce attriton rates. Nature reviews. Drug Discovery. 2004; 3(8):711- 715.

  30. Willingham A, Deveraux Q, Hampton G, Aza-Blanc P. RNAi and HTS: exploring cancer by systematic loss-of-function. Oncogen. 2004; 23:8392-8400.

  31. Méndez-Lucio O, Tran J, Medina-Franco JL, Meurice N, Muller M. Towards drug repurposing in epigenetics: Olsalazine as a novel hypomethylating compound active in a celular context. ChemMedChem, 2014; 9:560-565.

  32. De la Cruz-Hernandez E, Medina-Franco JL, Trujillo J, Chavez- Blanco A, Dominguez-Gomez, G, Perez-Cardenas E, et al. Ribavirin as a tri-targeted antitumor repositioned drug. Oncology Reports, 2015;33:2384-2392.

  33. Aguirre-Alvarado C, Segura-Cabrera A, Velázquez-Quesada I, Hernández-Esquivel MA, García-Pérez CA, Guerrero-Rodríguez, SL et al. Virtual screening-driven repositioning of etoposide as CD44 antagonist in breast cancer cells. Oncotarget. 2016; 7(17):23772-23784.

  34. García-Quiroz J, Camacho J. Astemizole: an old anti-histamine as a new promising anti-cancer drug. Anticancer Agents in Medicinal Chemistry. 2011; 11: 307–314.

  35. Jakha R, Paul S, Bhardwaj M, Kang SC. Astemizole-Histamide induce bedin-1 independent autophagy by targetin p53 dependent crosstalk between antophagy and apoptosis. Cancer latters. 2015; 372(1):89-100.

  36. Kadavakollu S, Stailey C, Kunapareddy CS, White S. Clotrimazole as a Cancer Drug: A Short Review. Medicinal Chemistry Journal (Los Angeles). 2014; 4(11):722–724.

  37. Turanli B, Grotli M, Boren J, Nielsen J, Uhlen M, Arga KY, et al. Drug repositioning of effective prostate cancer treatment. Frontiers is Physiology. 2018; 9:500.

  38. Dilly SJ, Clark AJ, Marsh A, Mitchell DA, Cain R, Fishwick CWG, et al. A chemical genomics approach to drug reprofiling in oncology: antipsychotic drug risperidone as a potential adenocarcinoma treatment. Cancer Letters. 2017; 393:16–21.

  39. Gayvert KM, Dardenne E, Cheung C, Boland MR, Lorberbaum T, Wanjala J, et al. A computational drug repositioning approach for targeting oncogenic transcription factors. Cell Reports. 2016; 15:2348–2356.

  40. Turanli B, Gulfidan G, Arga KY. Transcriptomic-guided drug repositioning supported by a new bioinformatics search tool: geneXpharma. OMICS 2017; 21:584–591.




2020     |     www.medigraphic.com