Desarrollo y síntesis de un análogo de los quimioterapéuticos inhibidores de la topoisomerasa I, por metodologías de cribado virtual basadas en cambios bioisostéricos y QSAR
La incidencia del cáncer colorrectal ha aumentado considerablemente, convirtiéndolo en un problema de salud pública global. Su complejidad genera diagnósticos tardíos y tratamientos con baja eficacia, lo que conlleva a una alta carga de efectos adversos para los pacientes. En respuesta a esta proble...
- Autores:
-
Rodriguez Velandia , Sara Daniela
Moreno Tranchita, Juan Pablo
- Tipo de recurso:
- https://purl.org/coar/resource_type/c_7a1f
- Fecha de publicación:
- 2024
- Institución:
- Universidad El Bosque
- Repositorio:
- Repositorio U. El Bosque
- Idioma:
- spa
- OAI Identifier:
- oai:repositorio.unbosque.edu.co:20.500.12495/12133
- Acceso en línea:
- https://hdl.handle.net/20.500.12495/12133
- Palabra clave:
- Cáncer colorrectal
AND topoisomerasa 1
Machine learning
QSAR
Farmacocinética
Farmacodinamia
615.19
Colorectal cancer
DNA topoisomerase 1
Machine learning
QSAR
Pharmacokinetics
Pharmacodynamics
- Rights
- closedAccess
- License
- Acceso cerrado
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Desarrollo y síntesis de un análogo de los quimioterapéuticos inhibidores de la topoisomerasa I, por metodologías de cribado virtual basadas en cambios bioisostéricos y QSAR |
| dc.title.translated.none.fl_str_mv |
Development and synthesis of an analog of the chemotherapeutic topoisomerase I inhibitors, using virtual screening methodologies based on bioisosteric changes and QSAR. |
| title |
Desarrollo y síntesis de un análogo de los quimioterapéuticos inhibidores de la topoisomerasa I, por metodologías de cribado virtual basadas en cambios bioisostéricos y QSAR |
| spellingShingle |
Desarrollo y síntesis de un análogo de los quimioterapéuticos inhibidores de la topoisomerasa I, por metodologías de cribado virtual basadas en cambios bioisostéricos y QSAR Cáncer colorrectal AND topoisomerasa 1 Machine learning QSAR Farmacocinética Farmacodinamia 615.19 Colorectal cancer DNA topoisomerase 1 Machine learning QSAR Pharmacokinetics Pharmacodynamics |
| title_short |
Desarrollo y síntesis de un análogo de los quimioterapéuticos inhibidores de la topoisomerasa I, por metodologías de cribado virtual basadas en cambios bioisostéricos y QSAR |
| title_full |
Desarrollo y síntesis de un análogo de los quimioterapéuticos inhibidores de la topoisomerasa I, por metodologías de cribado virtual basadas en cambios bioisostéricos y QSAR |
| title_fullStr |
Desarrollo y síntesis de un análogo de los quimioterapéuticos inhibidores de la topoisomerasa I, por metodologías de cribado virtual basadas en cambios bioisostéricos y QSAR |
| title_full_unstemmed |
Desarrollo y síntesis de un análogo de los quimioterapéuticos inhibidores de la topoisomerasa I, por metodologías de cribado virtual basadas en cambios bioisostéricos y QSAR |
| title_sort |
Desarrollo y síntesis de un análogo de los quimioterapéuticos inhibidores de la topoisomerasa I, por metodologías de cribado virtual basadas en cambios bioisostéricos y QSAR |
| dc.creator.fl_str_mv |
Rodriguez Velandia , Sara Daniela Moreno Tranchita, Juan Pablo |
| dc.contributor.advisor.none.fl_str_mv |
Guevara Pulido, James Oswaldo |
| dc.contributor.author.none.fl_str_mv |
Rodriguez Velandia , Sara Daniela Moreno Tranchita, Juan Pablo |
| dc.subject.none.fl_str_mv |
Cáncer colorrectal AND topoisomerasa 1 Machine learning QSAR Farmacocinética Farmacodinamia |
| topic |
Cáncer colorrectal AND topoisomerasa 1 Machine learning QSAR Farmacocinética Farmacodinamia 615.19 Colorectal cancer DNA topoisomerase 1 Machine learning QSAR Pharmacokinetics Pharmacodynamics |
| dc.subject.ddc.none.fl_str_mv |
615.19 |
| dc.subject.keywords.none.fl_str_mv |
Colorectal cancer DNA topoisomerase 1 Machine learning QSAR Pharmacokinetics Pharmacodynamics |
| description |
La incidencia del cáncer colorrectal ha aumentado considerablemente, convirtiéndolo en un problema de salud pública global. Su complejidad genera diagnósticos tardíos y tratamientos con baja eficacia, lo que conlleva a una alta carga de efectos adversos para los pacientes. En respuesta a esta problemática, se diseñó y sintetizó un nuevo análogo inhibidor de la ADN topoisomerasa 1. Este proceso se basó en el diseño racional de fármacos, utilizando cambios bioisostéricos y metodologías de SBVS y LBVS con machine learning. Se desarrollaron tres modelos predictivos validados en líneas celulares de cáncer colorrectal HCT-116, HCT-8 y HT-29. Entre 59 análogos diseñados, el A12 se destaca por sus mejoras en comparación con Irinotecan, Topotecan y Doxorubicina. A12 presenta una buena afinidad por la ADN topoisomerasa 1 (6,8 Kcal/mol) y un menor IC50 (0,23 μM en HCT- 116). Además, presenta mejoras farmacocinéticas (PPB 89,6%, Log P 3,22) y un perfil de toxicidad similar a los fármacos comerciales. El desarrollo del análogo A12 representa un avance significativo en la búsqueda de nuevas opciones terapéuticas para el cáncer colorrectal. Sus propiedades farmacológicas y de seguridad lo convierten en un candidato prometedor para futuras investigaciones preclínicas |
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2024 |
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2024-05-16T02:45:06Z |
| dc.date.available.none.fl_str_mv |
2024-05-16T02:45:06Z |
| dc.date.issued.none.fl_str_mv |
2024-04-30 |
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http://purl.org/coar/resource_type/c_7a1f |
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Tesis/Trabajo de grado - Monografía - Pregrado |
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https://purl.org/coar/resource_type/c_7a1f |
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https://hdl.handle.net/20.500.12495/12133 |
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Universidad El Bosque |
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reponame:Repositorio Institucional Universidad El Bosque |
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Cáncer colorrectal.” Accessed: Mar. 31, 2024. [Online]. Available: https://www.who.int/es/news-room/fact- sheets/detail/colorectal-cancer A. Goel et al., “Citation: Hossain, M Colorectal Cancer: A Review of Carcinogenesis, Global Epidemiology, Current Challenges, Risk Factors, Preventive and Treatment Strategies,” Cancers, vol. 2022, p. 1732, 1732, doi: 10.3390/cancers14071732. C. Luo, S. Cen, G. Ding, and W. Wu, “Mucinous colorectal adenocarcinoma: Clinical pathology and treatment options,” Cancer Communications, vol. 39, no. 1, Mar. 2019, doi: 10.1186/S40880-019-0361-0. V. K. Morris et al., “Treatment of Metastatic Colorectal Cancer: ASCO Guideline,” J Clin Oncol, vol. 41, pp. 678–700, 2022, doi: 10.1200/JCO.22. Y. Pommier, A. Nussenzweig, S. Takeda, and C. Austin, “Human topoisomerases and their roles in genome stability and organization”, doi: 10.1038/s41580-022-00452-3. Y. Sun et al., “Targeting neddylation sensitizes colorectal cancer to topoisomerase I inhibitors by inactivating the DCAF13-CRL4 ubiquitin ligase complex”, doi: 10.1038/s41467-023-39374-9. A. Talukdar, B. Kundu, D. Sarkar, S. Goon, and M. A. Mondal, “Topoisomerase I inhibitors: Challenges, progress and the road ahead,” European Journal of Medicinal Chemistry, vol. 236, p. 114304, Jun. 2022, doi: 10.1016/J.EJMECH.2022.114304. B. L. Staker et al., “Structures of Three Classes of Anticancer Agents Bound to the Human Topoisomerase I−DNA Covalent Complex,” Journal of Medicinal Chemistry, vol. 48, no. 7, pp. 2336–2345, 2005, doi: 10.1021/jm049146p G. F. Weber, “2 DNA Damaging Drugs,” 2015, doi: 10.1007/978-3-319-13278-5_2. J. Marinello et al., “ARTICLE OPEN Cellular and Molecular Biology Topoisomerase I poison-triggered immune gene activation is markedly reduced in human small-cell lung cancers by impairment of the cGAS/STING pathway,” British Journal of Cancer, vol. 127, pp. 1214–1225, 2022, doi: 10.1038/s41416-022-01894 Y. Pommier, “DNA Topoisomerase I Inhibitors: Chemistry, Biology and Interfacial Inhibition”, doi: 10.1021/cr900097c. A. Thomas and Y. Pommier, “Targeting Topoisomerase I in the Era of Precision Medicine”, doi: 10.1158/1078- 0432.CCR-19-1089. H. Soufi, · Mohammed Salah, · Said Belaaouad, and · Mohammed Moutaabbid, “An Insightful Evaluation of Evodiamine Analogs Effect as DNA Topoisomerase I Inhibitors Using QSAR Method,” India, Sect. B Biol. Sci, vol. 93, no. 3, pp. 639–657, doi: 10.1007/s40011-023-01450-x. S. Pal et al., “Ligand-based Pharmacophore Modeling, Virtual Screening and Molecular Docking Studies for Discovery of Potential Topoisomerase I Inhibitors,” Computational and Structural Biotechnology Journal, vol. 17, pp. 291–310, Jan. 2019, doi: 10.1016/J.CSBJ.2019.02.006. D. M. Khaled, M. E. Elshakre, M. A. Noamaan, H. Butt, M. M. A. Fattah, and D. A. Gaber, “A Computational QSAR, Molecular Docking and In Vitro Cytotoxicity Study of Novel Thiouracil-Based Drugs with Anticancer Activity against Human-DNA Topoisomerase II,” 2022, doi: 10.3390/ijms231911799 N. Berdigaliyev and M. Aljofan, “An overview of drug discovery and development,” https://doi.org/10.4155/fmc-2019-0307, vol. 12, no. 10, pp. 939–947, Apr. 2020, doi: 10.4155/FMC-2019-0307. Y. Kochnev, E. Hellemann, K. C. Cassidy, and J. D. Durrant, “Webina: an open-source library and web app that runs AutoDock Vina entirely in the web browser”, doi: 10.1093/bioinformatics/btaa S. Murail, S. J. de Vries, J. Rey, G. Moroy, and P. Tufféry, “SeamDock: An Interactive and Collaborative Online Docking Resource to Assist Small Compound Molecular Docking”, doi: 10.3389/fmolb.2021.716466. F. Grisoni, D. Ballabio, R. Todeschini, and V. Consonni, “Molecular Descriptors for Structure–Activity Applications: A Hands-On Approach,” Methods in Molecular Biology, vol. 1800, pp. 3–53, 2018, doi: 10.1007/978-1- 4939-7899-1_1. D. N. Jaramillo, D. Millán, and J. Guevara-Pulido, “Design, synthesis and cytotoxic evaluation of a selective serotonin reuptake inhibitor (SSRI) by virtual screening,” European Journal of Pharmaceutical Sciences, vol. 183, p. 106403, Apr. 2023, doi: 10.1016/J.EJPS.2023.106403. M. Prieto, A. Niño, P. Acosta-Guzmán, and J. Guevara-Pulido, “Design and synthesis of a potential selective JAK-3 inhibitor for the treatment of rheumatoid arthritis using predictive QSAR models,” Informatics in Med icine Unlocked, vol. 45, p. 101464, Jan. 2024, doi: 10.1016/J.IMU.2024.101464. J. Guevara-Pulido, R. A. Jiménez, S. J. Morantes, D. N. Jaramillo, and P. Acosta-Guzmán, “Design, Synthesis, and Development of 4-[(7-Chloroquinoline-4-yl)amino]phenol as a Potential SARS-CoV-2 Mpro Inhibitor,” 2022, doi: 10.1002/slct.202200125. A.Daina, O. Michielin, and V. Zoete, “SwissADME: a free web tool to evaluate pharmacokinetics, drug-likeness and medicinal chemistry friendliness of small molecules OPEN,” 2017, doi: 10.1038/srep42717 G. Xiong et al., “ADMETlab 2.0: an integrated online platform for accurate and comprehensive predictions of ADMET properties,” Nucleic Acids Research, vol. 49, 2021, doi: 10.1093/nar/gkab255. N. Brown, P. Ertl, R. Lewis, T. Luksch, D. Reker, and N. Schneider, “Artificial intelligence in chemistry and drug design,” Journal of Computer-Aided Molecular Design, vol. 34, no. 7, pp. 709–715, Jul. 2020, doi: 10.1007/S10822-020-00317-X/METRICS. S. Koosha, Z. Mohamed, A. Sinniah, and M. A. Alshawsh, “Investigation into the Molecular Mechanisms underlying the Anti-proliferative and Anti-tumorigenesis activities of Diosmetin against HCT-116 Human Colorectal Cancer”, doi: 10.1038/s41598-019-41685-1. T. J. Med Sci et al., “Turkish Journal of Medical Sciences Carvacrol alters soluble factors in HCT-116 and HT-29 cell lines”, doi: 10.3906/sag-1907-173. J. Dong et al., “ChemDes: An integrated web-based platform for molecular descriptor and fingerprint computation,” Journal of Cheminformatics, vol. 7, no. 1, pp. 1–10, Dec. 2015, doi: 10.1186/S13321-015-0109-Z/FIG- URES/2. A. Golbraikh and A. Tropsha, “Beware of q2!,” Journal of Molecular Graphics and Modelling, vol. 20, no. 4,pp. 269–276, Jan. 2002, doi: 10.1016/S1093-3263(01)00123-1. N. A. Meanwell, “The Influence of Bioisosteres in Drug Design: Tactical Applications to Address Developability Problems,” Top Med Chem, vol. 9, pp. 283–382, 2015, doi: 10.1007/7355_2013_29. A. Dick and S. Cocklin, “pharmaceuticals Bioisosteric Replacement as a Tool in Anti-HIV Drug Design”, doi: 10.3390/ph13030036. M. Coelho Santos Junior et al., “Structure-Based Virtual Screening: From Classical to Artificial Intelligence,” Frontiers in Chemistry | www.frontiersin.org, vol. 1, p. 343, 2020, doi: 10.3389/fchem.2020.00343. H. Zhu, Y. Zhang, W. Li, and N. Huang, “A Comprehensive Survey of Prospective Structure-Based Virtual Screening for Early Drug Discovery in the Past Fifteen Years,” 2022, doi: 10.3390/ijms232415961. H. M. Berman et al., “The Protein Data Bank,” 2000. [Online]. Available: http://www.rcsb.org/pdb/status.html C. Metallinos, S. Nerdinger, and V. Snieckus, “N-cumyl benzamide, sulfonamide, and aryl O-carbamate directed metalation groups. Mild hydrolytic lability for facile manipulation of directed ortho metalation derived aromatics,” Organic Letters, vol. 1, no. 8, pp. 1183–1186, Oct. 1999, doi: 10.1021/OL990846B/SUPPL_FILE/OL990846B_S.PDF. P. Acosta-Guzmán, A. Rodríguez-López, and D. Gamba-Sánchez, “Pummerer Synthesis of Chromanes Reveals a Competition between Cyclization and Reductive Chlorination,” Organic Letters, vol. 21, no. 17, pp. 6903–6908, Sep. 2019, doi: 10.1021/ACS.ORGLETT.9B02520/SUPPL_FILE/OL9B02520_SI_001.PDF. N. Srinivasan, A. Yurek-George, and A. Ganesan, “Rapid deprotection of N-Boc amines by TFA combined with freebase generation using basic ion-exchange resins,” Molecular Diversity, vol. 9, no. 4, pp. 291–293, Nov. 2005, doi: 10.1007/S11030-005-4386-8/METRICS. |
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Guevara Pulido, James OswaldoRodriguez Velandia , Sara DanielaMoreno Tranchita, Juan Pablo2024-05-16T02:45:06Z2024-05-16T02:45:06Z2024-04-30https://hdl.handle.net/20.500.12495/12133Universidad El Bosquereponame:Repositorio Institucional Universidad El Bosquerepourl:https://repositorio.unbosque.edu.coLa incidencia del cáncer colorrectal ha aumentado considerablemente, convirtiéndolo en un problema de salud pública global. Su complejidad genera diagnósticos tardíos y tratamientos con baja eficacia, lo que conlleva a una alta carga de efectos adversos para los pacientes. En respuesta a esta problemática, se diseñó y sintetizó un nuevo análogo inhibidor de la ADN topoisomerasa 1. Este proceso se basó en el diseño racional de fármacos, utilizando cambios bioisostéricos y metodologías de SBVS y LBVS con machine learning. Se desarrollaron tres modelos predictivos validados en líneas celulares de cáncer colorrectal HCT-116, HCT-8 y HT-29. Entre 59 análogos diseñados, el A12 se destaca por sus mejoras en comparación con Irinotecan, Topotecan y Doxorubicina. A12 presenta una buena afinidad por la ADN topoisomerasa 1 (6,8 Kcal/mol) y un menor IC50 (0,23 μM en HCT- 116). Además, presenta mejoras farmacocinéticas (PPB 89,6%, Log P 3,22) y un perfil de toxicidad similar a los fármacos comerciales. El desarrollo del análogo A12 representa un avance significativo en la búsqueda de nuevas opciones terapéuticas para el cáncer colorrectal. Sus propiedades farmacológicas y de seguridad lo convierten en un candidato prometedor para futuras investigaciones preclínicasPregradoQuímico FarmacéuticoThe incidence of colorectal cancer has significantly increased, turning it into a global public health issue. Its complexity leads to late diagnoses and treatments with low efficacy, resulting in a high burden of adverse effects for patients. In response to this challenge, a new analog inhibitor of DNA topoisomerase 1 was designed and synthesized. This process was based on rational drug design, utilizing bioisosteric changes and SBVS and LBVS methodologies with machine learning. Three predictive models were developed and validated in colorectal cancer cell lines HCT-116, HCT-8, and HT-29. Among 59 designed analogs, A12 stands out for its improvements compared to Irinotecan, Topotecan, and Doxorubicin. A12 shows good affinity for DNA topoisomerase 1 (6.8 Kcal/mol) and a lower IC50 (0.23 μM in HCT-116). Additionally, it exhibits improved pharmacokinetics (PPB 89.6%, Log P 3.22) and a toxicity profile similar to commercial drugs. The development of the A12 analog represents a significant advancement in the search for new therapeutic options for colorectal cancer. Its pharmacological properties and safety profile make it a promising candidate for future preclinical research.application/pdfCáncer colorrectalAND topoisomerasa 1Machine learningQSARFarmacocinéticaFarmacodinamia615.19Colorectal cancerDNA topoisomerase 1Machine learningQSARPharmacokineticsPharmacodynamicsDesarrollo y síntesis de un análogo de los quimioterapéuticos inhibidores de la topoisomerasa I, por metodologías de cribado virtual basadas en cambios bioisostéricos y QSARDevelopment and synthesis of an analog of the chemotherapeutic topoisomerase I inhibitors, using virtual screening methodologies based on bioisosteric changes and QSAR.Química FarmacéuticaUniversidad El BosqueFacultad de CienciasTesis/Trabajo de grado - Monografía - Pregradohttps://purl.org/coar/resource_type/c_7a1fhttp://purl.org/coar/resource_type/c_7a1finfo:eu-repo/semantics/bachelorThesishttps://purl.org/coar/version/c_ab4af688f83e57aaCáncer colorrectal.” Accessed: Mar. 31, 2024. [Online]. Available: https://www.who.int/es/news-room/fact- sheets/detail/colorectal-cancerA. Goel et al., “Citation: Hossain, M Colorectal Cancer: A Review of Carcinogenesis, Global Epidemiology, Current Challenges, Risk Factors, Preventive and Treatment Strategies,” Cancers, vol. 2022, p. 1732, 1732, doi: 10.3390/cancers14071732.C. Luo, S. Cen, G. Ding, and W. Wu, “Mucinous colorectal adenocarcinoma: Clinical pathology and treatment options,” Cancer Communications, vol. 39, no. 1, Mar. 2019, doi: 10.1186/S40880-019-0361-0.V. K. Morris et al., “Treatment of Metastatic Colorectal Cancer: ASCO Guideline,” J Clin Oncol, vol. 41, pp. 678–700, 2022, doi: 10.1200/JCO.22.Y. Pommier, A. Nussenzweig, S. Takeda, and C. Austin, “Human topoisomerases and their roles in genome stability and organization”, doi: 10.1038/s41580-022-00452-3.Y. Sun et al., “Targeting neddylation sensitizes colorectal cancer to topoisomerase I inhibitors by inactivating the DCAF13-CRL4 ubiquitin ligase complex”, doi: 10.1038/s41467-023-39374-9.A. Talukdar, B. Kundu, D. Sarkar, S. Goon, and M. A. Mondal, “Topoisomerase I inhibitors: Challenges, progress and the road ahead,” European Journal of Medicinal Chemistry, vol. 236, p. 114304, Jun. 2022, doi: 10.1016/J.EJMECH.2022.114304.B. L. Staker et al., “Structures of Three Classes of Anticancer Agents Bound to the Human Topoisomerase I−DNA Covalent Complex,” Journal of Medicinal Chemistry, vol. 48, no. 7, pp. 2336–2345, 2005, doi: 10.1021/jm049146pG. F. Weber, “2 DNA Damaging Drugs,” 2015, doi: 10.1007/978-3-319-13278-5_2.J. Marinello et al., “ARTICLE OPEN Cellular and Molecular Biology Topoisomerase I poison-triggered immune gene activation is markedly reduced in human small-cell lung cancers by impairment of the cGAS/STING pathway,” British Journal of Cancer, vol. 127, pp. 1214–1225, 2022, doi: 10.1038/s41416-022-01894Y. Pommier, “DNA Topoisomerase I Inhibitors: Chemistry, Biology and Interfacial Inhibition”, doi: 10.1021/cr900097c.A. Thomas and Y. Pommier, “Targeting Topoisomerase I in the Era of Precision Medicine”, doi: 10.1158/1078- 0432.CCR-19-1089.H. Soufi, · Mohammed Salah, · Said Belaaouad, and · Mohammed Moutaabbid, “An Insightful Evaluation of Evodiamine Analogs Effect as DNA Topoisomerase I Inhibitors Using QSAR Method,” India, Sect. B Biol. Sci, vol. 93, no. 3, pp. 639–657, doi: 10.1007/s40011-023-01450-x.S. Pal et al., “Ligand-based Pharmacophore Modeling, Virtual Screening and Molecular Docking Studies for Discovery of Potential Topoisomerase I Inhibitors,” Computational and Structural Biotechnology Journal, vol. 17, pp. 291–310, Jan. 2019, doi: 10.1016/J.CSBJ.2019.02.006.D. M. Khaled, M. E. Elshakre, M. A. Noamaan, H. Butt, M. M. A. Fattah, and D. A. Gaber, “A Computational QSAR, Molecular Docking and In Vitro Cytotoxicity Study of Novel Thiouracil-Based Drugs with Anticancer Activity against Human-DNA Topoisomerase II,” 2022, doi: 10.3390/ijms231911799N. Berdigaliyev and M. Aljofan, “An overview of drug discovery and development,” https://doi.org/10.4155/fmc-2019-0307, vol. 12, no. 10, pp. 939–947, Apr. 2020, doi: 10.4155/FMC-2019-0307.Y. Kochnev, E. Hellemann, K. C. Cassidy, and J. D. Durrant, “Webina: an open-source library and web app that runs AutoDock Vina entirely in the web browser”, doi: 10.1093/bioinformatics/btaaS. Murail, S. J. de Vries, J. Rey, G. Moroy, and P. 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Ganesan, “Rapid deprotection of N-Boc amines by TFA combined with freebase generation using basic ion-exchange resins,” Molecular Diversity, vol. 9, no. 4, pp. 291–293, Nov. 2005, doi: 10.1007/S11030-005-4386-8/METRICS.Acceso cerradoinfo:eu-repo/semantics/closedAccesshttp://purl.org/coar/access_right/c_14cbspaLICENSElicense.txtlicense.txttext/plain; charset=utf-82000https://repositorio.unbosque.edu.co/bitstreams/5e59e578-5032-4029-ba13-c272ba38a3f9/download17cc15b951e7cc6b3728a574117320f9MD51Carta de autorización.pdfapplication/pdf174365https://repositorio.unbosque.edu.co/bitstreams/9dadf8f4-ab3d-451f-adf1-1220bddf35cd/download02f104730c5607e3a1424c0f25d79b98MD56Anexo 1 acta de aprobacion.pdfapplication/pdf5797083https://repositorio.unbosque.edu.co/bitstreams/2d2a9d54-6dfd-45f5-939c-ca5462ef7e79/download3e9adae59ced16b84c7b52488160c1d1MD57ORIGINALTrabajo de Grado.pdfTrabajo de Grado.pdfapplication/pdf1401861https://repositorio.unbosque.edu.co/bitstreams/cb2eba66-ff56-4c2f-953a-3da119e82b3e/download93daf251c2db2d7ca540fe7cbcb3c15aMD53CC-LICENSElicense_rdflicense_rdfapplication/rdf+xml; charset=utf-8899https://repositorio.unbosque.edu.co/bitstreams/cf541dfc-5048-422b-87cf-c6c4c87b4674/download3b6ce8e9e36c89875e8cf39962fe8920MD55TEXTTrabajo de Grado.pdf.txtTrabajo de Grado.pdf.txtExtracted texttext/plain61924https://repositorio.unbosque.edu.co/bitstreams/11de6376-ea74-4a71-a29d-4aa406f9879e/download6fd6feb77892d23e6a3b0ef075487920MD58THUMBNAILTrabajo de Grado.pdf.jpgTrabajo de Grado.pdf.jpgGenerated Thumbnailimage/jpeg5289https://repositorio.unbosque.edu.co/bitstreams/ccae805c-dfcd-4b0d-9af0-2e317bf68e5c/download9b79f7f79616c7696149db48cbe76286MD5920.500.12495/12133oai:repositorio.unbosque.edu.co:20.500.12495/121332024-05-16 03:06:37.539embargo2026-05-04https://repositorio.unbosque.edu.coRepositorio Institucional Universidad El Bosquebibliotecas@biteca.com |