Evaluación comparativa de herramientas predictivas In Silico de variantes de cambio de sentido en genes de interés en farmacogenética: análisis bioinformático y poblacional para el gen DPYD
ilustraciones, diagramas
- Autores:
-
Saldaña Peñaloza, Diego Aeljandro
- Tipo de recurso:
- Fecha de publicación:
- 2024
- Institución:
- Universidad Nacional de Colombia
- Repositorio:
- Universidad Nacional de Colombia
- Idioma:
- spa
- OAI Identifier:
- oai:repositorio.unal.edu.co:unal/86050
- Palabra clave:
- 610 - Medicina y salud::615 - Farmacología y terapéutica
Farmacogenética
Biología Computacional/métodos
Investigación Genética
Pharmacogenetics
Computational Biology/methods
Genetic Research
Dihidropirimidina deshidrogenasa
Valor predictivo de las pruebas
Mutación de cambio de sentido
Análisis In Silico
Farmacogenética
Dihydropyrimidine dehydrogenase
Predictive value of tests
Missense mutation
In Silico analysis
Pharmacogenetics
- Rights
- openAccess
- License
- Atribución-NoComercial-SinDerivadas 4.0 Internacional
| id |
UNACIONAL2_013331de687d1888073c4f0473f51594 |
|---|---|
| oai_identifier_str |
oai:repositorio.unal.edu.co:unal/86050 |
| network_acronym_str |
UNACIONAL2 |
| network_name_str |
Universidad Nacional de Colombia |
| repository_id_str |
|
| dc.title.spa.fl_str_mv |
Evaluación comparativa de herramientas predictivas In Silico de variantes de cambio de sentido en genes de interés en farmacogenética: análisis bioinformático y poblacional para el gen DPYD |
| dc.title.translated.eng.fl_str_mv |
Comparative evaluation of in silico predictive tools of missense variants in genes of interest in pharmacogenetics: bioinformatics and population analysis for the DPYD gene |
| title |
Evaluación comparativa de herramientas predictivas In Silico de variantes de cambio de sentido en genes de interés en farmacogenética: análisis bioinformático y poblacional para el gen DPYD |
| spellingShingle |
Evaluación comparativa de herramientas predictivas In Silico de variantes de cambio de sentido en genes de interés en farmacogenética: análisis bioinformático y poblacional para el gen DPYD 610 - Medicina y salud::615 - Farmacología y terapéutica Farmacogenética Biología Computacional/métodos Investigación Genética Pharmacogenetics Computational Biology/methods Genetic Research Dihidropirimidina deshidrogenasa Valor predictivo de las pruebas Mutación de cambio de sentido Análisis In Silico Farmacogenética Dihydropyrimidine dehydrogenase Predictive value of tests Missense mutation In Silico analysis Pharmacogenetics |
| title_short |
Evaluación comparativa de herramientas predictivas In Silico de variantes de cambio de sentido en genes de interés en farmacogenética: análisis bioinformático y poblacional para el gen DPYD |
| title_full |
Evaluación comparativa de herramientas predictivas In Silico de variantes de cambio de sentido en genes de interés en farmacogenética: análisis bioinformático y poblacional para el gen DPYD |
| title_fullStr |
Evaluación comparativa de herramientas predictivas In Silico de variantes de cambio de sentido en genes de interés en farmacogenética: análisis bioinformático y poblacional para el gen DPYD |
| title_full_unstemmed |
Evaluación comparativa de herramientas predictivas In Silico de variantes de cambio de sentido en genes de interés en farmacogenética: análisis bioinformático y poblacional para el gen DPYD |
| title_sort |
Evaluación comparativa de herramientas predictivas In Silico de variantes de cambio de sentido en genes de interés en farmacogenética: análisis bioinformático y poblacional para el gen DPYD |
| dc.creator.fl_str_mv |
Saldaña Peñaloza, Diego Aeljandro |
| dc.contributor.advisor.spa.fl_str_mv |
Mahecha Lopez, Daniel Hernán Rey Buitrago, Mauricio Castro Rojas, Carlos |
| dc.contributor.author.spa.fl_str_mv |
Saldaña Peñaloza, Diego Aeljandro |
| dc.contributor.researchgroup.spa.fl_str_mv |
Genética clínica |
| dc.subject.ddc.spa.fl_str_mv |
610 - Medicina y salud::615 - Farmacología y terapéutica |
| topic |
610 - Medicina y salud::615 - Farmacología y terapéutica Farmacogenética Biología Computacional/métodos Investigación Genética Pharmacogenetics Computational Biology/methods Genetic Research Dihidropirimidina deshidrogenasa Valor predictivo de las pruebas Mutación de cambio de sentido Análisis In Silico Farmacogenética Dihydropyrimidine dehydrogenase Predictive value of tests Missense mutation In Silico analysis Pharmacogenetics |
| dc.subject.decs.spa.fl_str_mv |
Farmacogenética Biología Computacional/métodos Investigación Genética |
| dc.subject.decs.eng.fl_str_mv |
Pharmacogenetics Computational Biology/methods Genetic Research |
| dc.subject.proposal.spa.fl_str_mv |
Dihidropirimidina deshidrogenasa Valor predictivo de las pruebas Mutación de cambio de sentido Análisis In Silico Farmacogenética |
| dc.subject.proposal.eng.fl_str_mv |
Dihydropyrimidine dehydrogenase Predictive value of tests Missense mutation In Silico analysis Pharmacogenetics |
| description |
ilustraciones, diagramas |
| publishDate |
2024 |
| dc.date.accessioned.none.fl_str_mv |
2024-05-08T18:54:17Z |
| dc.date.available.none.fl_str_mv |
2024-05-08T18:54:17Z |
| dc.date.issued.none.fl_str_mv |
2024 |
| dc.type.spa.fl_str_mv |
Trabajo de grado - Maestría |
| dc.type.driver.spa.fl_str_mv |
info:eu-repo/semantics/masterThesis |
| dc.type.version.spa.fl_str_mv |
info:eu-repo/semantics/acceptedVersion |
| dc.type.content.spa.fl_str_mv |
Text |
| dc.type.redcol.spa.fl_str_mv |
http://purl.org/redcol/resource_type/TM |
| status_str |
acceptedVersion |
| dc.identifier.uri.none.fl_str_mv |
https://repositorio.unal.edu.co/handle/unal/86050 |
| dc.identifier.instname.spa.fl_str_mv |
Universidad Nacional de Colombia |
| dc.identifier.reponame.spa.fl_str_mv |
Repositorio Institucional Universidad Nacional de Colombia |
| dc.identifier.repourl.spa.fl_str_mv |
https://repositorio.unal.edu.co/ |
| url |
https://repositorio.unal.edu.co/handle/unal/86050 https://repositorio.unal.edu.co/ |
| identifier_str_mv |
Universidad Nacional de Colombia Repositorio Institucional Universidad Nacional de Colombia |
| dc.language.iso.spa.fl_str_mv |
spa |
| language |
spa |
| dc.relation.indexed.spa.fl_str_mv |
Bireme |
| dc.relation.references.spa.fl_str_mv |
Sanger F, Nicklen S, Coulson AR. DNA sequencing with chain-terminating inhibitors. Proc Natl Acad Sci U S A. diciembre de 1977;74(12):5463-7. Wake DT, Ilbawi N, Dunnenberger HM, Hulick PJ. Pharmacogenomics: Prescribing Precisely. Med Clin North Am. noviembre de 2019;103(6):977-90. Verma M, Kulshrestha S, Puri A. Genome Sequencing. En: Keith JM, editor. Bioinformatics: Volume I: Data, Sequence Analysis, and Evolution [Internet]. New York, NY: Springer; 2017 [citado 25 de marzo de 2023]. p. 3-33. (Methods in Molecular Biology). Disponible en: https://doi.org/10.1007/978-1-4939-6622-6_1 Petrosino M, Novak L, Pasquo A, Chiaraluce R, Turina P, Capriotti E, et al. Analysis and Interpretation of the Impact of Missense Variants in Cancer. Int J Mol Sci. 21 de mayo de 2021;22(11):5416. Rodrigues C, Santos-Silva A, Costa E, Bronze-da-Rocha E. Performance of In Silico Tools for the Evaluation of UGT1A1 Missense Variants. Hum Mutat. diciembre de 2015;36(12):1215-25. Tsimberidou AM, Fountzilas E, Nikanjam M, Kurzrock R. Review of precision cancer medicine: Evolution of the treatment paradigm. Cancer Treat Rev [Internet]. 1 de junio de 2020 [citado 25 de marzo de 2023];86. Disponible en: https://www.cancertreatmentreviews.com/article/S0305-7372(20)30057-8/fulltext Morganti S, Tarantino P, Ferraro E, D´Amico P, Achutti B, Curigliano G. Next Generation Sequencing (NGS): A Revolutionary Technology in Pharmacogenomics and Personalized Medicine in Cancer. In: Ruiz-Garcia, E., Astudillo-de la Vega, H. (eds) Translational Research and Onco-Omics Applications in the Era of Cancer Personal Genomics. Advances in Experimental Medicine and Biology [Internet]. Vol. 1168. Switzerland: Springer Nature; [citado 25 de marzo de 2023]. 9–30 p. Disponible en: https://link.springer.com/chapter/10.1007/978-3-030-24100-1_2 Micaglio E, Locati ET, Monasky MM, Romani F, Heilbron F, Pappone C. Role of Pharmacogenetics in Adverse Drug Reactions: An Update towards Personalized Medicine. Front Pharmacol. 2021;12(651720):1-17 Lunenburg CATC, Henricks LM, Guchelaar HJ, Swen JJ, Deenen MJ, Schellens JHM, et al. Prospective DPYD genotyping to reduce the risk of fluoropyrimidine-induced severe toxicity: Ready for prime time. Eur J Cancer. 1 de febrero de 2016;54:40-8. Sharma V, Gupta SK, Verma M. Dihydropyrimidine dehydrogenase in the metabolism of the anticancer drugs | SpringerLink. 4 de septiembre de 2019;84(6):1157-66. Campbell JM, Bateman E, Peters MD, Bowen JM, Keefe DM, Stephenson MD. Fluoropyrimidine and platinum toxicity pharmacogenetics: an umbrella review ofsystematic reviews and meta-analyses. Pharmacogenomics. marzo de 2016;17(4):435-51. Lunenburg CATC, van der Wouden CH, Nijenhuis M, Crommentuijn-van Rhenen MH, de Boer-Veger NJ, Buunk AM, et al. Dutch Pharmacogenetics Working Group (DPWG) guideline for the gene–drug interaction of DPYD and fluoropyrimidines. Eur J Hum Genet. abril de 2020;28(4):508-17. Amstutz U, Henricks LM, Offer SM, Barbarino J, Schellens JHM, Swen JJ, et al. Clinical Pharmacogenetics Implementation Consortium (CPIC) Guideline for Dihydropyrimidine Dehydrogenase Genotype and Fluoropyrimidine Dosing: 2017 Update - Amstutz - 2018 - Clinical Pharmacology & Therapeutics - Wiley Online Library. 103(2):210-2016. Cacabelos R, Naidoo V, Corzo L, Cacabelos N, Carril JC. Genophenotypic Factors and Pharmacogenomics in Adverse Drug Reactions. Int J Mol Sci. 10 de diciembre de 2021;22(24):13302. Deenen MJ, Meulendijks D, Cats A, Sechterberger MK, Severens JL, Boot H, et al. Upfront Genotyping of DPYD*2A to Individualize Fluoropyrimidine Therapy: A Safety and Cost Analysis. J Clin Oncol. 20 de enero de 2016;34(3):227-34. Henricks LM, Lunenburg CATC, Man FM de, Meulendijks D, Frederix GWJ, Kienhuis E, et al. DPYD genotype-guided dose individualisation of fluoropyrimidine therapy in patients with cancer: a prospective safety analysis. Lancet Oncol. 1 de noviembre de 2018;19(11):1459-67. Innocenti F, Mills SC, Sanoff H, Ciccolini J, Lenz HJ, Milano G. All You Need to Know About DPYD Genetic Testing for Patients Treated With Fluorouracil and Capecitabine: A Practitioner-Friendly Guide. JCO Oncol Pract. diciembre de 2020;16(12):793-8. Barin-Le Guellec C, Lafay-Chebassier C, Ingrand I, Tournamille JF, Boudet A, Lanoue MC, et al. Toxicities associated with chemotherapy regimens containing a fluoropyrimidine: A real-life evaluation in France. Eur J Cancer. 1 de enero de 2020;124:37-46. Farinango C, Gallardo-Cóndor J, Freire-Paspuel B, Flores-Espinoza R, Jaramillo-Koupermann G, López-Cortés A, et al. Genetic Variations of the DPYD Gene and Its Relationship with Ancestry Proportions in Different Ecuadorian Trihybrid Populations. J Pers Med. 10 de junio de 2022;12(6):950. Rodrigues JCG, Fernandes MR, Ribeiro-dos-Santos AM, de Araújo GS, de Souza SJ, Guerreiro JF, et al. Pharmacogenomic Profile of Amazonian Amerindians. J Pers Med. 10 de junio de 2022;12(6):952. Silgado-Guzmán DF, Angulo-Aguado M, Morel A, Niño-Orrego MJ, Ruiz-Torres DA, Contreras Bravo NC, et al. Characterization of ADME Gene Variation in Colombian Population by Exome Sequencing. Front Pharmacol. 2022;13(931531):1-14. Danchin A. In vivo, in vitro and in silico: an open space for the development of microbe-based applications of synthetic biology. Microb Biotechnol. 2022;15(1):42-64. Carvalho C, Varela SAM, Bastos LF, Orfão I, Beja V, Sapage M, et al. The Relevance of In Silico, In Vitro and Non-human Primate Based Approaches to Clinical Research on Major Depressive Disorder. Altern Lab Anim. 1 de julio de 2019;47(3-4):128-39. Coleman JJ, Pontefract SK. Adverse drug reactions. Clin Med. octubre de 2016;16(5):481-5. Montané E, Santesmases J. Reacciones adversas a medicamentos. Med Clínica. 13 de marzo de 2020;154(5):178-84. Elzagallaai AA, Carleton BC, Rieder MJ. Pharmacogenomics in Pediatric Oncology: Mitigating Adverse Drug Reactions While Preserving Efficacy. Annu Rev Pharmacol Toxicol. 2021;61(1):679-99. Lavan AH, O’Mahony D, Buckley M, O’Mahony D, Gallagher P. Adverse Drug Reactions in an Oncological Population: Prevalence, Predictability, and Preventability. The Oncologist. septiembre de 2019;24(9):e968-77. Freites-Martinez A, Santana N, Arias-Santiago S, Viera A. CTCAE versión 5.0. Evaluación de la gravedad de los eventos adversos dermatológicos de las terapias antineoplásicas. Actas Dermo-Sifiliográficas. 1 de enero de 2021;112(1):90-2. Schütte M, Ogilvie LA, Rieke DT, Lange BMH, Yaspo ML, Lehrach H. Cancer Precision Medicine: Why More Is More and DNA Is Not Enough. Public Health Genomics. 2017;20(2):70-80. Goetz LH, Schork NJ. Personalized medicine: motivation, challenges, and progress. Fertil Steril. 1 de junio de 2018;109(6):952-63. Grandori C, Kemp CJ. Personalized Cancer Models for Target Discovery and Precision Medicine. Trends Cancer. septiembre de 2018;4(9):634-42. Low S, Zembutsu H, Nakamura Y. Breast cancer: The translation of big genomic data to cancer precision medicine. Cancer Sci. marzo de 2018;109(3):497-506. Malki MA, Pearson ER. Drug–drug–gene interactions and adverse drug reactions. Pharmacogenomics J. 2020;20(3):355-66. Cacabelos R, Cacabelos N, Carril JC. The role of pharmacogenomics in adverse drug reactions. Expert Rev Clin Pharmacol. 4 de mayo de 2019;12(5):407-42. Rodríguez-Vicente AE, Lumbreras E, Hernández JM, Martín M, Calles A, Otín CL, et al. Pharmacogenetics and pharmacogenomics as tools in cancer therapy. Drug Metab Pers Ther. 1 de marzo de 2016;31(1):25-34. Mhandire DZ, Goey AKL. The Value of Pharmacogenetics to Reduce Drug-Related Toxicity in Cancer Patients. Mol Diagn Ther. marzo de 2022;26(2):137-51. Kobuchi S, Ito Y. Application of Pharmacometrics of 5-Fluorouracil to Personalized Medicine: A Tool for Predicting Pharmacokinetic–Pharmacodynamic/Toxicodynamic Responses. Anticancer Res. 1 de diciembre de 2020;40(12):6585-97. Sethy C, Kundu CN. 5-Fluorouracil (5-FU) resistance and the new strategy to enhance the sensitivity against cancer: Implication of DNA repair inhibition. Biomed Pharmacother. 1 de mayo de 2021;137:111285. Lam SW, Guchelaar HJ, Boven E. The role of pharmacogenetics in capecitabine efficacy and toxicity. Cancer Treat Rev. 1 de noviembre de 2016;50:9-22. Vodenkova S, Buchler T, Cervena K, Veskrnova V, Vodicka P, Vymetalkova V. 5-fluorouracil and other fluoropyrimidines in colorectal cancer: Past, present and future. Pharmacol Ther. 1 de febrero de 2020;206:107447. ABC Transporter-Mediated Multidrug-Resistant Cancer | SpringerLink [Internet]. [citado 24 de abril de 2023]. Disponible en: https://link.springer.com/chapter/10.1007/978-981-13-7647-4_12 Varma A, Mathaiyan J, Shewade D, Dubashi B, Sunitha K. Influence of ABCB-1, ERCC-1 and ERCC-2 gene polymorphisms on response to capecitabine and oxaliplatin (CAPOX) treatment in colorectal cancer (CRC) patients of South India. J Clin Pharm Ther. 2020;45(4):617-27. Nishibeppu K, Komatsu S, Imamura T, Kiuchi J, Kishimoto T, Arita T, et al. Plasma microRNA profiles: identification of miR-1229-3p as a novel chemoresistant and prognostic biomarker in gastric cancer. Sci Rep. 21 de febrero de 2020;10:3161. Thorn CF, Marsh S, Carrillo MW, McLeod HL, Klein TE, Altman RB. PharmGKB summary: fluoropyrimidine pathways. Pharmacogenet Genomics. abril de 2011;21(4):237-42. Castro-Rojas C, Ortiz-López R, Rojas-Martínez A. Farmacogenómica del tratamiento de primera línea en el cáncer gástrico: avances en la identificación de los biomarcadores genómicos de respuesta clínica. Investig Clínica. junio de 2014;55(2):185-202. Wu XP, Dolnick BJ. 5-Fluorouracil alters dihydrofolate reductase pre-mRNA splicing as determined by quantitative polymerase chain reaction. Mol Pharmacol. 1 de julio de 1993;44(1):22-9. Greenhalgh DA, Parish JH. Effect of 5-fluorouracil combination therapy on RNA processing in human colonic carcinoma cells. Br J Cancer. marzo de 1990;61(3):415-9. Noordhuis P, Holwerda U, Wilt CLV der, Groeningen CJV, Smid K, Meijer S, et al. 5-Fluorouracil incorporation into RNA and DNA in relation to thymidylate synthase inhibition of human colorectal cancers. Ann Oncol. 1 de julio de 2004;15(7):1025-32. Wei X, Elizondo G, Sapone A, McLeod HL, Raunio H, Fernandez-Salguero P, et al. Characterization of the Human Dihydropyrimidine Dehydrogenase Gene. Genomics. 1 de agosto de 1998;51(3):391-400. Johnson MR, Wang K, Tillmanns S, Albin N, Diasio RB. Structural Organization of the Human Dihydropyrimidine Dehydrogenase Gene1. Cancer Res. 1 de mayo de 1997;57(9):1660-3. Dobritzsch D, Ricagno S, Schneider G, Schnackerz KD, Lindqvist Y. Crystal Structure of the Productive Ternary Complex of Dihydropyrimidine Dehydrogenase with NADPH and 5-Iodouracil: IMPLICATIONS FOR MECHANISM OF INHIBITION AND ELECTRON TRANSFER∗. J Biol Chem. 12 de abril de 2002;277(15):13155-66. Dobritzsch D, Schneider G, Schnackerz KD, Lindqvist Y. Crystal structure of dihydropyrimidine dehydrogenase, a major determinant of the pharmacokinetics of the anti-cancer drug 5-fluorouracil. EMBO J. 15 de febrero de 2001;20(4):650-60. Brutcher E. 5-Fluorouracil and Capecitabine: Assessment and Treatment of Uncommon Early-Onset Severe Toxicities Associated With Administration. Number 6 Dec 2018. 1 de diciembre de 2018;22(6):627-34. García-González X, Kaczmarczyk B, Abarca-Zabalía J, Thomas F, García-Alfonso P, Robles L, et al. New DPYD variants causing DPD deficiency in patients treated with fluoropyrimidine. Cancer Chemother Pharmacol. julio de 2020;86(1):45-54. White C, Scott RJ, Paul C, Ziolkowski A, Mossman D, Ackland S. Ethnic Diversity of DPD Activity and the DPYD Gene: Review of the Literature. Pharmacogenomics Pers Med. 9 de diciembre de 2021;14:1603-17. Hamzic S, Schärer D, Offer SM, Meulendijks D, Nakas C, Diasio RB, et al. Haplotype structure defines effects of common DPYD variants c.85T > C (rs1801265) and c.496A > G (rs2297595) on dihydropyrimidine dehydrogenase activity: Implication for 5‐fluorouracil toxicity. Br J Clin Pharmacol. agosto de 2021;87(8):3234-43. Clinical Pharmacogenetics Implementation Consortium (CPIC) Guideline for Dihydropyrimidine Dehydrogenase Genotype and Fluoropyrimidine Dosing: 2017 Update - Amstutz - 2018 - Clinical Pharmacology & Therapeutics - Wiley Online Library [Internet]. [citado 2 de abril de 2023]. Disponible en: https://ascpt.onlinelibrary.wiley.com/doi/10.1002/cpt.911 Implementing DPYD*2A Genotyping in Clinical Practice: The Quebec, Canada, Experience - PMC [Internet]. [citado 24 de abril de 2023]. Disponible en: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8018309/ Ng PC, Henikoff S. Predicting Deleterious Amino Acid Substitutions. Genome Res. mayo de 2001;11(5):863-74. Adzhubei IA, Schmidt S, Peshkin L, Ramensky VE, Gerasimova A, Bork P, et al. A method and server for predicting damaging missense mutations. Nat Methods. abril de 2010;7(4):248-9. Tang B, Li B, Gao LD, He N, Liu XR, Long YS, et al. Optimization of in silico tools for predicting genetic variants: individualizing for genes with molecular sub-regional stratification. Brief Bioinform. 25 de septiembre de 2020;21(5):1776-86. Ng PC, Henikoff S. SIFT: Predicting amino acid changes that affect protein function. Nucleic Acids Res. 1 de julio de 2003;31(13):3812-4. Schwarz JM, Cooper DN, Schuelke M, Seelow D. MutationTaster2: mutation prediction for the deep-sequencing age. Nat Methods. abril de 2014;11(4):361-2. Reva B, Antipin Y, Sander C. Predicting the functional impact of protein mutations: application to cancer genomics. Nucleic Acids Res. 1 de septiembre de 2011;39(17):e118. Reva B, Antipin Y, Sander C. Determinants of protein function revealed by combinatorial entropy optimization. Genome Biol. 1 de noviembre de 2007;8(11):R232. 67. biocompute.org.uk. fathmm. [citado 22 de agosto de 2023]. Functional Analysis through Hidden Markov Models (v2.3). Disponible en: http://fathmm.biocompute.org.uk/about.html Quang D, Chen Y, Xie X. DANN: a deep learning approach for annotating the pathogenicity of genetic variants. Bioinformatics. 1 de marzo de 2015;31(5):761-3. Kircher M, Witten DM, Jain P, O’Roak BJ, Cooper GM, Shendure J. A general framework for estimating the relative pathogenicity of human genetic variants. Nat Genet. marzo de 2014;46(3):310-5. Dong C, Wei P, Jian X, Gibbs R, Boerwinkle E, Wang K, et al. Comparison and integration of deleteriousness prediction methods for nonsynonymous SNVs in whole exome sequencing studies. Hum Mol Genet. 15 de abril de 2015;24(8):2125-37. Davydov EV, Goode DL, Sirota M, Cooper GM, Sidow A, Batzoglou S. Identifying a High Fraction of the Human Genome to be under Selective Constraint Using GERP++. PLoS Comput Biol. 2 de diciembre de 2010;6(12):e1001025. Feng BJ. PERCH: A Unified Framework for Disease Gene Prioritization. Hum Mutat. 2017;38(3):243-51. Ioannidis NM, Rothstein JH, Pejaver V, Middha S, McDonnell SK, Baheti S, et al. REVEL: An Ensemble Method for Predicting the Pathogenicity of Rare Missense Variants. Am J Hum Genet. 6 de octubre de 2016;99(4):877-85. Wu Y, Li R, Sun S, Weile J, Roth FP. Improved pathogenicity prediction for rare human missense variants. Am J Hum Genet. 7 de octubre de 2021;108(10):1891-906. Choi Y, Sims GE, Murphy S, Miller JR, Chan AP. Predicting the Functional Effect of Amino Acid Substitutions and Indels. PLoS ONE. 8 de octubre de 2012;7(10):e46688. IONITA-LAZA I, MCCALLUM K, XU B, BUXBAUM J. A SPECTRAL APPROACH INTEGRATING FUNCTIONAL GENOMIC ANNOTATIONS FOR CODING AND NONCODING VARIANTS. Nat Genet. febrero de 2016;48(2):214-20. Li B, Seligman C, Thusberg J, Miller JL, Auer J, Whirl-Carrillo M, et al. In silico comparative characterization of pharmacogenomic missense variants. BMC Genomics. 20 de mayo de 2014;15(4):S4. Duzkale H, Shen J, McLaughlin H, Alfares A, Kelly M, Pugh T, et al. A systematic approach to assessing the clinical significance of genetic variants. Clin Genet. noviembre de 2013;84(5):453-63. Zloh M, Kirton SB. The benefits of in silico modeling to identify possible small-molecule drugs and their off-target interactions. Future Med Chem. febrero de 2018;10(4):423-32. Masica DL, Karchin R. Towards Increasing the Clinical Relevance of In Silico Methods to Predict Pathogenic Missense Variants. PLoS Comput Biol. 12 de mayo de 2016;12(5):e1004725. Tavtigian SV, Greenblatt MS, Lesueur F, Byrnes GB. In silico analysis of missense substitutions using sequence-alignment based methods. Hum Mutat. noviembre de 2008;29(11):1327-36. Zhou Y, Mkrtchian S, Kumondai M, Hiratsuka M, Lauschke VM. An optimized prediction framework to assess the functional impact of pharmacogenetic variants. Pharmacogenomics J. 2019;19(2):115-26. Lahti JL, Tang GW, Capriotti E, Liu T, Altman RB. Bioinformatics and variability in drug response: a protein structural perspective. J R Soc Interface. 7 de julio de 2012;9(72):1409. Pandi MT, Koromina M, Tsafaridis I, Patsilinakos S, Christoforou E, van der Spek PJ, et al. A novel machine learning-based approach for the computational functional assessment of pharmacogenomic variants. Hum Genomics. 9 de agosto de 2021;15:51. Zhou Y, Lauschke VM. Computational Tools to Assess the Functional Consequences of Rare and Noncoding Pharmacogenetic Variability. Clin Pharmacol Ther. septiembre de 2021;110(3):626-36. Farajzadeh-Dehkordi M, Mafakher L, Samiee-Rad F, Rahmani B. Computational analysis of missense variant CYP4F2*3 (V433M) in association with human CYP4F2 dysfunction: a functional and structural impact. BMC Mol Cell Biol. 9 de mayo de 2023;24:17. Joshi K, Kaur S, Kumar R. Cytochrome P450 2C19 gene polymorphisms (CYP2C19*2 and CYP2C19*3) in chronic myeloid leukemia patients: in vitro and in silico studies. J Biomol Struct Dyn. 2022;40(19):9389-402. Shrestha S, Zhang C, Jerde CR, Nie Q, Li H, Offer SM, et al. Gene-specific variant classifier (DPYD-Varifier) to identify deleterious alleles of dihydropyrimidine dehydrogenase. Clin Pharmacol Ther. octubre de 2018;104(4):709-18. Rodrigues-Soares F, Suarez-Kurtz G. Pharmacogenomics research and clinical implementation in Brazil. Basic Clin Pharmacol Toxicol. 2019;124(5):538-49. Cavalcante GC, Freitas NDSDC, Ribeiro-Dos-Santos AM, Carvalho DCD, Silva EMD, Assumpção PPD, et al. Investigation of Potentially Deleterious Alleles for Response to Cancer Treatment with 5-Fluorouracil. Anticancer Res. 1 de diciembre de 2015;35(12):6971-7. Zhou Y, Fujikura K, Mkrtchian S, Lauschke VM. Computational Methods for the Pharmacogenetic Interpretation of Next Generation Sequencing Data. Front Pharmacol. 4 de diciembre de 2018;9:1437. Pallet N, Hamdane S, Garinet S, Blons H, Zaanan A, Paillaud E, et al. A comprehensive population-based study comparing the phenotype and genotype in a pretherapeutic screen of dihydropyrimidine dehydrogenase deficiency. Br J Cancer. septiembre de 2020;123(5):811-8. Principi N, Petropulacos K, Esposito S. Impact of Pharmacogenomics in Clinical Practice. Pharmaceuticals. noviembre de 2023;16(11):1596. Frontiers | A practical guide for the generation of model-based virtual clinical trials [Internet]. [citado 25 de febrero de 2024]. Disponible en: https://www.frontiersin.org/articles/10.3389/fsysb.2023.1174647/full Alnasser B. A Review of Literature on the Economic Implications of Implementing Artificial Intelligence in Healthcare. E-Health Telecommun Syst Netw. 15 de septiembre de 2023;12(3):35-48. Brooks GA, Tapp S, Daly AT, Busam JA, Tosteson ANA. Cost-effectiveness of DPYD genotyping prior to fluoropyrimidine-based adjuvant chemotherapy for colon cancer. Clin Colorectal Cancer. septiembre de 2022;21(3):e189-95. Moldrup C. Ethical, social and legal implications of pharmacogenomics: a critical review. Community Genet. 2001;4(4):204-14. Methods to Develop an in silico Clinical Trial: Computational Head-to-Head Comparison of Lisdexamfetamine and Methylphenidate - PMC [Internet]. [citado 25 de febrero de 2024]. Disponible en: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8595241/ Dihydropyrimidine Dehydrogenase Testing prior to Treatment with 5-Fluorouracil, Capecitabine, and Tegafur: A Consensus Paper - FullText - Oncology Research and Treatment 2020, Vol. 43, No. 11 - Karger Publishers [Internet]. [citado 24 de abril de 2023]. Disponible en: https://www.karger.com/Article/FullText/510258 |
| dc.rights.coar.fl_str_mv |
http://purl.org/coar/access_right/c_abf2 |
| dc.rights.license.spa.fl_str_mv |
Atribución-NoComercial-SinDerivadas 4.0 Internacional |
| dc.rights.uri.spa.fl_str_mv |
http://creativecommons.org/licenses/by-nc-nd/4.0/ |
| dc.rights.accessrights.spa.fl_str_mv |
info:eu-repo/semantics/openAccess |
| rights_invalid_str_mv |
Atribución-NoComercial-SinDerivadas 4.0 Internacional http://creativecommons.org/licenses/by-nc-nd/4.0/ http://purl.org/coar/access_right/c_abf2 |
| eu_rights_str_mv |
openAccess |
| dc.format.extent.spa.fl_str_mv |
123 páginas |
| dc.format.mimetype.spa.fl_str_mv |
application/pdf |
| dc.publisher.spa.fl_str_mv |
Universidad Nacional de Colombia |
| dc.publisher.program.spa.fl_str_mv |
Bogotá - Medicina - Maestría en Genética Humana |
| dc.publisher.faculty.spa.fl_str_mv |
Facultad de Medicina |
| dc.publisher.place.spa.fl_str_mv |
Bogotá, Colombia |
| dc.publisher.branch.spa.fl_str_mv |
Universidad Nacional de Colombia - Sede Bogotá |
| institution |
Universidad Nacional de Colombia |
| bitstream.url.fl_str_mv |
https://repositorio.unal.edu.co/bitstream/unal/86050/3/license.txt https://repositorio.unal.edu.co/bitstream/unal/86050/4/1013628866.2024.pdf https://repositorio.unal.edu.co/bitstream/unal/86050/5/1013628866.2024.pdf.jpg |
| bitstream.checksum.fl_str_mv |
eb34b1cf90b7e1103fc9dfd26be24b4a 3c86d93f43766a3253ccde045dbdd4d7 31c319e969dc59cc98e596890cb604f6 |
| bitstream.checksumAlgorithm.fl_str_mv |
MD5 MD5 MD5 |
| repository.name.fl_str_mv |
Repositorio Institucional Universidad Nacional de Colombia |
| repository.mail.fl_str_mv |
repositorio_nal@unal.edu.co |
| _version_ |
1814090032419438592 |
| spelling |
Atribución-NoComercial-SinDerivadas 4.0 Internacionalhttp://creativecommons.org/licenses/by-nc-nd/4.0/info:eu-repo/semantics/openAccesshttp://purl.org/coar/access_right/c_abf2Mahecha Lopez, Daniel Hernán679fa3f48c7ea1c0a5995c74e9ca1197Rey Buitrago, Mauricio85294b9c1929dade21e4121623929fae600Castro Rojas, Carlos8ae2b2934eb1db6a025eaff1631aacdd600Saldaña Peñaloza, Diego Aeljandroae937eb415f07bf118adc06510091b5fGenética clínica2024-05-08T18:54:17Z2024-05-08T18:54:17Z2024https://repositorio.unal.edu.co/handle/unal/86050Universidad Nacional de ColombiaRepositorio Institucional Universidad Nacional de Colombiahttps://repositorio.unal.edu.co/ilustraciones, diagramasGran parte de la toxicidad en pacientes oncológicos tratados con fluoropirimidinas se debe a la pérdida de la actividad enzimática de la dihidropiridina deshidrogenasa, causada por variantes en el gen DPYD, por lo cual diversos estudios han propuesto la genotipificación al inicio del tratamiento con estos fármacos. Este estudio buscó determinar la eficacia de diferentes algoritmos In Silico de predicción de variantes de cambio de sentido nocivas en el gen DPYD, con el propósito de proponer un flujo de evaluación basado en herramientas de anotación con alta sensibilidad y especificidad en la predicción de variantes de interés en farmacogenética en la población colombiana. Para lo cual se planteó la evaluación comparativa de herramientas de anotación In Silico en el gen DPYD basadas en los hallazgos de una revisión sistemática de alcance, adicional a un análisis descriptivo estructural, la búsqueda de variantes conocidas y así como de variantes no reportadas previamente en un conjunto de datos de secuenciación de exoma de un laboratorio de biología molecular en la ciudad de Bogotá. A partir de la revisión sistemática se seleccionaron los algoritmos BayesDel addAF, BayesDel noAF, Eigen, Eigen-PC, SIFT, MetaSNP, Mutation Assessor, Revel y Provean, los cuales fueron evaluados en 137 variantes en el gen DPYD, encontrando que las herramientas con mejor rendimiento fueron PROVEAN, Revel y MetaSNP. En el análisis poblacional, se encontró que, en general, la frecuencia poblacional de variantes conocidas como nocivas, incluyendo DPYD*2A, era menor al 1%, lo cual es inferior a lo reportado para poblaciones caucásicas, y la de mayor frecuencia fue HapB3. Se identificó la variante c.1127A>C, la cual por herramientas de anotación podría ser nociva, sin embargo, se deben realizar estudios adicionales para confirmar el efecto de la variante. En conclusión, a pesar de que este es un primer acercamiento en el análisis computacional para identificar variantes en el gen DPYD en población colombiana, se debe profundizar en los hallazgos reportados en esta investigación, lo cual podría permitir una aplicación de flujos de análisis en farmacogenética acordes a las características poblacionales colombianas. (Texto tomado de la fuente).Much of the toxicity in cancer patients treated with fluoropyrimidines is due to the loss of the enzymatic activity of dihydropyrimidine dehydrogenase, caused by variants in the DPYD gene. Therefore, various studies have proposed genotyping before the initiation of these drugs. This study aimed to determine the efficacy of different in silico algorithms for predicting deleterious missense variants in the DPYD gene, with the purpose of proposing an in silico evaluation flow based on annotation tools with high sensitivity and specificity in predicting variants of interest in pharmacogenetics in the Colombian population. For this aim, a comparative evaluation of in silico annotation tools in the DPYD gene was proposed based on the findings of a rapid systematic review, in addition to a structural descriptive analysis, a search for known variants, as well as a search for previously unreported variants in the database of a molecular biology laboratory in Bogotá. Based on the systematic review, the BayesDel addAF,BayesDel noAF, Eigen, Eigen-PC, SIFT, MetaSNP, Mutation Assessor, Revel, and Provean algorithms were selected and evaluated on 137 variants in the DPYD gene, finding that the tools with the best performance were PROVEAN, Revel, and MetaSNP. In the population analysis, it was found that, in general, the population frequency of known harmful variants, including DPYD*2A, was less than 1%, which is lower than reported for Caucasian populations, and the most frequent was HapB3. The variant c.1127A>C was identified, and, according to annotation tools, could be harmful; however, additional studies should be conducted to confirm the effect of the variant. In conclusion, despite being an initial computational analysis to identify variants in the DPYD gene in the Colombian population, further investigation is required to validate the findings of this research. This could lead to the development of analysis workflows in pharmacogenetics tailored to the characteristics of the Colombian population.MaestríaMagíster en Genética HumanaGenética y cáncer123 páginasapplication/pdfspaUniversidad Nacional de ColombiaBogotá - Medicina - Maestría en Genética HumanaFacultad de MedicinaBogotá, ColombiaUniversidad Nacional de Colombia - Sede Bogotá610 - Medicina y salud::615 - Farmacología y terapéuticaFarmacogenéticaBiología Computacional/métodosInvestigación GenéticaPharmacogeneticsComputational Biology/methodsGenetic ResearchDihidropirimidina deshidrogenasaValor predictivo de las pruebasMutación de cambio de sentidoAnálisis In SilicoFarmacogenéticaDihydropyrimidine dehydrogenasePredictive value of testsMissense mutationIn Silico analysisPharmacogeneticsEvaluación comparativa de herramientas predictivas In Silico de variantes de cambio de sentido en genes de interés en farmacogenética: análisis bioinformático y poblacional para el gen DPYDComparative evaluation of in silico predictive tools of missense variants in genes of interest in pharmacogenetics: bioinformatics and population analysis for the DPYD geneTrabajo de grado - Maestríainfo:eu-repo/semantics/masterThesisinfo:eu-repo/semantics/acceptedVersionTexthttp://purl.org/redcol/resource_type/TMBiremeSanger F, Nicklen S, Coulson AR. DNA sequencing with chain-terminating inhibitors. Proc Natl Acad Sci U S A. diciembre de 1977;74(12):5463-7.Wake DT, Ilbawi N, Dunnenberger HM, Hulick PJ. Pharmacogenomics: Prescribing Precisely. Med Clin North Am. noviembre de 2019;103(6):977-90.Verma M, Kulshrestha S, Puri A. Genome Sequencing. En: Keith JM, editor. Bioinformatics: Volume I: Data, Sequence Analysis, and Evolution [Internet]. New York, NY: Springer; 2017 [citado 25 de marzo de 2023]. p. 3-33. (Methods in Molecular Biology). Disponible en: https://doi.org/10.1007/978-1-4939-6622-6_1Petrosino M, Novak L, Pasquo A, Chiaraluce R, Turina P, Capriotti E, et al. Analysis and Interpretation of the Impact of Missense Variants in Cancer. Int J Mol Sci. 21 de mayo de 2021;22(11):5416.Rodrigues C, Santos-Silva A, Costa E, Bronze-da-Rocha E. Performance of In Silico Tools for the Evaluation of UGT1A1 Missense Variants. Hum Mutat. diciembre de 2015;36(12):1215-25.Tsimberidou AM, Fountzilas E, Nikanjam M, Kurzrock R. Review of precision cancer medicine: Evolution of the treatment paradigm. Cancer Treat Rev [Internet]. 1 de junio de 2020 [citado 25 de marzo de 2023];86. Disponible en: https://www.cancertreatmentreviews.com/article/S0305-7372(20)30057-8/fulltextMorganti S, Tarantino P, Ferraro E, D´Amico P, Achutti B, Curigliano G. Next Generation Sequencing (NGS): A Revolutionary Technology in Pharmacogenomics and Personalized Medicine in Cancer. In: Ruiz-Garcia, E., Astudillo-de la Vega, H. (eds) Translational Research and Onco-Omics Applications in the Era of Cancer Personal Genomics. Advances in Experimental Medicine and Biology [Internet]. Vol. 1168. Switzerland: Springer Nature; [citado 25 de marzo de 2023]. 9–30 p. Disponible en: https://link.springer.com/chapter/10.1007/978-3-030-24100-1_2Micaglio E, Locati ET, Monasky MM, Romani F, Heilbron F, Pappone C. Role of Pharmacogenetics in Adverse Drug Reactions: An Update towards Personalized Medicine. Front Pharmacol. 2021;12(651720):1-17Lunenburg CATC, Henricks LM, Guchelaar HJ, Swen JJ, Deenen MJ, Schellens JHM, et al. Prospective DPYD genotyping to reduce the risk of fluoropyrimidine-induced severe toxicity: Ready for prime time. Eur J Cancer. 1 de febrero de 2016;54:40-8.Sharma V, Gupta SK, Verma M. Dihydropyrimidine dehydrogenase in the metabolism of the anticancer drugs | SpringerLink. 4 de septiembre de 2019;84(6):1157-66.Campbell JM, Bateman E, Peters MD, Bowen JM, Keefe DM, Stephenson MD. Fluoropyrimidine and platinum toxicity pharmacogenetics: an umbrella review ofsystematic reviews and meta-analyses. Pharmacogenomics. marzo de 2016;17(4):435-51.Lunenburg CATC, van der Wouden CH, Nijenhuis M, Crommentuijn-van Rhenen MH, de Boer-Veger NJ, Buunk AM, et al. Dutch Pharmacogenetics Working Group (DPWG) guideline for the gene–drug interaction of DPYD and fluoropyrimidines. Eur J Hum Genet. abril de 2020;28(4):508-17.Amstutz U, Henricks LM, Offer SM, Barbarino J, Schellens JHM, Swen JJ, et al. Clinical Pharmacogenetics Implementation Consortium (CPIC) Guideline for Dihydropyrimidine Dehydrogenase Genotype and Fluoropyrimidine Dosing: 2017 Update - Amstutz - 2018 - Clinical Pharmacology & Therapeutics - Wiley Online Library. 103(2):210-2016.Cacabelos R, Naidoo V, Corzo L, Cacabelos N, Carril JC. Genophenotypic Factors and Pharmacogenomics in Adverse Drug Reactions. Int J Mol Sci. 10 de diciembre de 2021;22(24):13302.Deenen MJ, Meulendijks D, Cats A, Sechterberger MK, Severens JL, Boot H, et al. Upfront Genotyping of DPYD*2A to Individualize Fluoropyrimidine Therapy: A Safety and Cost Analysis. J Clin Oncol. 20 de enero de 2016;34(3):227-34.Henricks LM, Lunenburg CATC, Man FM de, Meulendijks D, Frederix GWJ, Kienhuis E, et al. DPYD genotype-guided dose individualisation of fluoropyrimidine therapy in patients with cancer: a prospective safety analysis. Lancet Oncol. 1 de noviembre de 2018;19(11):1459-67.Innocenti F, Mills SC, Sanoff H, Ciccolini J, Lenz HJ, Milano G. All You Need to Know About DPYD Genetic Testing for Patients Treated With Fluorouracil and Capecitabine: A Practitioner-Friendly Guide. JCO Oncol Pract. diciembre de 2020;16(12):793-8.Barin-Le Guellec C, Lafay-Chebassier C, Ingrand I, Tournamille JF, Boudet A, Lanoue MC, et al. Toxicities associated with chemotherapy regimens containing a fluoropyrimidine: A real-life evaluation in France. Eur J Cancer. 1 de enero de 2020;124:37-46.Farinango C, Gallardo-Cóndor J, Freire-Paspuel B, Flores-Espinoza R, Jaramillo-Koupermann G, López-Cortés A, et al. Genetic Variations of the DPYD Gene and Its Relationship with Ancestry Proportions in Different Ecuadorian Trihybrid Populations. J Pers Med. 10 de junio de 2022;12(6):950.Rodrigues JCG, Fernandes MR, Ribeiro-dos-Santos AM, de Araújo GS, de Souza SJ, Guerreiro JF, et al. Pharmacogenomic Profile of Amazonian Amerindians. J Pers Med. 10 de junio de 2022;12(6):952.Silgado-Guzmán DF, Angulo-Aguado M, Morel A, Niño-Orrego MJ, Ruiz-Torres DA, Contreras Bravo NC, et al. Characterization of ADME Gene Variation in Colombian Population by Exome Sequencing. Front Pharmacol. 2022;13(931531):1-14.Danchin A. In vivo, in vitro and in silico: an open space for the development of microbe-based applications of synthetic biology. Microb Biotechnol. 2022;15(1):42-64.Carvalho C, Varela SAM, Bastos LF, Orfão I, Beja V, Sapage M, et al. The Relevance of In Silico, In Vitro and Non-human Primate Based Approaches to Clinical Research on Major Depressive Disorder. Altern Lab Anim. 1 de julio de 2019;47(3-4):128-39.Coleman JJ, Pontefract SK. Adverse drug reactions. Clin Med. octubre de 2016;16(5):481-5.Montané E, Santesmases J. Reacciones adversas a medicamentos. Med Clínica. 13 de marzo de 2020;154(5):178-84.Elzagallaai AA, Carleton BC, Rieder MJ. Pharmacogenomics in Pediatric Oncology: Mitigating Adverse Drug Reactions While Preserving Efficacy. Annu Rev Pharmacol Toxicol. 2021;61(1):679-99.Lavan AH, O’Mahony D, Buckley M, O’Mahony D, Gallagher P. Adverse Drug Reactions in an Oncological Population: Prevalence, Predictability, and Preventability. The Oncologist. septiembre de 2019;24(9):e968-77.Freites-Martinez A, Santana N, Arias-Santiago S, Viera A. CTCAE versión 5.0. Evaluación de la gravedad de los eventos adversos dermatológicos de las terapias antineoplásicas. Actas Dermo-Sifiliográficas. 1 de enero de 2021;112(1):90-2.Schütte M, Ogilvie LA, Rieke DT, Lange BMH, Yaspo ML, Lehrach H. Cancer Precision Medicine: Why More Is More and DNA Is Not Enough. Public Health Genomics. 2017;20(2):70-80.Goetz LH, Schork NJ. Personalized medicine: motivation, challenges, and progress. Fertil Steril. 1 de junio de 2018;109(6):952-63.Grandori C, Kemp CJ. Personalized Cancer Models for Target Discovery and Precision Medicine. Trends Cancer. septiembre de 2018;4(9):634-42.Low S, Zembutsu H, Nakamura Y. Breast cancer: The translation of big genomic data to cancer precision medicine. Cancer Sci. marzo de 2018;109(3):497-506.Malki MA, Pearson ER. Drug–drug–gene interactions and adverse drug reactions. Pharmacogenomics J. 2020;20(3):355-66.Cacabelos R, Cacabelos N, Carril JC. The role of pharmacogenomics in adverse drug reactions. Expert Rev Clin Pharmacol. 4 de mayo de 2019;12(5):407-42.Rodríguez-Vicente AE, Lumbreras E, Hernández JM, Martín M, Calles A, Otín CL, et al. Pharmacogenetics and pharmacogenomics as tools in cancer therapy. Drug Metab Pers Ther. 1 de marzo de 2016;31(1):25-34.Mhandire DZ, Goey AKL. The Value of Pharmacogenetics to Reduce Drug-Related Toxicity in Cancer Patients. Mol Diagn Ther. marzo de 2022;26(2):137-51.Kobuchi S, Ito Y. Application of Pharmacometrics of 5-Fluorouracil to Personalized Medicine: A Tool for Predicting Pharmacokinetic–Pharmacodynamic/Toxicodynamic Responses. Anticancer Res. 1 de diciembre de 2020;40(12):6585-97.Sethy C, Kundu CN. 5-Fluorouracil (5-FU) resistance and the new strategy to enhance the sensitivity against cancer: Implication of DNA repair inhibition. Biomed Pharmacother. 1 de mayo de 2021;137:111285.Lam SW, Guchelaar HJ, Boven E. The role of pharmacogenetics in capecitabine efficacy and toxicity. Cancer Treat Rev. 1 de noviembre de 2016;50:9-22.Vodenkova S, Buchler T, Cervena K, Veskrnova V, Vodicka P, Vymetalkova V. 5-fluorouracil and other fluoropyrimidines in colorectal cancer: Past, present and future. Pharmacol Ther. 1 de febrero de 2020;206:107447.ABC Transporter-Mediated Multidrug-Resistant Cancer | SpringerLink [Internet]. [citado 24 de abril de 2023]. Disponible en: https://link.springer.com/chapter/10.1007/978-981-13-7647-4_12Varma A, Mathaiyan J, Shewade D, Dubashi B, Sunitha K. Influence of ABCB-1, ERCC-1 and ERCC-2 gene polymorphisms on response to capecitabine and oxaliplatin (CAPOX) treatment in colorectal cancer (CRC) patients of South India. J Clin Pharm Ther. 2020;45(4):617-27.Nishibeppu K, Komatsu S, Imamura T, Kiuchi J, Kishimoto T, Arita T, et al. Plasma microRNA profiles: identification of miR-1229-3p as a novel chemoresistant and prognostic biomarker in gastric cancer. Sci Rep. 21 de febrero de 2020;10:3161.Thorn CF, Marsh S, Carrillo MW, McLeod HL, Klein TE, Altman RB. PharmGKB summary: fluoropyrimidine pathways. Pharmacogenet Genomics. abril de 2011;21(4):237-42.Castro-Rojas C, Ortiz-López R, Rojas-Martínez A. Farmacogenómica del tratamiento de primera línea en el cáncer gástrico: avances en la identificación de los biomarcadores genómicos de respuesta clínica. Investig Clínica. junio de 2014;55(2):185-202.Wu XP, Dolnick BJ. 5-Fluorouracil alters dihydrofolate reductase pre-mRNA splicing as determined by quantitative polymerase chain reaction. Mol Pharmacol. 1 de julio de 1993;44(1):22-9.Greenhalgh DA, Parish JH. Effect of 5-fluorouracil combination therapy on RNA processing in human colonic carcinoma cells. Br J Cancer. marzo de 1990;61(3):415-9.Noordhuis P, Holwerda U, Wilt CLV der, Groeningen CJV, Smid K, Meijer S, et al. 5-Fluorouracil incorporation into RNA and DNA in relation to thymidylate synthase inhibition of human colorectal cancers. Ann Oncol. 1 de julio de 2004;15(7):1025-32.Wei X, Elizondo G, Sapone A, McLeod HL, Raunio H, Fernandez-Salguero P, et al. Characterization of the Human Dihydropyrimidine Dehydrogenase Gene. Genomics. 1 de agosto de 1998;51(3):391-400.Johnson MR, Wang K, Tillmanns S, Albin N, Diasio RB. Structural Organization of the Human Dihydropyrimidine Dehydrogenase Gene1. Cancer Res. 1 de mayo de 1997;57(9):1660-3.Dobritzsch D, Ricagno S, Schneider G, Schnackerz KD, Lindqvist Y. Crystal Structure of the Productive Ternary Complex of Dihydropyrimidine Dehydrogenase with NADPH and 5-Iodouracil: IMPLICATIONS FOR MECHANISM OF INHIBITION AND ELECTRON TRANSFER∗. J Biol Chem. 12 de abril de 2002;277(15):13155-66.Dobritzsch D, Schneider G, Schnackerz KD, Lindqvist Y. Crystal structure of dihydropyrimidine dehydrogenase, a major determinant of the pharmacokinetics of the anti-cancer drug 5-fluorouracil. EMBO J. 15 de febrero de 2001;20(4):650-60.Brutcher E. 5-Fluorouracil and Capecitabine: Assessment and Treatment of Uncommon Early-Onset Severe Toxicities Associated With Administration. Number 6 Dec 2018. 1 de diciembre de 2018;22(6):627-34.García-González X, Kaczmarczyk B, Abarca-Zabalía J, Thomas F, García-Alfonso P, Robles L, et al. New DPYD variants causing DPD deficiency in patients treated with fluoropyrimidine. Cancer Chemother Pharmacol. julio de 2020;86(1):45-54.White C, Scott RJ, Paul C, Ziolkowski A, Mossman D, Ackland S. Ethnic Diversity of DPD Activity and the DPYD Gene: Review of the Literature. Pharmacogenomics Pers Med. 9 de diciembre de 2021;14:1603-17.Hamzic S, Schärer D, Offer SM, Meulendijks D, Nakas C, Diasio RB, et al. Haplotype structure defines effects of common DPYD variants c.85T > C (rs1801265) and c.496A > G (rs2297595) on dihydropyrimidine dehydrogenase activity: Implication for 5‐fluorouracil toxicity. Br J Clin Pharmacol. agosto de 2021;87(8):3234-43.Clinical Pharmacogenetics Implementation Consortium (CPIC) Guideline for Dihydropyrimidine Dehydrogenase Genotype and Fluoropyrimidine Dosing: 2017 Update - Amstutz - 2018 - Clinical Pharmacology & Therapeutics - Wiley Online Library [Internet]. [citado 2 de abril de 2023]. Disponible en: https://ascpt.onlinelibrary.wiley.com/doi/10.1002/cpt.911Implementing DPYD*2A Genotyping in Clinical Practice: The Quebec, Canada, Experience - PMC [Internet]. [citado 24 de abril de 2023]. Disponible en: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8018309/Ng PC, Henikoff S. Predicting Deleterious Amino Acid Substitutions. Genome Res. mayo de 2001;11(5):863-74.Adzhubei IA, Schmidt S, Peshkin L, Ramensky VE, Gerasimova A, Bork P, et al. A method and server for predicting damaging missense mutations. Nat Methods. abril de 2010;7(4):248-9.Tang B, Li B, Gao LD, He N, Liu XR, Long YS, et al. Optimization of in silico tools for predicting genetic variants: individualizing for genes with molecular sub-regional stratification. Brief Bioinform. 25 de septiembre de 2020;21(5):1776-86.Ng PC, Henikoff S. SIFT: Predicting amino acid changes that affect protein function. Nucleic Acids Res. 1 de julio de 2003;31(13):3812-4.Schwarz JM, Cooper DN, Schuelke M, Seelow D. MutationTaster2: mutation prediction for the deep-sequencing age. Nat Methods. abril de 2014;11(4):361-2.Reva B, Antipin Y, Sander C. Predicting the functional impact of protein mutations: application to cancer genomics. Nucleic Acids Res. 1 de septiembre de 2011;39(17):e118.Reva B, Antipin Y, Sander C. Determinants of protein function revealed by combinatorial entropy optimization. Genome Biol. 1 de noviembre de 2007;8(11):R232.67. biocompute.org.uk. fathmm. [citado 22 de agosto de 2023]. Functional Analysis through Hidden Markov Models (v2.3). Disponible en: http://fathmm.biocompute.org.uk/about.htmlQuang D, Chen Y, Xie X. DANN: a deep learning approach for annotating the pathogenicity of genetic variants. Bioinformatics. 1 de marzo de 2015;31(5):761-3.Kircher M, Witten DM, Jain P, O’Roak BJ, Cooper GM, Shendure J. A general framework for estimating the relative pathogenicity of human genetic variants. Nat Genet. marzo de 2014;46(3):310-5.Dong C, Wei P, Jian X, Gibbs R, Boerwinkle E, Wang K, et al. Comparison and integration of deleteriousness prediction methods for nonsynonymous SNVs in whole exome sequencing studies. Hum Mol Genet. 15 de abril de 2015;24(8):2125-37.Davydov EV, Goode DL, Sirota M, Cooper GM, Sidow A, Batzoglou S. Identifying a High Fraction of the Human Genome to be under Selective Constraint Using GERP++. PLoS Comput Biol. 2 de diciembre de 2010;6(12):e1001025.Feng BJ. PERCH: A Unified Framework for Disease Gene Prioritization. Hum Mutat. 2017;38(3):243-51.Ioannidis NM, Rothstein JH, Pejaver V, Middha S, McDonnell SK, Baheti S, et al. REVEL: An Ensemble Method for Predicting the Pathogenicity of Rare Missense Variants. Am J Hum Genet. 6 de octubre de 2016;99(4):877-85.Wu Y, Li R, Sun S, Weile J, Roth FP. Improved pathogenicity prediction for rare human missense variants. Am J Hum Genet. 7 de octubre de 2021;108(10):1891-906.Choi Y, Sims GE, Murphy S, Miller JR, Chan AP. Predicting the Functional Effect of Amino Acid Substitutions and Indels. PLoS ONE. 8 de octubre de 2012;7(10):e46688.IONITA-LAZA I, MCCALLUM K, XU B, BUXBAUM J. A SPECTRAL APPROACH INTEGRATING FUNCTIONAL GENOMIC ANNOTATIONS FOR CODING AND NONCODING VARIANTS. Nat Genet. febrero de 2016;48(2):214-20.Li B, Seligman C, Thusberg J, Miller JL, Auer J, Whirl-Carrillo M, et al. In silico comparative characterization of pharmacogenomic missense variants. BMC Genomics. 20 de mayo de 2014;15(4):S4.Duzkale H, Shen J, McLaughlin H, Alfares A, Kelly M, Pugh T, et al. A systematic approach to assessing the clinical significance of genetic variants. Clin Genet. noviembre de 2013;84(5):453-63.Zloh M, Kirton SB. The benefits of in silico modeling to identify possible small-molecule drugs and their off-target interactions. Future Med Chem. febrero de 2018;10(4):423-32.Masica DL, Karchin R. Towards Increasing the Clinical Relevance of In Silico Methods to Predict Pathogenic Missense Variants. PLoS Comput Biol. 12 de mayo de 2016;12(5):e1004725.Tavtigian SV, Greenblatt MS, Lesueur F, Byrnes GB. In silico analysis of missense substitutions using sequence-alignment based methods. Hum Mutat. noviembre de 2008;29(11):1327-36.Zhou Y, Mkrtchian S, Kumondai M, Hiratsuka M, Lauschke VM. An optimized prediction framework to assess the functional impact of pharmacogenetic variants. Pharmacogenomics J. 2019;19(2):115-26.Lahti JL, Tang GW, Capriotti E, Liu T, Altman RB. Bioinformatics and variability in drug response: a protein structural perspective. J R Soc Interface. 7 de julio de 2012;9(72):1409.Pandi MT, Koromina M, Tsafaridis I, Patsilinakos S, Christoforou E, van der Spek PJ, et al. A novel machine learning-based approach for the computational functional assessment of pharmacogenomic variants. Hum Genomics. 9 de agosto de 2021;15:51.Zhou Y, Lauschke VM. Computational Tools to Assess the Functional Consequences of Rare and Noncoding Pharmacogenetic Variability. Clin Pharmacol Ther. septiembre de 2021;110(3):626-36.Farajzadeh-Dehkordi M, Mafakher L, Samiee-Rad F, Rahmani B. Computational analysis of missense variant CYP4F2*3 (V433M) in association with human CYP4F2 dysfunction: a functional and structural impact. BMC Mol Cell Biol. 9 de mayo de 2023;24:17.Joshi K, Kaur S, Kumar R. Cytochrome P450 2C19 gene polymorphisms (CYP2C19*2 and CYP2C19*3) in chronic myeloid leukemia patients: in vitro and in silico studies. J Biomol Struct Dyn. 2022;40(19):9389-402.Shrestha S, Zhang C, Jerde CR, Nie Q, Li H, Offer SM, et al. Gene-specific variant classifier (DPYD-Varifier) to identify deleterious alleles of dihydropyrimidine dehydrogenase. Clin Pharmacol Ther. octubre de 2018;104(4):709-18.Rodrigues-Soares F, Suarez-Kurtz G. Pharmacogenomics research and clinical implementation in Brazil. Basic Clin Pharmacol Toxicol. 2019;124(5):538-49.Cavalcante GC, Freitas NDSDC, Ribeiro-Dos-Santos AM, Carvalho DCD, Silva EMD, Assumpção PPD, et al. Investigation of Potentially Deleterious Alleles for Response to Cancer Treatment with 5-Fluorouracil. Anticancer Res. 1 de diciembre de 2015;35(12):6971-7.Zhou Y, Fujikura K, Mkrtchian S, Lauschke VM. Computational Methods for the Pharmacogenetic Interpretation of Next Generation Sequencing Data. Front Pharmacol. 4 de diciembre de 2018;9:1437.Pallet N, Hamdane S, Garinet S, Blons H, Zaanan A, Paillaud E, et al. A comprehensive population-based study comparing the phenotype and genotype in a pretherapeutic screen of dihydropyrimidine dehydrogenase deficiency. Br J Cancer. septiembre de 2020;123(5):811-8.Principi N, Petropulacos K, Esposito S. Impact of Pharmacogenomics in Clinical Practice. Pharmaceuticals. noviembre de 2023;16(11):1596.Frontiers | A practical guide for the generation of model-based virtual clinical trials [Internet]. [citado 25 de febrero de 2024]. Disponible en: https://www.frontiersin.org/articles/10.3389/fsysb.2023.1174647/fullAlnasser B. A Review of Literature on the Economic Implications of Implementing Artificial Intelligence in Healthcare. E-Health Telecommun Syst Netw. 15 de septiembre de 2023;12(3):35-48.Brooks GA, Tapp S, Daly AT, Busam JA, Tosteson ANA. Cost-effectiveness of DPYD genotyping prior to fluoropyrimidine-based adjuvant chemotherapy for colon cancer. Clin Colorectal Cancer. septiembre de 2022;21(3):e189-95.Moldrup C. Ethical, social and legal implications of pharmacogenomics: a critical review. Community Genet. 2001;4(4):204-14.Methods to Develop an in silico Clinical Trial: Computational Head-to-Head Comparison of Lisdexamfetamine and Methylphenidate - PMC [Internet]. [citado 25 de febrero de 2024]. Disponible en: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8595241/Dihydropyrimidine Dehydrogenase Testing prior to Treatment with 5-Fluorouracil, Capecitabine, and Tegafur: A Consensus Paper - FullText - Oncology Research and Treatment 2020, Vol. 43, No. 11 - Karger Publishers [Internet]. [citado 24 de abril de 2023]. Disponible en: https://www.karger.com/Article/FullText/510258InvestigadoresPúblico generalLICENSElicense.txtlicense.txttext/plain; charset=utf-85879https://repositorio.unal.edu.co/bitstream/unal/86050/3/license.txteb34b1cf90b7e1103fc9dfd26be24b4aMD53ORIGINAL1013628866.2024.pdf1013628866.2024.pdfTesis de Maestría en Genética Humanaapplication/pdf1899950https://repositorio.unal.edu.co/bitstream/unal/86050/4/1013628866.2024.pdf3c86d93f43766a3253ccde045dbdd4d7MD54THUMBNAIL1013628866.2024.pdf.jpg1013628866.2024.pdf.jpgGenerated Thumbnailimage/jpeg5969https://repositorio.unal.edu.co/bitstream/unal/86050/5/1013628866.2024.pdf.jpg31c319e969dc59cc98e596890cb604f6MD55unal/86050oai:repositorio.unal.edu.co:unal/860502024-08-24 23:13:18.788Repositorio Institucional Universidad Nacional de Colombiarepositorio_nal@unal.edu.coUEFSVEUgMS4gVMOJUk1JTk9TIERFIExBIExJQ0VOQ0lBIFBBUkEgUFVCTElDQUNJw5NOIERFIE9CUkFTIEVOIEVMIFJFUE9TSVRPUklPIElOU1RJVFVDSU9OQUwgVU5BTC4KCkxvcyBhdXRvcmVzIHkvbyB0aXR1bGFyZXMgZGUgbG9zIGRlcmVjaG9zIHBhdHJpbW9uaWFsZXMgZGUgYXV0b3IsIGNvbmZpZXJlbiBhIGxhIFVuaXZlcnNpZGFkIE5hY2lvbmFsIGRlIENvbG9tYmlhIHVuYSBsaWNlbmNpYSBubyBleGNsdXNpdmEsIGxpbWl0YWRhIHkgZ3JhdHVpdGEgc29icmUgbGEgb2JyYSBxdWUgc2UgaW50ZWdyYSBlbiBlbCBSZXBvc2l0b3JpbyBJbnN0aXR1Y2lvbmFsLCBiYWpvIGxvcyBzaWd1aWVudGVzIHTDqXJtaW5vczoKCgphKQlMb3MgYXV0b3JlcyB5L28gbG9zIHRpdHVsYXJlcyBkZSBsb3MgZGVyZWNob3MgcGF0cmltb25pYWxlcyBkZSBhdXRvciBzb2JyZSBsYSBvYnJhIGNvbmZpZXJlbiBhIGxhIFVuaXZlcnNpZGFkIE5hY2lvbmFsIGRlIENvbG9tYmlhIHVuYSBsaWNlbmNpYSBubyBleGNsdXNpdmEgcGFyYSByZWFsaXphciBsb3Mgc2lndWllbnRlcyBhY3RvcyBzb2JyZSBsYSBvYnJhOiBpKSByZXByb2R1Y2lyIGxhIG9icmEgZGUgbWFuZXJhIGRpZ2l0YWwsIHBlcm1hbmVudGUgbyB0ZW1wb3JhbCwgaW5jbHV5ZW5kbyBlbCBhbG1hY2VuYW1pZW50byBlbGVjdHLDs25pY28sIGFzw60gY29tbyBjb252ZXJ0aXIgZWwgZG9jdW1lbnRvIGVuIGVsIGN1YWwgc2UgZW5jdWVudHJhIGNvbnRlbmlkYSBsYSBvYnJhIGEgY3VhbHF1aWVyIG1lZGlvIG8gZm9ybWF0byBleGlzdGVudGUgYSBsYSBmZWNoYSBkZSBsYSBzdXNjcmlwY2nDs24gZGUgbGEgcHJlc2VudGUgbGljZW5jaWEsIHkgaWkpIGNvbXVuaWNhciBhbCBww7pibGljbyBsYSBvYnJhIHBvciBjdWFscXVpZXIgbWVkaW8gbyBwcm9jZWRpbWllbnRvLCBlbiBtZWRpb3MgYWzDoW1icmljb3MgbyBpbmFsw6FtYnJpY29zLCBpbmNsdXllbmRvIGxhIHB1ZXN0YSBhIGRpc3Bvc2ljacOzbiBlbiBhY2Nlc28gYWJpZXJ0by4gQWRpY2lvbmFsIGEgbG8gYW50ZXJpb3IsIGVsIGF1dG9yIHkvbyB0aXR1bGFyIGF1dG9yaXphIGEgbGEgVW5pdmVyc2lkYWQgTmFjaW9uYWwgZGUgQ29sb21iaWEgcGFyYSBxdWUsIGVuIGxhIHJlcHJvZHVjY2nDs24geSBjb211bmljYWNpw7NuIGFsIHDDumJsaWNvIHF1ZSBsYSBVbml2ZXJzaWRhZCByZWFsaWNlIHNvYnJlIGxhIG9icmEsIGhhZ2EgbWVuY2nDs24gZGUgbWFuZXJhIGV4cHJlc2EgYWwgdGlwbyBkZSBsaWNlbmNpYSBDcmVhdGl2ZSBDb21tb25zIGJham8gbGEgY3VhbCBlbCBhdXRvciB5L28gdGl0dWxhciBkZXNlYSBvZnJlY2VyIHN1IG9icmEgYSBsb3MgdGVyY2Vyb3MgcXVlIGFjY2VkYW4gYSBkaWNoYSBvYnJhIGEgdHJhdsOpcyBkZWwgUmVwb3NpdG9yaW8gSW5zdGl0dWNpb25hbCwgY3VhbmRvIHNlYSBlbCBjYXNvLiBFbCBhdXRvciB5L28gdGl0dWxhciBkZSBsb3MgZGVyZWNob3MgcGF0cmltb25pYWxlcyBkZSBhdXRvciBwb2Ryw6EgZGFyIHBvciB0ZXJtaW5hZGEgbGEgcHJlc2VudGUgbGljZW5jaWEgbWVkaWFudGUgc29saWNpdHVkIGVsZXZhZGEgYSBsYSBEaXJlY2Npw7NuIE5hY2lvbmFsIGRlIEJpYmxpb3RlY2FzIGRlIGxhIFVuaXZlcnNpZGFkIE5hY2lvbmFsIGRlIENvbG9tYmlhLiAKCmIpIAlMb3MgYXV0b3JlcyB5L28gdGl0dWxhcmVzIGRlIGxvcyBkZXJlY2hvcyBwYXRyaW1vbmlhbGVzIGRlIGF1dG9yIHNvYnJlIGxhIG9icmEgY29uZmllcmVuIGxhIGxpY2VuY2lhIHNlw7FhbGFkYSBlbiBlbCBsaXRlcmFsIGEpIGRlbCBwcmVzZW50ZSBkb2N1bWVudG8gcG9yIGVsIHRpZW1wbyBkZSBwcm90ZWNjacOzbiBkZSBsYSBvYnJhIGVuIHRvZG9zIGxvcyBwYcOtc2VzIGRlbCBtdW5kbywgZXN0byBlcywgc2luIGxpbWl0YWNpw7NuIHRlcnJpdG9yaWFsIGFsZ3VuYS4KCmMpCUxvcyBhdXRvcmVzIHkvbyB0aXR1bGFyZXMgZGUgZGVyZWNob3MgcGF0cmltb25pYWxlcyBkZSBhdXRvciBtYW5pZmllc3RhbiBlc3RhciBkZSBhY3VlcmRvIGNvbiBxdWUgbGEgcHJlc2VudGUgbGljZW5jaWEgc2Ugb3RvcmdhIGEgdMOtdHVsbyBncmF0dWl0bywgcG9yIGxvIHRhbnRvLCByZW51bmNpYW4gYSByZWNpYmlyIGN1YWxxdWllciByZXRyaWJ1Y2nDs24gZWNvbsOzbWljYSBvIGVtb2x1bWVudG8gYWxndW5vIHBvciBsYSBwdWJsaWNhY2nDs24sIGRpc3RyaWJ1Y2nDs24sIGNvbXVuaWNhY2nDs24gcMO6YmxpY2EgeSBjdWFscXVpZXIgb3RybyB1c28gcXVlIHNlIGhhZ2EgZW4gbG9zIHTDqXJtaW5vcyBkZSBsYSBwcmVzZW50ZSBsaWNlbmNpYSB5IGRlIGxhIGxpY2VuY2lhIENyZWF0aXZlIENvbW1vbnMgY29uIHF1ZSBzZSBwdWJsaWNhLgoKZCkJUXVpZW5lcyBmaXJtYW4gZWwgcHJlc2VudGUgZG9jdW1lbnRvIGRlY2xhcmFuIHF1ZSBwYXJhIGxhIGNyZWFjacOzbiBkZSBsYSBvYnJhLCBubyBzZSBoYW4gdnVsbmVyYWRvIGxvcyBkZXJlY2hvcyBkZSBwcm9waWVkYWQgaW50ZWxlY3R1YWwsIGluZHVzdHJpYWwsIG1vcmFsZXMgeSBwYXRyaW1vbmlhbGVzIGRlIHRlcmNlcm9zLiBEZSBvdHJhIHBhcnRlLCAgcmVjb25vY2VuIHF1ZSBsYSBVbml2ZXJzaWRhZCBOYWNpb25hbCBkZSBDb2xvbWJpYSBhY3TDumEgY29tbyB1biB0ZXJjZXJvIGRlIGJ1ZW5hIGZlIHkgc2UgZW5jdWVudHJhIGV4ZW50YSBkZSBjdWxwYSBlbiBjYXNvIGRlIHByZXNlbnRhcnNlIGFsZ8O6biB0aXBvIGRlIHJlY2xhbWFjacOzbiBlbiBtYXRlcmlhIGRlIGRlcmVjaG9zIGRlIGF1dG9yIG8gcHJvcGllZGFkIGludGVsZWN0dWFsIGVuIGdlbmVyYWwuIFBvciBsbyB0YW50bywgbG9zIGZpcm1hbnRlcyAgYWNlcHRhbiBxdWUgY29tbyB0aXR1bGFyZXMgw7puaWNvcyBkZSBsb3MgZGVyZWNob3MgcGF0cmltb25pYWxlcyBkZSBhdXRvciwgYXN1bWlyw6FuIHRvZGEgbGEgcmVzcG9uc2FiaWxpZGFkIGNpdmlsLCBhZG1pbmlzdHJhdGl2YSB5L28gcGVuYWwgcXVlIHB1ZWRhIGRlcml2YXJzZSBkZSBsYSBwdWJsaWNhY2nDs24gZGUgbGEgb2JyYS4gIAoKZikJQXV0b3JpemFuIGEgbGEgVW5pdmVyc2lkYWQgTmFjaW9uYWwgZGUgQ29sb21iaWEgaW5jbHVpciBsYSBvYnJhIGVuIGxvcyBhZ3JlZ2Fkb3JlcyBkZSBjb250ZW5pZG9zLCBidXNjYWRvcmVzIGFjYWTDqW1pY29zLCBtZXRhYnVzY2Fkb3Jlcywgw61uZGljZXMgeSBkZW3DoXMgbWVkaW9zIHF1ZSBzZSBlc3RpbWVuIG5lY2VzYXJpb3MgcGFyYSBwcm9tb3ZlciBlbCBhY2Nlc28geSBjb25zdWx0YSBkZSBsYSBtaXNtYS4gCgpnKQlFbiBlbCBjYXNvIGRlIGxhcyB0ZXNpcyBjcmVhZGFzIHBhcmEgb3B0YXIgZG9ibGUgdGl0dWxhY2nDs24sIGxvcyBmaXJtYW50ZXMgc2Vyw6FuIGxvcyByZXNwb25zYWJsZXMgZGUgY29tdW5pY2FyIGEgbGFzIGluc3RpdHVjaW9uZXMgbmFjaW9uYWxlcyBvIGV4dHJhbmplcmFzIGVuIGNvbnZlbmlvLCBsYXMgbGljZW5jaWFzIGRlIGFjY2VzbyBhYmllcnRvIENyZWF0aXZlIENvbW1vbnMgeSBhdXRvcml6YWNpb25lcyBhc2lnbmFkYXMgYSBzdSBvYnJhIHBhcmEgbGEgcHVibGljYWNpw7NuIGVuIGVsIFJlcG9zaXRvcmlvIEluc3RpdHVjaW9uYWwgVU5BTCBkZSBhY3VlcmRvIGNvbiBsYXMgZGlyZWN0cmljZXMgZGUgbGEgUG9sw610aWNhIEdlbmVyYWwgZGUgbGEgQmlibGlvdGVjYSBEaWdpdGFsLgoKCmgpCVNlIGF1dG9yaXphIGEgbGEgVW5pdmVyc2lkYWQgTmFjaW9uYWwgZGUgQ29sb21iaWEgY29tbyByZXNwb25zYWJsZSBkZWwgdHJhdGFtaWVudG8gZGUgZGF0b3MgcGVyc29uYWxlcywgZGUgYWN1ZXJkbyBjb24gbGEgbGV5IDE1ODEgZGUgMjAxMiBlbnRlbmRpZW5kbyBxdWUgc2UgZW5jdWVudHJhbiBiYWpvIG1lZGlkYXMgcXVlIGdhcmFudGl6YW4gbGEgc2VndXJpZGFkLCBjb25maWRlbmNpYWxpZGFkIGUgaW50ZWdyaWRhZCwgeSBzdSB0cmF0YW1pZW50byB0aWVuZSB1bmEgZmluYWxpZGFkIGhpc3TDs3JpY2EsIGVzdGFkw61zdGljYSBvIGNpZW50w61maWNhIHNlZ8O6biBsbyBkaXNwdWVzdG8gZW4gbGEgUG9sw610aWNhIGRlIFRyYXRhbWllbnRvIGRlIERhdG9zIFBlcnNvbmFsZXMuCgoKClBBUlRFIDIuIEFVVE9SSVpBQ0nDk04gUEFSQSBQVUJMSUNBUiBZIFBFUk1JVElSIExBIENPTlNVTFRBIFkgVVNPIERFIE9CUkFTIEVOIEVMIFJFUE9TSVRPUklPIElOU1RJVFVDSU9OQUwgVU5BTC4KClNlIGF1dG9yaXphIGxhIHB1YmxpY2FjacOzbiBlbGVjdHLDs25pY2EsIGNvbnN1bHRhIHkgdXNvIGRlIGxhIG9icmEgcG9yIHBhcnRlIGRlIGxhIFVuaXZlcnNpZGFkIE5hY2lvbmFsIGRlIENvbG9tYmlhIHkgZGUgc3VzIHVzdWFyaW9zIGRlIGxhIHNpZ3VpZW50ZSBtYW5lcmE6CgphLglDb25jZWRvIGxpY2VuY2lhIGVuIGxvcyB0w6lybWlub3Mgc2XDsWFsYWRvcyBlbiBsYSBwYXJ0ZSAxIGRlbCBwcmVzZW50ZSBkb2N1bWVudG8sIGNvbiBlbCBvYmpldGl2byBkZSBxdWUgbGEgb2JyYSBlbnRyZWdhZGEgc2VhIHB1YmxpY2FkYSBlbiBlbCBSZXBvc2l0b3JpbyBJbnN0aXR1Y2lvbmFsIGRlIGxhIFVuaXZlcnNpZGFkIE5hY2lvbmFsIGRlIENvbG9tYmlhIHkgcHVlc3RhIGEgZGlzcG9zaWNpw7NuIGVuIGFjY2VzbyBhYmllcnRvIHBhcmEgc3UgY29uc3VsdGEgcG9yIGxvcyB1c3VhcmlvcyBkZSBsYSBVbml2ZXJzaWRhZCBOYWNpb25hbCBkZSBDb2xvbWJpYSAgYSB0cmF2w6lzIGRlIGludGVybmV0LgoKCgpQQVJURSAzIEFVVE9SSVpBQ0nDk04gREUgVFJBVEFNSUVOVE8gREUgREFUT1MgUEVSU09OQUxFUy4KCkxhIFVuaXZlcnNpZGFkIE5hY2lvbmFsIGRlIENvbG9tYmlhLCBjb21vIHJlc3BvbnNhYmxlIGRlbCBUcmF0YW1pZW50byBkZSBEYXRvcyBQZXJzb25hbGVzLCBpbmZvcm1hIHF1ZSBsb3MgZGF0b3MgZGUgY2Fyw6FjdGVyIHBlcnNvbmFsIHJlY29sZWN0YWRvcyBtZWRpYW50ZSBlc3RlIGZvcm11bGFyaW8sIHNlIGVuY3VlbnRyYW4gYmFqbyBtZWRpZGFzIHF1ZSBnYXJhbnRpemFuIGxhIHNlZ3VyaWRhZCwgY29uZmlkZW5jaWFsaWRhZCBlIGludGVncmlkYWQgeSBzdSB0cmF0YW1pZW50byBzZSByZWFsaXphIGRlIGFjdWVyZG8gYWwgY3VtcGxpbWllbnRvIG5vcm1hdGl2byBkZSBsYSBMZXkgMTU4MSBkZSAyMDEyIHkgZGUgbGEgUG9sw610aWNhIGRlIFRyYXRhbWllbnRvIGRlIERhdG9zIFBlcnNvbmFsZXMgZGUgbGEgVW5pdmVyc2lkYWQgTmFjaW9uYWwgZGUgQ29sb21iaWEuIFB1ZWRlIGVqZXJjZXIgc3VzIGRlcmVjaG9zIGNvbW8gdGl0dWxhciBhIGNvbm9jZXIsIGFjdHVhbGl6YXIsIHJlY3RpZmljYXIgeSByZXZvY2FyIGxhcyBhdXRvcml6YWNpb25lcyBkYWRhcyBhIGxhcyBmaW5hbGlkYWRlcyBhcGxpY2FibGVzIGEgdHJhdsOpcyBkZSBsb3MgY2FuYWxlcyBkaXNwdWVzdG9zIHkgZGlzcG9uaWJsZXMgZW4gd3d3LnVuYWwuZWR1LmNvIG8gZS1tYWlsOiBwcm90ZWNkYXRvc19uYUB1bmFsLmVkdS5jbyIKClRlbmllbmRvIGVuIGN1ZW50YSBsbyBhbnRlcmlvciwgYXV0b3Jpem8gZGUgbWFuZXJhIHZvbHVudGFyaWEsIHByZXZpYSwgZXhwbMOtY2l0YSwgaW5mb3JtYWRhIGUgaW5lcXXDrXZvY2EgYSBsYSBVbml2ZXJzaWRhZCBOYWNpb25hbCBkZSBDb2xvbWJpYSBhIHRyYXRhciBsb3MgZGF0b3MgcGVyc29uYWxlcyBkZSBhY3VlcmRvIGNvbiBsYXMgZmluYWxpZGFkZXMgZXNwZWPDrWZpY2FzIHBhcmEgZWwgZGVzYXJyb2xsbyB5IGVqZXJjaWNpbyBkZSBsYXMgZnVuY2lvbmVzIG1pc2lvbmFsZXMgZGUgZG9jZW5jaWEsIGludmVzdGlnYWNpw7NuIHkgZXh0ZW5zacOzbiwgYXPDrSBjb21vIGxhcyByZWxhY2lvbmVzIGFjYWTDqW1pY2FzLCBsYWJvcmFsZXMsIGNvbnRyYWN0dWFsZXMgeSB0b2RhcyBsYXMgZGVtw6FzIHJlbGFjaW9uYWRhcyBjb24gZWwgb2JqZXRvIHNvY2lhbCBkZSBsYSBVbml2ZXJzaWRhZC4gCgo= |