Modelo de aprendizaje automático para predecir el riesgo de evento adverso en analgésicos opioides aplicando datos de secuenciación de última generación (NGS) en una población de pacientes colombianos
ilustraciones
- Autores:
-
Garzón Venegas, Eliana del Pilar
- Tipo de recurso:
- Fecha de publicación:
- 2022
- Institución:
- Universidad Nacional de Colombia
- Repositorio:
- Universidad Nacional de Colombia
- Idioma:
- spa
- OAI Identifier:
- oai:repositorio.unal.edu.co:unal/83279
- Palabra clave:
- 570 - Biología::576 - Genética y evolución
610 - Medicina y salud::615 - Farmacología y terapéutica
Efectividad de los medicamentos
Aprendizaje - efectos de las drogas
Drugs-Effectiveness
Learning-effect of drugs on
Analgesicos opioides
Evento adverso
Farmacogenética
Aprendizaje automático
Opioid analgesics
Adverse event
Pharmacogenetics
Machine learning
- Rights
- openAccess
- License
- Atribución-NoComercial-SinDerivadas 4.0 Internacional
| id |
UNACIONAL2_92ee945f00779fae454f96882df618b6 |
|---|---|
| oai_identifier_str |
oai:repositorio.unal.edu.co:unal/83279 |
| network_acronym_str |
UNACIONAL2 |
| network_name_str |
Universidad Nacional de Colombia |
| repository_id_str |
|
| dc.title.spa.fl_str_mv |
Modelo de aprendizaje automático para predecir el riesgo de evento adverso en analgésicos opioides aplicando datos de secuenciación de última generación (NGS) en una población de pacientes colombianos |
| dc.title.translated.eng.fl_str_mv |
Machine learning model to predict the risk of adverse event in opioid analgesics using next-generation sequencing (NGS) data in a population of Colombian patients |
| title |
Modelo de aprendizaje automático para predecir el riesgo de evento adverso en analgésicos opioides aplicando datos de secuenciación de última generación (NGS) en una población de pacientes colombianos |
| spellingShingle |
Modelo de aprendizaje automático para predecir el riesgo de evento adverso en analgésicos opioides aplicando datos de secuenciación de última generación (NGS) en una población de pacientes colombianos 570 - Biología::576 - Genética y evolución 610 - Medicina y salud::615 - Farmacología y terapéutica Efectividad de los medicamentos Aprendizaje - efectos de las drogas Drugs-Effectiveness Learning-effect of drugs on Analgesicos opioides Evento adverso Farmacogenética Aprendizaje automático Opioid analgesics Adverse event Pharmacogenetics Machine learning |
| title_short |
Modelo de aprendizaje automático para predecir el riesgo de evento adverso en analgésicos opioides aplicando datos de secuenciación de última generación (NGS) en una población de pacientes colombianos |
| title_full |
Modelo de aprendizaje automático para predecir el riesgo de evento adverso en analgésicos opioides aplicando datos de secuenciación de última generación (NGS) en una población de pacientes colombianos |
| title_fullStr |
Modelo de aprendizaje automático para predecir el riesgo de evento adverso en analgésicos opioides aplicando datos de secuenciación de última generación (NGS) en una población de pacientes colombianos |
| title_full_unstemmed |
Modelo de aprendizaje automático para predecir el riesgo de evento adverso en analgésicos opioides aplicando datos de secuenciación de última generación (NGS) en una población de pacientes colombianos |
| title_sort |
Modelo de aprendizaje automático para predecir el riesgo de evento adverso en analgésicos opioides aplicando datos de secuenciación de última generación (NGS) en una población de pacientes colombianos |
| dc.creator.fl_str_mv |
Garzón Venegas, Eliana del Pilar |
| dc.contributor.advisor.none.fl_str_mv |
Aristizábal Gutiérrez, Fabio Ancízar Niño Vásquez, Luis Fernando |
| dc.contributor.author.none.fl_str_mv |
Garzón Venegas, Eliana del Pilar |
| dc.contributor.researchgroup.spa.fl_str_mv |
laboratorio de Investigación en Sistemas Inteligentes Lisi |
| dc.subject.ddc.spa.fl_str_mv |
570 - Biología::576 - Genética y evolución 610 - Medicina y salud::615 - Farmacología y terapéutica |
| topic |
570 - Biología::576 - Genética y evolución 610 - Medicina y salud::615 - Farmacología y terapéutica Efectividad de los medicamentos Aprendizaje - efectos de las drogas Drugs-Effectiveness Learning-effect of drugs on Analgesicos opioides Evento adverso Farmacogenética Aprendizaje automático Opioid analgesics Adverse event Pharmacogenetics Machine learning |
| dc.subject.lemb.spa.fl_str_mv |
Efectividad de los medicamentos Aprendizaje - efectos de las drogas |
| dc.subject.lemb.eng.fl_str_mv |
Drugs-Effectiveness Learning-effect of drugs on |
| dc.subject.proposal.spa.fl_str_mv |
Analgesicos opioides Evento adverso Farmacogenética Aprendizaje automático |
| dc.subject.proposal.eng.fl_str_mv |
Opioid analgesics Adverse event Pharmacogenetics Machine learning |
| description |
ilustraciones |
| publishDate |
2022 |
| dc.date.issued.none.fl_str_mv |
2022-11-02 |
| dc.date.accessioned.none.fl_str_mv |
2023-02-03T15:59:14Z |
| dc.date.available.none.fl_str_mv |
2023-02-03T15:59:14Z |
| 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/83279 |
| 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/83279 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.references.spa.fl_str_mv |
Simon, E. J., Hiller, J. M. & Edelman, I. 1973. Stereospecific binding of the potent narcotic analgesic [3H] etorphine to rat-brain homogenate. Proceedings of the National Academy Kieffer, B. L. 1995. Recent advances in molecular recognition and signal transduction of active peptides: receptors for opioid peptides. Cellular and molecular neurobiology, 15, 615- 635 Chen, Y., Mestek, A., Liu, J., Hurley, J. A. & Yu, L. 1993. Molecular cloning and functional expression of a mu-opioid receptor from rat brain. Molecular Pharmacology, 44, 8-12. 4. Chen YJ, Lu JT, Huang CW, Wu WH, Lee KF, Liu HT, Shih-Hsin Wu L. Pharmacogenetic study of methadone treatment for heroin addiction: associations between drug-metabolizing gene polymorphisms and treatment efficacy. Pharmacogenet Genomics. 2022 Jan 1;32(1):31-38. doi:10.1097/FPC.0000000000000450. PMID: 34380995. Breiman, Leo. “Random Forests.” Machine Learning 45 (1). Springer: 5-32 (2001) Isaza, C., Sepúlveda-Arias, J. C. & Henao, J. 2009. Pharmacogenomics in medicine. Madadi P, Ross CJ, Hayden MR, Carleton BC, Gaedigk A, Leeder JS, et al. Pharmacogenetics of neonatal opioid toxicity following maternal use of codeine during breastfeeding: a case-control study. Clinical pharmacology and therapeutics. 2009;85(1):31-5. doi: 10.1038/clpt.2008.157. PubMed PMID: 18719619 Koren G, Cairns J, Chitayat D, Gaedigk A, Leeder SJ. Pharmacogenetics of morphine poisoning in a breastfed neonate of a codeine-prescribed mother. Lancet. 2006;368(9536):704. doi: 10.1016/S0140- 6736(06)69255-6. PubMed PMID: 16920476 Owusu Obeng, A., Hamadeh, I., & Smith, M. (2017). Review of opioid pharmacogenetics and considerations for pain management. Pharmacotherapy: The Journal of Human Pharmacology and Drug Therapy, 37(9), 1105-1121 Laksman, Z., and Detsky, A. S. (2011). Personalized medicine: understanding probabilities and managing expectations. J. Gen. Intern. Med. 26, 204–206. doi: 10.1007/s116060101515- 6 Mueller, J. J., Schlappe, B. A., Kumar, R., Olvera, N., Dao, F., Abu-Rustum, N., et al. (2018). Massively parallel sequencing analysis of mucinous ovarian carcinomas: genomic profiling and differential diagnoses. Gynecol. Oncol. 150, 127–135. doi: 10.1016/j.ygyno.2018.05.008 Evans, W. E., and Relling, M. V. (2004). Moving towards individualized medicine with pharmacogenomics. Nature 429, 464–468. doi: 10.1038/nature02626 Feero, W. G., Guttmacher, A. E., and Collins, F. S. (2008). The genome gets personal – Almost. JAMA 299, 1351–1352. doi: 10.1001/jama.299.11.1351 12. Ann K Daly, Pharmacogenetics: a general review on progress to date, British Medical Bulletin, Volume 124, Issue 1, December 2017, Pages 65–79 Ho, D. S. W., Schierding, W., Wake, M., Saffery, R., & O’Sullivan, J. (2019). Machine Learning SNP Based Prediction for Precision Medicine. Frontiers in Genetics Capriotti, E., Calabrese, R., and Casadio, R. (2006). Predicting the insurgence of human 50 genetic diseases associated to single point protein mutations with support vector machines and evolutionary information. Bioinformatics 22, 2729–2734. doi: 10.1093/bioinformatics/btl423 Cruz, J. A., and Wishart, D. S. (2006). Applications of machine learning in cancer prediction and prognosis. Cancer Inform. 2, 59–77. doi: 10.1177/117693510600200030 Palaniappan, S., and Awang, R. (2008). “Intelligent heart disease prediction system using data mining techniques,” in Proceedings of the 2008 IEEE/ACS Int. Conf. Comput. Syst. Appl, (Doha), 108– 115. doi: 10.1109/AICCSA.2008.4493524 Yu, W. (2010). Application of support vector machine modeling for prediction of common diseases: the case of diabetes and pre-diabetes. BMC Med. Inform. Decis. Mak. 10:16. doi: 10.1186/1472-6947-10- 16 Zhang, D., and Shen, D. (2012). Multi-modal multi-task learning for joint prediction of multiple regression and classification variables in Alzheimer’s disease. Neuroimage 59, 895–907. doi: 10.1016/j.neuroimage.2011.09.069 López, B., Torrent-Fontbona, F., Viñas, R., and Fernández-Real, J. M. (2017). Single nucleotide polymorphism relevance learning with random forests for type 2 diabetes risk prediction. Artif. Intell. Med. 85, 43–49. doi: 10.1016/j.artmed.2017.09.005 Boulesteix, A. L., Janitza, S., Kruppa, J., and König, I. R. (2012). Overview of random forest methodology and practical guidance with emphasis on computational biology and bioinformatics. Wiley Interdiscip. Rev. Data Min. Knowl. Discov. 2, 493–507. doi: 10.1002/widm.1072 Chen, X., and Ishwaran, H. (2012). Random forests for genomic data analysis. Genomics 99, 323–329. doi: 10.1016/j.ygeno.2012.04.003 Austin, P. C., Tu, J. V., Ho, J. E., Levy, D., and Lee, D. S. (2013). Using methods from the data- mining and machine-learning literature for disease classification and prediction: a case study examining classification of heart failure subtypes. J. Clin. Epidemiol. 66, 398–407. doi: 10.1016/j.jclinepi.2012.11.008 Kringel, D., Geisslinger, G., Resch, E., Oertel, B. G., Thrun, M. C., Heinemann, S., & Lötsch, J. (2018). Machine-learned analysis of the association of next-generation sequencing–based human TRPV1 and TRPA1 genotypes with the sensitivity to heat stimuli and topically applied capsaicin. Pain, 159(7), 1366 Lo-Ciganic, W. H., Huang, J. L., Zhang, H. H., Weiss, J. C., Wu, Y., Kwoh, C. K., ... & Kuza, C. C. (2019). Evaluation of Machine-Learning Algorithms for Predicting Opioid Overdose Risk Among Medicare Beneficiaries With Opioid Prescriptions. JAMA network open, 2(3), e190968-e190968 Organización Mundial de la salud (OMS), Manejo del abuso de sustancias. Recuperado de https://www.who.int/substance_abuse/information-sheet/es/ Observatorio de Drogas de Colombia (O.D.C), Alerta informativa acerca del uso de sustancias y preparados con actividad psicoactiva utilizadas para el manejo del dolor (opioides), 08-12-019. Recuperado de http://www.odc.gov.co/Portals/1/SAT/Alerta%20Opioides.pdf EVANS, C. J., KEITH, D., MORRISON, H., MAGENDZO, K. & EDWARDS, R. H. 1992. Cloning of a delta opioid receptor by functional expression. SCIENCE-NEW YORK THEN WASHINGTON-, 258, 1952-1952. KIEFFER, B. L. 1995. Recent advances in molecular recognition and signal transduction of active peptides: receptors for opioid peptides. Cellular and molecular neurobiology, 15, 615- 635. CHEN, Y., MESTEK, A., LIU, J., HURLEY, J. A. & YU, L. 1993. Molecular cloning and functional expression of a mu-opioid receptor from rat brain. Molecular Pharmacology, 44, 8-12. Lorenzo P, Moreno A, Leza JC, Lizasoain I, Moro MA, Portolés A. Velazquez. Farmacología Básica y Clínica. 19a ed. Editorial Médica Panamericana; 2018. lórez J. Fármacos analgésicos opioides. En: Flórez J, Armijo JA, Mediavilla A, editores. Farmacología humana. 4a ed. Barcelona: Masson; 2003. p. 461-478 Rang HP, Dale MM, Ritter JM, Moore PK. editores. Farmacología. 5a ed. Madrid: Elsevier; 2004. p. 562-583 Cherny NI. Opioid analgesics. Drugs 1996; 51: 713-737 Mendoza Patiño, N. Farmacología médica (3a edición). Editorial Médica Panamericana. olleau, S. Evaluación de la escalera analgésica de la Organización Mundial de la Salud en su 20o aniversario. Cancer Pain Release. 2006;19:2. 2. Lacy Ch, Armstrong L, Goldman M, Lance L. Drug Information Handbook. Lexi Comp 15th Ed. p. 410-412. utstein HB, Akil H. Opioid Analgesics. En: Brunton L, Lazo J, Parker K. Goodman and Gilman ́s The Pharmacological Basis of Therapeutics. 2006. Thummel K. Shen D, Isoherranen, Smith H. Appendix II Design and Optimization of Dosage Regimens: Pharmacokinetic data. In: Brunton L, Lazo J, Parker K: Goodman and Gilman ́s The Pharmacological Basis of Therapeutics. 11th ed. 2006. p. 1.813 Vascello L and Maquillan R. Opioid Analgesics and Routes of Administration. De LeonCasasola O. Cancer Pain. Pharmacological Interventional and Palliative care Approaches. Elsevier inc. 2006:171-193. Smith HS. Variations in opioid responsiveness. Pain Physician. 2008 Mar-Apr; 11(2):237-48. Colleau, S M. Cancer Press Release 2006; Vol 19: p. 2: Evaluación de la escalera analgésica de la Organización Mundial de la Salud en su 20o aniversario. Leppert W, Luczak J. The role of tramadol in cancer pain treatment-a review. Support Care Cancer. 2005 Jan;13(1):5-17. Epub 2004 Nov 18 Grond S, Sablotzki A. Clinical pharmacology of tramadol. Clin Pharmacokinet. 2004;43(13):879-923 Leppert W, Luczak J. The role of tramadol in cancer pain treatment-a review. Support Care Cancer. 2005 Jan;13(1):5-17. Epub 2004 Nov 18. ascello L, Maquillan R. Opioid Analgesics and Routes of Administration. De LeonCasasola O. Cancer Pain. Pharmacological Interventional and Palliative Care Approaches. Elsevier inc. 2006:171-193. Lacy Ch, Armstrong L, Goldman M, Lance L. Drug Information Handbook. Lexi Comp 15th Ed. Page 1171-1175. Gutstein HB, Akil H. Opioid Analgesics, Chap 21. In: Brunton L; Lazo J; Parker K: Goodman and Gilman ́s The Pharmacological Basis of Therapeutics. 11th ed. 2006. Thummel K, Shen D Isoherranen, Smith H. Appendix II Design and Optimization of Dosage Regimens: Pharmacokinetic data. In: Brunton L; Lazo J; Parker K: Goodman and Gilman ́s. The Pharmacological Basis of Therapeutics. 11th ed. 2006. p.1851. Schumacher M, Basbaun A, Way W. in: Katzung B. Basic and clinical pharmacology. 10th Ed,Mc Graw-Hill Companies The; 2006. Chap 31 Ross FB, Smith MT. The intrinsic antinociceptive eects of oxycodone appear to be kappa-opioid receptor meditated. Pain. 1997 Nov;73(2):151-7. Benziger DP, Kaiko RF, Miotto JB, Fitzmartin RD, Reder RF, Chasin M. Differential effects of food and the bioavailability of controlled-releaseoxycodone tablets and immediate-release oxycodone solution. J Pharm Sci. 1996 Apr;85(4):407-10. Kaiko RF, Benziger DP, Fitzmartin RD, Burke BE, Reder RF, Goldenheim PD. Pharmacokinetic-pharmacodynamic relationships of controlled-release oxycodone. Clin Pharmacol Ther. 1996 Jan;59:52-61. Darwish M, Kirby M, Robertson P Jr, Tracewell W, Jiang JG. Absolute and relative bioavailability of fentanyl buccal tablet and oral transmucosal fentanyl citrate. J Clin Pharmacol. 2007 Mar;47(3):343-50. Lacy Ch, Armstrong L, Goldman M, Lance L. Drug Information Handbook. Lexi Comp 15th ed: p. 693-699 Huang P, Kehner GB, Cowan A, Liu-Chen LY. Comparison of pharmacological activities of buprenorphine and norbuprenorphine: norbuprenorphine is a potent opioid agonist. J Pharmacol Exp Ther. 2001 May;297(2):688-95. Böger RH. Renal impairment: a challenge for opioid treatment? The role of buprenorphine. Palliat Med. 2006;20 Suppl 1:s17-23. Zaki PA, Keith DE Jr, Brine GA, Carroll FI, Evans CJ. Ligand-induced changes in surface mu-opioid receptor number: relationship to G protein activation? J Pharmacol Exp Ther. 2000 Mar;292(3):1127-34 McNicol ED, Boyce D, Schumann R, Carr DB. Mu-opioid antagonists for opioidinduced bowel dysfunction. Cochrane Database Syst Rev. 2008 Apr 16;(2):CD006332. Nebreda C, Bruno U. Manual de fármacos utilizados en el tratamiento del dolor crónico. IAHPC 2001;42-43 Trescot AM, Datta S, Lee M, Hansen H. Opioid pharmacology. Pain Physician. 2008 Mar;11(2 Suppl):S133-53. Pappagallo M, Stewart W, Woods M. Constipation symptoms in long-term users of opioid analgesic therapy. Poster-Abstracts. American Pain Society Annual Meeting, Fort Lauderdale, Florida, October 21-24, 1999. Homann NG, Olofsson O, Salen B, Wickstrom L. Prevalence of abuse and dependency in chronic pain patients. Int J Addict. 1995 Jun;30(8):919-27. Hahn, D., Fukuda, T., Euteneuer, J. C., Mizuno, T., Vinks, A. A., Sadhasivam, S., & Emoto, C. (2020). Influence of MRP3 genetics and hepatic expression ontogeny for morphine disposition in neonatal and pediatric patients. The Journal of Clinical Pharmacology, 60(8), 992-998. Lam J, Woodall KL, Solbeck P, Ross CJ, Carleton BC, Hayden MR, Koren G, Madadi P. Codeine-related deaths: The role of pharmacogenetics and drug interactions. Forensic Sci Int. 2014 Jun;239:50-6. doi: 10.1016/j.forsciint.2014.03.018. Epub 2014 Mar 26. PMID: 24747667. Zhang J, Zhang L, Zhao X, Shen S, Luo X, Zhang Y. Association between MDR1/CYP3A4/OPRM1 gene polymorphisms and the post-caesarean fentanyl analgesic effect on Chinese women. Gene. 2018 Jun 30;661:78-84. doi: 10.1016/j.gene.2018.03.081. Epub 2018 Mar 27. PMID: 29601950. Naito T, Takashina Y, Yamamoto K, Tashiro M, Ohnishi K, Kagawa Y, Kawakami J. CYP3A5*3 affects plasma disposition of noroxycodone and dose escalation in cancer patients receiving oxycodone. J Clin Pharmacol. 2011 Nov;51(11):1529-38. doi: 10.1177/0091270010388033. Epub 2011 Jan 5. PMID: 21209234. Saiz-Rodríguez M, Ochoa D, Herrador C, Belmonte C, Román M, Alday E, Koller D, Zubiaur P, Mejía G, Hernández-Martínez M, Abad-Santos F. Polymorphisms associated with fentanyl pharmacokinetics, pharmacodynamics and adverse effects. Basic Clin Pharmacol Toxicol. 2019 Mar;124(3):321-329. doi: 10.1111/bcpt.13141. Epub 2018 Oct 30. PMID: 30281924. Fonseca F, Gratacòs M, Escaramís G, De Cid R, Martín-Santos R, Farré M, Estivill X, Torrens M. ALDH5A1 variability in opioid dependent patients could influence response to methadone treatment. Eur Neuropsychopharmacol. 2014 Mar;24(3):4204. doi: 10.1016/j.euroneuro.2013.10.003. Epub 2013 Oct 18. PMID: 24230997. Hung CC, Chiou MH, Huang BH, Hsieh YW, Hsieh TJ, Huang CL, Lane HY. Impact of genetic polymorphisms in ABCB1, CYP2B6, OPRM1, ANKK1 and DRD2 genes on methadone therapy in Han Chinese patients. Pharmacogenomics. 2011 54 Nov;12(11):1525-33. doi: 10.2217/pgs.11.96. Epub 2011 Sep 8. PMID: 21902500. de Cid R, Fonseca F, Gratacòs M, Gutierrez F, Martín-Santos R, Estivill X, Torrens M. BDNF variability in opioid addicts and response to methadone treatment: preliminary findings. Genes Brain Behav. 2008 Jul;7(5):515-22. doi: 10.1111/j.1601183X.2007.00386.x. PMID: 18182069. Yoshida K, Nishizawa D, Ichinomiya T, Ichinohe T, Hayashida M, Fukuda K, Ikeda K. Prediction formulas for individual opioid analgesic requirements based on genetic polymorphism analyses. PLoS One. 2015 Jan 23;10(1):e0116885. doi: 10.1371/journal.pone.0116885. PMID: 25615449; PMCID: PMC4304713. Kuo HW, Liu TH, Tsou HH, Hsu YT, Wang SC, Fang CP, Liu CC, Chen ACH, Liu YL. Inflammatory chemokine eotaxin-1 is correlated with age in heroin dependent patients under methadone maintenance therapy. Drug Alcohol Depend. 2018 Feb 1;183:19-24. doi: 10.1016/j.drugalcdep.2017.10.014. Epub 2017 Nov 28. PMID: 29222992. Cargnin S, Magnani F, Viana M, Tassorelli C, Mittino D, Cantello R, Sances G, Nappi G, Canonico PL, Genazzani AA, Raffaeli W, Terrazzino S. An opposite-direction modulation of the COMT Val158Met polymorphism on the clinical response to intrathecal morphine and triptans. J Pain. 2013 Oct;14(10):1097-106. doi: 10.1016/j.jpain.2013.04.006. Epub 2013 Jun 15. PMID: 23773341. Jones JD, Mumtaz M, Manubay JM, Mogali S, Sherwin E, Martinez S, Comer SD. Assessing the contribution of opioid- and dopamine-related genetic polymorphisms to the abuse liability of oxycodone. Pharmacol Biochem Behav. 2019 Nov;186:172778. doi: 10.1016/j.pbb.2019.172778. Epub 2019 Sep 4. PMID: 31493434; PMCID: PMC6801039. Chang HW, Ho WC, Huang CL, Wang RY. Precision therapeutic opioid dosing implications from genetic biomarkers and craving score. Medicine (Baltimore). 2020 May 29;99(22):e20429. doi: 10.1097/MD.0000000000020429. PMID: 32481444. 10.1016 Crettol S, Déglon JJ, Besson J, Croquette-Krokar M, Hämmig R, Gothuey I, Monnat M, Eap CB. ABCB1 and cytochrome P450 genotypes and phenotypes: influence on methadone plasma levels and response to treatment. Clin Pharmacol Ther. 2006 Dec;80(6):668-81. doi: 10.1016/j.clpt.2006.09.012. PMID: 17178267. Saiz-Rodríguez M, Ochoa D, Román M, Zubiaur P, Koller D, Mejía G, AbadSantos F. Involvement of CYP2D6 and CYP2B6 on tramadol pharmacokinetics. Pharmacogenomics. 2020 Jul;21(10):663-675. doi: 10.2217/pgs-2020-0026. Epub 2020 Jun 15. PMID: 32538291 Chen YJ, Lu JT, Huang CW, Wu WH, Lee KF, Liu HT, Shih-Hsin Wu L. Pharmacogenetic study of methadone treatment for heroin addiction: associations between drug-metabolizing gene polymorphisms and treatment efficacy. Pharmacogenet Genomics. 2022 Jan 1;32(1):31-38. doi: 10.1097/FPC.0000000000000450. PMID: 34380995. Kringen MK, Chalabianloo F, Bernard JP, Bramness JG, Molden E, Høiseth G. Combined Effect of CYP2B6 Genotype and Other Candidate Genes on a Steady-State Serum Concentration of Methadone in Opioid Maintenance Treatment. Ther Drug Monit. 2017 Oct;39(5):550-555. doi: 10.1097/FTD.0000000000000437. PMID: 28723731. Fonseca F, de la Torre R, Díaz L, Pastor A, Cuyàs E, Pizarro N, Khymenets O, Farré M, Torrens M. Contribution of cytochrome P450 and ABCB1 genetic variability on methadone pharmacokinetics, dose requirements, and response. PLoS One. 2011 May 12;6(5):e19527. doi: 10.1371/journal.pone.0019527. PMID: 21589866; PMCID: PMC3093392. Koren G, Cairns J, Chitayat D, Gaedigk A, Leeder SJ. Pharmacogenetics of morphine poisoning in a breastfed neonate of a codeine-prescribed mother. Lancet. 2006 Aug 19;368(9536):704. doi: 10.1016/S0140-6736(06)69255-6. PMID: 16920476. Badaoui S, Hopkins AM, Rodrigues AD, Miners JO, Sorich MJ, Rowland A. Application of Model Informed Precision Dosing to Address the Impact of Pregnancy Stage and CYP2D6 Phenotype on Foetal Morphine Exposure. AAPS J. 2021 Jan 6;23(1):15. doi: 10.1208/s12248-020-00541-1. PMID: 33404848. Halling J, Weihe P, Brosen K. CYP2D6 polymorphism in relation to tramadol metabolism: a study of faroese patients. Ther Drug Monit. 2008 Jun;30(3):271-5. doi: 10.1097/FTD.0b013e3181666b2f. PMID: 18520597. Samer CF, Daali Y, Wagner M, Hopfgartner G, Eap CB, Rebsamen MC, Rossier MF, Hochstrasser D, Dayer P, Desmeules JA. The effects of CYP2D6 and CYP3A activities on the pharmacokinetics of immediate release oxycodone. Br J Pharmacol. 2010 Jun;160(4):907-18. doi: 10.1111/j.1476-5381.2010.00673.x. PMID: 20590587; PMCID: PMC2935997. Dong ZL, Li H, Chen QX, Hu Y, Wu SJ, Tang LY, Gong WY, Xie GH, Fang XM. Effect of CYP3A4*1G on the fentanyl consumption for intravenous patient-controlled analgesia after total abdominal hysterectomy in Chinese Han population. J Clin Pharm Ther. 2012 Apr;37(2):153-6. doi: 10.1111/j.1365-2710.2011.01268.x. Epub 2011 Apr 29. PMID: 21535061. Zhu GD, Whitley P, LaRue L, Adkins B, Dawson E, Huskey A, Capparelli EV, Del Tredici AL. Impact of genetic variation in CYP2C19, CYP2D6, and CYP3A4 on oxycodone and its metabolites in a large database of clinical urine drug tests. anaka N, Naito T, Yagi T, Doi M, Sato S, Kawakami J. Impact of CYP3A5*3 on plasma exposure and urinary excretion of fentanyl and norfentanyl in the early postsurgical period. Ther Drug Monit. 2014 Jun;36(3):345-52. doi: 10.1097/FTD.0000000000000029. PMID: 24365989. Naito T, Takashina Y, Yamamoto K, Tashiro M, Ohnishi K, Kagawa Y, Kawakami J. CYP3A5*3 affects plasma disposition of noroxycodone and dose escalation in cancer patients receiving oxycodone. J Clin Pharmacol. 2011 Nov;51(11):1529-38. doi: 10.1177/0091270010388033. Epub 2011 Jan 5. PMID: 21209234. Arias AJ, Gelernter J, Gueorguieva R, Ralevski E, Petrakis IL. Pharmacogenetics of naltrexone and disulfiram in alcohol dependent, dually diagnosed veterans. Am J Addict. 2014 May-Jun;23(3):288-93. doi: 10.1111/j.1521-0391.2014.12102.x. PMID: 24724887; PMCID: PMC4600600. Doehring A, Hentig Nv, Graff J, Salamat S, Schmidt M, Geisslinger G, Harder S, Lötsch J. Genetic variants altering dopamine D2 receptor expression or function modulate the risk of opiate addiction and the dosage requirements of methadone substitution. Pharmacogenet Genomics. 2009 Jun;19(6):407-14. doi: 10.1097/FPC.0b013e328320a3fd. PMID: 19373123. Chidambaran V, Pilipenko V, Spruance K, Venkatasubramanian R, Niu J, Fukuda T, Mizuno T, Zhang K, Kaufman K, Vinks AA, Martin LJ, Sadhasivam S. Fatty acid amide hydrolase-morphine interaction influences ventilatory response to hypercapnia and postoperative opioid outcomes in children. Pharmacogenomics. 2017 Jan;18(2):143-156. doi: 10.2217/pgs-2016-0147. Epub 2016 Dec 15. PMID: 27977335; PMCID: PMC5558540. Shi Y, Li Y, Zhang J, Xiao Y, Yan P, Zhu Y. GAD1 but not GAD2 polymorphisms are associated with heroin addiction phenotypes. Neurosci Lett. 2020 Jan 19;717:134704. doi: 10.1016/j.neulet.2019.134704. Epub 2019 Dec 19. PMID: 31866536. Hung CC, Chiou MH, Huang BH, Hsieh YW, Hsieh TJ, Huang CL, Lane HY. Impact of genetic polymorphisms in ABCB1, CYP2B6, OPRM1, ANKK1 and DRD2 genes on methadone therapy in Han Chinese patients. Pharmacogenomics. 2011 Nov;12(11):1525-33. doi: 10.2217/pgs.11.96. Epub 2011 Sep 8. PMID: 21902500. Wendt FR, Rahikainen AL, King JL, Sajantila A, Budowle B. A genome-wide association study of tramadol metabolism from post-mortem samples. Pharmacogenomics J. 2020 Feb;20(1):94-103. doi: 10.1038/s41397-019-0088-y. Epub 2019 Apr 11. PMID: 30971809. Somogyi AA, Sia AT, Tan EC, Coller JK, Hutchinson MR, Barratt DT. Ethnicitydependent influence of innate immune genetic markers on morphine PCA 57 requirements and adverse effects in postoperative pain. Pain. 2016 Nov;157(11):24582466. doi: 10.1097/j.pain.0000000000000661. PMID: 27649267. Yoshida K, Nishizawa D, Ichinomiya T, Ichinohe T, Hayashida M, Fukuda K, Ikeda K. Prediction formulas for individual opioid analgesic requirements based on genetic polymorphism analyses. PLoS One. 2015 Jan 23;10(1):e0116885. doi: 10.1371/journal.pone.0116885. PMID: 25615449; PMCID: PMC4304713. Lötsch J, Prüss H, Veh RW, Doehring A. A KCNJ6 (Kir3.2, GIRK2) gene polymorphism modulates opioid effects on analgesia and addiction but not on pupil size. Pharmacogenet Genomics. 2010 May;20(5):291-7. doi: 10.1097/FPC.0b013e3283386bda. PMID: 20220551. Elens L, Norman E, Matic M, Rane A, Fellman V, van Schaik RH. Genetic Predisposition to Poor Opioid Response in Preterm Infants: Impact of KCNJ6 and COMT Polymorphisms on Pain Relief After Endotracheal Intubation. Ther Drug Monit. 2016 Aug;38(4):525-33. doi: 10.1097/FTD.0000000000000301. PMID: 27027462. Barratt DT, Klepstad P, Dale O, Kaasa S, Somogyi AA. Innate Immune Signalling Genetics of Pain, Cognitive Dysfunction and Sickness Symptoms in Cancer Pain Patients Treated with Transdermal Fentanyl. PLoS One. 2015 Sep 2;10(9):e0137179. doi: 10.1371/journal.pone.0137179. PMID: 26332828; PMCID: PMC4557995. Levran O, Peles E, Hamon S, Randesi M, Zhao C, Zhang B, Adelson M, Kreek MJ. Nerve growth factor β polypeptide (NGFB) genetic variability: association with the methadone dose required for effective maintenance treatment. Pharmacogenomics J. 2012 Aug;12(4):319-27. doi: 10.1038/tpj.2011.6. Epub 2011 Mar 1. PMID: 21358750; PMCID: PMC3130093. Levran O, Peles E, Randesi M, Shu X, Ott J, Shen PH, Adelson M, Kreek MJ. Association of genetic variation in pharmacodynamic factors with methadone dose required for effective treatment of opioid addiction. Pharmacogenomics. 2013 May;14(7):755-68. doi: 10.2217/pgs.13.58. PMID: 23651024; PMCID: PMC3867202. Kranzler HR, Lynch KG, Crist RC, Hartwell E, Le Moigne A, Laffont CM, Andorn AC. A Delta-Opioid Receptor Gene Polymorphism Moderates the Therapeutic Response to Extended-Release Buprenorphine in Opioid Use Disorder. Int J Neuropsychopharmacol. 2021 Feb 15;24(2):89-96. doi: 10.1093/ijnp/pyaa069. PMID: 32920647; PMCID: PMC7883889. Jones JD, Mumtaz M, Manubay JM, Mogali S, Sherwin E, Martinez S, Comer SD. Assessing the contribution of opioid- and dopamine-related genetic polymorphisms to the abuse liability of oxycodone. Pharmacol Biochem Behav. 2019 Nov;186:172778. doi: 10.1016/j.pbb.2019.172778. Epub 2019 Sep 4. PMID: 31493434; PMCID: PMC6801039. Luo R, Li X, Qin S, Luo Z, Luo X, Hu P, Liu Z, Feng X, Li X. Impact of SNP-SNP interaction among ABCB1, ARRB2, DRD1 and OPRD1 on methadone dosage 58 requirement in Han Chinese patients. Pharmacogenomics. 2017 Dec;18(18):16591670. doi: 10.2217/pgs-2017-0072. Epub 2017 Nov 27. PMID: 29173032. Chatti I, Woillard JB, Mili A, Creveaux I, Ben Charfeddine I, Feki J, Langlais S, Ben Fatma L, Saad A, Gribaa M, Libert F. Genetic Analysis of Mu and Kappa Opioid Receptor and COMT Enzyme in Cancer Pain Tunisian Patients Under Opioid Treatment. Iran J Public Health. 2017 Dec;46(12):1704-1711. PMID: 29259946; PMCID: PMC5734971. Zhang Q, Shi M, Tang H, Zhong H, Lu X. κ Opioid Receptor 1 Single Nucleotide Polymorphisms were Associated with the Methadone Dosage. Genet Test Mol Biomarkers. 2020 Jan;24(1):17-23. doi: 10.1089/gtmb.2019.0159. PMID: 31940240. Pu J, Wang N, Huang ZK, He XY, Yuan HB. Correlation between gene polymorphism and opioid efficacy in patients with gastric or intestinal cancer. Eur Rev Med Pharmacol Sci. 2019 Nov;23(21):9393-9410. doi: 10.26355/eurrev_201911_19432. PMID: 31773688. Pettini E, Micaglio M, Bitossi U, De Gaudio AR, Degl'Innocenti DR, Tofani L, Limatola V, Adembri C, Di Filippo A. Influence of OPRM1 Polymorphism on Postoperative Pain After Intrathecal Morphine Administration in Italian Patients Undergoing Elective Cesarean Section. Clin J Pain. 2018 Feb;34(2):178-181. doi: 10.1097/AJP.0000000000000520. PMID: 28591085. Ma J, Li W, Chai Q, Tan X, Zhang K. Correlation of P2RX7 gene rs1718125 polymorphism with postoperative fentanyl analgesia in patients with lung cancer. Medicine (Baltimore). 2019 Feb;98(7):e14445. doi: 10.1097/MD.0000000000014445. PMID: 30762755; PMCID: PMC6408006. Galvan A, Skorpen F, Klepstad P, Knudsen AK, Fladvad T, Falvella FS, Pigni A, Brunelli C, Caraceni A, Kaasa S, Dragani TA. Multiple Loci modulate opioid therapy response for cancer pain. Clin Cancer Res. 2011 Jul 1;17(13):4581-7. doi: 10.1158/1078-0432.CCR-10-3028. Epub 2011 May 27. PMID: 21622719. Tzvetkov MV, Saadatmand AR, Lötsch J, Tegeder I, Stingl JC, Brockmöller J. Genetically polymorphic OCT1: another piece in the puzzle of the variable pharmacokinetics and pharmacodynamics of the opioidergic drug tramadol. Clin Pharmacol Ther. 2011 Jul;90(1):143-50. doi: 10.1038/clpt.2011.56. Epub 2011 May 11. PMID: 21562485. Zhu XL, Han X, Xin XF, Song HJ, Guo H. Correlations of analgesic dosage of morphine with SLC6A4 gene polymorphisms in patients with lung cancer. Eur Rev Med Pharmacol Sci. 2020 May;24(9):5046-5052. doi: 10.26355/eurrev_202005_21197. PMID: 32432768. Rasool MI, Bairam AF, Gohal SA, El Daibani AA, Alherz FA, Abunnaja MS, Alatwi ES, Kurogi K, Liu MC. Effects of the human SULT1A1 polymorphisms on the sulfation 59 of acetaminophen,O-desmethylnaproxen, and tapentadol. Pharmacol Rep. 2019 Apr;71(2):257-265. doi: 10.1016/j.pharep.2018.12.001. Epub 2018 Dec 10. PMID: 30822619; PMCID: PMC6498441. Bairam AF, Rasool MI, Alherz FA, Abunnaja MS, El Daibani AA, Kurogi K, Liu MC. Effects of human SULT1A3/SULT1A4 genetic polymorphisms on the sulfation of acetaminophen and opioid drugs by the cytosolic sulfotransferase SULT1A3. Arch Biochem Biophys. 2018 Jun 15;648:44-52. doi: 10.1016/j.abb.2018.04.019. Epub 2018 Apr 26. PMID: 29705271; PMCID: PMC5969526. Duan L, Li X, Yan J, Chen Y, Luo R, Zhang Q, Feng X, Li X. Association of COMT Gene Polymorphisms with Response to Methadone Maintenance Treatment Among Chinese Opioid-Dependent Patients. Genet Test Mol Biomarkers. 2020 Jun;24(6):364369. doi: 10.1089/gtmb.2019.0275. Epub 2020 May 14. PMID: 32407152. Holthe M, Klepstad P, Zahlsen K, Borchgrevink PC, Hagen L, Dale O, Kaasa S, Krokan HE, Skorpen F. Morphine glucuronide-to-morphine plasma ratios are unaffected by the UGT2B7 H268Y and UGT1A1*28 polymorphisms in cancer patients on chronic morphine therapy. Eur J Clin Pharmacol. 2002 Aug;58(5):353-6. doi: 10.1007/s00228-002-0490-1. Epub 2002 Jul 13. PMID: 12185559. Muraoka W, Nishizawa D, Fukuda K, Kasai S, Hasegawa J, Wajima K, Nakagawa T, Ikeda K. Association between UGT2B7 gene polymorphisms and fentanyl sensitivity in patients undergoing painful orthognathic surgery. Mol Pain. 2016 JanDec;12:1744806916683182. doi: 10.1177/1744806916683182. PMID: 28256933; PMCID: PMC5521342. Kaya-Akyüzlü D, Özkan-Kotiloğlu S, Bal C, Yalçın-Şahiner Ş, Avcıoğlu G, Danışman M. Effects of UGT2B7 rs7662029 and rs7439366 polymorphisms on sublingual buprenorphine metabolism in heroin addicts: An improved PCR-RFLP assay for the detection of rs7662029 polymorphism. Environ Toxicol Pharmacol. 2022 Aug;94:103902. doi: 10.1016/j.etap.2022.103902. Epub 2022 Jun 10. PMID: 35697190. Baber M, Chaudhry S, Kelly L, Ross C, Carleton B, Berger H, Koren G. The pharmacogenetics of codeine pain relief in the postpartum period. Pharmacogenomics J. 2015 Oct;15(5):430-5. doi: 10.1038/tpj.2015.3. Epub 2015 Mar 10. PMID: 25752520. Mitchell T.M. Machine Learning. McGraw-hill; New York, NY, USA: 1997. Cilluffo G, Fasola S, Ferrante G, Malizia V, Montalbano L, La Grutta S. MachineLearning: An Overview and Applications in Pharmacogenetics. Genes (Basel). 2021 Sep 26;12(10):1511. doi: 10.3390/genes12101511. PMID: 34680905; PMCID: PMC8535911. Breiman, L. Random forests. Mach. learning 45, 5–32 (2001). Christian, Brian; Griffiths, Tom (April 2017), "Chapter 7: Overfitting", Algorithms To Live By: The computer science of human decisions, William Collins, pp. 149– 168, ISBN 978-0-00-754799-9 Pedregosa, F., G. Varoquaux, A. Gramfort, V. Michel, B. Thirion, O. Grisel, M. Blondel, P. Prettenhofer, R. Weiss, V. Dubourg, J. Vanderplas, A. Passos, D. Cournapeau, M. Brucher, M. Perrot y E. Duchesnay: Scikit-learn: Machine Learning in Python. Journal of Machine Learning Research, 12:2825–2830, 2011. Friedman N., Geiger D., Goldszmidt M. Bayesian networks classifiers. Machine Learning, 29:131–163, 1997. Friedman N., Linial M., Nachman I, Pe’er D. Using Bayesian Networks to Analyze Expression Data. Journal of Computational Biology, 7:601– 620, 2000. Needham C., Bradford J., Bulpitt A., Westhead D. A primer on learning in Bayesian networks for computational biology. PLoS Comput Biol, 3:e129, 2007. Editado de: https://www.illumina.com/products/bytype/informaticsproducts/dragen-bio-it- platform.html Breiman, Leo. “Random Forests.” Machine Learning 45 (1). Springer: 5-32 (2001). Galar M, Fernandez A, Barrenechea E, Bustince H, Herrera F. Una revisión de conjuntos para el problema del desequilibrio de clases: enfoques basados en embolsado, refuerzo e híbrido. sist. Hombre, Cibern. Parte C Aplic. Rev. IEEE Trans. 2012; 42 :463–484. doi: 10.1109/TSMCC.2011.2161285. Schubach M, Re M, Robinson PN, Valentini G. Imbalance-Aware Machine Learning for Predicting Rare and Common Disease-Associated Non-Coding Variants. Sci Rep. 2017 Jun 7;7(1):2959. doi: 10.1038/s41598-017-03011-5. PMID: 28592878; PMCID: PMC5462751. Chawla, N. V., Bowyer, K. W., Hall, L. O. & Kegelmeyer, W. P. SMOTE: Synthetic Minority Over-sampling Technique. J. Artif. Intell. Res. 321–357 (2002). |
| 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 |
xii, 61 páginas |
| dc.format.mimetype.spa.fl_str_mv |
application/pdf |
| dc.coverage.country.none.fl_str_mv |
Colombia |
| dc.publisher.spa.fl_str_mv |
Universidad Nacional de Colombia |
| dc.publisher.program.spa.fl_str_mv |
Bogotá - Ingeniería - Maestría en Bioinformática |
| dc.publisher.faculty.spa.fl_str_mv |
Facultad de Ingeniería |
| 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/83279/1/license.txt https://repositorio.unal.edu.co/bitstream/unal/83279/2/1032381985.2022.pdf https://repositorio.unal.edu.co/bitstream/unal/83279/3/1032381985.2022.pdf.jpg |
| bitstream.checksum.fl_str_mv |
eb34b1cf90b7e1103fc9dfd26be24b4a 637fa6b782370c42861c4fcdfcf37b84 f5817680f021c2c299bef9c17982752f |
| 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_ |
1814089931650236416 |
| 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_abf2Aristizábal Gutiérrez, Fabio Ancízar3110eab91faa704adf5a2529ba51f91fNiño Vásquez, Luis Fernandobc784b82735e16fe53653c3f5c8f3bbeGarzón Venegas, Eliana del Pilar777aac79391499b0a472d6b2f69f50d6laboratorio de Investigación en Sistemas Inteligentes Lisi2023-02-03T15:59:14Z2023-02-03T15:59:14Z2022-11-02https://repositorio.unal.edu.co/handle/unal/83279Universidad Nacional de ColombiaRepositorio Institucional Universidad Nacional de Colombiahttps://repositorio.unal.edu.co/ilustracionesOBJETIVO: Desarrollar un modelo basado en aprendizaje automático para la predicción del riesgo de evento adverso, a partir del genotipo en farmacogenes asociados con la farmacocinética y farmacodinámica de analgésicos opioides, a partir de datos de secuenciación de última generación (NGS), en una cohorte de pacientes colombianos. MÉTODOS: Se desarrolló un pipeline de anotación de variantes y anotación funcional en 39 genes asociados a la farmacocinética y farmacodinamia de 17 analgésicos opioides de uso común en Colombia a partir de 2080 VCF de exomas provenientes de la secuenciación de nueva generación (NGS). Se realizó un modelo de aprendizaje automático para la clasificación del riesgo efecto adverso utilizando bosques aleatorios, naive Bayes y perceptrón multicapa. RESULTADOS: El pipeline de anotación de variantes y anotación funcional en 39 genes asociados a la farmacocinética y farmacodinamia de 17 analgésicos opioides de uso común en Colombia detecta 9 variantes de riesgo. Los algoritmos de aprendizaje automático se entrenan y evalúan a partir de un dataset compuesto por 1900 variantes genéticas con score de riesgo desde 0.5 a 1.5, 50 variantes genéticas con un escore de 1.5 a 2 y 9 variantes genéticas con score de 2.5 a 4, las cuales están asociadas con efecto adverso, se analizan empleando bosques aleatorios, naive bayes y perceptrón multicapa, obteniendo resultados deficientes en la clasificación de la clase 2 y clase 3 debido al desbalance de datos en estas clase, con lo cual se realiza un enriquecimiento del dataset a partir de variantes de la base de datos PharmGKB, ampliando la data de la clase 3. Se evalúan varias combinaciones de clases por medio de bosques aleatorios y perceptrón multicapa, obteniendo los mejores resultados de clasificación, considerado únicamente dos clases, clase 1, suprimiendo valores de score de 0.5 y 1 y clase 2 compuesta por la unión de datos con score de 2.5 a 5. CONCLUSIONES: Fue posible desarrollar modelos computacionales de clasificación del riesgo de efecto adverso. Sin embargo, se concluye que el desequilibrio en las clases, genera problemas de clasificación, lo que resulta en una reducción significativa de la sensibilidad y la precisión de los modelos de aprendizaje automático. Para poder generar relaciones entre variantes genéticas y su asociación con la presentación de efecto adverso se hace indispensable considerar variables de diversas ómicas que le den un peso importante a la asociación, acompañado de la información clínica y de seguimiento de los pacientes. (Texto tomado de la fuente)GOAL: To develop a model based on machine learning for prediction of the risk of adverse event, from the genotype in associated pharmacogenes with the pharmacokinetics and pharmacodynamics of opioid analgesics, based on data from nextgeneration sequencing (NGS), in a cohort of colombian patients. METHODS: A variant annotation and functional annotation pipeline was developed for 39 genes associated with the pharmacokinetics and pharmacodynamics of 17 commonly used opioid analgesics in Colombia from 2080 VCF exomes from nextgeneration sequencing (NGS). A machine learning model was performed for risk adverse effect classification using random forest, naive Bayes, and multilayer perceptron. RESULTS: The variant annotation and functional annotation pipeline in 39 genes associated with the pharmacokinetics and pharmacodynamics of 17 commonly used opioid analgesics in Colombia detected 9 risk variants. Machine learning algorithms are trained and evaluated from a dataset composed of 1,900 genetic variants with risk scores from 0.5 to 1.5, 50 genetic variants with scores from 1.5 to 2, and 9 genetic variants with scores from 2.5 to 4. , which are associated with an adverse effect, will be analyzed using occasional forests, naive bayes and multilayer perceptron, obtaining poor results in the classification of class 2 and class 3 due to the imbalance of data in these classes, with which an enrichment is performed. of the dataset from variants of the PharmGKB database, expanding the data of class 3. Various combinations of classes are evaluated by means of random forests and multilayer perceptron, obtaining the best classification results, considering only two classes, class 1, suppressing score values of 0.5 and 1 and class 2 composed of the union of data with scores from 2.5 to 5. CONCLUSIONS: It was possible to develop computational models for classifying the risk of adverse effects. However, it is concluded that the imbalance in the classes generates classification problems, which results in a significant reduction in the sensitivity and accuracy of the machine learning models. In order to generate relationships between genetic variants and their association with the presentation of adverse effects, it is essential to consider variables of various omics that give significant weight to the association, accompanied by clinical information and patient follow-up.MaestríaMagíster en BioinformáticaFarmacogenómicaxii, 61 páginasapplication/pdfspaUniversidad Nacional de ColombiaBogotá - Ingeniería - Maestría en BioinformáticaFacultad de IngenieríaBogotá, ColombiaUniversidad Nacional de Colombia - Sede Bogotá570 - Biología::576 - Genética y evolución610 - Medicina y salud::615 - Farmacología y terapéuticaEfectividad de los medicamentosAprendizaje - efectos de las drogasDrugs-EffectivenessLearning-effect of drugs onAnalgesicos opioidesEvento adversoFarmacogenéticaAprendizaje automáticoOpioid analgesicsAdverse eventPharmacogeneticsMachine learningModelo de aprendizaje automático para predecir el riesgo de evento adverso en analgésicos opioides aplicando datos de secuenciación de última generación (NGS) en una población de pacientes colombianosMachine learning model to predict the risk of adverse event in opioid analgesics using next-generation sequencing (NGS) data in a population of Colombian patientsTrabajo de grado - Maestríainfo:eu-repo/semantics/masterThesisinfo:eu-repo/semantics/acceptedVersionTexthttp://purl.org/redcol/resource_type/TMColombiaSimon, E. J., Hiller, J. M. & Edelman, I. 1973. Stereospecific binding of the potent narcotic analgesic [3H] etorphine to rat-brain homogenate. Proceedings of the National AcademyKieffer, B. L. 1995. Recent advances in molecular recognition and signal transduction of active peptides: receptors for opioid peptides. Cellular and molecular neurobiology, 15, 615- 635Chen, Y., Mestek, A., Liu, J., Hurley, J. A. & Yu, L. 1993. Molecular cloning and functional expression of a mu-opioid receptor from rat brain. Molecular Pharmacology, 44, 8-12. 4.Chen YJ, Lu JT, Huang CW, Wu WH, Lee KF, Liu HT, Shih-Hsin Wu L. Pharmacogenetic study of methadone treatment for heroin addiction: associations between drug-metabolizing gene polymorphisms and treatment efficacy. Pharmacogenet Genomics. 2022 Jan 1;32(1):31-38. doi:10.1097/FPC.0000000000000450. PMID: 34380995.Breiman, Leo. “Random Forests.” Machine Learning 45 (1). Springer: 5-32 (2001)Isaza, C., Sepúlveda-Arias, J. C. & Henao, J. 2009. Pharmacogenomics in medicine.Madadi P, Ross CJ, Hayden MR, Carleton BC, Gaedigk A, Leeder JS, et al. Pharmacogenetics of neonatal opioid toxicity following maternal use of codeine during breastfeeding: a case-control study. Clinical pharmacology and therapeutics. 2009;85(1):31-5. doi: 10.1038/clpt.2008.157. PubMed PMID: 18719619Koren G, Cairns J, Chitayat D, Gaedigk A, Leeder SJ. Pharmacogenetics of morphine poisoning in a breastfed neonate of a codeine-prescribed mother. Lancet. 2006;368(9536):704. doi: 10.1016/S0140- 6736(06)69255-6. PubMed PMID: 16920476Owusu Obeng, A., Hamadeh, I., & Smith, M. (2017). Review of opioid pharmacogenetics and considerations for pain management. Pharmacotherapy: The Journal of Human Pharmacology and Drug Therapy, 37(9), 1105-1121Laksman, Z., and Detsky, A. S. (2011). Personalized medicine: understanding probabilities and managing expectations. J. Gen. Intern. Med. 26, 204–206. doi: 10.1007/s116060101515- 6Mueller, J. J., Schlappe, B. A., Kumar, R., Olvera, N., Dao, F., Abu-Rustum, N., et al. (2018). Massively parallel sequencing analysis of mucinous ovarian carcinomas: genomic profiling and differential diagnoses. Gynecol. Oncol. 150, 127–135. doi: 10.1016/j.ygyno.2018.05.008Evans, W. E., and Relling, M. V. (2004). Moving towards individualized medicine with pharmacogenomics. Nature 429, 464–468. doi: 10.1038/nature02626Feero, W. G., Guttmacher, A. E., and Collins, F. S. (2008). The genome gets personal – Almost. JAMA 299, 1351–1352. doi: 10.1001/jama.299.11.1351 12. Ann K Daly, Pharmacogenetics: a general review on progress to date, British Medical Bulletin, Volume 124, Issue 1, December 2017, Pages 65–79Ho, D. S. W., Schierding, W., Wake, M., Saffery, R., & O’Sullivan, J. (2019). Machine Learning SNP Based Prediction for Precision Medicine. Frontiers in GeneticsCapriotti, E., Calabrese, R., and Casadio, R. (2006). Predicting the insurgence of human 50 genetic diseases associated to single point protein mutations with support vector machines and evolutionary information. Bioinformatics 22, 2729–2734. doi: 10.1093/bioinformatics/btl423Cruz, J. A., and Wishart, D. S. (2006). Applications of machine learning in cancer prediction and prognosis. Cancer Inform. 2, 59–77. doi: 10.1177/117693510600200030Palaniappan, S., and Awang, R. (2008). “Intelligent heart disease prediction system using data mining techniques,” in Proceedings of the 2008 IEEE/ACS Int. Conf. Comput. Syst. Appl, (Doha), 108– 115. doi: 10.1109/AICCSA.2008.4493524Yu, W. (2010). Application of support vector machine modeling for prediction of common diseases: the case of diabetes and pre-diabetes. BMC Med. Inform. Decis. Mak. 10:16. doi: 10.1186/1472-6947-10- 16Zhang, D., and Shen, D. (2012). Multi-modal multi-task learning for joint prediction of multiple regression and classification variables in Alzheimer’s disease. Neuroimage 59, 895–907. doi: 10.1016/j.neuroimage.2011.09.069López, B., Torrent-Fontbona, F., Viñas, R., and Fernández-Real, J. M. (2017). Single nucleotide polymorphism relevance learning with random forests for type 2 diabetes risk prediction. Artif. Intell. Med. 85, 43–49. doi: 10.1016/j.artmed.2017.09.005Boulesteix, A. L., Janitza, S., Kruppa, J., and König, I. R. (2012). Overview of random forest methodology and practical guidance with emphasis on computational biology and bioinformatics. Wiley Interdiscip. Rev. Data Min. Knowl. Discov. 2, 493–507. doi: 10.1002/widm.1072Chen, X., and Ishwaran, H. (2012). Random forests for genomic data analysis. Genomics 99, 323–329. doi: 10.1016/j.ygeno.2012.04.003Austin, P. C., Tu, J. V., Ho, J. E., Levy, D., and Lee, D. S. (2013). Using methods from the data- mining and machine-learning literature for disease classification and prediction: a case study examining classification of heart failure subtypes. J. Clin. Epidemiol. 66, 398–407. doi: 10.1016/j.jclinepi.2012.11.008Kringel, D., Geisslinger, G., Resch, E., Oertel, B. G., Thrun, M. C., Heinemann, S., & Lötsch, J. (2018). Machine-learned analysis of the association of next-generation sequencing–based human TRPV1 and TRPA1 genotypes with the sensitivity to heat stimuli and topically applied capsaicin. Pain, 159(7), 1366Lo-Ciganic, W. H., Huang, J. L., Zhang, H. H., Weiss, J. C., Wu, Y., Kwoh, C. K., ... & Kuza, C. C. (2019). Evaluation of Machine-Learning Algorithms for Predicting Opioid Overdose Risk Among Medicare Beneficiaries With Opioid Prescriptions. JAMA network open, 2(3), e190968-e190968Organización Mundial de la salud (OMS), Manejo del abuso de sustancias. Recuperado de https://www.who.int/substance_abuse/information-sheet/es/Observatorio de Drogas de Colombia (O.D.C), Alerta informativa acerca del uso de sustancias y preparados con actividad psicoactiva utilizadas para el manejo del dolor (opioides), 08-12-019. Recuperado de http://www.odc.gov.co/Portals/1/SAT/Alerta%20Opioides.pdfEVANS, C. J., KEITH, D., MORRISON, H., MAGENDZO, K. & EDWARDS, R. H. 1992. Cloning of a delta opioid receptor by functional expression. SCIENCE-NEW YORK THEN WASHINGTON-, 258, 1952-1952.KIEFFER, B. L. 1995. Recent advances in molecular recognition and signal transduction of active peptides: receptors for opioid peptides. Cellular and molecular neurobiology, 15, 615- 635.CHEN, Y., MESTEK, A., LIU, J., HURLEY, J. A. & YU, L. 1993. Molecular cloning and functional expression of a mu-opioid receptor from rat brain. Molecular Pharmacology, 44, 8-12.Lorenzo P, Moreno A, Leza JC, Lizasoain I, Moro MA, Portolés A. Velazquez. Farmacología Básica y Clínica. 19a ed. Editorial Médica Panamericana; 2018.lórez J. Fármacos analgésicos opioides. En: Flórez J, Armijo JA, Mediavilla A, editores. Farmacología humana. 4a ed. Barcelona: Masson; 2003. p. 461-478Rang HP, Dale MM, Ritter JM, Moore PK. editores. Farmacología. 5a ed. Madrid: Elsevier; 2004. p. 562-583Cherny NI. Opioid analgesics. Drugs 1996; 51: 713-737Mendoza Patiño, N. Farmacología médica (3a edición). Editorial Médica Panamericana.olleau, S. Evaluación de la escalera analgésica de la Organización Mundial de la Salud en su 20o aniversario. Cancer Pain Release. 2006;19:2. 2. Lacy Ch, Armstrong L, Goldman M, Lance L. Drug Information Handbook. Lexi Comp 15th Ed. p. 410-412.utstein HB, Akil H. Opioid Analgesics. En: Brunton L, Lazo J, Parker K. Goodman and Gilman ́s The Pharmacological Basis of Therapeutics. 2006.Thummel K. Shen D, Isoherranen, Smith H. Appendix II Design and Optimization of Dosage Regimens: Pharmacokinetic data. In: Brunton L, Lazo J, Parker K: Goodman and Gilman ́s The Pharmacological Basis of Therapeutics. 11th ed. 2006. p. 1.813Vascello L and Maquillan R. Opioid Analgesics and Routes of Administration. De LeonCasasola O. Cancer Pain. Pharmacological Interventional and Palliative care Approaches. Elsevier inc. 2006:171-193.Smith HS. Variations in opioid responsiveness. Pain Physician. 2008 Mar-Apr; 11(2):237-48.Colleau, S M. Cancer Press Release 2006; Vol 19: p. 2: Evaluación de la escalera analgésica de la Organización Mundial de la Salud en su 20o aniversario.Leppert W, Luczak J. The role of tramadol in cancer pain treatment-a review. Support Care Cancer. 2005 Jan;13(1):5-17. Epub 2004 Nov 18Grond S, Sablotzki A. Clinical pharmacology of tramadol. Clin Pharmacokinet. 2004;43(13):879-923Leppert W, Luczak J. The role of tramadol in cancer pain treatment-a review. Support Care Cancer. 2005 Jan;13(1):5-17. Epub 2004 Nov 18.ascello L, Maquillan R. Opioid Analgesics and Routes of Administration. De LeonCasasola O. Cancer Pain. Pharmacological Interventional and Palliative Care Approaches. Elsevier inc. 2006:171-193.Lacy Ch, Armstrong L, Goldman M, Lance L. Drug Information Handbook. Lexi Comp 15th Ed. Page 1171-1175.Gutstein HB, Akil H. Opioid Analgesics, Chap 21. In: Brunton L; Lazo J; Parker K: Goodman and Gilman ́s The Pharmacological Basis of Therapeutics. 11th ed. 2006.Thummel K, Shen D Isoherranen, Smith H. Appendix II Design and Optimization of Dosage Regimens: Pharmacokinetic data. In: Brunton L; Lazo J; Parker K: Goodman and Gilman ́s. The Pharmacological Basis of Therapeutics. 11th ed. 2006. p.1851.Schumacher M, Basbaun A, Way W. in: Katzung B. Basic and clinical pharmacology. 10th Ed,Mc Graw-Hill Companies The; 2006. Chap 31Ross FB, Smith MT. The intrinsic antinociceptive eects of oxycodone appear to be kappa-opioid receptor meditated. Pain. 1997 Nov;73(2):151-7.Benziger DP, Kaiko RF, Miotto JB, Fitzmartin RD, Reder RF, Chasin M. Differential effects of food and the bioavailability of controlled-releaseoxycodone tablets and immediate-release oxycodone solution. J Pharm Sci. 1996 Apr;85(4):407-10.Kaiko RF, Benziger DP, Fitzmartin RD, Burke BE, Reder RF, Goldenheim PD. Pharmacokinetic-pharmacodynamic relationships of controlled-release oxycodone. Clin Pharmacol Ther. 1996 Jan;59:52-61.Darwish M, Kirby M, Robertson P Jr, Tracewell W, Jiang JG. Absolute and relative bioavailability of fentanyl buccal tablet and oral transmucosal fentanyl citrate. J Clin Pharmacol. 2007 Mar;47(3):343-50.Lacy Ch, Armstrong L, Goldman M, Lance L. Drug Information Handbook. Lexi Comp 15th ed: p. 693-699Huang P, Kehner GB, Cowan A, Liu-Chen LY. Comparison of pharmacological activities of buprenorphine and norbuprenorphine: norbuprenorphine is a potent opioid agonist. J Pharmacol Exp Ther. 2001 May;297(2):688-95.Böger RH. Renal impairment: a challenge for opioid treatment? The role of buprenorphine. Palliat Med. 2006;20 Suppl 1:s17-23.Zaki PA, Keith DE Jr, Brine GA, Carroll FI, Evans CJ. Ligand-induced changes in surface mu-opioid receptor number: relationship to G protein activation? J Pharmacol Exp Ther. 2000 Mar;292(3):1127-34McNicol ED, Boyce D, Schumann R, Carr DB. Mu-opioid antagonists for opioidinduced bowel dysfunction. Cochrane Database Syst Rev. 2008 Apr 16;(2):CD006332.Nebreda C, Bruno U. Manual de fármacos utilizados en el tratamiento del dolor crónico. IAHPC 2001;42-43Trescot AM, Datta S, Lee M, Hansen H. Opioid pharmacology. Pain Physician. 2008 Mar;11(2 Suppl):S133-53.Pappagallo M, Stewart W, Woods M. Constipation symptoms in long-term users of opioid analgesic therapy. Poster-Abstracts. American Pain Society Annual Meeting, Fort Lauderdale, Florida, October 21-24, 1999.Homann NG, Olofsson O, Salen B, Wickstrom L. Prevalence of abuse and dependency in chronic pain patients. Int J Addict. 1995 Jun;30(8):919-27.Hahn, D., Fukuda, T., Euteneuer, J. C., Mizuno, T., Vinks, A. A., Sadhasivam, S., & Emoto, C. (2020). Influence of MRP3 genetics and hepatic expression ontogeny for morphine disposition in neonatal and pediatric patients. The Journal of Clinical Pharmacology, 60(8), 992-998.Lam J, Woodall KL, Solbeck P, Ross CJ, Carleton BC, Hayden MR, Koren G, Madadi P. Codeine-related deaths: The role of pharmacogenetics and drug interactions. Forensic Sci Int. 2014 Jun;239:50-6. doi: 10.1016/j.forsciint.2014.03.018. Epub 2014 Mar 26. PMID: 24747667.Zhang J, Zhang L, Zhao X, Shen S, Luo X, Zhang Y. Association between MDR1/CYP3A4/OPRM1 gene polymorphisms and the post-caesarean fentanyl analgesic effect on Chinese women. Gene. 2018 Jun 30;661:78-84. doi: 10.1016/j.gene.2018.03.081. Epub 2018 Mar 27. PMID: 29601950.Naito T, Takashina Y, Yamamoto K, Tashiro M, Ohnishi K, Kagawa Y, Kawakami J. CYP3A5*3 affects plasma disposition of noroxycodone and dose escalation in cancer patients receiving oxycodone. J Clin Pharmacol. 2011 Nov;51(11):1529-38. doi: 10.1177/0091270010388033. Epub 2011 Jan 5. PMID: 21209234.Saiz-Rodríguez M, Ochoa D, Herrador C, Belmonte C, Román M, Alday E, Koller D, Zubiaur P, Mejía G, Hernández-Martínez M, Abad-Santos F. Polymorphisms associated with fentanyl pharmacokinetics, pharmacodynamics and adverse effects. Basic Clin Pharmacol Toxicol. 2019 Mar;124(3):321-329. doi: 10.1111/bcpt.13141. Epub 2018 Oct 30. PMID: 30281924.Fonseca F, Gratacòs M, Escaramís G, De Cid R, Martín-Santos R, Farré M, Estivill X, Torrens M. ALDH5A1 variability in opioid dependent patients could influence response to methadone treatment. Eur Neuropsychopharmacol. 2014 Mar;24(3):4204. doi: 10.1016/j.euroneuro.2013.10.003. Epub 2013 Oct 18. PMID: 24230997.Hung CC, Chiou MH, Huang BH, Hsieh YW, Hsieh TJ, Huang CL, Lane HY. Impact of genetic polymorphisms in ABCB1, CYP2B6, OPRM1, ANKK1 and DRD2 genes on methadone therapy in Han Chinese patients. Pharmacogenomics. 2011 54 Nov;12(11):1525-33. doi: 10.2217/pgs.11.96. Epub 2011 Sep 8. PMID: 21902500.de Cid R, Fonseca F, Gratacòs M, Gutierrez F, Martín-Santos R, Estivill X, Torrens M. BDNF variability in opioid addicts and response to methadone treatment: preliminary findings. Genes Brain Behav. 2008 Jul;7(5):515-22. doi: 10.1111/j.1601183X.2007.00386.x. PMID: 18182069.Yoshida K, Nishizawa D, Ichinomiya T, Ichinohe T, Hayashida M, Fukuda K, Ikeda K. Prediction formulas for individual opioid analgesic requirements based on genetic polymorphism analyses. PLoS One. 2015 Jan 23;10(1):e0116885. doi: 10.1371/journal.pone.0116885. PMID: 25615449; PMCID: PMC4304713.Kuo HW, Liu TH, Tsou HH, Hsu YT, Wang SC, Fang CP, Liu CC, Chen ACH, Liu YL. Inflammatory chemokine eotaxin-1 is correlated with age in heroin dependent patients under methadone maintenance therapy. Drug Alcohol Depend. 2018 Feb 1;183:19-24. doi: 10.1016/j.drugalcdep.2017.10.014. Epub 2017 Nov 28. PMID: 29222992.Cargnin S, Magnani F, Viana M, Tassorelli C, Mittino D, Cantello R, Sances G, Nappi G, Canonico PL, Genazzani AA, Raffaeli W, Terrazzino S. An opposite-direction modulation of the COMT Val158Met polymorphism on the clinical response to intrathecal morphine and triptans. J Pain. 2013 Oct;14(10):1097-106. doi: 10.1016/j.jpain.2013.04.006. Epub 2013 Jun 15. PMID: 23773341.Jones JD, Mumtaz M, Manubay JM, Mogali S, Sherwin E, Martinez S, Comer SD. Assessing the contribution of opioid- and dopamine-related genetic polymorphisms to the abuse liability of oxycodone. Pharmacol Biochem Behav. 2019 Nov;186:172778. doi: 10.1016/j.pbb.2019.172778. Epub 2019 Sep 4. PMID: 31493434; PMCID: PMC6801039.Chang HW, Ho WC, Huang CL, Wang RY. Precision therapeutic opioid dosing implications from genetic biomarkers and craving score. Medicine (Baltimore). 2020 May 29;99(22):e20429. doi: 10.1097/MD.0000000000020429. PMID: 32481444.10.1016Crettol S, Déglon JJ, Besson J, Croquette-Krokar M, Hämmig R, Gothuey I, Monnat M, Eap CB. ABCB1 and cytochrome P450 genotypes and phenotypes: influence on methadone plasma levels and response to treatment. Clin Pharmacol Ther. 2006 Dec;80(6):668-81. doi: 10.1016/j.clpt.2006.09.012. PMID: 17178267.Saiz-Rodríguez M, Ochoa D, Román M, Zubiaur P, Koller D, Mejía G, AbadSantos F. Involvement of CYP2D6 and CYP2B6 on tramadol pharmacokinetics. Pharmacogenomics. 2020 Jul;21(10):663-675. doi: 10.2217/pgs-2020-0026. Epub 2020 Jun 15. PMID: 32538291Chen YJ, Lu JT, Huang CW, Wu WH, Lee KF, Liu HT, Shih-Hsin Wu L. Pharmacogenetic study of methadone treatment for heroin addiction: associations between drug-metabolizing gene polymorphisms and treatment efficacy. Pharmacogenet Genomics. 2022 Jan 1;32(1):31-38. doi: 10.1097/FPC.0000000000000450. PMID: 34380995.Kringen MK, Chalabianloo F, Bernard JP, Bramness JG, Molden E, Høiseth G. Combined Effect of CYP2B6 Genotype and Other Candidate Genes on a Steady-State Serum Concentration of Methadone in Opioid Maintenance Treatment. Ther Drug Monit. 2017 Oct;39(5):550-555. doi: 10.1097/FTD.0000000000000437. PMID: 28723731.Fonseca F, de la Torre R, Díaz L, Pastor A, Cuyàs E, Pizarro N, Khymenets O, Farré M, Torrens M. Contribution of cytochrome P450 and ABCB1 genetic variability on methadone pharmacokinetics, dose requirements, and response. PLoS One. 2011 May 12;6(5):e19527. doi: 10.1371/journal.pone.0019527. PMID: 21589866; PMCID: PMC3093392.Koren G, Cairns J, Chitayat D, Gaedigk A, Leeder SJ. Pharmacogenetics of morphine poisoning in a breastfed neonate of a codeine-prescribed mother. Lancet. 2006 Aug 19;368(9536):704. doi: 10.1016/S0140-6736(06)69255-6. PMID: 16920476.Badaoui S, Hopkins AM, Rodrigues AD, Miners JO, Sorich MJ, Rowland A. Application of Model Informed Precision Dosing to Address the Impact of Pregnancy Stage and CYP2D6 Phenotype on Foetal Morphine Exposure. AAPS J. 2021 Jan 6;23(1):15. doi: 10.1208/s12248-020-00541-1. PMID: 33404848.Halling J, Weihe P, Brosen K. CYP2D6 polymorphism in relation to tramadol metabolism: a study of faroese patients. Ther Drug Monit. 2008 Jun;30(3):271-5. doi: 10.1097/FTD.0b013e3181666b2f. PMID: 18520597.Samer CF, Daali Y, Wagner M, Hopfgartner G, Eap CB, Rebsamen MC, Rossier MF, Hochstrasser D, Dayer P, Desmeules JA. The effects of CYP2D6 and CYP3A activities on the pharmacokinetics of immediate release oxycodone. Br J Pharmacol. 2010 Jun;160(4):907-18. doi: 10.1111/j.1476-5381.2010.00673.x. PMID: 20590587; PMCID: PMC2935997.Dong ZL, Li H, Chen QX, Hu Y, Wu SJ, Tang LY, Gong WY, Xie GH, Fang XM. Effect of CYP3A4*1G on the fentanyl consumption for intravenous patient-controlled analgesia after total abdominal hysterectomy in Chinese Han population. J Clin Pharm Ther. 2012 Apr;37(2):153-6. doi: 10.1111/j.1365-2710.2011.01268.x. Epub 2011 Apr 29. PMID: 21535061.Zhu GD, Whitley P, LaRue L, Adkins B, Dawson E, Huskey A, Capparelli EV, Del Tredici AL. Impact of genetic variation in CYP2C19, CYP2D6, and CYP3A4 on oxycodone and its metabolites in a large database of clinical urine drug tests.anaka N, Naito T, Yagi T, Doi M, Sato S, Kawakami J. Impact of CYP3A5*3 on plasma exposure and urinary excretion of fentanyl and norfentanyl in the early postsurgical period. Ther Drug Monit. 2014 Jun;36(3):345-52. doi: 10.1097/FTD.0000000000000029. PMID: 24365989.Naito T, Takashina Y, Yamamoto K, Tashiro M, Ohnishi K, Kagawa Y, Kawakami J. CYP3A5*3 affects plasma disposition of noroxycodone and dose escalation in cancer patients receiving oxycodone. J Clin Pharmacol. 2011 Nov;51(11):1529-38. doi: 10.1177/0091270010388033. Epub 2011 Jan 5. PMID: 21209234.Arias AJ, Gelernter J, Gueorguieva R, Ralevski E, Petrakis IL. Pharmacogenetics of naltrexone and disulfiram in alcohol dependent, dually diagnosed veterans. Am J Addict. 2014 May-Jun;23(3):288-93. doi: 10.1111/j.1521-0391.2014.12102.x. PMID: 24724887; PMCID: PMC4600600.Doehring A, Hentig Nv, Graff J, Salamat S, Schmidt M, Geisslinger G, Harder S, Lötsch J. Genetic variants altering dopamine D2 receptor expression or function modulate the risk of opiate addiction and the dosage requirements of methadone substitution. Pharmacogenet Genomics. 2009 Jun;19(6):407-14. doi: 10.1097/FPC.0b013e328320a3fd. PMID: 19373123.Chidambaran V, Pilipenko V, Spruance K, Venkatasubramanian R, Niu J, Fukuda T, Mizuno T, Zhang K, Kaufman K, Vinks AA, Martin LJ, Sadhasivam S. Fatty acid amide hydrolase-morphine interaction influences ventilatory response to hypercapnia and postoperative opioid outcomes in children. Pharmacogenomics. 2017 Jan;18(2):143-156. doi: 10.2217/pgs-2016-0147. Epub 2016 Dec 15. PMID: 27977335; PMCID: PMC5558540.Shi Y, Li Y, Zhang J, Xiao Y, Yan P, Zhu Y. GAD1 but not GAD2 polymorphisms are associated with heroin addiction phenotypes. Neurosci Lett. 2020 Jan 19;717:134704. doi: 10.1016/j.neulet.2019.134704. Epub 2019 Dec 19. PMID: 31866536.Hung CC, Chiou MH, Huang BH, Hsieh YW, Hsieh TJ, Huang CL, Lane HY. Impact of genetic polymorphisms in ABCB1, CYP2B6, OPRM1, ANKK1 and DRD2 genes on methadone therapy in Han Chinese patients. Pharmacogenomics. 2011 Nov;12(11):1525-33. doi: 10.2217/pgs.11.96. Epub 2011 Sep 8. PMID: 21902500.Wendt FR, Rahikainen AL, King JL, Sajantila A, Budowle B. A genome-wide association study of tramadol metabolism from post-mortem samples. Pharmacogenomics J. 2020 Feb;20(1):94-103. doi: 10.1038/s41397-019-0088-y. Epub 2019 Apr 11. PMID: 30971809.Somogyi AA, Sia AT, Tan EC, Coller JK, Hutchinson MR, Barratt DT. Ethnicitydependent influence of innate immune genetic markers on morphine PCA 57 requirements and adverse effects in postoperative pain. Pain. 2016 Nov;157(11):24582466. doi: 10.1097/j.pain.0000000000000661. PMID: 27649267.Yoshida K, Nishizawa D, Ichinomiya T, Ichinohe T, Hayashida M, Fukuda K, Ikeda K. Prediction formulas for individual opioid analgesic requirements based on genetic polymorphism analyses. PLoS One. 2015 Jan 23;10(1):e0116885. doi: 10.1371/journal.pone.0116885. PMID: 25615449; PMCID: PMC4304713.Lötsch J, Prüss H, Veh RW, Doehring A. A KCNJ6 (Kir3.2, GIRK2) gene polymorphism modulates opioid effects on analgesia and addiction but not on pupil size. Pharmacogenet Genomics. 2010 May;20(5):291-7. doi: 10.1097/FPC.0b013e3283386bda. PMID: 20220551.Elens L, Norman E, Matic M, Rane A, Fellman V, van Schaik RH. Genetic Predisposition to Poor Opioid Response in Preterm Infants: Impact of KCNJ6 and COMT Polymorphisms on Pain Relief After Endotracheal Intubation. Ther Drug Monit. 2016 Aug;38(4):525-33. doi: 10.1097/FTD.0000000000000301. PMID: 27027462.Barratt DT, Klepstad P, Dale O, Kaasa S, Somogyi AA. Innate Immune Signalling Genetics of Pain, Cognitive Dysfunction and Sickness Symptoms in Cancer Pain Patients Treated with Transdermal Fentanyl. PLoS One. 2015 Sep 2;10(9):e0137179. doi: 10.1371/journal.pone.0137179. PMID: 26332828; PMCID: PMC4557995.Levran O, Peles E, Hamon S, Randesi M, Zhao C, Zhang B, Adelson M, Kreek MJ. Nerve growth factor β polypeptide (NGFB) genetic variability: association with the methadone dose required for effective maintenance treatment. Pharmacogenomics J. 2012 Aug;12(4):319-27. doi: 10.1038/tpj.2011.6. Epub 2011 Mar 1. PMID: 21358750; PMCID: PMC3130093.Levran O, Peles E, Randesi M, Shu X, Ott J, Shen PH, Adelson M, Kreek MJ. Association of genetic variation in pharmacodynamic factors with methadone dose required for effective treatment of opioid addiction. Pharmacogenomics. 2013 May;14(7):755-68. doi: 10.2217/pgs.13.58. PMID: 23651024; PMCID: PMC3867202.Kranzler HR, Lynch KG, Crist RC, Hartwell E, Le Moigne A, Laffont CM, Andorn AC. A Delta-Opioid Receptor Gene Polymorphism Moderates the Therapeutic Response to Extended-Release Buprenorphine in Opioid Use Disorder. Int J Neuropsychopharmacol. 2021 Feb 15;24(2):89-96. doi: 10.1093/ijnp/pyaa069. PMID: 32920647; PMCID: PMC7883889.Jones JD, Mumtaz M, Manubay JM, Mogali S, Sherwin E, Martinez S, Comer SD. Assessing the contribution of opioid- and dopamine-related genetic polymorphisms to the abuse liability of oxycodone. Pharmacol Biochem Behav. 2019 Nov;186:172778. doi: 10.1016/j.pbb.2019.172778. Epub 2019 Sep 4. PMID: 31493434; PMCID: PMC6801039.Luo R, Li X, Qin S, Luo Z, Luo X, Hu P, Liu Z, Feng X, Li X. Impact of SNP-SNP interaction among ABCB1, ARRB2, DRD1 and OPRD1 on methadone dosage 58 requirement in Han Chinese patients. Pharmacogenomics. 2017 Dec;18(18):16591670. doi: 10.2217/pgs-2017-0072. Epub 2017 Nov 27. PMID: 29173032.Chatti I, Woillard JB, Mili A, Creveaux I, Ben Charfeddine I, Feki J, Langlais S, Ben Fatma L, Saad A, Gribaa M, Libert F. Genetic Analysis of Mu and Kappa Opioid Receptor and COMT Enzyme in Cancer Pain Tunisian Patients Under Opioid Treatment. Iran J Public Health. 2017 Dec;46(12):1704-1711. PMID: 29259946; PMCID: PMC5734971.Zhang Q, Shi M, Tang H, Zhong H, Lu X. κ Opioid Receptor 1 Single Nucleotide Polymorphisms were Associated with the Methadone Dosage. Genet Test Mol Biomarkers. 2020 Jan;24(1):17-23. doi: 10.1089/gtmb.2019.0159. PMID: 31940240.Pu J, Wang N, Huang ZK, He XY, Yuan HB. Correlation between gene polymorphism and opioid efficacy in patients with gastric or intestinal cancer. Eur Rev Med Pharmacol Sci. 2019 Nov;23(21):9393-9410. doi: 10.26355/eurrev_201911_19432. PMID: 31773688.Pettini E, Micaglio M, Bitossi U, De Gaudio AR, Degl'Innocenti DR, Tofani L, Limatola V, Adembri C, Di Filippo A. Influence of OPRM1 Polymorphism on Postoperative Pain After Intrathecal Morphine Administration in Italian Patients Undergoing Elective Cesarean Section. Clin J Pain. 2018 Feb;34(2):178-181. doi: 10.1097/AJP.0000000000000520. PMID: 28591085.Ma J, Li W, Chai Q, Tan X, Zhang K. Correlation of P2RX7 gene rs1718125 polymorphism with postoperative fentanyl analgesia in patients with lung cancer. Medicine (Baltimore). 2019 Feb;98(7):e14445. doi: 10.1097/MD.0000000000014445. PMID: 30762755; PMCID: PMC6408006.Galvan A, Skorpen F, Klepstad P, Knudsen AK, Fladvad T, Falvella FS, Pigni A, Brunelli C, Caraceni A, Kaasa S, Dragani TA. Multiple Loci modulate opioid therapy response for cancer pain. Clin Cancer Res. 2011 Jul 1;17(13):4581-7. doi: 10.1158/1078-0432.CCR-10-3028. Epub 2011 May 27. PMID: 21622719.Tzvetkov MV, Saadatmand AR, Lötsch J, Tegeder I, Stingl JC, Brockmöller J. Genetically polymorphic OCT1: another piece in the puzzle of the variable pharmacokinetics and pharmacodynamics of the opioidergic drug tramadol. Clin Pharmacol Ther. 2011 Jul;90(1):143-50. doi: 10.1038/clpt.2011.56. Epub 2011 May 11. PMID: 21562485.Zhu XL, Han X, Xin XF, Song HJ, Guo H. Correlations of analgesic dosage of morphine with SLC6A4 gene polymorphisms in patients with lung cancer. Eur Rev Med Pharmacol Sci. 2020 May;24(9):5046-5052. doi: 10.26355/eurrev_202005_21197. PMID: 32432768.Rasool MI, Bairam AF, Gohal SA, El Daibani AA, Alherz FA, Abunnaja MS, Alatwi ES, Kurogi K, Liu MC. Effects of the human SULT1A1 polymorphisms on the sulfation 59 of acetaminophen,O-desmethylnaproxen, and tapentadol. Pharmacol Rep. 2019 Apr;71(2):257-265. doi: 10.1016/j.pharep.2018.12.001. Epub 2018 Dec 10. PMID: 30822619; PMCID: PMC6498441.Bairam AF, Rasool MI, Alherz FA, Abunnaja MS, El Daibani AA, Kurogi K, Liu MC. Effects of human SULT1A3/SULT1A4 genetic polymorphisms on the sulfation of acetaminophen and opioid drugs by the cytosolic sulfotransferase SULT1A3. Arch Biochem Biophys. 2018 Jun 15;648:44-52. doi: 10.1016/j.abb.2018.04.019. Epub 2018 Apr 26. PMID: 29705271; PMCID: PMC5969526.Duan L, Li X, Yan J, Chen Y, Luo R, Zhang Q, Feng X, Li X. Association of COMT Gene Polymorphisms with Response to Methadone Maintenance Treatment Among Chinese Opioid-Dependent Patients. Genet Test Mol Biomarkers. 2020 Jun;24(6):364369. doi: 10.1089/gtmb.2019.0275. Epub 2020 May 14. PMID: 32407152.Holthe M, Klepstad P, Zahlsen K, Borchgrevink PC, Hagen L, Dale O, Kaasa S, Krokan HE, Skorpen F. Morphine glucuronide-to-morphine plasma ratios are unaffected by the UGT2B7 H268Y and UGT1A1*28 polymorphisms in cancer patients on chronic morphine therapy. Eur J Clin Pharmacol. 2002 Aug;58(5):353-6. doi: 10.1007/s00228-002-0490-1. Epub 2002 Jul 13. PMID: 12185559.Muraoka W, Nishizawa D, Fukuda K, Kasai S, Hasegawa J, Wajima K, Nakagawa T, Ikeda K. Association between UGT2B7 gene polymorphisms and fentanyl sensitivity in patients undergoing painful orthognathic surgery. Mol Pain. 2016 JanDec;12:1744806916683182. doi: 10.1177/1744806916683182. PMID: 28256933; PMCID: PMC5521342.Kaya-Akyüzlü D, Özkan-Kotiloğlu S, Bal C, Yalçın-Şahiner Ş, Avcıoğlu G, Danışman M. Effects of UGT2B7 rs7662029 and rs7439366 polymorphisms on sublingual buprenorphine metabolism in heroin addicts: An improved PCR-RFLP assay for the detection of rs7662029 polymorphism. Environ Toxicol Pharmacol. 2022 Aug;94:103902. doi: 10.1016/j.etap.2022.103902. Epub 2022 Jun 10. PMID: 35697190.Baber M, Chaudhry S, Kelly L, Ross C, Carleton B, Berger H, Koren G. The pharmacogenetics of codeine pain relief in the postpartum period. Pharmacogenomics J. 2015 Oct;15(5):430-5. doi: 10.1038/tpj.2015.3. Epub 2015 Mar 10. PMID: 25752520.Mitchell T.M. Machine Learning. McGraw-hill; New York, NY, USA: 1997.Cilluffo G, Fasola S, Ferrante G, Malizia V, Montalbano L, La Grutta S. MachineLearning: An Overview and Applications in Pharmacogenetics. Genes (Basel). 2021 Sep 26;12(10):1511. doi: 10.3390/genes12101511. PMID: 34680905; PMCID: PMC8535911.Breiman, L. Random forests. Mach. learning 45, 5–32 (2001).Christian, Brian; Griffiths, Tom (April 2017), "Chapter 7: Overfitting", Algorithms To Live By: The computer science of human decisions, William Collins, pp. 149– 168, ISBN 978-0-00-754799-9Pedregosa, F., G. Varoquaux, A. Gramfort, V. Michel, B. Thirion, O. Grisel, M. Blondel, P. Prettenhofer, R. Weiss, V. Dubourg, J. Vanderplas, A. Passos, D. Cournapeau, M. Brucher, M. Perrot y E. Duchesnay: Scikit-learn: Machine Learning in Python. Journal of Machine Learning Research, 12:2825–2830, 2011.Friedman N., Geiger D., Goldszmidt M. Bayesian networks classifiers. Machine Learning, 29:131–163, 1997.Friedman N., Linial M., Nachman I, Pe’er D. Using Bayesian Networks to Analyze Expression Data. Journal of Computational Biology, 7:601– 620, 2000.Needham C., Bradford J., Bulpitt A., Westhead D. A primer on learning in Bayesian networks for computational biology. PLoS Comput Biol, 3:e129, 2007.Editado de: https://www.illumina.com/products/bytype/informaticsproducts/dragen-bio-it- platform.htmlBreiman, Leo. “Random Forests.” Machine Learning 45 (1). Springer: 5-32 (2001).Galar M, Fernandez A, Barrenechea E, Bustince H, Herrera F. Una revisión de conjuntos para el problema del desequilibrio de clases: enfoques basados en embolsado, refuerzo e híbrido. sist. Hombre, Cibern. Parte C Aplic. Rev. IEEE Trans. 2012; 42 :463–484. doi: 10.1109/TSMCC.2011.2161285.Schubach M, Re M, Robinson PN, Valentini G. Imbalance-Aware Machine Learning for Predicting Rare and Common Disease-Associated Non-Coding Variants.Sci Rep. 2017 Jun 7;7(1):2959. doi: 10.1038/s41598-017-03011-5. PMID: 28592878; PMCID: PMC5462751.Chawla, N. V., Bowyer, K. W., Hall, L. O. & Kegelmeyer, W. P. SMOTE: Synthetic Minority Over-sampling Technique. J. Artif. Intell. Res. 321–357 (2002).Modelo de aprendizaje automático para predecir el riesgo de evento adverso en analgésicos opioides aplicando datos de secuenciación de última generación (NGS) en una población de pacientes colombianosEstudiantesInvestigadoresMaestrosLICENSElicense.txtlicense.txttext/plain; charset=utf-85879https://repositorio.unal.edu.co/bitstream/unal/83279/1/license.txteb34b1cf90b7e1103fc9dfd26be24b4aMD51ORIGINAL1032381985.2022.pdf1032381985.2022.pdfTesis de Maestría en Bioinformáticaapplication/pdf3614028https://repositorio.unal.edu.co/bitstream/unal/83279/2/1032381985.2022.pdf637fa6b782370c42861c4fcdfcf37b84MD52THUMBNAIL1032381985.2022.pdf.jpg1032381985.2022.pdf.jpgGenerated Thumbnailimage/jpeg4202https://repositorio.unal.edu.co/bitstream/unal/83279/3/1032381985.2022.pdf.jpgf5817680f021c2c299bef9c17982752fMD53unal/83279oai:repositorio.unal.edu.co:unal/832792023-08-15 23:03:58.7Repositorio Institucional Universidad Nacional de Colombiarepositorio_nal@unal.edu.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 |