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dc.creatorOyaga-Iriarte, E. (Esther)-
dc.creatorInsausti, A. (Asier)-
dc.creatorSayar, O. (Onintza)-
dc.creatorAldaz, A. (Azucena)-
dc.date.accessioned2021-09-02T07:03:27Z-
dc.date.available2021-09-02T07:03:27Z-
dc.date.issued2019-
dc.identifier.citationOyaga-Iriarte, E. (Esther); Insausti, A. (Asier); Sayar, O. (Onintza); et al. "Prediction of irinotecan toxicity in metastatic colorectal cancer patients based on machine learning models with pharmacokinetic parameters". Journal of Pharmacological Sciences. 140, 2019, 20 - 25es_ES
dc.identifier.issn1347-8613-
dc.identifier.otherPMID: 31105026-
dc.identifier.urihttps://hdl.handle.net/10171/61901-
dc.description.abstractIrinotecan (CPT-11) is a drug used against a wide variety of tumors, which can cause severe toxicity, possibly leading to the delay or suspension of the cycle, with the consequent impact on the prognosis of survival. The main goal of this work is to predict the toxicities derived from CPT-11 using artificial intelligence methods. The data for this study is conformed of 53 cycles of FOLFIRINOX, corresponding to patients with metastatic colorectal cancer. Supported by several demographic data, blood markers and pharmacokinetic parameters resulting from a non-compartmental pharmacokinetic study of CPT-11 and its metabolites (SN-38 and SN-38-G), we use machine learning techniques to predict high degrees of different toxicities (leukopenia, neutropenia and diarrhea) in new patients. We predict high degree of leukopenia with an accuracy of 76%, neutropenia with 75% and diarrhea with 91%. Among other variables, this study shows that the areas under the curve of CPT-11, SN-38 and SN-38-G play a relevant role in the prediction of the studied toxicities. The presented models allow to predict the degree of toxicity for each cycle of treatment according to the particularities of each patientes_ES
dc.description.sponsorshipThis work is partially supported by “Ayuda para Doctorados Industriales del Ministerio de Economía, Industria y Competitividad” (Ref. DI-15-07511).es_ES
dc.language.isoenges_ES
dc.publisherElsevier BVes_ES
dc.rightsinfo:eu-repo/semantics/openAccesses_ES
dc.subjectMaterias Investigacion::Ciencias de la Salud::Química médicaes_ES
dc.subjectColorectal canceres_ES
dc.subjectIrinotecanes_ES
dc.subjectMachine learninges_ES
dc.subjectPharmacokineticses_ES
dc.subjectToxicityes_ES
dc.titlePrediction of irinotecan toxicity in metastatic colorectal cancer patients based on machine learning models with pharmacokinetic parameterses_ES
dc.typeinfo:eu-repo/semantics/articlees_ES
dc.description.noteThis is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).es_ES
dc.identifier.doi10.1016/j.jphs.2019.03.004-
dadun.citation.endingPage25es_ES
dadun.citation.publicationNameJournal of Pharmacological Scienceses_ES
dadun.citation.startingPage20es_ES
dadun.citation.volume140es_ES

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