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dc.creatorOyaga-Iriarte, E. (Esther)-
dc.creatorInsausti, A. (Asier)-
dc.creatorBueno, L. (Lorea)-
dc.creatorSayar, O. (Onintza)-
dc.creatorAldaz, A. (Azucena)-
dc.date.accessioned2021-09-02T06:57:24Z-
dc.date.available2021-09-02T06:57:24Z-
dc.date.issued2019-
dc.identifier.citationOyaga-Iriarte, E. (Esther); Insausti, A. (Asier); Bueno, L. (Lorea); et al. "Mining Small Routine Clinical Data: A Population Pharmacokinetic Model and Optimal Sampling Times of Capecitabine and its Metabolites". Journal of Pharmacy & Pharmaceutical Sciences. 22, 2019, 112 - 121es_ES
dc.identifier.issn1482-1826-
dc.identifier.otherPMID: 30964613-
dc.identifier.urihttps://hdl.handle.net/10171/61900-
dc.description.abstractPurpose: The present study was performed to demonstrate that small amounts of routine clinical data allow to generate valuable knowledge. Concretely, the aims of this research were to build a joint population pharmacokinetic model for capecitabine and three of its metabolites (5-DFUR, 5-FU and 5-FUH2) and to determine optimal sampling times for therapeutic drug monitoring. Methods: We used data of 7 treatment cycles of capecitabine in patients with metastatic colorectal cancer. The population pharmacokinetic model was built as a multicompartmental model using NONMEM and was internally validated by visual predictive check. Optimal sampling times were estimated using PFIM 4.0 following D-optimality criterion. Results: The final model was a multicompartmental model which represented the sequential transformations from capecitabine to its metabolites 5-DFUR, 5-FU and 5-FUH2 and was correctly validated. The optimal sampling times were 0.546, 0.892, 1.562, 4.736 and 8 hours after the administration of the drug. For its correct implementation in clinical practice, the values were rounded to 0.5, 1, 1.5, 5 and 8 hours after the administration of the drug. Conclusions: Capecitabine, 5-DFUR, 5-FU and 5-FUH2 can be correctly described by the joint multicompartmental model presented in this work. The aforementioned times are optimal to maximize the information of samples. Useful knowledge can be obtained for clinical practice from small databases.es_ES
dc.description.sponsorshipThis work is partially supported by “Ayuda para Doctorados Industriales del Ministerio de Economía, Industria y Competitividad" (Ref. DI15-07511).es_ES
dc.language.isoenges_ES
dc.publisherUniversity of Alberta Librarieses_ES
dc.rightsinfo:eu-repo/semantics/openAccesses_ES
dc.subjectMaterias Investigacion::Ciencias de la Salud::Química médicaes_ES
dc.subjectTherapeutic drug monitoringes_ES
dc.subjectCapecitabinees_ES
dc.subjectMetaboliteses_ES
dc.subjectPharmacokinetic modeles_ES
dc.titleMining Small Routine Clinical Data: A Population Pharmacokinetic Model and Optimal Sampling Times of Capecitabine and its Metaboliteses_ES
dc.typeinfo:eu-repo/semantics/articlees_ES
dc.relation.publisherversion10.18433/jpps30392es_ES
dc.description.noteThis is an open access journal with free of charge submission and non-commercial download. At the time of submission, authors will be asked to transfer the copyright to the accepted article to the Journal of Pharmacy and Pharmaceutical Sciences. The author may purchase the copyright for $500 upon which he/she will have the exclusive copyright to the article. Nevertheless, acceptance of a manuscript for publication in the Journal is with the authors' approval of the terms and conditions of the Creative Commons copyright license Creative Common license (Attribution-ShareAlike) License for non-commercial uses.es_ES
dc.identifier.doi10.18433/jpps30392-
dadun.citation.endingPage121es_ES
dadun.citation.publicationNameJournal of Pharmacy & Pharmaceutical Scienceses_ES
dadun.citation.startingPage112es_ES
dadun.citation.volume22es_ES

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