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In parallel, an in\u2010house model was developed with prospective and retrospective data from 49 patients (727 concentrations). Predictive performance was assessed across different data integration levels: a priori predictions and sequential Bayesian forecasting with progressive incorporation of individual observations.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>All published models and the in\u2010house model showed poor a priori predictive performance, with even the best performing models yielding MAPE values near 50% and fewer than 30% of predictions within 30% of observed concentrations. Bayesian forecasting progressively improved bias, accuracy and dispersion of prediction errors. Initial incorporation of three trough concentrations produced limited benefit, but substantial improvements were observed upon integration of a pharmacokinetic curve composed of at least 3 post\u2010dose concentrations, at which point several published models and the in\u2010house model achieved high precision, with mean bias below 10% and MAPE below 30%. Two published models exhibited comparable performance to the in\u2010house model, which achieved marginally better results as Bayesian forecasting incorporated successive individual observations, without providing a meaningful overall advantage.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion<\/jats:title>\n                    <jats:p>Published tacrolimus popPK models can achieve clinically reliable predictions when combined with Bayesian updating, performing comparably to in\u2010house developed models. These findings support a resource\u2010efficient strategy for implementing tacrolimus MIPD without the need for local model development, provided that a pharmacokinetic curve is obtained early post\u2010transplantation.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1002\/bcp.70447","type":"journal-article","created":{"date-parts":[[2026,2,12]],"date-time":"2026-02-12T05:31:08Z","timestamp":1770874268000},"page":"2109-2126","source":"Crossref","is-referenced-by-count":0,"title":["Accelerating tacrolimus model\u2010informed precision dosing in kidney transplant recipients: Model evaluation and refinement strategies"],"prefix":"10.1002","volume":"92","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-1457-0894","authenticated-orcid":false,"given":"Mart\u00edn","family":"Umpi\u00e9rrez","sequence":"first","affiliation":[{"name":"Department of Pharmaceutical Sciences, Faculty of Chemistry Universidad de la Rep\u00fablica  Montevideo 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