{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,21]],"date-time":"2026-10-21T17:19:24Z","timestamp":1792603164003,"version":"4.5.0"},"reference-count":39,"publisher":"Springer Science and Business Media LLC","issue":"2","license":[{"start":{"date-parts":[[2025,2,28]],"date-time":"2025-02-28T00:00:00Z","timestamp":1740700800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2025,2,28]],"date-time":"2025-02-28T00:00:00Z","timestamp":1740700800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"funder":[{"DOI":"10.13039\/501100022107","name":"Universit\u00e9 de Limoges","doi-asserted-by":"crossref","id":[{"id":"10.13039\/501100022107","id-type":"DOI","asserted-by":"crossref"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["AAPS J"],"abstract":"<jats:title>Abstract<\/jats:title>\n          <jats:sec>\n            <jats:title>Introduction<\/jats:title>\n            <jats:p>Valganciclovir, a prodrug of ganciclovir (GCV), is used to prevent cytomegalovirus infection after transplantation, with doses adjusted based on creatinine clearance (CrCL) to target GCV AUC0-24\u00a0h of 40\u201360\u00a0mg*h\/L. This sometimes leads to overexposure or underexposure. This study aimed to train, test and validate machine learning (ML) algorithms for accurate GCV AUC0-24\u00a0h estimation in solid organ transplantation.<\/jats:p>\n          <\/jats:sec>\n          <jats:sec>\n            <jats:title>Methods<\/jats:title>\n            <jats:p>We simulated patients for different dosing regimen (900\u00a0mg\/24\u00a0h, 450\u00a0mg\/24\u00a0h, 450\u00a0mg\/48\u00a0h, 450\u00a0mg\/72\u00a0h) using two literature population pharmacokinetic models, allocating 75% for training and 25% for testing. Simulations from two other literature models and real patients provided validation datasets. Three independent sets of ML algorithms were created for each regimen, incorporating CrCL and 2 or 3 concentrations. We evaluated their performance on testing and validation datasets and compared them with MAP-BE.<\/jats:p>\n          <\/jats:sec>\n          <jats:sec>\n            <jats:title>Results<\/jats:title>\n            <jats:p>XGBoost using 3 concentrations generated the most accurate predictions. In testing dataset, they exhibited a relative bias of -0.02 to 1.5% and a relative RMSE of 2.6 to 8.5%. In the validation dataset, a relative bias of 1.5 to 5.8% and 8.9 to 16.5%, and a relative RMSE of 8.5 to 9.6% and 10.7% to 19.7% were observed depending on the model used. XGBoost algorithms outperformed or matched MAP-BE, showing enhanced generalization and robustness in their estimates. When applied to real patients\u2019 data, algorithms using 2 concentrations showed relative bias of 1.26% and relative RMSE of 12.68%.<\/jats:p>\n          <\/jats:sec>\n          <jats:sec>\n            <jats:title>Conclusions<\/jats:title>\n            <jats:p>XGBoost ML models accurately estimated GCV AUC0-24\u00a0h from limited samples and CrCL, providing a strategy for optimized therapeutic drug monitoring.<\/jats:p>\n          <\/jats:sec>\n          <jats:sec>\n            <jats:title>Graphical Abstract<\/jats:title>\n          <\/jats:sec>","DOI":"10.1208\/s12248-025-01034-9","type":"journal-article","created":{"date-parts":[[2025,2,28]],"date-time":"2025-02-28T20:20:23Z","timestamp":1740774023000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":7,"title":["Estimation of Ganciclovir Exposure in Adults Transplant Patients by Machine Learning"],"prefix":"10.1208","volume":"27","author":[{"given":"Hamza","family":"Sayadi","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yeleen","family":"Fromage","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Marc","family":"Labriffe","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Pierre-Andr\u00e9","family":"Billat","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Cyrielle","family":"Codde","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Selim","family":"Arraki Zava","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Pierre","family":"Marquet","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1695-0695","authenticated-orcid":false,"given":"Jean-Baptiste","family":"Woillard","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2025,2,28]]},"reference":[{"key":"1034_CR1","doi-asserted-by":"crossref","first-page":"e2034","DOI":"10.1002\/rmv.2034","volume":"29","author":"M Zuhair","year":"2019","unstructured":"Zuhair M, Smit GSA, Wallis G, Jabbar F, Smith C, Devleesschauwer B, et al. 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