{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,16]],"date-time":"2026-07-16T12:06:08Z","timestamp":1784203568109,"version":"3.55.0"},"reference-count":20,"publisher":"Wiley","issue":"7","license":[{"start":{"date-parts":[[2026,7,14]],"date-time":"2026-07-14T00:00:00Z","timestamp":1783987200000},"content-version":"vor","delay-in-days":13,"URL":"http:\/\/creativecommons.org\/licenses\/by-nc\/4.0\/"},{"start":{"date-parts":[[2026,7,1]],"date-time":"2026-07-01T00:00:00Z","timestamp":1782864000000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/doi.wiley.com\/10.1002\/tdm_license_1.1"}],"funder":[{"DOI":"10.13039\/501100001665","name":"Agence Nationale de la Recherche","doi-asserted-by":"publisher","award":["ANR\u201022\u2010PESN\u20100017"],"award-info":[{"award-number":["ANR\u201022\u2010PESN\u20100017"]}],"id":[{"id":"10.13039\/501100001665","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["ascpt.onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["CPT Pharmacom &amp; Syst Pharma"],"published-print":{"date-parts":[[2026,7]]},"abstract":"<jats:title>ABSTRACT<\/jats:title>\n                  <jats:p>\n                    A priori model\u2010informed precision dosing (MIPD) recommends an appropriate first dose based solely on the patient's covariates enabling faster target attainment without required concentration measurements. Population pharmacokinetic (PopPK) model ensembling and machine learning (ML) approaches were developed and evaluated to predict a first dose of amoxicillin in intensive care. Following a bibliographic review, a virtual patient population was simulated based on cohorts from four published adult amoxicillin PopPK models. Steady\u2010state trough concentrations were simulated using cohort\u2010specific dosing regimens. As reference methods, weighed model ensembling (WME) and classification tree (CT)\u2010informed ensembling were implemented. Two novel ensembling strategies were developed: regression tree (RT)\u2010informed ensembling, using RT to predict the log individual prediction\/observation ratio, and factor analysis of mixed data (FAMD), assigning model weights based on patient similarity to original model cohorts. In parallel, four ML algorithms (support vector machine,\n                    <jats:italic>k<\/jats:italic>\n                    \u2010nearest neighbors, random forest, and XGBoost) were trained to predict the dose achieving target concentrations based on covariates and dosing scheme. All approaches were compared with single\u2010model PopPK dosing, standard dosing, a nomogram, and externally validated using clinical data. Most MIPD methods outperformed standard dosing. On simulated data, ensembling (24%\u201340% correct predictions) and ML (36%\u201339%) exceeded single\u2010model approaches (17%\u201332%). ML\u2010based ensembling eliminates the need for model selection and increases target attainment in both simulated and clinical data. However, the generalizability of such results remains hampered by the lack of high\u2010quality clinical datasets to support model training.\n                  <\/jats:p>","DOI":"10.1002\/psp4.70296","type":"journal-article","created":{"date-parts":[[2026,7,14]],"date-time":"2026-07-14T13:18:02Z","timestamp":1784035082000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Model Ensembling and Machine Learning Approaches to Predict the First Dose of Amoxicillin in Intensive Care"],"prefix":"10.1002","volume":"15","author":[{"ORCID":"https:\/\/orcid.org\/0009-0002-1625-3128","authenticated-orcid":false,"given":"Mih\u00e1ly","family":"Leiwolf","sequence":"first","affiliation":[{"name":"Inserm U1070 Pharmacology of Antimicrobial Agents and Resistance University of Poitiers  Poitiers France"},{"name":"Inserm U1248 Pharmacology and Transplantation University of Limoges  Limoges France"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6436-3757","authenticated-orcid":false,"given":"Nicolas","family":"Gregoire","sequence":"additional","affiliation":[{"name":"Inserm U1070 Pharmacology of Antimicrobial Agents and Resistance University of Poitiers  Poitiers France"},{"name":"Department of Toxicology and Pharmacokinetics University Hospital of Poitiers  Poitiers France"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4786-3965","authenticated-orcid":false,"given":"Sophie","family":"Magr\u00e9ault","sequence":"additional","affiliation":[{"name":"Department of Pharmacology AP\u2010HP, Groupe Hospitalier Paris Seine Saint\u2010Denis  Bondy France"},{"name":"Inserm UMR1137, IAME University Paris Cit\u00e9 &amp; Sorbonne University  Paris France"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8150-3467","authenticated-orcid":false,"given":"B\u00e9n\u00e9dicte","family":"Franck","sequence":"additional","affiliation":[{"name":"Univ Rennes, CHU Rennes, EHESP, Irset (Institut de recherche en sant\u00e9, environnement et travail) \u2013 UMR S 1085  Rennes France"},{"name":"Inserm, Centre d'Investigation Clinique 1414  Rennes France"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0007-6624-2597","authenticated-orcid":false,"given":"Ombeline","family":"Krekounian","sequence":"additional","affiliation":[{"name":"Inserm U1070 Pharmacology of Antimicrobial Agents and Resistance University of Poitiers  Poitiers France"},{"name":"Department of Clinical Pharmacology Medical University of Vienna  Vienna Austria"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1695-0695","authenticated-orcid":false,"given":"Jean\u2010Baptiste","family":"Woillard","sequence":"additional","affiliation":[{"name":"Inserm U1248 Pharmacology and Transplantation University of Limoges  Limoges France"},{"name":"Department of Pharmacology, Toxicology, and Pharmacovigilance University Hospital of Limoges  Limoges France"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1258-8054","authenticated-orcid":false,"given":"Vincent","family":"Aranzana\u2010Climent","sequence":"additional","affiliation":[{"name":"Inserm U1070 Pharmacology of Antimicrobial Agents and Resistance University of Poitiers  Poitiers France"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"311","published-online":{"date-parts":[[2026,7,14]]},"reference":[{"key":"e_1_2_8_2_1","doi-asserted-by":"publisher","DOI":"10.1002\/cpt.2202"},{"key":"e_1_2_8_3_1","doi-asserted-by":"publisher","DOI":"10.3390\/antibiotics12040681"},{"key":"e_1_2_8_4_1","doi-asserted-by":"publisher","DOI":"10.1002\/psp4.12813"},{"key":"e_1_2_8_5_1","doi-asserted-by":"publisher","DOI":"10.1002\/psp4.13253"},{"key":"e_1_2_8_6_1","doi-asserted-by":"publisher","DOI":"10.1007\/s40262\u2010024\u201001425\u20109"},{"key":"e_1_2_8_7_1","doi-asserted-by":"publisher","DOI":"10.1002\/psp4.70084"},{"key":"e_1_2_8_8_1","doi-asserted-by":"publisher","DOI":"10.1007\/s40262\u2010024\u201001362\u20107"},{"key":"e_1_2_8_9_1","doi-asserted-by":"publisher","DOI":"10.1007\/s11095\u2010022\u201003351\u20106"},{"key":"e_1_2_8_10_1","doi-asserted-by":"publisher","DOI":"10.1093\/eurheartj\/ehv319"},{"key":"e_1_2_8_11_1","volume-title":"Les pneumopathies acquises sous ventilation m\u00e9canique [Internet]","author":"Trouillet J.","year":"2009"},{"key":"e_1_2_8_12_1","doi-asserted-by":"publisher","DOI":"10.1093\/cid\/ciu027"},{"key":"e_1_2_8_13_1","doi-asserted-by":"publisher","DOI":"10.1093\/jac\/dkt240"},{"key":"e_1_2_8_14_1","doi-asserted-by":"publisher","DOI":"10.1128\/AAC.00505\u201018"},{"key":"e_1_2_8_15_1","doi-asserted-by":"publisher","DOI":"10.1093\/jac\/dkaa368"},{"key":"e_1_2_8_16_1","doi-asserted-by":"publisher","DOI":"10.1093\/jac\/dkaa232"},{"key":"e_1_2_8_17_1","unstructured":"K. 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