{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,28]],"date-time":"2026-10-28T04:55:44Z","timestamp":1793163344145,"version":"4.5.0"},"reference-count":27,"publisher":"Wiley","issue":"1","license":[{"start":{"date-parts":[[2026,1,15]],"date-time":"2026-01-15T00:00:00Z","timestamp":1768435200000},"content-version":"vor","delay-in-days":14,"URL":"http:\/\/creativecommons.org\/licenses\/by-nc\/4.0\/"},{"start":{"date-parts":[[2026,1,1]],"date-time":"2026-01-01T00:00:00Z","timestamp":1767225600000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/doi.wiley.com\/10.1002\/tdm_license_1.1"}],"content-domain":{"domain":["ascpt.onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["CPT Pharmacom &amp;amp; Syst Pharma"],"published-print":{"date-parts":[[2026,1]]},"abstract":"<jats:title>ABSTRACT<\/jats:title>\n                  <jats:p>\n                    Physiologically Based Pharmacokinetic (PBPK) modeling is a powerful tool in drug development that integrates drug\u2010specific information with physiological parameters to predict drug concentrations. However, parameter estimation in PBPK models presents significant challenges due to the large number of parameters involved and limited observed data. This tutorial introduces a novel approach coupling whole\u2010body PBPK (WB\u2010PBPK) models with population estimation methods (popWB\u2010PBPK) to leverage individual data and estimate inter\u2010individual variability on physiologically relevant parameters. The framework employs an optimized Stochastic Approximation Expectation\u2013Maximization (SAEM) algorithm, reducing the estimation runtime through an adaptive parameter grid optimization and linear interpolation techniques. Using theophylline as a case study, we illustrate how this approach can accurately estimate drug\u2010specific parameters (CYP1A2 clearance and lipophilicity) while incorporating covariate effects (smoking status). The optimized algorithm significantly reduces computational time compared to the standard SAEM algorithm. Our implementation in the\n                    <jats:italic>saemixPBPK<\/jats:italic>\n                    R package provides an accessible framework for parameter estimation in PBPK models, enabling more robust predictions of pharmacokinetic behavior leveraging individual data. This approach represents an important advancement in mechanistic modeling, allowing simultaneous estimation of population parameters, variability, and uncertainty while maintaining the physiological relevance of PBPK models.\n                  <\/jats:p>","DOI":"10.1002\/psp4.70186","type":"journal-article","created":{"date-parts":[[2026,1,16]],"date-time":"2026-01-16T06:29:22Z","timestamp":1768544962000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":4,"title":["Integrating Population Approaches With Physiologically Based Pharmacokinetic Models: A Novel Framework for Parameter Estimation"],"prefix":"10.1002","volume":"15","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-4931-410X","authenticated-orcid":false,"given":"Donato","family":"Teutonico","sequence":"first","affiliation":[{"name":"Pharmacometrics, Translational Medicine Unit, Sanofi  Vitry\u2010sur\u2010Seine France"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"David","family":"Marchionni","sequence":"additional","affiliation":[{"name":"Pharmacometrics, Translational Medicine Unit  Sanofi Montpellier France"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2680-6339","authenticated-orcid":false,"given":"Marc","family":"Lavielle","sequence":"additional","affiliation":[{"name":"Inria  Saclay France"},{"name":"Ecole Polytechnique  Palaiseau France"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Laurent","family":"Nguyen","sequence":"additional","affiliation":[{"name":"Pharmacometrics, Translational Medicine Unit, Sanofi  Vitry\u2010sur\u2010Seine France"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"311","published-online":{"date-parts":[[2026,1,15]]},"reference":[{"key":"e_1_2_10_2_1","doi-asserted-by":"publisher","DOI":"10.1007\/s10928-007-9053-5"},{"key":"e_1_2_10_3_1","doi-asserted-by":"publisher","DOI":"10.1111\/j.1365-2125.2004.02225.x"},{"key":"e_1_2_10_4_1","doi-asserted-by":"publisher","DOI":"10.1002\/(SICI)1520-6017(200001)89:1<16::AID-JPS3>3.0.CO;2-E"},{"issue":"4","key":"e_1_2_10_5_1","doi-asserted-by":"crossref","first-page":"121","DOI":"10.1016\/S1478-5382(03)02342-4","article-title":"PK\u2010Sim: A Physiologically Based Pharmacokinetic \u2018Whole\u2010Body'model","volume":"1","author":"Willmann S.","year":"2003","journal-title":"Biosilico"},{"key":"e_1_2_10_6_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.cotox.2023.100390"},{"key":"e_1_2_10_7_1","doi-asserted-by":"publisher","DOI":"10.1002\/bdd.2257"},{"key":"e_1_2_10_8_1","doi-asserted-by":"publisher","DOI":"10.1002\/psp4.12134"},{"key":"e_1_2_10_9_1","doi-asserted-by":"publisher","DOI":"10.1080\/01621459.1996.10476961"},{"key":"e_1_2_10_10_1","doi-asserted-by":"publisher","DOI":"10.1016\/0378-4274(93)90103-5"},{"key":"e_1_2_10_11_1","doi-asserted-by":"crossref","DOI":"10.1186\/2193-9616-1-6","article-title":"Using Bayesian\u2010PBPK Modeling for Assessment of Inter\u2010Individual Variability and Subgroup Stratification","volume":"1","author":"Krauss M.","year":"2013","journal-title":"In Silico Pharmacology"},{"key":"e_1_2_10_12_1","volume-title":"Genetic Algorithms","author":"Goldberg D. 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