{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,20]],"date-time":"2026-08-20T04:25:45Z","timestamp":1787199945212,"version":"build-2736575974"},"reference-count":26,"publisher":"Wiley","issue":"8","license":[{"start":{"date-parts":[[2026,7,20]],"date-time":"2026-07-20T00:00:00Z","timestamp":1784505600000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by-nc\/4.0\/"},{"start":{"date-parts":[[2026,7,20]],"date-time":"2026-07-20T00:00:00Z","timestamp":1784505600000},"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; Syst Pharma"],"published-print":{"date-parts":[[2026,8]]},"abstract":"<jats:title>ABSTRACT<\/jats:title>\n                  <jats:p>Mechanistic PK\/PD models represent complex biological systems in which model parameters are in nonlinear, multidimensional parameter spaces and often require robust optimization informed by experimental data, making parameter estimation a critical yet challenging aspect of pharmacometric analysis. Moreover, optimization methods for nonlinear\u2010mixed effects PK\/PD modeling are highly sensitive to initial parameter values, and poor initial estimates frequently result in convergence failure or suboptimal fits. To address the challenges, this study introduces a systematic framework that integrates population\u2010based meta\u2010heuristic algorithms to identify optimal or near\u2010optimal initial estimates that facilitate subsequent nonlinear mixed\u2010effects modeling. Nineteen distinct population\u2010based meta\u2010heuristic algorithms, including evolutionary, swarm\u2010based, and bio\u2010inspired methods, were evaluated on two mechanistic ODE\u2010based models: (1) a two\u2010compartment pharmacokinetic model with linear and Michaelis\u2013Menten elimination and (2) the Friberg myelosuppression model. The temporal evolution of model goodness\u2010of\u2010fit over iterations was evaluated for each algorithm, and the effects of population size and iteration number on the algorithm performance were also examined. The results demonstrate population\u2010based optimization algorithms efficiently and autonomously refine parameter estimates over iterations. Jellyfish search optimizer, symbiotic organisms search, and memetic algorithm showed strong performance compared to others with respect to optimization accuracy and computational efficiency across both models. As expected, increasing population size and iteration number enhanced the optimization performance. Overall, population\u2010based meta\u2010heuristic algorithms demonstrate superior capability in navigating nonlinear, high\u2010dimensional parameter spaces, generating robust initial estimates for subsequent modeling workflows, and enhancing convergence stability, computational efficiency, and parameter identifiability.<\/jats:p>","DOI":"10.1002\/psp4.70301","type":"journal-article","created":{"date-parts":[[2026,7,21]],"date-time":"2026-07-21T03:20:39Z","timestamp":1784604039000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Application of Population\u2010Based Meta\u2010Heuristic Algorithms for Robust Initialization in Mechanistic Pharmacometric Modeling"],"prefix":"10.1002","volume":"15","author":[{"given":"Dongjin","family":"Lee","sequence":"first","affiliation":[{"name":"Clinical Pharmacology, Modeling and Simulation Amgen  Thousand Oaks California USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0002-3126-3981","authenticated-orcid":false,"given":"Chih\u2010Wei","family":"Lin","sequence":"additional","affiliation":[{"name":"Clinical Pharmacology, Modeling and Simulation Amgen  Thousand Oaks California USA"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"311","published-online":{"date-parts":[[2026,7,20]]},"reference":[{"key":"e_1_2_10_2_1","doi-asserted-by":"publisher","DOI":"10.1002\/cpt.1363"},{"key":"e_1_2_10_3_1","doi-asserted-by":"publisher","DOI":"10.1002\/cpt.1475"},{"key":"e_1_2_10_4_1","doi-asserted-by":"publisher","DOI":"10.1002\/psp4.13222"},{"key":"e_1_2_10_5_1","volume-title":"M15 General Principles for Model\u2010Informed Drug Development (Draft Guidance)","author":"U.S. Food and Drug Administration","year":"2023"},{"key":"e_1_2_10_6_1","doi-asserted-by":"publisher","DOI":"10.1111\/bcp.15496"},{"key":"e_1_2_10_7_1","doi-asserted-by":"publisher","DOI":"10.12793\/tcp.2016.24.3.119"},{"key":"e_1_2_10_8_1","doi-asserted-by":"publisher","DOI":"10.1111\/bcp.12179"},{"key":"e_1_2_10_9_1","doi-asserted-by":"publisher","DOI":"10.1007\/s10928-025-10000-z"},{"key":"e_1_2_10_10_1","doi-asserted-by":"publisher","DOI":"10.3390\/pharmaceutics13101708"},{"key":"e_1_2_10_11_1","doi-asserted-by":"publisher","DOI":"10.1002\/psp4.13284"},{"key":"e_1_2_10_12_1","doi-asserted-by":"publisher","DOI":"10.3390\/app12020896"},{"key":"e_1_2_10_13_1","doi-asserted-by":"publisher","DOI":"10.1007\/s10462-020-09952-0"},{"key":"e_1_2_10_14_1","doi-asserted-by":"publisher","DOI":"10.1038\/s43856-025-01054-8"},{"issue":"1","key":"e_1_2_10_15_1","article-title":"Metaheuristic Algorithms for Optimization: A Brief Review","volume":"59","author":"Tomar V.","year":"2024","journal-title":"Engineering Proceedings"},{"key":"e_1_2_10_16_1","doi-asserted-by":"publisher","DOI":"10.1002\/psp4.12445"},{"key":"e_1_2_10_17_1","doi-asserted-by":"publisher","DOI":"10.1002\/psp4.12422"},{"key":"e_1_2_10_18_1","doi-asserted-by":"publisher","DOI":"10.1002\/psp4.12404"},{"key":"e_1_2_10_19_1","doi-asserted-by":"publisher","DOI":"10.1007\/s10928-009-9142-8"},{"key":"e_1_2_10_20_1","doi-asserted-by":"publisher","DOI":"10.1200\/JCO.2002.02.140"},{"key":"e_1_2_10_21_1","doi-asserted-by":"publisher","DOI":"10.34028\/iajit\/21\/5\/4"},{"key":"e_1_2_10_22_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.amc.2020.125535"},{"key":"e_1_2_10_23_1","doi-asserted-by":"publisher","DOI":"10.1038\/psp.2013.24"},{"key":"e_1_2_10_24_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.cmpb.2005.04.005"},{"key":"e_1_2_10_25_1","doi-asserted-by":"publisher","DOI":"10.1007\/s10928-024-09932-9"},{"key":"e_1_2_10_26_1","volume-title":"Annual Meeting of the Population Approach Group in Europe","author":"Nordgren R.","year":"2021"},{"key":"e_1_2_10_27_1","doi-asserted-by":"publisher","DOI":"10.1186\/s12918-017-0428-y"}],"container-title":["CPT: Pharmacometrics &amp; 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