{"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":1787199945191,"version":"build-2736575974"},"reference-count":41,"publisher":"Wiley","issue":"8","license":[{"start":{"date-parts":[[2026,8,2]],"date-time":"2026-08-02T00:00:00Z","timestamp":1785628800000},"content-version":"vor","delay-in-days":1,"URL":"http:\/\/creativecommons.org\/licenses\/by-nc\/4.0\/"},{"start":{"date-parts":[[2026,8,1]],"date-time":"2026-08-01T00:00:00Z","timestamp":1785542400000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/doi.wiley.com\/10.1002\/tdm_license_1.1"}],"funder":[{"DOI":"10.13039\/501100006769","name":"Russian Science Foundation","doi-asserted-by":"publisher","award":["23\u201371\u201010051"],"award-info":[{"award-number":["23\u201371\u201010051"]}],"id":[{"id":"10.13039\/501100006769","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":[[2026,8]]},"abstract":"<jats:title>ABSTRACT<\/jats:title>\n                  <jats:p>\n                    Numerous clinical trials (CTs) and population pharmacokinetic (PK) models have been published on dapagliflozin, an approved SGLT2 inhibitor used to treat type 2 diabetes, heart failure, and chronic kidney disease. This study proposes a Bayesian workflow for the development of minimal physiologically\u2010based PK models (mPBPK) that integrates all available PK information, to support uncertainty quantification and informed drug development. First, a systematic collection of published dapagliflozin PK data and models was performed. A mPBPK model was then developed, and posterior parameter distributions were obtained based on data from parallel\u2010design CTs. Three Bayesian modeling packages:\n                    <jats:italic>NIMBLE<\/jats:italic>\n                    (v1.1.0),\n                    <jats:italic>MCSim<\/jats:italic>\n                    (v6.2.0), and\n                    <jats:italic>Torsten<\/jats:italic>\n                    (v0.89.0), and three alternative prior specifications were evaluated. Model validation was performed using PK data from crossover CTs and urinary recovery data, followed by sensitivity analysis. 18 studies reporting PK data and 10 reporting PK models were identified. Posterior distributions were comparable across programs: 95% credible intervals overlapped.\n                    <jats:italic>Torsten<\/jats:italic>\n                    showed superior sampling efficiency, as compared to\n                    <jats:italic>NIMBLE<\/jats:italic>\n                    and\n                    <jats:italic>MCSim<\/jats:italic>\n                    (5.7 and 10.0 times higher in tails and central posterior regions, respectively); however, the average effective sample size per hour was comparable for\n                    <jats:italic>Torsten<\/jats:italic>\n                    and\n                    <jats:italic>MCSim<\/jats:italic>\n                    . The predicted urinary recovery was 2.2%\u20134.4% (mean 3.2%), while the observed values were in the 0.8%\u20134.0% range (mean 2.0%). Glomerular filtration rate and fraction unbound were the main contributors to inter\u2010trial variability in urinary recovery, while volume of distribution and clearance had the highest influence on maximum concentration and area\u2010under\u2010the\u2010concentration curve, respectively. The proposed Bayesian workflow is flexible and transferable to other mechanistic model types.\n                  <\/jats:p>","DOI":"10.1002\/psp4.70307","type":"journal-article","created":{"date-parts":[[2026,8,3]],"date-time":"2026-08-03T04:20:40Z","timestamp":1785730840000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Bayesian Workflow for Minimal\n                    <scp>PBPK<\/scp>\n                    Models: Case Study of Dapagliflozin"],"prefix":"10.1002","volume":"15","author":[{"ORCID":"https:\/\/orcid.org\/0009-0007-1547-5200","authenticated-orcid":false,"given":"Anna","family":"Mikhailova","sequence":"first","affiliation":[{"name":"M&amp;S Decisions  Dubai UAE"},{"name":"Marchuk Institute of Numerical Mathematics RAS  Moscow Russian Federation"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0678-5729","authenticated-orcid":false,"given":"Kirill","family":"Peskov","sequence":"additional","affiliation":[{"name":"M&amp;S Decisions  Dubai UAE"},{"name":"Marchuk Institute of Numerical Mathematics RAS  Moscow Russian Federation"},{"name":"Research Center of Model\u2010Informed Drug Development Sechenov First Moscow State Medical University  Moscow Russian Federation"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4925-2463","authenticated-orcid":false,"given":"Gabriel","family":"Helmlinger","sequence":"additional","affiliation":[{"name":"Quantitative Medicines  Lexington Massachusetts USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7066-0811","authenticated-orcid":false,"given":"Victor","family":"Sokolov","sequence":"additional","affiliation":[{"name":"M&amp;S Decisions  Dubai UAE"},{"name":"Marchuk Institute of Numerical Mathematics RAS  Moscow Russian Federation"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"311","published-online":{"date-parts":[[2026,8,2]]},"reference":[{"key":"e_1_2_9_2_1","doi-asserted-by":"publisher","DOI":"10.1056\/NEJMoa1911303"},{"key":"e_1_2_9_3_1","doi-asserted-by":"publisher","DOI":"10.1056\/NEJMoa2024816"},{"key":"e_1_2_9_4_1","doi-asserted-by":"publisher","DOI":"10.1111\/bcp.15939"},{"key":"e_1_2_9_5_1","doi-asserted-by":"publisher","DOI":"10.1007\/s40262-020-00956-1"},{"key":"e_1_2_9_6_1","doi-asserted-by":"publisher","DOI":"10.1038\/psp.2013.20"},{"key":"e_1_2_9_7_1","doi-asserted-by":"publisher","DOI":"10.1111\/dom.14305"},{"key":"e_1_2_9_8_1","doi-asserted-by":"publisher","DOI":"10.1002\/psp4.12934"},{"key":"e_1_2_9_9_1","doi-asserted-by":"publisher","DOI":"10.1208\/s12248-011-9297-2"},{"key":"e_1_2_9_10_1","doi-asserted-by":"publisher","DOI":"10.1002\/jcph.2196"},{"key":"e_1_2_9_11_1","doi-asserted-by":"publisher","DOI":"10.1111\/bcp.13981"},{"key":"e_1_2_9_12_1","doi-asserted-by":"publisher","DOI":"10.1007\/s00125-024-06142-3"},{"key":"e_1_2_9_13_1","doi-asserted-by":"publisher","DOI":"10.1002\/jcph.2062"},{"key":"e_1_2_9_14_1","doi-asserted-by":"publisher","DOI":"10.1007\/s10928-024-09905-y"},{"key":"e_1_2_9_15_1","doi-asserted-by":"publisher","DOI":"10.1213\/ANE.0000000000000165"},{"key":"e_1_2_9_16_1","doi-asserted-by":"publisher","DOI":"10.1111\/cdev.12169"},{"key":"e_1_2_9_17_1","doi-asserted-by":"publisher","DOI":"10.1007\/s10928-012-9280-2"},{"key":"e_1_2_9_18_1","doi-asserted-by":"publisher","DOI":"10.1136\/bmj.n71"},{"key":"e_1_2_9_19_1","doi-asserted-by":"publisher","DOI":"10.1002\/psp4.12956"},{"key":"e_1_2_9_20_1","unstructured":"A.Gelman A.Vehtari D.Simpson et\u00a0al. \u201cBayesian Workflow \u201d (2020) https:\/\/doi.org\/10.48550\/ARXIV.2011.01808."},{"key":"e_1_2_9_21_1","doi-asserted-by":"publisher","DOI":"10.1214\/20-BA1221"},{"key":"e_1_2_9_22_1","volume-title":"Bayesian Data Analysis","author":"Gelman A.","year":"2014"},{"key":"e_1_2_9_23_1","unstructured":"NIMBLE Development Team \u201cNIMBLE: MCMC Particle Filtering and Programmable Hierarchical Modeling \u201d (2026) https:\/\/doi.org\/10.5281\/ZENODO.1211190."},{"key":"e_1_2_9_24_1","doi-asserted-by":"publisher","DOI":"10.18637\/jss.v002.i09"},{"key":"e_1_2_9_25_1","doi-asserted-by":"publisher","DOI":"10.18637\/jss.v076.i01"},{"key":"e_1_2_9_26_1","doi-asserted-by":"crossref","unstructured":"C. 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R.Gillespie \u201cFlexible and Efficient Bayesian Pharmacometrics Modeling Using Stan and Torsten Part I \u201d (2022) https:\/\/doi.org\/10.48550\/arXiv.2109.10184.","DOI":"10.1002\/psp4.12812"},{"key":"e_1_2_9_27_1","unstructured":"S.Watanabe \u201cAsymptotic Equivalence of Bayes Cross Validation and Widely Applicable Information Criterion in Singular Learning Theory \u201d (2010) https:\/\/doi.org\/10.48550\/ARXIV.1004.2316."},{"key":"e_1_2_9_28_1","doi-asserted-by":"publisher","DOI":"10.1007\/s11222-016-9696-4"},{"key":"e_1_2_9_29_1","doi-asserted-by":"publisher","DOI":"10.1002\/psp4.12063"},{"key":"e_1_2_9_30_1","doi-asserted-by":"publisher","DOI":"10.1056\/NEJMra054415"},{"key":"e_1_2_9_31_1","doi-asserted-by":"publisher","DOI":"10.1053\/ajkd.2003.50007"},{"key":"e_1_2_9_32_1","unstructured":"European Medicines Agency \u201cEMA\/689976\/2012\u2014Assessment Report Forxiga Dapagliflozin \u201d accessed in September 2025 https:\/\/www.ema.europa.eu\/en\/documents\/assessment\u2010report\/forxiga\u2010epar\u2010public\u2010assessment\u2010report_en.pdf."},{"key":"e_1_2_9_33_1","doi-asserted-by":"publisher","DOI":"10.1007\/s43441-023-00585-3"},{"key":"e_1_2_9_34_1","doi-asserted-by":"publisher","DOI":"10.1214\/17-AOAS1122"},{"key":"e_1_2_9_35_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.xphs.2025.103916"},{"key":"e_1_2_9_36_1","doi-asserted-by":"publisher","DOI":"10.1002\/sim.2112"},{"key":"e_1_2_9_37_1","doi-asserted-by":"publisher","DOI":"10.1007\/s40262-024-01425-9"},{"key":"e_1_2_9_38_1","doi-asserted-by":"publisher","DOI":"10.1214\/ss\/1009212673"},{"key":"e_1_2_9_39_1","doi-asserted-by":"publisher","DOI":"10.1111\/biom.12242"},{"key":"e_1_2_9_40_1","unstructured":"M.Beraha D.Falco andA.Guglielmi \u201cJAGS NIMBLE Stan: A Detailed Comparison Among Bayesian MCMC Software \u201d (2021) https:\/\/doi.org\/10.48550\/ARXIV.2107.09357."},{"key":"e_1_2_9_41_1","unstructured":"C. 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