{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,18]],"date-time":"2026-09-18T14:22:04Z","timestamp":1789741324884,"version":"4.0.1"},"reference-count":49,"publisher":"Wiley","issue":"1","license":[{"start":{"date-parts":[[2024,10,2]],"date-time":"2024-10-02T00:00:00Z","timestamp":1727827200000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by-nc\/4.0\/"}],"content-domain":{"domain":["ascpt.onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["CPT Pharmacom &amp; Syst Pharma"],"published-print":{"date-parts":[[2025,1]]},"abstract":"<jats:title>Abstract<\/jats:title><jats:p>Gastrointestinal first\u2010pass metabolism plays an important role in bioavailability and in drug\u2013drug interactions. Physiologically\u2010based pharmacokinetic (PBPK) modeling is a powerful tool to integrate these processes mechanistically. However, a correct bottom\u2010up prediction of GI first\u2010pass metabolism is challenging and depends on various model parameters like the level of enzyme expression and the basolateral intestinal mucosa permeability (<jats:italic>P<\/jats:italic><jats:sub>mucosa<\/jats:sub>). This work aimed to investigate if cytochrome P450 (CYP) 3A4 expression could help predict the first\u2010pass effect using PBPK modeling or whether additional factors like <jats:italic>P<\/jats:italic><jats:sub>mucosa<\/jats:sub> do play additional roles using PBPK modeling. To this end, a systematic review of the absolute CYP3A expression in the human gastrointestinal tract and liver was conducted. The resulting CYP3A4 expression profile and two previously published profiles were applied to PBPK models of seven CYP3A4 substrates (alfentanil, alprazolam, felodipine, midazolam, sildenafil, triazolam, and verapamil) built\u2010in PK\u2010Sim\u00ae. For each compound, it was assessed whether first\u2010pass metabolism could be adequately predicted based on the integrated CYP3A4 expression profile alone or whether an optimization of <jats:italic>P<\/jats:italic><jats:sub>mucosa<\/jats:sub> was required. Evaluation criteria were the precision of the predicted interstudy bioavailabilities and area under the concentration\u2013time curves. It was found that none of the expression profiles provided upfront an adequate description of the extent of GI metabolism and that optimization of <jats:italic>P<\/jats:italic><jats:sub>mucosa<\/jats:sub> as a compound\u2010specific parameter improved the prediction of most models. Our findings indicate that a pure bottom\u2010up prediction of gastrointestinal first\u2010pass metabolism is currently not possible and that compound\u2010specific features like <jats:italic>P<\/jats:italic><jats:sub>mucosa<\/jats:sub> must be considered as well.<\/jats:p>","DOI":"10.1002\/psp4.13249","type":"journal-article","created":{"date-parts":[[2024,10,3]],"date-time":"2024-10-03T06:24:34Z","timestamp":1727936674000},"page":"130-141","update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["PBPK modeling: What is the role of CYP3A4 expression in the gastrointestinal tract to accurately predict first\u2010pass metabolism?"],"prefix":"10.1002","volume":"14","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-7793-1637","authenticated-orcid":false,"given":"Justine","family":"Henriot","sequence":"first","affiliation":[{"name":"Universit\u00e9 de Lorraine Faculty of Pharmacy  Nancy France"},{"name":"Bayer AG, Pharmacometrics\/Modeling and Simulation Systems Pharmacology &amp; Medicine \u2013 PBPK  Leverkusen Germany"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1108-5719","authenticated-orcid":false,"given":"Andr\u00e9","family":"Dallmann","sequence":"additional","affiliation":[{"name":"Bayer HealthCare SAS (on behalf of Bayer AG, Model\u2010Informed Drug Development (MIDD), Research &amp; Development Pharmaceuticals, Leverkusen, Germany)  Lille France"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1947-3839","authenticated-orcid":false,"given":"Fran\u00e7ois","family":"Dupuis","sequence":"additional","affiliation":[{"name":"Universit\u00e9 de Lorraine CITHEFOR  Nancy France"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0001-9372-6315","authenticated-orcid":false,"given":"J\u00e9r\u00e9my","family":"Perrier","sequence":"additional","affiliation":[{"name":"PhinC Development  Massy France"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1170-9392","authenticated-orcid":false,"given":"Sebastian","family":"Frechen","sequence":"additional","affiliation":[{"name":"Bayer AG, Pharmacometrics\/Modeling and Simulation Systems Pharmacology &amp; Medicine \u2013 PBPK  Leverkusen Germany"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"311","published-online":{"date-parts":[[2024,10,2]]},"reference":[{"key":"e_1_2_11_2_1","doi-asserted-by":"publisher","DOI":"10.1146\/annurev.pharmtox.41.1.535"},{"key":"e_1_2_11_3_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.pharmthera.2012.12.007"},{"key":"e_1_2_11_4_1","unstructured":"United States Food and Drug Administration.Guidance for Industry: In\u00a0Vitro Drug Interaction Studies \u2014 Cytochrome P450 Enzyme\u2010 and Transporter\u2010Mediated Drug Interactions.2020.https:\/\/www.fda.gov\/regulatory\u2010information\/search\u2010fda\u2010guidance\u2010documents\/in\u2010vitro\u2010drug\u2010interaction\u2010studies\u2010cytochrome\u2010p450\u2010enzyme\u2010and\u2010transporter\u2010mediated\u2010drug\u2010interactions. 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