{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,27]],"date-time":"2026-08-27T16:51:11Z","timestamp":1787849471159,"version":"build-2784847793"},"reference-count":48,"publisher":"Wiley","issue":"4","license":[{"start":{"date-parts":[[2014,1,24]],"date-time":"2014-01-24T00:00:00Z","timestamp":1390521600000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/onlinelibrary.wiley.com\/termsAndConditions#vor"}],"content-domain":{"domain":["bpspubs.onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["British J Pharmacology"],"published-print":{"date-parts":[[2014,2]]},"abstract":"<jats:sec><jats:title>Background and purpose<\/jats:title><jats:p><jats:italic>In vitro<\/jats:italic>inhibitory potency (<jats:italic><jats:styled-content style=\"fixed-case\">K<\/jats:styled-content><\/jats:italic><jats:sub>i<\/jats:sub>)\u2010based predictions of<jats:styled-content style=\"fixed-case\">P<\/jats:styled-content>\u2010glycoprotein (<jats:styled-content style=\"fixed-case\">P<\/jats:styled-content>\u2010gp)\u2010mediated drug\u2010drug interactions (<jats:styled-content style=\"fixed-case\">DDIs<\/jats:styled-content>) are hampered by the substantial variability in inhibitory potency. In this study,<jats:italic>in vivo<\/jats:italic>\u2010based [<jats:styled-content style=\"fixed-case\">I<\/jats:styled-content>]\/<jats:italic><jats:styled-content style=\"fixed-case\">K<\/jats:styled-content><\/jats:italic><jats:sub>i<\/jats:sub>values were used to predict the<jats:styled-content style=\"fixed-case\">DDI<\/jats:styled-content>risks of a<jats:styled-content style=\"fixed-case\">P<\/jats:styled-content>\u2010gp substrate dabigatran etexilate (<jats:styled-content style=\"fixed-case\">DABE<\/jats:styled-content>) using physiologically based pharmacokinetic (<jats:styled-content style=\"fixed-case\">PBPK<\/jats:styled-content>) modelling.<\/jats:p><\/jats:sec><jats:sec><jats:title>Experimental approach<\/jats:title><jats:p>A baseline<jats:styled-content style=\"fixed-case\">PBPK<\/jats:styled-content>model was established with digoxin, a known<jats:styled-content style=\"fixed-case\">P<\/jats:styled-content>\u2010gp substrate. The<jats:italic><jats:styled-content style=\"fixed-case\">K<\/jats:styled-content><\/jats:italic><jats:sub>m<\/jats:sub>(<jats:styled-content style=\"fixed-case\">P<\/jats:styled-content>\u2010gp transport) of digoxin in the baseline<jats:styled-content style=\"fixed-case\">PBPK<\/jats:styled-content>model was adjusted to<jats:italic><jats:styled-content style=\"fixed-case\">K<\/jats:styled-content><\/jats:italic><jats:sub>m<\/jats:sub><jats:sup>i<\/jats:sup>to fit the change of digoxin pharmacokinetics in the presence of a<jats:styled-content style=\"fixed-case\">P<\/jats:styled-content>\u2010gp inhibitor. Then \u2018<jats:italic>in vivo<\/jats:italic>\u2019 [<jats:styled-content style=\"fixed-case\">I<\/jats:styled-content>]\/<jats:italic>K<\/jats:italic><jats:sub>i<\/jats:sub>of this<jats:styled-content style=\"fixed-case\">P<\/jats:styled-content>\u2010gp inhibitor was calculated using<jats:italic><jats:styled-content style=\"fixed-case\">K<\/jats:styled-content><\/jats:italic><jats:sub>m<\/jats:sub><jats:sup>i<\/jats:sup>\/<jats:italic><jats:styled-content style=\"fixed-case\">K<\/jats:styled-content><\/jats:italic><jats:sub>m<\/jats:sub>. Baseline<jats:styled-content style=\"fixed-case\">PBPK<\/jats:styled-content>model was developed for<jats:styled-content style=\"fixed-case\">DABE<\/jats:styled-content>, and the \u2018<jats:italic>in vivo<\/jats:italic>\u2019 [<jats:styled-content style=\"fixed-case\">I<\/jats:styled-content>]\/<jats:italic><jats:styled-content style=\"fixed-case\">K<\/jats:styled-content><\/jats:italic><jats:sub>i<\/jats:sub>was incorporated into this model to simulate the static effect of<jats:styled-content style=\"fixed-case\">P<\/jats:styled-content>\u2010gp inhibitor on<jats:styled-content style=\"fixed-case\">DABE<\/jats:styled-content>pharmacokinetics. This approach was verified by comparing the observed and the simulated<jats:styled-content style=\"fixed-case\">DABE<\/jats:styled-content>pharmacokinetics in the presence of five different<jats:styled-content style=\"fixed-case\">P<\/jats:styled-content>\u2010gp inhibitors.<\/jats:p><\/jats:sec><jats:sec><jats:title>Key results<\/jats:title><jats:p>This approach accurately predicted the effects of five<jats:styled-content style=\"fixed-case\">P<\/jats:styled-content>\u2010gp inhibitors on<jats:styled-content style=\"fixed-case\">DABE<\/jats:styled-content>pharmacokinetics (98\u2013133% and 89\u2013104% for the ratios of<jats:styled-content style=\"fixed-case\">AUC<\/jats:styled-content>and<jats:italic><jats:styled-content style=\"fixed-case\">C<\/jats:styled-content><\/jats:italic><jats:sub>max<\/jats:sub>respectively). The effects of 16 other<jats:styled-content style=\"fixed-case\">P<\/jats:styled-content>\u2010gp inhibitors on the pharmacokinetics of<jats:styled-content style=\"fixed-case\">DABE<\/jats:styled-content>were also confidently simulated.<\/jats:p><\/jats:sec><jats:sec><jats:title>Conclusions and implications<\/jats:title><jats:p>\u2018<jats:italic>In vivo<\/jats:italic>\u2019 [<jats:styled-content style=\"fixed-case\">I<\/jats:styled-content>]\/<jats:italic><jats:styled-content style=\"fixed-case\">K<\/jats:styled-content><\/jats:italic><jats:sub>i<\/jats:sub>and<jats:styled-content style=\"fixed-case\">PBPK<\/jats:styled-content>modelling, used in combination, can accurately predict<jats:styled-content style=\"fixed-case\">P<\/jats:styled-content>\u2010gp\u2010mediated<jats:styled-content style=\"fixed-case\">DDIs<\/jats:styled-content>. The described framework provides a mechanistic basis for the proper design of clinical DDI studies, as well as avoiding unnecessary clinical<jats:styled-content style=\"fixed-case\">DDI<\/jats:styled-content>studies.<\/jats:p><\/jats:sec>","DOI":"10.1111\/bph.12533","type":"journal-article","created":{"date-parts":[[2013,11,28]],"date-time":"2013-11-28T05:04:54Z","timestamp":1385615094000},"page":"1043-1053","update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":35,"title":["Physiologically based pharmacokinetic modelling and<i>in vivo<\/i>[I]\/<i>K<\/i><sub>i<\/sub>accurately predict<scp>P<\/scp>\u2010glycoprotein\u2010mediated drug\u2010drug interactions with dabigatran etexilate"],"prefix":"10.1111","volume":"171","author":[{"given":"Yuansheng","family":"Zhao","sequence":"first","affiliation":[{"name":"The Hamner Institutes for Health Sciences Research Triangle Park NC USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zhe\u2010Yi","family":"Hu","sequence":"additional","affiliation":[{"name":"Department of Clinical Pharmacy College of Pharmacy University of Tennessee Health Science Center Memphis TN USA"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"311","published-online":{"date-parts":[[2014,1,24]]},"reference":[{"key":"e_1_2_8_2_1","doi-asserted-by":"publisher","DOI":"10.1016\/S0009-9236(01)17221-8"},{"key":"e_1_2_8_3_1","doi-asserted-by":"publisher","DOI":"10.1124\/dmd.112.050500"},{"key":"e_1_2_8_4_1","doi-asserted-by":"publisher","DOI":"10.1002\/jps.2600600803"},{"key":"e_1_2_8_5_1","doi-asserted-by":"publisher","DOI":"10.1124\/dmd.107.019083"},{"key":"e_1_2_8_6_1","doi-asserted-by":"publisher","DOI":"10.1158\/1078-0432.CCR-06-2362"},{"key":"e_1_2_8_7_1","doi-asserted-by":"publisher","DOI":"10.1056\/NEJMoa0905561"},{"key":"e_1_2_8_8_1","doi-asserted-by":"crossref","first-page":"866","DOI":"10.1016\/S0090-9556(24)15235-X","article-title":"OATP and P\u2010glycoprotein transporters mediate the cellular uptake and excretion of fexofenadine","volume":"27","author":"Cvetkovic M","year":"1999","journal-title":"Drug Metab Dispos"},{"key":"e_1_2_8_9_1","doi-asserted-by":"publisher","DOI":"10.1111\/bcp.12055"},{"key":"e_1_2_8_10_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.clpt.2004.02.008"},{"key":"e_1_2_8_11_1","unstructured":"Draft Guidance\/Guidance for Industry(2012).Drug interaction studies\u2013study Design data analysis implications for dosing and labeling recommendations. 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