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Application of physiologically-based pharmacokinetic model approach to predict pharmacokinetics and drug–drug interaction of rivaroxaban: A case study of rivaroxaban and carbamazepineoa mark
  • Ngo, Lien Thi ;
  • Yang, Sung yoon ;
  • Shin, Sooyoung ;
  • Cao, Duc Tuan ;
  • Van Nguyen, Hung ;
  • Jung, Sangkeun ;
  • Lee, Jae Young ;
  • Lee, Jong Hwa ;
  • Yun, Hwi yeol ;
  • Chae, Jung woo
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dc.contributor.authorNgo, Lien Thi-
dc.contributor.authorYang, Sung yoon-
dc.contributor.authorShin, Sooyoung-
dc.contributor.authorCao, Duc Tuan-
dc.contributor.authorVan Nguyen, Hung-
dc.contributor.authorJung, Sangkeun-
dc.contributor.authorLee, Jae Young-
dc.contributor.authorLee, Jong Hwa-
dc.contributor.authorYun, Hwi yeol-
dc.contributor.authorChae, Jung woo-
dc.date.issued2022-11-01-
dc.identifier.issn2163-8306-
dc.identifier.urihttps://dspace.ajou.ac.kr/dev/handle/2018.oak/32959-
dc.description.abstractRivaroxaban (RIV; Xarelto; Janssen Pharmaceuticals, Beerse, Belgium) is one of the direct oral anticoagulants. The drug is a strong substrate of cytochrome P450 (CYP) enzymes and efflux transporters. This study aimed to develop a physiologically-based pharmacokinetic (PBPK) model for RIV. It contained three hepatic metabolizing enzyme reactions (CYP3A4, CYP2J2, and CYP-independent) and two active transporter-mediated transfers (P-gp and BCRP transporters). To illustrate the performance of the developed RIV PBPK model on the prediction of drug–drug interactions (DDIs), carbamazepine (CBZ) was selected as a case study due to the high DDI potential. Our study results showed that CBZ significantly reduces the exposure of RIV. The area under the concentration-time curve from zero to infinity (AUCinf) of RIV was reduced by 35.2% (from 2221.3 to 1438.7 ng*h/ml) and by 25.5% (from 2467.3 to 1838.4 ng*h/ml) after the first dose and at the steady-state, respectively, whereas the maximum plasma concentration (Cmax) of RIV was reduced by 37.7% (from 266.3 to 166.1 ng/ml) and 36.4% (from 282.3 to 179.5 ng/ml), respectively. The developed PBPK model of RIV could be paired with PBPK models of other interested perpetrators to predict DDI profiles. Further studies investigating the extent of DDI between CBZ and RIV should be conducted in humans to gain a full understanding of their safety and effects.-
dc.description.sponsorshipThis study was supported by the Institute of Information & Communications Technology Planning & Evaluation (IITP) grant funded by the Korean government (MSIT; No. 2020\u20100\u201001441, Artificial Intelligence Convergence Research Center [Chungnam National University], No. RS\u20102022\u201000155857, Artificial Intelligence Convergence Innovation Human Resources Development [Chungnam National University]) and National Research Foundation of Korea (NRF) grant funded by the Korea government (MSIT; No. NRF\u20102018R1C1B5085278, NRF\u20102022R1A2C1010929), and the Korea Environmental Industry & Technology Institute (KEITI) through Core Technology Development Project for Environmental Diseases Prevention and Management (2021003310001), funded by the Korea Ministry of Environment (MOE).-
dc.language.isoeng-
dc.publisherAmerican Society for Clinical Pharmacology and Therapeutics-
dc.subject.meshATP Binding Cassette Transporter, Subfamily G, Member 2-
dc.subject.meshCarbamazepine-
dc.subject.meshCytochrome P-450 CYP3A-
dc.subject.meshCytochrome P-450 Enzyme System-
dc.subject.meshDrug Interactions-
dc.subject.meshHumans-
dc.subject.meshModels, Biological-
dc.subject.meshNeoplasm Proteins-
dc.subject.meshRivaroxaban-
dc.titleApplication of physiologically-based pharmacokinetic model approach to predict pharmacokinetics and drug–drug interaction of rivaroxaban: A case study of rivaroxaban and carbamazepine-
dc.typeArticle-
dc.citation.endPage1442-
dc.citation.startPage1430-
dc.citation.titleCPT: Pharmacometrics and Systems Pharmacology-
dc.citation.volume11-
dc.identifier.bibliographicCitationCPT: Pharmacometrics and Systems Pharmacology, Vol.11, pp.1430-1442-
dc.identifier.doi10.1002/psp4.12844-
dc.identifier.pmid36193622-
dc.identifier.scopusid2-s2.0-85139134893-
dc.identifier.urlhttp://onlinelibrary.wiley.com/journal/10.1002/(ISSN)2163-8306-
dc.description.isoatrue-
dc.subject.subareaModeling and Simulation-
dc.subject.subareaPharmacology (medical)-
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