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Journal Abstract Search

140 related items for PubMed ID: 26099559

  • 1. Simulation of Metabolic Drug-Drug Interactions Perpetrated by Fluvoxamine Using Hybridized Two-Compartment Hepatic Drug-Pool-Based Tube Modeling and Estimation of In Vivo Inhibition Constants.
    Iga K.
    J Pharm Sci; 2015 Oct; 104(10):3565-77. PubMed ID: 26099559
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  • 3. Usefulness of Two-Compartment Model-Assisted and Static Overall Inhibitory-Activity Method for Prediction of Drug-Drug Interaction.
    Iga K, Kiriyama A.
    Biol Pharm Bull; 2017 Dec 01; 40(12):2024-2037. PubMed ID: 28993551
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  • 5. Use of three-compartment physiologically based pharmacokinetic modeling to predict hepatic blood levels of fluvoxamine relevant for drug-drug interactions.
    Iga K.
    J Pharm Sci; 2015 Apr 01; 104(4):1478-91. PubMed ID: 25558834
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  • 7. Physiologically-Based Pharmacokinetic Models for CYP1A2 Drug-Drug Interaction Prediction: A Modeling Network of Fluvoxamine, Theophylline, Caffeine, Rifampicin, and Midazolam.
    Britz H, Hanke N, Volz AK, Spigset O, Schwab M, Eissing T, Wendl T, Frechen S, Lehr T.
    CPT Pharmacometrics Syst Pharmacol; 2019 May 01; 8(5):296-307. PubMed ID: 30762305
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  • 9. Liver dysfunction markedly decreases the inhibition of cytochrome P450 1A2-mediated theophylline metabolism by fluvoxamine.
    Orlando R, Padrini R, Perazzi M, De Martin S, Piccoli P, Palatini P.
    Clin Pharmacol Ther; 2006 May 01; 79(5):489-99. PubMed ID: 16678550
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  • 10. Assessment of algorithms for predicting drug-drug interactions via inhibition mechanisms: comparison of dynamic and static models.
    Guest EJ, Rowland-Yeo K, Rostami-Hodjegan A, Tucker GT, Houston JB, Galetin A.
    Br J Clin Pharmacol; 2011 Jan 01; 71(1):72-87. PubMed ID: 21143503
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  • 12. Assessment of cytochrome P450-mediated drug-drug interaction potential of orteronel and exposure changes in patients with renal impairment using physiologically based pharmacokinetic modeling and simulation.
    Lu C, Suri A, Shyu WC, Prakash S.
    Biopharm Drug Dispos; 2014 Dec 01; 35(9):543-52. PubMed ID: 25264242
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  • 13. The utility of in vitro cytochrome P450 inhibition data in the prediction of drug-drug interactions.
    Obach RS, Walsky RL, Venkatakrishnan K, Gaman EA, Houston JB, Tremaine LM.
    J Pharmacol Exp Ther; 2006 Jan 01; 316(1):336-48. PubMed ID: 16192315
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  • 17. Semi-mechanistic physiologically-based pharmacokinetic modeling of clinical glibenclamide pharmacokinetics and drug-drug-interactions.
    Greupink R, Schreurs M, Benne MS, Huisman MT, Russel FG.
    Eur J Pharm Sci; 2013 Aug 16; 49(5):819-28. PubMed ID: 23806476
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  • 19. Fluvoxamine-theophylline interaction: gap between in vitro and in vivo inhibition constants toward cytochrome P4501A2.
    Yao C, Kunze KL, Kharasch ED, Wang Y, Trager WF, Ragueneau I, Levy RH.
    Clin Pharmacol Ther; 2001 Nov 16; 70(5):415-24. PubMed ID: 11719727
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  • 20. Application of the extended clearance concept classification system (ECCCS) to predict the victim drug-drug interaction potential of statins.
    Kunze A, Poller B, Huwyler J, Camenisch G.
    Drug Metab Pers Ther; 2015 Sep 16; 30(3):175-88. PubMed ID: 25996489
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