Literature DB >> 22010218

Prediction of relative in vivo metabolite exposure from in vitro data using two model drugs: dextromethorphan and omeprazole.

Justin D Lutz1, Nina Isoherranen.   

Abstract

Metabolites can have pharmacological or toxicological effects, inhibit metabolic enzymes, and be used as probes of drug-drug interactions or specific cytochrome P450 (P450) phenotypes. Thus, better understanding and prediction methods are needed to characterize metabolite exposures in vivo. This study aimed to test whether in vitro data could be used to predict and rationalize in vivo metabolite exposures using two model drugs and P450 probes: dextromethorphan and omeprazole with their primary metabolites dextrorphan, 5-hydroxyomeprazole (5OH-omeprazole), and omeprazole sulfone. Relative metabolite exposures were predicted using metabolite formation and elimination clearances. For dextrorphan, the formation clearances of dextrorphan glucuronide and 3-hydroxymorphinan from dextrorphan in human liver microsomes were used to predict metabolite (dextrorphan) clearance. For 5OH-omeprazole and omeprazole sulfone, the depletion rates of the metabolites in human hepatocytes were used to predict metabolite clearance. Dextrorphan/dextromethorphan in vivo metabolite/parent area under the plasma concentration versus time curve ratio (AUC(m)/AUC(p)) was overpredicted by 2.1-fold, whereas 5OH-omeprazole/omeprazole and omeprazole sulfone/omeprazole were predicted within 0.75- and 1.1-fold, respectively. The effect of inhibition or induction of the metabolite's formation and elimination on the AUC(m)/AUC(p) ratio was simulated. The simulations showed that unless metabolite clearance pathways are characterized, interpretation of the metabolic ratios is exceedingly difficult. This study shows that relative in vivo metabolite exposure can be predicted from in vitro data and characterization of secondary metabolism of probe metabolites is critical for interpretation of phenotypic data.

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Year:  2011        PMID: 22010218      PMCID: PMC3250047          DOI: 10.1124/dmd.111.042200

Source DB:  PubMed          Journal:  Drug Metab Dispos        ISSN: 0090-9556            Impact factor:   3.922


  36 in total

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3.  Inhibition of the sulfoxidation of omeprazole by ketoconazole in poor and extensive metabolizers of S-mephenytoin.

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7.  Contribution of itraconazole metabolites to inhibition of CYP3A4 in vivo.

Authors:  I E Templeton; K E Thummel; E D Kharasch; K L Kunze; C Hoffer; W L Nelson; N Isoherranen
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10.  Effect of cinacalcet hydrochloride, a new calcimimetic agent, on the pharmacokinetics of dextromethorphan: in vitro and clinical studies.

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  10 in total

1.  Inhibition of CYP2C19 and CYP3A4 by omeprazole metabolites and their contribution to drug-drug interactions.

Authors:  Yoshiyuki Shirasaka; Jennifer E Sager; Justin D Lutz; Connie Davis; Nina Isoherranen
Journal:  Drug Metab Dispos       Date:  2013-04-25       Impact factor: 3.922

2.  Pharmacokinetic comparison between the long-term anesthetized, short-term anesthetized and conscious rat models in nasal drug delivery.

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Authors:  Jennifer E Sager; Sasmita Tripathy; Lauren S L Price; Abhinav Nath; Justine Chang; Alyssa Stephenson-Famy; Nina Isoherranen
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4.  Infant Dextromethorphan and Dextrorphan Exposure via Breast Milk From Mothers Who Are CYP2D6 Extensive Metabolizers.

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5.  In vitro analysis and quantitative prediction of efavirenz inhibition of eight cytochrome P450 (CYP) enzymes: major effects on CYPs 2B6, 2C8, 2C9 and 2C19.

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6.  Hepatic Cyp2d and Cyp26a1 mRNAs and activities are increased during mouse pregnancy.

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7.  A physiologically based pharmacokinetic model to predict disposition of CYP2D6 and CYP1A2 metabolized drugs in pregnant women.

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8.  Effects of elagolix on the pharmacokinetics of omeprazole and its metabolites in healthy premenopausal women.

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9.  Physiologically-based pharmacokinetic modeling of dextromethorphan to investigate interindividual variability within CYP2D6 activity score groups.

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10.  Prediction of Metabolite-to-Parent Drug Exposure: Derivation and Application of a Mechanistic Static Model.

Authors:  Ernesto Callegari; Manthena V S Varma; R Scott Obach
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  10 in total

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