Inhibition of Drug‐Metabolizing Enzymes in the Gastrointestinal Tract and Its Influence on the Drug–Drug Interaction Prediction

Galetin, Aleksandra; Houston, J. Brian

doi:10.1002/9780470538951.ch9

Cited by 2 publications

(3 citation statements)

References 142 publications

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“…f other is the fraction of clearance by other routes such as renal or biliary excretion (f m ϩ f other ϭ 1). Experimental or clinical information of the effects of the inhibitor on the bioavailability across gastrointestinal tract (FgЈ/Fg) are generally not available (Galetin and Houston, 2009); therefore, this term was not included in our calculations. To assess the worst-case scenario of interaction, the clinical maximum plasma concentration of the inhibitor (C max ) was used to calculate the fA values.…”

Section: Resultsmentioning

confidence: 99%

Quantitative Prediction and Clinical Observation of a CYP3A Inhibitor-Based Drug-Drug Interactions with MLN3897, a Potent C-C Chemokine Receptor-1 Antagonist

Lu¹,

Balani²,

Qian³

et al. 2009

J Pharmacol Exp Ther

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A novel in vitro model was recently developed in our laboratories for the prediction of magnitude of clinical pharmacokinetic drugdrug interactions (DDIs), based on reversible hepatic cytochrome P450 (P450) inhibition. This approach, using inhibition data from human hepatocytes incubated in human plasma, and quantitative P450 phenotyping data from hepatic microsomal incubations, successfully predicted DDIs for 15 marketed drugs with ketoconazole, a strong competitive inhibitor of CYP3A4/5, generally used to demonstrate a "worst-case scenario" for CYP3A inhibition. In addition, this approach was successfully extended to DDI predictions with the moderate competitive CYP3A inhibitor fluconazole for nine marketed drugs. In the current report, the general applicability of the model has been demonstrated by prospectively predicting the degree of inhibition and then conducting DDI studies in the clinic for an investigational CCR1 antagonist MLN3897, which is cleared predominantly by CYP3A. The clinical studies involved treatment of healthy volunteers (n ϭ 17-20), in a crossover design, with ketoconazole (200 mg b.i.d.) or fluconazole (400 mg once a day), while receiving MLN3897. Administration of MLN3897 and ketoconazole led to an average 8.28-fold increase in area under the curve of plasma concentration-time plot (AUC) of MLN3897 at steady state, compared with the 8.33-fold increase predicted from the in vitro data. Similarly for fluconazole, an average increase of 3.93-fold in AUC was observed for MLN3897 in comparison with a predicted value of 3.26-fold. Thus, our model reliably predicted the exposure changes for MLN3897 in interaction studies with competitive CYP3A inhibitors in humans, further strengthening the utility of our in vitro model.Prediction of clinical DDIs using in vitro studies is one of the major challenges in the pharmaceutical industry. The main utility of such DDI predictions is to help foresee any safety issues anticipated from higher exposures and thus help design clinical trials with better safety. In some instances, clinical DDI studies can be avoided if no significant pharmacokinetic interaction is predicted. Traditionally, DDI predictions have been based on the ratio of the inhibitor concentration [I] and the enzyme inhibition constant K i ([I]/K i ratio) (Rostami-Hodjegan and Tucker, 2004;Ito et al., 2004;Obach et al., 2006; 2006 FDA draft guidance on DDI studies at http://WWW.FDA.gov/cder/ guidence/6695dft.html). Although this approach had some successes in predicting DDIs, reliability of predictions still remains a challenge (Blanchard et al., 2004;Cook et al., 2004;Ito et al., 2004;Bachmann 2006;Obach et al., 2006). Difficulty remains both in estimating the physiological concentration of the inhibitor at the enzyme site and in reliably determining K i , which often is affected by the experimental conditions used. Factors such as the protein concentration in the incubation, the substrate of choice, and the enzyme source (Thummel and Wilkinson 1998;Galetin et al., 2006;Lu et al.,...

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Section: Resultsmentioning

confidence: 99%

Quantitative Prediction and Clinical Observation of a CYP3A Inhibitor-Based Drug-Drug Interactions with MLN3897, a Potent C-C Chemokine Receptor-1 Antagonist

Lu¹,

Balani²,

Qian³

et al. 2009

J Pharmacol Exp Ther

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“…As recently reviewed by Galetin and Houston [38], several assumptions have to be made in this practice: an absolute inhibition of GI metabolizing enzymes by a single dose of 175 -250 mL of grapefruit juice; grapefruit juice has no effect on fraction absorbed in GI (Fa) and hepatic enzyme activity (Fh remains unchanged) on the co-administrated drug; and the coadministrated drug has minimal GI elimination or reabsorption. However, in reality, the information on bioavailability passing human GI wall (Fg) and the effects on the bioavailability from GI enzyme or transporter inhibition (Fg'/Fg) are not readily available due to the lack of practical ways to determine these values in humans.…”

Section: Ddi Model Considering Gastrointestinal Contributionsmentioning

confidence: 99%

“…7), hepatocyte suspensions in human plasma were prepared in a 96-well plate. Hence, one doesn't need to know the K i of the inhibitor and the [I] at the enzyme site to be able to predict DDI, because the enzyme activity remaining (f A ) could be used to represent the inversed factor of (1+[I]/K i ) [37,38]: (10) As mentioned previously, in the presence of an inhibitor, each clearance route is reduced by the factor of (1+[I]/K i ). Immediately following equilibration, the plasma samples were separated from the hepatocytes and the total inhibitor concentrations were determined in those plasma samples.…”

Section: The Limitation Of the [I]/k I Modelmentioning

confidence: 99%

Emerging In Vitro Tools to Evaluate Cytochrome P450 and Transporter-Mediated Drug-Drug Interactions

Concannon¹,

Hennelly²,

Noott³

et al. 2010

CDDT

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Drug-drug interactions comprise a significant cause of morbidity and mortality worldwide as they may lead to adverse clinical events, result in decrease/inactivation of the therapeutic effect of a drug, may enhance drug toxicity and indirectly compromise treatment outcomes and adherence. Drug transporters and drug metabolism enzymes govern drug absorption, distribution, metabolism, and elimination (ADME). Inhibition or induction of transporter and drug metabolism enzymes can alter the ADME of a co-administered drug, which may lead to drug-induced toxicity or lack of efficacy. This review assesses our current understanding of the in vitro methods of evaluating CYPs and transporter-mediated DDI. The DDI prediction models based on in vitro assays are also discussed in this review. The applications, advantages and limitations of each method are also addressed in this review.

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Inhibition of Drug‐Metabolizing Enzymes in the Gastrointestinal Tract and Its Influence on the Drug–Drug Interaction Prediction

Cited by 2 publications

References 142 publications

Quantitative Prediction and Clinical Observation of a CYP3A Inhibitor-Based Drug-Drug Interactions with MLN3897, a Potent C-C Chemokine Receptor-1 Antagonist

Quantitative Prediction and Clinical Observation of a CYP3A Inhibitor-Based Drug-Drug Interactions with MLN3897, a Potent C-C Chemokine Receptor-1 Antagonist

Emerging In Vitro Tools to Evaluate Cytochrome P450 and Transporter-Mediated Drug-Drug Interactions

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