Prediction of Pharmacokinetic Drug-Drug Interactions Using Human Hepatocyte Suspension in Plasma and Cytochrome P450 Phenotypic Data. III. In Vitro-in Vivo Correlation with Fluconazole

Lu, Chuang; Berg, Cicely; Prakash, Shimoga R.; Lee, Frank W.; Balani, Suresh K.

doi:10.1124/dmd.107.019000

Cited by 41 publications

(29 citation statements)

References 31 publications

(54 reference statements)

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“…Prediction of drug-drug interactions using in vitro data has been extensively explored using both static and dynamic models, and considerable success has been achieved (Ito et al, 2005;Obach et al, 2006;Lu et al, 2008;Fahmi et al, 2008;Rowland Yeo et al, 2010). For intravenously administered drugs, it has been suggested that this type of modeling can be used for prediction assuming a very low hepatic EH (Kirby and Unadkat, 2010).…”

Section: Discussionmentioning

confidence: 99%

“…NA, data not available (compounds are not time-dependent inhibitor of CYP3A4 based on data in Table 3). a A static model described by Kirby and Unadkat (2010) was used by incorporating parallel pathways (Ito et al, 2005;Lu et al, 2008) and with the consideration of EH.…”

Section: Discussionmentioning

confidence: 99%

“…Prediction of drug-drug interactions using in vitro inhibition data has been well described in the literature (Ito et al, 2005;Obach et al, 2006;Fahmi et al, 2008;Lu et al, 2008). The area under the curve (AUC) ratio (AUC9/AUC) of a victim drug is inversely related to the hepatic clearance ratio (CL H /CL H 9) in the presence and absence of an inhibitor, assuming little or no renal clearance.…”

Section: Downloaded Frommentioning

confidence: 99%

“…The area under the curve (AUC) ratio (AUC9/AUC) of a victim drug is inversely related to the hepatic clearance ratio (CL H /CL H 9) in the presence and absence of an inhibitor, assuming little or no renal clearance. Because parallel pathways (CYP2C8 and CYP3A4) are involved in paclitaxel metabolism, AUC changes can be simply described using equations as follows (Ito et al, 2005;Lu et al, 2008):…”

Section: Downloaded Frommentioning

confidence: 99%

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Pathway-Dependent Inhibition of Paclitaxel Hydroxylation by Kinase Inhibitors and Assessment of Drug–Drug Interaction Potentials

Wang

Pan

et al. 2014

Drug Metab Dispos

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Paclitaxel is often used in combination with small molecule kinase inhibitors to enhance antitumor efficacy against various malignancies. Because paclitaxel is metabolized by CYP2C8 and CYP3A4, the possibility of drug-drug interactions mediated by enzyme inhibition may exist between the combining agents. In the present study, a total of 12 kinase inhibitors were evaluated for inhibitory potency in human liver microsomes by monitoring the formation of CYP2C8 and CYP3A4 metabolites simultaneously. For reversible inhibition, nilotinib was found to be the most potent inhibitor against both CYP2C8 and CYP3A4, and the inhibition potency could be explained by strong hydrogen binding based on molecular docking simulations and type II binding based on spectral analysis. Comparison of K i values revealed that the CYP2C8 pathway was more sensitive toward some kinase inhibitors (such as axitinib), while the CYP3A4 pathway was preferentially inhibited by others (such as bosutinib). Pathwaydependent inactivation (time-dependent inhibition) was also observed for a number of kinase inhibitors against CYP3A4 but not CYP2C8. Further studies showed that axitinib had a K I of 0.93 mM and k inact of 0.0137 min 21 , and the observed inactivation toward CYP3A4 was probably due to the formation of reactive intermediate (s). Using a static model, a reasonably accurate prediction of drug-drug interactions was achieved by incorporating parallel pathways and hepatic extraction ratio. The present results suggest that potent and pathway-dependent inhibition of CYP2C8 and/or CYP3A4 pathways by kinase inhibitors may alter the ratio of paclitaxel metabolites in vivo, and that such changes can be clinically relevant as differential metabolism has been linked to paclitaxel-induced neurotoxicity in cancer patients.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Downloaded Frommentioning

confidence: 99%

Section: Downloaded Frommentioning

confidence: 99%

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Pathway-Dependent Inhibition of Paclitaxel Hydroxylation by Kinase Inhibitors and Assessment of Drug–Drug Interaction Potentials

Wang

Pan

et al. 2014

Drug Metab Dispos

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“…In our in vitro assays, we used human hepatocytes suspended in human plasma, with the plasma concentration of the inhibitors in the incubations being similar to the clinical C max of the inhibitor observed with standard dosing. The free concentration at the enzyme site would be same in the two systems (Lu et al, 2007(Lu et al, , 2008a. Consequently, our model eliminates the need to determine the value of [I] and removes the dependence on the traditional, less desirable rule of [I]/K i ratio to predict magnitude of DDIs.…”

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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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Evaluation of Inhibitors of Drug Metabolism in Human Hepatocytes

2009

Enzyme Inhibition in Drug Discovery and Development

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Prediction of Pharmacokinetic Drug-Drug Interactions Using Human Hepatocyte Suspension in Plasma and Cytochrome P450 Phenotypic Data. III. In Vitro-in Vivo Correlation with Fluconazole

Cited by 41 publications

References 31 publications

Pathway-Dependent Inhibition of Paclitaxel Hydroxylation by Kinase Inhibitors and Assessment of Drug–Drug Interaction Potentials

Pathway-Dependent Inhibition of Paclitaxel Hydroxylation by Kinase Inhibitors and Assessment of Drug–Drug Interaction Potentials

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

Evaluation of Inhibitors of Drug Metabolism in Human Hepatocytes

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