Ketoconazole Renders Poor CYP3A Phenotype Status With Midazolam as Probe Drug

Prediction of drug-drug interactions using physiologically-based pharmacokinetic models of CYP450 modulators included in Simcyp software

In recent years, physiologically based PharmacoKinetic (PBPK) modeling has received growing interest as a useful tool for the assessment of drug pharmacokinetics. It has been demonstrated to be informative and helpful to quantify the modification in drug exposure due to specific physio-pathological conditions, age, genetic polymorphisms, ethnicity and particularly drug-drug interactions (DDIs). In this paper, the prediction success of DDIs involving various cytochrome P450 isoenzyme (CYP) modulators namely ketoconazole (a competitive inhibitor of CYP3A), itraconazole (a competitive inhibitor of CYP3A), clarithromycin (a mechanism-based inhibitor of CYP3A), quinidine (a competitive inhibitor of CYP2D6), paroxetine (a mechanism-based inhibitor of CYP2D6), ciprofloxacin (a competitive inhibitor of CYP1A2), fluconazole (a competitive inhibitor of CYP2C9/2C19) and rifampicin (an inducer of CYP3A) were assessed using Simcyp^® software. The aim of this report was to establish confidence in each CYP-specific modulator file so they can be used in the future for the prediction of DDIs involving new victim compounds. Our evaluation of these PBPK models suggested that they can be successfully used to evaluate DDIs in untested scenarios. The only noticeable exception concerned a quinidine inhibitor model that requires further improvement. Additionally, other important aspects such as model validation criteria were discussed.

No Increased Risk of Ketoconazole Toxicity in Drug-Drug Interaction Studies

In July 2013 the U.S. Food and Drug Administration (FDA) released a safety announcement regarding the use of ketoconazole and its adverse drug reactions. The FDA report advised against the use ketoconazole tablets as a first-line treatment for any fungal infections because of the risk of potentially serious drug-drug interactions and liver and adrenal gland complications. The European Medicines Agency (EMA) also proposed to limit the use of oral ketoconazole in fungal infections because of the same risk of harmful effects and interactions. In addition, the FDA also advised against the use of oral ketoconazole in drug interaction studies, in which it has been extensively used as an index inhibitor of drug metabolism. The aim of this investigation was to evaluate the risks of ketoconazole-induced hepatotoxicity described by the FDA and EMA in published drug interaction studies with ketoconazole and compare these data with the toxicity reported for ketoconazole when used as antifungal treatment. In the drug interaction studies (2355 participants; healthy volunteers and patients; median treatment duration, 6 days), only 40 participants were reported to have increased liver transaminase activity (1.7%), and no deaths were reported or associated with ketoconazole. In studies investigating ketoconazole treatment, patients were treated for 276 days (median), and 5.6% of patients had elevated liver enzyme activity. Because of the short treatment period in drug interaction studies the risk of drug-induced hepatic injury is considered very low. As such, we recommend that ketoconazole remain a safe CYP3A index inhibitor for use in drug interaction studies with healthy volunteers.

Evaluation of partial area under the concentration time curve to estimate midazolam apparent oral clearance for cytochrome P450 3A phenotyping

BACKGROUND

Midazolam apparent oral clearance (CLORAL) is used to estimate intestinal and hepatic cytochrome P450 (CYP) 3A activity. A limited sampling approach was performed to access a midazolam partial area under the concentration time curve (AUC) to estimate CLORAL.

METHODS

Midazolam plasma concentrations from healthy adults were obtained during CYP3A baseline (n=116), inhibition (n=75), and induction or activation (n=66) from seven published studies. Observed CLORAL and partial AUCs of AUC0-2, AUC0-4, AUC0-6, AUC1-2, AUC1-4, AUC2-4, and AUC2-6 were determined by noncompartmental analysis. Subject data were randomly divided into a training set and a validation set. Linear regression equations, derived from partial AUCs, were developed from training set data. Predicted CLORAL was determined from these equations from validation set data. Preset criterion was a coefficient of determination (r2) greater than or equal to 0.9. Bias and precision were evaluated by relative percent mean prediction error (%MPE) and relative percent mean absolute error (%MAE).

RESULTS

During CYP3A baseline conditions, all of the evaluated CLORAL equations had unacceptable r2 (range: 0.34-0.86). During CYP3A inhibition, all of the evaluated CLORAL equations had unacceptable %MAE. Acceptable r2, %MPE, and %MAE were observed during CYP3A induction/activation with AUC0-4 (r2=0.99, %MPE=3.9, %MAE=12.5) and AUC1-4 (r2=0.99, %MPE=6%, %MAE=11.1%). The same equations also predicted the extent of CYP3A induction as a lack of equivalence was observed with AUC0-4 and AUC1-4.

CONCLUSIONS

Midazolam partial AUCs were unable to estimate CYP3A activity during the evaluated baseline and inhibitory conditions. Midazolam CLORAL utilizing a partial AUC0-4 and AUC1-4 was able to estimate CYP3A induction with rifampin and Ginkgo biloba extract.

Predictions of metabolic drug-drug interactions using physiologically based modelling: Two cytochrome P450 3A4 substrates coadministered with ketoconazole or verapamil

Nowadays, evaluation of potential risk of metabolic drug-drug interactions (mDDIs) is of high importance within the pharmaceutical industry, in order to improve safety and reduce the attrition rate of new drugs. Accurate and early prediction of mDDIs has become essential for drug research and development, and in vitro experiments designed to evaluate potential mDDIs are systematically included in the drug development plan prior to clinical assessment. The aim of this study was to illustrate the value and limitations of the classical and new approaches available to predict risks of DDIs in the research and development processes. The interaction of cytochrome P450 (CYP) 3A4 inhibitors (ketoconazole and verapamil) with midazolam was predicted using the inhibitor concentration/inhibition constant ([I]/K(i)) approach, the static approach with added variability (Simcyp(R)), and whole-body physiologically based pharmacokinetic (WB-PBPK) modelling (acslXtreme(R)). Then an in-house reference drug was used to challenge the different approaches based on the midazolam experience. Predicted values (pharmacokinetic parameters, the area under the plasma concentration-time curve [AUC] ratio and plasma concentrations) were compared with observed values obtained after intravenous and oral administration in order to assess the accuracy of the prediction methods. With the [I]/K(i) approach, the interaction risk was always overpredicted for the midazolam substrate, regardless of its route of administration and the coadministered inhibitor. However, the predictions were always satisfactory (within 2-fold) for the reference drug. For the Simcyp(R) calculations, two of the three interaction results for midazolam were overpredicted, both when midazolam was given orally, whereas the prediction obtained when midazolam was administered intravenously was satisfactory. For the reference drug, all predictions could be considered satisfactory. For the WB-PBPK approach, all predictions were satisfactory, regardless of the substrate, route of administration, dose and coadministered inhibitor. DDI risk predictions are performed throughout the research and development processes and are now fully integrated into decision-making processes. The regulatory approach is useful to provide alerts, even at a very early stage of drug development. The 'steady state' approach in Simcyp(R) improves the prediction by using physiological knowledge and mechanistic assumptions. The DDI predictions are very useful, as they provide a range of AUC ratios that include individuals at the extremes of the population, in addition to the 'average tendency'. Finally, the WB-PBPK approach improves the predictions by simulating the concentration-time profiles and calculating the related pharmacokinetic parameters, taking into account the time of administration of each drug - but it requires a good understanding of the absorption, distribution, metabolism and excretion properties of the compound.