Odette A. Fahmi

Comparison of Different Algorithms for Predicting Clinical Drug-Drug Interactions, Based on the Use of CYP3A4 in Vitro Data: Predictions of Compounds as Precipitants of Interaction

Cytochrome P450 3A4 (CYP3A4) is the most important enzyme in drug metabolism and because it is the most frequent target for pharmacokinetic drug-drug interactions (DDIs) it is highly desirable to be able to predict CYP3A4-based DDIs from in vitro data. In this study, the prediction of clinical DDIs for 30 drugs on the pharmacokinetics of midazolam, a probe substrate for CYP3A4, was done using in vitro inhibition, inactivation, and induction data. Two DDI prediction approaches were used, which account for effects at both the liver and intestine. The first was a model that simultaneously combines reversible inhibition, time-dependent inactivation, and induction data with static estimates of relevant in vivo concentrations of the precipitant drug to provide point estimates of the average magnitude of change in midazolam exposure. This model yielded a success rate of 88% in discerning DDIs with a mean -fold error of 1.74. The second model was a computational physiologically based pharmacokinetic model that uses dynamic estimates of in vivo concentrations of the precipitant drug and accounts for interindividual variability among the population (Simcyp). This model yielded success rates of 88 and 90% (for “steady-state” and “time-based” approaches, respectively) and mean -fold errors of 1.59 and 1.47. From these findings it can be concluded that in vivo DDIs for CYP3A4 can be predicted from in vitro data, even when more than one biochemical phenomenon occurs simultaneously.

A COMBINED MODEL FOR PREDICTING CYP3A4 CLINICAL NET DRUG-DRUG INTERACTION BASED ON CYP3A4 INHIBITION, INACTIVATION, AND INDUCTION DETERMINED IN VITRO

Although approaches to the prediction of drug-drug interactions (DDIs) arising via time-dependent inactivation have recently been developed, such approaches do not account for simple competitive inhibition or induction. Accordingly, these approaches do not provide accurate predictions of DDIs arising from simple competitive inhibition (e.g., ketoconazole) or induction of cytochromes P450 (e.g., phenytoin). In addition, methods that focus upon a single interaction mechanism are likely to yield misleading predictions in the face of mixed mechanisms (e.g., ritonavir). As such, we have developed a more comprehensive mathematical model that accounts for the simultaneous influences of competitive inhibition, time-dependent inactivation, and induction of CYP3A in both the liver and intestine to provide a net drug-drug interaction prediction in terms of area under the concentration-time curve ratio. This model provides a framework by which readily obtained in vitro values for competitive inhibition, time-dependent inactivation and induction for the precipitant compound as well as literature values for fm and FG for the object drug can be used to provide quantitative predictions of DDIs. Using this model, DDIs arising via inactivation (e.g., erythromycin) continue to be well predicted, whereas those arising via competitive inhibition (e.g., ketoconazole), induction (e.g., phenytoin), and mixed mechanisms (e.g., ritonavir) are also predicted within the ranges reported in the clinic. This comprehensive model quantitatively predicts clinical observations with reasonable accuracy and can be a valuable tool to evaluate candidate drugs and rationalize clinical DDIs.

Use of immortalized human hepatocytes to predict the magnitude of clinical drug-drug interactions caused by CYP3A4 induction.

Cytochrome P4503A4 (CYP3A4) is the principal drug-metabolizing enzyme in human liver. Drug-drug interactions (DDIs) caused by induction of CYP3A4 can result in decreased exposure to coadministered drugs, with potential loss of efficacy. Immortalized hepatocytes (Fa2N-4 cells) have been proposed as a tool to identify CYP3A4 inducers. The purpose of the current studies was to characterize the effect of known inducers on CYP3A4 in Fa2N-4 cells, and to determine whether these in vitro data could reliably project the magnitude of DDIs caused by induction. Twenty-four compounds were chosen for these studies, based on previously published data using primary human hepatocytes. Eighteen compounds had been shown to be positive for induction, and six compounds had been shown to be negative for induction. In Fa2N-4 cells, all 18 positive controls produced greater than 2-fold maximal CYP3A4 induction, and all 6 negative controls produced less than 1.5-fold maximal CYP3A4 induction. Subsequent studies were conducted to determine the relationship between in vitro induction data and in vivo induction response. The approach was to relate in vitro induction data (Emax and EC50 values) with efficacious free plasma concentrations to calculate a relative induction score. This score was then correlated with decreases in area under the plasma concentration versus time curve values for coadministered CYP3A4 object drugs (midazolam or ethinylestradiol) from previously published clinical DDI studies. Excellent correlations (r2 values >0.92) were obtained, suggesting that Fa2N-4 cells can be used for identification of inducers as well as prediction of the magnitude of clinical DDIs.

Cytochrome P450 3A4 mRNA Is a More Reliable Marker than CYP3A4 Activity for Detecting Pregnane X Receptor-Activated Induction of Drug-Metabolizing Enzymes

Induction of cytochrome P450 (P450) activity in the clinic can result in therapeutic failure such as tissue rejection in transplant patients or unwanted pregnancy, among others. CYP3A4 is by far the most abundant isoform and is responsible for the majority of P450-related metabolism of all marketed drugs. However, it is of importance to understand the significance of induction mediated through other P450 enzymes. The objective of this investigation was to evaluate several known inducers in vitro using cryopreserved human hepatocytes, with the aim of assessing the relevant induction of CYP3A4, CYP2B6, CYP2C9, CYP2C19, and CYP3A5, based on mRNA expression. CYP3A4 induction was also assessed based on enzymatic activity in three different lots to investigate whether mRNA expression data have any advantages over enzymatic activity. In general, the mRNA fold-induction data results were more sensitive compared with activity data, and more informative in cases in which the drug is also a P450 inhibitor. The induction of transcription of other drug-metabolizing enzymes including CYP2B6 and CYP2C enzymes occurred every time that CYP3A4 mRNA levels increased, but to a lesser extent, indicating that measurement of CYP3A4 mRNA is a sensitive marker for the induction of these other enzymes. This was the case even for enzymes and inducers that are known to also act via the constitutive androstane receptor pathway. Finally, the utility of in vitro induction measurements in the identification of clinically meaningful inducers was tested by using two simple binary classification approaches: 1) fold-induction versus vehicle control and 2) induction response relative to rifampin. The best classification was observed when the cutoff criteria based on fold induction relative to the vehicle control, using mRNA data are used.

Application of CYP3A4 in vitro data to predict clinical drug-drug interactions; predictions of compounds as objects of interaction.

WHAT IS ALREADY KNOWN ABOUT THIS SUBJECT Numerous retrospective analyses have shown the utility of in vitro systems for predicting potential drug-drug interactions (DDIs). Prediction of DDIs from in vitro data is commonly obtained using estimates of enzyme K(i), inhibitor and substrate concentrations and absorption rate for substrate and inhibitor. WHAT THIS STUDY ADDS Using a generic approach for all test compounds, the findings from the current study showed the use of recombinant P450s provide a more robust in vitro measure of P450 contribution (fraction metabolized, f(m)) than that achieved when using chemical inhibitors in combination with human liver microsomes, for the prediction of potential CYP3A4 drug-drug interactions prior to clinical investigation. The current study supported the use of SIMCYP(R), a modelling and simulation software in utilizing the in vitro measures in the prediction of potential drug-drug interactions. AIMS The aim of this study was to explore and optimize the in vitro and in silico approaches used for predicting clinical DDIs. A data set containing clinical information on the interaction of 20 Pfizer compounds with ketoconazole was used to assess the success of the techniques. METHODS The study calculated the fraction and the rate of metabolism of 20 Pfizer compounds via each cytochrome P450. Two approaches were used to determine fraction metabolized (f(m)); 1) by measuring substrate loss in human liver microsomes (HLM) in the presence and absence of specific chemical inhibitors and 2) by measuring substrate loss in individual cDNA expressed P450s (also referred to as recombinant P450s (rhCYP)) The fractions metabolized via each CYP were used to predict the drug-drug interaction due to CYP3A4 inhibition by ketoconazole using the modelling and simulation software SIMCYP. RESULTS When in vitro data were generated using Gentest supersomes, 85% of predictions were within two-fold of the observed clinical interaction. Using PanVera baculosomes, 70% of predictions were predicted within two-fold. In contrast using chemical inhibitors the accuracy was lower, predicting only 37% of compounds within two-fold of the clinical value. Poorly predicted compounds were found to either be metabolically stable and/or have high microsomal protein binding. The use of equilibrium dialysis to generate accurate protein binding measurements was especially important for highly bound drugs. CONCLUSIONS The current study demonstrated that the use of rhCYPs with SIMCYP provides a robust in vitro system for predicting the likelihood and magnitude of changes in clinical exposure of compounds as a consequence of CYP3A4 inhibition by a concomitantly administered drug.

PREDICTION OF DRUG-DRUG INTERACTIONS FROM IN VITRO INDUCTION DATA Application of the Relative Induction Score Approach Using Cryopreserved Human Hepatocytes

Cytochrome P450 induction-mediated drug-drug interaction (DDI) is one of the major concerns in clinical practice and for the pharmaceutical industry. Previously, a novel approach [the relative induction score (RIS)] was developed using the Fa2N-4 immortalized human hepatocyte line and proposed as a tool for predicting magnitude of clinical DDIs caused by induction of CYP3A. The approach is based on combining in vitro induction parameters (EC50 and Emax) with the efficacious free plasma concentrations to calculate a relative induction score, which is correlated to the magnitude of clinical DDI for midazolam or ethinyl estradiol. To expand the applicability of the RIS model, we have measured induction caused by ten drugs in two different lots of human cryopreserved hepatocytes and correlated the data to clinical DDIs using the RIS. The results demonstrated that, as with Fa2N-4 hepatocytes, sigmoidal relationships can be derived between RIS and magnitude of induction of midazolam and ethinyl estradiol clearance in cryopreserved human hepatocytes. This study demonstrates the general applicability of the relative induction score approach using the human cryopreserved hepatocyte model to predict clinical DDI.

Unexpected Effect of Rifampin on the Pharmacokinetics of Linezolid: In Silico and In Vitro Approaches to Explain Its Mechanism

The effect of rifampin on the steady‐state pharmacokinetics of linezolid was evaluated in an open‐label, multiple‐dose, crossover study in 16 healthy subjects. When coadministered with rifampin, area under the plasma concentration‐time curve over the dosing interval and maximum concentration values for linezolid were reduced approximately 32% and 21%, respectively. Time to maximum concentration and apparent volume of distribution were generally similar between treatments. The mean half‐life and apparent oral clearance were decreased for the combination treatment compared with linezolid alone. In vitro and in silico approaches were used to evaluate this interaction. In human hepatocytes, the metabolism of linezolid was increased by 1.3‐ to 1.6‐fold when the cells were pretreated with rifampin, compared with a 19‐ to 40‐fold increase in testosterone metabolism, a positive control for cytochrome P4503A activity. This increase in linezolid and testosterone metabolism was partially inhibited (∼50%) by ketoconazole. Modeling of these data using Simcyp suggested that rifampin inducible drug metabolizing enzymes, such as cytochrome P4503A, have a very minor contribution to linezolid clearance, which increases when rifampin is coadministered. The clinical significance of the decreased linezolid levels is unclear. Linezolid and rifampin administered alone or in combination was generally safe and well tolerated.

Development of an in vitro drug–drug interaction assay to simultaneously monitor five cytochrome P450 isoforms and performance assessment using drug library compounds

INTRODUCTION Inhibition of cytochrome P450 (CYP) is a principal mechanism for metabolism-based drug-drug interactions (DDIs). This article describes a robust, high-throughput CYP-mediated DDI assay using a cocktail of 5 clinically relevant probe substrates with quantification by liquid chromatography/tandem mass spectrometry (LC/MS-MS). METHODS The assay consisted of human liver microsomes and a cocktail of probe substrates metabolized by the five major CYP isoforms (tacrine for CYP1A2, diclofenac for CYP2C9, (S)-mephenytoin for CYP2C19, dextromethorphan for CYP2D6 and midazolam for CYP3A4). The assay was fully automated in both 96- and 384-well formats. RESULTS A series of experiments were conducted to define the optimal kinetic parameters and solvent concentrations, as well as, to assess potential reactant and product interference. The assay was validated against known CYP inhibitors (miconazole, sulfaphenazole, ticlopidine, quinidine, ketoconazole, itraconazole, fluoxetine) and evaluated in a screening environment by testing 9494 compounds. DISCUSSION Our findings show that this assay has application in early stage drug discovery to economically, reliably and accurately assess compounds for DDIs.

Evaluation of Various Static and Dynamic Modeling Methods to Predict Clinical CYP3A Induction Using In Vitro CYP3A4 mRNA Induction Data

Several drug–drug interaction (DDI) prediction models were evaluated for their ability to identify drugs with cytochrome P450 (CYP)3A induction liability based on in vitro mRNA data. The drug interaction magnitudes of CYP3A substrates from 28 clinical trials were predicted using (i) correlation approaches (ratio of the in vivo peak plasma concentration (Cmax) to in vitro half‐maximal effective concentration (EC50); and relative induction score), (ii) a basic static model (calculated R3 value), (iii) a mechanistic static model (net effect), and (iv) mechanistic dynamic (physiologically based pharmacokinetic) modeling. All models performed with high fidelity and predicted few false negatives or false positives. The correlation approaches and basic static model resulted in no false negatives when total Cmax was incorporated; these models may be sufficient to conservatively identify clinical CYP3A induction liability. Mechanistic models that include CYP inactivation in addition to induction resulted in DDI predictions with less accuracy, likely due to an overprediction of the inactivation effect.

Critical Review of Preclinical Approaches to Investigate Cytochrome P450–Mediated Therapeutic Protein Drug-Drug Interactions and Recommendations for Best Practices: A White Paper

Drug-drug interactions (DDIs) between therapeutic proteins (TPs) and small-molecule drugs have recently drawn the attention of regulatory agencies, the pharmaceutical industry, and academia. TP-DDIs are mainly caused by proinflammatory cytokine or cytokine modulator–mediated effects on the expression of cytochrome P450 enzymes. To build consensus among industry and regulatory agencies on expectations and challenges in this area, a working group was initiated to review the preclinical state of the art. This white paper represents the observations and recommendations of the working group on the value of in vitro human hepatocyte studies for the prediction of clinical TP-DDI. The white paper was developed following a “Workshop on Recent Advances in the Investigation of Therapeutic Protein Drug-Drug Interactions: Preclinical and Clinical Approaches” held at the Food and Drug Administration White Oak Conference Center on June 4 and 5, 2012. Results of a workshop poll, cross-laboratory data comparisons, and the overall recommendations of the in vitro working group are presented herein. The working group observed that evaluation of TP-DDI for anticytokine monoclonal antibodies is currently best accomplished with a clinical study in patients with inflammatory disease. Treatment-induced changes in appropriate biomarkers in phase 2 and 3 studies may indicate the potential for a clinically measurable treatment effect on cytochrome P450 enzymes. Cytokine-mediated DDIs observed with anti-inflammatory TPs cannot currently be predicted using in vitro data. Future success in predicting clinical TP-DDIs will require an understanding of disease biology, physiologically relevant in vitro systems, and more examples of well conducted clinical TP-DDI trials.