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The propensity for CYP3A4 induction by 2-(6-(5-chloro-2-methoxyphenyl)-4-oxo-2-thioxo-3,4-dihydropyrimidin-1(2H)-yl)acetamide (PF-06282999), an irreversible inactivator of myeloperoxidase, was examined in the present study. Studies using human hepatocytes revealed moderate increases in CYP3A4 mRNA and midazolam-1'-hydroxylase activity in a PF-06282999 dose-dependent fashion. At the highest tested concentration of 300 μM, PF-06282999 caused maximal induction in CYP3A4 mRNA and enzyme activity ranging from 56% to 86% and 47% t0 72%, respectively, of rifampicin response across the three hepatocyte donor pools. In a clinical drug-drug interaction (DDI) study, the mean midazolam Cmax and area under the curve (AUC) values following 14-day treatment with PF-06282999 decreased in a dose-dependent fashion with a maximum decrease in midazolam AUC0-inf and Cmax of ∼57.2% and 41.1% observed at the 500 mg twice daily dose. The moderate impact on midazolam pharmacokinetics at the 500 mg twice daily dose of PF-06282999 was also reflected in statistically significant changes in plasma 4β-hydroxycholesterol/cholesterol and urinary 6β-hydroxycortisol/cortisol ratios. Changes in plasma and urinary CYP3A4 biomarkers did not reach statistical significance at the 125 mg three times daily dose of PF-06282999, despite a modest decrease in midazolam systemic exposure. Predicted DDI magnitude based on the in vitro induction parameters and simulated pharmacokinetics of perpetrator (PF-06282999) and victim (midazolam) using the Simcyp (Simcyp Ltd., Sheffield, United Kingdom) population-based simulator were in reasonable agreement with the observed clinical data. Since the magnitude of the 4β-hydroxycholesterol or 6β-hydroxycortisol ratio change was generally smaller than the magnitude of the midazolam AUC change with PF-06282999, a pharmacokinetic interaction study with midazolam ultimately proved important for assessment of DDI via CYP3A4 induction.

The European Medicines Agency (EMA), the Pharmaceutical and Medical Devices Agency (PMDA), and the Food and Drug Administration (FDA) have issued guidelines for the conduct of drug-drug interaction studies. To examine the applicability of these regulatory recommendations specifically for induction, a group of scientists, under the auspices of the Drug Metabolism Leadership Group of the Innovation and Quality (IQ) Consortium, formed the Induction Working Group (IWG). A team of 19 scientists, from 16 of the 39 pharmaceutical companies that are members of the IQ Consortium and two Contract Research Organizations reviewed the recommendations, focusing initially on the current EMA guidelines. Questions were collated from IQ member companies as to which aspects of the guidelines require further evaluation. The EMA was then approached to provide insights into their recommendations on the following: 1) evaluation of downregulation, 2) in vitro assessment of CYP2C induction, 3) the use of CITCO as the positive control for CYP2B6 induction by CAR, 4) data interpretation (a 2-fold increase in mRNA as evidence of induction), and 5) the duration of incubation of hepatocytes with test article. The IWG conducted an anonymous survey among IQ member companies to query current practices, focusing specifically on the aforementioned key points. Responses were received from 19 companies. All data and information were blinded before being shared with the IWG. The results of the survey are presented, together with consensus recommendations on downregulation, CYP2C induction, and CYP2B6 positive control. Results and recommendations related to data interpretation and induction time course will be reported in subsequent articles.

Nine static models (seven basic and two mechanistic) and their respective cutoff values used for predicting cytochrome P450 3A (CYP3A) inhibition, as recommended by the US Food and Drug Administration and the European Medicines Agency, were evaluated using data from 119 clinical studies with orally administered midazolam as a substrate. Positive predictive error (PPE) and negative predictive error (NPE) rates were used to assess model performance, based on a cutoff of 1.25-fold change in midazolam area under the curve (AUC) by inhibitor. For reversible inhibition, basic models using total or unbound systemic inhibitor concentration [I] had high NPE rates (46-47%), whereas those using intestinal luminal ([I]gut) values had no NPE but a higher PPE. All basic models for time-dependent inhibition had no NPE and reasonable PPE rates (15-18%). Mechanistic static models that incorporate all interaction mechanisms and organ specific [I] values (enterocyte and hepatic inlet) provided a higher predictive precision, a slightly increased NPE, and a reasonable PPE. Various cutoffs for predicting the likelihood of CYP3A inhibition were evaluated for mechanistic models, and a cutoff of 1.25-fold change in midazolam AUC appears appropriate.

Drug-drug interactions (DDIs) due to CYP2B6 induction have recently gained prominence and clinical induction risk assessment is recommended by regulatory agencies. This work aimed to evaluate the potency of CYP2B6 versus CYP3A4 induction in vitro and from clinical studies and to assess the predictability of efavirenz versus bupropion as clinical probe substrates of CYP2B6 induction. The analysis indicates that the magnitude of CYP3A4 induction was higher than CYP2B6 both in vitro and in vivo. The magnitude of DDIs caused by induction could not be predicted for bupropion with static or dynamic models. On the other hand, the relative induction score, net effect, and physiologically based pharmacokinetics SimCYP models using efavirenz resulted in improved DDI predictions. Although bupropion and efavirenz have been used and are recommended by regulatory agencies as clinical CYP2B6 probe substrates for DDI studies, CYP3A4 contributes to the metabolism of both probes and is induced by all reference CYP2B6 inducers. Therefore, caution must be taken when interpreting clinical induction results because of the lack of selectivity of these probes. Although in vitro-in vivo extrapolation for efavirenz performed better than bupropion, interpretation of the clinical change in exposure is confounded by the coinduction of CYP2B6 and CYP3A4, as well as the increased contribution of CYP3A4 to efavirenz metabolism under induced conditions. Current methods and probe substrates preclude accurate prediction of CYP2B6 induction. Identification of a sensitive and selective clinical substrate for CYP2B6 (fraction metabolized > 0.9) is needed to improve in vitro-in vivo extrapolation for characterizing the potential for CYP2B6-mediated DDIs. Alternative strategies and a framework for evaluating the CYP2B6 induction risk are proposed.

WHAT IS ALREADY KNOWN ABOUT THIS SUBJECT • Tofacitinib (CP-690,550) is a novel, oral Janus kinase inhibitor being investigated as a targeted immunomodulator and disease-modifying therapy in rheumatoid arthritis. • Non-renal elimination accounts for 70% of the total clearance of tofacitinib and the metabolism is primarily mediated by cytochrome P450 (CYP) 3A4. • This study was required to determine the effect of tofacitinib on the in vivo pharmacokinetics of a sensitive CYP3A4 substrate. WHAT THIS STUDY ADDS • The pharmacokinetics of midazolam, a sensitive CYP3A4 substrate, are not altered when co-administered with tofacitinib in healthy subjects. • Tofacitinib is unlikely to affect the clearance of drugs metabolized by CYP enzymes. • There is no need for dose adjustments of CYP substrates when co-administered with tofacitinib. AIMS To investigate inhibitive and inductive effects of tofacitinib (CP-690,550), a Janus kinase inhibitor, on CYP3A4 function via in vitro and in vivo studies. METHODS In vitro experiments were conducted to assess the inhibition and induction potential of tofacitinib for major drug metabolizing enzymes (CYP1A2, CYP2B6, CYP2C8, CYP2C9, CYP2C19, CYP2D6 and CYP3A4). A phase 1, randomized, open-label, two-way crossover study (NCT00902460) was conducted to confirm the lack of inhibitive/inductive effect on a sensitive CYP3A4 substrate, midazolam, in healthy subjects. Midazolam pharmacokinetics were assessed over 24 h following single dose 2 mg administration prior to administering tofacitinib and after twice daily dosing of tofacitinib 30 mg for 6 days. The primary endpoint was midazolam area under the concentration–time profile, from time 0 to infinity (AUC(0,∞)). RESULTS In vitro studies demonstrated low potential for CYP inhibition (IC50 estimates tofacitinib >30 µm), CYP3A4 mRNA induction (observed at tofacitinib concentrations ≥25 µm) and no effect on enzymatic activity of CYP substrates. In the human study, AUC(0,∞) adjusted geometric mean ratio for midazolam plus tofacitinib to midazolam alone was 103.97% [90% confidence interval (CI) 95.57, 113.12], wholly within the pre-specified acceptance region (80, 125). The 90% CI for the ratio of adjusted geometric means of maximum plasma concentration (Cmax) (95.98, 108.87) was also wholly within this acceptance region. CONCLUSIONS These data confirm a lack of an inhibitive or inductive effect of tofacitinib on CYP3A activity in humans and, in conjunction with in vitro data, support the conclusion that tofacitinib is unlikely to influence the CYP enzyme system as a whole.

Increasing use of therapeutic proteins (TPs) in polypharmacy settings calls for more in-depth understanding of the biological interactions that can lead to increased toxicity or loss of pharmacological effect. Factors such as patient population, medications that are likely to be coadministered in that population, clearance mechanisms of a TP, and concomitant drugs have to be taken into account to determine the potential for drug-drug interactions (DDIs). The most well documented TP DDI mechanism involves cytokine-mediated changes in drug-metabolizing enzymes. Because of the limitations of the current preclinical models for addressing this type of DDI, clinical evaluation is currently the most reliable approach. Other DDI mechanisms need to be addressed on a case-by-case basis. These include altered clearance of TPs resulting from the changes in the target protein levels by the concomitant medication, displacement of TPs from binding proteins, modulation of Fcγ receptor expression, and others. The purpose of this review is to introduce the approach used by Pfizer scientists for evaluation of the DDI potential of novel TP products during drug discovery and development.

Objectives The aim of this study was to use time-lapse confocal laser scanning microscopy to establish a more sensitive and specific method for evaluating P-glycoprotein activity in Caco-2 cells. Methods The change in the fluorescence of residual rhodamine 123 at the apical and central regions of Caco-2 cells was measured in the presence of digoxin or St John's wort by using time-lapse confocal laser scanning microscopy. The data were compared with measurements made using conventional techniques, a fluorescence microplate reader and a fluorescence microscope. Key findings The percentage decrease of rhodamine 123 caused by 10 µm digoxin or 0.1 µg/ml St John's wort was significantly larger in the apical region of the Caco-2 cell than in the central region or in the whole cell. The digoxin-induced inhibition in the apical region as measured by time-lapse confocal laser scanning microscopy was greater than that measured in the whole cell by a microplate reader or a fluorescence microscope. Conclusions The assay of residual rhodamine 123 in the apical region of Caco-2 cells by confocal laser scanning microscopy was more sensitive than the conventional methods using a microplate reader or fluorescence microscopy. It will be a valuable screening tool for studying both the inhibition and induction of P-glycoprotein activity.

Drug-drug interactions caused by induction of metabolizing enzymes, particularly CYP3A, can impact the efficacy and safety of co-administered drugs. It is, therefore, important to understand a new compound's potential for enzyme induction and to understand how to use the induction data generated in vitro to predict potential for drug-drug interactions in vivo.Recent advances in methods for using in vitro data to predict potential for CYP3A induction in vivo are reviewed.The reader will gain a comprehensive understanding of the advantages and disadvantages of various prediction methods for induction and be able to directly compare different methods using a common in vitro data set.The various methods for predicting clinical CYP3A induction from in vitro induction data all have demonstrated utility; it is the authors' opinion that the correlation-based approaches offer as good or better predictivity and have simpler input requirements than more complex approaches. Of the different correlation approaches, the relatively simple unbound C(max)/EC(50) or AUC/EC(50) approaches are the simplest and yet show the best correlation to the observed clinical data. While the approaches discussed herein represent an improvement in our understanding of the predictive value of in vitro induction data, it is important to recognize that there is still room for improvement in quantitative prediction of magnitude of drug interactions due to induction.

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.

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.

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 (EC(50) and E(max)) 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.

1. Induction is an important mechanism contributing to drug–drug interactions. It is most commonly evaluated in the human hepatocyte assay over 48-h or 72-h incubation period. However, whether the overall exposure (i.e. Area Under the Curve (AUC) or Cave) or maximum exposure (i.e. Cmax) of the inducer is responsible for the magnitude of subsequent induction has not been thoroughly investigated. Additionally, in vitro induction assays are typically treated as static systems, which could lead to inaccurate induction potency estimation. Hence, European Medicines Agency (EMA) guidance now specifies quantitation of drug levels in the incubation. 2. This work treated the typical in vitro evaluation of rifampin induction as an in vivo system by generating various target engagement profiles, measuring free rifampin concentration over 3 d of incubation and evaluating the impact of these factors on final induction response. 3. This rifampin-based analysis demonstrates that the induction process is driven by time-averaged target engagement (i.e. AUC-driven). Additionally, depletion of rifampin in the incubation medium over 3 d as well as non-specific/specific binding were observed. 4. These findings should help aid the discovery of clinical candidates with minimal induction liability and further expand our knowledge in the quantitative translatability of in vitro induction assays.

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 (E(max) and EC(50) 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 (r(2) 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.

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.

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. Clinical Pharmacology & Therapeutics (2014); 95 2, 216–227. doi:10.1038/clpt.2013.170

The increase in cytochrome P450 (P450) enzyme activity noted upon exposure to therapeutics can elicit marked drug-drug interactions (DDIs) that may ultimately result in poor clinical outcome or adverse drug effects. As such, in vitro model systems that can rapidly and accurately determine whether potential therapeutics activate the human pregnane X receptor (PXR) and thus induce CYP3A P450 levels are highly sought after tools for drug discovery. To that end, we assessed whether DPX2 cells, a HepG2-derived cell line stably integrated with a PXR expression vector plus a luciferase reporter, could detect agents that not only cause PXR activation/CYP3A induction but also elicit clinical DDIs. All 20 clinical inducers and 9 of 15 clinical noninducers examined activated PXR in DPX2 cells (E(max) > 8-fold), although activation parameters obtained with the noninducers were not predictive of DDI. The relative induction score, calculated by combining PXR activation parameters (EC(50) and E(max)) in DPX2 cells for seven inducers plus four noninducers with their efficacious total plasma concentrations, strongly correlated (R(2) = 0.90) with the magnitude of induction of midazolam clearance. Thus, the DPX cell-based PXR activation system is not only capable of distinguishing potential inducers in a high-throughput manner but can also differentiate among compounds in predicting the magnitude of induction-mediated DDIs, providing a means for structure-activity relationship screening during discovery and development.

Increasing use of therapeutic proteins (TPs) in polypharmacy settings calls for more in-depth understanding of the biological interactions that can lead to increased toxicity or loss of pharmacological effect. Factors such as patient population, medications that are likely to be coadministered in that population, clearance mechanisms of a TP, and concomitant drugs have to be taken into account to determine the potential for drug-drug interactions (DDIs). The most well documented TP DDI mechanism involves cytokine-mediated changes in drug-metabolizing enzymes. Because of the limitations of the current preclinical models for addressing this type of DDI, clinical evaluation is currently the most reliable approach. Other DDI mechanisms need to be addressed on a case-by-case basis. These include altered clearance of TPs resulting from the changes in the target protein levels by the concomitant medication, displacement of TPs from binding proteins, modulation of Fcγ receptor expression, and others. The purpose of this review is to introduce the approach used by Pfizer scientists for evaluation of the DDI potential of novel TP products during drug discovery and development.

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.

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.

Inactivation of human P450 enzymes represents an important mechanism of drug–drug interactions (DDIs). Inactivators are distinct from other inhibitors in that the affected enzyme is responsible for bioactivating an otherwise inert drug into an intermediate that can irreversibly damage the enzyme, and recovery of activity in vivo requires the biosynthesis of new enzyme. Whether a new drug will be a mechanism-based inactivator depends on the identity of chemical substituents present in the substrate and their metabolism by the P450 enzyme. Experimental approaches used to define new drugs as possible time-dependent inhibitors and mechanism-based inactivators are described. Finally, it has been demonstrated that inactivation kinetic parameters generated in vitro can be used to predict DDI. The methods used to do this are described along with existing uncertainties in the input parameters needed for accurate predictions.

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 f(m) and F(G) 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.

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 Ki, 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, fm) 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®, 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 (fm); 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.

The relationship between lipophilicity and CYP2D6 affinity of cyclic tertiary (N-alkyl-4-phenyl-1,2,3,6-tetrahydropyridines) and quaternary (N-alkyl-4-phenylpyridinium) amines was examined. The 1,2,3,6-tetrahydropyridine scaffold was chosen due to its common occurrence in the structures of CYP2D6 ligands such as the Parkinsonian neurotoxin 1-methyl-4-phenyl-1,2,3,6-tetrahydropyridine (MPTP) and the dehydrated haloperidol metabolite N-[4-(4-fluorophenyl)-4-oxobutyl]-4-(4-chlorophenyl)-1,2,3,6-tetrahydropyridine (HPTP). Likewise, the pyridinium framework is found in and 4-(4-chlorophenyl)-1-[4-(4-fluorophenyl)-4-oxobutyl]pyridinium and N-methyl-4-phenylpyridinium (MPP(+)), the positively charged metabolites of MPTP and haloperidol. The lack of CYP2D6 inhibition by MPTP and its pyridinium metabolite MPP(+) was due to their hydrophilic nature since higher N-alkyl homologs revealed substantial increases in inhibitory potency against recombinant CYP2D6-mediated bufuralol-1'-hydroxylation. The reasonable correlation between lipophilicity and CYP2D6 inhibition by pyridiniums and 1,2,3,6-tetrahydropyridines was only limited to straight chain N-alkyl analogs, since certain N-alkylaryl analogs of lower lipophilicity were better CYP2D6 inhibitors. CYP2D6 substrate properties of straight chain N-alkyltetrahydropyridines were also governed by lipophilicity, and N-heptyl-4-phenyl-1,2,3,6-tetrahydropyridine was the optimal substrate (K(mapp) = 0.63 microM). Metabolism studies indicated that the N-heptyl analog underwent monohydroxylation on the aromatic ring and on the N-heptyl group suggesting that 1,2,3,6-tetrahydropyridines can bind in more than one conformation in the CYP2D6 active site. Increased lipophilicity of haloperidol metabolites did not correlate with inhibitory potency since the more lipophilic HPTP metabolite was less potent as an inhibitor than reduced-haloperidol and reduced-HPTP. Furthermore, HPTP and reduced-HPTP, of comparable lipophilicity to the N-heptyltetrahydropyridine analog were inactive as CYP2D6 substrates. This observation suggests that steric constraints rather than lipophilicity are responsible for the lack of CYP2D6 substrate properties of cyclic tertiary amines tethered to bulky N-substituents. This phenomenon appears to be a common theme among several cyclic tertiary amine-containing anti-depressants and should be taken into consideration when designing central nervous system agents devoid of CYP2D6 substrate properties.

The understanding of the processes of induction of human drug-metabolizing enzymes has advanced considerably over the past decade. If we concentrate on CYP3A4, the most abundant form of cytochrome P450 and the one most involved in the clearance of the majority of pharmaceuticals, a clear