A Novel Model for the Prediction of Drug-Drug Interactions in Humans Based on in Vitro Cytochrome P450 Phenotypic Data

Quantitative prediction of CYP3A-mediated drug-drug interactions by correctly estimating fraction metabolized using human liver chimeric mice

BACKGROUND AND PURPOSE

Fraction metabolized (fm ) and fraction transported (ft ) are important for understanding drug-drug interactions (DDIs) in drug discovery and development. However, current in vitro systems cannot accurately estimate in vivo fm due to inability to reflect the ft by efflux transporters (ft,efflux ). This study demonstrates how CYP3A-mediated DDI for CYP3A/P-gp substrates can be predicted using Hu-PXB mice as human liver chimeric mice.

EXPERIMENTAL APPROACH

For estimating human in vitro fm by CYP3A enzyme (fm,CYP3A,in vitro ), six drugs, including CYP3A/P-gp substrates (alprazolam, cyclosporine, docetaxel, midazolam, prednisolone, and theophylline) and human hepatocytes were incubated with or without ketoconazole as a CYP3A inhibitor. We calculated fm,CYP3A,in vitro based on hepatic intrinsic clearance. To estimate human in vivo fm,CYP3A (fm,CYP3A,in vivo ), we collected information on clinical DDI caused by ketoconazole for these six drugs. We calculated fm,CYP3A,in vivo using the change of total clearance (CLtotal ). For evaluating the human DDI predictability, the six drugs were administered intravenously to Hu-PXB and SCID mice with or without ketoconazole. We calculated the change of CLtotal caused by ketoconazole. We compared the CLtotal change in humans with that in Hu-PXB and SCID mice.

KEY RESULTS

The fm,CYP3A,in vitro was overestimated compared to the fm,CYP3A,in vivo . Hu-PXB mice showed much better correlation in the change of CLtotal with humans (R2  = 0.95) compared to SCID mice (R2  = 0.0058).

CONCLUSIONS AND IMPLICATIONS

CYP3A-mediated DDI can be predicted by correctly estimating human fm,CYP3A,in vivo using Hu-PXB mice. These mice could be useful predicting hepatic fm and ft,efflux .

Predictive In Vitro-In Vivo Extrapolation for Time Dependent Inhibition of CYP1A2, CYP2C8, CYP2C9, CYP2C19 and CYP2D6 Using Pooled Human Hepatocytes, Human Liver Microsomes, and a Simple Mechanistic Static Model

Inactivation of Cytochrome P450 (CYP450) enzymes can lead to significant increases in exposure of co-medicants. The majority of reported in vitro to in vivo extrapolation (IVIVE) data have historically focused on CYP3A4 leaving the assessment of other CYP isoforms insubstantial. To this end, the utility of human hepatocytes (HHEP) and microsome (HLM) to predict clinically relevant DDIs was investigated with a focus on CYP1A2, CYP2C8, CYP2C9, CYP2C19 and CYP2D6. Evaluation of IVIVE for CYP2B6 was limited to only weak inhibition. A search of the University of Washington Drug-Drug Interaction Database was conducted to identify a clinically relevant weak, moderate and strong inhibitor for selective substrates of CYP1A2, CYP2C8, CYP2C9, CYP2C19 and CYP2D6, resulting in 18 inhibitors for in vitro characterization against 119 clinical interaction studies. Pooled human hepatocytes and HLM were pre-incubated with increasing concentrations of inhibitors for designated timepoints. Time dependent inhibition (TDI) was detected in HLM for four moderate/strong inhibitors suggesting that some optimization of incubation conditions (i.e. lower protein concentrations) is needed to capture weak inhibition. Clinical risk assessment was conducted by incorporating the in vitro derived kinetic parameters k_inact and K_I into static equations recommended by regulatory authorities. Significant overprediction was observed when applying the basic models recommended by regulatory agencies. Mechanistic static models (MSM), which consider the fraction of metabolism through the impacted enzyme, using the unbound hepatic inlet concentration lead to the best overall prediction accuracy with 92% and 85% of data from HHEPs and HLM, respectively, within 2-fold of the observed value. Significance Statement Collectively, the data demonstrate that coupling time-dependent inactivation parameters derived from pooled human hepatocytes and HLM with a mechanistic static model provides an easy and quantitatively accurate means to determine clinical DDI risk from in vitro data. Weak and moderate inhibitors did not show TDI under standard incubation conditions using HLM and optimization of incubation conditions is warranted. Recommendations are made with respect to input parameters for IVIVE of TDI with non-CYP3A enzymes using available data from HLM and HHEPs.

Reversible Mechanisms of Enzyme Inhibition and Resulting Clinical Significance

Inhibition of a drug-metabolizing enzyme by the reversible interaction of a drug with the enzyme, thus decreasing the metabolism of another drug, is a major cause of clinically significant drug-drug interactions. This chapter defines the four reversible mechanisms of inhibition exhibited by drugs: competitive, noncompetitive, uncompetitive, and mixed competitive/noncompetitive. An in vitro procedure to determine the potential of a drug to be a reversible inhibitor is also provided. Finally, a number of examples of clinically significant drug-drug interactions resulting from reversible inhibition are described.

Impact of Intracellular Concentrations on Metabolic Drug-Drug Interaction Studies

Accurate prediction of drug-drug interactions (DDI) is a challenging task in drug discovery and development. It requires determination of enzyme inhibition in vitro which is highly system-dependent for many compounds. The aim of this study was to investigate whether the determination of intracellular unbound concentrations in primary human hepatocytes can be used to bridge discrepancies between results obtained using human liver microsomes and hepatocytes. Specifically, we investigated if Kp_uu could reconcile differences in CYP enzyme inhibition values (K_i or IC₅₀). Firstly, our methodology for determination of Kp_uu was optimized for human hepatocytes, using four well-studied reference compounds. Secondly, the methodology was applied to a series of structurally related CYP2C9 inhibitors from a Roche discovery project. Lastly, the Kp_uu values of three commonly used CYP3A4 inhibitors—ketoconazole, itraconazole, and posaconazole—were determined and compared to compound-specific hepatic enrichment factors obtained from physiologically based modeling of clinical DDI studies with these three compounds. Kp_uu obtained in suspended human hepatocytes gave good predictions of system-dependent differences in vitro. The Kp_uu was also in fair agreement with the compound-specific hepatic enrichment factors in DDI models and can therefore be used to improve estimations of enrichment factors in physiologically based pharmacokinetic modeling.

Allosteric Interactions in Human Cytochrome P450 CYP3A4: The Role of Phenylalanine 213

The role of Phe213 in the allosteric mechanism of human cytochrome P450 CYP3A4 was studied using a combination of progesterone (PGS) and carbamazepine (CBZ) as probe substrates. We expressed, purified, and incorporated into POPC Nanodiscs three mutants, F213A, F213S, and F213Y, and compared them with wild-type (WT) CYP3A4 by monitoring spectral titration, the rate of NADPH oxidation, and steady-state product turnover rates with pure substrates and substrate mixtures. All mutants demonstrated higher activity with CBZ, lower activity with PGS, and a reduced level of activation of CBZ epoxidation by PGS, which was most pronounced in the F213A mutant. Using all-atom molecular dynamics simulations, we compared the dynamics of WT CYP3A4 and the F213A mutant incorporated into the lipid bilayer and the effect of the presence of the PGS molecule at the allosteric peripheral site and evaluated the critical role of Phe213 in mediating the heterotropic allosteric interactions in CYP3A4.

Biomedical Informatics Approaches to Identifying Drug-Drug Interactions: Application to Insulin Secretagogues

BACKGROUND

Drug-drug interactions with insulin secretagogues are associated with increased risk of serious hypoglycemia in patients with type 2 diabetes. We aimed to systematically screen for drugs that interact with the five most commonly used secretagogues-glipizide, glyburide, glimepiride, repaglinide, and nateglinide-to cause serious hypoglycemia.

METHODS

We screened 400 drugs frequently coprescribed with the secretagogues as candidate interacting precipitants. We first predicted the drug-drug interaction potential based on the pharmacokinetics of each secretagogue-precipitant pair. We then performed pharmacoepidemiologic screening for each secretagogue of interest, and for metformin as a negative control, using an administrative claims database and the self-controlled case series design. The overall rate ratios (RRs) and those for four predefined risk periods were estimated using Poisson regression. The RRs were adjusted for multiple estimation using semi-Bayes method, and then adjusted for metformin results to distinguish native effects of the precipitant from a drug-drug interaction.

RESULTS

We predicted 34 pharmacokinetic drug-drug interactions with the secretagogues, nine moderate and 25 weak. There were 140 and 61 secretagogue-precipitant pairs associated with increased rates of serious hypoglycemia before and after the metformin adjustment, respectively. The results from pharmacokinetic prediction correlated poorly with those from pharmacoepidemiologic screening.

CONCLUSIONS

The self-controlled case series design has the potential to be widely applicable to screening for drug-drug interactions that lead to adverse outcomes identifiable in healthcare databases. Coupling pharmacokinetic prediction with pharmacoepidemiologic screening did not notably improve the ability to identify drug-drug interactions in this case.

Estimation of fractions metabolized by hepatic CYP enzymes using a concept of inter-system extrapolation factors (ISEFs) - a comparison with the chemical inhibition method

BACKGROUND

For estimation of fractions metabolized (fm) by different hepatic recombinant human CYP enzymes (rhCYP), calculation of inter-system extrapolation factors (ISEFs) has been proposed.

METHODS

ISEF values for CYP1A2, CYP2C19 and CYP3A4/5 were measured. A CYP2C9 ISEF was taken from a previous report. Using a set of compounds, fractions metabolized by CYP enzymes (fm,CYP) values calculated with the ISEFs based on rhCYP data were compared with those from the chemical inhibition data. Oral pharmacokinetics (PK) profiles of midazolam were simulated using the physiologically based pharmacokinetics (PBPK) model with the CYP3A ISEF. For other CYPs, the in vitro fm,CYP values were compared with the reference fm,CYP data back-calculated with, e.g. modeling of test substrates by feeding clinical PK data.

RESULTS

In vitro-in vitro fm,CYP3A4 relationship between the results from rhCYP incubation and chemical inhibition was drawn as an exponential correlation with R2=0.974. A midazolam PBPK model with the CYP3A4/5 ISEFs simulated the PK profiles within twofold error compared to the clinical observations. In a limited number of cases, the in vitro methods could not show good performance in predicting fm,CYP1A2, fm,CYP2C9 and fm,CYP2C19 values as reference data.

CONCLUSIONS

The rhCYP data with the measured ISEFs provided reasonable calculation of fm,CYP3A4 values, showing slight over-estimation compared to chemical inhibition.

Translational High-Dimensional Drug Interaction Discovery and Validation Using Health Record Databases and Pharmacokinetics Models

Polypharmacy increases the risk of drug-drug interactions (DDIs). Combining epidemiological studies with pharmacokinetic modeling, we detected and evaluated high-dimensional DDIs among 30 frequent drugs. Multidrug combinations that increased the risk of myopathy were identified in the US Food and Drug Administration Adverse Event Reporting System (FAERS) and electronic medical record (EMR) databases by a mixture drug-count response model. CYP450 inhibition was estimated among the 30 drugs in the presence of 1 to 4 inhibitors using in vitro / in vivo extrapolation. Twenty-eight three-way and 43 four-way DDIs had significant myopathy risk in both databases and predicted increases in the area under the concentration-time curve ratio (AUCR) >2-fold. The high-dimensional DDI of omeprazole, fluconazole, and clonidine was associated with a 6.41-fold (FAERS) and 18.46-fold (EMR) increased risk of myopathy local false discovery rate (<0.005); the AUCR of omeprazole in this combination was 9.35. The combination of health record informatics and pharmacokinetic modeling is a powerful translational approach to detect high-dimensional DDIs.

Translational Biomedical Informatics and Pharmacometrics Approaches in the Drug Interactions Research

Drug interaction is a leading cause of adverse drug events and a major obstacle for current clinical practice. Pharmacovigilance data mining, pharmacokinetic modeling, and text mining are computation and informatic tools on integrating drug interaction knowledge and generating drug interaction hypothesis. We provide a comprehensive overview of these translational biomedical informatics methodologies with related databases. We hope this review illustrates the complementary nature of these informatic approaches and facilitates the translational drug interaction research.

Improvement of the chemical inhibition phenotyping assay by cross-reactivity correction

BACKGROUND

The fraction of an absorbed drug metabolized by the different hepatic cytochrome P450 (CYP) enzymes, relative to total hepatic CYP metabolism (fmCYP), can be estimated by measuring the inhibitory effects of presumably selective CYP inhibitors on the intrinsic metabolic clearance of a drug using human liver microsomes. However, the chemical inhibition data are often affected by cross-reactivities of the chemical inhibitors used in this assay.

METHODS

To overcome this drawback, the cross-reactivities exhibited by six chemical inhibitors (furafylline, montelukast, sulfaphenazole, ticlopidine, quinidine and ketoconazole) were quantified using specific CYP enzyme marker reactions. The determined cross-reactivities were used to correct the in vitro fmCYPs of nine marketed drugs. The corrected values were compared with reference data obtained by physiologically based pharmacokinetics simulation using the software SimCYP.

RESULTS

Uncorrected in vitro fmCYPs of the nine drugs showed poor linear correlation with their reference data (R2=0.443). Correction by factoring in inhibitor cross-reactivities significantly improved the correlation (R2=0.736).

CONCLUSIONS

Correcting in vitro chemical inhibition results for cross-reactivities appear to offer a straightforward and easily adoptable approach to provide improved fmCYP data for a drug.

Quantitative prediction of the extent of drug-drug interaction using a physiologically based pharmacokinetic model that includes inhibition of drug metabolism determined in cryopreserved hepatocytes

1. A physiologically based pharmacokinetic (PBPK) model that includes inhibition constant evaluated in cryopreserved hepatocytes was used to predict drug-drug interactions (DDIs) between orally administered nifedipine, a CYP substrate, and fluconazole or ketoconazole, CYP inhibitors, in rats. 2. The Kp,uu, ratio of unbound inhibitor concentration in liver ([I]liver,u) to that in plasma ([I]sys,u), of fluconazole and ketoconazole was 1.0 and 13.0, indicating that ketoconazole accumulates in liver. The ratios of inhibition constants in rat liver microsomes (Ki,mic,u) to that in rat cryopreserved hepatocytes (Ki,hep,u) for fluconazole and ketoconazole were 1.5 and 25.5, which were similar to the Kp,uu and suggested that cryopreserved hepatocytes could mimic the hepatic accumulation of inhibitors. 3. The increases in AUC of nifedipine predicted by the minimal PBPK model using [I]liver,u/Ki,mic,u and [I]sys,u/Ki,hep,u were within 1.5-fold of the observed values for both inhibitors, whereas the model using [I]sys,u/Ki,mic,u underestimated the AUC increase caused by ketoconazole 21-fold. 4. These results indicated that hepatic accumulation factor of an inhibitor is required for a precise DDI projection and that cryopreserved hepatocytes would be useful to obtain the Ki including hepatic accumulation factor. It was demonstrated that PBPK model using Ki,hep,u could be a valuable approach for quantitative DDI projection.

Progress in Prediction and Interpretation of Clinically Relevant Metabolic Drug-Drug Interactions: a Minireview Illustrating Recent Developments and Current Opportunities

PURPOSE OF REVIEW

This review gives a perspective on the current "state of the art" in metabolic drug-drug interaction (DDI) prediction. We highlight areas of successful prediction and illustrate progress in areas where limits in scientific knowledge or technologies prevent us from having full confidence.

RECENT FINDINGS

Several examples of success are highlighted. Work done for bitopertin shows how in vitro and clinical data can be integrated to give a model-based understanding of pharmacokinetics and drug interactions. The use of interpolative predictions to derive explicit dosage recommendations for untested DDIs is discussed using the example of ibrutinib, and the use of DDI predictions in lieu of clinical studies in new drug application packages is exemplified with eliglustat and alectinib. Alectinib is also an interesting case where dose adjustment is unnecessary as the activity of a major metabolite compensates sufficiently for changes in parent drug exposure. Examples where "unusual" cytochrome P450 (CYP) and non-CYP enzymes are responsible for metabolic clearance have shown the importance of continuing to develop our repertoire of in vitro regents and techniques. The time-dependent inhibition assay using human hepatocytes suspended in full plasma allowed improved DDI predictions, illustrating the importance of continued in vitro assay development and refinement.

SUMMARY

During the past 10 years, a highly mechanistic understanding has been developed in the area of CYP-mediated metabolic DDIs enabling the prediction of clinical outcome based on preclinical studies. The combination of good quality in vitro data and physiologically based pharmacokinetic modeling may now be used to evaluate DDI risk prospectively and are increasingly accepted in lieu of dedicated clinical studies.

Evaluation of a New Molecular Entity as a Victim of Metabolic Drug-Drug Interactions-an Industry Perspective

Under the guidance of the International Consortium for Innovation and Quality in Pharmaceutical Development (IQ), scientists from 20 pharmaceutical companies formed a Victim Drug-Drug Interactions Working Group. This working group has conducted a review of the literature and the practices of each company on the approaches to clearance pathway identification (fCL), estimation of fractional contribution of metabolizing enzyme toward metabolism (fm), along with modeling and simulation-aided strategy in predicting the victim drug-drug interaction (DDI) liability due to modulation of drug metabolizing enzymes. Presented in this perspective are the recommendations from this working group on: 1) strategic and experimental approaches to identify fCL and fm, 2) whether those assessments may be quantitative for certain enzymes (e.g., cytochrome P450, P450, and limited uridine diphosphoglucuronosyltransferase, UGT enzymes) or qualitative (for most of other drug metabolism enzymes), and the impact due to the lack of quantitative information on the latter. Multiple decision trees are presented with stepwise approaches to identify specific enzymes that are involved in the metabolism of a given drug and to aid the prediction and risk assessment of drug as a victim in DDI. Modeling and simulation approaches are also discussed to better predict DDI risk in humans. Variability and parameter sensitivity analysis were emphasized when applying modeling and simulation to capture the differences within the population used and to characterize the parameters that have the most influence on the prediction outcome.

Ritonavir and efavirenz significantly alter the metabolism of erlotinib--an observation in primary cultures of human hepatocytes that is relevant to HIV patients with cancer

Erlotinib is approved for the treatment of non-small cell lung and pancreatic cancers, and is metabolized by CYP3A4. Inducers and inhibitors of CYP3A enzymes such as ritonavir and efavirenz, respectively, may be used as part of the highly active antiretroviral therapy drugs to treat patients with human immunodeficiency virus (HIV). When HIV patients with a malignancy need treatment with erlotinib, there is a potential of as-yet-undefined drug-drug interaction. We evaluated these interactions using human hepatocytes benchmarked against the interaction of erlotinib with ketoconazole and rifampin, the archetype cytochrome P450 inhibitor and inducer, respectively. Hepatocytes were treated with vehicle [0.1% dimethylsulfoxide, ritonavir (10 μM)], ketoconazole (10 μM), efavirenz (10 μM), or rifampin (10 μM) for 4 days. On day 5, erlotinib (5 μM) was incubated with the above agents for another 24-48 hours. Concentrations of erlotinib and O-desmethyl erlotinib were quantitated in collected samples (combined lysate and medium) using liquid chromatography and tandem mass spectrometry. The half-life (t(½)) of erlotinib increased from 10.6 ± 2.6 to 153 ± 80 and 23.9 ± 4.8 hours, respectively, upon treatment with ritonavir and ketoconazole. The apparent intrinsic clearance (C(Lint, app)) of erlotinib was lowered 16-fold by ritonavir and 1.9-fold by ketoconazole. Efavirenz and rifampin decreased t1/2 of erlotinib from 10.3 ± 1.1 to 5.0 ± 1.5 and 3.4 ± 0.2 hours, respectively. Efavirenz and rifampin increased the C(Lint, app) of erlotinib by 2.2- and 2-fold, respectively. Our results suggest that to achieve desired drug exposure, the clinically used dose (150 mg daily) of erlotinib may have to be significantly reduced (25 mg every other day) or increased (300 mg daily), respectively, when ritonavir or efavirenz is coadministered.

A quantitative framework and strategies for management and evaluation of metabolic drug-drug interactions in oncology drug development: new molecular entities as object drugs

This article outlines general strategies for the management and evaluation of pharmacokinetic drug-drug interactions (DDIs) resulting from perturbation of clearance of investigational anticancer drug candidates by concomitantly administered agents in a drug development setting, with a focus on drug candidates that cannot be evaluated in first-in-human studies in healthy subjects. A risk level classification is proposed, based on quantitative integration of knowledge derived from preclinical drug-metabolism studies evaluating the projected percentage contribution [f(i)(%)] of individual molecular determinants (e.g. cytochrome P450 isoenzymes) to the overall human clearance of the investigational agent. The following classification is proposed with respect to susceptibility to DDIs with metabolic inhibitors: a projected maximum DDI expected to result in a ≤1.33-fold increase in exposure, representing a low level of risk; a projected maximum DDI expected to result in a >1.33-fold but <2-fold increase in exposure, representing a moderate level of risk; and a projected maximum DDI expected to result in a ≥2-fold increase in exposure, representing a potentially high level of risk. For DDIs with metabolic inducers, the following operational classification is proposed, based on the sum of the percentage contributions of enzymes that are inducible via a common mechanism to the overall clearance of the investigational drug: <<25%, representing a low level of risk; <50%, representing a moderate level of risk; and ≥50%, representing a potentially high level of risk. To ensure patient safety and to minimize bias in determination of the recommended phase II dose (RP2D), it is recommended that strong and moderate inhibitors and inducers of the major contributing enzyme are excluded in phase I dose-escalation studies of high-risk compounds, whereas exclusion of strong inhibitors and inducers of the contributing enzyme(s) is recommended as being sufficient for moderate-risk compounds. For drugs that will be investigated in diseases such as glioblastoma, where there may be relatively frequent use of enzyme-inducing antiepileptic agents (EIAEDs), a separate dose-escalation study in this subpopulation is recommended to define the RP2D. For compounds in the high-risk category, if genetic deficiencies in the activity of the major drug-metabolizing enzyme are known, it is recommended that poor metabolizers be studied separately to define the RP2D for this subpopulation. Whereas concomitant medication exclusion criteria that are utilized in the phase I dose-escalation studies will probably also need to be maintained for high-risk compounds in phase II studies unless the results of a clinical DDI study indicate the absence of a clinically relevant interaction, these exclusion criteria can potentially be relaxed beyond phase I for moderate-risk compounds, if supported by the nature of clinical toxicities and the understanding of the therapeutic index in phase I. Adequately designed clinical DDI studies will not only inform potential relaxation of concomitant medication exclusion criteria in later-phase studies but, importantly, will also inform the development of pharmacokinetically derived dose-modification guidelines for use in clinical practice when coupled with adequate safety monitoring, as illustrated in the prescribing guidance for many recently approved oncology therapeutics.

Aminophylline for treating asthma and chronic obstructive pulmonary disease

Aminophylline is a complex of theophylline and ethylenediamine. Its main pharmacological action is relaxation of bronchial smooth muscle. Two meta-analyses examining the efficacy of aminophylline in acute asthma attacks in children and in adults have been reported by the Cochrane Collaboration. In the meta-analysis reporting results from studies in children, it was concluded that aminophylline does not add any benefit to standard care. Yet one study, which has the largest patient population, reports that aminophylline improves lung functions within 6-8 h and reduces the risk of intubation. The meta-analysis examining adult studies revealed that there is no outstanding difference between aminophylline and standard therapy in the management of adult acute asthma. In conclusion, aminophylline may be an alternative to intravenous infusion of beta-agonists, heliox or magnesium sulfate administration in children in whom respiratory fatigue begins to develop and intensive-care unit admission and mechanical ventilation seems to be the next treatment in line. However, in adults, it is not recommended for use in the treatment of acute asthma owing to its possible adverse effects.

Mechanism-based inactivation of cytochrome P450 2C9 by tienilic acid and (+/-)-suprofen: a comparison of kinetics and probe substrate selection

In vitro experiments were conducted to compare k(inact), K(I) and inactivation efficiency (k(inact)/K(I)) of cytochrome P450 (P450) 2C9 by tienilic acid and (+/-)-suprofen using (S)-flurbiprofen, diclofenac, and (S)-warfarin as reporter substrates. Although the inactivation of P450 2C9 by tienilic acid when (S)-flurbiprofen and diclofenac were used as substrates was similar (efficiency of approximately 9 ml/min/micromol), the inactivation kinetics were characterized by a sigmoidal profile. (+/-)-Suprofen inactivation of (S)-flurbiprofen and diclofenac hydroxylation was also described by a sigmoidal profile, although inactivation was markedly less efficient (approximately 1 ml/min/micromol). In contrast, inactivation of P450 2C9-mediated (S)-warfarin 7-hydroxylation by tienilic acid and (+/-)-suprofen was best fit to a hyperbolic equation, where inactivation efficiency was moderately higher (10 ml/min/micromol) and approximately 3-fold higher (3 ml/min/micromol), respectively, relative to that of the other probe substrates, which argues for careful consideration of reporter substrate when mechanism-based inactivation of P450 2C9 is assessed in vitro. Further investigations into the increased inactivation seen with tienilic acid relative to that with (+/-)-suprofen revealed that tienilic acid is a higher affinity substrate with a spectral binding affinity constant (K(s)) of 2 microM and an in vitro half-life of 5 min compared with a K(s) of 21 microM and a 50 min in vitro half-life for (+/-)-suprofen. Lastly, a close analog of tienilic acid with the carboxylate functionality replaced by an oxirane ring was devoid of inactivation properties, which suggests that an ionic binding interaction with a positively charged residue in the P450 2C9 active site is critical for recognition and mechanism-based inactivation by these close structural analogs.

In vitro evaluation of reversible and irreversible cytochrome P450 inhibition: current status on methodologies and their utility for predicting drug-drug interactions

It is widely accepted that today's practice of polypharmacy inevitably increases the incidence of drug-drug interactions (DDIs). Serious DDI is a major liability for any new chemical entity (NCE) entering the pharmaceutical market. As such, pharmaceutical companies employ various strategies to avoid problematic compounds for clinical development. A key cause for DDIs is the inhibition of cytochrome P450 enzymes (CYPs) that are responsible for metabolic clearance of many drugs. Screening for inhibition potency of CYPs by NCEs has therefore become a routine practice during the drug discovery stage. However, in order to make proper use of DDI data, an understanding of the strengths and weaknesses of the various experimental systems in current use is required. An illustrated review of experimental practices is presented with discussion of likely future developments. The combination of high quality in vitro data generation and the application of in vivo CYP inhibition modelling approaches should allow more informed decisions to be made in the search for drug molecules with acceptable DDI characteristics.

The use of hepatocytes in evaluating time-dependent inactivation of P450 in vivo

BACKGROUND

Time-dependent inactivation (TDI) of P450 is an important mechanism of drug interactions. The quantitative in vitro - in vivo correlation of TDI using systems such as human liver microsomes requires a comprehensive understanding of in vitro kinetics, pharmacokinetics, inhibition mechanisms, and homeostasis of the enzyme being inactivated.

OBJECTIVE

To evaluate the use of hepatocytes in predicting TDI.

METHODS

The theoretical basis of in vitro - in vivo correlation of TDI and the progress in using microsomes and hepatocytes to predict TDI in vivo are reviewed.

RESULTS/CONCLUSION

Factors that may impact prediction accuracy, such as nonspecific binding, metabolism of inactivator, active transport, and sequential inhibitory metabolites, can be assessed by performing 'in vitro-in vitro' correlation between microsomes and hepatocytes. Together with microsomal data and the aid of computer modeling and simulation, hepatocytes provide a powerful tool to optimize the integrated approaches aimed at quantitatively predicting TDI in vivo.

Prediction of pharmacokinetic drug-drug interactions using human hepatocyte suspension in plasma and cytochrome P450 phenotypic data. II. In vitro-in vivo correlation with ketoconazole

Traditional cytochrome P450 (P450) based drug-drug interaction (DDI) predictions are based on the ratio of an inhibitor's physiological concentration [I] and its inhibition constant K(i). Determining [I] at the enzymatic site, although critical for predicting clinical DDIs, remains a technical challenge. In our previous study, a novel approach using cryopreserved human hepatocytes suspended in human plasma was investigated to mimic the in vivo concentration of ketoconazole at the enzymatic site (Lu et al., 2007), effectively eliminating the estimation of the elusive [I] value. P450 inhibition in this system appears to model that in vivo. Using the ketoconazole inhibition information in a human hepatocyte-plasma suspension together with quantitative P450 phenotypic information, we successfully predicted the pharmacokinetic DDIs for a small set of drugs, such as theophylline, tolbutamide, omeprazole, desipramine, midazolam, loratadine, cyclosporine, and alprazolam, as well as an investigational compound. For the applicability of this model on a wider scale the in vitro-in vivo correlation data set needed to be expanded. However, for most drugs in the literature there is not enough quantitative information on the involvement of individual P450s to predict DDIs retrospectively. To facilitate that, in this study we determined quantitative P450 phenotyping for seven marketed drugs: budesonide, buprenorphine, loratadine, sirolimus, tacrolimus, docetaxel, and methylprednisolone. Augmentation of the new data set with the one generated previously produced broader a database that provided further support for the wider applicability of this approach using ketoconazole as a potent CYP3A inhibitor. This application is predicted to be equally effective with other P450 inhibitors that are not substrates of efflux pumps.

Clinically important drug interactions potentially involving mechanism-based inhibition of cytochrome P450 3A4 and the role of therapeutic drug monitoring

Cytochrome P450 (CYP) 3A4 is the most abundant enzyme of CYPs in the liver and gut that metabolizes approximately 50% currently available drugs. A number of important drugs have been identified as substrates, inducers, and/or inhibitors of CYP3A4. The substrates of CYP3A4 considerably overlap with those of P-glycoprotein. Both CYP3A4 and P-glycoprotein are subject to inhibition and induction by a number of factors. Mechanism-based inhibition of CYP3A4 is characterized by NADPH-, time-, and concentration-dependent enzyme inactivation occurring when some xenobiotics or drugs are converted by CYPs to reactive metabolites. Such an inhibition of CYP3A4 is caused by chemical modification of the heme, the protein, or both as a result of covalent binding of modified heme to the protein. To date, the identified clinically important mechanism-based CYP3A4 inhibitors mainly include macrolide antibiotics (eg, clarithromycin and erythromycin), anti-HIV agents (eg, ritonavir and delavirdine), antidepressants (eg, fluoxetine and fluvoxamine), calcium channel blockers (eg, verapamil and diltiazem), steroids and their modulators (eg, gestodene and mifepristone), and several herbal and dietary components. The inactivation of CYP3A4 by drugs often causes unfavorable and long-lasting drug-drug interactions and probably fatal toxicity, depending on many factors associated with the enzyme, drugs, and the patients. Clinicians are encouraged to have a sound knowledge of drug-induced, mechanism-based CYP3A4 inhibition; take proper cautions, and perform close monitoring for possible drug interactions when using drugs that are mechanism-based CYP3A4 inhibitors. To minimize drug-drug interactions involving mechanism-based CYP3A4 inhibition, it is necessary to choose safe drug combination regimens, adjust drug dosages appropriately, and conduct therapeutic drug monitoring for drugs with narrow therapeutic indices.

In vitro evaluation of metabolic drug-drug interactions: a descriptive and critical commentary

Adverse drug-drug interactions represent a major challenge for the pharmaceutical industry. Recently, in vitro approaches for the evaluation of metabolism-related drug-drug interactions have been developed. These in vitro approaches are found to be useful in the assessment of clinical drug-drug interaction potential of new chemical entities and to aid the understanding of clinically significant drug-drug interactions observed with existing drugs. The general methods for the evaluation of drug-drug interactions using in vitro, human-based experimental systems are described and critically reviewed.

Sequential metabolism is responsible for diltiazem-induced time-dependent loss of CYP3A

Kinetic parameters (k(inact) and K(I)) obtained in microsomes are often used to predict time-dependent inactivation. We previously reported that microsomal inactivation kinetic parameters of diltiazem underpredicted CYP3A inactivation in hepatocytes. In this study, we evaluated the contributions of inactivation and reversible inhibition of CYP3A by diltiazem and its N-desmethyl (MA) and N,N-didesmethyl (MD) metabolites. In human liver microsomes, MA was a more potent time-dependent inactivator of CYP3A than its parent drug, with apparent k(inact) approximately 4-fold higher than that of diltiazem at a microsomal protein concentration of 0.2 mg/ml. MD did not inactivate CYP3A. Inactivation of CYP3A by diltiazem was dependent on microsomal protein concentration (25, 36, and 41% decrease in CYP3A activity at 0.2, 0.4, and 0.8 mg/ml microsomal protein, respectively, incubated with 10 microM diltiazem over 20 min), whereas inactivation by MA did not seem to be protein concentration-dependent. MA and MD were reversible inhibitors of CYP3A with competitive Ki values of 2.7 and 0.2 microM, respectively. In cryopreserved hepatocytes incubated with diltiazem, time-dependent loss of CYP3A was accompanied by increased formation of MA and MD, with the MA level similar to its K(I) at higher diltiazem concentrations. In addition, the metabolites appeared to be accumulated inside the cells. In summary, time-dependent CYP3A inactivation by MA seems to be the major contributor responsible for the loss of CYP3A in human liver microsomes and human hepatocytes incubated with diltiazem. These findings suggest that prediction of CYP3A loss based solely on microsomal inactivation parameters of parent drug may be inadequate.