Drug metabolites as cytochrome p450 inhibitors: a retrospective analysis and proposed algorithm for evaluation of the pharmacokinetic interaction potential of metabolites in drug discovery and development

Metabolite Bioanalysis in Drug Development: Recommendations from the IQ Consortium Metabolite Bioanalysis Working Group

The intent of this perspective is to share the recommendations of the International Consortium for Innovation and Quality in Pharmaceutical Development Metabolite Bioanalysis Working Group on the fit-for-purpose metabolite bioanalysis in support of drug development and registration. This report summarizes the considerations for the trigger, timing, and rigor of bioanalysis in the various assessments to address unique challenges due to metabolites, with respect to efficacy and safety, which may arise during drug development from investigational new drug (IND) enabling studies, and phase I, phase II, and phase III clinical trials to regulatory submission. The recommended approaches ensure that important drug metabolites are identified in a timely manner and properly characterized for efficient drug development.

Analyzing the metabolic fate of oral administration drugs: A review and state-of-the-art roadmap

The key orally delivered drug metabolism processes are reviewed to aid the assessment of the current in vivo/vitro experimental systems applicability for evaluating drug metabolism and the interaction potential. Orally administration is the most commonly used state-of-the-art road for drug delivery due to its ease of administration, high patient compliance and cost-effectiveness. Roles of gut metabolic enzymes and microbiota in drug metabolism and absorption suggest that the gut is an important site for drug metabolism, while the liver has long been recognized as the principal organ responsible for drugs or other substances metabolism. In this contribution, we explore various experimental models from their development to the application for studying oral drugs metabolism of and summarized advantages and disadvantages. Undoubtedly, understanding the possible metabolic mechanism of drugs in vivo and evaluating the procedure with relevant models is of great significance for screening potential clinical drugs. With the increasing popularity and prevalence of orally delivered drugs, sophisticated experimental models with higher predictive capacity for the metabolism of oral drugs used in current preclinical studies will be needed. Collectively, the review seeks to provide a comprehensive roadmap for researchers in related fields.

How Science Is Driving Regulatory Guidances

This chapter provides regulatory perspectives on how to translate in vitro drug metabolism findings into in vivo drug-drug interaction (DDI) predictions and how this affects the decision of conducting in vivo DDI evaluation. The chapter delineates rationale and analyses that have supported the recommendations in the U.S. Food and Drug Administration (FDA) DDI guidances in terms of in vitro-in vivo extrapolation of cytochrome P450 (CYP) inhibition-mediated DDI potential for investigational new drugs and their metabolites as substrates or inhibitors. The chapter also describes the framework and considerations to assess UDP-glucuronosyltransferase (UGT) inhibition-mediated DDI potential for drugs as substrates or inhibitors. The limitations of decision criteria and further improvements needed are also discussed. Case examples are provided throughout the chapter to illustrate how decision criteria have been utilized to evaluate in vivo DDI potential from in vitro data.

Do Inhibitory Metabolites Impact DDI Risk Assessment? Analysis of in vitro and in vivo Data from NDA Reviews Between 2013 and 2018

Evaluating the potential of new drugs and their metabolites to cause drug-drug interactions (DDIs) is critical for understanding drug safety and efficacy. Although multiple analyses of proprietary metabolite testing data have been published, no systematic analyses of metabolite data collected according to current testing criteria have been conducted. To address this knowledge gap, 120 new molecular entities approved between 2013 and 2018 were reviewed. Comprehensive data on metabolite-to-parent area under the curve ratios (AUC_M /AUC_P ), inhibitory potency of parent and metabolites, and clinical DDIs were collected. Sixty-four percent of the metabolites quantified in vivo had AUC_M /AUC_P  ≥ 0.25 and 75% of these metabolites were tested for cytochrome P450 (CYP) inhibition in vitro, resulting in 15 metabolites with potential DDI risk identification. Although 50% of the metabolites with AUC_M /AUC_P  < 0.25 were also tested in vitro, none of them showed meaningful CYP inhibition potential. The metabolite percentage of plasma total radioactivity cutoff of ≥ 10% did not appear to add value to metabolite testing strategies. No relationship between metabolite versus parent drug polarity and inhibition potency was observed. Comparison of metabolite and parent maximum concentration (C_max ) divided by inhibition constant (K_i ) values suggested that metabolites can contribute to in vivo DDIs and, hence, quantitative prediction of clinical DDI magnitude may require both parent and metabolite data. This systematic analysis of metabolite data for newly approved drugs supports an AUC_M /AUC_P cutoff of ≥ 0.25 to warrant metabolite in vitro CYP screening to adequately characterize metabolite inhibitory DDI potential and support quantitative DDI predictions.

Prediction of Drug-Drug Interactions with Bupropion and Its Metabolites as CYP2D6 Inhibitors Using a Physiologically-Based Pharmacokinetic Model

The potential of inhibitory metabolites of perpetrator drugs to contribute to drug-drug interactions (DDIs) is uncommon and underestimated. However, the occurrence of unexpected DDI suggests the potential contribution of metabolites to the observed DDI. The aim of this study was to develop a physiologically-based pharmacokinetic (PBPK) model for bupropion and its three primary metabolites—hydroxybupropion, threohydrobupropion and erythrohydrobupropion—based on a mixed “bottom-up” and “top-down” approach and to contribute to the understanding of the involvement and impact of inhibitory metabolites for DDIs observed in the clinic. PK profiles from clinical researches of different dosages were used to verify the bupropion model. Reasonable PK profiles of bupropion and its metabolites were captured in the PBPK model. Confidence in the DDI prediction involving bupropion and co-administered CYP2D6 substrates could be maximized. The predicted maximum concentration (C_max) area under the concentration-time curve (AUC) values and C_max and AUC ratios were consistent with clinically observed data. The addition of the inhibitory metabolites into the PBPK model resulted in a more accurate prediction of DDIs (AUC and C_max ratio) than that which only considered parent drug (bupropion) P450 inhibition. The simulation suggests that bupropion and its metabolites contribute to the DDI between bupropion and CYP2D6 substrates. The inhibitory potency from strong to weak is hydroxybupropion, threohydrobupropion, erythrohydrobupropion, and bupropion, respectively. The present bupropion PBPK model can be useful for predicting inhibition from bupropion in other clinical studies. This study highlights the need for caution and dosage adjustment when combining bupropion with medications metabolized by CYP2D6. It also demonstrates the feasibility of applying the PBPK approach to predict the DDI potential of drugs undergoing complex metabolism, especially in the DDI involving inhibitory metabolites.

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

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

Retrospective use of PBPK modelling to understand a clinical drug-drug interaction between dextromethorphan and GSK1034702

1. In a clinical trial, a strong drug-drug interaction (DDI) was observed between dextromethorphan (DM, the object or victim drug) and GSK1034702 (the precipitant or perpetrator drug), following single and repeat doses. This study determined the inhibition parameters of GSK1034702 in vitro and applied PBPK modelling approaches to simulate the clinical observations and provide mechanistic hypotheses to understand the DDI. 2. In vitro assays were conducted to determine the inhibition parameters of human CYP2D6 by GSK1034702. PBPK models were populated with the in vitro parameters and DDI simulations conducted and compared to the observed data from a clinical study with DM and GSK1034702. 3. GSK1034702 was a potent direct and metabolism-dependent inhibitor of human CYP2D6, with inhibition parameters of: IC₅₀=1.6 μM, K_inact = 3.7 h^-1 and K_I = 0.8 μM. Incorporating these data into PBPK models predicted a DDI after repeat, but not single, 5 mg doses of GSK1034702. 4. The DDI observed with repeat administration of GSK1034702 (5 mg) can be attributed to metabolism-dependent inhibition of CYP2D6. Further, in vitro data were generated and several potential mechanisms proposed to explain the interaction observed following a single dose of GSK1034702.

An S-warfarin and AZD1981 interaction: in vitro and clinical pilot data suggest the N-deacetylated amino acid metabolite as the primary perpetrator

AIM

AZD1981 is an orally bioavailable chemoattractant receptor-homologous molecule expressed on Th2 cells (CRTh2) receptor antagonist progressed to phase II trials for the treatment of allergic asthma. Previously performed in vitro human hepatocyte incubations identified N-deacetylated AZD1981 as a primary metabolite. We report on metabolite exposure from a clinical excretion balance, on in vitro studies performed to determine the likelihood of a metabolite-dependent drug-drug interaction (DDI) and on a clinical warfarin DDI study. The aim was to demonstrate that N-deacetylated AZD1981 is responsible for the observed interaction.

METHODS

The excretion and biotransformation of [¹⁴ C]-AZD1981 were studied in healthy male volunteers, and subsequently in vitro cytochrome P450 (CYP) inhibition and hepatocyte uptake investigations were carried out with metabolites and the parent drug. A clinical DDI study using coadministered twice-daily 100 mg and 400 mg AZD1981 with 25 mg warfarin was performed.

RESULTS

The excretion balance study showed N-deacetylated AZD1981 to be the most abundant metabolite present in plasma. In vitro data revealed the metabolite to be a weak CYP2C9 time-dependent inhibitor, subject to more active hepatic uptake than the parent molecule. Clinically, the S-warfarin area under the plasma concentration-time curve increased, on average, 1.4-fold [95% confidence interval (CI) 1.22, 1.50] and 2.4-fold (95% CI 2.11, 2.64) after 100 mg (n = 13) and 400 mg (n = 11) AZD1981 administration, respectively. In vitro CYP inhibition and hepatocyte uptake data were used to explain the interaction.

CONCLUSIONS

N-deacetylated AZD1981 can be added to the small list of drug metabolites reported as sole contributors to clinical drug-drug interactions, with weak time-dependent inhibition exacerbated by efficient hepatic uptake being the cause.

Quantitative Prediction of Drug-Drug Interactions Involving Inhibitory Metabolites in Drug Development: How Can Physiologically Based Pharmacokinetic Modeling Help?

This subteam under the Drug Metabolism Leadership Group (Innovation and Quality Consortium) investigated the quantitative role of circulating inhibitory metabolites in drug-drug interactions using physiologically based pharmacokinetic (PBPK) modeling. Three drugs with major circulating inhibitory metabolites (amiodarone, gemfibrozil, and sertraline) were systematically evaluated in addition to the literature review of recent examples. The application of PBPK modeling in drug interactions by inhibitory parent-metabolite pairs is described and guidance on strategic application is provided.

The Use of In Vitro Data and Physiologically-Based Pharmacokinetic Modeling to Predict Drug Metabolite Exposure: Desipramine Exposure in Cytochrome P4502D6 Extensive and Poor Metabolizers Following Administration of Imipramine

Major circulating drug metabolites can be as important as the drugs themselves in efficacy and safety, so establishing methods whereby exposure to major metabolites following administration of parent drug can be predicted is important. In this study, imipramine, a tricyclic antidepressant, and its major metabolite desipramine were selected as a model system to develop metabolite prediction methods. Imipramine undergoes N-demethylation to form the active metabolite desipramine, and both imipramine and desipramine are converted to hydroxylated metabolites by the polymorphic enzyme CYP2D6. The objective of the present study is to determine whether the human pharmacokinetics of desipramine following dosing of imipramine can be predicted using static and dynamic physiologically-based pharmacokinetic (PBPK) models from in vitro input data for CYP2D6 extensive metabolizer (EM) and poor metabolizer (PM) populations. The intrinsic metabolic clearances of parent drug and metabolite were estimated using human liver microsomes (CYP2D6 PM and EM) and hepatocytes. Passive diffusion clearance of desipramine, used in the estimation of availability of the metabolite, was predicted from passive permeability and hepatocyte surface area. The predicted area under the curve (AUCm/AUCp) of desipramine/imipramine was 12- to 20-fold higher in PM compared with EM subjects following i.v. or oral doses of imipramine using the static model. Moreover, the PBPK model was able to recover simultaneously plasma profiles of imipramine and desipramine in populations with different phenotypes of CYP2D6. This example suggested that mechanistic PBPK modeling combined with information obtained from in vitro studies can provide quantitative solutions to predict in vivo pharmacokinetics of drugs and major metabolites in a target human population.

Use of Human Plasma Samples to Identify Circulating Drug Metabolites that Inhibit Cytochrome P450 Enzymes

Drug interactions elicited through inhibition of cytochrome P450 (P450) enzymes are important in pharmacotherapy. Recently, greater attention has been focused on not only parent drugs inhibiting P450 enzymes but also on possible inhibition of these enzymes by circulating metabolites. In this report, an ex vivo method whereby the potential for circulating metabolites to be inhibitors of P450 enzymes is described. To test this method, seven drugs and their known plasma metabolites were added to control human plasma at concentrations previously reported to occur in humans after administration of the parent drug. A volume of plasma for each drug based on the known inhibitory potency and time-averaged concentration of the parent drug was extracted and fractionated by high-pressure liquid chromatography-mass spectrometry, and the fractions were tested for inhibition of six human P450 enzyme activities (CYP1A2, CYP2C8, CYP2C9, CYP2C19, CYP2D6, and CYP3A4). Observation of inhibition in fractions that correspond to the retention times of metabolites indicates that the metabolite has the potential to contribute to P450 inhibition in vivo. Using this approach, norfluoxetine, hydroxyitraconazole, desmethyldiltiazem, desacetyldiltiazem, desethylamiodarone, hydroxybupropion, erythro-dihydrobupropion, and threo-dihydrobupropion were identified as circulating metabolites that inhibit P450 activities at a similar or greater extent as the parent drug. A decision tree is presented outlining how this method can be used to determine when a deeper investigation of the P450 inhibition properties of a drug metabolite is warranted.

Influence of CYP2D6 and CYP2C19 genetic polymorphism on the pharmacokinetics of tolperisone in healthy volunteers

PURPOSE

This is the first study that connects pharmacokinetics of tolperisone with genetic polymorphism of the enzymes involved in its metabolism in human. We aimed to identify the influence of polymorphism of two main enzymes (CYP2D6 and CYP2C19) on pharmacokinetic profile of parent drug.

METHODS

In a single-dose study, 28 healthy Caucasian male volunteers received an oral dose of 150 mg of tolperisone. The subjects were genotyped with respect to CYP2D6 and CYP2C19 enzymes. Plasma was sampled for up to 12 h post dose, followed by quantification of tolperisone by a fully validated HPLC-tandem mass spectrometry (MS/MS) method. The pharmacokinetic parameters were estimated using a non-compartmental method and compared statistically at level p < 0.05 across the genotyped groups.

RESULTS

High variability (exceeded 100%) of main bioavailability parameters (AUCt, AUC(inf), C(max)) was observed in the whole group of subjects. An essential difference in the pharmacokinetics of tolperisone of quick metabolizers whose genotype expressed wild homozygote CYP2D6 *1/*1 with respect to heterozygous *1/*4 and *1/*5 subjects was demonstrated. The mean AUC(inf) was 2.1- and 3.4-fold higher in *1/*4 and *1/*5, respectively, than in *1/*1 subjects. In case of Cmax, the differences were greater and reached maximally 3.8 times (mean values 54.00, 98.85, and 205.20 ng/mL for CYP2D6 *1/*1, *1/*4, and *1/*5, respectively). Values of the parameters for the one subject that expressed *4/*4 genotype were even 8.5 times higher than in subjects with extensive or intermediate phenotype. Although CYP2C19 *1/*2 subjects had higher AUCt, AUC(inf), and Cmax values than *1/*1, no statistically significant differences were observed. Oral clearance (CL/F) significantly decreased by 65.7% in heterozygous *1/*2 relative to homozygous *1/*1 extensive metabolizers.

CONCLUSION

In this study, we first demonstrated the effect of CYP2D6 polymorphism on pharmacokinetics of tolperisone in Caucasian subjects. The contribution of CYP2C19 enzyme seems to be less important.

Quantitative Rationalization of Gemfibrozil Drug Interactions: Consideration of Transporters-Enzyme Interplay and the Role of Circulating Metabolite Gemfibrozil 1-O-β-Glucuronide

Gemfibrozil has been suggested as a sensitive cytochrome P450 2C8 (CYP2C8) inhibitor for clinical investigation by the U.S. Food and Drug Administration and the European Medicines Agency. However, gemfibrozil drug-drug interactions (DDIs) are complex; its major circulating metabolite, gemfibrozil 1-O-β-glucuronide (Gem-Glu), exhibits time-dependent inhibition of CYP2C8, and both parent and metabolite also behave as moderate inhibitors of organic anion transporting polypeptide 1B1 (OATP1B1) in vitro. Additionally, parent and metabolite also inhibit renal transport mediated by OAT3. Here, in vitro inhibition data for gemfibrozil and Gem-Glu were used to assess their impact on the pharmacokinetics of several victim drugs (including rosiglitazone, pioglitazone, cerivastatin, and repaglinide) by employing both static mechanistic and dynamic physiologically based pharmacokinetic (PBPK) models. Of the 48 cases evaluated using the static models, about 75% and 98% of the DDIs were predicted within 1.5- and 2-fold of the observed values, respectively, when incorporating the interaction potential of both gemfibrozil and its 1-O-β-glucuronide. Moreover, the PBPK model was able to recover the plasma profiles of rosiglitazone, pioglitazone, cerivastatin, and repaglinide under control and gemfibrozil treatment conditions. Analyses suggest that Gem-Glu is the major contributor to the DDIs, and its exposure needed to bring about complete inactivation of CYP2C8 is only a fraction of that achieved in the clinic after a therapeutic gemfibrozil dose. Overall, the complex interactions of gemfibrozil can be quantitatively rationalized, and the learnings from this analysis can be applied in support of future predictions of gemfibrozil DDIs.

Prediction of human pharmacokinetics in 2013 and beyond

The utility of in vitro generated kinetic data to provide quantitative prediction of in vivo pharmacokinetic behavior is well established and forms a cornerstone of many research projects in drug metabolism and disposition, particularly within the pharmaceutical industry. This issue provides several excellent examples of the use of in vitro techniques for prediction of human pharmacokinetics (PK). The general area of in vitro-in vivo extrapolation (IVIVE) is broad and hence the spectrum of topics covers various aspects drug clearance and distribution and drug-drug interactions. Some articles were commissioned whereas others were identified during the reviewing process. Overall they provide a snapshot of activity at the end of 2013. They document that whereas the translation of some in vitro approaches is now established, other areas are in their infancy and need more development.