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

Screening of 16 major drug glucuronides for time-dependent inhibition of nine drug-metabolizing CYP enzymes - detailed studies on CYP3A inhibitors

Time-dependent inhibition of cytochrome P450 (CYP) enzymes has been observed for a few glucuronide metabolites of clinically used drugs. Here, we investigated the inhibitory potential of 16 glucuronide metabolites towards nine major CYP enzymes in vitro. Automated substrate cocktail methods were used to screen time-dependent inhibition of CYP1A2, 2A6, 2B6, 2C8, 2C9, 2C19, 2D6, 2J2 and 3A in human liver microsomes. Seven glucuronides (carvedilol β-D-glucuronide, diclofenac acyl-β-D-glucuronide, 4-hydroxyduloxetine β-D-glucuronide, ezetimibe phenoxy-β-D-glucuronide, raloxifene 4'-glucuronide, repaglinide acyl-β-D-glucuronide and valproic acid β-D-glucuronide) caused NADPH- and time-dependent inhibition of at least one of the CYPs investigated, including CYP2A6, CYP2C19 and CYP3A. In more detailed experiments, we focused on the glucuronides of carvedilol and diclofenac, which inhibited CYP3A. Carvedilol β-D-glucuronide showed weak time-dependent inhibition of CYP3A, but the parent drug carvedilol was found to be a more potent inhibitor of CYP3A, with the half-maximal inhibitor concentration (IC50) decreasing from 7.0 to 1.1 µM after a 30-min preincubation with NADPH. The maximal inactivation constant (kinact) and the inhibitor concentration causing half of kinact (KI) for CYP3A inactivation by carvedilol were 0.051 1/min and 1.8 µM, respectively. Diclofenac acyl-β-D-glucuronide caused time-dependent inactivation of CYP3A at high concentrations, with a 4-fold IC50 shift (from 400 to 98 µM after a 30-min preincubation with NADPH) and KI and kinact values of >2,000 µM and >0.16 1/min. In static predictions, carvedilol caused significant (>1.25-fold) increase in the exposure of the CYP3A substrates midazolam and simvastatin. In conclusion, we identified several glucuronide metabolites with CYP inhibitory properties. Based on detailed experiments, the inactivation of CYP3A by carvedilol may cause clinically significant drug-drug interactions.

Dose Optimization Informed by PBPK Modeling: State-of-the Art and Future

Model-informed drug development (MIDD) is a powerful quantitative approach that plays an integral role in drug development and regulatory review. While applied throughout the life cycle of the development of new drugs, a key application of MIDD is to inform clinical trial design including dose selection and optimization. To date, physiologically-based pharmacokinetic (PBPK) modeling, an established component of the MIDD toolkit, has mainly been used for assessment of drug-drug interactions (DDIs) and consequential dose adjustments in regulatory submissions. As a result of recent scientific advances and growing confidence in the utility of the approach, PBPK models are being increasingly applied to provide dose recommendations for subjects with differing ages, genetics, and disease states. In this review, we present our perspective on the current landscape of regulatory acceptance of PBPK applications supported by relevant case studies. We also discuss the recent progress and future challenges associated with expanding the utility of PBPK models into emerging areas for regulatory decision making, especially dose optimization in highly vulnerable and understudied populations and facilitating diversity in clinical trials.

Evaluation of the potential impact on pharmacokinetics of various cytochrome P450 substrates of increasing IL-6 levels following administration of the T-cell bispecific engager glofitamab

Glofitamab is a novel T cell bispecific antibody developed for treatment of relapsed-refractory diffuse large B cell lymphoma and other non-Hodgkin's lymphoma indications. By simultaneously binding human CD20-expressing tumor cells and CD3 on T cells, glofitamab induces tumor cell lysis, in addition to T-cell activation, proliferation, and cytokine release. Here, we describe physiologically-based pharmacokinetic (PBPK) modeling performed to assess the impact of glofitamab-associated transient increases in interleukin 6 (IL-6) on the pharmacokinetics of several cytochrome P450 (CYP) substrates. By refinement of a previously described IL-6 model and inclusion of in vitro CYP suppression data for CYP3A4, CYP1A2, and 2C9, a PBPK model was established in Simcyp to capture the induced IL-6 levels seen when glofitamab is administered at the intended dose and dosing regimen. Following model qualification, the PBPK model was used to predict the potential impact of CYP suppression on exposures of various CYP probe substrates. PBPK analysis predicted that, in the worst-case, the transient elevation of IL-6 would increase exposures of CYP3A4, CYP2C9, and CYP1A2 substrates by less than or equal to twofold. Increases for CYP3A4, CYP2C9, and CYP1A2 substrates were projected to be 1.75, 1.19, and 1.09-fold following the first administration and 2.08, 1.28, and 1.49-fold following repeated administrations. It is recommended that there are no restrictions on concomitant treatment with any other drugs. Consideration may be given for potential drug-drug interaction during the first cycle in patients who are receiving concomitant CYP substrates with a narrow therapeutic index via monitoring for toxicity or for drug concentrations.

Quantitative prediction of transporter-mediated drug-drug interactions using the mechanistic static pharmacokinetic (MSPK) model

Guidance/guidelines on drug-drug interactions (DDIs) have been issued in Japan, the United States, and Europe. These guidance/guidelines provide decision trees for conducting metabolizing enzyme-mediated clinical DDI studies; however, the decision trees for transporter-mediated DDIs lack quantitative prediction methods. In this study, the accuracy of a net-effect mechanistic static pharmacokinetics (MSPK) model containing the fraction transported (ft) of transporters was examined to predict transporter-mediated DDIs. This study collected information on 25 oral drugs with new active reagents that were used in clinical DDI studies as perpetrators (42 cases) from drugs approved in Japan between April 2016 and June 2020. The AUCRs (AUC ratios with and without perpetrators) of victim drugs were predicted using the net-effect MSPK model. As a result, 83 and 95% of the predicted AUCRs were within 1.5- and 2-fold error in the observed AUCRs, respectively. In cases where the victims were statins in which pharmacokinetics several transporters are involved, 70 and 91% of the predicted AUCRs were within 1.5- and 2-fold errors, respectively. Therefore, the net-effect MSPK model was applicable for predicting the AUCRs of victims, which are substrates for multiple transporters.

Hydroxychloroquine is Metabolized by Cytochrome P450 2D6, 3A4, and 2C8, and Inhibits Cytochrome P450 2D6, while its Metabolites also Inhibit Cytochrome P450 3A in vitro

This study aimed to explore the cytochrome P450 (CYP) metabolic and inhibitory profile of hydroxychloroquine (HCQ). Hydroxychloroquine metabolism was studied using human liver microsomes (HLMs) and recombinant CYP enzymes. The inhibitory effects of HCQ and its metabolites on nine CYPs were also determined in HLMs, using an automated substrate cocktail method. Our metabolism data indicated that CYP3A4, CYP2D6, and CYP2C8 are the key enzymes involved in HCQ metabolism. All three CYPs formed the primary metabolites desethylchloroquine (DCQ) and desethylhydroxychloroquine (DHCQ) to various degrees. Although the intrinsic clearance (CL_int) value of HCQ depletion by recombinant CYP2D6 was > 10-fold higher than that by CYP3A4 (0.87 versus 0.075 µl/min/pmol), scaling of recombinant CYP CL_int to HLM level resulted in almost equal HLM CL_int values for CYP2D6 and CYP3A4 (11 and 14 µl/min/mg, respectively). The scaled HLM CL_int of CYP2C8 was 5.7 µl/min/mg. Data from HLM experiments with CYP-selective inhibitors also suggested relatively equal roles for CYP2D6 and CYP3A4 in HCQ metabolism, with a smaller contribution by CYP2C8. In CYP inhibition experiments, HCQ, DCQ, DHCQ, and the secondary metabolite didesethylchloroquine were direct CYP2D6 inhibitors, with 50% inhibitory concentration (IC₅₀) values between 18 and 135 µM. HCQ did not inhibit other CYPs. Furthermore, all metabolites were time-dependent CYP3A inhibitors (IC₅₀ shift 2.2-3.4). To conclude, HCQ is metabolized by CYP3A4, CYP2D6, and CYP2C8 in vitro. HCQ and its metabolites are reversible CYP2D6 inhibitors, and HCQ metabolites are time-dependent CYP3A inhibitors. These data can be used to improve physiologically-based pharmacokinetic models and update drug-drug interaction risk estimations for HCQ. SIGNIFICANCE STATEMENT: While CYP2D6, CYP3A4, and CYP2C8 have been shown to mediate chloroquine biotransformation, it appears that the role of CYP enzymes in hydroxychloroquine (HCQ) metabolism has not been studied. In addition, little is known about the CYP inhibitory effects of HCQ. Here, we demonstrate that CYP2D6, CYP3A4, and CYP2C8 are the key enzymes involved in HCQ metabolism. Furthermore, our findings show that HCQ and its metabolites are inhibitors of CYP2D6, which likely explains the previously observed interaction between HCQ and metoprolol.

Kratom Alkaloids: Interactions With Enzymes, Receptors, and Cellular Barriers

Parallel to the growing use of kratom, there is a wealth of evidence from self-report, preclinical, and early clinical studies on therapeutic benefits of its alkaloids in particular for treating pain, managing substance use disorder, and coping with emotional or mental health conditions. On the other hand, there are also reports on potential health risks concerning kratom use. These two aspects are often discussed in reviews on kratom. Here, we aim to highlight specific areas that are of importance to give insights into the mechanistic of kratom alkaloids pharmacological actions. This includes their interactions with drug-metabolizing enzymes and predictions of clinical drug-drug interactions, receptor-binding properties, interactions with cellular barriers in regards to barrier permeability, involvement of membrane transporters, and alteration of barrier function when exposed to the alkaloids.

Physiologically-Based Pharmacokinetic Modelling of Entrectinib Parent and Active Metabolite to Support Regulatory Decision-Making

BACKGROUND AND OBJECTIVE

Entrectinib is a selective inhibitor of ROS1/TRK/ALK kinases, recently approved for oncology indications. Entrectinib is predominantly cleared by cytochrome P450 (CYP) 3A4, and modulation of CYP3A enzyme activity profoundly alters the pharmacokinetics of both entrectinib and its active metabolite M5. We describe development of a combined physiologically based pharmacokinetic (PBPK) model for entrectinib and M5 to support dosing recommendations when entrectinib is co-administered with CYP3A4 inhibitors or inducers.

METHODS

A PBPK model was established in Simcyp^® Simulator. The initial model based on in vitro-in vivo extrapolation was refined using sensitivity analysis and non-linear mixed effects modeling to optimize parameter estimates and to improve model fit to data from a clinical drug-drug interaction study with the strong CYP3A4 inhibitor, itraconazole. The model was subsequently qualified against clinical data, and the final qualified model used to simulate the effects of moderate to strong CYP3A4 inhibitors and inducers on entrectinib and M5 pharmacokinetics.

RESULTS

The final model showed good predictive performance for entrectinib and M5, meeting commonly used predictive performance acceptance criteria in each case. The model predicted that co-administration of various moderate CYP3A4 inhibitors (verapamil, erythromycin, clarithromycin, fluconazole, and diltiazem) would result in an average increase in entrectinib exposure between 2.2- and 3.1-fold, with corresponding average increases for M5 of approximately 2-fold. Co-administration of moderate CYP3A4 inducers (efavirenz, carbamazepine, phenytoin) was predicted to result in an average decrease in entrectinib exposure between 45 and 79%, with corresponding average decreases for M5 of approximately 50%.

CONCLUSIONS

The model simulations were used to derive dosing recommendations for co-administering entrectinib with CYP3A4 inhibitors or inducers. PBPK modeling has been used in lieu of clinical studies to enable regulatory decision-making.

Translational aspects of cytochrome P450-mediated drug-drug interactions: A case study with clopidogrel

Multimorbidity, polypharmacotherapy and drug interactions are increasingly common in the ageing population. Many drug-drug interactions (DDIs) are caused by perpetrator drugs inhibiting or inducing cytochrome P450 (CYP) enzymes, resulting in alterations of the plasma concentrations of a victim drug. DDIs can have a major negative health impact, and in the past, unrecognized DDIs have resulted in drug withdrawals from the market. Signals to investigate DDIs may emerge from a variety of sources. Nowadays, standard methods are widely available to identify and characterize the mechanisms of CYP-mediated DDIs in vitro. Clinical pharmacokinetic studies, in turn, provide experimental data on pharmacokinetic outcomes of DDIs. Physiologically based pharmacokinetic (PBPK) modelling utilizing both in vitro and in vivo data is a powerful tool to predict different DDI scenarios. Finally, epidemiological studies can provide estimates on the health outcomes of DDIs. Thus, to fully characterize the mechanisms, clinical effects and implications of CYP-mediated DDIs, translational research approaches are required. This minireview provides an overview of translational approaches to study CYP-mediated DDIs, going beyond regulatory DDI guidelines, and an illustrative case study of how the DDI potential of clopidogrel was unveiled by combining these different methods.

The application of precision dosing in the use of sertraline throughout pregnancy for poor and ultrarapid metabolizer CYP 2C19 subjects: A virtual clinical trial pharmacokinetics study

Sertraline is known to undergo changes in pharmacokinetics during pregnancy. CYP 2C19 has been implicated in the interindividual variation in clinical effect associated with sertraline activity. However, knowledge of suitable dose titrations during pregnancy and within CYP 2C19 phenotypes is lacking. A pharmacokinetic modeling virtual clinical trials approach was implemented to: (i) assess gestational changes in sertraline trough plasma concentrations for CYP 2C19 phenotypes, and (ii) identify appropriate dose titration strategies to stabilize sertraline levels within a defined therapeutic range throughout gestation. Sertraline trough plasma concentrations decreased throughout gestation, with maternal volume expansion and reduction in plasma albumin being identified as possible causative reasons. All CYP 2C19 phenotypes required a dose increase throughout gestation. For extensive metabolizer (EM) and ultrarapid metabolizer (UM) phenotypes, doses of 100-150 mg daily are required throughout gestation. For poor metabolizers (PM), 50 mg daily during trimester 1 followed by a dose of 100 mg daily in trimesters 2 and 3 are required.

Efficacy of Gemfibrozil as an Adjunct to Sertraline in Major Depressive Disorder, A Double-Blind, Randomized, and Placebo-Controlled Clinical Trial

Objective: Major depressive disorder (MDD) is predicted to be one of the biggest disease burden in the future. The antidepressant activity of gemfibrozil has been recently considered. In this study, we assessed the effectiveness of gemfibrozil as a sertraline adjunct in treating patients with MDD.

Method: In this study, 46 patients with MDD based on the DSM-V criteria with a minimum score of 22 on the 17-item Hamilton Rating Scale for Depression (HAM-D) were divided into two groups. One group was treated with 300 mg of gemfibrozil daily and the other group treated with placebo. Each group was treated simultaneously with 100 mg of sertraline daily for 8 weeks. The trial was randomized and double-blind. To assess the response to treatment, patients were evaluated at baseline and then at weeks 2, 4 and 8 using the HAM-D score.

Results: The study was completed by 45 patients up to the final stages and follow-up visits. Repeated measure ANOVA with a Greenhouse-Geisser correction showed a significant difference for time×treatment interaction on within-subjects HAM-D scores [p–value= 0.026]. A notable difference was seen in time [p–value < 0.001]. The test of between-subject effects also represented a remarkable consequence of treatment on HAM-D scores at weeks 2, 4, and 8 [p–value = 0.07]. Using Kaplan-Meier estimate curves, time to remission periods were notable different between the 2 trial groups [Log-Rank p–value = 0.003].

Conclusion: Gemfibrozil is an effective adjunctive treatment in MDD and can be used to reduce depression symptoms.

Variability in In Vitro OATP1B1/1B3 Inhibition Data: Impact of Incubation Conditions on Variability and Subsequent Drug Interaction Predictions

As the research into the organic anion transporting polypeptides (OATPs) continues to grow, it is important to ensure that the data generated are accurate and reproducible. In the in vitro evaluation of OATP1B1/1B3 inhibition, there are many variables that can contribute to variability in the resulting inhibition constants, which can then, in turn, contribute to variable results when clinical predictions (R-values) are performed. Currently, the only experimental condition recommended by the US Food and Drug Administration (FDA) is the inclusion of a pre-incubation period.¹ To identify other potential sources of variability, a descriptive analysis of available in vitro inhibition data was completed. For each of the 21 substrate/inhibitor pairs evaluated, cell type and pre-incubation were found to have the greatest effect on half-maximal inhibitory concentration (IC₅₀ ) variability. Indeed, when only HEK293 cells and co-incubation conditions were included, the observed variability for the entire data set (highest IC₅₀ /lowest) was reduced from 12.4 to 5.2. The choice of probe substrate used in the study also had a significant effect on inhibitor constant variability. Interestingly, despite the broad range of inhibitory constants identified, these two factors showed little effect on the calculated R-values relative to the FDA evaluation cutoff of 1.1 triggering a clinical evaluation for the inhibitors evaluated. However, because of the small data set available, further research is needed to confirm these preliminary results and define best practice for the study of OATPs.

Transporter-Mediated Disposition, Clinical Pharmacokinetics and Cholestatic Potential of Glyburide and Its Primary Active Metabolites

Glyburide is widely used for the treatment of type 2 diabetes. We studied the mechanisms involved in the disposition of glyburide and its pharmacologically active hydroxy metabolites M1 and M2b and evaluated their clinical pharmacokinetics and the potential role in glyburide-induced cholestasis employing physiologically based pharmacokinetic (PBPK) modeling. Transport studies of parent and metabolites in human hepatocytes and transfected cell systems imply hepatic uptake mediated by organic anion-transporting polypeptides. Metabolites are also subjected to basolateral and biliary efflux by P-glycoprotein, breast cancer resistance protein, and multidrug resistance-associated proteins, and are substrates to renal organic anion transporter 3. A PBPK model in combination with a Bayesian approach was developed considering the identified disposition mechanisms. The model reasonably described plasma concentration time profiles and urinary recoveries of glyburide and the metabolites, implying the role of multiple transport processes in their pharmacokinetics. Predicted free liver concentrations of the parent (∼30-fold) and metabolites (∼4-fold) were higher than their free plasma concentrations. Finally, all three compounds showed bile salt export pump inhibition in vitro; however, significant in vivo inhibition was not apparent for any compound on the basis of a predicted unbound liver exposure-response effect model using measured in vitro IC₅₀ values. In conclusion, this study demonstrates the important role of multiple drug transporters in the disposition of glyburide and its active metabolites, suggesting that variability in the function of these processes may lead to pharmacokinetic variability in the parent and the metabolites, potentially translating to pharmacodynamic variability.