Considerations from the Innovation and Quality Induction Working Group in Response to Drug-Drug Interaction Guidances from Regulatory Agencies: Focus on CYP3A4 mRNA In Vitro Response Thresholds, Variability, and Clinical Relevance

Induction of human hepatic cytochrome P-450 3A4 expression by antifungal succinate dehydrogenase inhibitors

Succinate dehydrogenase inhibitors (SDHIs) are widely-used fungicides, to which humans are exposed and for which putative health risks are of concern. In order to identify human molecular targets for these agrochemicals, the interactions of 15 SDHIs with expression and activity of human cytochrome P-450 3A4 (CYP3A4), a major hepatic drug metabolizing enzyme, were investigated in vitro. 12/15 SDHIs, i.e., bixafen, boscalid, fluopyram, flutolanil, fluxapyroxad, furametpyr, isofetamid, isopyrazam, penflufen, penthiopyrad, pydiflumetofen and sedaxane, were found to enhance CYP3A4 mRNA expression in human hepatic HepaRG cells and primary human hepatocytes exposed for 48 h to 10 µM SDHIs, whereas 3/15 SDHIs, i.e., benzovindiflupyr, carboxin and thifluzamide, were without effect. The inducing effects were concentrations-dependent for boscalid (EC50=22.5 µM), fluopyram (EC50=4.8 µM) and flutolanil (EC50=53.6 µM). They were fully prevented by SPA70, an antagonist of the Pregnane X Receptor, thus underlining the implication of this xenobiotic-sensing receptor. Increase in CYP3A4 mRNA in response to SDHIs paralleled enhanced CYP3A4 protein expression for most of SDHIs. With respect to CYP3A4 activity, it was directly inhibited by some SDHIs, including bixafen, fluopyram, fluxapyroxad, isofetamid, isopyrazam, penthiopyrad and sedaxane, which therefore appears as dual regulators of CYP3A4, being both inducer of its expression and inhibitor of its activity. The inducing effect nevertheless predominates for these SDHIs, except for isopyrazam and sedaxane, whereas boscalid and flutolanil were pure inducers of CYP3A4 expression and activity. Most of SDHIs appear therefore as in vitro inducers of CYP3A4 expression in cultured hepatic cells, when, however, used at concentrations rather higher than those expected in humans in response to environmental or dietary exposure to these agrochemicals.

Quantitative Cytochrome P450 3A4 Induction Risk Assessment Using Human Hepatocytes Complemented with Pregnane X Receptor-Activating Profiles

Reliable in vitro to in vivo translation of cytochrome P450 (CYP) 3A4 induction potential is essential to support risk mitigation for compounds during pharmaceutical discovery and development. In this study, a linear correlation of CYP3A4 mRNA induction potential in human hepatocytes with the respective pregnane-X receptor (PXR) activation in a reporter gene assay using DPX2 cells was successfully demonstrated for 13 clinically used drugs. Based on this correlation, using rifampicin as a positive control, the magnitude of CYP3A4 mRNA induction for 71 internal compounds at several concentrations up to 10 µM (n = 90) was predicted within 2-fold error for 64% of cases with only a few false positives (19%). Furthermore, the in vivo area under the curve reduction of probe CYP substrates was reasonably predicted for eight marketed drugs (carbamazepine, dexamethasone, enzalutamide, nevirapine, phenobarbital, phenytoin, rifampicin, and rufinamide) using the static net effect model using both the PXR activation and CYP3A4 mRNA induction data. The liver exit concentrations were used for the model in place of the inlet concentrations to avoid false positive predictions and the concentration achieving twofold induction (F2) was used to compensate for the lack of full induction kinetics due to cytotoxicity and solubility limitations in vitro. These findings can complement the currently available induction risk mitigation strategy and potentially influence the drug interaction modeling work conducted at clinical stages. SIGNIFICANCE STATEMENT: The established correlation of CYP3A4 mRNA in human hepatocytes to PXR activation provides a clear cut-off to identify a compound showing an in vitro induction risk, complementing current regulatory guidance. Also, the demonstrated in vitro-in vivo translation of induction data strongly supports a clinical development program although limitations remain for drug candidates showing complex disposition pathways, such as involvement of auto-inhibition/induction, active transport and high protein binding.

Can Mechanistic Static Models for Drug-Drug Interactions Support Regulatory Filing for Study Waivers and Label Recommendations?

BACKGROUND AND OBJECTIVE

Mechanistic static and dynamic physiologically based pharmacokinetic models are used in clinical drug development to assess the risk of drug-drug interactions (DDIs). Currently, the use of mechanistic static models is restricted to screening DDI risk for an investigational drug, while dynamic physiologically based pharmacokinetic models are used for quantitative predictions of DDIs to support regulatory filing. As physiologically based pharmacokinetic model development by sponsors as well as a review of models by regulators require considerable resources, we explored the possibility of using mechanistic static models to support regulatory filing, using representative cases of successful physiologically based pharmacokinetic submissions to the US Food and Drug Administration under different classes of applications.

METHODS

Drug-drug interaction predictions with mechanistic static models were done for representative cases in the different classes of applications using the same data and modelling workflow as described in the Food and Drug Administration clinical pharmacology reviews. We investigated the hypothesis that the use of unbound average steady-state concentrations of modulators as driver concentrations in the mechanistic static models should lead to the same conclusions as those from physiologically based pharmacokinetic modelling for non-dynamic measures of DDI risk assessment such as the area under the plasma concentration-time curve ratio, provided the same input data are employed for the interacting drugs.

RESULTS

Drug-drug interaction predictions of area under the plasma concentration-time curve ratios using mechanistic static models were mostly comparable to those reported in the Food and Drug Administration reviews using physiologically based pharmacokinetic models for all representative cases in the different classes of applications.

CONCLUSIONS

The results reported in this study should encourage the use of models that best fit an intended purpose, limiting the use of physiologically based pharmacokinetic models to those applications that leverage its unique strengths, such as what-if scenario testing to understand the effect of dose staggering, evaluating the role of uptake and efflux transporters, extrapolating DDI effects from studied to unstudied populations, or assessing the impact of DDIs on the exposure of a victim drug with concurrent mechanisms. With this first step, we hope to trigger a scientific discussion on the value of a routine comparison of the two methods for regulatory submissions to potentially create a best practice that could help identify examples where the use of dynamic changes in modulator concentrations could make a difference to DDI risk assessment.

Ensemble Docking Approach to Mitigate Pregnane X Receptor-Mediated CYP3A4 Induction Risk

Three structurally closely related dopamine D1 receptor positive allosteric modulators (D1 PAMs) based on a tetrahydroisoquinoline (THIQ) scaffold were profiled for their CYP3A4 induction potentials. It was found that the length of the linker at the C5 position greatly affected the potentials of these D1 PAMs as CYP3A4 inducers, and the level of induction correlated well with the activation of the pregnane X receptor (PXR). Based on the published PXR X-ray crystal structures, we built a binding model specifically for these THIQ-scaffold-based D1 PAMs in the PXR ligand-binding pocket via an ensemble docking approach and found the model could explain the observed CYP induction disparity. Combined with our previously reported D1 receptor homology model, which identified the C5 position as pointing toward the solvent-exposed space, our PXR-binding model coincidentally suggested that structural modifications at the C5 position could productively modulate the CYP induction potential while maintaining the D1 PAM potency of these THIQ-based PAMs.

Beyond the Michaelis-Menten: Accurate Prediction of Drug Interactions through Cytochrome P450 3A4 Induction

The US Food and Drug Administration (FDA) guidance has recommended several model-based predictions to determine potential drug-drug interactions (DDIs) mediated by cytochrome P450 (CYP) induction. In particular, the ratio of substrate area under the plasma concentration-time curve (AUCR) under and not under the effect of inducers is predicted by the Michaelis-Menten (MM) model, where the MM constant ( K m $$ {K}_{\mathrm{m}} $$ ) of a drug is implicitly assumed to be sufficiently higher than the concentration of CYP enzymes that metabolize the drug ( E T $$ {E}_{\mathrm{T}} $$ ) in both the liver and small intestine. Furthermore, the fraction absorbed from gut lumen ( F a $$ {F}_{\mathrm{a}} $$ ) is also assumed to be one because F a $$ {F}_{\mathrm{a}} $$ is usually unknown. Here, we found that such assumptions lead to serious errors in predictions of AUCR. To resolve this, we propose a new framework to predict AUCR. Specifically, F a $$ {F}_{\mathrm{a}} $$ was re-estimated from experimental permeability values rather than assuming it to be one. Importantly, we used the total quasi-steady-state approximation to derive a new equation, which is valid regardless of the relationship between K m $$ {K}_{\mathrm{m}} $$ and E T $$ {E}_{\mathrm{T}} $$ , unlike the MM model. Thus, our framework becomes much more accurate than the original FDA equation, especially for drugs with high affinities, such as midazolam or strong inducers, such as rifampicin, so that the ratio between K m $$ {K}_{\mathrm{m}} $$ and E T $$ {E}_{\mathrm{T}} $$ becomes low (i.e., the MM model is invalid). Our work greatly improves the prediction of clinical DDIs, which is critical to preventing drug toxicity and failure.

Artemisia extracts differ from artemisinin effects on human hepatic CYP450s 2B6 and 3A4 in vitro

ETHNOPHARMACOLOGICAL RELEVANCE

The Chinese medicinal herb, Artemisia annua L., has been used for >2,000 yr as traditional tea infusions to treat a variety of infectious diseases including malaria, and its use is spreading globally (along with A. afra Jacq. ex Willd.) mainly through grassroots efforts.

AIM OF THE STUDY

Artemisinin is more bioavailable delivered from the plant, Artemisia annua L. than the pure drug, but little is known about how delivery via a hot water infusion (tea) alters induction of hepatic CYP2B6 and CYP3A4 that metabolize artemisinin.

MATERIALS AND METHODS

HepaRG cells were treated with 10 μM artemisinin or rifampicin (positive control), and teas (10 g/L) of A. annua SAM, and A. afra SEN and MAL with 1.6, 0.05 and 0 mg/g DW artemisinin in the leaves, respectively; qPCR and Western blots were used to measure CYP2B6 and CYP3A4 responses. Enzymatic activity of these P450s was measured using human liver microsomes and P450-Glo assays.

RESULTS

All teas inhibited activity of CYP2B6 and CYP3A4. Artemisinin and the high artemisinin-containing tea infusion (SAM) induced CYP2B6 and CYP3A4 transcription, but artemisinin-deficient teas, MAL and SEN, did not. Artemisinin increased CYP2B6 and CYP3A4 protein levels, but none of the three teas did, indicating a post-transcription inhibition by all three teas.

CONCLUSIONS

This study showed that Artemisia teas inhibit activity and artemisinin autoinduction of CYP2B6 and CYP3A4 post transcription, a response likely the effect of other phytochemicals in these teas. Results are important for understanding Artemisia tea posology.

Characterization of Correction Factors to Enable Assessment of Clinical Risk from In Vitro CYP3A4 Induction Data and Basic Drug-Drug Interaction Models

Background and Objective

Induction of drug-metabolizing enzymes can lead to drug-drug interactions (DDIs); therefore, early assessment is often conducted. Previous reports focused on true positive cytochrome P450 3A (CYP3A) inducers leaving a gap in translation for in vitro inducers which do not manifest in clinical induction. The goal herein was to expand the in vitro induction dataset by including true negative clinical inducers to identify a correction factor to basic DDI models, which reduces false positives without impacting false negatives.

Methods

True negative clinical inducers were identified through a literature search, in vitro induction parameters were generated in three human hepatocyte donors, and the performance of basic induction models proposed by regulatory agencies, concentration producing twofold induction (F2), basic static model (R3) and relative induction score (RIS), was used to characterize clinical induction risk.

Results

The data demonstrated the importance of correcting for in vitro binding and metabolism to derive induction parameters. The aggregate analysis indicates that the RIS with a positive cut-off of < 0.7-fold area under the curve ratio (AUCR) provides the best quantitative prediction. Additionally, correction factors of ten and two times the unbound peak plasma concentration at steady state (C_max,ss,u) can be confidently used to identify true positive inducers when referenced against the concentration resulting in twofold increase in messenger ribonucleic acid (mRNA) or using the R3 equation, respectively.

Conclusions

These iterative improvements, which reduce the number of false positives, could aid regulatory recommendations and limit unnecessary clinical explorations into CYP3A induction.

Graphical abstract

Supplementary Information

The online version contains supplementary material available at 10.1007/s13318-022-00763-y.

Current Practices, Gap Analysis, and Proposed Workflows for PBPK Modeling of Cytochrome P450 Induction: An Industry Perspective

The International Consortium for Innovation and Quality (IQ) Physiologically Based Pharmacokinetic (PBPK) Modeling Induction Working Group (IWG) conducted a survey across participating companies around general strategies for PBPK modeling of induction, including experience with its utility to address various questions, regulatory interactions, and regulatory acceptance. The results highlight areas where PBPK modeling is used with high confidence and identifies opportunities where confidence is lower and further evaluation is needed. To enhance the survey results, the PBPK-IWG also collected case studies and analyzed recent literature examples where PBPK models were applied to predict CYP3A induction-mediated drug-drug interactions. PBPK modeling of induction has evolved and progressed significantly, proving to have great potential to accelerate drug discovery and development. With the aim of enabling optimal use for new molecular entities that are either substrates and/or inducers of CYP3A, the PBPK-IWG proposes initial workflows for PBPK application, discusses future trends, and identifies gaps that need to be addressed.

Broad application of CYP3A4 LC-MS protein quantification in hepatocyte cytochrome P450 induction assays identifies nonuniformity in mRNA and protein induction responses

Screening for cytochrome P450 (CYP) induction potential is routine in drug development. Induction results in a net increase in CYP protein and is assessed typically by measuring indirect endpoints, i.e., enzyme activity and mRNA in vitro. Recent methodological advancements have made CYP protein quantification by LC-MS in in vitro induction studies more accessible and amenable to routine testing. In this study, we evaluated CYP3A4 concentration dependence of induction response for 11 compounds (rifampin, rifabutin, carbamazepine, efavirenz, nitrendipine, flumazenil, pioglitazone, rosiglitazone, troglitazone, pazopanib, and ticagrelor) in plated hepatocytes from two or three donors incorporating in the assessment all three endpoints. In addition, the time-dependence of the induction was examined over 1, 2 or 3 days of treatment. For most compounds, mRNA, enzyme activity and protein endpoints exhibited similarity in induction responses. Pazopanib and ticagrelor were notable exceptions as neither protein nor enzyme activity were induced despite mRNA induction of a magnitude similar to efavirenz, pioglitazone or rosiglitazone, which clearly induced in all three endpoints. Static modeling of clinical induction responses supported a role for protein as a predictive endpoint. These data highlight the value of including CYP protein quantification as an induction assay endpoint to provide a more comprehensive assessment of induction liability. Significance Statement Direct, LC-MS-based quantification of CYP protein is a desirable induction assay endpoint, however the application of protein as an endpoint has been limited due to inefficient workflows. Here, we incorporate recent advancements in protein quantitation methods to efficiently quantify CYP3A4 protein in in vitro induction assays with 11 compounds in up to 3 donors. The data indicate induction responses from mRNA do not always align with those of protein suggesting assessment of induction liability is more complex than thought previously.

In vitro investigations into the roles of CYP450 enzymes and drug transporters in the drug interactions of zanubrutinib, a covalent Bruton's tyrosine kinase inhibitor

Zanubrutinib is a highly selective, potent, orally available, targeted covalent inhibitor (TCI) of Bruton's tyrosine kinase (BTK). This work investigated the in vitro drug metabolism and transport of zanubrutinib, and its potential for clinical drug-drug interactions (DDIs). Phenotyping studies indicated cytochrome P450 (CYP) 3A are the major CYP isoform responsible for zanubrutinib metabolism, which was confirmed by a clinical DDI study with itraconazole and rifampin. Zanubrutinib showed mild reversible inhibition with half maximal inhibitory concentration (IC50 ) of 4.03, 5.69, and 7.80 μM for CYP2C8, CYP2C9, and CYP2C19, respectively. Data in human hepatocytes disclosed induction potential for CYP3A4, CYP2B6, and CYP2C enzymes. Transport assays demonstrated that zanubrutinib is not a substrate of human breast cancer resistance protein (BCRP), organic anion transporting polypeptide (OATP)1B1/1B3, organic cation transporter (OCT)2, or organic anion transporter (OAT)1/3 but is a potential substrate of the efflux transporter P-glycoprotein (P-gp). Additionally, zanubrutinib is neither an inhibitor of P-gp at concentrations up to 10.0 μM nor an inhibitor of BCRP, OATP1B1, OATP1B3, OAT1, and OAT3 at concentrations up to 5.0 μM. The in vitro results with CYPs and transporters were correlated with the available clinical DDIs using basic models and mechanistic static models. Zanubrutinib is not likely to be involved in transporter-mediated DDIs. CYP3A inhibitors and inducers may impact systemic exposure of zanubrutinib. Dose adjustments may be warranted depending on the potency of CYP3A modulators.

Expression dynamics of pregnane X receptor-controlled genes in 3D primary human hepatocyte spheroids

The pregnane X receptor (PXR) is a ligand-activated nuclear receptor controlling hepatocyte expression of numerous genes. Although expression changes in xenobiotic-metabolizing, lipogenic, gluconeogenic and bile acid synthetic genes have been described after PXR activation, the temporal dynamics of their expression is largely unknown. Recently, 3D spheroids of primary human hepatocytes (PHHs) have been characterized as the most phenotypically relevant hepatocyte model. We used 3D PHHs to assess time-dependent expression profiles of 12 prototypic PXR-controlled genes in the time course of 168 h of rifampicin treatment (1 or 10 µM). We observed a similar bell-shaped time-induction pattern for xenobiotic-handling genes (CYP3A4, CYP2C9, CYP2B6, and MDR1). However, we observed either biphasic profiles for genes involved in endogenous metabolism (FASN, GLUT2, G6PC, PCK1, and CYP7A1), a decrease for SHP or oscillation for PDK4 and PXR. The rifampicin concentration determined the expression profiles for some genes. Moreover, we calculated half-lives of CYP3A4 and CYP2C9 mRNA under induced or basal conditions and we used a mathematical model to describe PXR-mediated regulation of CYP3A4 expression employing 3D PHHs. The study shows the importance of long-term time-expression profiling of PXR target genes in phenotypically stable 3D PHHs and provides insight into PXR function in liver beyond our knowledge from conventional 2D in vitro models.

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.

Designing Out PXR Activity on Drug Discovery Projects: A Review of Structure-Based Methods, Empirical and Computational Approaches

This perspective discusses the role of pregnane xenobiotic receptor (PXR) in drug discovery and the impact of its activation on CYP3A4 induction. The use of structural biology to reduce PXR activity on drug discovery projects has become more common in recent years. Analysis of this work highlights several important molecular interactions, and the resultant structural modifications to reduce PXR activity are summarized. The computational approaches undertaken to support the design of new drugs devoid of PXR activation potential are also discussed. Finally, the SAR of empirical design strategies to reduce PXR activity is reviewed, and the key SAR transformations are discussed and summarized. In conclusion, this perspective demonstrates that PXR activity can be greatly diminished or negated on active drug discovery projects with the knowledge now available. This perspective should be useful to anyone who seeks to reduce PXR activity on a drug discovery project.

2020 FDA Drug-drug Interaction Guidance: A Comparison Analysis and Action Plan by Pharmaceutical Industrial Scientists

Background:

In January 2020, the US FDA published two final guidelines, one entitled “In vitro Drug Interaction Studies - Cytochrome P450 Enzyme- and Transporter-Mediated Drug Interactions Guidance for Industry” and the other entitled “Clinical Drug Interaction Studies - Cytochrome P450 Enzyme- and Transporter-Mediated Drug Interactions Guidance for Industry”. These were updated from the 2017 draft in vitro and clinical DDI guidance.

Methods:

This study is aimed to provide an analysis of the updates along with a comparison of the DDI guidelines published by the European Medicines Agency (EMA) and Japanese Pharmaceuticals and Medical Devices Agency (PMDA) along with the current literature.

Results:

The updates were provided in the final FDA DDI guidelines and explained the rationale of those changes based on the understanding from research and literature. Furthermore, a comparison among the FDA, EMA, and PMDA DDI guidelines are presented in Tables 1, 2 and 3.

Conclusion:

The new 2020 clinical DDI guidance from the FDA now has even higher harmonization with the guidance (or guidelines) from the EMA and PMDA. A comparison of DDI guidance from the FDA 2017, 2020, EMA, and PMDA on CYP and transporter based DDI, mathematical models, PBPK, and clinical evaluation of DDI is presented in this review.

Hepatic Transporter Alterations by Nuclear Receptor Agonist T0901317 in Sandwich-Cultured Human Hepatocytes: Proteomic Analysis and PBPK Modeling to Evaluate Drug-Drug Interaction Risk

In vitro approaches for predicting drug-drug interactions (DDIs) caused by alterations in transporter protein regulation are not well established. However, reports of transporter regulation via nuclear receptor (NR) modulation by drugs are increasing. This study examined alterations in transporter protein levels in sandwich-cultured human hepatocytes (SCHH; n = 3 donors) measured by liquid chromatography-tandem mass spectrometry-based proteomic analysis after treatment with N-[4-(1,1,1,3,3,3-hexafluoro-2-hydroxypropan-2-yl)phenyl]-N-(2,2,2-trifluoroethyl)benzenesulfonamide (T0901317), the first described synthetic liver X receptor agonist. T0901317 treatment (10 μM, 48 hours) decreased the levels of organic cation transporter (OCT) 1 (0.22-, 0.43-, and 0.71-fold of control) and organic anion transporter (OAT) 2 (0.38-, 0.38-, and 0.53-fold of control) and increased multidrug resistance protein (MDR) 1 (1.37-, 1.48-, and 1.59-fold of control). The induction of NR downstream gene expression supports the hypothesis that T0901317 off-target effects on farnesoid X receptor and pregnane X receptor activation are responsible for the unexpected changes in OCT1, OAT2, and MDR1. Uptake of the OCT1 substrate metformin in SCHH was decreased by T0901317 treatment. Effects of decreased OCT1 levels on metformin were simulated using a physiologically-based pharmacokinetic (PBPK) model. Simulations showed a clear decrease in metformin hepatic exposure resulting in a decreased pharmacodynamic effect. This DDI would not be predicted by the modest changes in simulated metformin plasma concentrations. Altogether, the current study demonstrated that an approach combining SCHH, proteomic analysis, and PBPK modeling is useful for revealing tissue concentration-based DDIs caused by unexpected regulation of hepatic transporters by NR modulators. SIGNIFICANCE STATEMENT: This study utilized an approach combining sandwich-cultured human hepatocytes, proteomic analysis, and physiologically based pharmacokinetic modeling to evaluate alterations in pharmacokinetics (PK) and pharmacodynamics (PD) caused by transporter regulation by nuclear receptor modulators. The importance of this approach from a mechanistic and clinically relevant perspective is that it can reveal drug-drug interactions (DDIs) caused by unexpected regulation of hepatic transporters and enable prediction of altered PK and PD changes, especially for tissue concentration-based DDIs.

Evaluation of drug-drug interactions in drug metabolism: Differences and harmonization in guidance/guidelines

The U.S. Drugs and Food Administration (FDA) and the Ministry of Health, Labor and Welfare of Japan (MHLW) issued the drastically revised draft guidance and final guideline on drug-drug interactions (DDI) in 2017 and 2018, respectively. One of the most drastic changes for the evaluation of inhibition potential of drug metabolizing enzymes in the liver using a basic model in these guidance and guideline are represented by the concept to use the unbound maximum concentration in the systemic circulation as the investigational drug concentration instead of the total maximum concentration and the corresponding cutoff values are applied in harmonization with the current DDI guideline of Europe. In this review, the current DDI guidance and guidelines of the three regions are compared and the points which are in common are described. In addition, several issues to be considered and/or clarified such as a criterion for the metabolites to be evaluated as perpetrator drugs, details of in vitro study design etc. are also briefly summarized. Based on further accumulation of data and information, and their continuous international scientific discussion, these issues are expected to be solved to make the current DDI guidance and guidelines be much more harmonized and practically available standards.

Perspectives from the Innovation and Quality Consortium Induction Working Group on Factors Impacting Clinical Drug-Drug Interactions Resulting from Induction: Focus on Cytochrome 3A Substrates

A recent publication from the Innovation and Quality Consortium Induction Working Group collated a large clinical data set with the goal of evaluating the accuracy of drug-drug interaction (DDI) prediction from in vitro data. Somewhat surprisingly, comparison across studies of the mean- or median-reported area under the curve ratio showed appreciable variability in the magnitude of outcome. This commentary explores the possible drivers of this range of outcomes observed in clinical induction studies. While recommendations on clinical study design are not being proposed, some key observations were informative during the aggregate analysis of clinical data. Although DDI data are often presented using median data, individual data would enable evaluation of how differences in study design, baseline expression, and the number of subjects contribute. Since variability in perpetrator pharmacokinetics (PK) could impact the overall DDI interpretation, should this be routinely captured? Maximal induction was typically observed after 5-7 days of dosing. Thus, when the half-life of the inducer is less than 30 hours, are there benefits to a more standardized study design? A large proportion of CYP3A4 inducers were also CYP3A4 inhibitors and/or inactivators based on in vitro data. In these cases, using CYP3A selective substrates has limitations. More intensive monitoring of changes in area under the curve over time is warranted. With selective CYP3A substrates, the net effect was often inhibition, whereas less selective substrates could discern induction through mechanisms not susceptible to inhibition. The latter included oral contraceptives, which raise concerns of reduced efficacy following induction. Alternative approaches for modeling induction, such as applying biomarkers and physiologically based pharmacokinetic modeling (PBPK), are also considered. SIGNIFICANCE STATEMENT: The goal of this commentary is to stimulate discussion on whether there are opportunities to optimize clinical drug-drug interaction study design. The overall aim is to reduce, understand and contextualize the variability observed in the magnitude of induction across reported clinical studies. A large clinical CYP3A induction dataset was collected and further analyzed to identify trends and gaps. Reporting individual victim PK data, characterizing perpetrator PK and including additional PK assessments for mixed-mechanism perpetrators may provide insights into how these factors impact differences observed in clinical outcomes. The potential utility of biomarkers and PBPK modeling are discussed in considering future directions.

The optimization of methadone dosing whilst treating with rifampicin: A pharmacokinetic modeling study

BACKGROUND

The use of oral methadone in opioid substitution treatment (OST) for the management of opioid use disorder is established clinical practice. Confounding treatment is the increased risks of contracting Mycobacterium tuberculosis, the mainstay treatment of which incorporates the potent CYP 2B6 inducer rifampicin.

METHODS

This study applied pharmacokinetic modelling using virtual clinical trials, to pharmacokinetically quantify the extent and impact of rifampicin-mediated drug-drug interactions (DDI) on methadone plasma concentrations. An R-methadone model was developed and validated against 11 retrospective clinical studies prior to use in all subsequent studies. The aims were to investigate: (i) the impact of the DDI on daily methadone doses of 60 mg, 90 mg and 120 mg; (ii) dose escalation during rifampicin and (iii) dose reduction following rifampicin cessation.

RESULTS

A dose increase to 160 mg daily during rifampicin treatment phases was required to maintain peak methadone plasma concentrations within a derived therapeutic window of 80-700 ng/mL. Dose escalation prior to rifampicin initiation was not required and resulted in an increase in subjects with supra-therapeutic concentrations. However, during rifampicin cessation, a dose reduction of 10 mg every 2 days commencing prior to rifampicin cessation, ensured that most patients possessed a peak methadone plasma concentration within an optimal therapeutic window.

IMPLICATIONS

Rifampicin significantly alters methadone plasma concentrations and necessitates dose adjustments. Daily doses of almost double those used perhaps more commonly in clinical practice are required for optimal plasma concentration and careful consideration of dose reduction strategies would be required during the deinduction phase.

Does In Vitro Cytochrome P450 Downregulation Translate to In Vivo Drug-Drug Interactions? Preclinical and Clinical Studies With 13-cis-Retinoic Acid

All‐trans‐retinoic acid (atRA) downregulates cytochrome P450 (CYP)2D6 in several model systems. The aim of this study was to determine whether all active retinoids downregulate CYP2D6 and whether in vitro downregulation translates to in vivo drug–drug interactions (DDIs). The retinoids atRA, 13cisRA, and 4‐oxo‐13cisRA all decreased CYP2D6 mRNA in human hepatocytes in a concentration‐dependent manner. The in vitro data predicted ~ 50% decrease in CYP2D6 activity in humans after dosing with 13cisRA. However, the geometric mean area under plasma concentration‐time curve (AUC) ratio for dextromethorphan between treatment and control was 0.822, indicating a weak induction of dextromethorphan clearance following 13cisRA treatment. Similarly, in mice treatment with 4‐oxo‐13cisRA–induced mRNA expression of multiple mouse Cyp2d genes. In comparison, a weak induction of CYP3A4 in human hepatocytes translated to a weak in vivo induction of CYP3A4. These data suggest that in vitro CYP downregulation may not translate to in vivo DDIs, and better understanding of the mechanisms of CYP downregulation is needed.