Enzyme-mediated drug-drug interactions: a review of in vivo and in vitro methodologies, regulatory guidance, and translation to the clinic

Enzyme-mediated pharmacokinetic drug-drug interactions can be caused by altered activity of drug metabolizing enzymes in the presence of a perpetrator drug, mostly via inhibition or induction. We identified a gap in the literature for a state-of-the art detailed overview assessing this type of DDI risk in the context of drug development. This manuscript discusses in vitro and in vivo methodologies employed during the drug discovery and development process to predict clinical enzyme-mediated DDIs, including the determination of clearance pathways, metabolic enzyme contribution, and the mechanisms and kinetics of enzyme inhibition and induction. We discuss regulatory guidance and highlight the utility of in silico physiologically-based pharmacokinetic modeling, an approach that continues to gain application and traction in support of regulatory filings. Looking to the future, we consider DDI risk assessment for targeted protein degraders, an emerging small molecule modality, which does not have recommended guidelines for DDI evaluation. Our goal in writing this report was to provide early-career researchers with a comprehensive view of the enzyme-mediated pharmacokinetic DDI landscape to aid their drug development efforts.

Overview of preclinical and clinical studies investigating pharmacokinetics and drug-drug interactions of padsevonil

BACKGROUND

Padsevonil is an antiseizure medication candidate intended to benefit patients with drug-resistant epilepsy. Our investigations aimed at characterizing pharmacokinetics and drug-drug interaction (DDI) profile of padsevonil.

RESEARCH DESIGN AND METHODS

An overview of preclinical and clinical pharmacology studies conducted during padsevonil development is provided.

RESULTS

In preclinical studies, cytochrome (CYP) 3A4 was identified as the main P450 isoform involved in padsevonil metabolism, with potential minor contribution from CYP2C19. Padsevonil was shown to be a time-dependent CYP2C19-inhibitor, weak CYP3A4-inducer, weak inhibitor of P-gp/OCT1/MATE2-K, and potent OCT2-inhibitor. Initial clinical pharmacology studies in healthy participants showed that padsevonil had (i) good absorption, (ii) clearance mediated mainly by metabolism, and (iii) time-dependent kinetics. A study in genotyped participants confirmed the role of CYP2C19 in clearance and time-dependent kinetics; the major contribution of CYP3A4 was confirmed in DDI studies with CYP3A4-inducers (carbamazepine, oxcarbazepine) and -inhibitor (erythromycin). Padsevonil did not affect pharmacokinetics of valproate/lamotrigine/levetiracetam/oxcarbazepine or oral contraceptives. In a cocktail clinical study, padsevonil showed moderate CYP2C19 inhibition (omeprazole) and weak CYP3A4 induction (oral midazolam). No specific effects on CYP1A2 (caffeine), CYP2C9 (S-warfarin), and CYP2D6 (dextromethorphan) were observed.

CONCLUSIONS

The studies presented helped in understanding padsevonil disposition and risks of DDIs, which would inform dosing and prescribing.

CLINICAL TRIAL REGISTRATION

https://www.clinicaltrials.gov identifiers are NCT04131517, NCT03480243, NCT03695094, NCT04075409.

Applying Physiologically Based Pharmacokinetic Modeling to Interpret Carbamazepine's Nonlinear Pharmacokinetics and Its Induction Potential on Cytochrome P450 3A4 and Cytochrome P450 2C9 Enzymes

Carbamazepine (CBZ) is commonly prescribed for epilepsy and frequently used in polypharmacy. However, concerns arise regarding its ability to induce the metabolism of other drugs, including itself, potentially leading to the undertreatment of co-administered drugs. Additionally, CBZ exhibits nonlinear pharmacokinetics (PK), but the root causes have not been fully studied. This study aims to investigate the mechanisms behind CBZ's nonlinear PK and its induction potential on CYP3A4 and CYP2C9 enzymes. To achieve this, we developed and validated a physiologically based pharmacokinetic (PBPK) parent-metabolite model of CBZ and its active metabolite Carbamazepine-10,11-epoxide in GastroPlus®. The model was utilized for Drug-Drug Interaction (DDI) prediction with CYP3A4 and CYP2C9 victim drugs and to further explore the underlying mechanisms behind CBZ's nonlinear PK. The model accurately recapitulated CBZ plasma PK. Good DDI performance was demonstrated by the prediction of CBZ DDIs with quinidine, dolutegravir, phenytoin, and tolbutamide; however, with midazolam, the predicted/observed DDI AUClast ratio was 0.49 (slightly outside of the two-fold range). CBZ's nonlinear PK can be attributed to its nonlinear metabolism caused by autoinduction, as well as nonlinear absorption due to poor solubility. In further applications, the model can help understand DDI potential when CBZ serves as a CYP3A4 and CYP2C9 inducer.

Mechanistic Static Model based Prediction of Transporter Substrate Drug-Drug Interactions Utilizing Atorvastatin and Rifampicin

OBJECTIVE

An in vitro relative activity factor (RAF) technique combined with mechanistic static modeling was examined to predict drug-drug interaction (DDI) magnitude and analyze contributions of different clearance pathways in complex DDIs involving transporter substrates. Atorvastatin and rifampicin were used as a model substrate and inhibitor pair.

METHODS

In vitro studies were conducted with transfected HEK293 cells, hepatocytes and human liver microsomes. Prediction success was defined as predictions being within twofold of observations.

RESULTS

The RAF method successfully translated atorvastatin uptake from transfected cells to hepatocytes, demonstrating its ability to quantify transporter contributions to uptake. Successful translation of atorvastatin's in vivo intrinsic hepatic clearance (CLint,h,in vivo) from hepatocytes to liver was only achieved through consideration of albumin facilitated uptake or through application of empirical scaling factors to transporter-mediated clearances. Transporter protein expression differences between hepatocytes and liver did not affect CLint,h,in vivo predictions. By integrating cis and trans inhibition of OATP1B1/OATP1B3, atorvastatin-rifampicin (single dose) DDI magnitude could be accurately predicted (predictions within 0.77-1.0 fold of observations). Simulations indicated that concurrent inhibition of both OATP1B1 and OATP1B3 caused approximately 80% of atorvastatin exposure increases (AUCR) in the presence of rifampicin. Inhibiting biliary elimination, hepatic metabolism, OATP2B1, NTCP, and basolateral efflux are predicted to have minimal to no effect on AUCR.

CONCLUSIONS

This study demonstrates the effective application of a RAF-based translation method combined with mechanistic static modeling for transporter substrate DDI predictions and subsequent mechanistic interpretation.

Use of In Vivo Imaging and Physiologically-Based Kinetic Modelling to Predict Hepatic Transporter Mediated Drug-Drug Interactions in Rats

Gadoxetate, a magnetic resonance imaging (MRI) contrast agent, is a substrate of organic-anion-transporting polypeptide 1B1 and multidrug resistance-associated protein 2. Six drugs, with varying degrees of transporter inhibition, were used to assess gadoxetate dynamic contrast enhanced MRI biomarkers for transporter inhibition in rats. Prospective prediction of changes in gadoxetate systemic and liver AUC (AUCR), resulting from transporter modulation, were performed by physiologically-based pharmacokinetic (PBPK) modelling. A tracer-kinetic model was used to estimate rate constants for hepatic uptake (khe), and biliary excretion (kbh). The observed median fold-decreases in gadoxetate liver AUC were 3.8- and 1.5-fold for ciclosporin and rifampicin, respectively. Ketoconazole unexpectedly decreased systemic and liver gadoxetate AUCs; the remaining drugs investigated (asunaprevir, bosentan, and pioglitazone) caused marginal changes. Ciclosporin decreased gadoxetate khe and kbh by 3.78 and 0.09 mL/min/mL, while decreases for rifampicin were 7.20 and 0.07 mL/min/mL, respectively. The relative decrease in khe (e.g., 96% for ciclosporin) was similar to PBPK-predicted inhibition of uptake (97-98%). PBPK modelling correctly predicted changes in gadoxetate systemic AUCR, whereas underprediction of decreases in liver AUCs was evident. The current study illustrates the modelling framework and integration of liver imaging data, PBPK, and tracer-kinetic models for prospective quantification of hepatic transporter-mediated DDI in humans.

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.

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.

Predicting Clinical Effects of CYP3A4 Modulators on Abemaciclib and Active Metabolites Exposure Using Physiologically Based Pharmacokinetic Modeling

Abemaciclib, a selective inhibitor of cyclin‐dependent kinases 4 and 6, is metabolized mainly by cytochrome P450 (CYP)3A4. Clinical studies were performed to assess the impact of strong inhibitor (clarithromycin) and inducer (rifampin) on the exposure of abemaciclib and active metabolites. A physiologically based pharmacokinetic (PBPK) model incorporating the metabolites was developed to predict the effect of other strong and moderate CYP3A4 inhibitors and inducers. Clarithromycin increased the area under the plasma concentration‐time curve (AUC) of abemaciclib and potency‐adjusted unbound active species 3.4‐fold and 2.5‐fold, respectively. Rifampin decreased corresponding exposures 95% and 77%, respectively. These changes influenced the fraction metabolized via CYP3A4 in the model. An absolute bioavailability study informed the hepatic and gastric availability. In vitro data and a human radiolabel study determined the fraction and rate of formation of the active metabolites as well as absorption‐related parameters. The predicted AUC ratios of potency‐adjusted unbound active species with rifampin and clarithromycin were within 0.7‐ and 1.25‐fold of those observed. The PBPK model predicted 3.78‐ and 7.15‐fold increases in the AUC of the potency‐adjusted unbound active species with strong CYP3A4 inhibitors itraconazole and ketoconazole, respectively; and 1.62‐ and 2.37‐fold increases with the concomitant use of moderate CYP3A4 inhibitors verapamil and diltiazem, respectively. The model predicted modafinil, bosentan, and efavirenz would decrease the AUC of the potency‐adjusted unbound active species by 29%, 42%, and 52%, respectively. The current PBPK model, which considers changes in unbound potency‐adjusted active species, can be used to inform dosing recommendations when abemaciclib is coadministered with CYP3A4 perpetrators.

Evaluation of human primary intestinal monolayers for drug metabolizing capabilities

BACKGROUND

The intestinal epithelium is a major site of drug metabolism in the human body, possessing enterocytes that house brush border enzymes and phase I and II drug metabolizing enzymes (DMEs). The enterocytes are supported by a porous extracellular matrix (ECM) that enables proper cell adhesion and function of brush border enzymes, such as alkaline phosphatase (ALP) and alanyl aminopeptidase (AAP), phase I DMEs that convert a parent drug to a more polar metabolite by introducing or unmasking a functional group, and phase II DMEs that form a covalent conjugate between a functional group on the parent compound or sequential metabolism of phase I metabolite. In our effort to develop an in vitro intestinal epithelium model, we investigate the impact of two previously described simple and customizable scaffolding systems, a gradient cross-linked scaffold and a conventional scaffold, on the ability of intestinal epithelial cells to produce drug metabolizing proteins as well as to metabolize exogenously added compounds. While the scaffolding systems possess a range of differences, they are most distinguished by their stiffness with the gradient cross-linked scaffold possessing a stiffness similar to that found in the in vivo intestine, while the conventional scaffold possesses a stiffness several orders of magnitude greater than that found in vivo.

RESULTS

The monolayers on the gradient cross-linked scaffold expressed CYP3A4, UGTs 2B17, 1A1 and 1A10, and CES2 proteins at a level similar to that in fresh crypts/villi. The monolayers on the conventional scaffold expressed similar levels of CYP3A4 and UGTs 1A1 and 1A10 DMEs to that found in fresh crypts/villi but significantly decreased expression of UGT2B17 and CES2 proteins. The activity of CYP3A4 and UGTs 1A1 and 1A10 was inducible in cells on the gradient cross-linked scaffold when the cells were treated with known inducers, whereas the CYP3A4 and UGT activities were not inducible in cells grown on the conventional scaffold. Both monolayers demonstrate esterase activity but the activity measured in cells on the conventional scaffold could not be inhibited with a known CES2 inhibitor. Both monolayer culture systems displayed similar ALP and AAP brush border enzyme activity. When cells on the conventional scaffold were incubated with a yes-associated protein (YAP) inhibitor, CYP3A4 activity was greatly enhanced suggesting that mechano-transduction signaling can modulate drug metabolizing enzymes.

CONCLUSIONS

The use of a cross-linked hydrogel scaffold for expansion and differentiation of primary human intestinal stem cells dramatically impacts the induction of CYP3A4 and maintenance of UGT and CES drug metabolizing enzymes in vitro making this a superior substrate for enterocyte culture in DME studies. This work highlights the influence of mechanical properties of the culture substrate on protein expression and the activity of drug metabolizing enzymes as a critical factor in developing accurate assay protocols for pharmacokinetic studies using primary intestinal cells.

GRAPHICAL ABSTRACT

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.

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.

From Endogenous Compounds as Biomarkers to Plasma-Derived Nanovesicles as Liquid Biopsy; Has the Golden Age of Translational Pharmacokinetics-Absorption, Distribution, Metabolism, Excretion-Drug-Drug Interaction Science Finally Arrived?

It is now established that a drug's pharmacokinetics (PK) absorption, distribution, metabolism, excretion (ADME) and drug-drug interaction (DDI) profile can be modulated by age, disease, and genotype. In order to facilitate subject phenotyping and clinical DDI assessment, therefore, various endogenous compounds (in plasma and urine) have been pursued as drug-metabolizing enzyme and transporter biomarkers. Compared with biomarkers, however, the topic of circulating extracellular vesicles as "liquid biopsy" has received little attention within the ADME community; most organs secrete nanovesicles (e.g., exosomes) into the blood that contain luminal "cargo" derived from the originating organ (proteins, messenger RNA, and microRNA). As such, ADME profiling of plasma exosomes could be leveraged to better define genotype-phenotype relationships and the study of ontogeny, disease, and complex DDIs. If methods to support the isolation of tissue-derived plasma exosomes are successfully developed and validated, it is envisioned that they will be used jointly with genotyping, biomarkers, and modeling tools to greatly progress translational PK-ADME-DDI science.

Assessment of Transporter-Mediated and Passive Hepatic Uptake Clearance Using Rifamycin-SV as a Pan-Inhibitor of Active Uptake

The use of in vitro data for the quantitative prediction of transporter-mediated clearance is critical. Central to this evaluation is the use of hepatocytes, since they contain the full complement of transporters and metabolic enzymes. In general, uptake clearance (CL_uptake) is evaluated by measuring the appearance of compound in the cell. Passive clearance (CL_pd) is often determined by conducting parallel studies at 4 °C or by attempting to saturate uptake pathways. Both approaches have their limitations. Recent studies have proposed the use of Rifamycin-SV (RFV) as a pan-inhibitor of hepatic uptake pathways. In our studies, we confirm that transport activity of all major hepatic uptake transporters is inhibited significantly by RFV at 1 mM (OATP1B1, 1B3, and 2B1 = NTCP (80%), OCT1 (65%), OAT2 (60%)). Under these incubation conditions, we found that the free intracellular concentration of RFV is ∼175 μM and that several major CYPs and UGTs can be reversibly inhibited. Using this approach, we also determined CL_uptake and CL_pd of nine known OATP substrates across three different lots of human hepatocytes. The scaling factors generated for these compounds at 37 °C with RFV and 4 °C were found to be similar. The CL_pd of passively permeable compounds like metoprolol and semagacestat were found to be higher at 37 °C compared to 4 °C, indicating a temperature effect on these compounds. In addition, our data also suggests that incorporation of medium concentrations into CL_uptake and CL_pd calculations may be critical for highly protein bound and highly lipophilic drugs. Overall, our data indicate that RFV, instead of 4 °C, can be reliably used to measure CL_uptake and CL_pd of drugs.

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

The Innovation and Quality Induction Working Group presents an assessment of best practice for data interpretation of in vitro induction, specifically, response thresholds, variability, application of controls, and translation to clinical risk assessment with focus on CYP3A4 mRNA. Single concentration control data and Emax/EC₅₀ data for prototypical CYP3A4 inducers were compiled from many human hepatocyte donors in different laboratories. Clinical CYP3A induction and in vitro data were gathered for 51 compounds, 16 of which were proprietary. A large degree of variability was observed in both the clinical and in vitro induction responses; however, analysis confirmed in vitro data are able to predict clinical induction risk. Following extensive examination of this large data set, the following recommendations are proposed. a) Cytochrome P450 induction should continue to be evaluated in three separate human donors in vitro. b) In light of empirically divergent responses in rifampicin control and most test inducers, normalization of data to percent positive control appears to be of limited benefit. c) With concentration dependence, 2-fold induction is an acceptable threshold for positive identification of in vitro CYP3A4 mRNA induction. d) To reduce the risk of false positives, in the absence of a concentration-dependent response, induction ≥ 2-fold should be observed in more than one donor to classify a compound as an in vitro inducer. e) If qualifying a compound as negative for CYP3A4 mRNA induction, the magnitude of maximal rifampicin response in that donor should be ≥ 10-fold. f) Inclusion of a negative control adds no value beyond that of the vehicle control.

Simultaneous Assessment of Clearance, Metabolism, Induction, and Drug-Drug Interaction Potential Using a Long-Term In Vitro Liver Model for a Novel Hepatitis B Virus Inhibitor

Long-term in vitro liver models are now widely explored for human hepatic metabolic clearance prediction, enzyme phenotyping, cross-species metabolism, comparison of low clearance drugs, and induction studies. Here, we present studies using a long-term liver model, which show how metabolism and active transport, drug-drug interactions, and enzyme induction in healthy and diseased states, such as hepatitis B virus (HBV) infection, may be assessed in a single test system to enable effective data integration for physiologically based pharmacokinetic (PBPK) modeling. The approach is exemplified in the case of (3S)-4-[[(4R)-4-(2-Chloro-4-fluorophenyl)-5-methoxycarbonyl-2-thiazol-2-yl-1,4-dihydropyrimidin-6-yl]methyl]morpholine-3-carboxylic acid RO6889678, a novel inhibitor of HBV with a complex absorption, distribution, metabolism, and excretion (ADME) profile. RO6889678 showed an intracellular enrichment of 78-fold in hepatocytes, with an apparent intrinsic clearance of 5.2 µl/min per mg protein and uptake and biliary clearances of 2.6 and 1.6 µl/min per mg protein, respectively. When apparent intrinsic clearance was incorporated into a PBPK model, the simulated oral human profiles were in good agreement with observed data at low doses but were underestimated at high doses due to unexpected overproportional increases in exposure with dose. In addition, the induction potential of RO6889678 on cytochrome P450 (P450) enzymes and transporters at steady state was assessed and cotreatment with ritonavir revealed a complex drug-drug interaction with concurrent P450 inhibition and moderate UDP-glucuronosyltransferase induction. Furthermore, we report on the first evaluation of in vitro pharmacokinetics studies using HBV-infected HepatoPac cocultures. Thus, long-term liver models have great potential as translational research tools exploring pharmacokinetics of novel drugs in vitro in health and disease.

Comparison of Species and Cell-Type Differences in Fraction Unbound of Liver Tissues, Hepatocytes, and Cell Lines

Fraction unbound (f_u) of liver tissue, hepatocytes, and other cell types is an essential parameter used to estimate unbound liver drug concentration and intracellular free drug concentration. f_u,liver and f_u,cell are frequently measured in multiple species and cell types in drug discovery and development for various applications. A comparison study of 12 matrices for f_u,liver and f_u,cell of hepatocytes in five different species (mouse, rat, dog, monkey, and human), as well as f_u,cell of Huh7 and human embryonic kidney 293 cell lines, was conducted for 22 structurally diverse compounds with the equilibrium dialysis method. Using an average bioequivalence approach, our results show that the average difference in binding to liver tissue, hepatocytes, or different cell types was within 2-fold of that of the rat f_u,liver Therefore, we recommend using rat f_u,liver as a surrogate for liver binding in other species and cell types in drug discovery. This strategy offers the potential to simplify binding studies and reduce cost, thereby enabling a more effective and practical determination of f_u for liver tissues, hepatocytes, and other cell types. In addition, f_u under hepatocyte stability incubation conditions should not be confused with f_u,cell, as one is a diluted f_u and the other is an undiluted f_u Cell density also plays a critical role in the accurate measurement of f_u,cell.

Metabolism and Disposition of a Novel Selective α7 Neuronal Acetylcholine Receptor Agonist ABT-126 in Humans: Characterization of the Major Roles for Flavin-Containing Monooxygenases and UDP-Glucuronosyl Transferase 1A4 and 2B10 in Catalysis

Mass balance, metabolism, and excretion of ABT-126, an α7 neuronal acetylcholine receptor agonist, were characterized in healthy male subjects (n = 4) after a single 100-mg (100 μCi) oral dose. The total recovery of the administered radioactivity was 94.0% (±2.09%), with 81.5% (±10.2%) in urine and 12.4% (±9.3%) in feces. Metabolite profiling indicated that ABT-126 had been extensively metabolized, with 6.6% of the dose remaining as unchanged parent drug in urine. Parent drug accounted for 12.2% of the administered radioactivity in feces. The primary metabolic transformations of ABT-126 involved aza-adamantane N-oxidation (M1, 50.3% in urine) and aza-adamantane N-glucuronidation (M11, 19.9% in urine). M1 and M11 were also major circulating metabolites, accounting for 32.6% and 36.6% of the drug-related material in plasma, respectively. These results demonstrated that ABT-126 is eliminated primarily by hepatic metabolism, followed by urinary excretion. Enzymatic studies suggested that M1 formation is mediated primarily by human liver flavin-containing monooxygenase (FMO)3 and, to a lesser extent, by human kidney FMO1; M11 is generated mainly by human uridine 5'-diphospho-glucuronosyltransferase (UGT) 1A4, whereas UGT 2B10 also contributes to ABT-126 glucuronidation. Species-dependent formation of M11 was observed in hepatocytes; M11 was formed in human and monkey hepatocytes, but not in rat and dog hepatocytes, suggesting that monkeys constitute an appropriate model for predicting the fate of compounds undergoing significant N-glucuronidation. M1 and M11 are not expected to have clinically relevant on- or off-target pharmacologic activities. In summary, this study characterized ABT-126 metabolites in the circulation and excreta and the primary elimination pathways of ABT-126 in humans.

Simple Evaluation Method for CYP3A4 Induction from Human Hepatocytes: The Relative Factor Approach with an Induction Detection Limit Concentration Based on the Emax Model

We investigated the robustness and utility of the relative factor (RF) approach based on the maximum induction effect (E_max) model, using the mRNA induction data of 10 typical CYP3A4 inducers in cryopreserved human hepatocytes. The RF value is designated as the ratio of the induction detection limit concentration (IDLC) for a standard inducer, such as rifampicin (RIF) or phenobarbital (PB), to that for the compound (e.g., RF_RIF is IDLC_RIF/IDLC_cpd; RF_PB is IDLC_PB/IDLC_cpd). An important feature of the RF approach is that the profiles of the induction response curves on the logarithmic scale remain unchanged irrespective of inducers but are shifted parallel depending on the EC₅₀ values. A key step in the RF approach is to convert the induction response curve by finding the IDLC of a standard inducer. The relative induction score was estimated not only from E_max and EC₅₀ values but also from those calculated by the RF approach. These values showed good correlation, with a correlation coefficient of more than 0.974, which revealed the RF approach to be a robust analysis irrespective of its simplicity. Furthermore, the relationship between RF_RIF or RF_PB multiplied by the steady-state unbound plasma concentration and the in vivo induction ratio plotted using 10 typical inducers gives adequate thresholds for CYP3A4 drug-drug interaction risk assessment. In light of these findings, the simple RF approach using the IDLC value could be a useful method to adequately assess the risk of CYP3A4 induction in humans during drug discovery and development without evaluation of E_max and EC₅₀.