Clinical pharmacokinetics of leriglitazone and a translational approach using PBPK modeling to guide the selection of the starting dose in children.

Leriglitazone is a unique peroxisome proliferator-activated receptor-gamma (PPARγ) agonist that crosses the blood-brain barrier in humans and clinical trials have shown evidence of efficacy in neurodegenerative diseases. At clinical doses which are well-tolerated, leriglitazone reaches the target central nervous system (CNS) concentrations that are needed for PPARγ engagement and efficacy; PPARγ engagement is also supported by clinical and anti-inflammatory biomarker changes in the Cerebrospinal fluid in the CNS. Plasma pharmacokinetics (PK) of leriglitazone were determined in a phase 1 study in male healthy volunteers comprising a single ascending dose (SAD) and a multiple ascending dose (MAD) at oral doses of 30, 90, and 270 mg and 135 and 270 mg, respectively. Leriglitazone was rapidly absorbed with no food effect on overall exposure and showed a linear PK profile with dose-exposure correlation. A physiologically based pharmacokinetic (PBPK) model was developed for leriglitazone based on phase 1 data (SAD part) and incorporated CYP3A4 (fmCYP3A4 = 24%) and CYP2C8-mediated (fmCYP2C8 = 45%) metabolism, as well as biliary clearance (feBIL = 19.5%) derived from in vitro data, and was verified by comparing the observed versus predicted concentration-time profiles from the MAD part. The PBPK model was prospectively applied to predict the starting pediatric doses and was preliminarily verified with data from five pediatric patients.

Physiologically-based pharmacokinetic modeling of the drug-drug interaction between ivacaftor and lefamulin in cystic fibrosis patients.

Lefamulin is being evaluated as a treatment for bacterial exacerbations in cystic fibrosis (CF). Ivacaftor is approved for the treatment of patients with CF. Lefamulin is a moderate CYP3A inhibitor and co-administration with ivacaftor may result in a drug-drug interaction (DDI). A CF population was built based on literature using the Simcyp Simulator. A previously developed and validated physiologically-based pharmacokinetic (PBPK) model for ivacaftor was used. A PBPK model for lefamulin was developed and verified. Predicted concentrations and pharmacokinetic (PK) parameters for both ivacaftor and lefamulin in healthy subjects and patients with CF were in reasonable agreement with observed data (within 1.4-fold, majority within 1.25-fold). The lefamulin model as a CYP3A4 perpetrator was validated using a different Ki value for oral (p.o.) and intravenous (i.v.) routes. The simulated changes in area under the curve of ivacaftor in patients with CF when co-administered with p.o. and i.v. lefamulin were weak-to-moderate. The predicted change in ivacaftor PK when co-administered with oral lefamulin was less than observed between ivacaftor and fluconazole. These results suggest a low liability for a DDI between lefamulin and ivacaftor in patients with CF.

Prediction of CYP-mediated DDIs involving inhibition: Approaches to address the requirements for system qualification of the Simcyp Simulator.

Physiologically-based pharmacokinetic (PBPK) modeling is being increasingly used in drug development to avoid unnecessary clinical drug-drug interaction (DDI) studies and inform drug labels. Thus, regulatory agencies are recommending, or indeed requesting, more rigorous demonstration of the prediction accuracy of PBPK platforms in the area of their intended use. We describe a framework for qualification of the Simcyp Simulator with respect to competitive and mechanism-based inhibition (MBI) of CYP1A2, CYP2D6, CYP2C8, CYP2C9, CYP2C19, and CYP3A4/5. Initially, a DDI matrix, consisting of a range of weak, moderate, and strong inhibitors and substrates with varying fraction metabolized by specific CYP enzymes that were susceptible to different degrees of inhibition, were identified. Simulations were run with 123 clinical DDI studies involving competitive inhibition and 78 clinical DDI studies involving MBI. For competitive inhibition, the overall prediction accuracy was good with an average fold error (AFE) of 0.91 and 0.92 for changes in the maximum plasma concentration (Cmax ) and area under the plasma concentration (AUC) time profile, respectively, as a consequence of the DDI. For MBI, an AFE of 1.03 was determined for both Cmax and AUC. The prediction accuracy was generally comparable across all CYP enzymes, irrespective of the isozyme and mechanism of inhibition. These findings provide confidence in application of the Simcyp Simulator (V19 R1) for assessment of the DDI potential of drugs in development either as inhibitors or victim drugs of CYP-mediated interactions. The approach described herein and the identified DDI matrix can be used to qualify subsequent versions of the platform.

Estimation of brain receptor occupancy for trazodone immediate release and once a day formulations.

Trazodone is approved for the treatment of major depressive disorders, marketed as immediate release (IR), prolonged release, and once a day (OAD) formulation. The different formulations allow different administration schedules and may be useful to facilitate patients' compliance to the antidepressant treatment. A previously verified physiologically-based pharmacokinetic model based on in vitro and in vivo information on trazodone pharmacokinetics was applied, aiming at predicting brain receptor occupancy (RO) after single and repeated dosing of the IR formulation and repeated dosing of the OAD formulation in healthy subjects. Receptors included in the simulations were selected using static calculations of RO based on the maximum unbound brain concentration (Cmax,brain,u ) of trazodone for each formulation and dosing scheme, resulting in 16 receptors being simulated. Seven receptors were simulated for the IR low dose formulation (30 mg), with similar tonset and duration of coverage (range: 0.09-0.25 h and 2.1->24 h, respectively) as well as RO (range: 0.64-0.92) predicted between day 1 and day 7 of dosing. The 16 receptors evaluated for the OAD formulation (300 mg) showed high RO (range: 0.97-0.84 for the receptors also covered by the IR formulation and 0.73-0.48 for the remaining) correlating with affinity and similar duration of time above the target threshold to the IR formulation (range: 2->24 h). The dose-dependent receptor coverage supports the multimodal activity of trazodone, which may further contribute to its fast antidepressant action and effectiveness in controlling different symptoms in depressed patients.

Physiologically Based Pharmacokinetic Modeling and Allometric Scaling in Pediatric Drug Development: Where Do We Draw the Line?

Developing medicines for children is now established in legislation in both the United States and Europe; new drugs require pediatric study or investigation plans as part of their development. Particularly in early age groups, many developmental processes are not reflected by simple scalars such as body weight or body surface area, and even projecting doses based on simple allometric scaling can lead to significant overdoses in certain age groups. Modeling and simulation methodology, including physiologically based modeling, has evolved as part of the drug development toolkit and is being increasingly applied to various aspects of pediatric drug development. Pediatric physiologically based pharmacokinetic (PBPK) models account for the development of organs and the ontogeny of specific enzymes and transporters that determine the age-related pharmacokinetic profiles. However, when should this approach be used, and when will simpler methods such as allometric scaling suffice in answering specific problems? The aim of this review article is to illustrate the application of allometric scaling and PBPK in pediatric drug development and explore the optimal application of the latter approach with reference to case examples. In reality, allometric scaling included as part of population pharmacokinetic and PBPK approaches are all part of a model-informed drug development toolkit helping with decision making during the process of drug discovery and development; to that end, they should be viewed as complementary.

PBPK modeling to predict drug-drug interactions of ivosidenib as a perpetrator in cancer patients and qualification of the Simcyp platform for CYP3A4 induction.

Ivosidenib is a potent, targeted, orally active, small-molecule inhibitor of mutant isocitrate dehydrogenase 1 (IDH1) that has been approved in the United States for the treatment of adults with newly diagnosed acute myeloid leukemia (AML) who are greater than or equal to 75 years of age or ineligible for intensive chemotherapy, and those with relapsed or refractory AML, with a susceptible IDH1 mutation. Ivosidenib is an inducer of the CYP2B6, CYP2C8, CYP2C9, and CYP3A4 and an inhibitor of P-glycoprotein (P-gp), organic anion transporting polypeptide-1B1/1B3 (OATP1B1/1B3), and organic anion transporter-3 (OAT3) in vitro. A physiologically-based pharmacokinetic (PK) model was developed to predict drug-drug interactions (DDIs) of ivosidenib in patients with AML. The in vivo CYP3A4 induction effect of ivosidenib was quantified using 4ß-hydroxycholesterol and was subsequently verified with the PK data from an ivosidenib and venetoclax combination study. The verified model was prospectively applied to assess the effect of multiple doses of ivosidenib on a sensitive CYP3A4 substrate, midazolam. The simulated midazolam geometric mean area under the curve (AUC) and maximum plasma concentration (Cmax ) ratios were 0.18 and 0.27, respectively, suggesting ivosidenib is a strong inducer. The model was also used to predict the DDIs of ivosidenib with CYP2B6, CYP2C8, CYP2C9, P-gp, OATP1B1/1B3, and OAT3 substrates. The AUC ratios following multiple doses of ivosidenib and a single dose of CYP2B6 (bupropion), CYP2C8 (repaglinide), CYP2C9 (warfarin), P-gp (digoxin), OATP1B1/1B3 (rosuvastatin), and OAT3 (methotrexate) substrates were 0.90, 0.52, 0.84, 1.01, 1.02, and 1.27, respectively. Finally, in accordance with regulatory guidelines, the Simcyp modeling platform was qualified to predict CYP3A4 induction using known inducers and sensitive substrates.

Physiologically based pharmacokinetic modeling and simulation to predict drug-drug interactions of ivosidenib with CYP3A perpetrators in patients with acute myeloid leukemia.

PURPOSE: Develop a physiologically based pharmacokinetic (PBPK) model of ivosidenib using in vitro and clinical PK data from healthy participants (HPs), refine it with clinical data on ivosidenib co-administered with itraconazole, and develop a model for patients with acute myeloid leukemia (AML) and apply it to predict ivosidenib drug-drug interactions (DDI). METHODS: An HP PBPK model was developed in Simcyp Population-Based Simulator (version 15.1), with the CYP3A4 component refined based on a clinical DDI study. A separate model accounting for the reduced apparent oral clearance in patients with AML was used to assess the DDI potential of ivosidenib as the victim of CYP3A perpetrators. RESULTS: For a single 250 mg ivosidenib dose, the HP model predicted geometric mean ratios of 2.14 (plasma area under concentration-time curve, to infinity [AUC0-∞]) and 1.04 (maximum plasma concentration [Cmax]) with the strong CYP3A4 inhibitor, itraconazole, within 1.26-fold of the observed values (2.69 and 1.0, respectively). The AML model reasonably predicted the observed ivosidenib concentration-time profiles across all dose levels in patients. Predicted ivosidenib geometric mean steady-state AUC0-∞ and Cmax ratios were 3.23 and 2.26 with ketoconazole, and 1.90 and 1.52 with fluconazole, respectively. Co-administration of the strong CYP3A4 inducer, rifampin, predicted a greater DDI effect on a single dose of ivosidenib than on multiple doses (AUC ratios 0.35 and 0.67, Cmax ratios 0.91 and 0.81, respectively). CONCLUSION: Potentially clinically relevant DDI effects with CYP3A4 inducers and moderate and strong inhibitors co-administered with ivosidenib were predicted. Considering the challenges of conducting clinical DDI studies in patients, this PBPK approach is valuable in ivosidenib DDI risk assessment and management.

Impact of Disease on Plasma and Lung Exposure of Chloroquine, Hydroxychloroquine and Azithromycin: Application of PBPK Modeling.

We use a mechanistic lung model to demonstrate that accumulation of chloroquine (CQ), hydroxychloroquine (HCQ), and azithromycin (AZ) in the lungs is sensitive to changes in lung pH, a parameter that can be affected in patients with coronavirus disease 2019 (COVID-19). A reduction in pH from 6.7 to 6 in the lungs, as observed in respiratory disease, led to 20-fold, 4.0-fold, and 2.7-fold increases in lung exposure of CQ, HCQ, and AZ, respectively. Simulations indicated that the relatively high concentrations of CQ and HCQ in lung tissue were sustained long after administration of the drugs had stopped. Patients with COVID-19 often present with kidney failure. Our simulations indicate that renal impairment (plus lung pH reduction) caused 30-fold, 8.0-fold, and 3.4-fold increases in lung exposures for CQ, HCQ, and AZ, respectively, with relatively small accompanying increases (20 to 30%) in systemic exposure. Although a number of different dosage regimens were assessed, the purpose of our study was not to provide recommendations for a dosing strategy, but to demonstrate the utility of a physiologically-based pharmacokinetic modeling approach to estimate lung concentrations. This, used in conjunction with robust in vitro and clinical data, can help in the assessment of COVID-19 therapeutics going forward.

A physiologically based pharmacokinetic - pharmacodynamic modelling approach to predict incidence of neutropenia as a result of drug-drug interactions of paclitaxel in cancer patients.

Paclitaxel is the backbone of standard chemotherapeutic regimens used in a number of malignancies and is frequently given with concomitant medications. Newly developed oncolytic agents, including tyrosine kinase inhibitors are often shown to be CYP3A4 and P-gp inhibitors. The aim of this study was to develop a PBPK model for intravenously administered paclitaxel in order to predict the incidence of neutropenia and to estimate the DDI potential as a victim drug. The dose-dependent effects on paclitaxel plasma protein binding, volume of distribution and drug clearance were considered for dose levels of 80 mg/m2, 135 mg/m2 and 175 mg/m2. A pharmacodynamics model that incorporate the impact of paclitaxel on the neutrophil was developed. The relative metabolic clearance via CYP3A4 and CYP2C8, the renal clearance as well as P-gp mediated biliary clearance were incorporated in the model in order to assess the neutropenia in the presence of DDI. The developed PBPK-PD model was able to recover the drop in neutrophils observed after the administration of 175mg/m2 of paclitaxel over a 3-h duration. The mean nadir observed was 1.9 × 109 neutrophils/L and was attained after 10 days of treatment, and a fraction of 47% of the population was predicted to have at some point a neutropenia including 12% with severe neutropenia. In the case of concomitant administration of ketoconazole, 39% of the population was predicted to suffer from severe neutropenia. In summary, PBPK-PD modeling allows a priori prediction of DDIs and safety events involving complex combination therapies which are often utilized in an oncology setting.

Estimation of an Appropriate Dose of Trazodone for Pediatric Insomnia and the Potential for a Trazodone-Atomoxetine Interaction.

There is a paucity of clinical trials for the treatment of pediatric insomnia. This study was designed to predict the doses of trazodone to guide dosing in a clinical trial for pediatric insomnia using physiologically-based pharmacokinetic (PBPK) modeling. Data on the pharmacokinetics of trazodone in children are currently lacking. The interaction potential between trazodone and atomoxetine was also predicted. Doses predicted in the following age groups, with exposures corresponding to adult dosages of 30, 75, and 150 mg once a day (q.d.), respectively, were: (i) 2- to 6-year-old group, doses of 0.35, 0.8, and 1.6 mg/kg q.d.; (ii) >6- to 12-year-old group, doses of 0.4, 1.0, and 1.9 mg/kg q.d.; (iii) >12- to 17-year-old group, doses of 0.4, 1.1, and 2.1 mg/kg q.d. An interaction between trazodone and atomoxetine was predicted to be unlikely. Clinical trials based on the aforementioned predicted dosing are currently in progress, and pharmacokinetic data obtained will enable further refinement of the PBPK models.

Evaluation of Maternal Drug Exposure Following the Administration of Antenatal Corticosteroids During Late Pregnancy Using Physiologically-Based Pharmacokinetic Modeling.

Betamethasone and dexamethasone are the most widely studied antenatal corticosteroids (ACS) administered to pregnant women, just prior to the birth of a preterm neonate, to accelerate fetal lung maturation. Although betamethasone, predominantly used in developed countries, has been shown to be an effective and safe intervention for reducing neonatal mortality, the choice of ACS and optimal dosing in low and middle income countries (LMICs) remains unclear. This is primarily because the exposure-response relationships have not been established for ACS despite the long history of use. As the first step toward the optimal use of ACS in LMICs, we developed physiologically-based pharmacokinetic (PBPK) models to describe the kinetics of ACS following i.v., p.o., or i.m. dosing. In vitro data describing the cytochrome P450 3A4 enzyme contribution were incorporated and this was refined using clinical data. The models can be applied prospectively to predict kinetics of ACS in pregnant women receiving various dosing regimens.

Model-Informed Drug Development for Ixazomib, an Oral Proteasome Inhibitor.

Model-informed drug development (MIDD) was central to the development of the oral proteasome inhibitor ixazomib, facilitating internal decisions (switch from body surface area (BSA)-based to fixed dosing, inclusive phase III trials, portfolio prioritization of ixazomib-based combinations, phase III dose for maintenance treatment), regulatory review (model-informed QT analysis, benefit-risk of 4 mg dose), and product labeling (absolute bioavailability and intrinsic/extrinsic factors). This review discusses the impact of MIDD in enabling patient-centric therapeutic optimization during the development of ixazomib.

Drug Dosing in Pregnant Women: Challenges and Opportunities in Using Physiologically Based Pharmacokinetic Modeling and Simulations.

The unmet medical need of providing evidence-based pharmacotherapy for pregnant women is recognized by the regulatory bodies. Physiologically based pharmacokinetic (PBPK) modeling offers an attractive platform to quantify anticipated changes in the pharmacokinetics (PKs) of drugs during pregnancy. Recent publications applying a pregnancy PBPK module to the prediction of maternal and fetal exposure of drugs are summarized. Future opportunities to use PBPK models to predict breast milk exposure and assess human fetotoxicity risks are presented.

Effects of Strong CYP3A Inhibition and Induction on the Pharmacokinetics of Ixazomib, an Oral Proteasome Inhibitor: Results of Drug-Drug Interaction Studies in Patients With Advanced Solid Tumors or Lymphoma and a Physiologically Based Pharmacokinetic Analysis.

At clinically relevant ixazomib concentrations, in vitro studies demonstrated that no specific cytochrome P450 (CYP) enzyme predominantly contributes to ixazomib metabolism. However, at higher than clinical concentrations, ixazomib was metabolized by multiple CYP isoforms, with the estimated relative contribution being highest for CYP3A at 42%. This multiarm phase 1 study (Clinicaltrials.gov identifier: NCT01454076) investigated the effect of the strong CYP3A inhibitors ketoconazole and clarithromycin and the strong CYP3A inducer rifampin on the pharmacokinetics of ixazomib. Eighty-eight patients were enrolled across the 3 drug-drug interaction studies; the ixazomib toxicity profile was consistent with previous studies. Ketoconazole and clarithromycin had no clinically meaningful effects on the pharmacokinetics of ixazomib. The geometric least-squares mean area under the plasma concentration-time curve from 0 to 264 hours postdose ratio (90%CI) with vs without ketoconazole coadministration was 1.09 (0.91-1.31) and was 1.11 (0.86-1.43) with vs without clarithromycin coadministration. Reduced plasma exposures of ixazomib were observed following coadministration with rifampin. Ixazomib area under the plasma concentration-time curve from time 0 to the time of the last quantifiable concentration was reduced by 74% (geometric least-squares mean ratio of 0.26 [90%CI 0.18-0.37]), and maximum observed plasma concentration was reduced by 54% (geometric least-squares mean ratio of 0.46 [90%CI 0.29-0.73]) in the presence of rifampin. The clinical drug-drug interaction study results were reconciled well by a physiologically based pharmacokinetic model that incorporated a minor contribution of CYP3A to overall ixazomib clearance and quantitatively considered the strength of induction of CYP3A and intestinal P-glycoprotein by rifampin. On the basis of these study results, the ixazomib prescribing information recommends that patients should avoid concomitant administration of strong CYP3A inducers with ixazomib.

In vitro to in vivo extrapolation for high throughput prioritization and decision making.

In vitro chemical safety testing methods offer the potential for efficient and economical tools to provide relevant assessments of human health risk. To realize this potential, methods are needed to relate in vitro effects to in vivo responses, i.e., in vitro to in vivo extrapolation (IVIVE). Currently available IVIVE approaches need to be refined before they can be utilized for regulatory decision-making. To explore the capabilities and limitations of IVIVE within this context, the U.S. Environmental Protection Agency Office of Research and Development and the National Toxicology Program Interagency Center for the Evaluation of Alternative Toxicological Methods co-organized a workshop and webinar series. Here, we integrate content from the webinars and workshop to discuss activities and resources that would promote inclusion of IVIVE in regulatory decision-making. We discuss properties of models that successfully generate predictions of in vivo doses from effective in vitro concentration, including the experimental systems that provide input parameters for these models, areas of success, and areas for improvement to reduce model uncertainty. Finally, we provide case studies on the uses of IVIVE in safety assessments, which highlight the respective differences, information requirements, and outcomes across various approaches when applied for decision-making.

Metformin's Intrinsic Blood-to-Plasma Partition Ratio (B/P): Reconciling the Perceived High In Vivo B/P > 10 with the In Vitro Equilibrium Value of Unity.

Blood cells are considered an important distributional compartment for metformin based on the high blood-to-plasma partition ratio (B/P) in humans (>10 at Cmin). However, literature reports of metformin's intrinsic in vitro B/P values are lacking. At present, the extent and rate of metformin cellular partitioning was determined in incubations of fresh human and rat blood with [(14)C]metformin for up to 1 week at concentrations spanning steady-state plasma Cmin, Cmax, and a concentration associated with lactic acidosis. The results showed that metformin's intrinsic equilibrium B/P was ∼0.8-1.4 in blood, which is <10% of the reported clinical value. Kinetics of metformin partitioning into human blood cells and repartitioning back into plasma were slow (repartitioning half-life ∼32-39 hours). These data, along with in vivo rapid and efficient renal clearance of plasma metformin (plasma renal extraction ratio ∼90%-100%), explain why the clinical terminal half-life of metformin in plasma (6 hours) is 3- to 4-fold shorter than the half-life in whole blood (18 hours) and erythrocytes (23 hours). The rate constant for metformin repartitioning from blood cells to plasma (∼0.02 h(-1)) is far slower than the clinical renal elimination rate constant (0.3 h(-1)). Blood distributional rate constants were incorporated into a metformin physiologically-based pharmacokinetic model, which predicted the differential elimination half-life in plasma and blood. The present study demonstrates that the extent of cellular drug partitioning in blood observed in a dynamic in vivo system may be very different from the static in vitro values when repartitioning from blood cells is far slower than clearance of drug in plasma.

Assessment of the persistence of anacetrapib and evacetrapib concentrations using two pharmacokinetic modeling approaches.

Anacetrapib, a cholesterol ester transfer protein (CETP) inhibitor, has been reported to have longer elimination half-life after longer treatment. Two pharmacokinetic model-based approaches were used to assess whether evacetrapib, another CETP inhibitor, could behave similarly. Using population pharmacokinetic (PopPK) modeling, evacetrapib and anacetrapib pharmacokinetics were characterized using available concentration-time data, and steady-state conditions were simulated. Published 2-compartment models for each compound were adapted to include a hypothetical third compartment representing a depot into which drug could partition. Physiologically based pharmacokinetic (PBPK) modeling was used to predict steady-state conditions and terminal half-life based on known physicochemical and dispositional properties. The PopPK model described the anacetrapib data well, showing a likely third compartment with estimated apparent volume of 40,700 L. Anacetrapib's estimated half-life for this compartment was 550 days. Simulations for evacetrapib using a hypothetical 3-compartment model, the third compartment being consistent with that of the anacetrapib model, produced predictions inconsistent with reported results, indicating that evacetrapib did not substantially accumulate into a large compartment. The PBPK simulations were consistent with PopPK results, predicting accumulation for anacetrapib (but not evacetrapib) followed by very slow elimination. Based on available data and known physicochemical properties, evacetrapib is not expected to accumulate substantially during long-term treatment.

Clinical CYP3A inhibitor alternatives to ketoconazole, clarithromycin and itraconazole, are not transported into the liver by hepatic organic anion transporting polypeptides and organic cation transporter 1.

Ketoconazole is no longer available for clinical determination of worst-case victim drug-drug interaction (DDI) potential for cytochrome P450 3A (CYP3A)-substrate drugs; clarithromycin and itraconazole are the proposed replacements. Ketoconazole DDIs are described by unbound systemic exposures due to absence of carrier-facilitated hepatic uptake, but this aspect of clarithromycin and itraconazole disposition has not been investigated. At present, transport of clarithromycin, itraconazole, and hydroxyitraconazole by hepatic organic anion transporting polypeptides (OATPs) and organic cation transporter 1 (OCT1) was examined in vitro and in vivo. As for ketoconazole, uptake of clarithromycin, itraconazole, and hydroxyitraconazole into OATP1B1, OATP1B3, OATP2B1, and OCT1 expressing human embryonic kidney 293 (HEK293) cells was not greater than in vector controls. Uptake into these HEK293 cells and human hepatocytes was not impaired by the prototypical OATP, OCT, and sodium/taurocholate cotransporting polypeptide inhibitors bromosulfophthalein, imipramine, and taurocholate, respectively. In contrast, uptake of the positive controls, atorvastatin for OATPs and metformin for OCT1, was significantly enhanced by relevant transporter expression, and uptake into both these HEK293 cells and human hepatocytes was significantly impaired by prototypical inhibitors. In Oatp1a/1b gene cluster knockout mice, which lack the major hepatic Oatps, and in Oct1/2 knockout mice, ketoconazole, clarithromycin, itraconazole, and hydroxyitraconazole oral exposure was not increased, and the liver-to-blood partition coefficient (Kp) was not decreased. By contrast relative to wild-type mice, in Oatp1a/1b- and Oct1/2-knockout mice, atorvastatin and metformin oral exposure was significantly increased, and liver Kp was significantly decreased. The present studies provide in vitro and in vivo evidence that, like ketoconazole, clarithromycin, itraconazole, and hydroxyitraconazole are not transported into the liver by hepatic uptake transporters, including OATPs and OCT1.

Investigational small-molecule drug selectively suppresses constitutive CYP2B6 activity at the gene transcription level: physiologically based pharmacokinetic model assessment of clinical drug interaction risk.

The glycogen synthase kinase-3 inhibitor LY2090314 specifically impaired CYP2B6 activity during in vitro evaluation of cytochrome P450 (P450) enzyme induction in human hepatocytes. CYP2B6 catalytic activity was significantly decreased following 3-day incubation with 0.1-10 µM LY2090314, on average by 64.3% ± 5.0% at 10 µM. These levels of LY2090314 exposure were not cytotoxic to hepatocytes and did not reduce CYP1A2 and CYP3A activities. LY2090314 was not a time-dependent CYP2B6 inhibitor, did not otherwise inhibit enzyme activity at concentrations ≤10 µM, and was not metabolized by CYP2B6. Thus, mechanism-based inactivation or other direct interaction with the enzyme could not explain the observed reduction in CYP2B6 activity. Instead, LY2090314 significantly reduced CYP2B6 mRNA levels (Imax = 61.9% ± 1.4%; IC50 = 0.049 ± 0.043 µM), which were significantly correlated with catalytic activity (r(2) = 0.87, slope = 0.77; Imax = 57.0% ± 10.8%, IC50 = 0.057 ± 0.027 µM). Direct inhibition of constitutive androstane receptor by LY2090314 is conceptually consistent with the observed CYP2B6 transcriptional suppression (Imax = 100.0% ± 10.8% and 57.1% ± 2.4%; IC50 = 2.5 ± 1.2 and 2.1 ± 0.4 µM for isoforms 1 and 3, respectively) and may be sufficiently extensive to overcome the weak but potent activation of pregnane X receptor by ≤10 µM LY2090314 (19.3% ± 2.2% of maximal rifampin response, apparent EC50 = 1.2 ± 1.1 nM). The clinical relevance of these findings was evaluated through physiologically based pharmacokinetic model simulations. CYP2B6 suppression by LY2090314 is not expected clinically, with a projected <1% decrease in hepatic enzyme activity and <1% decrease in hydroxybupropion exposure following bupropion coadministration. However, simulations showed that observed CYP2B6 suppression could be clinically relevant for a drug with different pharmacokinetic properties from LY2090314.