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.

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.

Pharmacokinetic and Pharmacodynamic Drug-Drug Interactions: Research Methods and Applications

Because of the high research and development cost of new drugs, the long development process of new drugs, and the high failure rate at later stages, combining past drugs has gradually become a more economical and attractive alternative. However, the ensuing problem of drug-drug interactions (DDIs) urgently need to be solved, and combination has attracted a lot of attention from pharmaceutical researchers. At present, DDI is often evaluated and investigated from two perspectives: pharmacodynamics and pharmacokinetics. However, in some special cases, DDI cannot be accurately evaluated from a single perspective. Therefore, this review describes and compares the current DDI evaluation methods based on two aspects: pharmacokinetic interaction and pharmacodynamic interaction. The methods summarized in this paper mainly include probe drug cocktail methods, liver microsome and hepatocyte models, static models, physiologically based pharmacokinetic models, machine learning models, in vivo comparative efficacy studies, and in vitro static and dynamic tests. This review aims to serve as a useful guide for interested researchers to promote more scientific accuracy and clinical practical use of DDI studies.

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.

Prospective Prediction of Dapaconazole Clinical Drug-Drug Interactions Using an In Vitro to In Vivo Extrapolation Equation and PBPK Modeling

This study predicted dapaconazole clinical drug−drug interactions (DDIs) over the main Cytochrome P450 (CYP) isoenzymes using static (in vitro to in vivo extrapolation equation, IVIVE) and dynamic (PBPK model) approaches. The in vitro inhibition of main CYP450 isoenzymes by dapaconazole in a human liver microsome incubation medium was evaluated. A dapaconazole PBPK model (Simcyp version 20) in dogs was developed and qualified using observed data and was scaled up for humans. Static and dynamic models to predict DDIs following current FDA guidelines were applied. The in vitro dapaconazole inhibition was observed for all isoforms investigated, including CYP1A2 (IC50 of 3.68 µM), CYP2A6 (20.7 µM), 2C8 (104.1 µM), 2C9 (0.22 µM), 2C19 (0.05 µM), 2D6 (0.87 µM), and 3A4 (0.008−0.03 µM). The dynamic (PBPK) and static DDI mechanistic model-based analyses suggest that dapaconazole is a weak inhibitor (AUCR > 1.25 and <2) of CYP1A2 and CYP2C9, a moderate inhibitor (AUCR > 2 and <5) of CYP2C8 and CYP2D6, and a strong inhibitor (AUCR ≥ 5) of CYP2C19 and CYP3A, considering a clinical scenario. The results presented may be a useful guide for future in vivo and clinical dapaconazole studies.

Cumulative Risk Meets Inter-Individual Variability: Probabilistic Concentration Addition of Complex Mixture Exposures in a Population-Based Human In Vitro Model

Although humans are continuously exposed to complex chemical mixtures in the environment, it has been extremely challenging to investigate the resulting cumulative risks and impacts. Recent studies proposed the use of “new approach methods,” in particular in vitro assays, for hazard and dose−response evaluation of mixtures. We previously found, using five human cell-based assays, that concentration addition (CA), the usual default approach to calculate cumulative risk, is mostly accurate to within an order of magnitude. Here, we extend these findings to further investigate how cell-based data can be used to quantify inter-individual variability in CA. Utilizing data from testing 42 Superfund priority chemicals separately and in 8 defined mixtures in a human cell-based population-wide in vitro model, we applied CA to predict effective concentrations for cytotoxicity for each individual, for “typical” (median) and “sensitive” (first percentile) members of the population, and for the median-to-sensitive individual ratio (defined as the toxicodynamic variability factor, TDVF). We quantified the accuracy of CA with the Loewe Additivity Index (LAI). We found that LAI varies more between different mixtures than between different individuals, and that predictions of the population median are generally more accurate than predictions for the “sensitive” individual or the TDVF. Moreover, LAI values were generally <1, indicating that the mixtures were more potent than predicted by CA. Together with our previous studies, we posit that new approach methods data from human cell-based in vitro assays, including multiple phenotypes in diverse cell types and studies in a population-wide model, can fill critical data gaps in cumulative risk assessment, but more sophisticated models of in vitro mixture additivity and bioavailability may be needed. In the meantime, because simple CA models may underestimate potency by an order of magnitude or more, either whole-mixture testing in vitro or, alternatively, more stringent benchmarks of cumulative risk indices (e.g., lower hazard index) may be needed to ensure public health protection.

Clinical Investigation on Endogenous Biomarkers to Predict Strong OAT-Mediated Drug-Drug Interactions

BACKGROUND

Endogenous biomarkers are promising tools to assess transporter-mediated drug-drug interactions early in humans.

METHODS

We evaluated on a common and validated in vitro system the selectivity of 4-pyridoxic acid (PDA), homovanillic acid (HVA), glycochenodeoxycholate-3-sulphate (GCDCA-S) and taurine towards different renal transporters, including multidrug resistance-associated protein, and assessed the in vivo biomarker sensitivity towards the strong organic anion transporter (OAT) inhibitor probenecid at 500 mg every 6 h to reach close to complete OAT inhibition.

RESULTS

PDA and HVA were substrates of the OAT1/2/3, OAT4 (PDA only) and multidrug resistance-associated protein 4; GCDCA-S was more selective, having affinity only towards OAT3 and multidrug resistance-associated protein 2. Taurine was not a substrate of any of the investigated transporters under the in vitro conditions tested. Plasma exposure of PDA and HVA significantly increased and the renal clearance of GCDCA-S, PDA and HVA decreased; the magnitude of these changes was comparable to those of known clinical OAT probe substrates. PDA and GCDCA-S were the most promising endogenous biomarkers of the OAT pathway activity: PDA plasma exposure was the most sensitive to probenecid inhibition, and, in contrast, GCDCA-S was the most sensitive OAT biomarker based on renal clearance, with higher selectivity towards the OAT3 transporter.

CONCLUSIONS

The current findings illustrate a clear benefit of measuring PDA plasma exposure during phase I studies when a clinical drug candidate is suspected to be an OAT inhibitor based on in vitro data. Subsequently, combined monitoring of PDA and GCDCA-S in both urine and plasma is recommended to tease out the involvement of OAT1/3 in the inhibition interaction.

CLINICAL TRIAL REGISTRATION

EudraCT number: 2016-003923-49.

Time-dependent enzyme inactivation: Numerical analyses of in vitro data and prediction of drug-drug interactions

Cytochrome P450 (CYP) enzyme kinetics often do not conform to Michaelis-Menten assumptions, and time-dependent inactivation (TDI) of CYPs displays complexities such as multiple substrate binding, partial inactivation, quasi-irreversible inactivation, and sequential metabolism. Additionally, in vitro experimental issues such as lipid partitioning, enzyme concentrations, and inactivator depletion can further complicate the parameterization of in vitro TDI. The traditional replot method used to analyze in vitro TDI datasets is unable to handle complexities in CYP kinetics, and numerical approaches using ordinary differential equations of the kinetic schemes offer several advantages. Improvement in the parameterization of CYP in vitro kinetics has the potential to improve prediction of clinical drug-drug interactions (DDIs). This manuscript discusses various complexities in TDI kinetics of CYPs, and numerical approaches to model these complexities. The extrapolation of CYP in vitro TDI parameters to predict in vivo DDIs with static and dynamic modeling is discussed, along with a discussion on current gaps in knowledge and future directions to improve the prediction of DDI with in vitro data for CYP catalyzed drug metabolism.

Use of generally recognized as safe or dietary compounds to inhibit buprenorphine metabolism: potential to improve buprenorphine oral bioavailability

The present study evaluated the potential of five generally recognized as safe (GRAS) or dietary compounds (α-mangostin, chrysin, ginger extract, pterostilbene and silybin) to inhibit oxidative (CYP) and conjugative (UGT) metabolism using pooled human intestinal and liver microsomes. Buprenorphine was chosen as the model substrate as it is extensively metabolized by CYPs to norbuprenorphine and by UGTs to buprenorphine glucuronide. Chrysin, ginger extract, α-mangostin, pterostilbene and silybin were tested for their inhibition of the formation of norbuprenorphine or buprenorphine glucuronide in both intestinal and liver microsomes. Pterostilbene was the most potent inhibitor of norbuprenorphine formation in both intestinal and liver microsomes, with IC50 values of 1.3 and 0.8 μM, respectively, while α-mangostin and silybin most potently inhibited buprenorphine glucuronide formation. The equipotent combination of pterostilbene and ginger extract additively inhibited both pathways in intestinal microsomes. Since pterostilbene and ginger extract showed potent CYP and/or UGT inhibition of buprenorphine metabolism, their equipotent combination was tested to assess the presence of synergistic inhibition. However, because the combination showed additive inhibition, it was not used while performing IVIVE analysis. Based on quantitative in vitro-in vivo extrapolation, pterostilbene (21 mg oral dose) appeared to be most effective in improving the mean predicted Foral and AUC∞ PO of buprenorphine from 3 ± 2% and 340 ± 330 ng*min/ml to 75 ± 8% and 36,000 ± 25,000 ng*min/ml, respectively. At a 10-fold lower dose of pterostilbene, the predicted buprenorphine Foral approximated sublingual bioavailability (~35%) and showed a 2-4 fold reduction in the variability around the predicted AUC∞ PO of buprenorphine. These results demonstrate the feasibility of using various GRAS/dietary compounds to inhibit substantially the metabolism by CYP and UGT enzymes to achieve higher and less variable oral bioavailability. This inhibitor strategy may be useful for drugs suffering from low and variable oral bioavailability due to extensive presystemic oxidative and/or conjugative metabolism.

A Novel PBPK Modeling Approach to Assess Cytochrome P450 Mediated Drug-Drug Interaction Potential of the Cytotoxic Prodrug Evofosfamide

Evofosfamide is a cytotoxic small‐molecule prodrug preferentially activated under hypoxic conditions. The cytotoxicity of evofosfamide impacted the generation of in vitro drug‐drug interaction (DDI) data, especially in vitro induction results. Therefore, a novel physiologically based pharmacokinetic (PBPK) approach was used, which involved available in vitro and clinical data of evofosfamide and combined it with induction data from the prototypical cytochrome P450 (CYP)3A inducer rifampicin. The area under the concentration‐time curve (AUC) ratios of midazolam were above 0.80, indicating that induction of CYP3A by evofosfamide administered weekly is unlikely to occur in humans. Moreover, static and PBPK modeling showed no clinically relevant inhibition via CYP2B6, CYP2D6, and CYP3A4. In conclusion, PBPK models were used to supplement in vitro information of a cytotoxic compound. This approach may set a precedent for future studies of cytotoxic drugs, potentially reducing the need for clinical DDI studies and providing more confidence in the clinical use of approved cytotoxic compounds for which DDI information is sparse.

Improved Predictions of Drug-Drug Interactions Mediated by Time-Dependent Inhibition of CYP3A

Time-dependent inactivation (TDI) of cytochrome P450s (CYPs) is a leading cause of clinical drug-drug interactions (DDIs). Current methods tend to overpredict DDIs. In this study, a numerical approach was used to model complex CYP3A TDI in human-liver microsomes. The inhibitors evaluated included troleandomycin (TAO), erythromycin (ERY), verapamil (VER), and diltiazem (DTZ) along with the primary metabolites N-demethyl erythromycin (NDE), norverapamil (NV), and N-desmethyl diltiazem (NDD). The complexities incorporated into the models included multiple-binding kinetics, quasi-irreversible inactivation, sequential metabolism, inhibitor depletion, and membrane partitioning. The resulting inactivation parameters were incorporated into static in vitro-in vivo correlation (IVIVC) models to predict clinical DDIs. For 77 clinically observed DDIs, with a hepatic-CYP3A-synthesis-rate constant of 0.000 146 min-1, the average fold difference between the observed and predicted DDIs was 3.17 for the standard replot method and 1.45 for the numerical method. Similar results were obtained using a synthesis-rate constant of 0.000 32 min-1. These results suggest that numerical methods can successfully model complex in vitro TDI kinetics and that the resulting DDI predictions are more accurate than those obtained with the standard replot approach.

Derivation of CYP3A4 and CYP2B6 degradation rate constants in primary human hepatocytes: A siRNA-silencing-based approach

The first-order degradation rate constant (k_deg) of cytochrome P450 (CYP) enzymes is a known source of uncertainty in the prediction of time-dependent drug-drug interactions (DDIs) in physiologically-based pharmacokinetic (PBPK) modelling. This study aimed to measure CYP k_deg using siRNA to suppress CYP expression in primary human hepatocytes followed by incubation over a time-course and tracking of protein expression and activity to observe degradation. The magnitude of gene knockdown was determined by qPCR and activity was measured by probe substrate metabolite formation and CYP2B6-Glo™ assay. Protein disappearance was determined by Western blotting. During a time-course of 96 and 60 h of incubation, over 60% and 76% mRNA knockdown was observed for CYP3A4 and CYP2B6, respectively. The k_deg of CYP3A4 and CYP2B6 protein was 0.0138 h^-1 (±0.0023) and 0.0375 h^-1 (±0.025), respectively. The k_deg derived from probe substrate metabolism activity was 0.0171 h^-1 (±0.0025) for CYP3A4 and 0.0258 h^-1 (±0.0093) for CYP2B6. The CYP3A4 k_deg values derived from protein disappearance and metabolic activity were in relatively good agreement with each other and similar to published values. This novel approach can now be used for other less well-characterised CYPs.

Preincubation-dependent and long-lasting inhibition of organic anion transporting polypeptide (OATP) and its impact on drug-drug interactions

Preincubation with cyclosporin A (CsA), a potent inhibitor of organic anion transporting polypeptide 1B1 (OATP1B1) and OATP1B3, enhanced its inhibitory effects on these transporters in vitro. A similar effect was observed upon preincubation with some other inhibitors. Removing these from the incubation media did not readily reverse the inhibition on OATP1B1 and OATP1B3. This preincubation-dependent long-lasting inhibition appeared to be related to CsA concentration in the cells in addition to that in the incubation media. Thus, we hypothesized that CsA inhibits OATP1B1 and OATP1B3 from inside (trans-inhibition) as well as outside (cis-inhibition) the cells and constructed the cis- and trans-inhibition model. The enhanced inhibitory effect of CsA on OATP1B1 observed after preincubation was quantitatively described using K_i,out and K_i,in as inhibition constants for cis- and trans-inhibitions, respectively. In addition, a long-lasting inhibition was also described by this model. Additional factors taken into consideration when simulating in vivo pharmacokinetic alterations by CsA are potential inhibition by AM1, a major metabolite of CsA, which has been reported to inhibit OATP1B1 and OATP1B3. Based on the physiologically based pharmacokinetic model incorporating trans- and cis-inhibition of OATP1B1 by CsA, the simulation showed that OATP1B1-mediated drug-drug interaction with CsA was suggested to be time-dependent also in vivo although further clinical studies are required for confirmation.

A Model for Predicting the Interindividual Variability of Drug-Drug Interactions

Pharmacokinetic drug-drug interactions are frequently characterized and quantified by an AUC ratio (Rauc). The typical value of the AUC ratio in case of cytochrome-mediated interactions may be predicted by several approaches, based on in vitro or in vivo data. Prediction of the interindividual variability of Rauc would help to anticipate more completely the consequences of a drug-drug interaction. We propose and evaluate a simple approach for predicting the standard deviation (sd) of Ln(Rauc), a metric close to the interindividual coefficient of variation of Rauc. First, a model was derived to link sd(Ln Rauc) with the substrate fraction metabolized by each cytochrome and the potency of the interactors, in case of induction or inhibition. Second, the parameters involved in these equations were estimated by a Bayesian hierarchical model, using the data from 56 interaction studies retrieved from the literature. Third, the model was evaluated by several metrics based on the fold prediction error (PE) of sd(Ln Rauc). The median PE was 0.998 (the ideal value is 1) and the interquartile range was 0.96-1.03. The PE was in the acceptable interval (0.5 to 2) in 52 cases out of 56. Fourth, a surface plot of sd(Ln Rauc) as a function of the characteristics of the substrate and the interactor has been built. The minimal value of sd(Ln Rauc) was about 0.08 (obtained for Rauc = 1) while the maximal value, 0.7, was obtained for interactions involving highly metabolized substrates with strong interactors.

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

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

Predicting Drug Extraction in the Human Gut Wall: Assessing Contributions from Drug Metabolizing Enzymes and Transporter Proteins using Preclinical Models

Intestinal metabolism can limit oral bioavailability of drugs and increase the risk of drug interactions. It is therefore important to be able to predict and quantify it in drug discovery and early development. In recent years, a plethora of models-in vivo, in situ and in vitro-have been discussed in the literature. The primary objective of this review is to summarize the current knowledge in the quantitative prediction of gut-wall metabolism. As well as discussing the successes of current models for intestinal metabolism, the challenges in the establishment of good preclinical models are highlighted, including species differences in the isoforms; regional abundances and activities of drug metabolizing enzymes; the interplay of enzyme-transporter proteins; and lack of knowledge on enzyme abundances and availability of empirical scaling factors. Due to its broad specificity and high abundance in the intestine, CYP3A is the enzyme that is frequently implicated in human gut metabolism and is therefore the major focus of this review. A strategy to assess the impact of gut wall metabolism on oral bioavailability during drug discovery and early development phases is presented. Current gaps in the mechanistic understanding and the prediction of gut metabolism are highlighted, with suggestions on how they can be overcome in the future.

Evaluation of a New Molecular Entity as a Victim of Metabolic Drug-Drug Interactions-an Industry Perspective

Under the guidance of the International Consortium for Innovation and Quality in Pharmaceutical Development (IQ), scientists from 20 pharmaceutical companies formed a Victim Drug-Drug Interactions Working Group. This working group has conducted a review of the literature and the practices of each company on the approaches to clearance pathway identification (fCL), estimation of fractional contribution of metabolizing enzyme toward metabolism (fm), along with modeling and simulation-aided strategy in predicting the victim drug-drug interaction (DDI) liability due to modulation of drug metabolizing enzymes. Presented in this perspective are the recommendations from this working group on: 1) strategic and experimental approaches to identify fCL and fm, 2) whether those assessments may be quantitative for certain enzymes (e.g., cytochrome P450, P450, and limited uridine diphosphoglucuronosyltransferase, UGT enzymes) or qualitative (for most of other drug metabolism enzymes), and the impact due to the lack of quantitative information on the latter. Multiple decision trees are presented with stepwise approaches to identify specific enzymes that are involved in the metabolism of a given drug and to aid the prediction and risk assessment of drug as a victim in DDI. Modeling and simulation approaches are also discussed to better predict DDI risk in humans. Variability and parameter sensitivity analysis were emphasized when applying modeling and simulation to capture the differences within the population used and to characterize the parameters that have the most influence on the prediction outcome.

Functional Comparison of Human Colonic Carcinoma Cell Lines and Primary Small Intestinal Epithelial Cells for Investigations of Intestinal Drug Permeability and First-Pass Metabolism

To further the development of a model for simultaneously assessing intestinal absorption and first-pass metabolism in vitro, Caco-2, LS180, T84, and fetal human small intestinal epithelial cells (fSIECs) were cultured on permeable inserts, and the integrity of cell monolayers, CYP3A4 activity, and the inducibility of enzymes and transporters involved in intestinal drug disposition were measured. Caco-2, T84, and fSIECs all formed tight junctions, as assessed by immunofluorescence microscopy for zonula occludens-1, which was well organized into circumscribing strands in T84, Caco-2, and fSIECs but was diffuse in LS180 cells. The transepithelial electrical resistance value for LS180 monolayers was lower than that for Caco-2, T84, and fSIECs. In addition, the apical-to-basolateral permeability of the paracellular marker Lucifer yellow across LS180 monolayers was greater than in fSIECs, T84, and Caco-2 monolayers. The transcellular marker propranolol exhibited similar permeability across all cells. With regard to metabolic capacity, T84 and LS180 cells showed comparable basal midazolam hydroxylation activity and was inducible by rifampin and 1α,25(OH)2D3 in LS180 cells, but only marginally so in T84 cells. The basal CYP3A4 activity of fSIECs and Caco-2 cells was much lower and not inducible. Interestingly, some of the drug transporters expressed in LS180 and Caco-2 cells were induced by either 1α,25(OH)2D3 or rifampin or both, but effects were limited in the other two cell lines. These results suggest that none of the cell lines tested fully replicated the drug disposition properties of the small intestine and that the search for an ideal screening tool must continue.

Prediction of Drug-Drug Interactions with Crizotinib as the CYP3A Substrate Using a Physiologically Based Pharmacokinetic Model

An orally available multiple tyrosine kinase inhibitor, crizotinib (Xalkori), is a CYP3A substrate, moderate time-dependent inhibitor, and weak inducer. The main objectives of the present study were to: 1) develop and refine a physiologically based pharmacokinetic (PBPK) model of crizotinib on the basis of clinical single- and multiple-dose results, 2) verify the crizotinib PBPK model from crizotinib single-dose drug-drug interaction (DDI) results with multiple-dose coadministration of ketoconazole or rifampin, and 3) apply the crizotinib PBPK model to predict crizotinib multiple-dose DDI outcomes. We also focused on gaining insights into the underlying mechanisms mediating crizotinib DDIs using a dynamic PBPK model, the Simcyp population-based simulator. First, PBPK model-predicted crizotinib exposures adequately matched clinically observed results in the single- and multiple-dose studies. Second, the model-predicted crizotinib exposures sufficiently matched clinically observed results in the crizotinib single-dose DDI studies with ketoconazole or rifampin, resulting in the reasonably predicted fold-increases in crizotinib exposures. Finally, the predicted fold-increases in crizotinib exposures in the multiple-dose DDI studies were roughly comparable to those in the single-dose DDI studies, suggesting that the effects of crizotinib CYP3A time-dependent inhibition (net inhibition) on the multiple-dose DDI outcomes would be negligible. Therefore, crizotinib dose-adjustment in the multiple-dose DDI studies could be made on the basis of currently available single-dose results. Overall, we believe that the crizotinib PBPK model developed, refined, and verified in the present study would adequately predict crizotinib oral exposures in other clinical studies, such as DDIs with weak/moderate CYP3A inhibitors/inducers and drug-disease interactions in patients with hepatic or renal impairment.

Simulation of Metabolic Drug-Drug Interactions Perpetrated by Fluvoxamine Using Hybridized Two-Compartment Hepatic Drug-Pool-Based Tube Modeling and Estimation of In Vivo Inhibition Constants

Co-administration of fluvoxamine (FLV) (perpetrator) and ramelteon (victim, high-clearance CYP1A2 substrate) reportedly showed a 130-fold increase in the area under blood-ramelteon-levels curve (AUCR), which is unpredictable by any method assuming the traditional well-stirred hepatic extraction (Eh ) model. Thus, in order to predict this drug interaction (DDI), a mathematical method that allows simulation of dynamic changes in blood victim levels in response to metabolic inhibition by a perpetrator, without the use of any specialized tools, was derived using hybridized two-compartment hepatic drug-pool-based tube modeling. Using this method, the ramelteon-victimized DDI could be simulated in comparison with other victim DDIs, assuming a consistent FLV dosing regimen. Despite large differences in AUCRs, CYP1A2 or CYP2C19 substrate-victimized DDIs resulted in equivalent inhibition constants (Ki , around 3 nM) and net enzymatic inhibitory activities calculated by eliminating hepatic availability increases for victims. Thus, the unusually large ramelteon DDI could be attributed to the Eh of ramelteon itself. This DDI risk could also be accurately predicted from Ki s estimated in the other CYP1A2 or CYP2C19-substrate interactions. Meanwhile, dynamic changes in blood perpetrator levels were demonstrated to have a small effect on DDI, thus suggesting the usefulness of a tube-based static method for DDI prediction.

Assessment of interaction potential of AZD2066 using in vitro metabolism tools, physiologically based pharmacokinetic modelling and in vivo cocktail data

PURPOSE

Static and dynamic (PBPK) prediction models were applied to estimate the drug-drug interaction (DDI) risk of AZD2066. The predictions were compared to the results of an in vivo cocktail study. Various in vivo measures for tolbutamide as a probe agent for cytochrome P450 2C9 (CYP2C9) were also compared.

METHODS

In vitro inhibition data for AZD2066 were obtained using human liver microsomes and CYP-specific probe substrates. DDI prediction was performed using PBPK modelling with the SimCYP simulator™ or static model. The cocktail study was an open label, baseline, controlled interaction study with 15 healthy volunteers receiving multiple doses of AD2066 for 12 days. A cocktail of single doses of 100 mg caffeine (CYP1A2 probe), 500 mg tolbutamide (CYP2C9 probe), 20 mg omeprazole (CYP2C19 probe) and 7.5 mg midazolam (CYP3A probe) was simultaneously applied at baseline and during the administration of AZD2066. Bupropion as a CYP2B6 probe (150 mg) and 100 mg metoprolol (CYP2D6 probe) were administered on separate days. The pharmacokinetic parameters for the probe drugs and their metabolites in plasma and urinary recovery were determined.

RESULTS

In vitro AZD2066 inhibited CYP1A2, CYP2B6, CYP2C9, CYP2C19 and CYP2D6. The static model predicted in vivo interaction with predicted AUC ratio values of >1.1 for all CYP (except CYP3A4). The PBPK simulations predicted no risk for clinical relevant interactions. The cocktail study showed no interaction for the CYP2B6 and CYP2C19 enzymes, a possible weak inhibition of CYP1A2, CYP2C9 and CYP3A4 activities and a slight inhibition (29 %) of CYP2D6 activity. The tolbutamide phenotyping metrics indicated that there were significant correlations between CLform and AUCTOL, CL, Aemet and LnTOL24h. The MRAe in urine showed no correlation to CLform.

CONCLUSIONS

DDI prediction using the static approach based on total concentration indicated that AZD20066 has a potential risk for inhibition. However, no DDI risk could be predicted when a more in vivo-like dynamic prediction method with the PBPK with SimCYP™ software based on early human PK data was used and more parameters (i.e. free fraction in plasma, no DDI risk) were taken into account. The clinical cocktail study showed no or low risks for clinical relevant DDI interactions. Our findings are in line with the hypothesis that the dynamic prediction method predicts DDI in vivo in humans better than the static model based on total plasma concentrations.