Inhibitory effect of ketoconazole on the pharmacokinetics of a multireceptor tyrosine kinase inhibitor BMS-690514 in healthy participants: assessing the mechanism of the interaction with physiologically-based pharmacokinetic simulations

The Application of Physiologically Based Pharmacokinetic Modeling to Predict the Role of Drug Transporters: Scientific and Regulatory Perspectives

Transporters play an important role in drug absorption, disposition, and drug action. The evaluation of drug transporters requires a comprehensive understanding of transporter biology and pharmacology. Physiologically based pharmacokinetic (PBPK) models may offer an integrative platform to quantitatively evaluate the role of drug transporters and its interplay with other drug disposition processes such as passive drug diffusion and elimination by metabolizing enzymes. To date, PBPK modeling and simulations integrating drug transporters lag behind that for drug-metabolizing enzymes. In addition, predictive performance of PBPK has not been well established for predicting the role of drug transporters in the pharmacokinetics of a drug. To enhance overall predictive performance of transporter-based PBPK models, it is necessary to have a detailed understanding of transporter biology for proper representation in the models and to have a quantitative understanding of the contribution of transporters in the absorption and metabolism of a drug. This article summarizes PBPK-based submissions evaluating the role of drug transporters to the Office of Clinical Pharmacology of the US Food and Drug Administration.

Direct inhibition of Re Du Ning Injection and its active compounds on human liver cytochrome P450 enzymes by a cocktail method

The aim of this study was to investigate the direct inhibitory effects of Re Du Ning Injection (RDN) and its active compounds on the major cytochrome P450 enzyme (CYP) isoforms (CYP1A2, CYP2B6, CYP2C8, CYP2C9, CYP2C19, CYP2D6 and CYP3A4) of human liver microsomes by 'a cocktail method'. The activity of each CYP isform was represented as the formation rate of the specific metabolite from relevant substrate. Then a sensitive and specific ultra-performance liquid chromatography-tandem mass spectrometry (UPLC-MS/MS) method was developed and validated to simultaneously analyze the seven metabolites. RDN (0.035-2.26 mg/mL) showed a strong inhibitiory effect on CYP2C8, followed by CYP2C9, CYP2B6, CYP2C19, CYP1A2 and CYP3A4. The IC₅₀ value for each enzyme was 0.19, 0.66, 0.72, 1.27, 1.66 and 2.13 mg/mL, respectively. RDN competitively inhibited the activities of CYP1A2 (K_i  = 1.22 mg/mL), CYP2B6 (K_i  = 0.65 mg/mL) and CYP3A4 (K_i  = 0.88 mg/mL); it also exhibited mixed inhibition of CYP2C8, CYP2C9 and CYP2C19 with a K_i value of 0.26, 0.64 and 0.82 mg/mL, respectively. However, the activity of CYP2D6 was not significantly inhibited even by 2.26 mg/mL RDN. Moreover, the data of nine active compounds on the CYPs showed that cryptochlorogenin acid, sochlorogenic acid B and sochlorogenic acid C were the major contributors to the inhibitory effect of RDN on CYP2C8, while the inhibitory effect of RDN on CYP2C9 might be caused by sochlorogenic acid A and sochlorogenic acid C. Moreover, neochlorogenic acid might be the major contributor to the inhibitory effect on CYP2B6. All of the findings suggested that drug-drug interactions may occur and great caution should be taken when RDN is combined with drugs metabolized by these CYPs.

Comparative Hepatotoxicity of Fluconazole, Ketoconazole, Itraconazole, Terbinafine, and Griseofulvin in Rats

Oral ketoconazole was recently the subject of regulatory safety warnings because of its association with increased risk of inducing hepatic injury. However, the relative hepatotoxicity of antifungal agents has not been clearly established. The aim of this study was to compare the hepatotoxicity induced by five commonly prescribed oral antifungal agents. Rats were treated with therapeutic oral doses of griseofulvin, fluconazole, itraconazole, ketoconazole, and terbinafine. After 14 days, only ketoconazole had significantly higher ALT levels (p = 0.0017) and AST levels (p = 0.0008) than the control group. After 28 days, ALT levels were highest in the rats treated with ketoconazole followed by itraconazole, fluconazole, griseofulvin, and terbinafine, respectively. The AST levels were highest in the rats treated with ketoconazole followed by itraconazole, fluconazole, terbinafine, and griseofulvin, respectively. All drugs significantly elevated ALP levels after 14 days and 28 days of treatment (p < 0.0001). The liver enzyme levels suggested that ketoconazole had the highest risk in causing liver injury followed by itraconazole, fluconazole, terbinafine, and griseofulvin. However, histopathological changes revealed that fluconazole was the most hepatotoxic, followed by ketoconazole, itraconazole, terbinafine, and griseofulvin, respectively. Given the poor correlation between liver enzymes and the extent of liver injury, it is important to confirm liver injury through histological examination.

Applications of physiologically based pharmacokinetic modeling for the optimization of anti-infective therapies

INTRODUCTION

The pharmacokinetic properties of anti-infective drugs are a determinant part of treatment success. Pathogen replication is inhibited if adequate drug levels are achieved in target sites, whereas excessive drug concentrations linked to toxicity are to be avoided. Anti-infective distribution can be predicted by integrating in vitro drug properties and mathematical descriptions of human anatomy in physiologically based pharmacokinetic models. This method reduces the need for animal and human studies and is used increasingly in drug development and simulation of clinical scenario such as, for instance, drug-drug interactions, dose optimization, novel formulations and pharmacokinetics in special populations.

AREAS COVERED

We have assessed the relevance of physiologically based pharmacokinetic modeling in the anti-infective research field, giving an overview of mechanisms involved in model design and have suggested strategies for future applications of physiologically based pharmacokinetic models.

EXPERT OPINION

Physiologically based pharmacokinetic modeling provides a powerful tool in anti-infective optimization, and there is now no doubt that both industry and regulatory bodies have recognized the importance of this technology. It should be acknowledged, however, that major challenges remain to be addressed and that information detailing disease group physiology and anti-infective pharmacodynamics is required if a personalized medicine approach is to be achieved.

Effects of Ketoconazole on the Pharmacokinetics of Lenvatinib (E7080) in Healthy Participants

BACKGROUND

Lenvatinib is an oral, multitargeted, tyrosine kinase inhibitor under clinical investigation in solid tumors. In vitro evidence indicates that lenvatinib metabolism may be modulated by ketoconazole, an inhibitor of CYP3A4 and p-glycoprotein.

METHODS

In this Phase I, single-center, randomized, open-label, two-period, crossover study, healthy adults (18-55 years; N = 18) were randomized to one of two sequences (ketoconazole → placebo or vice versa). Ketoconazole (400 mg) or placebo was administered orally once daily for 18 days; a 5 mg dose of lenvatinib was orally administered on Day 5 of each treatment period. Blood samples were collected over 14 days and lenvatinib plasma concentrations measured by high-performance liquid chromatography/tandem mass spectrometry.

RESULTS

Systemic exposure to lenvatinib increased slightly (15-19%) with coadministration of ketoconazole. Although the 90% confidence interval (CI) for area under the plasma concentration-time curve (AUC) was within the prespecified bioequivalence interval of 80-125%, C_max slightly exceeded the 125% CI bound (134%). No changes in t_max, t_lag, or t_½ were observed. Thirteen subjects (72%) experienced treatment-emergent adverse events (11 mild, 2 moderate), most commonly headache (22%) and diarrhea (17%).

CONCLUSIONS

Lenvatinib exposure was slightly increased by ketoconazole; however, the magnitude of the change was relatively small, and likely not clinically meaningful.

PBPK model describes the effects of comedication and genetic polymorphism on systemic exposure of drugs that undergo multiple clearance pathways

An important goal in drug development is to understand the effects of intrinsic and/or extrinsic factors (IEFs) on drug pharmacokinetics. Although clinical studies investigating a given IEF can accomplish this goal, they may not be feasible for all IEFs or for situations when multiple IEFs exist concurrently. Physiologically based pharmacokinetic (PBPK) models may serve as a complementary tool for forecasting the effects of IEFs. We developed PBPK models for four drugs that are eliminated by both cytochrome P450 (CYP)3A4 and CYP2D6, and evaluated model prediction of the effects of comedications and/or genetic polymorphism on drug exposure. PBPK models predicted 100 and ≥70% of the observed results when the conventional "twofold rule" and the more conservative 25% deviation cut point were applied, respectively. These findings suggest that PBPK models can be used to infer effects of individual or combined IEFs and should be considered to optimize studies that evaluate these factors, specifically drug interactions and genetic polymorphism of drug-metabolizing enzymes.

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.