Stochastic prediction of CYP3A-mediated inhibition of midazolam clearance by ketoconazole

Tyrosine Kinase Inhibitors-Induced Arrhythmias: From Molecular Mechanisms, Pharmacokinetics to Therapeutic Strategies

With the development of anti-tumor drugs, tyrosine kinase inhibitors (TKIs) are an indispensable part of targeted therapy. They can be superior to traditional chemotherapeutic drugs in selectivity, safety, and efficacy. However, they have been found to be associated with serious adverse effects in use, such as myocardial infarction, fluid retention, hypertension, and rash. Although TKIs induced arrhythmia with a lower incidence than other cardiovascular diseases, much clinical evidence indicated that adequate attention and management should be provided to patients. This review focuses on QT interval prolongation and atrial fibrillation (AF) which are conveniently monitored in clinical practice. We collected data about TKIs, and analyzed the molecule mechanism, discussed the actual clinical evidence and drug-drug interaction, and provided countermeasures to QT interval prolongation and AF. We also pooled data to show that both QT prolongation and AF are related to their multi-target effects. Furthermore, more than 30 TKIs were approved by the FDA, but most of the novel drugs had a small sample size in the preclinical trial and risk/benefit assessments were not perfect, which led to a suspension after listing, like nilotinib. Similarly, vandetanib exhibits the most significant QT prolongation and ibrutinib exhibits the highest incidence in AF, but does not receive enough attention during treatment.

Quantitative Prediction of CYP3A4- and CYP3A5-Mediated Drug Interactions

We verified a physiologically-based pharmacokinetic (PBPK) model to predict cytochrome P450 3A4/5-mediated drug-drug interactions (DDIs). A midazolam (MDZ)-ketoconazole (KTZ) interaction study in 24 subjects selected by CYP3A5 genotype, and liquid chromatography and mass spectroscopy quantification of CYP3A4/5 abundance from independently acquired and genotyped human liver (n = 136) and small intestinal (N = 12) samples, were conducted. The observed CYP3A5 genetic effect on MDZ systemic and oral clearance was successfully replicated by a mechanistic framework incorporating the proteomics-informed CYP3A abundance and optimized small intestinal CYP3A4 abundance based on MDZ intestinal availability (F_G ) of 0.44. Furthermore, combined with a modified KTZ PBPK model, this framework recapitulated the observed geometric mean ratio of MDZ area under the curve (AUCR) following 200 or 400 mg KTZ, which was, respectively, 2.7-3.4 and 3.9-4.7-fold in intravenous administration and 11.4-13.4 and 17.0-19.7-fold in oral administration, with AUCR numerically lower (P > 0.05) in CYP3A5 expressers than nonexpressers. In conclusion, the developed mechanistic framework supports dynamic prediction of CYP3A-mediated DDIs in study planning by bridging DDIs between CYP3A5 expressers and nonexpressers.

A quantitative systems pharmacology approach, incorporating a novel liver model, for predicting pharmacokinetic drug-drug interactions

All pharmaceutical companies are required to assess pharmacokinetic drug-drug interactions (DDIs) of new chemical entities (NCEs) and mathematical prediction helps to select the best NCE candidate with regard to adverse effects resulting from a DDI before any costly clinical studies. Most current models assume that the liver is a homogeneous organ where the majority of the metabolism occurs. However, the circulatory system of the liver has a complex hierarchical geometry which distributes xenobiotics throughout the organ. Nevertheless, the lobule (liver unit), located at the end of each branch, is composed of many sinusoids where the blood flow can vary and therefore creates heterogeneity (e.g. drug concentration, enzyme level). A liver model was constructed by describing the geometry of a lobule, where the blood velocity increases toward the central vein, and by modeling the exchange mechanisms between the blood and hepatocytes. Moreover, the three major DDI mechanisms of metabolic enzymes; competitive inhibition, mechanism based inhibition and induction, were accounted for with an undefined number of drugs and/or enzymes. The liver model was incorporated into a physiological-based pharmacokinetic (PBPK) model and simulations produced, that in turn were compared to ten clinical results. The liver model generated a hierarchy of 5 sinusoidal levels and estimated a blood volume of 283 mL and a cell density of 193 × 106 cells/g in the liver. The overall PBPK model predicted the pharmacokinetics of midazolam and the magnitude of the clinical DDI with perpetrator drug(s) including spatial and temporal enzyme levels changes. The model presented herein may reduce costs and the use of laboratory animals and give the opportunity to explore different clinical scenarios, which reduce the risk of adverse events, prior to costly human clinical studies.

Effects of Cytochrome P450 3A4 Inhibitors-Ketoconazole and Erythromycin-on Bitopertin Pharmacokinetics and Comparison with Physiologically Based Modelling Predictions

OBJECTIVE

To assess the effect of strong and moderate cytochrome P450 (CYP) 3A4 inhibition on exposure of bitopertin, a glycine reuptake inhibitor primarily metabolized by CYP3A4, and to compare the results with predictions based on physiologically based pharmacokinetic (PBPK) modelling.

METHODS

The effects of ketoconazole and erythromycin were assessed in two male volunteer studies with open-label, two-period, fixed-sequence designs. Twelve subjects were enrolled in each of the studies. In period 1, a single dose of bitopertin was administered; in period 2, 400 mg ketoconazole was administered once daily for 17 days or 500 mg erythromycin was administered twice daily for 21 days. A single dose of bitopertin was coadministered on day 5. Pharmacokinetic parameters were derived by non-compartmental methods. Simulated bitopertin profiles using dynamic PBPK modelling for a typical healthy volunteer in GastroPlus(®) were used to predict changes in pharmacokinetic parameters.

RESULTS

In healthy volunteers, coadministration of ketoconazole increased the bitopertin area under the plasma concentration-time curve (AUC) from 0 to 312 h (AUC0-312h) 4.2-fold (90 % confidence interval [CI] 3.5-5.0) and erythromycin increased the AUC from time zero to infinity (AUC0-inf) 2.1-fold (90 % CI 1.9-2.3). The peak concentration (C max) increased by <25 % in both studies. Simulated bitopertin profiles using PBPK modelling showed good agreement with the observed AUC ratios in both studies. The predicted AUC0-inf ratios for the interaction with ketoconazole and erythromycin were 7.7 and 1.9, respectively.

CONCLUSION

Strong CYP3A4 inhibitors increase AUC0-inf of bitopertin 7- to 8-fold and hence should not be administered concomitantly with bitopertin. Moderate CYP3A4 inhibitors double AUC0-inf.

Ibrutinib Dosing Strategies Based on Interaction Potential of CYP3A4 Perpetrators Using Physiologically Based Pharmacokinetic Modeling

Based on ibrutinib pharmacokinetics and potential sensitivity towards CYP3A4-mediated drug-drug interactions (DDIs), a physiologically based pharmacokinetic approach was developed to mechanistically describe DDI with various CYP3A4 perpetrators in healthy men under fasting conditions. These models were verified using clinical data for ketoconazole (strong CYP3A4 inhibitor) and used to prospectively predict and confirm the inducing effect of rifampin (strong CYP3A4 inducer); DDIs with mild (fluvoxamine, azithromycin) and moderate inhibitors (diltiazem, voriconazole, clarithromycin, itraconazole, erythromycin), and moderate (efavirenz) and strong CYP3A4 inducers (carbamazepine), were also predicted. Ketoconazole increased ibrutinib area under the curve (AUC) by 24-fold, while rifampin decreased ibrutinib AUC by 10-fold; coadministration of ibrutinib with strong inhibitors or inducers should be avoided. The ibrutinib dose should be reduced to 140 mg (quarter of maximal prescribed dose) when coadministered with moderate CYP3A4 inhibitors so that exposures remain within observed ranges at therapeutic doses. Thus, dose recommendations for CYP3A4 perpetrator use during ibrutinib treatment were developed and approved for labeling.

Metabolic mapping of A3 adenosine receptor agonist MRS5980

(1S,2R,3S,4R,5S)-4-(2-((5-Chlorothiophen-2-yl)ethynyl)-6-(methylamino)-9H-purin-9-yl)-2,3-dihydroxy-N-methylbicyclo[3.1.0]hexane-1-carboxamide (MRS5980) is an A3AR selective agonist containing multiple receptor affinity- and selectivity-enhancing modifications and a therapeutic candidate drug for many inflammatory diseases. Metabolism-related poor pharmacokinetic behavior and toxicities are a major reason for drug R&D failure. Metabolomics with UPLC-MS was employed to profile the metabolism of MRS5980 and MRS5980-induced disruption of endogenous compounds. Recombinant drug-metabolizing enzymes screening experiment were used to determine the enzymes involved in MRS5980 metabolism. Analysis of lipid metabolism-related genes was performed to investigate the reason for MRS5980-induced lipid metabolic disorders. Unsupervised principal components analysis separated the control and MRS5980 treatment groups in feces, urine, and liver samples, but not in bile and serum. The major ions mainly contributing to the separation of feces and urine were oxidized MRS5980, glutathione (GSH) conjugates and cysteine conjugate (degradation product of the GSH conjugates) of MRS5980. The major ions contributing to the group separation of liver samples were phosphatidylcholines. In vitro incubation experiments showed the involvement of CYP3A enzymes in the oxidative metabolism of MRS5980 and direct GSH reactivity of MRS5980. The electrophilic attack by MRS5980 is a minor pathway and did not alter GSH levels in liver or liver histology, and thus may be of minor clinical consequence. Gene expression analysis further showed decreased expression of PC biosynthetic genes choline kinase a and b, which further accelerated conversion of lysophosphatidylcholine to phosphatidylcholines through increasing the expression of lysophosphatidylcholine acyltransferase 3. These data will be useful to guide rational design of drugs targeting A3AR, considering efficacy, metabolic elimination, and electrophilic reactivity.

Understanding Disease-Drug Interactions in Cancer Patients: Implications for Dosing Within the Therapeutic Window

The human inflammatory response can result in the alteration of drug clearance through effects on metabolizing enzymes or transporters. In this article we briefly review the theory of how cancer can lead to indirect changes in drug metabolism, review acute phase proteins and cytokines as markers of changes in cytochrome P450 (CYP) activity in cancer patients, and provide clinical case examples of how the inflammation in advanced cancer patients can lead to altered CYP-mediated drug clearance.

Effect of axitinib on the QT interval in healthy volunteers

PURPOSE

Axitinib is a potent and selective inhibitor of vascular endothelial growth factor receptors 1-3, approved for second-line treatment of advanced renal cell carcinoma (RCC). Preclinical studies did not indicate potential for axitinib-induced delayed cardiac repolarization.

METHODS

The effect of axitinib on corrected QT (QTc) prolongation was evaluated with one-stage concentration-QTc response modeling using data from a definitive randomized crossover QT phase I study in healthy volunteers administered one single 5-mg axitinib dose alone or in the presence of steady-state ketoconazole (400 mg once daily).

RESULTS

Axitinib and ketoconazole had opposite effects on heart rate: Axitinib lowered it, ketoconazole raised it. The final analysis showed a flat relationship between QTc and axitinib concentration (slope -0.0314 ms·mL/ng) for axitinib alone. Mean highest placebo-matched change from baseline in QTc was -3.0 [90 % confidence interval (CI) -5.4, -0.6] ms. At supratherapeutic axitinib exposures achieved with potent cytochrome P450 3A4/5 inhibition by ketoconazole, the model predicted mean QTc change of 6.5 (90 % CI 4.4-8.5) ms. The slope population mean estimate was -0.331 (95 % CI -0.860, 0.198) ms·mL/µg for ketoconazole alone and 0.0725 (0.0445-0.1005) ms·mL/ng for axitinib in the presence of ketoconazole. The results were then compared with those obtained based on more widely used Fridericia's, Bazett's, and study-specific correction methods.

CONCLUSIONS

Since axitinib plasma concentrations observed in this study exceeded the range of concentrations observed in patients with RCC at the highest approved clinical dose (10 mg twice daily), axitinib was not associated with clinically significant QTc prolongation in target populations.

The effects of milk thistle (Silybum marianum) on human cytochrome P450 activity

Milk thistle (Silybum marianum) extracts are widely used as a complementary and alternative treatment of various hepatic conditions and a host of other diseases/disorders. The active constituents of milk thistle supplements are believed to be the flavonolignans contained within the extracts. In vitro studies have suggested that some milk thistle components may significantly inhibit specific cytochrome P450 (P450) enzymes. However, determining the potential for clinically significant drug interactions with milk thistle products has been complicated by inconsistencies between in vitro and in vivo study results. The aim of the present study was to determine the effect of a standardized milk thistle supplement on major P450 drug-metabolizing enzymes after a 14-day exposure period. CYP1A2, CYP2C9, CYP2D6, and CYP3A4/5 activities were measured by simultaneously administering the four probe drugs, caffeine, tolbutamide, dextromethorphan, and midazolam, to nine healthy volunteers before and after exposure to a standardized milk thistle extract given thrice daily for 14 days. The three most abundant falvonolignans found in plasma, following exposure to milk thistle extracts, were silybin A, silybin B, and isosilybin B. The concentrations of these three major constituents were individually measured in study subjects as potential perpetrators. The peak concentrations and areas under the time-concentration curves of the four probe drugs were determined with the milk thistle administration. Exposure to milk thistle extract produced no significant influence on CYP1A2, CYP2C9, CYP2D6, or CYP3A4/5 activities.

A semiphysiological population pharmacokinetic model for dynamic inhibition of liver and gut wall cytochrome P450 3A by voriconazole

BACKGROUND

Accurate predictions of cytochrome P450 (CYP) 3A-mediated drug-drug interactions (DDIs) account for dynamic changes of CYP3A activity at both major expression sites (liver and gut wall) by considering the full pharmacokinetic profile of the perpetrator and the substrate. Physiological-based in vitro-in vivo extrapolation models have become of increasing interest. However, due to discrepancies between the predicted and observed magnitude of DDIs, the role of models fully based on in vivo data is still essential.

OBJECTIVE

The primary objective of this study was to develop a coupled dynamic model for the interaction of the CYP3A inhibitor voriconazole and the prototypical CYP3A substrate midazolam.

METHODS

Raw concentration data were obtained from a DDI study. Ten subjects were given either no pretreatment (control) or voriconazole twice daily orally. Midazolam was given either intravenously or orally after the last voriconazole dose and during control phases. Data analysis was performed by the population pharmacokinetic approach using non-linear mixed effects modelling (NONMEM 7.2.0). Model evaluation was performed using visual predictive checks and bootstrap analysis.

RESULTS

A semiphysiological model was able to describe the pharmacokinetics of midazolam, its major metabolite and voriconazole simultaneously. By considering the temporal disposition of all three substances in the liver and gut wall, a time-varying CYP3A inhibition process was implemented. Only the incorporation of hypothetical enzyme site compartments resulted in an adequate fit, suggesting a sustained inhibitory effect through accumulation. Novel key features of this analysis are the identification of (1) an apparent sustained inhibitory effect by voriconazole due to a proposed quasi accumulation at the enzyme site, (2) a significantly reduced inhibitory potency of intravenous voriconazole for oral substrates, (3) voriconazole as a likely uridine diphosphate glucuronosyltransferase (UGT) 2B inhibitor and (4) considerable sources of interindividual variability.

CONCLUSION

The proposed semiphysiological modelling approach generated a mechanistic description of the complex DDI occurring at major CYP3A expression sites and thus may serve as a powerful tool to maximise information acquired from clinical DDI studies. The model has been shown to draw precise and accurate predictions. Therefore, simulations based on this kind of models may be used for various clinical scenarios to improve pharmacotherapy.

Fluoxetine- and norfluoxetine-mediated complex drug-drug interactions: in vitro to in vivo correlation of effects on CYP2D6, CYP2C19, and CYP3A4

Fluoxetine and its circulating metabolite norfluoxetine comprise a complex multiple-inhibitor system that causes reversible or time-dependent inhibition of the cytochrome P450 (CYP) family members CYP2D6, CYP3A4, and CYP2C19 in vitro. Although significant inhibition of all three enzymes in vivo was predicted, the areas under the concentration-time curve (AUCs) for midazolam and lovastatin were unaffected by 2-week dosing of fluoxetine, whereas the AUCs of dextromethorphan and omeprazole were increased by 27- and 7.1-fold, respectively. This observed discrepancy between in vitro risk assessment and in vivo drug-drug interaction (DDI) profile was rationalized by time-varying dynamic pharmacokinetic models that incorporated circulating concentrations of fluoxetine and norfluoxetine enantiomers, mutual inhibitor-inhibitor interactions, and CYP3A4 induction. The dynamic models predicted all DDIs with less than twofold error. This study demonstrates that complex DDIs that involve multiple mechanisms, pathways, and inhibitors with their metabolites can be predicted and rationalized via characterization of all the inhibitory species in vitro.

Text mining for drug-drug interaction

In order to understand the mechanisms of drug-drug interaction (DDI), the study of pharmacokinetics (PK), pharmacodynamics (PD), and pharmacogenetics (PG) data are significant. In recent years, drug PK parameters, drug interaction parameters, and PG data have been unevenly collected in different databases and published extensively in literature. Also the lack of an appropriate PK ontology and a well-annotated PK corpus, which provide the background knowledge and the criteria of determining DDI, respectively, lead to the difficulty of developing DDI text mining tools for PK data collection from the literature and data integration from multiple databases.To conquer the issues, we constructed a comprehensive pharmacokinetics ontology. It includes all aspects of in vitro pharmacokinetics experiments, in vivo pharmacokinetics studies, as well as drug metabolism and transportation enzymes. Using our pharmacokinetics ontology, a PK corpus was constructed to present four classes of pharmacokinetics abstracts: in vivo pharmacokinetics studies, in vivo pharmacogenetic studies, in vivo drug interaction studies, and in vitro drug interaction studies. A novel hierarchical three-level annotation scheme was proposed and implemented to tag key terms, drug interaction sentences, and drug interaction pairs. The utility of the pharmacokinetics ontology was demonstrated by annotating three pharmacokinetics studies; and the utility of the PK corpus was demonstrated by a drug interaction extraction text mining analysis.The pharmacokinetics ontology annotates both in vitro pharmacokinetics experiments and in vivo pharmacokinetics studies. The PK corpus is a highly valuable resource for the text mining of pharmacokinetics parameters and drug interactions.

Combined in vitro-in vivo approach to assess the hepatobiliary disposition of a novel oral thrombin inhibitor

Two clinical trials and a large set of in vitro transporter experiments were performed to investigate if the hepatobiliary disposition of the direct thrombin inhibitor prodrug AZD0837 is the mechanism for the drug-drug interaction with ketoconazole observed in a previous clinical study. In Study 1, [(3)H]AZD0837 was administered to healthy male volunteers (n = 8) to quantify and identify the metabolites excreted in bile. Bile was sampled directly from the jejunum by duodenal aspiration via an oro-enteric tube. In Study 2, the effect of ketoconazole on the plasma and bile pharmacokinetics of AZD0837, the intermediate metabolite (AR-H069927), and the active form (AR-H067637) was investigated (n = 17). Co-administration with ketoconazole elevated the plasma exposure to AZD0837 and the active form approximately 2-fold compared to placebo, which may be explained by inhibited CYP3A4 metabolism and reduced biliary clearance, respectively. High concentrations of the active form was measured in bile with a bile-to-plasma AUC ratio of approximately 75, indicating involvement of transporter-mediated excretion of the compound. AZD0837 and its metabolites were further investigated as substrates of hepatic uptake and efflux transporters in vitro. Studies in MDCK-MDR1 cell monolayers and P-glycoprotein (P-gp) expressing membrane vesicles identified AZD0837, the intermediate, and the active form as substrates of P-gp. The active form was also identified as a substrate of the multidrug and toxin extrusion 1 (MATE1) transporter and the organic cation transporter 1 (OCT1), in HEK cells transfected with the respective transporter. Ketoconazole was shown to inhibit all of these three transporters; in particular, inhibition of P-gp and MATE1 occurred in a clinically relevant concentration range. In conclusion, the hepatobiliary transport pathways of AZD0837 and its metabolites were identified in vitro and in vivo. Inhibition of the canalicular transporters P-gp and MATE1 may lead to enhanced plasma exposure to the active form, which could, at least in part, explain the clinical interaction with ketoconazole.

Integration of in vitro binding mechanism into the semiphysiologically based pharmacokinetic interaction model between ketoconazole and midazolam

In vitro screening for drug–drug interactions is an integral component of drug development, with larger emphasis now placed on the use of in vitro parameters to predict clinical inhibition. However, large variability exists in K_i reported for ketoconazole with midazolam, a model inhibitor–substrate pair for CYP3A. We reviewed the literature and extracted K_i for ketoconazole as measured by the inhibition of hydroxymidazolam formation in human liver microsomes. The superset of data collected was analyzed for the impact of microsomal binding, using Langmuir and phase equilibrium binding models, and fitted to various inhibition models: competitive, noncompetitive, and mixed. A mixed inhibition model with binding corrected by an independent binding model was best able to fit the data (K_ic = 19.2 nmol/l and K_in = 39.8 nmol/l) and to predict clinical effect of ketoconazole on midazolam area under the concentration–time curve. The variability of reported K_i may partially be explained by microsomal binding and choice of inhibition model.

A semi-physiologically-based pharmacokinetic model characterizing mechanism-based auto-inhibition to predict stereoselective pharmacokinetics of verapamil and its metabolite norverapamil in human

Verapamil and its major metabolite norverapamil were identified to be both mechanism-based inhibitors and substrates of CYP3A and reported to have non-linear pharmacokinetics in clinic. Metabolic clearances of verapamil and norverapmil as well as their effects on CYP3A activity were firstly measured in pooled human liver microsomes. The results showed that S-isomers were more preferential to be metabolized than R-isomers for both verapamil and norverapamil, and their inhibitory effects on CYP3A activity were also stereoselective with S-isomers more potent than R-isomers. A semi-physiologically based pharmacokinetic model (semi-PBPK) characterizing mechanism-based auto-inhibition was developed to predict the stereoselective pharmacokinetic profiles of verapamil and norverapamil following single or multiple oral doses. Good simulation was obtained, which indicated that the developed semi-PBPK model can simultaneously predict pharmacokinetic profiles of S-verapamil, R-verapamil, S-norverapamil and R-norverapamil. Contributions of auto-inhibition to verapamil and norverapamil accumulation were also investigated following the 38th oral dose of verapamil sustained-release tablet (240mg once daily). The predicted accumulation ratio was about 1.3-1.5 fold, which was close to the observed data of 1.4-2.1-fold. Finally, the developed semi-PBPK model was further applied to predict drug-drug interactions (DDI) between verapamil and other three CYP3A substrates including midazolam, simvastatin, and cyclosporine A. Successful prediction was also obtained, which indicated that the developed semi-PBPK model incorporating auto-inhibition also showed great advantage on DDI prediction with CYP3A substrates.

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

BMS-690514, a selective inhibitor of the ErbB and vascular endothelial growth factor receptors, has shown antitumor activity in early clinical development. The compound is metabolized by multiple enzymes, with CYP3A4 responsible for the largest fraction (34%) of metabolism. It is also a substrate of P-glycoprotein (P-gp) in vitro. To assess the effect of ketoconazole on BMS-690514 pharmacokinetics, 17 healthy volunteers received 200 mg BMS-690514 alone followed by 100 mg BMS-690514 with ketoconazole (400 mg once daily for 4 days). The AUC(∞) of 100 mg BMS-690514 concomitantly administered with ketoconazole was similar to that of 200 mg BMS-690514 alone. The dose-normalized C(max) and AUC(∞) of BMS-690514 from the 100-mg BMS-690514/400-mg ketoconazole treatment increased by 55% and 127%, respectively, relative to those from 200 mg BMS-690514 alone. Prediction of the drug-drug interaction (DDI) using a population-based simulator (Simcyp) indicated that, in addition to CYP3A4 inhibition, the inhibition of P-gp by ketoconazole in the intestine, liver, and kidneys must be invoked to fully account for the DDI observed. This finding suggests that the inhibition of P-gp by ketoconazole, along with its effect on CYP3A4, needs to be considered when designing a DDI study of ketoconazole with a victim drug that is a dual substrate.

Effects of ketoconazole on the pharmacokinetics of ponatinib in healthy subjects

Ponatinib is a BCR-ABL tyrosine kinase inhibitor (TKI) approved for the treatment of chronic myeloid leukemia and Philadelphia chromosome–positive acute lymphoblastic leukemia in patients resistant or intolerant to prior TKIs. In vitro studies suggested that metabolism of ponatinib is partially mediated by CYP3A4. The effects of CYP3A4 inhibition on the pharmacokinetics of ponatinib and its CYP3A4-mediated metabolite, AP24567, were evaluated in a single-center, randomized, two-period, two-sequence crossover study in healthy volunteers. Subjects (N = 22) received two single doses (orally) of ponatinib 15 mg, once given alone and once coadministered with daily (5 days) ketoconazole 400 mg, a CYP3A4 inhibitor. Ponatinib plus ketoconazole increased ponatinib maximum plasma concentration (C_max) and area under the concentration–time curve (AUC) compared with ponatinib alone. The estimated mean ratios for AUC_0–∞, AUC_0–t, and C_max indicated increased exposures to ponatinib of 78%, 70%, and 47%, respectively; exposure to AP24567 decreased by 71%. Exposure to AP24567 was marginal after ponatinib alone (no more than 4% of the exposure to ponatinib). These results suggest that caution should be exercised with the concurrent use of ponatinib and strong CYP3A4 inhibitors and that a ponatinib dose decrease to 30 mg daily, from the 45 mg daily starting dose, could be considered.

An integrated pharmacokinetics ontology and corpus for text mining

Background

Drug pharmacokinetics parameters, drug interaction parameters, and pharmacogenetics data have been unevenly collected in different databases and published extensively in the literature. Without appropriate pharmacokinetics ontology and a well annotated pharmacokinetics corpus, it will be difficult to develop text mining tools for pharmacokinetics data collection from the literature and pharmacokinetics data integration from multiple databases.

Description

A comprehensive pharmacokinetics ontology was constructed. It can annotate all aspects of in vitro pharmacokinetics experiments and in vivo pharmacokinetics studies. It covers all drug metabolism and transportation enzymes. Using our pharmacokinetics ontology, a PK-corpus was constructed to present four classes of pharmacokinetics abstracts: in vivo pharmacokinetics studies, in vivo pharmacogenetic studies, in vivo drug interaction studies, and in vitro drug interaction studies. A novel hierarchical three level annotation scheme was proposed and implemented to tag key terms, drug interaction sentences, and drug interaction pairs. The utility of the pharmacokinetics ontology was demonstrated by annotating three pharmacokinetics studies; and the utility of the PK-corpus was demonstrated by a drug interaction extraction text mining analysis.

Conclusions

The pharmacokinetics ontology annotates both in vitro pharmacokinetics experiments and in vivo pharmacokinetics studies. The PK-corpus is a highly valuable resource for the text mining of pharmacokinetics parameters and drug interactions.

Pharmacokinetic interaction between prasugrel and ritonavir in healthy volunteers

The new anti-aggregating agent prasugrel is bioactivated by cytochromes P450 (CYP) 3A and 2B6. Ritonavir is a potent CYP3A inhibitor and was shown in vitro as a CYP2B6 inhibitor. The aim of this open-label cross-over study was to assess the effect of ritonavir on prasugrel active metabolite (prasugrel AM) pharmacokinetics in healthy volunteers. Ten healthy male volunteers received 10 mg prasugrel. After at least a week washout, they received 100 mg ritonavir, followed by 10 mg prasugrel 2 hr later. We used dried blood spot sampling method to monitor prasugrel AM pharmacokinetics (C_max, t_1/2, t_max, AUC_{0–6 hr}) at 0, 0.25, 0.5, 1, 1.5, 2, 4 and 6 hr after prasugrel administration. A ‘cocktail’ approach was used to measure CYP2B6, 2C9, 2C19 and 3A activities. In the presence of ritonavir, prasugrel AM C_max and AUC were decreased by 45% (mean ratio: 0.55, CI 90%: 0.40–0.7, p = 0.007) and 38% (mean ratio: 0.62, CI 90%: 0.54–0.7, p = 0.005), respectively, while t_1/2 and t_max were not affected. Midazolam metabolic ratio (MR) dramatically decreased in presence of ritonavir (6.7 ± 2.6 versus 0.13 ± 0.07) reflecting an almost complete inhibition of CYP3A4, whereas omeprazole, flurbiprofen and bupropion MR were not affected. These data demonstrate that ritonavir is able to block prasugrel CYP3A4 bioactivation. This CYP-mediated drug–drug interaction might lead to a significant reduction of prasugrel efficacy in HIV-infected patients with acute coronary syndrome.

A Semi-Mechanistic Metabolism Model of CYP3A Substrates in Pregnancy: Predicting Changes in Midazolam and Nifedipine Pharmacokinetics

Physiological changes in pregnancy, including changes in body composition and metabolic enzyme activity, can alter drug pharmacokinetics. A semi-mechanistic metabolism model was developed to describe the pharmacokinetics of two cytochrome P450 3A (CYP3A) substrates, midazolam and nifedipine, in obstetrics patients. The model parameters were optimized to fit the data of oral midazolam pharmacokinetics in pregnant women, by increasing CYP3A-induced hepatic metabolism 1.6-fold in the model with no change in gut wall metabolism. Fetal metabolism had a negligible effect on maternal plasma drug concentrations. Validation of the model was performed by applying changes in volume of distribution and metabolism, consistent with those observed for midazolam, to the pharmacokinetics parameters of immediate-release nifedipine in healthy volunteers. The predicted steady-state areas under the concentration–time curve (AUCs) for nifedipine were within 15% of the data observed in pregnant women undergoing treatment for preterm labor. This model predicts the pharmacokinetics of two CYP3A substrates in pregnancy, and may be applicable to other CYP3A substrates as well.

Ascending single-dose study of the safety profile, tolerability, and pharmacokinetics of bosutinib coadministered with ketoconazole to healthy adult subjects

BACKGROUND

Bosutinib (SKI-606) is an orally bioavailable, competitive tyrosine kinase inhibitor that selectively targets both Src and Abl tyrosine kinases. Bosutinib is metabolized primarily through the cytochrome P450 3A4 pathway. Inhibition of bosutinib metabolism by coadministration with the potent cytochrome P450 3A4 inhibitor ketoconazole could potentially increase plasma concentrations of bosutinib, allowing for the study of bosutinib tolerability at supratherapeutic concentrations in a healthy subject population.

OBJECTIVE

This study assessed the safety profile, tolerability, and pharmacokinetics of different dose combinations of bosutinib coadministered with ketoconazole in healthy adults, and determined whether supratherapeutic concentrations of bosutinib can be achieved with ketoconazole.

METHODS

This was a randomized, Phase I, double-blind, placebo-controlled, sequential-group study conducted in healthy adults. Single oral doses of bosutinib 100, 200, 300, 400, 500, and 600 mg or placebo were administered with ketoconazole and food on day 1; daily single oral doses of ketoconazole 400 mg were administered on days -1 and 1 through 4.

RESULTS

Forty-eight subjects were enrolled. Their mean (SD) age was 32.0 (10.7) years (range, 18-50 years). The majority of the subjects (n = 44 [92%]) were white, 2 (4%) were black or African American, and 2 (4%) were of other races. Bosutinib was associated with acceptable tolerability at doses from 100 to 600 mg, with adverse events either mild (n = 30 [63%]) or moderate (n = 12 [25%]) in severity; no subject discontinued treatment due to adverse events, and no serious events were reported. Mean (SD) values for bosutinib 100 to 600 mg ranged from 58.4 (13.3) to 426 (100) ng/mL for C(max) and 2980 (802) to 23,000 (4020) ng·h/mL for AUC(0-∞); mean AUC(0-24) and AUC(0-last) ranged from 876 (234) to 7080 (1640) ng· h/mL and from 2740 (854) to 22,200 (3630) ng · h/mL, respectively. C(max) and AUC were linear and dose proportional. Mean C(max) at 600 mg was 2.1-fold higher than the steady-state C(max) previously observed for patients with chronic myelogenous leukemia who received bosutinib 500 mg once daily with food.

CONCLUSIONS

Single doses of bosutinib up to 600 mg coadministered with multiple doses of ketoconazole were acceptably well tolerated in this small, selected group of healthy male volunteers. In addition, supratherapeutic exposure was achieved within this range for bosutinib when coadministered with ketoconazole. ClinicalTrials.gov identifier: NCT00777530.

Use of physiologically based pharmacokinetic modeling for assessment of drug-drug interactions

Interactions between co-administered medicines can reduce efficacy or lead to adverse effects. Understanding and managing such interactions is essential in bringing safe and effective medicines to the market. Ideally, interaction potential should be recognized early and minimized in compounds that reach late stages of drug development. Physiologically based pharmacokinetic models combine knowledge of physiological factors with compound-specific properties to simulate how a drug behaves in the human body. These software tools are increasingly used during drug discovery and development and, when integrating relevant in vitro data, can simulate drug interaction potential. This article provides some background and presents illustrative examples. Physiologically based models are an integral tool in the discovery and development of drugs, and can significantly aid our understanding and prediction of drug interactions.

In vitro-to-in vivo predictions of drug-drug interactions involving multiple reversible inhibitors

INTRODUCTION

Predictions of drug-drug interactions (DDIs) are commonly performed for single inhibitors, but interactions involving multiple inhibitors also frequently occur. Predictions of such interactions involving stereoisomer pairs, parent/metabolite combinations and simultaneously administered multiple inhibitors are increasing in importance. This review provides the framework for predicting inhibitory DDIs of multiple inhibitors with any combination of reversible inhibition mechanism.

AREAS COVERED

The review provides an overview of the reliability of the in vitro determined reversible inhibition mechanism. Furthermore, the article provides a method to predict DDIs for multiple reversible inhibitors that allows substituting the inhibition constant (K(i)) with an inhibitor affinity (IC(50)) value determined at S << K(M).

EXPERT OPINION

A better understanding and the prediction methods of DDIs, resulting from multiple inhibitors, are important. The inhibition mechanism of a reversible inhibitor is often equivocal across studies and unreliable. Determination of the K(i) requires the assignment of reversible inhibition mechanism but in vitro-to-in vivo prediction of DDI risk can be achieved for multiple inhibitors from estimates of the inhibitor affinity (IC(50)) only, regardless of the inhibition mechanism.

Pharmacokinetics of oral neratinib during co-administration of ketoconazole in healthy subjects

AIM

The primary objective was to evaluate the pharmacokinetics of a single dose of neratinib, a potent, low-molecular-weight, orally administered, irreversible pan-ErbB (ErbB-1, -2, -4) receptor tyrosine kinase inhibitor, during co-administration with ketoconazole, a potent CYP3A4 inhibitor.

METHODS

This was an open-label, randomized, two-period, crossover study. Fasting healthy adults received a single oral dose of neratinib 240 mg alone and with multiple oral doses of ketoconazole 400 mg. Blood samples were collected up to 72 h after each neratinib dose. Plasma concentration data were analyzed using a noncompartmental method. The least square geometric mean ratios [90% confidence interval (CI)] of C(max) (neratinib+ketoconazole): C(max) (neratinib alone), and AUC(neratinib+ketoconazole): AUC(neratinib alone) were assessed.

RESULTS

Twenty-four subjects were enrolled. Compared with neratinib administered alone, co-administration of ketoconazole increased neratinib C(max) by 3.2-fold (90% CI: 2.4, 4.3) and AUC by 4.8-fold (3.6, 6.5). Median t(max) was 6.0 h with both regimens. Ketoconazole decreased mean apparent oral clearance of neratinib from 346 lh(-1) to 87.1 lh(-1) and increased mean elimination half-life from 11.7 h to 18.0 h. The incidence of adverse events was comparable between the two regimens (50% neratinib alone, 65% co-administration with ketoconazole).

CONCLUSION

Co-administration of neratinib with ketoconazole, a potent CYP3A inhibitor, increased neratinib C(max) by 3.2-fold and AUC by 4.8-fold compared with administration of neratinib alone. These results indicate that neratinib is a substrate of CYP3A and is susceptible to interaction with potent CYP3A inhibitors and, thus, dose adjustments may be needed if neratinib is administered with such compounds.

Assessment of algorithms for predicting drug-drug interactions via inhibition mechanisms: comparison of dynamic and static models

WHAT IS ALREADY KNOWN ABOUT THIS SUBJECT

The prediction of drug-drug interactions (DDIs) from in vitro data usually utilizes an average dosing interval estimate of inhibitor concentration in an equation-based static model. Simcyp®, a population-based ADME simulator, is becoming widely used for the prediction of DDIs and has the ability to incorporate the time-course of inhibitor concentration and hence generate a temporal profile of the inhibition process within a dynamic model.

WHAT THIS PAPER ADDS

Prediction of DDIs for 35 clinical studies incorporating a representative range of drug-drug interactions, with multiple studies across different inhibitors and victim drugs. Assessment of whether the inclusion of the time course of inhibition in the dynamic model improves prediction in comparison with the static model. Investigation of the impact of different inhibitor and victim drug parameters on DDI prediction accuracy including dosing time and the inclusion of active metabolites. Assessment of ability of the dynamic model to predict inter-individual variability in the DDI magnitude.

AIMS

Static and dynamic models (incorporating the time course of the inhibitor) were assessed for their ability to predict drug-drug interactions (DDIs) using a population-based ADME simulator (Simcyp®V8). The impact of active metabolites, dosing time and the ability to predict inter-individual variability in DDI magnitude were investigated using the dynamic model.

METHODS

Thirty-five in vivo DDIs involving azole inhibitors and benzodiazepines were predicted using the static and dynamic model; both models were employed within Simcyp for consistency in parameters. Simulations comprised of 10 trials with matching population demographics and dosage regimen to the in vivo studies. Predictive utility of the static and dynamic model was assessed relative to the inhibitor or victim drug investigated.

RESULTS

Use of the dynamic and static models resulted in comparable prediction success, with 71 and 77% of DDIs predicted within two-fold, respectively. Over 40% of strong DDIs (>five-fold AUC increase) were under-predicted by both models. Incorporation of the itraconazole metabolite into the dynamic model resulted in increased prediction accuracy of strong DDIs (80% within two-fold). Bias and imprecision in prediction of triazolam DDIs were higher in comparison with midazolam and alprazolam; >50% of triazolam DDIs were under-predicted regardless of the model used. Predicted inter-individual variability in the AUC ratio (coefficient of variation of 45%) was consistent with the observed variability (50%).

CONCLUSIONS

High prediction accuracy was observed using both the Simcyp dynamic and static models. The differences observed with the dose staggering and the incorporation of active metabolite highlight the importance of these variables in DDI prediction.

Effect of ketoconazole on the pharmacokinetics of oral bosutinib in healthy subjects

Bosutinib (SKI-606), a dual inhibitor of Src and Abl tyrosine kinases, is being developed for the treatment of chronic myelogenous leukemia. The effect of coadministration of ketoconazole on the pharmacokinetic (PK) profile of bosutinib was evaluated in an open-label, randomized, 2-period, crossover study. Healthy subjects (fasting) received a single dose of oral bosutinib 100 mg alone and with multiple once-daily doses of oral ketoconazole 400 mg. PK sampling occurred through 96 hours. The least square geometric mean treatment ratios (90% confidence interval [CI]) of C(max(bosutinib+ketoconazole))/C(max(bosutinib alone)), AUC(T(bosutinib+ketoconazole))/AUC(T(bosutinib alone)), and AUC((bosutinib+ketoconazole))/AUC((bosutinib alone)) were assessed. Compared with bosutinib administered alone, coadministration with ketoconazole increased bosutinib C(max) 5.2-fold, AUC(T) 7.6-fold, and AUC 8.6-fold. Ketoconazole coadministration decreased the mean apparent clearance of bosutinib approximately 9-fold and increased the mean (SD) terminal half-life from 46.2 (16.4) hours to 69.0 (29.1) hours. The incidence of adverse events (AEs) was comparable between the 2 treatments. The most common AEs were headache, nausea, and increased blood creatinine. No safety-related discontinuations or serious AEs occurred. These PK results indicate that bosutinib is susceptible to interaction with potent CYP3A4 inhibitors.

Physiologically based predictions of the impact of inhibition of intestinal and hepatic metabolism on human pharmacokinetics of CYP3A substrates

The first objective of the present study was to predict the pharmacokinetics of selected CYP3A substrates administered at a single oral dose to human. The second objective was to predict pharmacokinetics of the selected drugs in presence of inhibitors of the intestinal and/or hepatic CYP3A activity. We developed a whole-body physiologically based pharmacokinetics (WB-PBPK) model accounting for presystemic elimination of midazolam (MDZ), alprazolam (APZ), triazolam (TRZ), and simvastatin (SMV). The model also accounted for concomitant administration of the above-mentioned drugs with CYP3A inhibitors, namely ketoconazole (KTZ), itraconazole (ITZ), diltiazem (DTZ), saquinavir (SQV), and a furanocoumarin contained in grape-fruit juice (GFJ), namely 6',7'-dihydroxybergamottin (DHB). Model predictions were compared to published clinical data. An uncertainty analysis was performed to account for the variability and uncertainty of model parameters when predicting the model outcomes. We also briefly report on the results of our efforts to develop a global sensitivity analysis and its application to the current WB-PBPK model. Considering the current criterion for a successful prediction, judged satisfied once the clinical data are captured within the 5th and 95th percentiles of the predicted concentration-time profiles, a successful prediction has been obtained for a single oral administration of MDZ and SMV. For APZ and TRZ, however, a slight deviation toward the 95th percentile was observed especially for C(max) but, overall, the in vivo profiles were well captured by the PBPK model. Moreover, the impact of DHB-mediated inhibition on the extent of intestinal pre-systemic elimination of MDZ and SMV has been accurately predicted by the proposed PBPK model. For concomitant administrations of MDZ and ITZ, APZ and KTZ, as well as SMV and DTZ, the in vivo concentration-time profiles were accurately captured by the model. A slight deviation was observed for SMV when coadministered with ITZ, whereas more important deviations have been obtained between the model predictions and in vivo concentration-time profiles of MDZ coadministered with SQV. The same observation was made for TRZ when administered with KTZ. Most of the pharmacokinetic parameters predicted by the PBPK model were successfully predicted within a two-fold error range either in the absence or presence of metabolism-based inhibition. Overall, the present study demonstrated the ability of the PBPK model to predict DDI of CYP3A substrates with promising accuracy.

Inhibition of oral midazolam clearance by boosting doses of ritonavir, and by 4,4-dimethyl-benziso-(2H)-selenazine (ALT-2074), an experimental catalytic mimic of glutathione oxidase

WHAT IS ALREADY KNOWN ABOUT THIS SUBJECT

* The viral protease inhibitor ritonavir is known to inhibit clearance of intravenous midazolam. * ALT-2074, a catalytic mimic of glutathione oxidase, inhibits human cytochrome P450 3A (CYP3A) isoforms in vitro.

WHAT THIS STUDY ADDS

* Short-term administration of low-dose ritonavir increases area under the plasma concentration curve following oral midazolam by a factor of 28. * Therefore ritonavir is an appropriate positive control inhibitor for clinical drug interaction studies involving CYP3A substrates. * Midazolam clearance is weakly inhibited by ALT-2074, consistent with its in vitro profile.

AIMS

We evaluated whether 'boosting' doses of ritonavir can serve as a positive control inhibitor for pharmacokinetic drug-drug interaction studies involving cytochrome P450 3A (CYP3A). The study also determined whether 4,4-dimethyl-benziso-(2H)-selenazine (ALT-2074), an investigational organoselenium compound that acts as a catalytic mimic of glutathione oxidase, inhibits CYP3A metabolism in vivo.

METHODS

Thirteen healthy volunteers received single 3-mg oral doses of midazolam on three occasions: in the control condition, during co-treatment with low-dose ritonavir (three oral doses of 100 mg over 24 h), and during co-treatment with ALT-2074 (three oral doses of 80 mg over 24 h).

RESULTS

Ritonavir increased mean (+/-SE) total area under the curve (AUC) for midazolam by a factor of 28.4 +/- 4.2 (P < 0.001), and reduced oral clearance to 4.2 +/- 0.5% of control (P < 0.001). In contrast, ALT-2074 increased midazolam AUC by 1.25 +/- 0.11 (P < 0.05), and reduced oral clearance to 88 +/- 8% of control.

CONCLUSIONS

Low-dose ritonavir produces extensive CYP3A inhibition exceeding that of ketoconazole (typically 10- to 15-fold midazolam AUC enhancement), and is a suitable positive control index inhibitor for drug-drug interaction studies. ALT-2074 inhibits CYP3A metabolism to a small degree that is of uncertain clinical importance.

Drug-drug interaction prediction assessment

Model-based drug-drug interaction (DDI) is an important in-silico tool to assess the in vivo consequences of in vitro DDI. Before its general application to new drug compounds, the DDI model is always established from known interaction data. For the first time, tests for difference and equivalent tests are implemented to compare reported and model-base simulated DDI (log AUCR) in the sample mean and variance. The biases and predictive confidence interval coverage probabilities are introduced to assess the DDI prediction performance. Sample size and power guidelines are developed for DDI model simulations. These issues have never been discussed in trial simulation studies to investigate DDI prediction. A ketoconazole (KETO)/midazolam (MDZ) example is employed to demonstrate these statistical methods. Based on published KETO and MDZ pharmacokinetics data and their in vitro inhibition rate constant data, current model-based DDI prediction underpredicts the area under concentration curve ratio (AUCR) and its between-subject variance compared to the reported study.

Physiologically based pharmacokinetic model of mechanism-based inhibition of CYP3A by clarithromycin

The prediction of clinical drug-drug interactions (DDIs) due to mechanism-based inhibitors of CYP3A is complicated when the inhibitor itself is metabolized by CYP3Aas in the case of clarithromycin. Previous attempts to predict the effects of clarithromycin on CYP3A substrates, e.g., midazolam, failed to account for nonlinear metabolism of clarithromycin. A semiphysiologically based pharmacokinetic model was developed for clarithromycin and midazolam metabolism, incorporating hepatic and intestinal metabolism by CYP3A and non-CYP3A mechanisms. CYP3A inactivation by clarithromycin occurred at both sites. K(I) and k(inact) values for clarithromycin obtained from in vitro sources were unable to accurately predict the clinical effect of clarithromycin on CYP3A activity. An iterative approach determined the optimum values to predict in vivo effects of clarithromycin on midazolam to be 5.3 microM for K(i) and 0.4 and 4 h(-1) for k(inact) in the liver and intestines, respectively. The incorporation of CYP3A-dependent metabolism of clarithromycin enabled prediction of its nonlinear pharmacokinetics. The predicted 2.6-fold change in intravenous midazolam area under the plasma concentration-time curve (AUC) after 500 mg of clarithromycin orally twice daily was consistent with clinical observations. Although the mean predicted 5.3-fold change in the AUC of oral midazolam was lower than mean observed values, it was within the range of observations. Intestinal CYP3A activity was less sensitive to changes in K(I), k(inact), and CYP3A half-life than hepatic CYP3A. This semiphysiologically based pharmacokinetic model incorporating CYP3A inactivation in the intestine and liver accurately predicts the nonlinear pharmacokinetics of clarithromycin and the DDI observed between clarithromycin and midazolam. Furthermore, this model framework can be applied to other mechanism-based inhibitors.

Semiphysiologically based pharmacokinetic models for the inhibition of midazolam clearance by diltiazem and its major metabolite

Prediction of the extent and time course of drug-drug interactions (DDIs) between the mechanism-based inhibitor diltiazem (DTZ) and the CYP3A4 substrate midazolam (MDZ) is confounded by time- and concentration-dependent clearance of the inhibitor. Semiphysiologically based pharmacokinetic (PBPK) models were developed for DTZ and MDZ with the major metabolite of DTZ, N-desmethyldiltiazem (nd-DTZ), incorporated in the DTZ model. Enzyme kinetic parameters (k(inact) and K(I)) for DTZ and nd-DTZ were estimated in vitro and used to model the time course of changes in the amount of CYP3A4 in the liver and gut wall, which in turn, determined the nonlinear elimination of MDZ and DTZ, and the corresponding DDI. The robustness of the model prediction was assessed by comparing the results of the prediction to published DTZ pharmacokinetic and DTZ/MDZ interaction data. A clinical study was conducted to further validate the predicted increase of MDZ exposure after DTZ treatment. The model predicted the nonlinear disposition of DTZ after single and multiple oral doses. The clinical study showed that DTZ treatment resulted in 4.1- and 1.6-fold increases in MDZ exposure after oral and intravenous MDZ administration, respectively, suggesting that the DDI in the gut wall plays an important role in the DTZ/MDZ interaction. The semi-PBPK model incorporating the DDI at the gut wall, and the effect of nd-DTZ successfully predicted the nonlinear disposition of DTZ and its interaction with MDZ. Moreover, model simulation suggested that both DTZ and nd-DTZ contributed to the overall inhibitory effect after DTZ administration, and the values of the in vitro estimated inhibition parameters and CYP3A4 turnover rate are critical for the prediction.

Predicting drug-drug interactions: an FDA perspective

Pharmacokinetic drug interactions can lead to serious adverse events, and the evaluation of a new molecular entity's drug-drug interaction potential is an integral part of drug development and regulatory review prior to its market approval. Alteration of enzyme and/or transporter activities involved in the absorption, distribution, metabolism, or excretion of a new molecular entity by other concomitant drugs may lead to a change in exposure leading to altered response (safety or efficacy). Over the years, various in vitro methodologies have been developed to predict drug interaction potential in vivo. In vitro study has become a critical first step in the assessment of drug interactions. Well-executed in vitro studies can be used as a screening tool for the need for further in vivo assessment and can provide the basis for the design of subsequent in vivo drug interaction studies. Besides in vitro experiments, in silico modeling and simulation may also assist in the prediction of drug interactions. The recent FDA draft drug interaction guidance highlighted the in vitro models and criteria that may be used to guide further in vivo drug interaction studies and to construct informative labeling. This report summarizes critical elements in the in vitro evaluation of drug interaction potential during drug development and uses a case study to highlight the impact of in vitro information on drug labeling.

A Bayesian meta-analysis on published sample mean and variance pharmacokinetic data with application to drug-drug interaction prediction

In drug-drug interaction (DDI) research, a two-drug interaction is usually predicted by individual drug pharmacokinetics (PK). Although subject-specific drug concentration data from clinical PK studies on inhibitor or inducer and substrate PK are not usually published, sample mean plasma drug concentrations and their standard deviations have been routinely reported. Hence there is a great need for meta-analysis and DDI prediction using such summarized PK data. In this study, an innovative DDI prediction method based on a three-level hierarchical Bayesian meta-analysis model is developed. The three levels model sample means and variances, between-study variances, and prior distributions. Through a ketoconazle-midazolam example and simulations, we demonstrate that our meta-analysis model can not only estimate PK parameters with small bias but also recover their between-study and between-subject variances well. More importantly, the posterior distributions of PK parameters and their variance components allow us to predict DDI at both population-average and study-specific levels. We are also able to predict the DDI between-subject/study variance. These statistical predictions have never been investigated in DDI research. Our simulation studies show that our meta-analysis approach has small bias in PK parameter estimates and DDI predictions. Sensitivity analysis was conducted to investigate the influences of interaction PK parameters, such as the inhibition constant Ki, on the DDI prediction.

Potential role of intestinal first-pass metabolism in the prediction of drug-drug interactions

BACKGROUND

The contribution of intestine to the magnitude of drug-drug interactions (DDI) may be significant, considering high levels of inhibitors in the gut lumen achieved during absorption and the abundance of metabolic enzymes in the mature enterocytes. Intestinal inhibition is incorporated in the DDI prediction models as the ratio of the intestinal wall availability in the presence and absence of the inhibitor (F(G)(') and F(G), respectively).

OBJECTIVE

This review will focus on the ability of the current approaches to estimate the extent of intestinal DDI accurately, addressing predominantly the most abundant intestinal P450 enzyme, CYP3A4.

METHODS

Considering the sensitivity of the DDI prediction models to the accuracy of the F(G) estimates, the current study focuses on 3 different in vitro and in vivo approaches to assess this parameter.

RESULTS/CONCLUSION

The advantages and limitations of each of F(G) methods are outlined. Accurate assessment of this parameter is essential for the prediction of human drug clearance and drug-drug interaction potential.

Pharmacokinetics of the oral direct renin inhibitor aliskiren in combination with digoxin, atorvastatin, and ketoconazole in healthy subjects: the role of P-glycoprotein in the disposition of aliskiren

This study investigated the potential pharmacokinetic interaction between the direct renin inhibitor aliskiren and modulators of P-glycoprotein and cytochrome P450 3A4 (CYP3A4). Aliskiren stimulated in vitro P-glycoprotein ATPase activity in recombinant baculovirus-infected Sf9 cells with high affinity (K(m) 2.1 micromol/L) and was transported by organic anion-transporting peptide OATP2B1-expressing HEK293 cells with moderate affinity (K(m) 72 micromol/L). Three open-label, multiple-dose studies in healthy subjects investigated the pharmacokinetic interactions between aliskiren 300 mg and digoxin 0.25 mg (n = 22), atorvastatin 80 mg (n = 21), or ketoconazole 200 mg bid (n = 21). Coadministration with aliskiren resulted in changes of <30% in AUC(tau) and C(max,ss) of digoxin, atorvastatin, o-hydroxy-atorvastatin, and rho-hydroxy-atorvastatin, indicating no clinically significant interaction with P-glycoprotein or CYP3A4 substrates. Aliskiren AUC(tau) was significantly increased by coadministration with atorvastatin (by 47%, P < .001) or ketoconazole (by 76%, P < .001) through mechanisms most likely involving transporters such as P-glycoprotein and organic anion-transporting peptide and possibly through metabolic pathways such as CYP3A4 in the gut wall. These results indicate that aliskiren is a substrate for but not an inhibitor of P-glycoprotein. On the basis of the small changes in exposure to digoxin and atorvastatin and the <2-fold increase in exposure to aliskiren during coadministration with atorvastatin and ketoconazole, the authors conclude that the potential for clinically relevant drug interactions between aliskiren and these substrates and/or inhibitors of P-glycoprotein/CPY3A4/OATP is low.