Alexander Staab

Population pharmacokinetic analysis of the oral thrombin inhibitor dabigatran etexilate in patients with non-valvular atrial fibrillation from the RE-LY trial.

Summary.  Background: Dabigatran etexilate (DE) is an orally absorbed prodrug of dabigatran, a thrombin inhibitor that exerts potent anticoagulant and antithrombotic activity. Objectives: To characterize the pharmacokinetics of dabigatran in patients with non‐valvular atrial fibrillation (AF) from the Randomized Evaluation of Long‐term Anticoagulant Therapy (RE‐LY) trial and to quantify the effect of selected factors on pharmacokinetic (PK) model parameters. Patients and methods: A total of 27 706 dabigatran plasma concentrations from 9522 patients who received DE 110 or 150 mg twice daily were analyzed with non‐linear mixed‐effects modeling. Results: The pharmacokinetics of dabigatran were best described by a two‐compartment disposition model with first‐order absorption. The covariates creatinine clearance (CRCL), age, sex, heart failure and the ethnic subgroup ‘South Asian’ exhibited statistically significant effects on apparent clearance of dabigatran. Body weight and hemoglobin significantly influenced the apparent volume of distribution of the central compartment. Concomitant medication with proton‐pump inhibitors, amiodarone and verapamil significantly affected the bioavailability. However, all of the statistically significant factors that were identified, except for renal function status, showed only small to moderate effects (< 26% change in exposure at steady state). On the basis of simulations from the final population PK model, a dose of 75 mg twice daily would result in similar exposure for severely renally impaired patients with CRCL of 15–30 mL min−1 and patients with normal renal function receiving 150 mg twice daily. Conclusions: The analysis provides a thorough PK characterization of dabigatran in the AF patient population from RE‐LY. None of the covariates investigated, with the exception of renal function, warrants dose adjustment.

Dabigatran Etexilate in Atrial Fibrillation Patients With Severe Renal Impairment: Dose Identification Using Pharmacokinetic Modeling and Simulation

Dabigatran, administered orally as the prodrug dabigatran etexilate (DE), is a direct thrombin inhibitor shown to be effective in the prevention of stroke and systemic embolism in patients with atrial fibrillation (AF). The aim of this analysis was to derive a modeling and simulation‐based dose and dosing regimen for AF patients with severe renal failure who could potentially benefit from the use of DE. The exposure was simulated for AF patients with severe renal impairment for several combinations of doses (75, 110, 150 mg) and posologies (BID, QD, Q2D). Simulations were based on a population pharmacokinetic model derived from data from 9522 patients from the pivotal phase III study (RE‐LY). Atrial fibrillation patients with a creatinine clearance (CRCL) of <30 to ≥15 mL/min treated with a dose of 75 mg DE BID have target plasma level and exposure data largely within the concentration range proven to be safe and effective in AF patients with CRCL >30 mL/min receiving 150 mg BID. This dosing algorithm was also confirmed and supported by the United States Food and Drug Administration Clinical Pharmacology Division using their model based on the data from the dedicated renal impairment study and taking into account the safety and efficacy information from RE‐LY.

Binding to dipeptidyl peptidase-4 determines the disposition of linagliptin (BI 1356)--investigations in DPP-4 deficient and wildtype rats.

Linagliptin (BI 1356) is a novel dipeptidyl peptidase‐4 (DPP‐4) inhibitor in clinical development for the treatment of type 2 diabetes. It exhibits non‐linear pharmacokinetics and shows concentration‐dependent plasma protein binding to its target, DPP‐4. The aim of this study was to investigate the impact of saturable binding of linagliptin to plasma and tissue DPP‐4 by comparing the pharmacokinetics of linagliptin in wildtype and DPP‐4 deficient Fischer rats using non‐compartmental and model‐based data analysis. The non‐compartmental analysis revealed a significantly reduced AUC in DPP‐4 deficient rats compared with wildtype rats when single intravenous doses ⩽1 mg/kg were administered, but the exposure was similar in both strains at higher doses. The terminal half‐lives were significantly shorter in DPP‐4 deficient rats compared with wildtype rats. For doses ⩽1 mg/kg, DPP‐4 deficient rats exhibited linear pharmacokinetics, whereas the pharmacokinetics of wildtype rats was non‐linear. In the model‐based analysis these differences could be accounted for by assuming concentration‐dependent protein binding in the central and one peripheral compartment in wildtype rats. In the model, disposition parameters for unbound linagliptin were assumed to be identical in both rat strains. Simulations with different doses of linagliptin and different concentrations of binding sites further illustrated that the interdependence of linagliptin and DPP‐4 in plasma and in the periphery has a major influence on the disposition of linagliptin in wildtype rats. In conclusion, the study showed that the concentration‐dependent binding of linagliptin to its target DPP‐4 has a major impact on the plasma pharmacokinetics of linagliptin. Copyright

Impact of target-mediated drug disposition on Linagliptin pharmacokinetics and DPP-4 inhibition in type 2 diabetic patients.

The pharmacokinetics of the novel dipeptidyl‐peptidase 4 (DPP‐4) inhibitor linagliptin is nonlinear. Based on in vitro experiments, concentration‐dependent binding to DPP‐4 is the most likely cause for the nonlinearity. Population pharmacokinetic/pharmacodynamic modeling was performed using linagliptin plasma concentrations and plasma DPP‐4 activities from 2 phase 2a studies. In these studies, type 2 diabetic patients received either 1, 2.5, 5, or 10 mg of linagliptin once daily over 12 days (study 1) or 2.5, 5, or 10 mg of linagliptin once daily over 28 days (study 2). The modeling results supported the hypothesis that linagliptin exhibits target‐mediated drug disposition. The linagliptin plasma concentrations were best described by a 2‐compartment model including concentration‐dependent protein binding in the central and peripheral compartment. The plasma DPP‐4 activity was included in the model in a semi‐mechanistic way by relating it to the model‐calculated plasma DPP‐4 occupancy with linagliptin. The target binding has a major impact on linagliptin pharmacokinetics. Although unbound linagliptin is cleared efficiently (CL/F220 L/h), the concentration‐dependent binding is responsible for the long terminal half‐life (∼120 hours) of linagliptin and its nonlinear pharmacokinetics. The model allowed a comprehensive understanding of the impact of target‐mediated drug disposition and provides a useful tool to support clinical development.

A combined pharmacometric analysis of dabigatran etexilate in healthy volunteers and patients with atrial fibrillation or undergoing orthopaedic surgery

Dabigatran etexilate is the orally bioavailable pro-drug of dabigatran, a direct thrombin inhibitor. Using data from eight clinical studies in healthy volunteers and patients with non-valvular atrial fibrillation (AF) or undergoing orthopaedic surgery (OS), population pharmacokinetic (PK) and pharmacodynamic (PD) models were developed to investigate whether the PK and PD of dabigatran differ across different populations. In both healthy volunteers (n=80) and patients (n=1,965), the PK of dabigatran was best described by a two-compartment disposition model with first-order absorption and elimination. Renal function was the only covariate shown to have a clinically relevant impact on dabigatran exposure. The patient PK model was successfully applied in predicting exposure observed in the RE-LY trial evaluating dabigatran treatment in patients with non-valvular AF. The relationship between dabigatran plasma concentrations and activated partial thromboplastin time in healthy volunteers and patients (n=762) was best described with a combination of a linear model and a maximum effect (Emax) model, consistent with previous reports. PK/PD relationships were robust across the various populations tested and were not affected by any of the covariates examined. In summary, the PK of dabigatran is sufficiently consistent to allow extrapolation of data generated in healthy volunteers to patients with AF or undergoing OS.

Pharmacokinetics and pharmacodynamics of single rising intravenous doses (0.5 mg-10 mg) and determination of absolute bioavailability of the dipeptidyl peptidase-4 inhibitor linagliptin (BI 1356) in healthy male subjects.

Background and ObjectivesLinagliptin (BI 1356) is a highly specific inhibitor of dipeptidyl peptidase (DPP)-4, which is currently in phase III clinical development for the treatment of type 2 diabetes mellitus. Linagliptin exhibits nonlinear pharmacokinetics after oral administration, which are mainly related to concentration-dependent binding of linagliptin to its target, DPP-4. The objectives of the study were to investigate the pharmacokinetics and pharmacodynamics after intravenous administration of linagliptin and to determine its absolute bioavailability (F).Subjects and MethodsThis was a single rising-dose, randomized, four-group, placebo-controlled, singleblind (within dose groups) study. Thirty-six healthy men aged 18–50 years were enrolled and randomized into four sequential treatment groups. Group 1 received linagliptin 0.5mg intravenously, group 2 received 2.5mg intravenously and group 4 received 10mg intravenously. In group 3, subjects underwent a two-way randomized crossover, receiving 5mg intravenously and a 10mg oral tablet. Linagliptin concentrations in plasma and urine, as well as plasma DPP-4 activity, were determined by validated assays. Noncompart-mental analysis and population pharmacokinetic modelling were performed.ResultsLinagliptin showed nonlinear pharmacokinetics after intravenous infusion of 0.5–10 mg, with a less than dose-proportional increase in exposure. Noncompartmental parameters were calculated on the basis of total (i.e. bound and unbound) plasma concentrations. The total clearance value was low and increased with dose from 2.51 to 14.3 L/h. The apparent steady-state volume of distribution (Vss) increased with dose from 380 to 1540L. Renal excretion of the unchanged parent compound increased with increasing plasma concentrations from 2.72% in the 0.5 mg dose group to 23.0% in the 10 mg dose group. The terminal elimination half-life was comparable across dose groups (126–139 hours). Because of the nonlinear pharmacokinetics, the standard approach of comparing the area under the plasma concentration-time curve (AUC) after oral administration with the AUC after intravenous administration led to dose-dependent estimates of the absolute bioavailability. Therefore, a population pharmacokinetic model was developed, accounting for the concentration-dependent protein binding of linagliptin to its target enzyme, DPP-4. The model-derived estimates of the Vss and clearance of linagliptin not bound to DPP-4 were 402.2 L and 26.9 L/h, respectively. The absolute bioavailability was estimated to be about 30% for the linagliptin 10mg tablet.ConclusionThe nonlinear pharmacokinetic characteristics and the pharmacokinetic/pharmacodynamic relationship of linagliptin were independent of the mode of administration (intravenous or oral). Because of the nonlinear pharmacokinetics, the standard approach of comparing the AUC after oral administration with the AUC after intravenous administration was inappropriate to determine the absolute bioavailability of linagliptin. By a modelling approach, the absolute bioavailability of the 10mg linagliptin tablet was estimated to be about 30%.

A quantitative enterohepatic circulation model: development and evaluation with tesofensine and meloxicam.

Background and ObjectiveDrugs undergoing enterohepatic circulation (EHC) are associated with typical pharmacokinetic characteristics such as multiple-peak phenomenon in the plasma concentration-time profile and prolongation of the apparent elimination half-life (t1/2). Currently, versatile pharmacokinetic models are lacking that could test the hypothesis of an EHC for observed multiple-peak phenomenon in pharmacokinetic profiles and its quantitative contribution. The aim of this analysis was to accomplish a model that is able to describe typical plasma concentration-time profiles of compounds undergoing EHC using data from intravenous studies of tesofensine and meloxicam. In addition, the developed model should be able to quantify the contribution of an EHC to the pharmacokinetics by determining the influence of interrupting the EHC of tesofensine and meloxicam to various extents.MethodsTwo studies were investigated retrospectively for model development and model evaluation. Twentyone healthy subjects received a single 6-hour infusion of tesofensine (0.3, 0.6, 0.9, 1.2 mg) in a double-blind, randomized, placebo-controlled, single rising-dose study. Twelve healthy subjects were treated in a randomized, crossover study with meloxicam 30 mg as a single dose given intravenously (bolus) either alone or concomitantly with cholestyramine. The EHC model was developed based on data from the tesofensine study, where EHC is suspected. Model evaluation was performed with data from the meloxicam trial. Modelling and simulation analyses were performed using the software programs NONMEM, SAS and Berkeley Madonna.ResultsPlasma concentration-time profiles of tesofensine were best described by a three-compartment model (absorption, central and gallbladder) with first-order elimination. The release of the bile compartment was controlled by a sine function model, switching the bile compartment periodically on and off using the actual clock time as the control element. A four-compartment model (absorption, central, peripheral and gallbladder) with first-order elimination and the sine function for gallbladder control described the meloxicam data best. Coadministration of cholestyramine resulted in a predicted 56% withdrawal of meloxicam from the EHC process causing a reduction in the t1/2 from ∼19 hours to ∼12 hours.ConclusionA quantitative EHC model was successfully developed that was capable of describing the multiple peaks in plasma concentration-time profiles of tesofensine and meloxicam very well. Additionally, the model successfully quantified the observed results for an interruption of the meloxicam EHC. The model offers an in silico method to support an EHC hypothesis using standard pharmacokinetic data and might help to guide dosing recommendations of compounds undergoing EHC.

A novel model-based meta-analysis to indirectly estimate the comparative efficacy of two medications: an example using DPP-4 inhibitors, sitagliptin and linagliptin, in treatment of type 2 diabetes mellitus

Objectives To develop a longitudinal statistical model to indirectly estimate the comparative efficacies of two drugs, using model-based meta-analysis (MBMA). Comparison of two oral dipeptidyl peptidase (DPP)-4 inhibitors, sitagliptin and linagliptin, for type 2 diabetes mellitus (T2DM) treatment was used as an example. Design Systematic review with MBMA. Data sources MEDLINE, EMBASE, http://www.ClinicalTrials.gov, Cochrane review of DPP-4 inhibitors for T2DM, sitagliptin trials on Food and Drug Administration website to December 2011 and linagliptin data from the manufacturer. Eligibility criteria for selecting studies Double-blind, randomised controlled clinical trials, ≥12 weeks’ duration, that analysed sitagliptin or linagliptin efficacies as changes in glycated haemoglobin (HbA1c) levels, in adults with T2DM and HbA1c >7%, irrespective of background medication. Model development and application A Bayesian model was fitted (Markov Chain Monte Carlo method). The final model described HbA1c levels as function of time, dose, baseline HbA1c, washout status/duration and ethnicity. Other covariates showed no major impact on model parameters and were not included. For the indirect comparison, a population of 1000 patients was simulated from the model with a racial composition reflecting the average racial distribution of the linagliptin trials, and baseline HbA1c of 8%. Results The model was developed using longitudinal data from 11 234 patients (10 linagliptin, 15 sitagliptin trials), and assessed by internal evaluation techniques, demonstrating that the model adequately described the observations. Simulations showed both linagliptin 5 mg and sitagliptin 100 mg reduced HbA1c by 0.81% (placebo-adjusted) at week 24. Credible intervals for participants without washout were −0.88 to −0.75 (linagliptin) and −0.89 to −0.73 (sitagliptin), and for those with washout, −0.91 to −0.76 (linagliptin) and −0.91 to −0.75 (sitagliptin). Conclusions This study demonstrates the use of longitudinal MBMA in the field of diabetes treatment. Based on an example evaluating HbA1c reduction with linagliptin versus sitagliptin, the model used seems a valid approach for indirect drug comparisons.

Contribution of the active metabolite M1 to the pharmacological activity of tesofensine in vivo: a pharmacokinetic‐pharmacodynamic modelling approach

Tesofensine is a centrally acting drug under clinical development for Alzheimers disease, Parkinsons disease and obesity. In vitro, the major metabolite of tesofensine (M1) displayed a slightly higher activity, which however has not been determined in vivo. The aims of this investigation were (i) to simultaneously accomplish a thorough characterization of the pharmacokinetic (PK) properties of tesofensine and M1 in mice and (ii) to evaluate the potency (pharmacodynamics, PD) and concentration‐time course of the active metabolite M1 relative to tesofensine and their impact in vivo using the PK/PD modelling approach.

Integration of absorption, distribution, metabolism, and elimination genotyping data into a population pharmacokinetic analysis of nevirapine.

Objectives The aim of this analysis was to show the applicability of a newly developed algorithm to assess the influence of genetic variants and other covariates on nevirapine’s drug disposition. The algorithm combines high-throughput genotyping data and nonlinear mixed effects modeling methods. Methods Patients, who participated in the 2NN pharmacokinetic sub study, were reconsented and reenrolled into a clinical trial for genotyping analysis. Overall, 198 single nucleotide polymorphisms located in 45 absorption, distribution, metabolism, and elimination related genes were genotyped using the Illumina BeadArray technology. Data analysis was performed using NONMEM VI and SAS 9.1.3. Results Overall, 1260 nevirapine plasma concentrations were obtained from 271 genotyped patients. Plasma concentration−time profiles of nevirapine were best described by a one-compartment model with auto-induced first-order elimination process. Nevirapine clearance was 19.4% reduced in Asian/Black patients, compared with Caucasian/Hispanic patients. For single nucleotide polymorphism rs3745274 (CYP2B6 516G>T) heterozygous patients (GT) showed a 15.3% reduced clearance; patients with homozygous CYP2B6 516TT alleles showed a 30.6% reduced clearance compared to patients with homozygous 516GG alleles. Patients carrying the homozygote genotype of rs12768009 (CYP2C19 8403AA), highly linked to rs4244285 (CYP2C19*2), showed a 26.8% reduced clearance compared with patients with CYP2C19 8403 AG and GG alleles. Conclusion By integration of high-throughput genotyping data into a pharmacometric analysis of nevirapine, the impact of the CYP2B6 516G>T polymorphism on nevirapine’s exposure was confirmed and quantified. In addition, a new hypothesis with regard to CYP2C19 involvement in nevirapine metabolism has been generated. The analysis presented might help to optimize and individualize the therapy for patients treated with nevirapine to add to their therapeutic benefit.