Modeling Exposure-Driven Adverse Event Time Courses in Oncology Exemplified by Afatinib.

Models were developed to characterize the relationship between afatinib exposure and diarrhea and rash/acne adverse event (AE) trajectories, and their predictive ability was assessed. Based on pooled data from seven phase II/III clinical studies including 998 patients, mixed-effects models for ordered categorical data were applied to describe daily AE severity. Clinical trial simulation aided by trial execution models was used for internal and external model evaluation. The final exposure-safety model consisted of longitudinal logistic regression models with first-order Markov elements for both AEs. Drug exposure was included as daily area under the concentration-time curve (AUC), and drug effects on the AEs were correlated. Clinical trial simulation allowed adequate prediction of maximum AE grades and AE severity time courses but overestimated the proportion of AE-dependent dose reductions and discontinuations. Both diarrhea and rash/acne were correlated with afatinib exposure. The developed modeling framework allows a prospective comparison of dosing strategies and study designs with respect to safety.

Model-Informed Drug Discovery and Development: Current Industry Good Practice and Regulatory Expectations and Future Perspectives.

Good practices around model-informed drug discovery and development (MID3) aim to improve the implementation, standardization, and acceptance of these approaches within drug development and regulatory review. A survey targeted to clinical pharmacology and pharmacometric colleagues across industry, the US Food and Drug Administration (FDA), and the European Medicines Agency (EMA) was conducted to understand current and future roles of MID3. The documented standards were generally affirmed as a "good match" to current industry practice and regulatory expectations, with some identified gaps that are discussed. All have seen at least a "modest" step forward in MID3 implementation associated with greater organizational awareness and share the expectation for a future wider use and impact. The priority within organizations was identified as a limitation with respect to the future of MID3. Finally, potential solutions, including a global overarching MID3 regulatory guideline, to facilitate greater acceptance by industry and regulatory decision makers are discussed.

Model-based evaluation of pulmonary pharmacokinetics in asthmatic and COPD patients after oral olodaterol inhalation.

AIMS: Olodaterol is an orally inhaled ß2 -agonist for treatment of chronic obstructive pulmonary disease (COPD). The aims of this population pharmacokinetic (PK) analysis were: (1) to investigate systemic PK and thereby make inferences about pulmonary PK in asthmatic patients, COPD patients and healthy volunteers, and (2) to assess whether differences in pulmonary efficacy might be expected based on pulmonary PK characteristics. METHODS: Plasma and urine data after olodaterol inhalation were available from six clinical trials comprising 710 patients and healthy volunteers (single and multiple dosing). To investigate the relevance of covariates, full fixed-effect modelling was applied based on a previously developed healthy volunteer systemic disposition model. RESULTS: A pulmonary model with three parallel absorption processes best described PK after inhalation in patients. The pulmonary bioavailable fraction (PBIO) was 48.7% (46.1-51.3%, 95% confidence interval) in asthma, and 53.6% (51.1-56.2%) in COPD. In asthma 87.2% (85.4-88.8%) of PBIO was slowly absorbed with an absorption half-life of 18.5 h (16.3-21.4 h), whereas in COPD 80.1% (78.0-82.2%) was absorbed with a half-life of 37.8 h (31.1-47.8 h). In healthy volunteers absorption was faster, with a half-life of 18.5 h (16.3-21.4 h) of the slowest absorbed process, which characterized 74.6% (69.1-80.2%) of PBIO. CONCLUSIONS: The modelling approach successfully described data after olodaterol inhalation in patients and healthy volunteers. Slow pulmonary absorption was demonstrated both in asthma and COPD. Absorption characteristics after olodaterol inhalation indicated even more beneficial lung targeting in patients compared to healthy volunteers.

Investigating pulmonary and systemic pharmacokinetics of inhaled olodaterol in healthy volunteers using a population pharmacokinetic approach.

AIMS: Olodaterol, a novel ß2-adrenergic receptor agonist, is a long-acting, once-daily inhaled bronchodilator approved for the treatment of chronic obstructive pulmonary disease. The aim of the present study was to describe the plasma and urine pharmacokinetics of olodaterol after intravenous administration and oral inhalation in healthy volunteers by population pharmacokinetic modelling and thereby to infer its pulmonary fate. METHODS: Plasma and urine data after intravenous administration (0.5-25 µg) and oral inhalation (2.5-70 µg via the Respimat® inhaler) were available from a total of 148 healthy volunteers (single and multiple dosing). A stepwise model building approach was applied, using population pharmacokinetic modelling. Systemic disposition parameters were fixed to estimates obtained from intravenous data when modelling data after inhalation. RESULTS: A pharmacokinetic model, including three depot compartments with associated parallel first-order absorption processes (pulmonary model) on top of a four-compartment body model (systemic disposition model), was found to describe the data the best. The dose reaching the lung (pulmonary bioavailable fraction) was estimated to be 49.4% [95% confidence interval (CI) 46.1, 52.7%] of the dose released from the device. A large proportion of the pulmonary bioavailable fraction [70.1% (95% CI 66.8, 73.3%)] was absorbed with a half-life of 21.8 h (95% CI 19.7, 24.4 h). CONCLUSIONS: The plasma and urine pharmacokinetics of olodaterol after intravenous administration and oral inhalation in healthy volunteers were adequately described. The key finding was that a high proportion of the pulmonary bioavailable fraction had an extended pulmonary residence time. This finding was not expected based on the physicochemical properties of olodaterol.

Pharmacometric Models for Characterizing the Pharmacokinetics of Orally Inhaled Drugs.

During the last decades, the importance of modeling and simulation in clinical drug development, with the goal to qualitatively and quantitatively assess and understand mechanisms of pharmacokinetic processes, has strongly increased. However, this increase could not equally be observed for orally inhaled drugs. The objectives of this review are to understand the reasons for this gap and to demonstrate the opportunities that mathematical modeling of pharmacokinetics of orally inhaled drugs offers. To achieve these objectives, this review (i) discusses pulmonary physiological processes and their impact on the pharmacokinetics after drug inhalation, (ii) provides a comprehensive overview of published pharmacokinetic models, (iii) categorizes these models into physiologically based pharmacokinetic (PBPK) and (clinical data-derived) empirical models, (iv) explores both their (mechanistic) plausibility, and (v) addresses critical aspects of different pharmacometric approaches pertinent for drug inhalation. In summary, pulmonary deposition, dissolution, and absorption are highly complex processes and may represent the major challenge for modeling and simulation of PK after oral drug inhalation. Challenges in relating systemic pharmacokinetics with pulmonary efficacy may be another factor contributing to the limited number of existing pharmacokinetic models for orally inhaled drugs. Investigations comprising in vitro experiments, clinical studies, and more sophisticated mathematical approaches are considered to be necessary for elucidating these highly complex pulmonary processes. With this additional knowledge, the PBPK approach might gain additional attractiveness. Currently, (semi-)mechanistic modeling offers an alternative to generate and investigate hypotheses and to more mechanistically understand the pulmonary and systemic pharmacokinetics after oral drug inhalation including the impact of pulmonary diseases.

Population Pharmacokinetics and Pharmacodynamics of Linagliptin in Patients with Type 2 Diabetes Mellitus.

BACKGROUND AND OBJECTIVES: Linagliptin is a dipeptidyl peptidase (DPP)-4 inhibitor, used to treat type 2 diabetes mellitus (T2DM). Population pharmacokinetic and pharmacodynamic analyses were performed to characterize the impact of clinically relevant intrinsic/extrinsic factors (covariates) on linagliptin exposure and DPP-4 inhibition in patients with T2DM. METHODS: Linagliptin plasma concentrations and DPP-4 activities were obtained from four studies (two phase 1, two phase 2b). Non-linear mixed-effects modelling techniques were implemented using NONMEM software. The covariates that were studied comprised demographic information and laboratory values, including liver enzyme levels and creatinine clearance, as well as study-related factors such as metformin co-treatment. Covariate effects on parameters describing the pharmacokinetics and pharmacokinetic/pharmacodynamic relationship were investigated using stepwise forward inclusion/backward elimination. RESULTS: The pharmacokinetic analysis included 6,907 measurements of plasma linagliptin concentrations from 462 patients; the pharmacokinetic/pharmacodynamic analysis included 9,674 measurements of plasma DPP-4 activity and linagliptin plasma concentrations from 607 patients. The non-linear pharmacokinetics were described by a target-mediated drug disposition model accounting for the concentration-dependent binding of linagliptin to its target, DPP-4. The difference in exposure between the 5th and 95th percentiles of the covariate distributions and median was <20 % for each single covariate. Likewise, the impact of the covariates on both the half-maximum effect (EC50) and the concentration leading to 80 % DPP-4 inhibition was <20 %. CONCLUSION: These analyses show that the investigated factors do not alter the pharmacokinetics and DPP-4 inhibitory activity of linagliptin to a clinically relevant extent and that dose adjustment is not necessary on the basis of factors including age, sex and weight.

Exposure-response modelling for empagliflozin, a sodium glucose cotransporter 2 (SGLT2) inhibitor, in patients with type 2 diabetes.

AIMS: To provide model-based clinical development decision support including dose selection guidance for empagliflozin, an orally administered sodium glucose cotransporter 2 inhibitor, through developed exposure-response (E-R) models for efficacy and tolerability in patients with type 2 diabetes mellitus (T2DM). METHODS: Five randomized, placebo-controlled, multiple oral dose studies of empagliflozin in patients with T2DM (n = 974; 1-100 mg once daily, duration ≤12 weeks) were used to develop E-R models for efficacy (glycosylated haemoglobin [HbA1c ], fasting plasma glucose [FPG] and urinary glucose excretion). Two studies (n = 748, 12 weeks) were used to evaluate tolerability E-R. RESULTS: The efficacy model predicted maximal decreases in FPG and HbA1c of 16% and 0.6%, respectively, assuming a baseline FPG concentration of 8 mm (144 mg dl(-1) ) and 10-25 mg every day empagliflozin targeted 80-90% of these maximums. Increases in exposure had no effect on incidence rates of hypoglycaemia (n = 4), urinary tract infection (n = 17) or genital/vulvovaginal-related (n = 16) events, although low prevalence rates may have precluded more accurate evaluation. CONCLUSIONS: E-R analyses indicated that 10 and 25 mg once daily empagliflozin doses achieved near maximal glucose lowering efficacy.

Population pharmacokinetics of afatinib, an irreversible ErbB family blocker, in patients with various solid tumors.

PURPOSE: This population pharmacokinetic model was developed to characterize the pharmacokinetics of the oral irreversible ErbB family blocker afatinib in patients with solid tumors and to investigate the impact of selected intrinsic and extrinsic factors. METHODS: Data from 927 patients (4,460 plasma concentrations) with advanced solid tumors in 7 Phase II or III studies were analyzed. Afatinib was administered orally in continuous 3 or 4 week cycles (starting dose 20, 40 or 50 mg once-daily). Plasma concentration-time data for up to 7 months dosing were analyzed using nonlinear mixed-effects modeling. RESULTS: The pharmacokinetic profile of afatinib was best described by a two-compartment disposition model with first-order absorption and linear elimination. There was a slightly more than proportional increase in exposure with increasing dose, accounted for by a dose-dependent relative bioavailability. For the therapeutic dose of 40 mg, the estimated apparent total clearance and distribution volume at steady state were 734 mL/min and 2,370 L, respectively. While food intake, body weight, gender, Eastern Cooperative Oncology Group performance score, renal function, and the level of alkaline phosphatase, lactate dehydrogenase or total protein were statistically significant covariates influencing afatinib exposure, none resulted in a proportional change in exposure of more than 27.8 % in a typical patient at model extremes (2.5th and 97.5th percentiles of baseline values for continuous covariates). In simulations of the individual covariate effects, none caused a change in the typical profile exceeding the observed variability range (90 % prediction interval) of afatinib. CONCLUSION: This population pharmacokinetic model adequately described the pharmacokinetics of afatinib in different cancer patient populations and therefore can be used for simulations exploring covariate effects and possible dose adaptations. The effect size for each of the individual covariates is not considered clinically relevant.

Population pharmacokinetics of empagliflozin, a sodium glucose cotransporter 2 inhibitor, in patients with type 2 diabetes.

Data from five randomized, placebo-controlled, multiple oral dose studies of empagliflozin in patients with type 2 diabetes mellitus (T2DM; N = 974; 1-100 mg q.d.; ≤12 weeks) were used to develop a population pharmacokinetic (PK) model for empagliflozin. The model consisted of two-compartmental disposition, lagged first-order absorption and first-order elimination, and incorporated appropriate covariates. Population estimates (interindividual variance, CV%) of oral apparent clearance, central and peripheral volumes of distribution, and inter-compartmental clearance were 9.87 L/h (26.9%), 3.02 L, 60.4 L (30.8%), and 5.16 L/h, respectively. An imposed allometric weight effect was the most influential PK covariate effect, with a maximum effect on exposure of ±30%, using 2.5th and 97.5th percentiles of observed weights, relative to the median observed weight. Sex and race did not lend additional description to PK variability beyond allometric weight effects, other than ∼25% greater oral absorption rate constant for Asian patients. Age, total protein, and smoking/alcohol history did not affect PK parameters. Predictive check plots were consistent with observed data, implying an adequate description of empagliflozin PKs following multiple dosing in patients with T2DM. The lack of marked covariate effects, including weight, suggests that no exposure-based dose adjustments were required within the study population and dose range.

A semi-physiological model of amyloid-ß biosynthesis and clearance in human cerebrospinal fluid: a tool for alzheimer's disease research and drug development.

Stable isotope labeling kinetics (SILK) was successfully applied to quantify endogenous amyloid-ß (Aß) metabolism in human cerebrospinal fluid (CSF). A semi-physiological model describing Aß biosynthesis and degradation in human CSF and the impact of the γ-secretase inhibitor semagacestat should be developed and validated based on digitized data from three published SILK studies. Aß biosynthesis was adequately characterized by six transit compartments. At each transition step, a first-order degradation process was implemented. A two-compartment model best described semagacestat CSF concentration-time profiles. Semagacestat concentrations were linked to the Aß production by an inhibitory Emax model. For model validation, three individual Aß profiles from literature were successfully predicted. Model application demonstrated a 35% decreased Aß elimination rate constant in Alzheimer's disease (AD) patients. Study design optimization revealed that SILK studies could be conducted with significant less sampling points compared to the standard protocol without losing information about the Aß metabolism, if analyzed by the presented model. In conclusion, the analysis outlined the advantages and opportunities of integrating all available data and knowledge into a semi-physiological model. The model can serve as valuable tool for researchers and clinicians interested in the pathology of AD as well as in the development of new therapeutics for AD.

Pharmacometric characterization of dabigatran hemodialysis.

BACKGROUND: Hemodialysis has been shown to be a useful method of decreasing dabigatran plasma levels in situations that require rapid elimination of this thrombin inhibitor. However, there is currently no clinical recommendation for the accelerated/optimized elimination of dabigatran via hemodialysis (e.g., flow rates, filter type, duration of dialysis). OBJECTIVES: The primary objective of the present work was to characterize, via pharmacometric methods, the effects of different blood flow rates in hemodialysis on the pharmacokinetics of dabigatran, using data from a dedicated phase I dialysis study of end-stage renal disease (ESRD) patients. In addition, the effects of various clinically relevant hemodialysis settings were evaluated by simulation to assess their potential use in non-ESRD situations. METHODS: Seven patients with ESRD were investigated in an open-label, fixed-sequence, two-period comparison trial. A population pharmacokinetic model was developed to fit the data and then used for various simulations. Data analyses were performed using NONMEM(®), Berkeley Madonna, or SAS. RESULTS: The pharmacokinetics of dabigatran were best described by a two-compartment model with first-order absorption and a lag time. In addition to total body clearance in ESRD subjects, a first-order dialysis clearance was implemented which was greater than zero during hemodialysis and zero during the interdialytic periods. The relationship between the dialysis clearance and the blood flow rate was best described by the Michaels function. Simulations showed that varying clinically relevant dialysis settings such as filter properties or flow rates had only minor effects. Dialysis duration had the strongest impact on dabigatran plasma concentration. The observed geometric mean redistribution effect after hemodialysis was low (<16 %). The final model was successfully evaluated through the prediction of plasma concentrations from a case report undergoing dialysis. CONCLUSIONS: This analysis allowed the influences of various hemodialysis parameters on the dabigatran plasma concentration to be predicted in detail for the first time. Dialysis duration was identified as having the strongest impact on the reduction in dabigatran plasma concentration. The model developed here can potentially serve as a tool to provide guidance when considering the use of hemodialysis in patients who have received dabigatran.

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.

Population pharmacokinetic/pharmacodynamic analysis of the DPP-4 inhibitor linagliptin in Japanese patients with type 2 diabetes mellitus.

OBJECTIVES: Linagliptin is a novel, highly selective and long acting DPP-4 inhibitor for the treatment of type 2 diabetes mellitus (T2DM). Linagliptin exhibits non-linear pharmacokinetics (PK) due to saturable binding to plasma and tissue DPP-4. The aim of this study was to characterize the PK and PK/DPP-4 inhibition relationship of linagliptin in Japanese patients with T2DM using a population PK/DPP-4 model and to support the rationale for the therapeutic dose in Japanese patients by simulation. METHODS: Linagliptin plasma concentration and DPP-4 inhibition measurements from a placebo-controlled, parallel group multiple (28 days) dose trial that included 36 T2DM patients (18 patients each in 2.5 mg and 10 mg dose group) were used for analysis. Modeling was performed using FOCE INTERACTION estimation method implemented in NONMEM V. The linagliptin plasma concentration- and DPP-4 inhibition- time profiles were simulated for Japanese patients receiving 5 mg linagliptin once daily by the model established. RESULTS: Nonlinear PK of linagliptin in T2DM patients were well described by a 2-compartment model assuming concentration-dependent binding to DPP-4 in the central and peripheral compartment. Plasma DPP-4 inhibition was integrated in the model by relating the model-predicted DPP-4 occupancy with linagliptin linearly to DPP-4 inhibition. The simulation predicted that for the 5 mg dose group the trough DPP-4 inhibition at steady-state was 84.2%, which is higher than the target inhibition (≥80%) for an effective dose of DPP-4 inhibitor. In 2.5 mg dose group, steady-state DPP-4 inhibition of >80% was not maintained over 24 hours (observed and simulated). CONCLUSIONS: The nonlinear PK of linagliptin and its plasma DPP-4 inhibition in patients were well characterized by a target-mediated drug disposition model relating DPP-4 occupancy with linagliptin to DPP-4 inhibition. Simulations of plasma DPP-4 inhibition suggest that 5 mg linagliptin once daily is an appropriate therapeutic dose for Japanese patients with T2DM.

Joint population pharmacokinetic/pharmacodynamic model for the heart rate effects at rest and at the end of exercise for cilobradine.

PURPOSE: To develop a semi-mechanistic population pharmacokinetic/pharmacodynamic (PKPD) model for the selective bradycardic agent cilobradine describing simultaneously the heart rate (HR) measured at rest and just after the end of exercise sharing the same set of PKPD parameters. METHODS: Healthy subjects received cilobradine orally once daily over 2 weeks at 0.25-5 mg doses or placebo. Plasma drug concentrations and HR were measured at rest and following 3 min of exercise over the entire study period. PK and HR data were analyzed using the population approach with NONMEM VII. RESULTS: Plasma disposition of cilobradine was described with a three compartment model. Cilobradine showed dose proportional and time independent pharmacokinetics. HR response was drug concentration dependent and appeared with a significant delay with respect to PK profiles, a phenomenon modeled using two transit compartments. Perturbation in HR at rest as a consequence of exercise was described assuming that physiological processes controlling cardiac frequency were constantly increased over the period of exercise only. CONCLUSIONS: The selected model provides a useful modeling tool for cases where the PD response measured is the result of a temporal experimental induced perturbation.

Population pharmacokinetic/pharmacodynamic model for the sedative effects of flibanserin in healthy volunteers.

PURPOSE: Flibanserin is being developed for treating hypoactive sexual desire disorder in women; the main side effect is sedation. The analysis objective was to relate flibanserin plasma concentrations with acute sedative effects using a population pharmacokinetic/pharmacodynamic (PK/PD) model. METHODS: The population model was developed with NONMEM based on data from 24 healthy volunteers. "Drowsiness" was serially assessed by a Visual Analogue Scale (VAS) on a baseline day and after morning oral administration of 100 mg flibanserin together with PK sampling. RESULTS: PK was best described by a three-compartment disposition model and transit compartments accounting for the lag time in absorption. VAS "drowsiness" baseline profiles were modeled using linear splines with three breakpoints located at clock times at first and last observation, and at the median of the observation time across subjects. The drug effect followed a sigmoidal E(MAX) model using predicted effect site concentrations (C(e)). The VAS vs. C(e) relationship was very steep and effect site and plasma concentration-time profiles were very similar thus suggesting little delay between the occurrence of maximum flibanserin plasma concentrations and drowsiness. CONCLUSIONS: At effect site concentrations lower than ≈ 200 ng/mL that are reached approximately 4 h after administration, flibanserin shows hardly any effect on the VAS "drowsiness" scale.

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.

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.

Comparison of different semi-mechanistic models for chemotherapy-related neutropenia: application to BI 2536 a Plk-1 inhibitor.

PURPOSE: The aim of this investigation was to compare the performance of a commonly used semi-mechanistic model for drug-related neutropenia with other semi-mechanistic models published in the literature. METHODS: After their implementation in NONMEM VI, five semi-mechanistic models were assessed using the pharmacokinetic and absolute neutrophil count data obtained from 95 patients with non-small cell lung cancer receiving either 200 mg on day 1 or 50 or 60 mg on days 1, 2 and 3 of a 21-day treatment course with the new Plk-1 inhibitor BI 2536. The model performance was compared by means of predictive (visual and numerical) checks, precision in the parameter estimates and objective function-based measures. Details of model parameterization, model stability and run times are also provided. RESULTS: The time course of the drug plasma concentrations was described by a three compartment model with a first-order elimination rate. With respect to neutropenia, all models were successfully implemented in NONMEM and provided reasonable fits for the median (although not all models described all percentiles of the data well), and in general precise parameter estimates. CONCLUSION: In the current evaluation performed in a single drug, none of the models showed superior performance compared to the most commonly used model first described by Friberg et al. (J Clin Oncol 20:4713-4721, 2002).

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 OBJECTIVES: Linagliptin (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 METHODS: This was a single rising-dose, randomized, four-group, placebo-controlled, single-blind (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 0.5 mg intravenously, group 2 received 2.5 mg intravenously and group 4 received 10 mg intravenously. In group 3, subjects underwent a two-way randomized crossover, receiving 5 mg intravenously and a 10 mg oral tablet. Linagliptin concentrations in plasma and urine, as well as plasma DPP-4 activity, were determined by validated assays. Noncompartmental analysis and population pharmacokinetic modelling were performed. RESULTS: Linagliptin 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 (V(ss)) increased with dose from 380 to 1540 L. 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 V(ss) 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 10 mg tablet. CONCLUSION: The 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 10 mg linagliptin tablet was estimated to be about 30%.