Bayesian estimation in NONMEM.

Bayesian estimation is a powerful but underutilized tool for answering drug development questions. In this tutorial, the principles of Bayesian model development, assessment, and prior selection will be outlined. An example pharmacokinetic (PK) model will be used to demonstrate the implementation of Bayesian modeling using the nonlinear mixed-effects modeling software NONMEM.

Population pharmacokinetics and exposure-overall survival analysis of the transforming growth factor-ß inhibitor galunisertib in patients with pancreatic cancer.

PURPOSE: To evaluate the exposure-overall survival (OS) relationship in patients with advanced pancreatic cancer treated with galunisertib plus gemcitabine (GG) or gemcitabine plus placebo (GP). METHODS: Galunisertib 300 mg/day was given orally as intermittent dosing and gemcitabine as per label. Galunisertib exposure metrics for each patient in the GG arm (n = 99) of a phase 2 study of pancreatic cancer were calculated. Parametric survival models were used to identify influential baseline and response covariates on OS. RESULTS: The population pharmacokinetics dataset included data from 297 patients/healthy subjects (age: 22-84 years, weight: 39-126 kg) across multiple studies, including this pancreatic cancer study. Galunisertib was rapidly absorbed with peak concentrations attained within 0.5-2 h and had an elimination half-life of 8 h. Between-subject variance on apparent clearance was estimated to be 47%. Age was the only characteristic to have a statistically significant effect on apparent clearance. A parametric Weibull survival model with treatment effect (dose) estimated a hazard ratio of 0.796, after adjusting for patient baseline factors that were significantly associated with OS. There was also a flat daily exposure-OS relationship within the observed exposure range, once all significant baseline covariates were included. Response covariates, such as reduction in CA19-9, time on treatment, and cumulative exposure over treatment cycles were also identified as significant factors for OS for patients with pancreatic cancer. CONCLUSIONS: This analysis suggests that 300 mg/day galunisertib administered as 150 mg twice daily for 14 days on/14 days off treatment is an appropriate dosing regimen for patients with pancreatic cancer.

Population Pharmacokinetics of LY2623091 in Patients With Hypertension and Chronic Kidney Disease.

LY2623091 is a selective, orally active, nonsteroidal, competitive mineralocorticoid receptor antagonist that blocks the actions of aldosterone and other mineralocorticoid receptor ligands at the receptor level. The aim of this work was to explore and establish a population pharmacokinetic model, quantify the degree of interindividual variability, and identify significant disease-, patient-, and study-specific covariates that alter the disposition of LY2623091. The data included concentrations from 294 healthy subjects and patients with hypertension and/or chronic kidney disease (CKD), sampled in 5 phase 1 and 2 studies. The pharmacokinetics of LY2623091 was well described by a 2-compartment model with first-order absorption and elimination. Formulation (on oral bioavailability) as well as weight and age (both on apparent central volume of distribution) were found to be significant covariates. The relative bioavailability of the capsule formulation was 68.4% compared to that of the solution. Hypertension and CKD status were not significant covariates. The pharmacokinetic model suggests that given the same dose, patients with hypertension and/or CKD would receive a similar exposure compared to subjects without these disease conditions.

A d-optimal designed population pharmacokinetic study of oral itraconazole in adult cystic fibrosis patients.

AIM: The primary objective of the study was to estimate the population pharmacokinetic parameters for itraconazole and hydroxy-itraconazole, in particular, the relative oral bioavailability of the capsule compared with solution in adult cystic fibrosis patients, in order to develop new dosing guidelines. A secondary objective was to evaluate the performance of a population optimal design. METHODS: The blood sampling times for the population study were optimized previously using POPT v.2.0. The design was based on the administration of solution and capsules to 30 patients in a cross-over study. Prior information suggested that itraconazole is generally well described by a two-compartment disposition model with either linear or saturable elimination. The pharmacokinetics of itraconazole and the metabolite were modelled simultaneously using NONMEM. Dosing schedules were simulated to assess their ability to achieve a trough target concentration of 0.5 mg ml(-1). RESULTS: Out of 241 blood samples, 94% were taken within the defined optimal sampling windows. A two-compartment model with first order absorption and elimination best described itraconazole kinetics, with first order metabolism to the hydroxy-metabolite. For itraconazole the absorption rate constants (between-subject variability) for capsule and solution were 0.0315 h(-1) (91.9%) and 0.125 h(-1) (106.3%), respectively, and the relative bioavailability of the capsule was 0.82 (62.3%) (confidence interval 0.36, 1.97), compared with the solution. There was no evidence of nonlinearity. Simulations from the final model showed that a dosing schedule of 500 mg twice daily for both formulations provided the highest chance of target success. CONCLUSION: The optimal design performed well and the pharmacokinetics of itraconazole and hydroxy-itraconazole were described adequately by the model. The relative bioavailability for itraconazole capsules was 82% compared with the solution.

Optimal design for model discrimination and parameter estimation for itraconazole population pharmacokinetics in cystic fibrosis patients.

Optimal sampling times are found for a study in which one of the primary purposes is to develop a model of the pharmacokinetics of itraconazole in patients with cystic fibrosis for both capsule and solution doses. The optimal design is expected to produce reliable estimates of population parameters for two different structural PK models. Data collected at these sampling times are also expected to provide the researchers with sufficient information to reasonably discriminate between the two competing structural models.