Population Pharmacokinetic-Pharmacodynamic Modeling of Inotersen, an Antisense Oligonucleotide for Treatment of Patients with Hereditary Transthyretin Amyloidosis.

A population pharmacokinetic (PK) and pharmacodynamic (PD) model was developed for inotersen to evaluate exposure-response relationships and to optimize therapeutic dosing regimen in patients with hereditary transthyretin (TTR) amyloidosis polyneuropathy (hATTR-PN). Inotersen PK and TTR level (PD) data were composed of one Phase 1 study in healthy subjects, one Phase 2/3 study in hATTR patients, and its one open-label extension study. Effects of intrinsic and extrinsic factors (covariates) on PK and PK/PD of inotersen were evaluated using a full model approach. Inotersen PK was characterized by a two-compartment model with elimination from the central compartment. The population PK analysis identified disease status and lean body mass (LBM) as significant covariates for inotersen PK. Nonetheless, the contribution of disease status and LBM on PK was small, as the difference in clearance (CL/F) was 11.1% between healthy subjects and patients with hATTR-PN and 38% between the lowest and highest LBM quartiles of the patient population. Age, race, sex, baseline renal function estimated glomerular filtration rate, and hepatic function markers (baseline albumin, bilirubin, and alanine aminotransferase values) were not statistically significant covariates affecting inotersen PK. An inhibitory effect indirect-response model (inhibition of TTR production) was used to describe the drug effect on TTR-time profiles, with baseline TTR included as a covariate. The overall population Imax and IC50, together with 95% confidence interval, was estimated to be 0.913 (0.899-0.925) and 9.07 (8.08-10.1) ng/mL, respectively. V30M mutation showed no effect on the estimated IC50 value for hATTR patients. The final population PK and PK/PD model was used to simulate four different treatment regimens. The population PK/PD model developed well described the PK and PD of inotersen in patients with hATTR-PN and has been used for label recommendation and trial simulations.

Comparison of tenofovir plasma and tissue exposure using a population pharmacokinetic model and bootstrap: a simulation study from observed data.

Sparse tissue sampling with intensive plasma sampling creates a unique data analysis problem in determining drug exposure in clinically relevant tissues. Tissue exposure may govern drug efficacy, as many drugs exert their actions in tissues. We compared tissue area-under-the-curve (AUC) generated from bootstrapped noncompartmental analysis (NCA) methods and compartmental nonlinear mixed effect (NLME) modeling. A model of observed data after single-dose tenofovir disoproxil fumarate was used to simulate plasma and tissue concentrations for two destructive tissue sampling schemes. Two groups of 100 data sets with densely-sampled plasma and one tissue sample per individual were created. The bootstrapped NCA (SAS 9.3) used a trapezoidal method to calculate geometric mean tissue AUC per dataset. For NLME, individual post hoc estimates of tissue AUC were determined, and the geometric mean from each dataset calculated. Median normalized prediction error (NPE) and absolute normalized prediction error (ANPE) were calculated for each method from the true values of the modeled concentrations. Both methods produced similar tissue AUC estimates close to true values. Although the NLME-generated AUC estimates had larger NPEs, it had smaller ANPEs. Overall, NLME NPEs showed AUC under-prediction but improved precision and fewer outliers. The bootstrapped NCA method produced more accurate estimates but with some NPEs > 100%. In general, NLME is preferred, as it accommodates less intensive tissue sampling with reasonable results, and provides simulation capabilities for optimizing tissue distribution. However, if the main goal is an accurate AUC for the studied scenario, and relatively intense tissue sampling is feasible, the NCA bootstrap method is a reasonable, and potentially less time-intensive solution.