An alternative method for population pharmacokinetic data analysis under noncompliance

Abstract

Noncompliance presents a persistent problem while analyzing PK data from outpatient clinical studies. Ignoring dose omission or making uninformed assumptions about patient drug intake history can prove detrimental to the objectives of the analysis (e.g. determining the PK model parameters or identifying covariates) and ultimately compromise the interpretation of the data. In order to overcome this problem, an alternative method of handling noncompliant data is evaluated in this report. The proposed approach is based on the principle of superposition and works by separating the estimation of the elimination rate from the model based steady-state PK concentration. Simulations implementing this method under different scenarios of noncompliance demonstrate that it performs better than the conventional method of analyzing population PK data when compared on the basis of bias and imprecision in parameter estimation and power (and type I error) for covariate detection. Overall, the new method exhibits great potential to address the issue of uncertain/unreliable dosing histories frequently encountered in clinical trials.

Model-Based Approach for Optimization of Atazanavir Dose Recommendations for HIV-Infected Pediatric Patients

“…Postprocessing of NONMEM output was performed using S-Plus (version 7.0 for Linux; Tibco Software Inc., Palo Alto, CA). (10). The ATV structural model was parameterized in terms of first-order absorption rate (k a ), elimination rate (k e ), apparent volume of distribution (V/F), and relative bioavailability (F rel ).…”

“…The ATV structural model was parameterized in terms of first-order absorption rate (k a ), elimination rate (k e ), apparent volume of distribution (V/F), and relative bioavailability (F rel ). A parameter to describe the predose concentration (C 0 ) was estimated as a separate term in the model to guard against introducing bias in the compartmental PK parameters due to nonadherence (10). The ATV plasma concentration (C) at time t following an ATV dose (D) was described using the following expression:…”

“…This modeling approach has been shown to perform better than the conventional method (which assumes 100% treatment adherence), based on the bias and imprecision of parameter estimates and the power and type I error in the likelihood ratio test for covariate effects (10,18). RTV coadministration was included as a fixed effect in C 0 in view of the higher ATV trough concentration with an ATV/ RTV regimen than with ATV alone.…”

A pharmacometric case study regarding the sensitivity of structural model parameter estimation to error in patient reported dosing times

“…This metric is analogous to those used previously for simulation studies dealing with noncompliance [4,10] and may be expressed as a percent difference by multiplying the fraction by 100. Overlaid plots of PAR diff versus the magnitude of reporting error in the data set, for each set of data-with and without reporting error-provide insight into the effects of the presence of reporting error on parameter estimation in the data (Figs.…”

“…In the complex case, prior dosing history may be critical. The current methodological body of literature around improving parameter estimation in the presence of dosing inaccuracies has mainly focused on the moderate case [3,4,10,11]; however, over the last 2 years, around 45 % of currently approved oral therapies (geometric mean across 2013 and 2014), intended for once daily dosing, fall into the complex scenario of having a terminal half-life-of the active moiety-of [24 h [12] (see supplementary material worksheet for specific examples). To our knowledge, among the approaches to mitigate the effects of dosing inaccuracies on parameter estimation, only one investigation has considered the complex scenario [1]; however, no attempt was made to specifically characterize the effects stemming from error in reported dosing times, or to include subjects with different dosing times relative to sampling times.…”