Methods to detect non-compliance and reduce its impact on population PK parameter estimates

Abstract

This work proposes and evaluates two methods (CM1 and CM2) for detecting non-compliance using concentration-time data and for obtaining estimates of population pharmacokinetic model parameters in a population with prevalent non-compliance. CM1 estimates individual residual variability (RV) and identifies subjects with higher than average RV as non-compliant. Exclusion of subjects with high RV from the analysis dataset reduces the bias in the estimates of the model parameters. Various methods of identification and exclusion of non-compliant subjects were tested, compared, and shown to reduce or eliminate bias in parameter estimates associated with non-compliance. The tested methods were (i) a pre-defined cutoff value of the random effect on RV, (ii) sequential exclusion of subjects with the highest RV percentiles, and (iii) use of a mixture model for RV. CM2 is applicable for the data with a specific sampling pattern that includes a potentially non-compliant outpatient part with several trough samples followed by a dense profile after the inpatient (compliant) dose. It relies only on the doses known to be administered (e.g., inpatient doses). In this method, all concentration measurements during the outpatient part of the study (except the trough value immediately preceding the inpatient dose) are removed from the dataset and an additional parameter (individual relative bioavailability of the outpatient doses) is introduced. For a number of simulated datasets with various sampling schemes and non-compliance patterns the proposed methods allowed to identify subjects with compliance problems and to reduce or eliminate bias in the estimates of the model parameters.

Population pharmacokinetics of tenofovir and tenofovir‐diphosphate in healthy women

“…Nonadherence in the trial was taken into account by implementing Gibiansky's method in which a bioavailability parameter was applied to the previous day's dose by fixing the bioavailability parameter to 0.5 and its ω distribution to a high value, in our case 10, allowing the model to account for dose omissions or multiple doses. where F1 is an individual's bioavailability on the preclinic dose.…”

“…A method proposed first by Sheiner et al but recently described which uses a bioavailability adjustment to a preclinic dose was of interest to us as it was simpler to implement and has been said to be equivalent to the alternative approach and can be applied to the nonlinear models. Herein this method will be referred to as Gibiansky's method .…”

“…A method proposed first by Sheiner et al but recently described which uses a bioavailability adjustment to a preclinic dose was of interest to us as it was simpler to implement and has been said to be equivalent to the alternative approach and can be applied to the nonlinear models. Herein this method will be referred to as Gibiansky's method . Our objective was to develop and qualify a population pharmacokinetic model for plasma TFV to PBMC TFV‐DP in healthy volunteers from MTN001 trial using a method that accounts for nonadherence to facilitate unbiased parameter estimation.…”

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

“…Because most PK analyses need to assume that there is no error in the measurements of the independent variable (time), there has yet to be a systematic evaluation of the effect that error in patient reported dosing times may have on the accuracy of structural model parameter estimation, and how different estimation protocols may perform under these conditions. While there is a subset of work incorporating inaccurate dosing times [1][2][3][4] into their methodology, none have specifically investigated the effect of these inaccuracies on structural model parameter estimation. And while it is unlikely that a real data set would only contain error in reported times of doses (and no error in the dosing event history as well), the body of literature surrounding the effects of dosing inaccuracies should include investigations specifically parsing potential effects of error in the reported dosing times.…”

“…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.…”