Estimation of population pharmacokinetic parameters in the presence of non-compliance.

In population pharmacokinetic (PK) studies, patients' drug plasma profiles are routinely analyzed assuming that all patients took their drug at the times and in the amounts specified. However, patient non-compliance with the prescribed drug regimen is a leading source of failure to drug therapy. It has been reported that over 30% of patients routinely skip doses regardless of their disease, prognosis, or symptoms. This brings into question the assumption regarding full compliance for population PK analyses. This paper describes the estimation of population PK parameters in the presence and absence of non-compliance while either assuming full compliance or estimating compliance using a hierarchical Bayesian approach. Assessment of compliance for a given dose was limited to one of three possibilities: no dose was taken at the prescribed time, the prescribed dose was taken at the prescribed time, or twice the prescribed dose was taken at the prescribed time. Simulated data sets based on a one-compartment pharmacokinetic model with first order elimination were analyzed using WinBUGS (Bayesian inference Using Gibbs Sampling) software. An initial feasibility simulation experiment, using a simple, but informative PK sampling design with bolus input of drug, was performed. A second simulation study was then carried out using a more realistic sampling design and first-order input of drug. The simulated sampling design included observations after known doses as well as after uncertain doses. Results from the feasibility study revealed that when compliance was estimated instead of being assumed to be 100%, the relative prediction error for clearance (CL) decreased from 0.25 to 0.10 for 60% compliance and from 0.6 to 0.2 for 35% compliance. Estimates of the interoccasion variability of clearance were improved by compliance estimation but still had substantial positive bias. Estimated of interindividual variability were relatively insensitive to compliance estimation. Estimates for volume of distribution (V) and its associated variances were not affected by incorporation of compliance estimates, perhaps due to the specific sampling design that was used. The design was relatively uninformative regarding V. In the more realistic study, estimates for CL, V and the difference between the absorption rate constant and the elimination rate constant (KA-K) were improved by the incorporation of compliance estimation. The median relative errors were reduced from 0.51 to -0.01 for CL, from 0.49 to 0.04 for V, and from 0.49 to -0.02 for Ka-K. The bias in interoccasion variances for V and CL appeared to be reduced by compliance estimation while estimates of interindividual variability were not affected in a systematic fashion. The bias in the residual error variance was decreased from a relative error of about 2 to close to 0. The use of hierarchical Bayesian modeling with the incorporation of compliance estimation decreased the bias in the typical value parameter but the effects on variance parameters were less consistent. The encouraging results of these simulation experiments will hopefully stimulate further evaluation of this methodology for the estimation of population pharmacokinetic parameters in the presence of potential patient noncompliance.