Electronic monitoring of variation in drug intakes can reduce bias and improve precision in pharmacokinetic/pharmacodynamic population studies.

摘要

Population pharmacokinetic (PK) and pharmacodynamic (PD) studies evaluate drug concentration profiles and pharmacological effects over time when standard drug dosage regimens are assigned. They constitute a scientific basis for the determination of the optimal dosage of a new drug. Population PK/PD analyses can be performed on relatively few measures per patient enabling the study of a sizable sample of patients who take the drug over a possibly long period of time. We expose the problem of bias in PK/PD estimators in the presence of partial compliance with assigned treatment as it occurs in practice. We propose to solve this by recording accurate data on a number of previous dose timings and using timing-explicit hierarchical non-linear models for analysis. In practice, we rely on electronic measures of an ambulatory patient's drug dosing histories. Especially for non-linear PD estimation, we found that not only bias can be reduced, but higher precision can also be retrieved from the same number of data points when irregular drug intake times occur in well-controlled studies. We apply methods proposed by Mentre et al. to investigate the information matrix for hierarchical non-linear models. This confirms that a substantial gain in precision can be expected due to irregular drug intakes. Intuitively, this is explained by the fact that regular takers experience a relatively small range of concentrations, which makes it hard to estimate any deviation from linearity in the effect model. We conclude that estimators of PK/PD parameters can benefit greatly from information that enters through greater variation in the drug exposure process.