Selecting reliable pharmacokinetic data for explanatory analyses of clinical trials in the presence of possible noncompliance.
For single-dose concentration-time data collected in clinical trials to be useful for explanatory pharmacokinetic (PK) or pharmacokinetic-pharmacodynamic (PK-PD) analyses, the following two assumptions on the data must hold: (i) the times of the concentration (PK) observations are known, and (ii) the patient's recent past dosing history (times and amounts) is known. If either (or both) of these assumptions does not hold, and data analysis proceeds as if it did, biased estimates may result. Assumption (i) usually does hold as study personnel observe and record PK sampling times. Assumption (ii) is a problem when, as is often the case of outpatient studies, one must rely on patient recall for past dosing history. This paper presents a technique to avoid assumption (ii) by identifying for deletion those PK observation occasions likely exhibiting unreliable preceding dose histories. To so identify occasions, a Bayes objective function (posterior density) for the data is maximized in its parameters for each individual. The likelihood factor of this function is a mixture pharmacostatistical model expressing the likelihood of the observed concentration(s) under three mutually exclusive events: the prescribed dose preceding the occasion was not taken at all (NT), the prescribed dose was taken at the specified time (T), or the prescribed dose was taken at an unspecified time (U). Suspect observations are identified as those whose maximum corresponding likelihood component is other than T. The approach as defined here relies on the following assumptions in addition to (i): (ii) population PK (i.e., the distribution of PK parameters in the population being sampled) is known, at least approximately. (iii) PK samples (at least 1 or 2 per occasion) are available, (iv) doses taken are of the stated magnitude, and (v) the drug has a short half-life. Simulations reveal that especially when more than one PK sample is available per study occasion, the methodology chooses a set of PK observations that should perform better in subsequent explanatory analyses, or as a basis for estimating individual PK parameters, than do other simpler methods.
Lu
J
J
Department of Biopharmaceutical Sciences, School of Pharmacy, University of California, San Francisco, San Francisco, California 94143, USA.
Gries
J M
JM
Verotta
D
D
Sheiner
L B
LB
D002986:Clinical Trials as Topic; D006801:Humans; D008954:Models, Biological; D010349:Patient Compliance; D010599:Pharmacokinetics