Is mixed effects modeling or naive pooled data analysis preferred for the interpretation of single sample per subject toxicokinetic data?

The purpose of this study,vas to evaluate whether mixed effects modeling (M EM) performs better than either noncompartmental or compartmental naive poo led data (NPD) analysis for the interpretation of single sample per subject pharmacokinetic (PK) data. Using PK parameters determined during a toxicok inetic study in rats, we simulated data sets that might emerge from similar experiments. Data sets were simulated with varying numbers of animals at e ach sampling lime (4-48) and the number of samples taken (1-3) from each in dividual. Each data set was replicated 50 times and analyzed using several variations of MEM that differed in the assumptions made regarding intraindi vidual error, NPD, and a graphical noncompartmental method. These analyses attempted to retrieve the underlying parameter and covariate effect values. We compared these analysis methods,vith respect to how well the underlying values were retrieved. All analysis methods performed poorly with single s ample per subject data hut MEM gave less biased estimates under the simulat ed conditions used here. MEM performance increased when covariate effects,w ere sought in the analysis compared with analyses seeking only PK parameter s. Decreasing the number of animals used per sampling time from 48 to 16 di d not influence the quality of parameter estimates but further reductions ( < 16 animals per sampling time) resulted in a reduced proportion of accepta ble estimates. Parameter estimate quality improved and worsened with MEM an d NPD, respectively, when additional samples were obtained from each indivi dual. Assumptions made regarding the magnitude of intraindividual error wer e unimportant with single sample per subject data but influenced parameter estimates if more samples were obtained from each individual. MEM is prefer able to both NPD and noncompartmental approaches for the analysis of single sample per subject data but even with MEM estimates of clearance are often biased.