RESUMO
Sparse tissue sampling with intensive plasma sampling creates a unique data analysis problem in determining drug exposure in clinically relevant tissues. Tissue exposure may govern drug efficacy, as many drugs exert their actions in tissues. We compared tissue area-under-the-curve (AUC) generated from bootstrapped noncompartmental analysis (NCA) methods and compartmental nonlinear mixed effect (NLME) modeling. A model of observed data after single-dose tenofovir disoproxil fumarate was used to simulate plasma and tissue concentrations for two destructive tissue sampling schemes. Two groups of 100 data sets with densely-sampled plasma and one tissue sample per individual were created. The bootstrapped NCA (SAS 9.3) used a trapezoidal method to calculate geometric mean tissue AUC per dataset. For NLME, individual post hoc estimates of tissue AUC were determined, and the geometric mean from each dataset calculated. Median normalized prediction error (NPE) and absolute normalized prediction error (ANPE) were calculated for each method from the true values of the modeled concentrations. Both methods produced similar tissue AUC estimates close to true values. Although the NLME-generated AUC estimates had larger NPEs, it had smaller ANPEs. Overall, NLME NPEs showed AUC under-prediction but improved precision and fewer outliers. The bootstrapped NCA method produced more accurate estimates but with some NPEs > 100%. In general, NLME is preferred, as it accommodates less intensive tissue sampling with reasonable results, and provides simulation capabilities for optimizing tissue distribution. However, if the main goal is an accurate AUC for the studied scenario, and relatively intense tissue sampling is feasible, the NCA bootstrap method is a reasonable, and potentially less time-intensive solution.