Comparison of tenofovir plasma and tissue exposure using a population pharmacokinetic model and bootstrap: a simulation study from observed data

Abstract

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.

Population pharmacokinetics of tenofovir given as either tenofovir disoproxil fumarate or tenofovir alafenamide in an African population

“…Several population pharmacokinetic models for tenofovir have been reported previously, the majority when dosed as TDF. [17][18][19][20][21][22][23] An exception is the model by Greene et al, 24 in which the authors describe tenofovir pharmacokinetics after both TDF and TAF administration in men living with HIV in the United States. However, they use two separate models, one when TDF is administered and another when TAF is administered.…”

Increasing transparency in machine learning through bootstrap simulation and shapely additive explanations

“…to the complexity of the non-parametric algorithms that are common in machine-learning methods, it is impossible for a human to analyze each tree and execute an explanation of how the machine-learning method works [1,[62][63][64][65]. Thus, using SHAP allows for a similar covariate interpretation as linear regression even if the exact effect-sizes of the covariates cannot be interpreted the way it can in linear regression [15,22,49,[66][67][68]…”

Modeling HIV Pre-Exposure Prophylaxis

“…(Centers for Disease Control and Prevention, 2018) Collins et al recently published a population PK model relating plasma and rectal tissue concentrations of TFV, demonstrating that non-linear mixed-effects (NLME) modeling is a viable approach for predicting TFV tissue exposures using a sparse tissue and rich plasma sampling scheme. (Collins et al, 2017) A diagram of the compartmental model used by Collins et al can be found in Supplementary Figure 2.…”