(W-093) Integration of Population Pharmacokinetic Model for Tenofovir Alafenamide by External Evaluation

Soyoung Lee, Pharm.D., Ph.D. – Assistant Professor, Chungnam National University; Jung-woo CHAE, Ph.D. – Associate Professor, Chungnam National University; Hwi-yeol Yun, Ph.D. – Associate Professor, Chungnam National University

Objectives: Tenofovir (TFV) is a nucleotide analogue antiviral agent used for the treatment of chronic hepatitis B and the prevention and treatment of HIV-1 infection. TFV is commercially available as two prodrugs: tenofovir disoproxil fumarate (TDF) or tenofovir alafenamide (TAF). TAF was developed more recently than TDF and is reported to be 10 times more potent.[1] Unlike TDF, TAF directly absorbs and acts on the target cells. This results in a lower incidence of adverse events, which can occur due to high plasma concentrations of TFV.[2] Several studies have reported population pharmacokinetic (popPK) models considering TAF and TFV. Still, they have limitations in accounting for the distinctive kinetics of TAF (flip-flop kinetics), covariates, and co-administered drugs, making it challenging to propose a generalized popPK model. This study aims to investigate a popPK model that can characterize the in vivo kinetics of TAF and TFV with high predictive performance using previously reported models. Moreover, we would like to propose an integrated popPK model incorporating these results.

Methods: Model estimation and simulation were performed using a Non-linear Mixed Effect Modeling (NONMEM, 7.5.1) program supported by Perl speak NONMEM (PsN 5.2.6) to verify the reported models. Four popPK models were collected with an extensive inspection to collect the models that simultaneously describe TAF and TFV through the PubMed database.[1], [2], [3], [4] These models were evaluated using an in-house plasma TAF and TFV concentration dataset. The evaluation of the models was performed by comparing prediction accuracy measurements, such as mean absolute error (MAE), mean square error (MSE), root mean square error (RMSE), mean absolute percentage error (MAPE), mean percentage error (MPE), and the distribution of prediction error (PE). Based on the evaluation results, an integrated popPK model exhibiting low residuals and predictive power has been identified.

Results: External validation was conducted using 1,484 plasma TAF concentrations and 1,772 plasma TFV concentrations measured after administering 25 mg of TAF to 60 healthy adults. The evaluation results indicated that for TAF, a one-compartment model implementing the effects of P-glycoprotein inhibitors on bioavailability or a transit compartment to account for food effects in the absorption phase, with gender effect on the clearance parameter, exhibited the lowest residuals between predicted and observed values. For TFV, a two-compartment model with body weight effect on the clearance and volume of distribution parameters was derived.

Conclusions: Through using previously developed popPK models for TAF and TFV, this study explored the model structure and covariates that adequately describe the in vivo kinetics of TAF and TFV. The suggested TAF and TFV popPK models can be further developed to account for special populations such as pediatrics, geriatrics, and disease states.

Citations: [1] Paul Thoueille et al., Population pharmacokinetic modelling to characterize the effect of chronic kidney disease on tenofovir exposure after tenofovir alafenamide administration, Journal of Antimicrobial Chemotherapy, 2023.
[2] Aida N. Kawuma et al., Population Pharmacokinetics of tenofovir given as either tenofovir disoproxil fumarate or tenofovir alafenamide in an African population, CPT Pharmacometrics Syst Pharmacol, 2023.
[3] Stephen A. Greene et al., Population Modeling Highlights Drug Disposition Differences Between Tenofovir Alafenamide and Tenofovir Disoproxil Fumarate in the Blood and Semen, CLINICAL PHARMACOLOGY & THERAPEUTICS, 2019.
[4] Xingfang Ji et al., Population Pharmacokinetics of Tenofovir Alafenamide Fumarate and Its Metabolite Tenofovir in Healthy Chinese Volunteers, Clinical Pharmacology in Drug Development, 2024.