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Development of a Semi-Mechanistic Population Pharmacokinetic Model for Predicting Tenofovir Disoproxil Fumarate and Tenofovir Alafenamide Exposure in Plasma and Cellular Matrices During Pregnancy and Postpartum
Tenofovir (TFV)-based regimens are backbones of both HIV treatment and pre-exposure prophylaxis during pregnancy. Multiple studies have shown up to one-third decreases in dried blood spot tenofovir-diphosphate concentrations during pregnancy among participants taking tenofovir disoproxil fumarate (TDF). Currently, there are no mechanism-based models describing the pharmacokinetics of tenofovir diphosphate (the active anabolite) in peripheral blood mononuclear cells (PBMCs) of pregnant individuals receiving TDF or tenofovir alafenamide (TAF), and the mechanisms behind observed differences between dried blood spots and PBMCs remain unclear.
Methods
To address this gap, we developed a semi-mechanistic model to simultaneously describe the pharmacokinetics of all clinically relevant TDF and TAF-derived moieties and conducted clinical trial simulations to compare TDF and TAF pharmacokinetics during pregnancy and postpartum.
Results
The pharmacokinetics of plasma TAF and TFV were best described by one-compartment and two-compartment models, respectively, with first-order absorption. A transit compartment was included to reflect the slower elimination rate of plasma TFV after receiving TAF. Cellular matrix PBMC and dried blood spots were included using a biophase model. For TDF, plasma TFV apparent clearance increased by 24.9% and 13.1% during the second and third trimesters of pregnancy, respectively, compared with non-pregnant populations. In the postpartum period, plasma TFV apparent clearance in pregnant women was 9.3% lower than in non-pregnant women. The bioavailability for TAF decreased by 17.3% and 5.1% during the second and third trimesters, respectively, and increased by 18% during the postpartum period relative to non-pregnant women. In pregnant women, simulations showed that TAF maintains approximately five times higher tenofovir diphosphate concentrations in PBMCs compared with TDF during the second and third trimesters, despite a decrease in PBMC tenofovir diphosphate concentrations for both drugs. This finding is consistent with the higher PBMC loading effect of TAF observed in non-pregnant populations.
Conclusions
Our semi-mechanistic model provides a framework for understanding pregnancy-associated pharmacokinetic changes and supports future research to refine dosing strategies for HIV treatment and prevention in pregnancy.
A semi-mechanistic model was developed to simultaneously describe the pharmacokinetics of all clinically relevant tenofovir disoproxil fumarate and tenofovir alafenamide-derived moieties.
Clinical trial simulations were conducted to compare tenofovir disoproxil fumarate and tenofovir alafenamide pharmacokinetics during pregnancy and postpartum.
Our semi-mechanistic model provides a framework for understanding pregnancy-associated pharmacokinetic changes and supports future research to refine dosing strategies for HIV treatment and prevention in pregnancy.
1 Introduction
The nucleoside/nucleotide reverse-transcriptase inhibitors tenofovir disoproxil fumarate (TDF) or tenofovir alafenamide (TAF), plus emtricitabine (FTC) are preferred backbones of antiretroviral therapy for the treatment of HIV during pregnancy [1]. Additionally, TDF/FTC (F/TDF) is currently the only US Food and Drug Administration-approved oral option for HIV pre-exposure prophylaxis (PrEP) with published pharmacokinetic (PK) and safety data in pregnant women and individuals assigned female at birth (subsequently referred to as women) [2].
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Tenofovir disoproxil fumarate and TAF are prodrugs of tenofovir (TFV) with distinct metabolic pathways. Within target cells, their active anabolite, tenofovir diphosphate (TFV-dp), competes with endogenous deoxynucleotide triphosphate during reverse transcription and terminates chain elongation [3]. Tenofovir disoproxil fumarate in plasma is rapidly converted to TFV monoester and TFV by esterases after absorption, and plasma TFV monoester and TFV may be transported into cells and converted by cellular kinases into TFV-dp [4]. Plasma TFV is mainly eliminated renally by a combination of active tubular secretion and glomerular filtration [5]. Tenofovir alafenamide is relatively stable in plasma and enters peripheral blood mononuclear cells (PBMCs) rapidly after absorption. Tenofovir alafenamide is a substrate of cathepsin A, and is converted to a TFV alanine intermediate. It is further metabolized by other acidic hydrolases to form TFV and is then converted to form TFV-dp [6, 7]. However, a small portion of TFV is formed by esterase activity in blood; this free TFV can then be taken up by red blood cells and converted to TFV-dp. Based on the physiochemical properties of both prodrugs, while there is up to a seven-fold increase in TFV-dp generated in HIV target cells with TAF, there is a 90% reduction in plasma TFV concentrations [8]. This results in lower TFV uptake and TFV-dp production in red blood cells. As a consequence, there are different analytical considerations when measuring TFV-dp concentrations from persons on TDF-based regimens versus TAF-based regimens.
Peripheral blood mononuclear cells and dried blood spots (DBS) are two accepted sampling matrices for measuring TFV-dp. Peripheral blood mononuclear cells, specifically CD4+ cells, serve as the primary target for the drug’s antiviral activity. Intra-erythrocytic TFV-dp in red blood cells (RBCs) can also be quantified using DBS, providing an effective adherence marker. With a half-life of approximately 17 days [9], TFV-dp concentrations in DBS reflect the average dosing over the past 6–12 weeks, offering valuable insight into long-term adherence [10]. Multiple clinical studies conducted in the USA, Malawi, South Africa, Uganda, Zimbabwe, and Kenya have evaluated the pharmacokinetics of TDF in pregnant women and have reported reduced plasma TFV and TFV-dp concentrations in DBS compared with non-pregnant or postpartum populations [11‐15]. These reductions are attributed to the increased glomerular filtration rate observed during pregnancy [16]. Although TAF is minimally eliminated through renal excretion [17] and cytochrome P450 3A metabolism [18], TAF pharmacokinetics may also be impacted by pregnancy-related physiologic changes [19]. Bukkems et al. reported that TAF plasma concentrations were reduced by about 50% in the third trimester compared with postpartum [20]. Similarly, the International Maternal, Pediatric, Adolescent AIDS Clinical Trials (IMPAACT) Network P1026 and P2026s study found lower antepartum TAF exposure compared with postpartum, though these concentrations remained comparable to those in non-pregnant populations [21, 22]. The mechanisms underlying reduced TAF concentrations during pregnancy remain unclear.
Recently, two additional studies have reported TFV-dp concentrations in PBMCs in pregnant and postpartum populations. Joseph Davey et al. conducted a study in South Africa and found that PBMC TFV-dp concentrations in women receiving F/TDF were similar during the second and third trimesters and postpartum. However, in women receiving F/TAF, PBMC TFV-dp concentrations were lower during pregnancy compared with the postpartum period [15]. Mugwanya et al. conducted a PK study in Kenyan non-pregnant and pregnant women using directly observed F/TDF PrEP, and reported that DBS TFV-dp concentrations were more than 40% lower in pregnant women compared with non-pregnant women, while PBMC TFV-dp concentrations remained similar between the two groups [23]. Brooks et al. (IMPAACT 2026) reported approximately 30% lower PBMC TFV-dp concentrations in pregnant women receiving TAF, compared with historical data in non-pregnant adults [24, 25]. This study also reported a 26% lower DBS TFV-dp concentration between the third trimester and postpartum in 19 participants receiving TAF.
During pregnancy, the expansion of plasma volume may exceed the increase in RBC mass, leading to function hemodilution. As DBS TFV-dp measurements are dependent on RBC concentrations, hemodilution during pregnancy can further reduce DBS TFV-dp concentrations. Further, the divergent metabolic pathways in plasma can differentially direct RBC and PBMC loading, which could impact PK parameters. However, the mechanisms behind the differing observations between DBS and PBMC remain unclear. Our previous modeling showed a significant increase in apparent clearance of TFV during pregnancy, but it focused solely on plasma TFV concentrations and was based on sparse PK data informed by medication event monitoring system-based adherence [26]. To address these gaps, we developed a semi-mechanism-based population PK (popPK) model of TDF and TAF (for either HIV treatment or prevention) in pregnancy that incorporates the impact of pregnancy on PK parameters. This model was used to compare these two prodrugs in pregnant and postpartum populations and to predict TFV-dp concentrations in PBMCs and DBS after administration of TDF or TAF. Although TAF is not currently approved as PrEP for women, we hypothesized that its higher PBMC loading and lower systemic TFV exposure may be beneficial in maintaining therapeutic/protective drug concentrations in the pregnant population.
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2 Methods
This study is a secondary analysis of deidentified data from multiple previously published studies [10, 13, 21, 22, 27]. The original studies were approved by their respective institutional review boards, and all data were deidentified prior to data sharing and this analysis. The University at Buffalo reviewed this study (institutional review board ID: STUDY00006180) and determined that as this research does not involve human subjects, institutional review board approval was not required.
2.1 Pharmacokinetic (PK) Data Sources
We considered all accessible PK data on TAF and TDF in women, including pregnant and non-pregnant women with or without HIV. The selection of datasets was guided by three key considerations. First, to model all the clinically relevant TDF and TAF-derived moieties in different sample matrices following TDF and TAF administration, we prioritized studies that simultaneously evaluated the pharmacokinetics of both drugs and measured a broad range of analytes. For example, the CONRAD 137 study evaluated three TFV dose regimens (TDF 300 mg, TAF 25 mg, TAF 15 mg) in women and provided PK data in both plasma and PBMCs, making it particularly valuable for our analysis [27]. Second, we preferred studies with a directly observed adherence assessment, such as directly observed therapy (DOT). Compared with adherence measured by self-reported questionnaire or medication event monitoring system bottle caps, DOT studies provide a more accurate dosing time record and improve the reliability of parameter estimation [28]. Finally, as co-administration of TAF with PK boosters (e.g., cobicistat, ritonavir) can increase TAF and TFV concentrations owing to the inhibition of the p-glycoprotein transporter, we excluded PK data from participants co-administered TAF with PK boosters from this analysis [21]. Populations were not matched by age or race because of limited covariate data available in the included datasets and neither factor has been identified as a significant covariate on the disposition of TDF or TAF [6, 29, 30].
2.2 Modeling Software
The popPK analysis was performed using NONMEM version 7.5 (ICON Development Solutions, Gaithersburg, MD, USA) with Perl speaks NONMEM (PsN version 4.9.0) and Pirana as the interfaces. The Laplacian method in NONMEM was used to estimate parameters. Data visualization, NONMEM dataset preparation, NONMEM post-run diagnostics, and figure preparation were conducted using R version 4.3.2 (R Development Core Team; http://www.r-project.org/) and R studio (2024.09.1+394). Simulations were conducted in R software version 4.0.2 using the mrgsolve package (1.3.0).
2.3 Below-the-Limit-of-Quantification Record Handling
Below-the-limit-of-quantification data for plasma TAF, TFV, and DBS TFV-dp were handled using the Beal M3 method [31]. The below-the-limit-of-quantification values for each chemical entity from each clinical trial were converted to molar concentrations and included in the likelihood calculation of the M3 method. For TFV-dp in PBMCs, where each sample had a unique lower limit of quantification, the Beal M1 method was applied to handle below-the-limit-of-quantification data.
2.4 PK Model Development
A semi-mechanistic multi-compartmental model was developed to simultaneously describe the pharmacokinetics of plasma TAF and TFV, and the intracellular active anabolite TFV-dp in PBMCs and DBS. For modeling purposes, all TAF and TFV plasma concentrations were converted to molar concentrations. Tenofovir diphosphate concentrations in PBMCs were converted from fmol/million cells to µmol/L based on the volume of a single PBMC (282 fL/cell) [30]. Tenofovir diphosphate concentrations in DBS were converted from fmol/punch to µmol/L based on the volume of a single RBC and the average number of cells contained in a 3-mm DBS punch for TDF [32]. Given the complexity of the system and the heterogenicity of the available datasets, we employed a stepwise approach in base model development. First, we modeled TAF and TFV in plasma. We then incorporated the intracellular components, the PBMC and RBC compartments, into the base model. Model performance was assessed using diagnostic plots, including observed versus population and individual predicted concentrations and conditional weighted residuals versus predicted concentrations or time. Model selection was guided by decrease in the objective function value, visual inspection of goodness-of-fit plots, and biological mechanism and plausibility. For nested models, a decrease of the objective function value more than 3.84 was considered significant for one degree of freedom (p < 0.05). Internal validation was performed using visual predictive checks.
2.4.1 Statistical Model
The between-subject variability is described by an exponential model:
where P represents the individual value of the parameter P, \(TVP\) represents the typical value of the parameter P, and \({\eta }_{P}\) denotes the between-subject variability, which is assumed to have a normal distribution with a mean equal to 0 and variance equal to \({\omega }_{P}^{2}\).
For each analyte, we evaluated proportional, additive, and combined additive and proportional residual variability models:
where \({C}_{ij}\) represents the observed concentration of participant i at time j, \({C}_{ij}\) represents the predicted concentration, and \({\varepsilon }_{1ij}\) and \({\varepsilon }_{2ij}\) represent the proportional and additive error. \({\varepsilon }_{1ij}\) and \({\varepsilon }_{2ij}\) were assumed to have a normal distribution with a mean of 0 and variances \({\sigma }_{1}^{2}\) and \({\sigma }_{2}^{2}.\)
2.4.2 Plasma PK Model
We first utilized a popPK modeling approach to characterize the disposition of TAF and TFV in plasma in healthy non-pregnant women with a single or daily dose of F/TAF or F/TDF. Because of the very short half-life of TDF in plasma (approximately 24 seconds) [33], direct measurement of TDF concentrations is not possible. We therefore assumed an equivalent 136-mg dose of TFV was directly given when taking 300 mg of TDF [34].
Plasma TAF and TFV PK data from TAF and TDF arms in CONRAD 137 were combined and jointly fitted to this model. A shared central and peripheral volume of distribution for TFV was assumed for both TAF and TDF, based on the assumption that the volume of distribution for TFV remains the same between the two prodrugs once it appears in plasma.
2.4.3 Intracellular PK Model
The base model was extended to include PBMCs and RBCs, included as two biophase compartments. Linear and nonlinear saturable formation of TFV-dp were evaluated. Plasma TAF, TFV, and PBMC TFV-dp PK data from the CONRAD 137 and the DOT-DBS studies [10], and DBS TFV-dp PK data from DOT-DBS were combined and jointly fitted to this extended model. Pharmacokinetic parameters for both parent drug and metabolites were simultaneously estimated.
2.4.4 TDF PK Model in Pregnancy
We developed popPK models for TDF and TAF in pregnant and postpartum women using clinical PK data from IMPAACT P1026s and IMPAACT 2026 studies. Pharmacokinetic data from non-pregnant, pregnant, and postpartum women were jointly fitted to the model to evaluate the impact of pregnancy on PK parameters. We conducted a covariate search with a specific focus on pregnancy trimesters. The impact of pregnancy trimester on PK parameters can be described as:
where TRI2, TRI3, and PP are indicator variables, and are set to zero unless the second trimester, third trimester, or postpartum are reached, respectively. \({\theta }_{1}\) represents the parameter value for a non-pregnant population, with independent additive effects for each state/stage of pregnancy.
2.4.5 Clinical Trial Simulation
We incorporated the identified covariate relationship between pregnancy trimester and PK parameters into the previously developed semi-mechanistic PK model to simulate the PK profiles of plasma TFV, PBMC TFV-dp, and DBS TFV-dp for individuals initiating daily TDF or TAF at different states/stages of pregnancy: non-pregnant, second trimester, third trimester, and 6–12 weeks postpartum. For plasma and PBMCs, concentration–time profiles were generated for 1000 virtual women who received 14 days of oral TAF (25 mg) or TDF (300 mg) dosed every 24 h in each pregnancy stage until a steady state was achieved. For DBS, to reach steady state, PK profiles were simulated for 1000 virtual women who received daily oral TDF (300 mg) for 12 weeks. The steady-state trough concentrations of plasma TFV, PBMC TFV-dp, and DBS TFV-dp were compared to the non-pregnant state.
3 Results
3.1 PK Data
The final popPK analysis included PK data from four trials: two PK studies of HIV PrEP in healthy, HIV-negative, non-pregnant volunteers (CONRAD 137 and DOT-DBS) and two PK studies in pregnant women with HIV taking TAF or TDF (IMPAACT P1026s and 2026). Two out of four datasets reported intercellular TFV-dp in PBMCs; one out of four datasets reported intracellular TFV-dp measured in DBS, which has been widely used as an adherence marker in HIV PrEP research. Bioanalytical methods of each study are described in detail in their referenced publications [10, 13, 14, 21, 27]. Table 1 summarizes the PK data utilized.
Table 1
Summary of study characteristics included in the population pharmacokinetic modeling and simulation
Study
Tenofovir dose regimen
Analyte and sample matrix included in analysis (number of observations/number of BLQ/% BLQ)
Lower limit of quantification
HIV Status
Number of female participants and pregnancy status
DBS dried blood spots, BLQ below the limit of quantification, NA , PBMC peripheral blood mononuclear cells, TAF tenofovir alafenamide, TDF tenofovir disoproxil fumarate, TFV tenofovir, TFV-dp tenofovir diphosphate
aFor PBMC, each sample had its unique lower limit of quantification value based on the cell count
3.2 PK Model Development
3.2.1 Non-Pregnant PK Model
The TAF and TDF PK data were best described by an eight-compartment model. Final parameters are presented in Table 2. The model structure is illustrated in Fig. 1, with ordinary differential equations outlined below.
Table 2
Final estimates of TAF and TDF pharmacokinetic parameters
ADD additive error, BSV between-subject variability, CL1/F plasma TAF apparent clearance, CL2/F plasma TFV apparent clearance, %CV coefficient of variation in percentage terms, Ka1 TAF absorption rate constant, Ka2 TDF absorption rate constant, \({F}_{\text{fast}}\) fraction of TAF can be metabolized to TFV in plasma immediately after absorption, \({F}_{\text{TFV}}\) fraction of absorbed TAF that can convert to plasma TFV, \({K}_{\text{el}-\text{DBS}}\) elimination rate from DBS, \({K}_{\text{el}-\text{PBMC}}\) elimination rate from PBMC, \({K}_{\text{TAF}-\text{TFV}-\text{dp}-\text{PBMC}}\) intracellular uptake and activation rate from plasma TAF to PBMC TFV-dp, \({K}_{\text{TFV}-\text{TFV}-\text{dp}-\text{DBS}}\) intracellular uptake and activation rate from plasma TFV to DBS TFV-dp, \({K}_{\text{TFV}-\text{TFV}-\text{dp}-\text{PBMC}}\) intracellular uptake and activation rate from plasma TFV to PBMC TFV-dp, PBMC peripheral blood mononuclear cells, PROP proportional error, Q/F plasma TFV apparent intercompartmental clearance, %RSE relative standard error, Shr. shrinkage, TAF tenofovir alafenamide, TDF tenofovir disoproxil fumarate, TFV tenofovir, TFV-dp tenofovir diphosphate, V2/F TAF apparent volume of distribution, V5/F TFV central compartment apparent volume of distribution, V6/F TFV peripheral compartment apparent volume of distribution
Fig. 1
Model structure of tenofovir disoproxil fumarate (TDF) and tenofovir alafenamide (TAF) population pharmacokinetic model. CL clearance, DBS dried blood spots, Ka absorption rate constant, PBMC peripheral blood mononuclear cells, TFV tenofovir, TFV-dp tenofovir diphosphate, V volume of distribution
A one-compartment and two-compartment model adequately described the disposition of TAF following TAF administration and TFV following TDF administration, respectively. Subsequently, we combined these models to characterize the disposition of TFV after TAF administration, utilizing a set of ordinary differential equations to describe the conversion of plasma TAF to plasma TFV. In this model, the terms \({F}_{\text{TFV}}\) represent the fraction of absorbed TAF that can convert to plasma TFV. Within \({F}_{\text{TFV}}\), we assumed that a fraction \({(F}_{\text{fast}})\) of TAF can be metabolized to TFV in plasma immediately after absorption. The remaining fraction of TAF (\({F}_{\text{TFV}}\left(1-{F}_{\text{fast}}\right))\) will first enter a transit compartment, and then slowly convert to systemic TFV via a rate constant \({(K}_{\text{slow}})\). \({F}_{\text{fast}}\) was estimated to be 27.8%, consistent with the effective cellular uptake of TAF [6]. \({K}_{\text{slow}}\) was estimated to be 0.0212/h, resulting in a longer half-life of TFV after taking TAF, compared with TDF. Visual predictive check plots (Fig. S1 of the Electronic Supplementary Material) showed that the observed medians and the majority of the observed variability across strata were well captured within the model-predicted 95% prediction intervals.
Tenofovir diphosphate concentrations in cellular matrices were adequately described using the biophase model. Following TDF administration, plasma TFV is the sole chemical moiety taken up by PBMCs and sequentially activated to TFV-dp. This intracellular uptake and activation process was described by the rate constant \({K}_{\text{TFV}-\text{TFVd}p-\text{PBMC}}\). In contrast, following TAF administration, TAF is rapidly absorbed into PBMCs, serving as the primary chemical moiety. Tenofovir alafenamide is subsequently converted to TFV-dp through enzymatic processes mediated by cathepsin A and nucleotide kinases, described by the rate constant \({K}_{\text{TAF}-\text{TFVd}p-\text{PBMC}}\). For both TAF and TDF, as cathepsin A is not present in RBCs, we assumed that plasma TFV is the only source of RBC TFV-dp. The conversion between plasma TFV and RBC TFV-dp was described by the rate constant \({K}_{\text{TFV}-\text{TFVdp}-\text{DBS}}\). Visual predictive check plots (Figs. 2 and 3) indicated that the observed medians and most of the observed variability in TFVdp concentrations in PBMCs and DBS were contained within the model-predicted 95% prediction intervals. The calculated half-life after the administration of TDF for DBS TFV-dp is 433 h, while for PBMC TFV-dp it is 60 h. Because of the extensive computational time required for bootstrapping, it was not performed in this analysis. Instead, parameter uncertainty was assessed using the relative standard error derived from the covariance step reported in Table 2.
Fig. 2
Visual predictive check for CONRAD 137 peripheral blood mononuclear cell (PBMC) tenofovir diphosphate (TFV-dp) concentrations versus time, stratified by dose regimen. The dashed red and solid red lines depict the 5th, 50th, and 95th percentiles of the observed concentrations. The shaded area represents the 95% confidence interval around the simulated percentiles. TAF tenofovir alafenamide, TDF tenofovir disoproxil fumarate
Visual predictive check for the DOT-DBS study, stratified by measured analyte. The dashed red and solid red lines depict the 5th, 50th, and 95th percentiles of the observed concentrations. The shaded area represents the 95% confidence interval around the simulated percentiles. DBS dried blood spots, PBMC peripheral blood mononuclear cells, TFV tenofovir, TFV-dp tenofovir diphosphate
A two-compartment model with first-order absorption adequately described the disposition of TFV following TDF administration in the non-pregnant state (Fig. 4A). When pregnancy trimesters were included as the only categorical covariate, the parameter estimates indicated that plasma TFV apparent clearance increased by 24.9% and 13.1% during the second and third trimesters, respectively, compared with the non-pregnant state. In the 6–12 weeks postpartum period, plasma TFV apparent clearance decreased by 9.3% relative to non-pregnant values.
Fig. 4
Population pharmacokinetic model structure used to describe plasma tenofovir (TFV) and tenofovir alafenamide (TAF) concentrations during pregnancy and postpartum. CL clearance, Ka absorption rate constant, TDF tenofovir disoproxil fumarate, V volume of distribution
For TAF, a one-compartment model with first-order absorption was selected to describe plasma concentrations during pregnancy and postpartum (Fig. 4B). When the bioavailability of TAF was estimated separately for each trimester, the results showed a decrease of 17.3% and 5.1% during the second and third trimesters, respectively, compared with non-pregnant women. In contrast, TAF bioavailability increased by 18% during the 6–12 week postpartum period relative to non-pregnant women.
3.3 Clinical Trial Simulation
The simulated concentration–time profiles of TFV and TFV-dp in non-pregnant, pregnant, and postpartum individuals receiving TDF or TAF are shown in Fig. 5. For TDF, simulations indicate that the median simulated plasma TFV steady-state trough concentration decreased by 30.6% (21.3–35.2%) and 18.0% (12.4–21.1%) during the second and third trimesters, respectively, relative to non-pregnant women. Correspondingly, the median simulated PBMC TFV-dp steady-state trough concentrations decreased by 20.3% (19.6–21.0%) in the second trimester and 11.8% (11.3–12.2%) in the third trimester. For TAF, plasma TFV steady-state trough concentrations were predicted to decrease by 36.0% (35.1–37.4%) in the second trimester and 17.7% (17.0–18.6%) in the third trimester, compared with non-pregnant women. Similarly, PBMC TFV-dp steady-state trough concentrations decreased by 17.6% (17.4–18.3%) in the second trimester and 5.3% (5.1–5.8%) in the third trimester. Figure 6 presents the simulated PK profiles of DBS TFV-dp in non-pregnant, pregnant, and postpartum individuals receiving daily TDF. At steady state, DBS TFV-DP trough concentrations decreased by 20.0% (19.9–20.0%) in the second trimester and 11.6% (11.5–11.6%) in the third trimester. The simulated steady-state PBMC TFV-dp trough concentrations are shown in Fig. 7. Despite the reductions mentioned above, in all scenarios, TAF consistently resulted in higher steady-state PBMC TFV-dp trough concentrations compared with TDF.
Fig. 5
Simulated concentration–time profiles for tenofovir (TFV) and tenofovir diphosphate (TFV-dp) in non-pregnant, pregnant, and postpartum women taking tenofovir disoproxil fumarate (TDF) and tenofovir alafenamide (TAF). Population pharmacokinetic simulations for 14 days oral TAF 25-mg or TDF 300-mg dosing every 24 h in 1000 virtual patients. (A) Plasma concentration of TFV after TAF dosing, (B) Plasma concentration of TFV after TDF dosing, (C) peripheral blood mononuclear cell (PBMC) TFV-dp concentration after TAF dosing, and (D) PBMC TFV-dp concentration after TDF dosing. The solid lines represent the predicted median pharmacokinetic profile for non-pregnant (black), second trimester (red), third trimester (green), and postpartum (blue) populations. The regions encompass the 5% and 95% percentiles of prediction for each population
Simulated concentration–time profiles for tenofovir and tenofovir diphosphate (TFV-dp) in non-pregnant, pregnant, and postpartum women taking tenofovir disoproxil fumarate (TDF). Population pharmacokinetic simulations for 12 weeks oral TDF 300-mg dosing every 24 h in 1000 virtual patients. The solid lines represent the predicted median pharmacokinetic profile for non-pregnant (black), second trimester (red), third trimester (green), and postpartum (blue) populations. The regions encompass the 5% and 95% percentiles of prediction for each population. The dashed line represents fitted 50th percentiles for women with 100% adherence in the DOT-DBS study [10]
Simulated steady-state peripheral blood mononuclear cell (PBMC) tenofovir diphosphate (TFV-dp) trough concentrations in non-pregnant, pregnant, and postpartum women for two regimens: daily oral tenofovir disoproxil fumarate (TDF) 300 mg and daily oral tenofovir alafenamide (TAF) 25 mg. Boxplots show the distribution of simulated TFV-dp concentrations, individual data points and connected lines represent concentrations from the same virtual subject
This report describes the development of a semi-mechanism-based TAF and TDF popPK model in women, focused on PK changes during pregnancy and postpartum. The model jointly characterizes pharmacokinetics of all clinically relevant TDF and TAF-derived moieties in the sampling matrices of interest following TDF and TAF administration, utilizing a unified set of PK parameters. The model represents a framework to incorporate pregnancy-related PK parameter changes and enhance understanding of TDF and TAF pharmacokinetics in this understudied population.
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Several prior analyses have used model-based methods to compare TDF and TAF. Among these, only one study employed a universal model structure to describe plasma TFV disposition after administration of TDF or TAF [6]; other studies used two separate models and two significantly different estimates for the apparent clearance of TFV from plasma to highlight the difference between TDF and TAF [35, 36]. These approaches emphasize differences in TDF and TAF metabolism and intracellular uptake, but do so by using distinct PK parameter values for the same chemical entity in the same sampling matrix. In our model, PK profiles of all clinically relevant TDF and TAF-derived moieties are simultaneously described with unified PK parameters.
Many previous modeling efforts have simultaneously modeled the parent drug and active anabolite. For PBMC TFV-dp concentrations after TDF administration, biophase models are commonly used. First-order kinetics [30, 34‐37], non-linear saturable formation [38], and some more advanced models have been applied to link plasma TFV concentration and the biophase compartment. Baheti et al. used an indirect response model to link plasma concentrations to the formation of intracellular TFV-dp, assuming that TFV-dp formation in PBMCs occurs via a zero-order rate constant and can be stimulated by plasma TFV concentrations [39, 40]. Chen et al. used a hybrid of first-order and saturation formation kinetics and included a recycle compartment to better describe the disposition of PBMC TFV-dp after taking TDF [3]. Unlike in PBMCs, TFV-dp in DBS was usually modeled alone [14, 41]. A one-compartment PK model with first-order or constant input adequately described the PK profile of TFV-dp in DBS [14, 41]. In this study, a simple first-order input effectively linked plasma TFV and TAF concentration with intracellular TFV-dp concentrations; the calculated half-lives of TFV-dp after TDF administration in DBS (18 days vs 17 days) and PBMCs (2.5 days vs 2.9 days) align with previously reported values [10, 24]. For non-pregnant women, the simulated median steady-state DBS TFV-dp concentration is consistent with fitted 50th percentiles for women with 100% adherence in the DOT-DBS study (1685 fmol/punch) [10].
The impact of pregnancy-related physiological changes was evaluated and incorporated into the final PK model for simulation. For oral TDF, reduced TFV exposure during pregnancy has been consistently reported in clinical studies [11‐13, 29]. An increased glomerular filtration rate during pregnancy leads to a higher apparent clearance of plasma TFV, resulting in lower systemic exposure. Brooks et al. reported a significant association between plasma TFV monoester concentrations and TFV-dp concentrations in PBMCs and DBS, suggesting that TFV monoester may be the chemical moiety responsible for facilitating cell loading [4]. However, TFV monoester was not measured in any of the datasets included in this analysis. We made an assumption that the plasma TFV is the only chemical molecule actively depositing into PBMCs and RBCs. For TDF-based regimens, reduced plasma TFV concentrations will further diminish PBMC and DBS TFV-dp concentrations, consistent with findings from the Partners Demonstration Projects and IMPAACT 2009 study [12, 14]. However, the predicted reduction in DBS TFV-dp is smaller than previously reported values [12], indicating that hemodilution may need to be considered. While these changes may not be clinically significant for HIV treatment in the context of a multi-drug regimen, their implications for HIV PrEP remain unclear.
Mechanisms contributing to altered plasma TAF concentrations during pregnancy and postpartum are not fully understood. Potential factors include increased plasma volume and changes in bioavailability. As a substrate of P-glycoprotein and BCRP, TAF concentration can be boosted when administered with cobicistat, owing to the inhibition of efflux transport [42]. In this analysis, we separately estimated the bioavailability of TAF at different pregnancy trimesters. Consistent with the previous IMPAACT P1026s study [21], a higher bioavailability was estimated for the postpartum period compared with the non-pregnant state. Further research is required to elucidate the mechanisms behind this and evaluate the clinical significance.
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A limitation of this study is the lack of a comprehensive covariate search. Instead of evaluating all possible candidate covariates, we solely focused on the impact of pregnancy on PK parameters. Previous studies have identified serum creatinine, creatinine clearance, sex, and body weight as significant covariates influencing the pharmacokinetics of TDF and TAF [29, 30, 34]. For example, Eke et al. identified serum creatinine as the only statistically significant covariate in a multivariate analysis [29]. While serum creatinine may be a better predictor of apparent clearance, we prioritized trimester-specific trends to describe general PK changes in this population, inclusive of the associated physiologic changes of pregnancy (such as serum creatinine). The impact of hemodilution of DBS TFV-dp pharmacokinetics was also not evaluated as a covariate, as no DBS TFV-dp data from pregnant women were included in the modeling process.
Another limitation is that, for modeling purposes, we converted all units (e.g., ng/mL, fmol/million cells, and fmol/punch) to a molar unit (µmol/L). While plasma concentrations of TAF and TFV can be accurately converted based on the molecular weight, PBMC and DBS unit conversion requires assumption of PBMC volume, RBC volume, and the average number of cells contained in a 3-mm DBS punch for TDF [32], potentially introducing bias. In this analysis, we included PK data from both women without HIV and pregnant women living with HIV, we assumed no difference in TDF and TAF pharmacokinetics between these two populations by HIV status. We did exclude PK data from patients co-administered TAF with PK boosters.
Because of the absence of PK data in the first trimester, our model cannot address potential changes in TAF and TDF disposition during early pregnancy. In addition, based on available data, our model could not fully explain discrepancies between DBS and PBMC observations during pregnancy. Using linear kinetics to link plasma TFV and the intracellular compartment predicts simultaneous reductions in PBMCs and DBS TFV-dp, inconsistent with findings by Joseph Davey et al. and Mugwanya et al. [15, 23]. Further refinement of the model structure with more PBMC and DBS TFV-dp PK data in pregnant and postpartum populations could potentially address this limitation. Current treatment and prevention guidelines do not recommend a dose adjustment of TDF during pregnancy. The clinical significance of the predicted reduction in intracellular concentrations during the second and third trimesters remains unknown for PrEP. No studies or real-world data to date indicate decreased effectiveness of TDF-based PrEP in pregnancy. That said, the potential impact on PrEP efficacy requires further investigation in translational and clinical studies.
5 Conclusions
We developed a semi-mechanistic popPK model that simultaneously describes the PK profile of TFV in plasma and TFV-dp in PBMCs and DBS in women receiving TAF or TDF. The simulation indicated that PBMC TFV-dp concentrations for TDF decreased by approximately 20% during pregnancy, suggesting the decrease may not be as significant as previously anticipated. Our simulation also predicted that PBMC TFV-dp concentrations would remain approximately five times higher for TAF compared with TDF during the second and third trimesters of pregnancy, suggesting a potential advantage of TAF over TDF in maintaining higher intracellular TFV-dp concentrations during pregnancy and postpartum, consistent with previous observations in the non-pregnant population. These findings align with clinical observations of reduced plasma TFV exposure during pregnancy and highlight the importance of considering physiological changes when optimizing PrEP regimens in pregnancy. This semi-mechanistic model provides a framework for understanding pregnancy-associated PK changes and supports future research to refine dosing strategies and enhance PrEP effectiveness in pregnant women.
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Acknowledgements
The CONRAD 137 study was funded by the United States Agency for International Development (USAID)/the President’s Emergency Plan for AIDS Relief (PEPFAR) through Cooperative Agreement AID-OAA-A-14-00011 with CONRAD/Eastern Virginia Medical School. The views of the authors do not necessarily reflect those of the funding agency or the US Government. The original publication of CONRAD 137 findings resulted in part from research supported by the University of North Carolina at Chapel Hill Center for AIDS Research (CFAR), a National Institutes of Health (NIH)-funded program P30 AI050410. Gilead Sciences donated the study drugs. The DOT-DBS study was funded by U01 AI106499 and R01 AI122298. Support for the International Maternal Pediatric Adolescent AIDS Clinical Trials Network (IMPAACT) was provided by the National Institute of Allergy and Infectious Diseases (NIAID) with co-funding from the Eunice Kennedy Shriver National Institute of Child Health and Human Development (NICHD) and the National Institute of Mental Health (NIMH), all components of the NIH, under Award Numbers UM1AI068632-15 (IMPAACT LOC), UM1AI068616-15 (IMPAACT SDMC), and UM1AI106716-15 (IMPAACT LC), and by NICHD contract number HHSN275201800001I. The content is solely the responsibility of the authors and does not necessarily represent the official views of the NIH.
Declarations
Conflict of interest
Jeremiah D. Momper is an Editorial Board member of Clinical Pharmacokinetics. Jeremiah D. Momper was not involved in the selection of peer reviewers for the manuscript nor any of the subsequent editorial decisions. Renee Heffron serves as a consultant to Merck and Sharp & Dohme and has been a Principal Investigator for studies that received donated FTC/TDF medication from Gilead Sciences. Robert Bies serves as a consultant for Advanced Bioscience Laboratories. Craig W. Hendrix holds several patents and receives royalties related to PrEP topical microbicides, and is a founder of Prionde Biopharma, LLC, a PrEP microbicide company; all conflicts are managed by Johns Hopkins University and the HPTN. Dvora Joseph Davey receives research support from ViiV Healthcare, Merck, and Gilead Sciences. Kristina M. Brooks previously received consulting fees from ViiV Healthcare. Peter Anderson receives research contracts from Gilead Sciences, with payments made to his institution. Mark Mirochnick receives research support through his institution from ViiV Healthcare and Merck. Rachel K. Scott has participated in advisory boards and received investigator-sponsored research funding from ViiV Healthcare and Gilead Sciences, managed by her institution. Yifan Yu, Gustavo F. Doncel, Brookie M. Best, Mark A. Marzinke, Landon Myer, Connie Celum, and Jenell Coleman have no conflicts of interest that are directly relevant to the content of this article.
Ethics approval
This study is a secondary analysis of deidentified data from multiple previously published studies [10, 13, 21, 22, 27]. The original studies were approved by their respective institutional review boards, and all data were deidentified prior to data sharing and this analysis. The University at Buffalo reviewed this study (institutional review board ID: STUDY00006180) and determined that as this research does not involve human subjects, institutional review board approval was not required.
Consent to participate
Not applicable.
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Consent for publication
Not applicable.
Availability of data and material
The data cannot be made publicly available because of the ethical restrictions in the study’s informed consent documents and in the International Maternal Pediatric Adolescent AIDS Clinical Trials (IMPAACT) Network’s approved human subjects protection plan; public availability may compromise participant confidentiality. However, data are available to all interested researchers upon request to the IMPAACT Statistical and Data Management Center’s data access committee (e-mail: sdac.data@fstrf.org) with the agreement of the IMPAACT Network.
Code availability
Not applicable.
Author contributions
RKS acquired funding for the study. RKS, RB, and CWH contributed to the conceptualization of the study. RKS, RB, CWH, and JDM supervised the research. YY conducted the data analysis and drafted the original manuscript. KMB, GFD, BMB, MAM, MM, PA, LM, CC, RH, JC, DJD, CW, JDM, and RKS contributed to data curation. All authors reviewed and edited the manuscript.
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Open Access This article is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License, which permits any non-commercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by-nc/4.0/.
Development of a Semi-Mechanistic Population Pharmacokinetic Model for Predicting Tenofovir Disoproxil Fumarate and Tenofovir Alafenamide Exposure in Plasma and Cellular Matrices During Pregnancy and Postpartum
Verfasst von
Yifan Yu
Kristina M. Brooks
Gustavo F. Doncel
Brookie M. Best
Mark A. Marzinke
Mark Mirochnick
Peter Anderson
Landon Myer
Connie Celum
Renee Heffron
Jenell Coleman
Dvora Joseph Davey
Craig W. Hendrix
Jeremiah D. Momper
Robert Bies
Rachel K. Scott
Eke AC, Lockman S, Mofenson LM. Antiretroviral treatment of HIV/AIDS during pregnancy. JAMA. 2023;329(15):1308–9.CrossRefPubMedPubMedCentral
2.
Stalter RM, Pintye J, Mugwanya KK. Safety review of tenofovir disoproxil fumarate/emtricitabine pre-exposure prophylaxis for pregnant women at risk of HIV infection. Expert Opin Drug Saf. 2021;20(11):1367–73.CrossRefPubMedPubMedCentral
3.
Chen X, Seifert SM, Castillo-Mancilla JR, Bushman LR, Zheng JH, Kiser JJ, et al. Model linking plasma and intracellular tenofovir/emtricitabine with deoxynucleoside triphosphates. PLoS ONE. 2016;11(11):e0165505.CrossRefPubMedPubMedCentral
4.
Brooks KM, Ibrahim ME, Castillo-Mancilla JR, MaWhinney S, Alexander K, Tilden S, et al. Pharmacokinetics of tenofovir monoester and association with intracellular tenofovir diphosphate following single-dose tenofovir disoproxil fumarate. J Antimicrob Chemother. 2019;74(8):2352–9.CrossRefPubMedPubMedCentral
5.
Cressey TR, Avihingsanon A, Halue G, Leenasirimakul P, Sukrakanchana PO, Tawon Y, et al. Plasma and intracellular pharmacokinetics of tenofovir disoproxil fumarate 300 mg every 48 hours vs 150 mg once daily in HIV-infected adults with moderate renal function impairment. Clin Infect Dis. 2015;61(4):633–9.CrossRefPubMedPubMedCentral
6.
Kawuma AN, Wasmann RE, Dooley KE, Boffito M, Maartens G, Denti P. Population pharmacokinetic model and alternative dosing regimens for dolutegravir coadministered with rifampicin. Antimicrob Agents Chemother. 2022;66(6):e0021522.CrossRefPubMed
7.
Birkus G, Kutty N, He G-X, Mulato A, Lee W, McDermott M, et al. Activation of 9-[(R)-2-[[(S)-[[(S)-1-(isopropoxycarbonyl)ethyl]amino] phenoxyphosphinyl]-methoxy]propyl]adenine (GS-7340) and other tenofovir phosphonoamidate prodrugs by human proteases. Mol Pharmacol. 2008;74(1):92–100.CrossRefPubMed
8.
Ray AS, Fordyce MW, Hitchcock MJM. Tenofovir alafenamide: a novel prodrug of tenofovir for the treatment of human immunodeficiency virus. Antiviral Res. 2016;125:63–70.CrossRefPubMed
9.
Castillo-Mancilla JR, Zheng JH, Rower JE, Meditz A, Gardner EM, Predhomme J, et al. Tenofovir, emtricitabine, and tenofovir diphosphate in dried blood spots for determining recent and cumulative drug exposure. AIDS Res Hum Retroviruses. 2013;29(2):384–90.CrossRefPubMedPubMedCentral
10.
Anderson PL, Liu AY, Castillo-Mancilla JR, Gardner EM, Seifert SM, McHugh C, et al. Intracellular tenofovir-diphosphate and emtricitabine-triphosphate in dried blood spots following directly observed therapy. Antimicrob Agents Chemother. 2018;62(1):e01710–17.CrossRefPubMed
11.
Benaboud S, Hirt D, Launay O, Pannier E, Firtion G, Rey E, et al. Pregnancy-related effects on tenofovir pharmacokinetics: a population study with 186 women. Antimicrob Agents Chemother. 2012;56(2):857–62.CrossRefPubMedPubMedCentral
12.
Pyra M, Anderson PL, Hendrix CW, Heffron R, Mugwanya K, Haberer JE, et al. Tenofovir and tenofovir-diphosphate concentrations during pregnancy among HIV-uninfected women using oral preexposure prophylaxis. AIDS. 2018;32(13):1891–8.CrossRefPubMed
13.
Best BM, Burchett S, Li H, Stek A, Hu C, Wang J, et al. Pharmacokinetics of tenofovir during pregnancy and postpartum. HIV Med. 2015;16(8):502–11.CrossRefPubMedPubMedCentral
14.
Stranix-Chibanda L, Anderson PL, Kacanek D, Hosek S, Huang S, Nematadzira TG, et al. Tenofovir diphosphate concentrations in dried blood spots from pregnant and postpartum adolescent and young women receiving daily observed pre-exposure prophylaxis in Sub-Saharan Africa. Clin Infect Dis. 2021;73(7):e1893–900.CrossRefPubMed
15.
Joseph Davey D, Dadan S, Bheemraj K, Waitt C, Khoo S, Myer L, et al. Evaluation of pharmacokinetics of tenofovir alafenamide (TAF) and tenofovir disoproxil (TDF) in pregnant and postpartum women in South Africa: PrEP-PP pk study. Antivir Res. 2024;231:106014.CrossRefPubMed
16.
van Balen VAL, van Gansewinkel TAG, de Haas S, Spaan JJ, Ghossein-Doha C, van Kuijk SMJ, et al. Maternal kidney function during pregnancy: systematic review and meta-analysis. Ultrasound Obstet Gynecol. 2019;54(3):297–307.CrossRef
17.
Jin FFM, Garner W, et al. Pharmacokinetics, metabolism, and excretion of tenofovir alafenamide (TAF). In: 14th International workshop on clinical pharmacology of HIV therapy; April 22–24, 2013. Amsterdam, the Netherlands
Eke AC, Brooks KM, Gebreyohannes RD, Sheffield JS, Dooley KE, Mirochnick M. Tenofovir alafenamide use in pregnant and lactating women living with HIV. Expert Opin Drug Metab Toxicol. 2020;16(4):333–42.CrossRefPubMedPubMedCentral
20.
Bukkems VE, Necsoi C, Hidalgo Tenorio C, Garcia C, Alba Alejandre I, Weiss F, et al. Tenofovir alafenamide plasma concentrations are reduced in pregnant women living with human immunodeficiency virus (HIV): data from the PANNA network. Clin Infect Dis. 2022;75(4):623–9.CrossRefPubMed
21.
Brooks KM, Momper JD, Pinilla M, Stek AM, Barr E, Weinberg A, et al. Pharmacokinetics of tenofovir alafenamide with and without cobicistat in pregnant and postpartum women living with HIV. AIDS. 2021;35(3):407–17.CrossRefPubMed
22.
Brooks KM, Pinilla M, Stek AM, Shapiro DE, Barr E, Febo IL, et al. Pharmacokinetics of tenofovir alafenamide with boosted protease inhibitors in pregnant and postpartum women living with HIV: results from IMPAACT P1026s. J Acquir Immune Defic Syndr. 2022;90(3):343–50.CrossRefPubMedPubMedCentral
23.
Mugwanya KK, Nelly RM, Donnell D, Saina M, Brown CE, Schaafsma TT, et al. Adherence benchmarks for TFV-DP in DBS and PBMCs for African women using FTC/TDF PrEP. CROI 2024; March 3–6, 2024. Denver, Colorado, US.
24.
Yager JL, Brooks KM, Castillo-Mancilla JR, Nemkov C, Morrow M, Peterson S, et al. Tenofovir-diphosphate in peripheral blood mononuclear cells during low, medium and high adherence to emtricitabine/ tenofovir alafenamide vs. emtricitabine/ tenofovir disoproxil fumarate. AIDS. 2021;35(15):2481–7.CrossRefPubMed
25.
Brooks K. Intracellular tenofovir-diphosphate concentrations with tenofovir alafenamide during pregnancy and postpartum in people with HIV: results from IMPAACT 2026. In: International workshop on clinical pharmacology of HIV, hepatitis, and other antiviral drugs; September 11-13, 2023. Rome, Italy.
26.
Scott RK, Yu Y, Marzinke MA, Coleman JS, Hendrix CW, Bies R. Clinical trial simulation to evaluate tenofovir disoproxil fumarate/emtricitabine HIV pre-exposure prophylaxis dosing during pregnancy. Front Reprod Health. 2023;5:1224580.CrossRefPubMedPubMedCentral
27.
Thurman AR, Schwartz JL, Cottrell ML, Brache V, Chen BA, Cochón L, et al. Safety and pharmacokinetics of a tenofovir alafenamide fumarate-emtricitabine based oral antiretroviral regimen for prevention of HIV acquisition in women: a randomized controlled trial. EClinicalMedicine. 2021;36:100893.CrossRefPubMedPubMedCentral
28.
Anghel LA, Farcas AM, Oprean RN. An overview of the common methods used to measure treatment adherence. Med Pharm Rep. 2019;92(2):117–22.CrossRefPubMedPubMedCentral
29.
Eke AC, Shoji K, Best BM, Momper JD, Stek AM, Cressey TR, et al. Population pharmacokinetics of tenofovir in pregnant and postpartum women using tenofovir disoproxil fumarate. Antimicrob Agents Chemother. 2021;65(3):e02168-e2172.CrossRefPubMedPubMedCentral
30.
Tanaudommongkon A, Chaturvedula A, Hendrix CW, Fuchs EJ, Shieh E, Bakshi RP, et al. Population pharmacokinetics of tenofovir, emtricitabine and intracellular metabolites in transgender women. Br J Clin Pharmacol. 2022;88(8):3674–82.CrossRefPubMed
31.
Beal SL. Ways to fit a PK model with some data below the quantification limit. J Pharmacokinet Pharmacodyn. 2001;28(5):481–504.CrossRefPubMed
32.
Zheng JH, Rower C, McAllister K, Castillo-Mancilla J, Klein B, Meditz A, et al. Application of an intracellular assay for determination of tenofovir-diphosphate and emtricitabine-triphosphate from erythrocytes using dried blood spots. J Pharm Biomed Anal. 2016;122:16–20.CrossRefPubMedPubMedCentral
33.
Lee WA, He GX, Eisenberg E, Cihlar T, Swaminathan S, Mulato A, et al. Selective intracellular activation of a novel prodrug of the human immunodeficiency virus reverse transcriptase inhibitor tenofovir leads to preferential distribution and accumulation in lymphatic tissue. Antimicrob Agents Chemother. 2005;49(5):1898–906.CrossRefPubMedPubMedCentral
34.
Burns RN, Hendrix CW, Chaturvedula A. Population pharmacokinetics of tenofovir and tenofovir-diphosphate in healthy women. J Clin Pharmacol. 2015;55(6):629–38.CrossRefPubMedPubMedCentral
35.
Greene SA, Chen J, Prince HMA, Sykes C, Schauer AP, Blake K, et al. Population modeling highlights drug disposition differences between tenofovir alafenamide and tenofovir disoproxil fumarate in the blood and semen. Clin Pharmacol Ther. 2019;106(4):821–30.CrossRefPubMed
36.
Garrett KL, Chen J, Maas BM, Cottrell ML, Prince HA, Sykes C, et al. A pharmacokinetic/pharmacodynamic model to predict effective HIV prophylaxis dosing strategies for people who inject drugs. J Pharmacol Exp Ther. 2018;367(2):245–51.CrossRefPubMedPubMedCentral
37.
Jayachandran P, Garcia-Cremades M, Vučićević K, Bumpus NN, Anton P, Hendrix C, et al. A mechanistic in vivo/ex vivo pharmacokinetic-pharmacodynamic model of tenofovir for HIV prevention. CPT Pharmacometr Syst Pharmacol. 2021;10(3):179–87.CrossRef
38.
Duwal S, Schütte C, von Kleist M. Pharmacokinetics and pharmacodynamics of the reverse transcriptase inhibitor tenofovir and prophylactic efficacy against HIV-1 infection. PLoS ONE. 2012;7(7):e40382.CrossRefPubMedPubMedCentral
39.
Baheti G, Kiser JJ, Havens PL, Fletcher CV. Plasma and intracellular population pharmacokinetic analysis of tenofovir in HIV-1-infected patients. Antimicrob Agents Chemother. 2011;55(11):5294–9.CrossRefPubMedPubMedCentral
40.
Baheti G, King JR, Acosta EP, Fletcher CV. Age-related differences in plasma and intracellular tenofovir concentrations in HIV-1-infected children, adolescents and adults. AIDS. 2013;27(2):221–5.CrossRefPubMed
41.
Devanathan AS, Dumond JB, Anderson DJC, Moody K, Poliseno AJ, Schauer AP, et al. A novel algorithm to improve PrEP adherence monitoring using dried blood spots. Clin Pharmacol Ther. 2023;113(4):896–903.CrossRefPubMed
42.
Tseng A, Hughes CA, Wu J, Seet J, Phillips EJ. Cobicistat versus ritonavir: similar pharmacokinetic enhancers but some important differences. Ann Pharmacother. 2017;51(11):1008–22.CrossRefPubMedPubMedCentral
