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Journal of Antimicrobial Chemotherapy logoLink to Journal of Antimicrobial Chemotherapy
. 2022 Feb 23;77(5):1396–1403. doi: 10.1093/jac/dkac051

Predictors of 007 triphosphate concentrations in dried blood spots in persons with hepatitis C and active drug or alcohol use

Kristina M Brooks 1, Jose R Castillo-Mancilla 2, Mary Morrow 3, Samantha Mawhinney 4, Sarah E Rowan 5,6, David Wyles 7,8, Joshua Blum 9, Ryan Huntley 10, Lana Salah 11, Arya Tehrani 12, Leah C Jimmerson 13, Laura Roon 14, Lane R Bushman 15, Peter L Anderson 16, Jennifer J Kiser 17,✉
PMCID: PMC9633722  PMID: 35194648

Abstract

Background

Sofosbuvir is converted to its active form, 007 triphosphate (007-TP), within cells. To date, the association between treatment adherence and 007-TP in dried blood spots (DBS) and factors that influence this relationship remain unknown.

Objectives

To examine relationships between adherence and 007-TP concentrations in DBS and identify factors that influence 007-TP in DBS.

Methods

Persons with HCV or HIV/HCV coinfection and self-reported drug and/or alcohol use were randomized to one of two technology-based approaches for monitoring 12 week adherence to once-daily ledipasvir/sofosbuvir. Convenience blood samples were collected every 2 weeks during treatment. 007-TP in DBS was quantified using LC/MS and analysed using mixed-effects models.

Results

A total of 337 observations were available from 58 participants (78% male; 21% black; 22% Hispanic/Latino; 26% cirrhotic; 78% HIV-coinfected). The mean half-life of 007-TP in DBS was 142 h (95% CI 127–156) and concentrations increased by 7.3% (95% CI 2.2–12.6) for every 10% increase in between-visit adherence. Geometric mean (95% CI) 007-TP concentrations in DBS were 301 (247–368), 544 (462–639) and 647 (571–723) fmol/punch by adherence categories of ≤50%, >50 to ≤80%, and >80%. Adherence, time on therapy, increasing age and decreased estimated glomerular filtration rate were associated with higher 007-TP, whereas increased time since last dose, male sex, black race and higher BMI were associated with lower 007-TP.

Conclusions

007-TP has an extended half-life in DBS and concentrations increased with adherence. Further research is needed to examine additional factors that affect 007-TP and the clinical utility of this measure.

Introduction

Direct-acting antiviral (DAA) therapies cure hepatitis C in more than 95% of those treated,1–4 suggesting a high rate of forgiveness for missed doses. However, there are limited data on adherence–efficacy relationships for DAA. Defining adherence–efficacy relationships for DAA is important for clinical decision-making. For instance, it is unclear whether to continue or stop DAA treatment for likely futility when DAA doses are missed. DAA adherence has been captured using traditional tools such as self-report/pill counts,5–8 but these tend to overestimate true adherence.9 A few studies have evaluated DAA adherence using electronic blister packs/pill boxes9–13 and more novel technology approaches that capture actual medication ingestion.13–16 Though these approaches have improved our understanding of adherence–efficacy relationships, some approaches may be challenging to implement on a large scale or may require proficiency with the use of technology-based methods. Other objective adherence monitoring approaches, such as pharmacology-based measures in the blood or other matrices, could be alternative, simple adherence monitoring tools for HCV treatment.

Adherence monitoring using dried blood spots (DBS) has been examined extensively with other nucleos(t)ide analogues used in HIV treatment and prevention, notably tenofovir and emtricitabine. Concentrations of the phosphorylated forms of these nucleos(t)ide analogues in DBS has been used to measure cumulative medication adherence17–22 and assess relationships with a variety of clinical outcomes.23–30 However, this research has not been extended to other nucleos(t)ide analogues. Sofosbuvir (GS-7977) is a key component of multiple recommended DAA regimens to cure chronic HCV infection.31 Sofosbuvir is a phosphoramidate nucleotide prodrug that inhibits viral NS5b polymerase following conversion to its active uridine triphosphate form, GS-461203 (also known as 007-TP), within hepatocytes.32–34 Similar to other nucleos(t)ide inhibitors, 007-TP has an extended half-life within cells in comparison with the prodrug form, with estimated half-lives of ~14–26 h in PBMCs and ~69–104 h in RBCs35,36 versus ~0.4 h for sofosbuvir in plasma.34,37 The conversion of sofosbuvir first proceeds through a series of hydrolysis steps, initially by carboxylesterase 1 (CES1) in hepatocytes and then histidine triad nucleotide-binding protein 1 (HINT1) to yield the monophosphate form, GS-331007 MP. Intracellular kinases then sequentially convert the monophosphate to the active triphosphate form.38,39

The extended half-life of 007-TP in RBCs may confer the ability to examine cumulative medication adherence to sofosbuvir-containing DAA regimens in persons with HCV. Data on the intracellular pharmacokinetics of sofosbuvir in persons with HCV and HIV/HCV are limited.35 Previous estimates of 007-TP half-life in DBS were based on a limited sample size of sparsely collected samples in the absence of adherence monitoring.35 Furthermore, no studies have examined relationships between adherence and 007-TP concentrations in persons with HCV or HIV/HCV coinfection. Active drug use and the concomitant use of antiretroviral medications in persons with HIV/HCV coinfection may also alter 007-TP pharmacokinetics. Collectively, there is a need to prospectively examine whether 007-TP in DBS could be used as an objective, pharmacokinetic-based adherence monitoring tool in persons with HCV and better understand factors that may affect 007-TP concentrations in RBCs.

INCLUD was an open-label study to characterize adherence to and the pharmacology of ledipasvir/sofosbuvir (Harvoni®, Gilead Sciences, Inc.) in persons with HCV and HIV/HCV and active drug and/or alcohol use. Sustained virological response (SVR) rates were 86.7% in the ITT population and 94.5% in the as-treated population in this study, and we previously identified a number of factors that were associated with treatment adherence.13 The objectives of these analyses were to examine relationships between 007-TP concentrations in DBS and ledipasvir/sofosbuvir adherence, as measured using technology-based adherence monitoring, and to identify factors that influence the pharmacokinetics of 007-TP in DBS.

Materials and methods

Study population and design

Data originated from the INCLUD study, as previously described (NCT02573376).13 Briefly, participants had to be actively using drugs and/or alcohol by self-report within 30 days of screening. Eligible participants were then randomized 1:1 to either video-based directly observed therapy (vDOT, miDOT by emocha®) or wirelessly observed therapy (Wisepill®) to assess DAA adherence during treatment. Randomization was stratified by cirrhosis status and injection drug use (IDU) at entry. All participants received ledipasvir 90 mg/sofosbuvir 400 mg once daily for 12 weeks, and study visits occurred every 2 weeks over the 12 week treatment period. Alcohol and drug use was assessed by self-report and urine toxicology screen at each of these study visits.

Ethics

This study was reviewed and approved by the Colorado Multiple Institutional Review Board (15-0809) and all study procedures were performed in accordance with the Declaration of Helsinki. All participants provided written informed consent.

Pharmacokinetic sampling and analysis

Timed blood samples were collected during the 24 h intensive pharmacokinetic assessment on Day 1 and single convenience blood samples were collected at Weeks 2, 4, 6, 8, 10 and 12 of ledipasvir/sofosbuvir treatment. A single washout sample was also collected following treatment completion. Blood samples were processed for DBS, plasma and PBMCs across all timepoints using previously described methods.18,40 007-TP concentrations in DBS were quantified from a single 7 mm punch using LC-MS/MS methods.35 The half-life of 007-TP in DBS was estimated using a one-compartment extravascular model and multiplicative-additive error model (Phoenix NLME® v8.3.1, Certara LP, Princeton, NJ, USA). In this model, the absorption rate constant (ka) reflected the formation rate of 007-TP, volume was a scalar and the elimination rate constant (ke) was reflective of 007-TP elimination from RBCs.

Statistical analysis

Participant demographics and drug use were summarized using descriptive statistics. Adherence was calculated as the number of pillbox openings or video-recorded medication ingestions divided by the number of days between study visits. 007-TP concentrations were log-transformed prior to analysis with mixed models to account for repeated measures (SAS version 9.4). Geometric mean (95% CI) 007-TP concentrations by adherence categories of <50%, 50% to ≤80%, and >80% were calculated using steady-state data from Weeks 4–10 of treatment. For predictors of 007-TP concentrations in DBS, the base model included between-visit adherence, number of days on study, and time since the last sofosbuvir dose. Additional predictors of 007-TP concentrations were then screened on an individual basis with the base model, which included age, sex at birth, race (black versus non-black), estimated glomerular filtration rate (eGFR), BMI, albumin, haematocrit, HIV coinfection, cirrhosis and adherence monitoring device; and use of alcohol (binge and heavy),41 marijuana, opioids, methamphetamine, cocaine and IDU by self-report and/or positive urine toxicology screen. Individual covariates with P < 0.20 were then carried forward into the final multivariable model, followed by backward selection of covariates with P < 0.10. Sensitivity of the model to high-influence datapoints based on Cook’s D test was reviewed. In the subset of participants with HIV coinfection, the concomitant use of certain antiretroviral medications was also screened on the basis of previous drug–drug interaction studies [use of pharmacoenhancer (ritonavir or cobicistat), darunavir, elvitegravir and NRTIs].34,42 The proportion of variance that was accounted for by fixed and random effects in the final multivariable mixed model was determined based on previously published approaches for longitudinal data.43–46

Results

Participant demographics

A total of 58 participants had pharmacokinetic results from at least one study visit, equating to 337 observations available for analysis. Participant demographics and drug use throughout treatment are summarized in Tables 1 and 2, respectively, by HIV status. Among the subset of persons with HIV (PWH) (n = 45), concomitant HIV medications included NRTIs (98%), integrase strand transfer inhibitors (INSTIs) (71%), boosted PIs (36%) and NNRTIs (7%). Of those taking NRTIs, 16 (36%) were on tenofovir disoproxil fumarate, 18 (41%) were on tenofovir alafenamide and 10 (23%) were on abacavir.

Table 1.

Baseline demographics

Characteristic Without HIV (N = 13) With HIV (N = 45) Total (N = 58)
Sex at birth, n (%)
 Male 9 (69) 36 (80) 45 (78)
 Female 4 (31) 9 (20) 13 (22)
Transgender women, n (%) 0 (0) 2 (4) 2 (3)
Race, n (%)
 White 12 (92) 31 (69) 43 (74)
 Black 1 (8) 11 (24) 12 (21)
 Native American/Alaska native 0 (0) 3 (7) 3 (5)
 Hispanic or Latino 3 (23) 10 (22) 13 (22)
Age (years), median (IQR) 54 (50–56) 50 (46–55) 51 (46–55)
Weight (kg), median (IQR) 84 (74–86) 70 (63–78) 71 (63–84)
BMI (kg/m2), median (IQR) 25 (24–29) 24 (21–26) 24 (22–28)
eGFR (mL/min/1.73 m2), median (IQR) 98 (92–105) 84 (71–103) 90 (72–104)
Haematocrit (%), median (IQR) 45 (43–46) 44 (41–46) 44 (41–46)
HCV genotype, n (%)
 1 1 (8) 4 (9) 5 (9)
 1a 9 (69) 28 (62) 37 (64)
 1b 2 (15) 11 (24) 13 (22)
 4/4a 1 (8) 2 (4) 3 (5)
Transient elastography score, median (range) 9 (5–57) 8 (4–71) 8 (4–71)
Cirrhosis present, n (%) 5 (38) 10 (22) 15 (26)
IVDU, n (%) 1 (8) 15 (33) 16 (28)
Adherence monitoring, n (%)
 WOT 7 (54) 23 (51) 30 (52)
 DOT 6 (46) 22 (49) 28 (48)
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WOT, wirelessly observed therapy using wireless pillboxes.

Table 2.

Person-visits with drug use by self-report or positive urine toxicology screen by HIV status

Drug or alcohol use Without HIV (N = 74) With HIV (N = 263) Overall (N = 337)
Marijuana, n (%) 21 (28) 179 (68) 200 (59)
Stimulants, n (%) 34 (46) 135 (51) 169 (50)
Methamphetamine, n (%) 28 (38) 98 (37) 126 (37)
Opioids, n (%) 19 (26) 53 (20) 72 (21)
Cocaine, n (%) 14 (19) 43 (16) 57 (17)
Alcohol 43 (58) 145 (55) 188 (56)
 Heavy drinking, n (%)a 26 (35) 37 (14) 63 (19)
 Binge drinking, n (%)b 24 (32) 43 (16) 67 (20)
 Average number of self-reported daily drinksc      
  Mean (SD) 3 (3) 1 (2) 1 (3)
  Range 0–17 0–16 0–17
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a

Heavy alcohol use was defined as ≥8 drinks per week for women and ≥15 drinks per week for men.

b

Binge alcohol use was defined as ≥4 drinks per day for women and ≥5 drinks per day for men.

c

One observation missing for average number of daily drinks.

007-TP pharmacokinetics and relationships with adherence

The mean (95% CI) half-life of 007-TP in DBS was 142 (127–156) h. 007-TP concentrations increased by 7% (95% CI 2.2–12.6) for every 10% increase in between-visit adherence after controlling for clinical and demographic factors (Figure 1). When between-visit adherence was categorized to <50%, 50% to ≤80%, and >80%, geometric mean 007-TP concentrations were 301, 544 and 647 fmol/punch, respectively (Figure 2). 007-TP concentrations ranges were 31–807, 79–1371 and 227–1602 fmol/punch in each of these categories, respectively.

Figure 1.

Figure 1.

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Log-transformed 007-TP concentrations in DBS by average between-visit adherence. Each individual point corresponds to an individual observation (N = 224 in total). The shaded blue line represents the main effect and 95% CI from a multivariable mixed model adjusting for time on study, days since last dose, age, sex at birth, race (black versus non-black), eGFR and BMI. This figure appears in colour in the online version of JAC and in black and white in the print version of JAC.

Figure 2.

Figure 2.

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007-TP concentrations in DBS by average between-visit adherence (Weeks 4–10). Data presented as geometric mean (95% CI) from a mixed model accounting for repeated measures. n reflects total number of observations available in each category. The number of participants contributing to each category included 10 in the ≤50% adherence category, 19 in the >50 to ≤80% adherence category, and 52 in the >80% adherence category (observations from individual participants may have contributed to more than one adherence category). This figure appears in colour in the online version of JAC and in black and white in the print version of JAC.

Predictors of 007-TP concentrations

The base model included between-visit adherence, time on study and time since the last sofosbuvir dose. After controlling for these factors, individual covariates that met the threshold for inclusion in the backward selection process included age [8.5% increase per 10 years (95% CI −4.3 to 23.0); P = 0.199], sex at birth [24.1% lower in male versus female participants (95% CI −40.6 to −2.9); P = 0.029], race [30% lower in black versus non-black (95% CI −45.4 to −10.2%); P = 0.006], BMI [−2.8% decrease per every 1 kg/m2 increase (95% CI −5.0 to −0.6%); P = 0.012], eGFR [3.7% increase for every 10 mL/min/1.73 m2 decrease (95% CI 1.0–6.1%); P = 0.007] and methamphetamine use [7.0% higher in users versus non-users (95% CI −2.7 to 17.6); P = 0.16]. In the final multivariable model, age, sex at birth, race, eGFR and BMI were retained as significant predictors of 007-TP concentrations in DBS (see Figure 3). Screening using Cook’s D identified four observations that met criteria for high-influence points (one from a black female participant, two from non-black female participants and one from a non-black male participant). Running a sensitivity analysis without these four datapoints, the effect of black race was attenuated [17.6% lower (95% CI −33.2 to 1.7); P = 0.071], but other predictors were similar to the full model. Collectively, the identified covariates in the final multivariable models accounted for 41%–46% of the variability in 007-TP. Inclusion of random effects increased the total amount of variability that was accounted for up to 80%–84%.

Figure 3.

Figure 3.

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Forest plot of factors that influenced 007-TP concentrations in DBS in the full study population (top) and HIV subset (bottom) from the final multivariable models. Data presented as point estimates (95% CI) of the change in 007-TP concentrations following back transformation to the original scale.

Subset of population with HIV coinfection

A total of 45 participants (263 observations) were included in the subset of PWH. In comparison with the full model, eGFR and age became less significant (see Figure 3). The concomitant use of pharmacoenhancers, darunavir and elvitegravir did not significantly affect 007-TP concentrations (P = 0.81, 0.63 and 0.56, respectively). However, the concomitant use of NRTIs was significant (P = 0.007). Pairwise comparisons of tenofovir alafenamide versus tenofovir disoproxil fumarate, tenofovir alafenamide versus abacavir, and tenofovir disoproxil fumarate versus abacavir revealed that persons on tenofovir disoproxil fumarate-containing regimens had significantly lower 007-TP concentrations. This finding was further probed by examining categories of tenofovir disoproxil fumarate use, pharmacoenhancer use and the interaction of tenofovir and pharmacoenhancer use, given pharmacokinetic enhancers can substantially increase plasma and intracellular concentrations of tenofovir and tenofovir diphosphate, respectively.47 007-TP concentrations were 32.1% lower (95% CI −48.4 to −10.8; P = 0.006) in participants on tenofovir disoproxil fumarate, whereas the presence of a pharmacoenhancer alone and the interaction of tenofovir disoproxil fumarate with an enhancer were not significant [14.5% lower (95% CI −30.9 to 5.9), P = 0.15; and 38.3% higher (95% CI −3.7 to 98.6), P = 0.079, respectively]. The use of tenofovir disoproxil fumarate was retained as a significant covariate in the final multivariable model in PWH [geometric mean ratio of −19.1% with tenofovir disoproxil fumarate versus no tenofovir disoproxil fumarate (95% CI −32.7 to −2.9); P = 0.023].

Discussion

007-TP has an extended half-life in RBCs, permitting use of this moiety in DBS as an adherence monitoring tool in persons with HCV receiving sofosbuvir-containing DAA therapy. 007-TP concentrations in DBS increased as adherence to ledipasvir/sofosbuvir increased. Several covariates were identified that influenced 007-TP concentrations in DBS in our study population, including adherence, time on study, time since the last sofosbuvir dose, age, sex at birth, race, eGFR and BMI. Tenofovir disoproxil fumarate was also associated with lower 007-TP concentrations in DBS in the subset of PWH. Covariates collectively explained ~45% of the variability in 007-TP concentrations in DBS. Our findings provide valuable information towards further developing and implementing this pharmacological tool as an adherence biomarker in persons with HCV.

Our group has performed extensive work with DBS adherence monitoring with NRTI medications in the HIV prevention and treatment fields. For tenofovir specifically, adherence gradients for both tenofovir disoproxil fumarate and tenofovir alafenamide have been identified, which can be used to assess adherence over the previous 2–3 months of therapy.17,18,20 These gradients continue to be further refined based on demographic, clinical and biological factors that may affect tenofovir diphosphate concentrations,26,48 and a number of relationships between intracellular concentrations of these drugs and clinical outcomes have been examined. Our original goal in INCLUD was to examine whether this methodology could be extended to sofosbuvir as well as to serve as a pharmacology-based tool to assess adherence to DAA regimens containing sofosbuvir. The extended half-life of 007-TP in RBCs in our previous analysis35 and this controlled follow-up study suggested that this would be possible. However, substantial variability in 007-TP concentrations in DBS was observed, even in those with 100% adherence between visits.

Given the wide variability in 007-TP concentrations in DBS after controlling for adherence, we examined additional factors that influenced 007-TP concentrations in DBS. Age, sex at birth, race, eGFR and BMI were among the factors identified after controlling for adherence, time since last dose and time on therapy. A previous study by our group in persons receiving sofosbuvir with ribavirin identified that 007-TP concentrations in RBCs were 42% lower among male participants versus female participants.35 Some of the newly identified factors overlap with other population pharmacokinetic analyses that have been performed for plasma sofosbuvir and its GS-331007 metabolite. Factors previously associated with higher plasma sofosbuvir exposures include hepatic impairment,49 female sex,50–52 decreased renal function,49,52 food51 and concomitant use of elvitegravir/cobicistat.42,53 Factors associated with higher exposures of GS-331007, the major plasma metabolite of sofosbuvir, included decreased renal function49,50 and female sex,49,51 whereas lower GS-331007 exposures were associated with non-white race49,51 and the concomitant use of darunavir/ritonavir.53 Collectively, the similarity between patterns identified in plasma variables previously and in our analyses suggest that alterations in plasma exposures affect what is ultimately measured within cells.

Follow-up analyses to examine the degree of variability in 007-TP concentrations in DBS revealed that 41%–46% of variability in the 007-TP levels in DBS was accounted for by the covariates in our final model, which included between-visit adherence, days on study, time since last sofosbuvir dose, age, sex at birth, race, eGFR and BMI. When accounting for random effects, the total degree of variability that was accounted for ranged between 80% and 84%. This indicates that there are other factors (not captured) that are likely contributing to the variability in 007-TP in DBS. A previous pharmacokinetic analysis identified a lack of dose proportionality in sofosbuvir plasma exposures across a wide array of doses.54 Stable parent-to-metabolite ratios and greater parent and metabolite recovery in urine with lower doses were revealed, suggesting that variable drug absorption rather than differences in clearance may have been a contributing factor. Relationships between plasma and intracellular sofosbuvir, the potential influence of comorbid conditions, the additive influence of ledipasvir42 and genetic polymorphisms in hydrolysing enzymes or phosphorylating kinases involved in 007-TP formation may be worth investigating in future studies.

When restricting our analysis to PWH, many of the same clinical and demographic predictors of 007-TP in DBS were identified. Renal function lost significance, which may be due in part to the lower baseline eGFRs that were measured in this population and thus a lesser range of values for comparison. Age also lost significance, but this may be explained in part by its borderline significance in the full study population and reduced number of observations in the HIV subset. The mechanism behind lower 007-TP concentrations in DBS with the concomitant use of tenofovir disoproxil fumarate is unclear, but unlikely to have clinical significance. These lower concentrations were not explained by differences in baseline renal function between those on tenofovir disoproxil fumarate versus other NRTIs. Lower plasma sofosbuvir concentrations were identified previously with the combination of ritonavir-boosted darunavir, tenofovir disoproxil fumarate and emtricitabine.37,42 However, we did not find an effect of darunavir or the use of pharmacoenhancers on 007-TP in DBS. Our group previously detailed the drug–drug interaction between sofosbuvir and tenofovir disoproxil fumarate that resulted in high concentrations of tenofovir diphosphate in PBMCs and DBS,47,55 which has been primarily attributed to sofosbuvir’s inhibition of CES2-mediated tenofovir disoproxil fumarate hydrolysis.56 Tenofovir disoproxil fumarate and/or tenofovir monoester may also be hydrolysed by CES1 in the liver, which is the primary hydrolysing enzyme for sofosbuvir, and other hydrolysing enzymes and kinases involved in tenofovir conversion in RBCs may also overlap with sofosbuvir’s conversion in this cell type. RBCs have been shown to preferentially phosphorylate non-thymidine, and more so adenosine, analogues in comparison with PBMCs. Therefore, it is possible that with greater delivery of the prodrug and intermediate form of tenofovir delivered into the blood,57 and ~18-fold higher tenofovir diphosphate concentrations in RBCs,47 hydrolysing enzymes or intracellular kinases involved in sofosbuvir activation may either be saturated or preferentially convert tenofovir with this combination. Though tenofovir alafenamide is also hydrolysed by CES1, no clinically significant drug–drug interactions have been identified with the combined use of sofosbuvir and tenofovir alafenamide,58,59 nor was an interaction identified in this study (which may be due to the specificity of the sofosbuvir–tenofovir disoproxil fumarate interaction through CES2 inhibition).60

Our findings provide novel insights into the use of an easy-to-collect, quantitative pharmacological measure of adherence among a real-world population of persons with HCV and active drug/alcohol use. However, there are limitations to this study. Though technology-based measures of adherence were used, only video-based DOT captured ingestion of ledipasvir/sofosbuvir tablets, whereas opening of the Wisepill® device was assumed to indicate medication ingestion. The type of adherence monitoring device used was not a significant predictor in the initial covariate screen, suggesting any discrepancies between openings and actual medication ingestions were not widespread, but a carefully controlled video-based DOT study could potentially help with further reducing variability in our measures. Our findings are limited to a total of 58 participants, and the majority of observations available for analysis fell in the >80% adherence category. However, we collected up to eight blood samples per participant over 12 weeks of treatment, which strengthened our ability to examine intra- and intersubject variability and better understand the influence of various covariates over time. Due to the limited sample size and small number of confirmed failures in the original study [3/55 (5.5%) in the as-treated population],13 further assessments of associations between 007-TP concentrations in DBS and the probability of cure or failure were not pursued. However, this is an area that may warrant further investigation in larger studies to better determine the clinical utility of this adherence monitoring tool. Whether the 007-TP concentrations in DBS measured in this study are similar across other sofosbuvir-containing DAA regimens will also be important to evaluate as ledipasvir can increase plasma sofosbuvir exposures by 130%, though velpatasvir increases plasma sofosbuvir exposures by a similar magnitude.51 Additional studies to compare average concentrations across different sofosbuvir-containing DAA regimens may need to be conducted to better understand how concomitant DAAs affect 007-TP in DBS.

007-TP has an extended half-life in RBCs as measured in DBS, and 007-TP concentrations increase as adherence increases, collective factors of which may confer the ability to use this measure as an adherence monitoring tool in persons with HCV. After controlling for adherence, age, sex at birth, race, eGFR and BMI were identified as factors that significantly influenced 007-TP in RBCs. Collectively, ~45% of 007-TP variability in DBS was explained by these factors, suggesting additional research is needed to identify other factors that may influence 007-TP concentrations in RBCs, further refine adherence benchmarks and to assess the clinical utility of this adherence monitoring approach in persons with HCV.

Acknowledgements

Portions of these data were presented at the International Workshop on Clinical Pharmacology of Antiviral Therapy in Baltimore, MD, USA in June 2018 (Abstract #O_02) and virtually at the Conference on Retroviruses and Opportunistic Infections (CROI) in March 2020 (Abstract #576LB).

We would like to thank the study participants; clinical staff at the University of Colorado Clinical Translational Research Center (CTRC), the Infectious Disease Group Practice at the University of Colorado Hospital, and Denver Health and Hospital Authority; and members of the Colorado Antiviral Pharmacology Laboratory. The University of Colorado is a Certara Center of Excellence. The Center of Excellence programme supports leading institutions with Certara’s state-of-the-art model-informed drug development software.

Contributor Information

Kristina M Brooks, Department of Pharmaceutical Sciences, University of Colorado Anschutz Medical Campus, Aurora, CO, USA.

Jose R Castillo-Mancilla, Department of Medicine, Division of Infectious Diseases, University of Colorado Anschutz Medical Campus, Aurora, CO, USA.

Mary Morrow, Colorado School of Public Health, University of Colorado Anschutz Medical Campus, Aurora, CO, USA.

Samantha Mawhinney, Colorado School of Public Health, University of Colorado Anschutz Medical Campus, Aurora, CO, USA.

Sarah E Rowan, Department of Medicine, Division of Infectious Diseases, University of Colorado Anschutz Medical Campus, Aurora, CO, USA; Denver Health and Hospital Authority, Denver, CO, USA.

David Wyles, Department of Medicine, Division of Infectious Diseases, University of Colorado Anschutz Medical Campus, Aurora, CO, USA; Denver Health and Hospital Authority, Denver, CO, USA.

Joshua Blum, Denver Health and Hospital Authority, Denver, CO, USA.

Ryan Huntley, Department of Pharmaceutical Sciences, University of Colorado Anschutz Medical Campus, Aurora, CO, USA.

Lana Salah, Department of Pharmaceutical Sciences, University of Colorado Anschutz Medical Campus, Aurora, CO, USA.

Arya Tehrani, Department of Pharmaceutical Sciences, University of Colorado Anschutz Medical Campus, Aurora, CO, USA.

Leah C Jimmerson, Department of Pharmaceutical Sciences, University of Colorado Anschutz Medical Campus, Aurora, CO, USA.

Laura Roon, Department of Pharmaceutical Sciences, University of Colorado Anschutz Medical Campus, Aurora, CO, USA.

Lane R Bushman, Department of Pharmaceutical Sciences, University of Colorado Anschutz Medical Campus, Aurora, CO, USA.

Peter L Anderson, Department of Pharmaceutical Sciences, University of Colorado Anschutz Medical Campus, Aurora, CO, USA.

Jennifer J Kiser, Department of Pharmaceutical Sciences, University of Colorado Anschutz Medical Campus, Aurora, CO, USA.

Funding

This study was supported by the National Institute on Drug Abuse at the National Institutes of Health (R01 DA040499 to J.J.K.) and by NIH/NCATS Colorado CTSA (UL1 TR002535). Study medication was donated by Gilead Sciences, Inc. Contents are the authors’ sole responsibility and do not necessarily represent official views by the NIH or Gilead Sciences, Inc.

Transparency declarations

P.L.A. receives consulting fees, research funding (paid to his institution) and donated study medication from Gilead Sciences, Inc. J.J.K. receives research funding (paid to her institution) and received donated study medication for this study from Gilead Sciences, Inc. For the remaining authors, none were declared.

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