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. Author manuscript; available in PMC: 2019 Jan 1.
Published in final edited form as: Antivir Ther. 2018;23(4):345–351. doi: 10.3851/IMP3209

Inflammation investigated as a source of pharmacokinetic variability of atazanavir in AIDS Clinical Trials Group protocol A5224s

Charles S Venuto 1,2,*, Jihoon Lim 1, Susan Messing 3, Peter W Hunt 4, Grace A McComsey 5, Gene D Morse 2
PMCID: PMC5967996  NIHMSID: NIHMS958625  PMID: 29171837

Abstract

Background

Inflammation is associated with the downregulation of drug metabolizing enzymes and transporters. Thus, we investigated the chronic inflammatory state associated with HIV-infection as a source of pharmacokinetic variability of atazanavir. We also explored the association of total bilirubin concentrations with markers of inflammation and endothelial activation.

Methods

Apparent oral clearance (CL/F) of atazanavir was estimated from plasma samples collected from participants in AIDS Clinical Trials Group Study A5202. Several inflammatory and endothelial activation biomarkers were measured at baseline and weeks 24 and 96 as part of metabolic sub-study A5224s: high-sensitivity C-reactive protein (hsCRP), interleukin-6, tumor necrosis factor alpha and its soluble receptors, soluble vascular cellular and intracellular adhesion molecules, and total bilirubin. Statistical analysis was performed by a matrix of correlation coefficients between atazanavir CL/F and biomarker concentrations measured at week 24. The correlation between atazanavir clearance and percentage change in bilirubin from baseline to weeks 24 and 96, and between biomarkers and bilirubin concentrations at each week were also evaluated.

Results

Among 107 participants, there were no significant correlations observed between atazanavir CL/F and inflammatory and endothelial activation biomarkers measured at week 24 (P ≥ 0.24). As expected, bilirubin increased with increasing exposure to atazanavir (rho = −0.25, P = 0.01). Bilirubin concentrations were inversely correlated (P < 0.01) with each of the biomarkers except hsCRP.

Conclusions

Atazanavir CL/F did not correlate with the inflammatory biomarkers changes. Inflammatory-mediated inhibition of cytochrome P450 3A may have been attenuated due to atazanavir-associated increases of bilirubin, which has known anti-inflammatory properties.

BACKGROUND

Despite antiretroviral therapy (ART), persons with HIV-1 have persistent, low-grade inflammation and immune activation that are associated with an increased risk of AIDS and non-AIDS events [1,2]. As part of the inflammatory response to HIV infection, endothelial activation and release of vascular adhesion molecules occurs, and several different markers reflecting ongoing inflammation and endothelial activation increase in HIV infected persons even after long-term combination ART [3]. Although multiple studies have demonstrated improvements in inflammatory cytokine levels after undetectable virus levels are achieved with ART, some cytokine levels do not completely normalize compared to HIV uninfected individuals [4–6]. The physiological changes accompanying inflammation may alter the pharmacokinetics of certain medications. These changes include an increase in gastric pH, increasing and decreasing concentrations of plasma proteins, and reduction in the expression and function of drug metabolizing enzymes and membrane transporters [7,8]. For instance, inflammatory cytokines and other agents associated with inflammatory responses, such as interleukin-1 (IL-1), IL-6, interferon-γ, IL-1β, and bacterial lipopolysaccharaide are associated with the downregulation of cytochrome P450 (CYP) 3A mRNA and protein expression [9]. Similarly, IL-6 and perhaps tumor necrosis factor-alpha (TNF-α) suppress P-glycoprotein (P-gp) expression in hepatic, intestinal, and brain tissues [10].

Clinical effects resulting from these inflammatory-induced alterations are observed in various acute and chronic inflammatory conditions [11–14]. In lung and breast cancer patients, clearance of erythromycin (a CYP3A substrate and moderate inhibitor) was inversely correlated with plasma levels of the inflammatory acute-phase protein, C-reactive protein (CRP) and IL-6 [12]. In patients receiving bone marrow transplantations, elevated levels of IL-6 and TNF-α were correlated with reduced drug metabolism of cyclosporine (a CYP3A and P-gp substrate and inhibitor) [14]. Also, plasma trough concentrations of voriconazole (a CYP2C19, 3A4, and 2C9 substrate) are estimated to increase on average by 0.014 mg/liter for every 1 mg/liter increase in CRP concentration due to reduction in CYP-mediated metabolism [15]. Given the chronic inflammatory state associated with HIV-infection, inflammatory-mediated cytokines may be a source of pharmacokinetic variability of antiretrovirals, especially those that are substrates or inhibitors of CYP3A or P-gp [16,17]. In a recent clinical study, HIV-infected ART-naïve participants had decreased intestinal expression of CYP3A4, measured from intestinal biopsies, compared to healthy uninfected controls. Interestingly, however, HIV-infected individuals receiving ART had increased gene and protein expression of ABCB1/P-gp compared to the HIV-infected ART-naïve group [18].

Atazanavir is a potent once-daily protease inhibitor that undergoes extensive hepatic metabolism via CYP3A4/5, with subsequent uridine diphosphate glucuronsyltransferase (UGT)-mediated glucuronidation [19]. It is also a substrate for the drug efflux transporter, P-gp. In the present study, we analyzed plasma atazanavir exposure and markers of residual inflammation and vascular endothelial activation, as well as total bilirubin levels measured in HIV-infected individuals to determine their association with one another. The primary objective was to explore the relationship between atazanavir exposure and plasma concentrations of inflammatory and endothelial activation markers, with the expectation that with 24 weeks or more of ARV therapy, atazanavir exposure would be higher in those with increased inflammation. We also assessed the relationship between total bilirubin levels and plasma concentrations of inflammatory and endothelial activation markers because of the potential anti-inflammatory effects of bilirubin [20–22].

METHODS

Study Design

Data for this exploratory analysis came from AIDS Clinical Trials Group A5224s, which was a metabolic substudy of A5202 (ClinicalTrials.gov NCT00118898). In A5202, HIV-1-infected treatment naïve adults (≥ 16 years of age), with HIV-1 RNA > 1000 copies/mL were randomized to open-label efavirenz (600 mg) or atazanavir/ritonavir (300 mg/100 mg), with double-blinded placebo-controlled tenofovir disproxil fumarate/emtricitabine (300 mg/200 mg) or abacavir/lamivudine (600 mg/300 mg) [23–25]. Randomization was stratified by HIV-1 RNA (< or ≥ 100,000 copies/mL) and by intent to participate in the metabolic substudy A5224s, in which select markers of inflammation and endothelial activation were measured longitudinally [26,27]. Enrollment inclusion criteria included participants to have a screening total bilirubin ≤ 2.5 x upper limit of normal (ULN), and aspartate aminotransferase, alanine aminotransferase, and alkaline phosphatase ≤ 5 x ULN. Human subjects committees of all sites approved the protocol, and informed consent was obtained from all participants. Primary analyses from these studies have been previously reported [23–27].

Measurements of Biomarkers

Fasting plasma samples for measurement of biomarkers were collected at study entry (baseline), week 24, and week 96. Markers of inflammation that were measured included high-sensitivity C-reactive protein (hsCRP), interleukin-6 (IL-6), tumor necrosis factor-α (TNF-α), and soluble receptors of TNF-α (sTNFR-I, and -II). Endothelial activation markers that were measured included soluble vascular cellular adhesion molecule (sVCAM-1) and soluble intracellular adhesion molecule (sICAM-1). Biomarker assays were analyzed as previously described [2,27]. Bilirubin was assayed at research site clinical laboratories at baseline, week 24, and week 96 as part of the parent A5202 protocol.

Measurement of Atazanavir Exposure

In A5202, plasma samples for atazanavir assay were collected at steady-state during the first 24 weeks of study follow-up. A sparse sampling study design was used to collect at least three samples per participant for population pharmacokinetic modeling, as has been previously described [28]. Adherence to medications was monitored using the ACTG self-reported adherence questionnaire. Additionally, the last three doses of antiretrovirals prior to collection of pharmacokinetic samples were reviewed by the University at Buffalo Pharmacology Specialty Laboratory to ensure adherence was documented on the case report forms. Atazanavir concentrations were quantified using a validated high-pressure liquid chromatography assay [29]. The atazanavir concentration-time data were used to construct a one-compartment population pharmacokinetic model with NONMEM VII (ICON Development Solutions, Ellicott City, MD). Individual Bayesian estimates of apparent oral clearance (CL/F) for atazanavir were generated for each study participant, such that an individual had a single CL/F value (liters/hour) representative of their exposure to atazanavir over the first 24 weeks of study participation.

Statistical Analysis

For this analysis, participant data were included if they had inflammation and endothelial activation markers measured at baseline and week(s) 24 and/or 96, and had an atazanavir plasma CL/F value estimated. Baseline characteristics, biomarker concentrations, and atazanavir CL/F were summarized by proportions or medians with interquartile ranges (IQR). Continuous measurements were compared using Wilcoxon rank-sum tests or Kruskal-Wallis tests. Spearman rank correlation tests were performed to determine the correlation between atazanavir CL/F and week 24 biomarker concentrations, while week 96 biomarker data were used for sensitivity analyses. Spearman rank correlation tests were also used to determine the correlation between atazanavir CL/F and the percent change in bilirubin levels from baseline to weeks 24 and 96 [(bilirubinweek24 − bilirubinweek0)/bilirubinweek0]. Pearson correlation coefficients are reported for analyses between total bilirubin and each of the biomarkers of inflammation and endothelial activation after they were log-transformed. For paired data with more than one time point for each study participant, an ordinary correlation coefficient is not appropriate because it does not take into account the lack of independence between repeated measurements of the same participant [30]. Instead, we calculated a “within participants” correlation coefficient, which removes the variation between individuals to examine, for example, whether an increase in bilirubin within the same individual is associated with a decrease in an inflammation marker. The level of significance was set at two-sided P-values <0.05. Power calculations were not performed, as this was an exploratory analysis that took advantage of data collected from previous conducted studies. Analyses were performed in either R 3.3.2 or STATA 14 (College Station, TX: StataCorp LP).

RESULTS

There were 269 study participants enrolled in A5224s, of which 130 were randomized to receive atazanavir/ritonavir-based treatment. A total of 107 participants had both biomarkers and atazanavir CL/F measured and were therefore included in this analysis. There were 82 participants who had biomarker concentrations measured at baseline and both weeks 24 and 96; 20 participants who had biomarker concentrations measured at baseline and week 24 only; and 5 participants with baseline and week 96 data only. Baseline characteristics of study participants are summarized in Table 1.

Table 1.

Baseline characteristics of study participants

Variable Study Participants (n=107)
Male; No. (%) 95 (89)
Self-reported race/ethnicity; n (%)
 White, non-Hispanic 45 (42)
 Black, non-Hispanic 38 (36)
 Hispanic 20 (19)
 Other 4 (4)
Randomized NRTI arm; n (%)
 Abacavir/Lamivudine 55 (51)
 Tenofovir/Emtricitabine 52 (49)
Age in years; median (IQR) 37 (31, 43)
BMI in kg/m2; median (IQR) 24.8 (21.7, 28.1)
CD4 in cells/mm3; median (IQR) 234 (78, 321)
Total bilirubin in mg/dL; median (IQR) 0.5 (0.4, 0.7)
Biomarkers; median (IQR)
 hsCRP in μg/mL 1.7 (0.7, 4.1)
 IL-6 in pg/mL 0.8 (0.6, 1.3)
 TNF-α in pg/mL 11.0 (7.8, 14.5)
 sTNFR-I in pg/mL 1224 (1034, 1596)
 sTNFR-II in pg/mL 4930 (3573, 7964)
 sVCAM-1 in ng/mL 1146 (920, 1542)
 sICAM-1 in ng/mL 329 (261, 418)
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The overall median [IQR] atazanavir CL/F was 7.6 [6.7, 9.4] liters/hour. Atazanavir CL/F was significantly faster in the randomized tenofovir disproxil fumarate/emtricitabine group at 8.7 [6.8, 10.1] liters/hour compared to the abacavir/lamivudine group of 7.3 [6.5, 8.0] liters/hour (p=0.006). Atazanavir CL/F did not differ significantly by sex or race/ethnicity, and was not significantly correlated with baseline body mass index or HIV-1 RNA laboratory values.

Correlations between atazanavir clearance and biomarkers

At baseline, none of the biomarkers were significantly correlated with atazanavir CL/F (p ≥ 0.19). The correlations between atazanavir CL/F with week 24 biomarker concentrations among all study participants, and by randomized nucleos(t)ide reverse transcriptase inhibitor (NRTI) treatment arm are presented in Table 2. There were no significant correlations between atazanavir CL/F and biomarker concentrations measured at week 24 for all study participants. Similar results were observed at week 96 (data not shown), except for sVCAM-1, in which there were weak inverse correlations with atazanavir CL/F in the overall study population (rho = −0.244, p = 0.02) and among those randomized to abacavir/lamivudine (rho = −0.326, p = 0.03).

Table 2.

Spearman’s rho correlation coefficients (p-values) between apparent oral clearance of atazanavir (ATV CL/F) and week 24 biomarker concentrations

Biomarker ATV CL/F Overall ATV CL/F in TDF/FTC randomized group ATV CL/F in ABC/3TC randomized group
Week 24 Week 24 Week 24
hsCRP −0.117
(0.24)		 −0.103
(0.47)		 −0.042
(0.77)
IL-6 0.010
(0.92)		 0.017
(0.91)		 0.161
(0.26)
TNF-α −0.067
(0.50)		 −0.107
(0.46)		 −0.074
(0.61)
sTNFR-I −0.059
(0.56)		 −0.095
(0.51)		 0.012
(0.94)
sTNFR-II −0.083
(0.41)		 −0.172
(0.23)		 −0.032
(0.82)
sVCAM-1 −0.110
(0.27)		 −0.156
(0.27)		 −0.109
(0.45)
sICAM-1 −0.055
(0.58)		 −0.104
(0.47)		 −0.012
(0.93)
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As expected with atazanavir treatment initiation, the median [IQR] bilirubin concentrations increased at weeks 24 and 96 to 2.1 [1.3, 2.6] and 1.9 [1.1, 2.9], respectively. Percent change in bilirubin concentration from baseline to week 24 was weakly negatively correlated with atazanavir CL/F among all participants (rho = −0.251, p-value = 0.01) but appeared to differ by randomized NRTI treatment arm. Those in the tenofovir disoproxil fumarate/emtricitabine arm had moderate inverse correlation between atazanavir CL/F and percentage change in bilirubin concentration (rho = −0.370, p-value = 0.008); meaning as atazanavir exposure increased (i.e. CL/F decreased), the change in bilirubin levels increased. Those in the abacavir/lamivudine arm did not exhibit significant correlations between atazanavir CL/F and bilirubin changes (rho = −0.056, p-value = 0.69). Results were similar for the week 96 bilirubin changes (data not shown).

Correlations between biomarkers and total bilirubin

The correlations between total bilirubin concentrations and each of the inflammatory and endothelial activation marker concentrations were determined for all measurements collected at each time point (baseline, and weeks 24 and 96) to assess for the potential anti-inflammatory effects of bilirubin (Table 3). Within participants, there were moderate-to-strong inverse correlations observed between bilirubin concentrations and TNF-α (r = −0.68), sTNFR-II (r = −0.69), and sVCAM-1 (r = −0.68; p < 0.00001); moderate inverse correlations with sTNFR-I (r = −0.43) and sICAM-1 (r = −0.45; p < 0.00001); and, a weak inverse correlation with IL-6 (r = −0.21; p = 0.003). Collectively, these negative correlations suggest that increased bilirubin concentrations correlate with decreased concentrations of markers of inflammation and endothelial activation within individuals, except with hsCRP, for which there was not a significant correlation in the overall study group. These patterns of correlation remained largely the same within each randomized NRTI arm, except for IL-6 which was driven primarily by the tenofovir disproxil fumurate/emtricitabine containing regimen (Table 3).

Table 3.

Within participants’ Pearson’s correlation coefficients (p-values) between log-transformed inflammation markers and bilirubin at baseline, and weeks 24 and 96

Biomarker Overall TDF/FTC ABC/3TC
hsCRP −0.07
(0.36)		 −0.22
(0.03) 		0.11
(0.29)
IL-6 −0.21
(0.003) 		−0.35
(0.0005) 		−0.07
(0.48)
TNF-α −0.68
(< 0.0001) 		−0.70
(< 0.0001) 		−0.68
(< 0.0001)
sTNFR-I −0.43
(< 0.0001) 		−0.39
(0.0001) 		−0.46
(< 0.0001)
sTNFR-II −0.69
(< 0.0001) 		−0.66
(< 0.0001) 		−0.72
(< 0.0001)
sVCAM-1 −0.68
(< 0.0001) 		−0.63
(< 0.0001) 		−0.73
(< 0.0001)
sICAM-1 −0.45
(< 0.0001) 		−0.41
(< 0.0001) 		−0.58
(< 0.0001)
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DISCUSSION

Inflammatory processes can alter the expression and activity of different drug-metabolizing enzymes and transporters thereby affecting the pharmacokinetics of drugs. Previous studies have shown that infections and inflammation, both acute and chronic, can reduce the metabolic clearance of cytochrome P450 (CYP) substrates by 20 – 70% [31]. These effects likely stem primarily from the altered expression of CYP and drug transporter proteins that are down-regulated during the generation of host defense mechanisms. Both atazanavir and ritonavir are substrates and inhibitors of CYP isoenzymes 3A4/5, which led us to hypothesize that a source of the atazanavir pharmacokinetic heterogeneity in AIDS Clinical Trial Group study A5202 was mediated, in part, by ongoing inflammation associated with chronic HIV-1 infection. However, among study participants who received atazanavir-based treatment, we did not generally observe significant associations between the apparent plasma clearance of atazanavir and markers of inflammation and endothelial activation that would suggest a reduction in CYP-mediated metabolism due to higher inflammatory activity.

The underlying mechanisms of immune activation during HIV infection are multifactorial and result directly from the replication of HIV itself and indirectly through other mechanisms like the reactivation of chronic infections, oxidized lipids, and the translocation of microbial products from the gastrointestinal tract to systemic circulation. One of the direct consequences of activation of the immune system is secretion of pro-inflammatory cytokines, such as IFN-α, TNF-α, IL-1, IL-6, and IL-18, which can contribute to additional immune activation and apoptosis of immune cells [32,32]. Although cytokine perturbations may be partially corrected by antiretroviral therapy, some immune and endothelial activation markers have been shown to remain elevated despite months to years of treatment [4,17,34–36]. For example, markers such as TNF-α, IL-6, hsCRP, and sVCAM-1 are elevated in HIV-infected patients receiving stable antiretroviral therapy compared to healthy individuals [37,38]. In A5224s, antiretroviral therapy with atazanavir- or efavirenz-based regimens was effective in reducing all inflammation and endothelial activation markers measured overall, except for hsCRP [27]. However, the magnitudes in reduction differed for each biomarker. For instance, levels of sTNFR-I decreased by only 12% from baseline after 96 weeks of ART, while sTNFR-II decreased by approximately 50%.

The various CYP enzyme families and drug transporters also vary dramatically in response to diverse inflammatory stimuli, as the different cytokines can display a diverse spectrum of activity towards individual enzyme forms. For example, TNF-α reduces CYP2C11 and -3A2 but has no effect on CYP2A1 and -2C6 in rats [39]. IL-1 but not IL-6 reduce CYP2E1 mRNA and protein expression but only when this enzyme is induced, leaving the constitutive form of the enzyme unchanged [40]. Contrary, IL-6, blocks rifampicin-mediated induction of CYP3A4 activity by reducing the expression of pregnane X and constitutively activated receptors in primary human hepatocytes [41,42]. Changes in drug disposition have also been observed clinically in various disease states that involve an inflammatory response (e.g. cancer, sepsis, cardiovascular disease, arthritis) with different inflammatory markers being negatively correlated with different CYP enzyme function [12,13,43].

The lack of correlation between inflammation markers and atazanavir CL/F in this analysis may be due to elevated indirect bilirubin concentrations through the inhibition of UGT1A1-mediated bilirubin glucuronidation by atazanavir. Bilirubin has known anti-oxidant, cytoprotective, and anti-inflammatory properties, thus potentially blunting the effects of inflammation on CYP metabolism [21,22,44,45]. We did observe inverse correlations within study participants between their bilirubin concentrations and the majority of the markers of inflammation and endothelial activation. The strongest correlations were with sTNFR-II, TNF-α, and sVCAM-1. This is in agreement with prior studies reporting that bilirubin reduces TNF-α-induced gene upregulation of VCAM-1 and ICAM-1 [46].

In this study, if those with the most vigorous inflammatory conditions do indeed have impaired drug metabolism that result in higher atazanavir concentrations, they would ultimately also have greater elevations in their bilirubin levels, thereby attenuating the inflammatory-mediated alterations on metabolism we initially sought to observe. Furthermore, ritonavir exposures were not accounted for in this analysis, which may have also influenced associations between atazanavir metabolism and inflammatory processes. Thus, future analyses that will investigate the effects of inflammatory processes on drug metabolism in HIV-infected individuals should include antiretrovirals or other drugs that do not increase bilirubin or other endogenous signals of anti-inflammatory mechanisms (e.g. anti-inflammatory cytokines, transforming growth factor-β, IL-10 and IL-1 receptor antagonist, HDL). For example, an antiretroviral like darunavir, might be a more appropriate agent to study because of its substantial interindividual pharmacokinetic variability, its primary metabolism by CYP3A and absence of treatment-related hyperbilirubinemia, as well as its limited direct effects on some of the inflammatory markers that evoke metabolism and disposition changes [46].

In conclusion, atazanavir exposure was not correlated with levels of endothelial activation markers (sVCAM-1 and sICAM-1) or inflammatory markers (hsCRP, IL-6, TNF-α, sTNR-I, sTNFR-II). In contrast, bilirubin levels were positively correlated with atazanavir exposure and inversely correlated with the majority of the biomarkers (except hsCRP), possibly due to the endogenous anti-oxidant and anti-inflammatory properties of bilirubin. These anti-inflammatory effects of bilirubin have clinical implications for inflammatory disorders. For example, through disruption of endothelial VCAM-1 and ICAM-1-mediated leukocyte migration, bilirubin has demonstrated the ability to suppress atherosclerotic plaque formation [47]. This would explain, in part, the recent observations that HIV-infected individuals with elevated bilirubin have a decreased risk for heart failure, and the lack of association between atazanavir/ritonavir and cardiovascular disease compared to other protease inhibitors [48–50]. Future studies, including dedicated clinical pharmacokinetic studies, should continue to examine whether ongoing systemic inflammation in HIV-infected individuals affect the metabolism of antiretrovirals and other therapeutic agents that are important to this potentially vulnerable population to drug-disease interactions.

Acknowledgments

We would like to thank the staff of the University at Buffalo ACTG Pharmacology Specialty Laboratory, Mr. Douglas Kitch, Dr. Susan Rosenkranz, PhD, and Ms. Darlene Lu, MS from the Statistical and Data Analysis Center (Harvard T.H. Chan School of Public Health, Boston, MA) of the AIDS Clinical Trials Group, as well as Laurie Myers, MS, and Anthony Bloom, BA, from Frontier Science & Technology Research Foundation, Inc. (Amherst, NY) for their assistance in data management. We would also like to thank Abbott Laboratories, Bristol-Myers Squibb, Gilead Inc. and GlaxoSmithKline for providing antiretroviral agents used in this trial. Finally, we would like to especially thank the investigators, study coordinators and study participants of ACTG A5202 and A5224s.

Other investigators and contributors included the following: Hector H. Bolivar, MD and Sandra Navarro, MD- University of Miami (Site 901) CTU Grant #AI069477, ACTG Grant #AI27675, CFAR Grant #AI073961; Susan L. Koletar, MD and Diane Gochnour, RN- The Ohio State University (Site 2301) CTU Grant #AI069474; Edward Seefried, RN and Julie Hoffman, RN- UCSD (Site 701) CTU Grant #AI69432; Judith Feinberg, MD and Michelle Saemann, RN- University of Cincinnati (Site 2401) CTU Grant #AI069513; Kristine Patterson, MD, Donna Pittard, RN and David Currin, RN- University of North Carolina (Site 3201) CTU Grant #AI69423, CFAR Grant #AI50410, GCRC Grant #RR00046 and Grant #RR025747; Kerry Upton, RN, BSN and Michael Saag, MD- University of Alabama (Site 5801) CTU Grant # U01 AI069452, CCTS Grant #1UL1 RR025777-01; Graham Ray and Steven Johnson- University of Colorado Health Sciences Center (Site 6101) CTU Grant #AI69450, Grant #AI054907, Grant # RR00051; Bartolo Santos, RN and Connie A. Funk, RN, MPH- University of Southern California (Site 1201) CTU Grant #5U01 AI069428; Michael Morgan, FNP and Brenda Jackson, RN- Vanderbilt Therapeutics CRS (Site 3652) CTU Grant #AI069439; Pablo Tebas, MD and Aleshia Thomas, RN- University of PA, sub unit of Children’s Hospital of Philadelphia (Site 6201) CTU Grant #U01 AI069467-03, CFAR Grant #5P30 AI045008-10; Ge-Youl Kim, RN, BSN and Michael K. Klebert, PhD, RN, ANP-BC- Washington University (Site 2101) CTU Grant #AI069495; Jorge L. Santana and Santiago Marrero- University of Puerto Rico (Site 5401) CTU Grant #5U01 AI069415-03; Jane Norris, PA-C and Sandra Valle, PA-C- Stanford University (Site 501) CTU Grant #AI69556; Gary Matthew Cox, MD and Martha Silberman, RN- Duke University Medical Center (Site 1601) CTU Grant #5U01 AI069484-02; Sadia Shaik and Ruben Lopez- Harbor-UCLA Medical Center (Site 603) CTU Grant #AI069424; Margie Vasquez, RN and Demetre Daskalakis, MD- New York University/NYC HHC at Bellevue Hospital Center (Site 401) CTU Grant #AI069532; Christina Megill, RPA-C and Todd Stroberg, RN- Cornell Chelsea (Site 7804) CTU Grant #AI69419, CSTC Grant #RR024996; Jessica Shore, BSN and Babafemi Taiwo, MBBS- Northwestern University CRS (Site 2701) CTU Grant #AI069471; Mitchell Goldman, MD and Molly Boston, RN- Indiana University (Site 2601) CTU Grant #UO1 AI025859; Dr. Jeffrey Lennox and Dr. Carlos del Rio- The Ponce de Leon Center (A5802) CTU Grant #5U01 AI069418, CFAR Grant #P30 AI050409; Timothy W. Lane, MD and Kim Epperson, RN- Moses H Cone Memorial Hospital (Site 3203) CTU Grant #1U01 A1069423-01; Annie Luetkemeyer, MD and Mary Payne, RN- UCSF (Site 801) CTU Grant #1U01 AI069502-01; Barbara Gripshover, MD and Dawn Antosh, RN- Case Western Reserve University (Site 2501) CTU Grant #AI69501; Jane Reid RN, MS, APN-BC and Mary Adams, RN, MPh- University of Rochester (Site 1101) CTU Grant #U01 AI069511, GCRC Grant #UL1 RR024160; Sheryl S. Storey, PA-C, Shelia B. Dunaway, MD- University of Washington (Site 1401) CTU Grant #AI069434; Joel Gallant, MD and Ilene Wiggins, RN- Johns Hopkins University (Site 201) CTU Grant #AI69465; Kimberly Y. Smith, MD MPH and Joan A. Swiatek, RN, APN- Rush University Medical Center (Site 2702) CTU Grant #5U01 AI069471; Joseph Timpone, MD and Princy Kumar, MD- Georgetown University (Site 1008) CTU Grant #1U01 AI069494-01; Ardis Moe, MD, Maria Palmer PA-C- UCLA Care Center (Site 601) CTU Grant #AI069424; Jon Gothing, RN, BSN, ACRN and Joanne Delaney, RN, BSN - Brigham and Women’s Hospital Boston MA (Site 107) CTU Grant #AI069472; Kim Whitely, RN and Ann Marie Anderson, RN- Metro Health Center (Site 2503) CTU Grant #AI069501; Scott M. Hammer and Michael T. Yin- HIV Prevention & Treatment (Columbia University) (Site 30329) CTU Grant #5U01 AI069470, Grant #1UL1 RR024156; Mamta Jain, MD and Tianna Petersen, MSUT Southwestern Medical Center at Dallas (Site 3751) CTU Grant #3U01 AI046376 05S4; Roberto Corales, DO and Christine Hurley, RN- AIDS Community Health Center (Site 1108) CTU Grant #U01 AI069511, GCRC Grant #UL1 RR024160; Keith Henry, MD and Bette Bordenave, RN- Hennepin County Medical Center (Site 1502) Grant #N01 AI72626; Amanda Youmans, NP and Mary Albrecht, MD- Beth Israel Deaconess (Partners/Harvard) CRS (Site 103) CTU Grant #UOI A106947203; Richard B. Pollard, MD and Abimbola Olusanya, NP- University of California, Davis Medical Center (Site 3851) Grant #AI38858; Paul R. Skolnik, MD and Betsy Adams, RN- Boston Medical Center CRS (Site 104) CTU Grant #AI069472; Karen T. Tashima and Helen Patterson- The Miriam Hospital-Brown University (Partners/Harvard) (Site 2951) CTU Grant #1U01 AI069472-01; Michelle Ukwu and Lauren Rogers- Peabody Health Center (Site 31443) CTU Grant #AI069471; Henry H. Balfour, Jr., MD and Kathy A. Fox, RN, MBA- University of Minnesota (Site 1501) CTU Grant #AI27661; Susan Swindells, MBBS and Frances Van Meter, APRN- University of Nebraska Medical Center (Site 1505) CTU Grant #AI27661; University of Hawaii (Site5201) CTU Grant #AI34853; Gregory Robbins, MD and Nicole Burgett-Yandow, RN, BSN- Massachusetts General Hospital from the Partners/Harvard/BMC ACTU (Site 101) CTU Grant #1U01 AI069472-01; Dr. Charles E. Davis, Jr. and Colleen Boyce, RN- IHV Baltimore Treatment CRS (Site 4651) CTU Grant #5U01 AI069447 03; William A. O’Brien, MD and Gerianne Casey-University of Texas Medical Branch (Site 6301) CTU Grant #AI032782; Dr. Gene D. Morse, PharmD and Dr. Chiu-Bin Hsaio, MD- SUNY- Buffalo (Site 1102) CTU Grant #5U01 A1027658; San Mateo County AIDS Program (Site 505) CTU Grant #AI27666; Jeffrey L. Meier and Jack T. Stapleton- University of Iowa Healthcare (Site 1504) NIAID Grant #AI27661 and Grant #AI58740; Donna Mildvan, MD and Manuel Revuelta, MD- Beth Israel Medical Center ACTU (Site 2851) CTU Grant #AI46370; David Currin, RN- Wake County HHS (Site 30076) CTU Grant #AI25868; Wafaa El Sadr, MD, MPH, MPA and Avelino Loquere, RN- Harlem ACTG CRS (Site 31483) CTU Grant #5U01 AI069470-03; Nyef El-Daher, MD and Tina Johnson, RN- McCree McCuller Wellness Center (Site 1107) CTU Grant #U01 AI069511, GCRC Grant #UL1 RR024160; Robert Gross MD, MSCE and Kathyrn Maffei, RN, BSN- University of Pennsylvania Health (Site 6206) CTU Grant #1U01 AI69467-01; Valery Hughes, FNP and Glenn Sturge, BSCornell Uptown (Site 7803) CTU Grant #1U01 AI069419-01; Deborah McMahon, MD and Barbara Rutecki, CRNP, MPH- University of Pittsburgh (Site 1001) CTU Grant #1UO1 AI069494-01; Michael Wulfsohn, MD PhD, Andrew Cheng, MD PhD, Norbert Bischofberger PhD- Gilead Sciences; Lynn Dix, PhD and Qiming Liao, PhD- GlaxoSmithKline, Inc.

Funding

This research was supported by grants from the National Institute of Allergy and Infectious Diseases, National Institutes of Health 5K23AI108355-03 (to CSV), (AI38858, to the ACTG Central Group; AI68636, to the ACTG Network; and AI68634 and AI38855, to the ACTG Statistical and Data Analysis Center, U01AI068636 ( and UM1AI106701-01 to the University at Buffalo Pharmacology Support Laboratory), AI069434 to the University of Washington, AI069477 to the University of Miami. Additional support from the General Clinical Research Center units funded by the National Center for Research Resources and P30 AI50410, University of North Carolina at Chapel Hill CFAR. The project described was also supported by Award Number U01AI068636 from the National Institute of Allergy and Infectious Diseases and supported by National Institute of Mental Health (NIMH), National Institute of Dental and Craniofacial Research (NIDCR). The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institute of Allergy and Infectious Diseases or the National Institutes of Health.

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