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Logo of nihpaAbout Author manuscriptsSubmit a manuscriptNIH Public Access; Author Manuscript; Accepted for publication in peer reviewed journal;
 
AIDS. Author manuscript; available in PMC Dec 28, 2009.
Published in final edited form as:
PMCID: PMC2798571
NIHMSID: NIHMS148400
A Randomized Trial of Therapeutic Drug Monitoring of Protease Inhibitors in Antiretroviral-Experienced, HIV-1-Infected Patients
Lisa M. Demeter,1 Hongyu Jiang,2 A. Lisa Mukherjee,2 Gene D. Morse,3 Robin DiFrancesco,3,1 Carrie Dykes,1 Prakash Sista,4 Lee Bacheler,4 Karin Klingman,5 Alex Rinehart,6 and Mary Albrecht7
1Infectious Diseases Division, University of Rochester School of Medicine and Dentistry, Rochester, NY
2Statistical and Data Analysis Center, Harvard School of Public Health, Boston, MA
3Department of Pharmacy Practice, SUNY at Buffalo, Buffalo, NY
4VircoLab, Inc., Durham, NC
5Division of AIDS, National Institutes of Health, Bethesda, MD
6Tibotec Therapeutics, Bridgewater, NJ
7Division of Infectious Diseases, Beth Israel Deaconess Medical Center, Boston, MA
Corresponding author: Lisa M. Demeter, M.D., 601 Elmwood Avenue, Box 689, Rochester, NY 14642, Phone: 585-275-4764, Fax: 585-442-9328, lisa_demeter/at/urmc.rochester.edu
Objective
Whether therapeutic drug monitoring of protease inhibitors (PIs) improves outcomes in HIV-infected patients is controversial. We evaluated this strategy in a randomized, open-label clinical trial, using a normalized inhibitory quotient (NIQ), which incorporates drug exposure and viral drug resistance. NIQs≤1 may predict poor outcome and identify patients who could benefit from dose escalation.
Design/Methods
Eligible patients had a viral load ≥1,000 copies/mL on a failing regimen, and began a new PI-containing regimen at entry. All FDA-approved PIs available during study recruitment (June 2002-May 2006) were allowed. One-hundred-eighty-three participants with NIQ≤1, based on their week 2 PI trough concentration and pre-entry drug resistance test, were randomized at week 4 to standard of care (SOC) or PI dose escalation (TDM). The primary endpoint was change in log10 plasma HIV-1 RNA concentration from randomization to 20 weeks later.
Results
Ninety-one subjects were randomized to SOC, 92 to TDM. NIQs increased more in the TDM arm compared to SOC (+69% versus +25%, p=0.01). Despite this, TDM and SOC arms showed no difference in outcome (+0.09 versus +0.02 log10, p=0.17). In retrospective subgroup analyses, patients with less HIV resistance to their PIs benefited from TDM (p=0.002), as did black and Hispanic patients (p=0.035 and 0.05, respectively). Differences between black and white patients persisted when accounting for PI susceptibility.
Conclusions
There was no overall benefit of TDM. In post-hoc, subgroup analyses, TDM appeared beneficial in black and Hispanic patients, and in patients whose virus retained some susceptibility to the PIs in their regimen.
Keywords: antiretroviral therapy, clinical trials, protease inhibitors, pharmacokinetics, HIV drug resistance, therapeutic drug monitoring
Protease inhibitors (PIs) play a pivotal role in treating HIV infection[1]. Plasma PI trough concentrations demonstrate substantial inter-patient variability[2], and correlate with treatment outcome in retrospective studies [3]. These observations suggest that therapeutic drug monitoring could further optimize therapy with PIs. In antiretroviral-experienced patients, neither plasma PI concentration nor magnitude of HIV-1 resistance (50% inhibitory concentration, IC50) correlated as well with treatment outcome as an inhibitory quotient (IQ=trough concentration÷IC50), which incorporates both parameters[4-15].
Despite encouraging retrospective data, a prospective trial to assess the impact of PI dose escalation based on trough concentrations showed no benefit in antiretroviral-experienced patients[16]. We therefore designed a randomized trial to evaluate whether PI dose escalation based on an IQ improves treatment outcomes in antiretroviral-experienced patients.
Participants
Eligible patients were HIV-infected adults with: virologic failure on ≥1 PI-containing regimen, plasma HIV-1 RNA concentration ≥1,000 copies/mL and a virtual phenotype resistance test (virco®TYPE HIV-1, Virco BVBA, Mechelen, Belgium) showing resistance to at least one drug on the current failing regimen. A new PI-containing regimen was begun at entry. Medications having significant pharmacokinetic interactions with PIs were prohibited. Recruitment occurred from June 2002 through May 2006 at 45 AIDS Clinical Trials Units in the US and Puerto Rico. The last subject completed follow-up through 20 weeks after randomization on September 26, 2006. Each institutional review board approved the protocol; all subjects provided written informed consent.
Normalized Inhibitory Quotient
This study used a normalized IQ (NIQ), which correlates retrospectively with treatment responses (NIQ=patient IQ÷reference IQ; IQ=PI trough concentration÷fold-change in IC50 before regimen initiation)[15]. The reference IQ for each PI was derived from patient populations in earlier studies[4, 5, 17-19], and represented the ratio of patient trough concentration to the fold-change in IC50 of their virus that was associated with virologic success on that PI (see a previous publication summarizing the design and execution of this clinical trial for specific reference IQ values used in this study[20]). Having an NIQ≤1 meant that the patient's IQ was less than the postulated threshold for virologic success for that PI. Therefore, NIQ≤1 was empirically chosen to identify patients with a low likelihood of virologic response, who could potentially benefit from dose escalation. Low NIQs could be due to low trough concentrations, high levels of HIV drug resistance, or both, relative to the reference population.
Study Design and Interventions
Patients with NIQs≤1 two weeks after initiating a new PI-containing regimen were randomized at week 4 to a strategy of PI dose escalation with subsequent trough concentration monitoring (TDM), or to standard of care in which fixed doses were administered (SOC) (Figure 1). Subjects receiving dual PI regimens were eligible for randomization if either NIQ was ≤1. Randomization was performed at the Data Management Center using permuted blocks, stratified by stratification factors in place at the time of randomization, and communicated to the site. Subjects who initiated incorrect dosing, received a prohibited medication, or experienced toxicities/intolerance were ineligible for randomization. Site personnel, subjects and protocol team members were not blinded to subjects' treatment assignments; however, protocol team members did not have access to subjects' viral load and CD4 responses during the study. Dose escalations for TDM subjects were recommended by the protocol chairs and implemented within 72 hours of randomization. Subsequent trough concentrations were measured two and six weeks after randomization in both arms; only TDM subjects received those NIQs, with an additional dose escalation for NIQ≤1.
Figure 1
Figure 1
Study design and disposition of subjects
Subjects with NIQs>1 were followed on an Observational arm, with the same follow-up and management as SOC. Once the Observational arm reached its accrual limit of 50 subjects in July 2004, those with NIQs>1 discontinued study at week 4. The primary endpoint of the study was 20 weeks after randomization (24 weeks after study entry).
Clinical and Laboratory Evaluations
Clinical evaluations and laboratory tests were performed at study entry and two weeks later; at Step 2 entry (randomization); and at 2, 6, 12, 16, and 20 weeks after randomization. Timed plasma PI trough concentrations were obtained two weeks after study entry, and at two and six weeks after randomization (10-14 hours after dosing for twice daily PIs; 22-26 hours for once daily atazanavir). Phone contact between site personnel and patients to reinforce adherence occurred 48 hours prior to the scheduled trough (see reference [20] for further detail on how accuracy of trough concentrations was assured). PI concentrations were measured at SUNY Buffalo, which was certified to quantitate PIs using high-pressure liquid chromatography (CLIA #333D0999173). Adherence (self-reported number of missed doses over the prior four days) and electrocardiograms were obtained before treatment initiation and at each trough concentration visit.
Antiretroviral Regimens
All FDA-approved antiretroviral drugs available during recruitment (June 2002-May 2006) were allowed; darunavir was therefore not allowed. Subjects' physicians prescribed their antiretroviral regimen, guided by the screening resistance test. Subjects initiated protocol-specified doses of PIs, given in combination with low-dose ritonavir to augment their plasma concentration. Dual PI regimens with no known adverse pharmacokinetic interactions were also allowed.
Pre-specified algorithms were developed for each PI regimen. For the first dose escalation, the dose increase affected ritonavir only for the PIs atazanavir, amprenavir, fos-amprenavir, indinavir, and tipranavir; the primary PI only for saquinavir; and both the primary PI and ritonavir for lopinavir. We did not increase ritonavir dosing for saquinavir recipients because saquinavir trough concentrations are not increased with ritonavir doses >200mg daily[21]. Second dose escalations were not undertaken for tipranavir and atazanavir, because of concerns about dose-related hepatic and cardiac toxicities, respectively. For all remaining PIs, second dose escalations affected the primary PI, with the exception of lopinavir/ritonavir, for which the ritonavir dose was selectively increased by adding a 100 mg ritonavir capsule twice daily. Dose escalations were not undertaken if there was non-adherence or dose-limiting toxicity.
Outcomes and sample size
The primary comparison was the difference between TDM and SOC in the median change in log10 plasma HIV-1 RNA concentration from randomization to 20 weeks later. Assuming a standard deviation of 1.3 (based on available data at the time this study was designed), 90 subjects in each randomized arm gave 80% power to detect a 0.6 log10 difference. Secondary endpoints included change in plasma HIV-1 RNA concentration from Step 1 entry to 20 weeks after randomization, frequency of viral load suppression to <400 and <50 copies/ml, time to virologic failure, clinical and laboratory toxicities, and RNA and CD4 trajectories.
Study monitoring
The Division of AIDS Data and Safety Monitoring Board reviewed the trial's efficacy and safety results yearly. The O'Brien-Fleming stopping rule with Lan and DeMets spending function was used to evaluate interim efficacy results[22]. During the study, no early stopping boundary was reached.
Antiretroviral regimen activity
Over the course of the study, the most current version of the virtual phenotype assay was used. In order to allow direct comparison of regimen activity among subjects, a single version of the software (VT 4.1.00, May 2007) was used to re-analyze all screening resistance tests from subjects that entered the study. The number of active drugs was calculated using a continuous phenotypic susceptibility score that compared the IC50 fold-change to reported resistance cutoffs, and ranged from zero (no activity) to one (full activity) for each drug, similar to previously published studies[23-26]. Using the two clinical cutoffs (CCOs) for resistance defined in the virtual phenotype report, we assigned a score of one if the IC50 fold-change of the patient's virus was <CCO1 (i.e., the virus had maximal susceptibility to the drug). If the IC50 fold-change was >CCO2 (i.e. the virus had minimal susceptibility), we assigned a score of zero. If the IC50 fold-change was between CCO1 and CCO2, we assigned a fractional score equal to (IC50 fold-change − CCO1) ÷ (CCO2 − CCO1). The number of active PIs was obtained by summing the scores for each PI in the regimen, excluding ritonavir, and ranged from zero to 2.0 (1.0 if a subject was taking only one PI). The number of active drugs in the regimen was obtained similarly. For enfuvirtide, a score of one was assigned for naïve subjects, and a score of zero for those who were enfuvirtide-experienced.
Statistical analyses
All analyses were intent-to-treat, unless specified otherwise. Since the primary endpoint of the study was change in viral load between Step 2 randomization and 20 weeks later, the intent-to-treat analysis excluded subjects with no results at either time point. If both values were <50 copies/mL the change in viral load was assumed to be zero. Since it was rare that the viral load was <50 copies/mL at randomization only, a value of 50 was imputed in those cases. If the viral load was <50 copies/mL at follow-up only, the primary endpoint was bounded above by determinable value (left-censoring). The Gehan-Wilcoxon test was used for the primary comparison. Secondary analyses of the primary endpoint included an as-treated analysis and an analysis stratified by inclusion of a new class of drug.
Censored regression models were used to explore the effect of baseline characteristics on the primary endpoint and their possible interaction with the TDM effect. Subgroup analysis of the primary endpoint was performed when regression results suggested that there was an interaction between any baseline characteristic and response to TDM. For all censored regression models, the model residuals were assumed to be normally distributed.
Toxicities were graded using the Division of AIDS 1992 grading scale[27]; protocol-specific grading was used for electrocardiographic changes and hyperlipidemia. Pre-specified criteria were established for treatment interruption or delayed dose escalation.
Quantitative endpoints between groups were compared using the Wilcoxon rank-sum or Kruskal-Wallis tests. Categorical endpoints between groups were compared with exact tests. Time-to-event endpoints were estimated and compared using the Kaplan-Meier method and logrank test. All statistical tests were two-sided with a significance level of 0.05. Nominal p-values are presented for the primary endpoint, without adjustment for sequential monitoring.
Patients
Figure 1 and Table 1 summarize the follow-up of subjects and their baseline characteristics, respectively. The baseline characteristics of the two randomized arms were similar. There were more past intravenous drug users, fewer prior PIs, more active PIs, and a higher proportion of single PI regimens in the Observational arm (Table 1). These differences persisted when the analysis was limited to subjects that enrolled before closure of the Observational arm.
Table 1
Table 1
Baseline Characteristics at Step 1 Entry
Antiretroviral Regimens
The most common PI regimens in the randomized arms were saquinavir+fos-amprenavir (18%), saquinavir+fixed-dose lopinavir/ritonavir (17%), and fos-amprenavir (14%). The different regimens were evenly distributed between the randomized arms. In the Observational arm, fixed-dose lopinavir/ritonavir (34%) and fos-amprenavir (22%) were used most frequently. There was more frequent use of ritonavir-boosted dual PI regimens by subjects in the randomized arms, compatible with the more extensive HIV resistance to PIs in these arms compared to the Observational arm. The most commonly used nucleoside analogs in all arms were tenofovir (62%), lamivudine (27%), and abacavir (24%). A new class of antiretroviral drug was started in 13% of subjects; 73% of the subjects that began a new drug used enfuvirtide (9% of all subjects).
Interventions
Sixty-two of 85 subjects (73%) in the intent-to-treat TDM group undertook all recommended dose escalations. Eight of 23 subjects did not comply with dose escalations because of protocol-mandated toxicity management; the remaining 15 deviations were due to site errors and patient or physician preference. No SOC subjects reported dose escalations.
The median percent change in trough concentration from weeks 2 to 10 in the SOC and Observational arms (which did not undergo dose escalation), was 13% (1Q -21%, 3Q 72%). Median trough concentrations increased more in the TDM arm compared to SOC for all PIs, except fos-amprenavir (Figure 2A). Overall, there was a significant increase in NIQ for subjects in the TDM versus SOC arms; in post-hoc analyses, this increase was more pronounced when subjects taking fos-amprenavir were excluded (Figure 2B). The proportion of subjects reporting >95% adherence was similar in the SOC and TDM arms: 86% and 80% of the SOC arm were adherent at weeks 2 and 24, respectively, versus 79% and 75% for the TDM arm.
Figure 2
Figure 2
Figure 2
Change in protease inhibitor trough concentration and NIQ, by study arm between weeks 2 and 10
Efficacy
The primary endpoint, which was the change in log10 viral load from Step 2 randomization to 20 weeks later, was not significantly different in TDM versus SOC (+0.09 versus +0.02 log10, p=0.17). There were no differences between TDM versus SOC in the proportion of subjects who had viral loads <400 or <50 copies/mL at the primary endpoint visit (25% versus 18% had <400 copies/mL, p=0.3; 19% versus 12% had <50 copies/mL, p=0.3; missing=failure analysis). There also were no differences between the two randomized arms in any of the other planned secondary analyses of efficacy, or in as-treated analyses. Compared to the randomized arms, subjects in the Observational arm had greater declines in plasma HIV-1 RNA, primarily between Step 1 and Step 2 entry (Figure 3). This difference persisted when the analysis was limited to subjects that enrolled before closure of the Observational arm (data not shown).
Figure 3
Figure 3
Plasma HIV-1 RNA responses over time, by study arm
Safety
Thirty-three subjects reported grade 3/4 signs and symptoms during Step 2, up to 20 weeks post randomization, primarily general and gastro-intestinal. Seventy-seven subjects had grade 3/4 laboratory toxicities, primarily metabolic and pancreatic. There was one grade 3 QTc toxicity each in TDM and SOC. There were no differences in toxicities between TDM and SOC (data not shown).
Subgroup analyses
Multiple regression analysis revealed that effects of race/ethnicity and number of active PIs in the study regimen (calculated using a virtual phenotype sensitivity score for all PIs except ritonavir) potentially had interaction with the TDM effect (p=0.04 and 0.003, respectively). In post-hoc analyses, we compared the difference in the primary endpoint between TDM and SOC arms in specific subgroups of patients, according to gender, race and ethnicity, number of active PIs in the study regimen, and whether fos-amprenavir was used. The most striking finding was that subjects who had at least the median of 0.7 active PIs in their study regimen benefited from being on the TDM arm (p=0.002), whereas those with fewer active PIs did not (p=0.35, Figure 4A). Hispanic and black non-Hispanic subjects also appeared to have better responses to TDM. The three racial/ethnic groups did not differ statistically in confounding variables potentially explaining the apparent difference in response to TDM, including body mass index, number of active PIs, proportion starting a new drug class, PI trough concentrations, Step 1 and Step 2 entry HIV-1 RNA concentrations or CD4 counts, adherence at week 2, or use of fos-amprenavir. In additional analyses, black patients with ≥0.7 active PIs did better on TDM than SOC, whereas whites with <0.7 active PIs did worse (Figure 4B).
Figure 4
Figure 4
Figure 4
Impact of baseline characteristics on the difference in the primary endpoint between the TDM and SOC arms
Threshold for the number of active PIs that predicts response to TDM
A censored regression model was used to define a threshold for the number of active PIs that best identified which subjects benefited from this study's TDM strategy. For each active PI threshold that was analyzed (range 0-1.5 in increments of 0.05), the primary endpoint in TDM subjects whose number of active PIs was equal to or above the threshold was compared to a control group that included all SOC subjects as well as TDM subjects whose number of active PIs was below the threshold being examined. In all models, the TDM effect was also adjusted for the effect of the number of active PIs on the primary endpoint. The optimal threshold was defined as the one whose corresponding model fit the data the best (i.e., had the highest log-likelihood). In this analysis, 0.4 active PIs was the lowest threshold that best discriminated subjects with a better outcome on the TDM arm (data not shown). A subsequent subgroup analysis using 0.4 active PIs as a threshold confirmed that subjects with ≥0.4 active PIs in their regimen did better on TDM versus SOC (intent-to-treat analysis [n=107] p=0.0017, Gehan-Wilcoxon; as-treated analysis [n=86] p=0.03).
This is the first randomized trial designed to assess the efficacy of a strategy of therapeutic drug monitoring using an IQ in antiretroviral-experienced, HIV-infected patients. Because randomization occurred after drug concentration monitoring, all randomized subjects were eligible for dose escalation, in contrast to earlier trials, in which only 20-40% were eligible for dose escalation[16, 28, 29].
Based on the reference IQs in this study, the majority of PI-experienced subjects eligible for this trial had an NIQ≤1. HIV drug resistance to the PI(s) in the study regimen contributed substantially to low NIQs, as indicated by the greater number of prior PIs and reduced number of currently active PIs in the randomized arms compared to the Observational arm. Subjects in the three arms had similar week 2 trough concentrations, suggesting that in most patients, low trough concentrations were not a major contributor to low NIQs. Thus, the unique clinical question addressed in this trial is whether patients with PI-resistant virus would have better treatment outcomes by increasing drug exposure.
We found that dose escalation in response to an NIQ was feasible within the first four weeks of therapy. Of note is that nearly three-quarters of subjects in our study undertook all recommended dose escalations. Dose escalation generally resulted in increased trough concentrations and NIQs in the TDM arm, with no evidence for increased toxicity.
Surprisingly, increased trough concentrations were not observed with fos-amprenavir dose escalation. There is evidence, which was not available at the time that this study was designed, that amprenavir trough concentrations do not increase proportionally after increasing either fos-amprenavir or ritonavir doses; this finding was postulated to be due to induction of amprenavir metabolism by hepatic CYP3A4[31]. However, our study's observation that amprenavir trough concentrations increased after dose escalation of amprenavir-containing regimens, suggests instead that this finding may be due to differences in intraluminal metabolism or absorption of amprenavir after dose escalation of fos-amprenavir/ritonavir regimens. Since this study was not designed to directly compare dose escalation of fos-amprenavir versus amprenavir, we cannot rule out inter-patient variability in hepatic metabolism to explain the observed difference in response to dose escalation of amprenavir versus fos-amprenavir.
Despite the increased NIQs in the TDM arm, there was no overall benefit of this strategy with regard to the primary endpoint, which was the change in viral load between Step 2 randomization and 20 weeks later. A potential limitation of this study is its sample size, which provided only 80% power. Our ability to detect a significant difference between the two treatment arms may have been further limited by the heterogeneous antiretroviral regimens and different dose escalation strategies used in the study. In addition, these subjects in general had HIV with extensive drug resistance, as shown by subjects in the randomized arms having an average of only 1.5 active drugs in their regimen. It seems unlikely, however, that limited activity of the background regimen would have obscured a beneficial effect of TDM, since the primary endpoint of the study was change in viral load.
We chose to use an NIQ based on a predicted IC50 fold-change from a virtual phenotype report. Previous retrospective studies have demonstrated that virologic outcome also correlates with a genotypic inhibitory quotient (GIQ), in which the number of resistance mutations, rather than IC50 fold-change, is used to account for drug resistance [6, 10, 32, 33]. More study is needed to determine whether there are any differences in the predictive power of these different approaches to calculating an IQ. It should also be noted that our use of an NIQ threshold of ≤1 was empiric, although its use is supported by the finding that those in the Observational arm (who by definition had NIQ>1) had better virologic outcomes than patients in the two randomized arms, who had NIQ≤1.
Although the benefit of therapeutic drug monitoring in patients who had at least 0.7 active PIs in their regimen was identified in retrospective subgroup analyses, the effect was highly statistically significant. A subsequent analysis to determine the optimal number of active PIs associated with a response to therapeutic drug monitoring found that a threshold for number of active PIs of 0.4 best discriminated between those that did and did not benefit from therapeutic drug monitoring. It is plausible that increasing drug concentrations of PIs that have no or little activity against HIV would have limited benefit, and that the clinical impact of this strategy may be highest in those patients who have HIV with intermediate levels of resistance to the PI(s) in their regimen. Since the same dosing recommendations were made independent of how low the NIQ was, we cannot completely rule out a benefit of a TDM strategy in patients with highly resistant HIV.
An unexpected observation was that black and Hispanic patients appeared more likely than whites to benefit from TDM; these post-hoc analyses need to be interpreted with caution. However, the TDM benefit in blacks compared to whites persisted when the number of active PIs was taken into account, supporting an effect of race on response to this strategy. It is important to note that in these subgroup analyses whites with limited PI resistance had no benefit from TDM and those with high levels of PI resistance did worse with dose escalation, which is concerning given the more widespread use of TDM in some European countries. We found no evidence for potential confounding variables that could explain the racial differences, raising the question of whether genetic differences in responses to dose escalation could account for the racial difference that was observed. There is some evidence that single nucleotide polymorphisms in genes for cytochrome P450-3A4 or P-glycoprotein have different frequencies among racial groups[34, 35].
Although these retrospective analyses need to be interpreted cautiously, they strongly suggest that there was heterogeneity in response to interventions in the TDM arm, according to race and extent of HIV resistance to the PI(s) in the regimen. We believe that this heterogeneity, as well as our inability to increase amprenavir trough concentrations in fos-amprenavir recipients, could account for the lack of a detectable benefit of therapeutic drug monitoring in the study population as a whole.
In conclusion, no overall benefit of PI dose escalation was observed in antiretroviral-experienced patients with an NIQ≤1. Retrospective analyses suggest that including patients with high levels of resistance to the PI(s) in their new regimen may have limited our ability to detect a potential benefit of TDM in this study, and that TDM may be most useful in patients whose virus has intermediate levels of resistance. Further study is needed to confirm these findings, and to explore whether different racial and ethnic groups respond differently to PI dose escalation.
Acknowledgments
We would like to acknowledge the invaluable support provided by: Barbara Bastow, R.N., B.S.N.; data managers David Rusin and Bernadette Jarocki; Jennifer Nowak; Nancy Reynolds, Ph.D., R.N.; Margie Vasquez, R.N.; Thorner Harris; Alejandro Sanchez, M.D.; and Robert A. Salata, M.D.
Sources of Funding
Supported in part by the AIDS Clinical Trials Group, which is funded by the NIAID, National Institutes of Health (AI-25859, AI-27665, AI-27761, AI-32782, AI-32783, AI-34853, AI-38855, AI-38858, AI-46370, AI-68634, AI-68636, AI-69411, AI-69415, AI-69419, AI-69423, AI-69424, AI-69428, AI-69432, AI-69434, AI-69439, AI-69450, AI-69452, AI-69471, AI-69472, AI-69474, AI-69477, AI-69484, AI-69494, AI-69495, AI-69501, AI-69511, AI-69513, AI-69532, AI-69556); P30-AI-45008; and the General Clinical Research Center Units funded by the National Center for Research Resources (RR-00032, RR-00044, RR-00047, RR-00051, RR-00075, RR-00096). Virco performed drug resistance testing and provided partial financial support for drug concentration testing for the study, which was done in Dr. Morse's laboratory at SUNY Buffalo (also supported by AI-68636).
 
Author contributions:
Drs. Demeter and Albrecht had full access to all of the data in the study and take responsibility for the integrity of the data and the accuracy of the data analysis.
Dr. Albrecht served as protocol co-chair and supervised the overall conduct of the study, participated in study concept and design, analyzed and interpreted data, participated in drafting the manuscript, and revised the manuscript for important intellectual content.
Dr. Bacheler analyzed and interpreted data, revised the manuscript for important intellectual content, provided expertise in resistance testing and interpretation, and supervised the performance of real-time resistance testing.
Dr. Demeter served as protocol chair and supervised the overall conduct of the study, participated in the study concept and design, analyzed and interpreted data, and drafted the manuscript.
Dr. DiCenzo participated in the study concept and design, analyzed and interpreted data, participated in the supervision of the study by providing pharmacologic expertise, and revised the manuscript for important intellectual content.
Dr. Dykes participated in the study concept and design, analyzed and interpreted data, participated in the supervision of the study by providing drug resistance expertise, and revised the manuscript for important intellectual content
Dr. Morse participated in the study concept and design, analyzed and interpreted data, participated in the supervision of the study by providing pharmacologic expertise, revised the manuscript for important intellectual content, and supervised the real-time drug concentration testing.
Dr. Rinehart participated in the study concept and design, participated in the supervision of the study by providing expertise in resistance testing and interpretation, provided oversight of real-time resistance testing, obtained funding for the performance of drug level testing, analyzed and interpreted data, and revised the manuscript for important intellectual content.
Ms. DiFrancesco supervised the performance of real-time drug level testing, acquired and transmitted data, participated in the supervision of the study by providing expertise in the interpretation of drug level testing, analyzed and interpreted data, and revised the manuscript for important intellectual content.
Dr. Jiang performed and supervised the statistical analyses, revised the manuscript for important intellectual content, participated in drafting the manuscript, and analyzed and interpreted data.
Dr. Klingman participated in the study concept and design, participated in study supervision by providing clinical and regulatory expertise, analyzed and interpreted data, and revised the manuscript for important intellectual content.
Ms. Mukherjee performed statistical analyses, participated in the supervision of the trial by preparing real-time reports, analyzed and interpreted data, participated in drafting the manuscript, and revised the manuscript for important intellectual content.
Dr. Sista analyzed and interpreted data, and revised the manuscript for important intellectual content.
Participating Site Staff Members: Frances M. Canchola, RN and Luis M. Mendez- University of Southern California (A1201); Barbara Philpotts, RN and Dawn Antosh, RN- Case Clinical Research Site (A2501); Charles B. Hicks, MD and Joan C. Riddle, RN- Duke University School of Medicine (A1601); Sharon Riddler, MD, MPH and Carol Oriss, BSN, RN- University of Pittsburgh (A1001); Robert R. Redfield, MD and Charles E. Davis, Jr., MD- University of Maryland (A4651); Kim Scarsi, PharmD and Rosie Miles-Jamison- Northwestern University (A2701); Susan L. Koletar, MD and Mark D. Hite, RN- The Ohio State University (A2301); Jorge L. Santana Bagur, MD and Santiago Marrero, MD - University of Puerto Rico (A5401); Roy Gulick, MD, MPH and Valery Hughes, FNP- Cornell Clinical Trials Unit (A7803); Timothy Wilkin, MD, MPH and Todd Stroberg, RN- Cornell Chelsea Center (A7804); Helen Fitch, RN and Neah Kim Ling, MSN, APRN- Beth Israel Deaconess Medical Center (A0103); Paul R. Skolnik, MD and Betsy Adams, RN- Boston Medical Center (A0104); Dr. Paul Sax and Lynn Dumas, RN, BSN- Brigham and Women's Hospital (A0107) Grant #2200-102690; Jane Reid, RNc, MS, ANP and Carol Greisberger, RN, BS- University of Rochester (A1101); Chiu-Bin Hsaio, MD and Tammy O'Hara- SUNY- Buffalo (A1102); Margie Vasquez, RN and Judith A. Aberg, MD- New York University/NYC HHC at Bellevue Hospital Center (A0401); Dr Karen Tashima and Deborah Perez, RN- Miriam Hospital (A2951); Lorna Nagamine, RN (University of Hawaii at Manoa) and Scott Souza, PharmD (Queen's Medical Center)- University of Hawaii (A5201); Kerry Upton and Dana Green- University of Alabama (A5801); Paulina Rebolledo and Igho Ofotokun, MD- Emory University (A5802); Dr. Judith Feinberg and Michelle Saemann, RN- University of Cincinnati (A2401); Donna Mildvan, MD and Manuel Revuelta, MD- Beth Israel Medical Center (A2851); Steven Johnson and Beverly Putnam- University of Colorado Health Sciences Center (A6101); Linda Meixner, RN and Susan Cahill, RN- University of California San Diego (A0701); Hector H. Bolivar, MD and Margaret A. Fischl, MD- University of Miami (A0901); Mitchell Goldman, MD and Deborah O'Connor, RN, MSN- Indiana University (A2601); Michael Morgan, FNP and Huso Erdem- Vanderbilt University (A3652); Rob Roy MacGregor MD and Wayne Wagner RN- University of Pennsylvania (A6201); Eric Daar and Mario Guerrero- Harbor- UCLA Medical Center (A0603); Debra DeMarco, BSN, ACRN and Mark Rodriguez, RN, BSN- Washington University in St. Louis (A2101); Hector H. Bolivar, MD and Margaret A. Fischl, MD- University of Miami (A0901); Henry H Balfour, Jr. MD and Kathy Fox, RN, MBA- University of Minnesota (A1501); Kristine B. Patterson, MD and Susan Richard, ANP-C- University of North Carolina (A3201); Sheila Dunaway, MD and Ann C. Collier, MD- University of Washington, Seattle (A1401); Richard Pollard, MD and Abby Olusanya, NP- University of California, Davis Medical Center (A3851); Jane Norris, PA-C and Sandra Valle, PA-C- Stanford University (A0501); William A. O'Brien, MD, MS and Gerianne Casey, RN- University of Texas Medical Branch, Galveston (A6301).
Footnotes
This study was previously presented in part at the 15th Conference on Retroviruses and Opportunistic Infections, Boston, MA. February 4, 2008. Abstract #35.
Trial Registration: ClinicalTrials.gov #NCT00041769
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