Differences in Calculated Glomerular Filtration Rates (GFR) in Efavirenz (EFV) or Tenofovir (TDF)-treated Adults in ESS40006

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1 13th Conference on Retroviruses and Opportunistic Infections Denver, CO, USA. February 5-9, 2006 Poster Number 777 Differences in Calculated Glomerular Filtration Rates (GFR) in Efavirenz (EFV) or Tenofovir (TDF)-treated Adults in ESS40006 M Thompson 1, R Haubrich 2, D Margolis 3, S Schneider 4, R Schooley 5, K Pappa 6, J Sall 6, L Yau 6 and J Hernandez 6 1 AIDS Res. Con. of Atlanta, GA; 2 UCSD, San Diego, CA; 3 UNC, Chapel Hill, NC; 4 Saint Mary Med. Ctr. Care Clinic, Long Beach, CA; 5 UCSD, San Diego, CA; 6 GlaxoSmithKline, RTP, NC. Introduction Kidney disease is an important complication of HIV infection. Patients infected with HIV may develop HIV associated nephropathy (HIVAN) or, less commonly, an IgA-mediated anti-hiv immune complex glomerulonephritis. Likewise, patients with HIV and comorbidities such as Hepatitis B or C, diabetes, and hypertension may develop an array of associated nephropathies. In addition, antiretroviral therapies (ART) have been associated with renal dysfunction. ART drugs most commonly reported to cause nephrotoxic effects include: indinavir, adefovir and tenofovir disoproxil fumarate (TDF). 1-3 Several observational cohorts have reported a higher degree of nephrotoxicity in patients treated with TDF compared to nucleosides. 4-6 Recently the HIV Medicine Association of the Infectious Disease Society of America published guidelines for the management of chronic kidney disease in HIV infected patients. 7 The guidelines recommend that renal function be followed in patients infected with HIV by monitoring either creatinine clearance which can be calculated by the Cockcroft-Gault equation or by following glomerular filtration rate (GFR) as calculated by the simplified Modification of Diet in Renal Disease equation (MDRD). The MDRD formula includes race as a variable, while the Cockcroft-Gault method does not. Since this study includes a significant number of African-Americans, and because race has been strongly associated with HIVAN, we used the MDRD formula for these analyses. To explore the issue of tenofovir-associated nephrotoxicity, we analyzed the clinical trial database from ESS This study was designed as a randomized comparison of two regimens of amprenavir/ritonavir (APV/r) at doses of 600/100 mg vs 900/100 mg twice daily in subjects failing their current ART regimen. All patients also took abacavir. In addition, a non-randomized assignment was made to efavirenz (EFV) for NNRTI-naïve subjects or to TDF for NNRTI-experienced subjects. Patients took one additional nucleoside reverse transcriptase inhibitor as part of their regimen (Figure 1). To examine the effect of these regimens on renal function, we calculated GFR by MDRD using serum creatinine values obtained during the course of the trial. Because patients were not randomized to TDF or EFV in this study, this exploration should be regarded as an observational analysis, although within the closely monitored environment of a clinical trial.

2 Figure 1 Study Design PI failure >12 wks HIV RNA >1000 cpm CD4 >50/mm 3 FC Abacavir <5x FC Amprenavir <4x FC NRTIs <4x NNRTI naïve NNRTI failure 38 patients 76 patients r/apv 600/900 abacavir efavirenz 1 other NRTI r/apv 600/900 abacavir tenofovir 1 other NRTI Methods Descriptive statistics were summarized for subjects treated with EFV or TDF. Glomerular filtration rate was calculated for both groups on retrospective data by using the MDRD equation: GFR (ml/min/1.73m 2 ) = 186 X serum creatinine (mg/dl) X [age (yrs)] X [0.742 if female] X [1.212 if black] Potential predictors of GFR decline over 48 weeks of therapy were assessed using multiple regression analyses and include: baseline (BL) demographic data (age, weight, sex, and race) and other baseline characteristics (CDC HIV-1 classification, HIV risk factors, CD4+ cell count, plasma HIV-1 RNA, prior therapy, concurrent ART, and clinical laboratory results). No data were available for history of hypertension, Hepatitis C or diabetes but serum blood glucose at baseline and at Weeks 24 and 48 was used as a surrogate for diabetes. Data on the presence or absence of proteinuria were not available. Each potential predictor was studied using a univariate regression model with the Week 48 change from baseline in the calculated GFR as the dependent variable. Then predictors with a p-value of <0.1 in the univariate analyses were included a multiple regression model for the Week 48 change from baseline in calculated GFR (adjusted for the baseline calculated GFR). Significant covariates (p-value <0.05) were selected to remain in the final regression model based on the stepwise selection method. Results A total of 114 subjects were randomized in the ITT population for the comparison of APV/r dosing. Of these, 76 were assigned to TDF-containing regimens and 38 were assigned to EFV-containing regimens. Baseline (BL) demographics and disease characteristics, including BL serum creatinine and calculated GFR, are summarized for the ITT population in both groups (Table 1).

3 Table 1 Baseline Demographics and Disease Characteristics Median (range) or % APV + TDF (n=76) APV + EFV (n=38) Age (years) 42 (26-65) 43 (24-64) Sex (% male) 80% 92% Race (% white, black) 59%, 25% 34%, 50% Weight (kg) 78.9 (46-126) 81.5 (52-123) CDC Classification, % (A, B, C) 37%, 30%, 33% 37%, 29%, 34% HIV Risk Factors Heterosexual contact 33% 37% Homosexual contact 66% 74% Injectable drug use 8% 5% HIV-1 RNA, log 10 copies/ml 4.09 ( ) 4.08 ( ) CD4, cells/mm ( ) 229 (53-896) Serum Creatinine (mg/dl) 0.8 ( ) 0.9 ( ) Calculated GFR (ml/min/1.73m 2 ) 108 (58-196) 107 (51-237) Serum Glucose (mg/dl) 92 (67-224) 93 (67-236) Duration of Prior ART (months) ( ) ( ) Duration of Prior PI Therapy (months) ( ) ( ) Duration of Prior NRTI Therapy (months) ( ) ( ) The baseline characteristics for both groups of patients were comparable with respect to age, weight and HIV-disease; a higher proportion of patients in the EFV group were male and black. Due to the study design, the duration of prior ART therapy was generally longer in the TDF group compared to the EFV group (57 vs 37 months); however, there were no differences in the baseline median glucose levels, creatinine levels or the calculated GFR. At baseline, the median calculated GFR in the EFV-treated subjects was 107 ml/min/1.73m 2 and in the TDF-treated subjects was 108 ml/min/1.73m 2. In an intent-to-treat observed analysis, the group receiving TDF had a statistically significant median reduction from baseline in calculated GFR were seen at both Weeks 24 (p <0.001) and 48 (p <0.001) (Figure 2). At 24 weeks, the magnitude of the change from baseline in the TDF treated group was -10 ml/min/1.73m 2, and the change from baseline in the EFV group was +12 ml/min/1.73m 2. At 48 weeks, the magnitude of change in the TDF treated group was -11 ml/min/1.73m 2, and the change from baseline in the EFV group was +0.4 ml/min/1.73m 2. The group receiving EFV had a statistically significant median increase from baseline in calculated GFR at Week 24 (+12 ml/min/1.73m 2 ) but there was no difference at Week 48.

4 Figure 2 Change in Calculated GFR from Baseline for Individual Subjects at Weeks 24 and APV+TDF APV+EFV 60 Median Change from Baseline Week 24 Week 48 Week 24 Week 48 n = Signed Rank p-value = <0.001 < Statistically significant differences in the median change in calculated GFR from BL between the EFV-treated and TDF-treated subjects were observed at both weeks 24 (p<0.001) and 48 (p=0.004) (Figure 3). Figure 3 Fold Change in IC50 (+APL / APL) Determined by Two Different Assays Wk 2 Wk 4 Wk 8 Wk 12 Wk 16 WK 24 Wk 32 Wk 40 Wk Median change from baseline with IQR p<0.001* p=0.004* -30 n = n = TDF EFV IQR = interquartile range, Q1 = 25 th percentile, Q3 = 75 th percentile * p-values were based on the Wilcoxon rank-sum test The results of the univariate regression analysis exploring for predictors of the change in calculated GFR are shown in Table 2. Univariate predictors of change in GFR were baseline calculated GFR and serum creatinine (p <0.001), baseline viral load >50,000 or >100,000 copies/ml, TDF in the regimen (p <0.1), and baseline glucose (p <0.05). The parameter estimates of the final multiple regression model adjusted for BL calculated GFR are shown in Figure 4. The only predictor for the decline in calculated GFR over 48 weeks in the final multiple regression model after adjusting for BL calculated GFR was TDF use in the current regimen (p <0.001).

5 Table 2 Results of Univariate Regression Models Predicting Week 48 Change in Calculated GFR from Baseline Age (years) Weight (kg) Race White Black Hispanic Sex BL CDC Classification HIV Risk Factors BL calculated GFR*** BL serum creatinine (mg/dl)*** BL serum glucose (mg/dl)** BL CD4+ cell count (cells/mm 3 ) BL CD4+ cell count <50 cells/mm3 BL CD4+ cell count <200 cells/mm3 TDF in current regimen* 3TC in current regimen d4t in current regimen ddi in current regimen ZDV in current regimen BL viral load (log 10 copies/ml) BL viral load >5000 copies/ml BL viral load >10,000 copies/ml BL viral load >20,000 copies/ml BL viral load >50,000 copies/ml* BL viral load >100,000 copies/ml* Prior NNRTI experience (yes/no) Prior PI experience (yes/no) Prior NRTI exposure (months) Prior NNRTI exposure (months) Prior PI exposure (months) *p-value <0.1, **p-value<0.05, ***p-value<0.001 Figure 4 Multiple Regression Model Predicting Week 48 Change in Calculated GFR from Baseline TDF in current regimen (p<0.001) BL calculated GFR (p<0.001) Parameter Estimates and 95% Confidence Intervals

6 Discussion Randomized, controlled clinical trials of tenofovir given over at least 48 weeks (GS 934, 903, 907) have not shown evidence of renal dysfunction associated with TDF use. Reports of renal disorders associated with TDF use have been sporadic over the past few years The majority of these cases of renal impairment occurred in subjects with other identified risk factors such as lower CD4 cell count, prior adefovir use, low body weight, decreased renal function at baseline, and diabetes. However, in this study population none of these risk factors were significantly different between the subjects treated with TDF or EFV-containing regimens at baseline. Since this trial was not a randomized comparison of TDF compared with EFV, it is possible that factors other than TDF use alone were responsible for the reduction in calculated GFR from baseline. Patients on TDF had significantly longer duration of prior therapy compared to the EFV group. However, none of the parameters explored to evaluate these differences, including months of prior NRTIs were identified to be predictors of the change in calculated GFR. The time on prior therapy for individual drugs was not ascertained. In addition, nucleoside agents have shown not to exhibit clinically significant changes in calculated GFR. 11 Other factors that were not accounted for in this analysis include non-antiretroviral concomitant therapies, hypertension, diabetes, and Hepatitis C that may have influenced renal function. Baseline proteinuria was not assessed. Because all patients in this trial were on a boosted PI regimen, we were unable to address the issue of whether ritonavir plays a role in TDF nephrotoxicity. Conclusion A small but statistically significant decline in the median calculated GFR was observed at 24 and 48 weeks of therapy for NNRTI-experienced subjects treated with TDF in this study. The clinical significance of this change in calculated GFR is not known. This decline was not seen in the NNRTI-naïve subjects treated with EFV, and an increase in median calculated GFR was observed in this group at 24 weeks. TDF use was the only predictor of calculated GFR decline using multiple regression analysis (after adjusting for BL calculated GFR). Glomerular filtration rates should be followed in patients at risk for chronic kidney disease and in those on antiretroviral therapy with higher risk of causing nephrotoxicity. Calculated GFR should be routinely monitored in clinical trials of antiretroviral therapy and results which include GFR grouped by National Kidney Foundation (NKF) category should be reported for regimens in clinical trials. 12 Further analysis of other trials involving TDF may be helpful in further identifying high risk patients for the development of nephrotoxicity. References 1. Perazella MA. Drug induced renal failure: Update on new medications and new mechanisms of nephrotoxicity. Am J Med Sci 2003; 325: Benhamou Y et al. Safety and efficacy of adefovir dipivoxil in patients coinfected with HIV and lamivudine resistant hepatitis B viruses: an open label pilot study. Lancet 2001; 3587: Rifkin BS et al. Tenofovir-associated nephrotoxicity: Fanconi syndrome and renal failure. Am J Med 2004; 117: Gallant JE et al. Changes in renal function associated with tenofovir disoproxil fumarate treatment, compared with nucleoside reverse-transcriptase inhibitor treatment. CID 2005; 40: Mauss S et al. Antiretroviral therapy with tenofovir is associated with mild renal dysfunction. AIDS 2005; 19(1): Stebbing J et al. Case-control data regarding renal dysfunction and tenofovir DF. Contagion 2005; 2(7): Gupta SK et al. Guidelines for the management of chronic kidney disease in HIV-infected patients: recommendations of the HIV Medicine Association of the Infectious Diseases Society of America. CID 2005; 40: Gaspar G et al. Fanconi syndrome and acute renal failure in a patient treated with tenofovir: a call to action. AIDS 2004; 18: Peyriere H et al. Renal tubular dysfunction associated with tenofovir therapy: report of 7 cases. JAIDS 2004; 35: Karras A et al. Tenofovir-related nephrotoxicity in human immunodeficiency virus-infected patients: three cases of renal failure, Fanconi syndrome, and nephrogenic diabetes insipidus. CID 2003; 36: Sutherland-Phillips D et al. Regimens containing abacavir (ABC), lamivudine (3TC), zidovudine (ZDV), and efavirenz (EFV) do not affect GFR during long-term treatment of HIV naive subjects. Poster H-349 at 45th ICAAC, Dec 2005, Washington, DC. 12. Levey AS et al. National kidney foundation practice guidelines for chronic kidney disease: evaluation, classification, and stratification. Ann Intern Med 2003; 139: Acknowledgements We gratefully acknowledge the many study participants, clinical investigators and staff, and the Clinical Trials Management Services (CTMS) and GlaxoSmithKline (GSK) study teams PEARL Study Investigators: A Barile, J Baxter, S Becker, P Benson, D Blazes, A Burnside Jr, D Butcher, P Cimoch, D Cohen, G Coodley, T File, J Glaser, M Goetz, B Gripshover, S Hammer, S Jacobson, A Kelly, T Larson, D Parks, G Perez, P Piliero, D Richman, M Sands, M Sension, A Taege, J Timpone, P Wolfe, W Woodward, and D Wright CTMS: M Carrier, W Crumpton, N Haige, T Hardin, P Kilgore, S McKinney, R Perkins, and G Sproles GSK: T Becom, L Chandler, A Pierce, S Ross, and S Hessenthaler