CD4+ T cell count recovery in HIV type 1-infected patients is independent of class of antiretroviral therapy

BACKGROUND: In recent years, treatment options for human immunodeficiency virus type 1 (HIV-1) infection have changed from nonboosted protease inhibitors (PIs) to nonnucleoside reverse-transcriptase inhibitors (NNRTIs) and boosted PI-based antiretroviral drug regimens, but the impact on immunological recovery remains uncertain. METHODS: During January 1996 through December 2004 [corrected] all patients in the Swiss HIV Cohort were included if they received the first combination antiretroviral therapy (cART) and had known baseline CD4(+) T cell counts and HIV-1 RNA values (n = 3293). For follow-up, we used the Swiss HIV Cohort Study database update of May 2007 [corrected] The mean (+/-SD) duration of follow-up was 26.8 +/- 20.5 months. The follow-up time was limited to the duration of the first cART. CD4(+) T cell recovery was analyzed in 3 different treatment groups: nonboosted PI, NNRTI, or boosted PI. The end point was the absolute increase of CD4(+) T cell count in the 3 treatment groups after the initiation of cART. RESULTS: Two thousand five hundred ninety individuals (78.7%) initiated a nonboosted-PI regimen, 452 (13.7%) initiated an NNRTI regimen, and 251 (7.6%) initiated a boosted-PI regimen. Absolute CD4(+) T cell count increases at 48 months were as follows: in the nonboosted-PI group, from 210 to 520 cells/muL; in the NNRTI group, from 220 to 475 cells/muL; and in the boosted-PI group, from 168 to 511 cells/muL. In a multivariate analysis, the treatment group did not affect the response of CD4(+) T cells; however, increased age, pretreatment with nucleoside reverse-transcriptase inhibitors, serological tests positive for hepatitis C virus, Centers for Disease Control and Prevention stage C infection, lower baseline CD4(+) T cell count, and lower baseline HIV-1 RNA level were risk factors for smaller increases in CD4(+) T cell count. CONCLUSION: CD4(+) T cell recovery was similar in patients receiving nonboosted PI-, NNRTI-, and boosted PI-based cART.

Effects of HIV type-1 immune selection on susceptability to integrase inhibitor resistance

BACKGROUND: All site-specific interactions between HIV type-1 (HIV-1) subtype, human leukocyte antigen (HLA)-associated immune selection and integrase inhibitor resistance are not completely understood. We examined naturally occurring polymorphisms in HIV-1 integrase sequences from 342 antiretroviral-naive individuals from the Western Australian HIV Cohort Study and the Swiss HIV Cohort Study. METHODS: Standard bulk sequencing and sequence-based typing were used to generate integrase sequences and high-resolution HLA genotypes, respectively. Viral residues were examined with respect to drug resistance mutations and CD8(+) T-cell escape mutations. RESULTS: In both predominantly subtype B cohorts, 12 of 38 sites that mediate integrase inhibitor resistance mutations were absolutely conserved, and these included the primary resistance mutations. There were 18 codons with non-primary drug resistance-associated substitutions at rates of up to 58.8% and eight sites with alternative polymorphisms. Five viral residues were potentially subject to dual-drug and HLA-associated immune selection in which both selective pressures either drove the same amino acid substitution (codons 72, 157 and 163) or HLA alleles were associated with an alternative polymorphism that would alter the genetic barrier to resistance (codons 125 and 193). The common polymorphism T125A, which was characteristic of non-subtype B and was also associated with carriage of HLA-B*57/*5801, increased the mutational barrier to the resistance mutation T125K. CONCLUSIONS: Primary integrase inhibitor resistance mutations were not detected in the absence of drug exposure in keeping with sites of high constraint. Viral polymorphisms caused by immune selection and/or associated with non-subtype B might alter the genetic barrier to some non-primary resistance-associated mutations.

Long-term trends of HIV type 1 drug resistance prevalence among antiretroviral treatment-experienced patients in Switzerland

BACKGROUND:Accurate quantification of the prevalence of human immunodeficiency virus type 1 (HIV-1) drug resistance in patients who are receiving antiretroviral therapy (ART) is difficult, and results from previous studies vary. We attempted to assess the prevalence and dynamics of resistance in a highly representative patient cohort from Switzerland. METHODS:On the basis of genotypic resistance test results and clinical data, we grouped patients according to their risk of harboring resistant viruses. Estimates of resistance prevalence were calculated on the basis of either the proportion of individuals with a virologic failure or confirmed drug resistance (lower estimate) or the frequency-weighted average of risk group-specific probabilities for the presence of drug resistance mutations (upper estimate). RESULTS:Lower and upper estimates of drug resistance prevalence in 8064 ART-exposed patients were 50% and 57% in 1999 and 37% and 45% in 2007, respectively. This decrease was driven by 2 mechanisms: loss to follow-up or death of high-risk patients exposed to mono- or dual-nucleoside reverse-transcriptase inhibitor therapy (lower estimates range from 72% to 75%) and continued enrollment of low-risk patients who were taking combination ART containing boosted protease inhibitors or nonnucleoside reverse-transcriptase inhibitors as first-line therapy (lower estimates range from 7% to 12%). A subset of 4184 participants (52%) had >or= 1 study visit per year during 2002-2007. In this subset, lower and upper estimates increased from 45% to 49% and from 52% to 55%, respectively. Yearly increases in prevalence were becoming smaller in later years. CONCLUSIONS:Contrary to earlier predictions, in situations of free access to drugs, close monitoring, and rapid introduction of new potent therapies, the emergence of drug-resistant viruses can be minimized at the population level. Moreover, this study demonstrates the necessity of interpreting time trends in the context of evolving cohort populations.