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ACTG 333 Study Objectives

ACTG 333 The Antiviral Effect of Switching from Saquinavir to the New Formulation of Saquinavir vs. Switching to Indinavir After >1 year of Saquinavir Use DAP Analysis. ACTG 333 Study Objectives. Primary Objective

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ACTG 333 Study Objectives

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  1. ACTG 333The Antiviral Effect of Switching from Saquinavir to the New Formulation of Saquinavir vs. Switching to Indinavir After >1 year of Saquinavir UseDAP Analysis

  2. ACTG 333 Study Objectives Primary Objective To determine if after >1 year SQVhc use, there was an fall in HIV-RNA upon switching to IDV or SQVsgc. Secondary Objective for the DAP To determine if amino acid substitutions at protease positions associated with in vitro SQV or IDV resistance at baseline predicted RNA responses.

  3. Protocol Design Protocol Design Protocol Design Protocol Design Patients with more than 48 weeks of prior SQVhc Randomized, open label, 3 arm trial for 24 weeks Weeks 0 8 24 Weeks 0 8 24 Weeks 0 8 24 Weeks 0 8 24 SQVhc SQVhc SQVhc SQVhc IDV IDV IDV IDV 600mg tid 600mg tid 600mg tid 600mg tid SQVsgc SQVsgc SQVsgc SQVsgc SQVhc SQVhc SQVhc SQVhc test HIV-RNA, if no response cross to other PI 1200mg tid 1200mg tid 1200mg tid 1200mg tid IDV IDV IDV IDV 800mg q 8h 800mg q 8h 800mg q 8h 800mg q 8h Pre-entry-no antiretroviral change for 2 months Pre-entry-no antiretroviral change for 2 months Pre-entry-no antiretroviral change for 2 months Pre-entry-no antiretroviral change for 2 months

  4. Baseline Characteristics (n=89) Gender 89% male Race 72% W, 12% Afr-AM Age median 42 yr Prior SQV use median 105 weeks % on ≥ 2 NRTI at entry 85% HIV-RNA median 12,451 (4.1 log10) 25% <2500 and 25%>48,000 CD4 median 240 25% < 144 and 25% > 320

  5. Determination of Protease Genotype Population based sequencing plasma RNA -> RT ->PCR -> sequence Multiple PCR, independent clonal sequencing plasma RNA -> RT -> multiple parallel PCR -> insert into vectors -> sequence 1 clone each PCR Amino acid positions of protease related to IDV and SQV resistance were selected for analysis. Mix = mutant. Primary 32, 48, 82, 84, 90 Secondary 10, 20, 24, 33, 36, 46, 47, 54, 71, 73, 77, 88 (counted only if primary substitution present) Differences in methods resolved.

  6. Number of PI Mutations No. of Substitutions Subjects None 28 (31%) 1 5 (6%) 2 5 (6%) 3 17 (19%) 4 11 (12%) 5 7 (8%) 6 7 (8%) 7 1 (1% Missing 8 (9%)

  7. Failures Rates by Baseline Covariates ParameterCategory Subjects with RNA>500 (DAF) Baseline RNA<3.5 3/15 (20%) 3.5 - 4.5 10/15 (67%) 4.5 - 5.5 15/16 (94%) No.ofPI mutations zero 5/14 (36%) one 2/4 (50%) two 1/1 (100%) three 9/12 (75%) four 3/3 (100%) five 4/4 (100%) six 4/4 (100%)

  8. Regression Model for Genotype Data Dropouts as Failures ModelParameterp-valueOdds Ratio95% CI A Baseline RNA 0.0001 7.67 2.91-26.98 1 log change D Number of PI 0.0002 2.12 1.38 - 3.73 mutations F No. PI mutations 0.0015 2.71 1.39 - 7.36 Baseline RNA 0.0002 12.99 2.87 - 140.3 1 log change

  9. Conclusions 1. HIV-RNA change was greater for IDV than SQVsgc and for SQVsgc than SQVhc. 2. There was trend between increasing number of protease mutations and greater use of SQV, higher baseline RNA, and lower CD4. 3. The number of protease mutations and viral load at baseline were predictive of HIV-1 RNA response. Patients with 0 or 1 select protease mutation had a greater RNA decrease. Subjects with ≥ 2 total protease mutations had a blunted RNA response.

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