1. 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

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

4. Baseline Characteristics (n=89) Gender89% male Race72% W, 12% Afr-AM Age median 42 yr Prior SQV use median105 weeks % on ≥ 2 NRTI at entry85% HIV-RNA median12,451 (4.1 log 10 ) 25% 48,000 25% 48,000 CD4 median240 25% 320 25% 320

6. 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 Primary 32, 48, 82, 84, 90 Secondary 10, 20, 24, 33, 36, 46, 47, 54, 71, 73, 77, 88 Secondary 10, 20, 24, 33, 36, 46, 47, 54, 71, 73, 77, 88 (counted only if primary substitution present) Differences in methods resolved.

7. Number of PI Mutations No. of SubstitutionsSubjects None28 (31%) 1 5 (6%) 2 5 (6%) 317 (19%) 411 (12%) 5 7 (8%) 6 7 (8%) 7 1 (1% Missing 8 (9%)

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

9. Regression Model for Genotype Data Dropouts as Failures Model Parameter p-valueOdds Ratio 95% CI ABaseline RNA 0.00017.672.91-26.98 ABaseline RNA 0.00017.672.91-26.98 1 log change DNumber of PI 0.00022.121.38 - 3.73 DNumber of PI 0.00022.121.38 - 3.73mutations F No. PI mutations 0.00152.711.39 - 7.36 F No. PI mutations 0.00152.711.39 - 7.36 Baseline RNA 0.000212.992.87 - 140.3 Baseline RNA 0.000212.992.87 - 140.3 1 log change 1 log change

10. 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.