1. Connection Domain Mutations in Treatment-Experienced Patients in the OPTIMA (Options in Management with Antiretrovirals) Trial Birgitt Dau, M.D. Postdoctoral Fellow in Infectious Diseases US Department of Veterans Affairs and Stanford University

2. Connection Domain (CD) Background and Rationale for Analysis Codons 316-437 of HIV reverse transcriptase Codons 316-437 of HIV reverse transcriptase Connects the DNA polymerase (1-315) and RNase H (438-560) domains Connects the DNA polymerase (1-315) and RNase H (438-560) domains Most clinically available genotypic resistance tests have not sequenced the CD or RNase H domains Most clinically available genotypic resistance tests have not sequenced the CD or RNase H domains RNase H works during reverse transcription to degrade RNA from the DNA:RNA duplex RNase H works during reverse transcription to degrade RNA from the DNA:RNA duplex Mutations in RNase H slow its activity, allowing time for NRTI excision, and thus NRTI resistance 1 Mutations in RNase H slow its activity, allowing time for NRTI excision, and thus NRTI resistance 1 Mutations in the CD also affect RNase H efficiency 2 Mutations in the CD also affect RNase H efficiency 2 1. Nikolenko et al, Proc Natl Acad Sci USA 2005 2. Julias et al, J Virol 2003

3. HIV-1 Reverse Transcriptase

4. In Vitro and In Vivo Data on CD Mutations Many CD mutations are associated with ARV resistance to zidovudine, lamivudine, nevirapine and efavirenz in vitro Many CD mutations are associated with ARV resistance to zidovudine, lamivudine, nevirapine and efavirenz in vitro CD mutations increase fold change caused by TAMS 1 and K103N 2 in vitro CD mutations increase fold change caused by TAMS 1 and K103N 2 in vitro Appearance of N348I was associated with an increase in viral load 3 Appearance of N348I was associated with an increase in viral load 3 A371V is associated with a history of AZT exposure 4 A371V is associated with a history of AZT exposure 4 1. GN Nikolenko et al, Proc Natl Acad Sci U S A 2005 2. Harrigan et al, J Virol 2002 3. SH Yap et al, PLoS Med 2007 4. Santos et al, PLoS One, 2008

5. Methods HIV-1 reverse transcriptase gene sequences (codons 1- 400) and virtual phenotypes were analyzed from 345 patients randomized in the OPTIMA trial HIV-1 reverse transcriptase gene sequences (codons 1- 400) and virtual phenotypes were analyzed from 345 patients randomized in the OPTIMA trial Phenotypic susceptibility scores (PSS) were calculated by adding the score for each drug in the patient’s initial on- study ARV regimen Phenotypic susceptibility scores (PSS) were calculated by adding the score for each drug in the patient’s initial on- study ARV regimen –0 = no activity (FC > CCO2), 0.5 = partial activity (FC > CCO1 and CCO2), 0.5 = partial activity (FC > CCO1 and < CCO2), 1 = full activity (< CCO1) Virologic response was defined as a HIV viral load reduction of > 1 log10/mL after 24 weeks of ARV treatment Virologic response was defined as a HIV viral load reduction of > 1 log10/mL after 24 weeks of ARV treatment Statistical analysis Statistical analysis –Fisher’s Exact Test, Logistic regression, Chi-square

6. OPTIMA Trial 1 : Introduction OPTIMA is a large treatment interruption trial from 2001-2006 OPTIMA is a large treatment interruption trial from 2001-2006 Open, randomized, prospective, multi-center management trial in patients with MDR who failed at least two ARV regimens Open, randomized, prospective, multi-center management trial in patients with MDR who failed at least two ARV regimens A 2 x 2 factorial design: A 2 x 2 factorial design: –randomized to ARV drug free period (ARDFP) for 3 months or not (no ARDFP); –and to treatment by either standard antiretroviral therapy (ART) ( 5 ARV drugs) Primary outcomes: time to a new or recurrent AIDS event or death Primary outcomes: time to a new or recurrent AIDS event or death Secondary outcomes: changes in CD4 count and HIV-1 viral load Secondary outcomes: changes in CD4 count and HIV-1 viral load Minimum follow-up = 1 year Minimum follow-up = 1 year 1. See Poster LBPE1145

7. OPTIMA 1 Trial: Results 368 subjects randomized: 98% male, mean age 49 years, mean CD4 130/mm3 and viral load 4.71 log 10 copies/mL 368 subjects randomized: 98% male, mean age 49 years, mean CD4 130/mm3 and viral load 4.71 log 10 copies/mL Prior ARV use Prior ARV use –96% > 3 NRTI (median 5) –97% 1 NNRTI (median 1) –63% > PIs (median 3) –2.5% were enfuvirtide experienced. Baseline PSS: standard ART 1.8, Mega-ART 2.4 Baseline PSS: standard ART 1.8, Mega-ART 2.4 Median ARDFP was 12 weeks (IQR: 12-14 weeks) Median ARDFP was 12 weeks (IQR: 12-14 weeks) Comparing standard vs. Mega-ART; or ARDFP vs. No- ARDFP Comparing standard vs. Mega-ART; or ARDFP vs. No- ARDFP –No significant difference in time to primary outcome for AIDS or death –No significant difference in CD4 count or HIV viral load changes between the treatment arms 1. See poster LBPE1145

8. Epidemiology of CD Mutations Mutation OPTIMA # (%) n=345 ARV Naïve 1 # (%) (sample size) P Value Frequency E312Q 2 (0.58%) 9 (0.9) (993) P = 0.7385 Y318F 11 (3.2%) 0 (0) (989) P < 0.0001 G333D 5 (1.5%) 4 (0.4) (910) P = 0.0702 G333E 40 (11.6%) 69 (7.6) (910) P = 0.0323 G335C 1 (0.3%) 7 (0.8) (851) P = 0.4508 G335D 13 (3.8%) 10 (1.2) (851) P = 0.0047 N348I 39 (11.3%) 1 (0.2) (358) P < 0.0001 A360I 2 (0.6%) 0 (0) (352) P = 0.2446 A360V 12 (3.5%) 6 (1.7) (352) P = 0.1579 V365I 23 (6.7%) 13 (3.6) (352) P = 0.0019 A371V 61 (17.7%) 19 (5.4) (349) P < 0.0001 A376S 43 (12.5%) 16 (4.5) (348) P = 0.0013 E399G 7 (2.0%) 1 (0.2) (352) P = 0.0363 1. Stanford HIV Database

9. Association of CD Mutations with Primary ARV Mutations Y118I30.5%M184V50.7%G190A6.5%L210W33.7%T215F14.6%T215Y49.2%219E7.3%219Q14.1% G333E11.6%P=0.08740.4000 P=0.61 74 P=0.855 0 P=0.301 3 P=0.309 3 P=0.285 1 P=0.795 0 N348I11.3%P=0.4437P<0.05P<0.05 P=0.356 9 P=0.114 7 P=0.174 2 P<0.0050.1048 V365I6.7%NSNSNSNSP<0.05NSNSP<0.05 A371V17.7%P<0.005P<0.05 P=0.20 29 P<0.001P=1.000P<0.0010.04090.6622 A376S12.5%P<0.010.0999P<0.05P<0.050.0781P<0.050.73481.000 * CD mutations were not significantly associated with each other

10. Univariate Analysis: Association of CD Mutations with Diminished Virologic Response to ART CDMutation P value for lack of virologic response (< 1log 10 /mL decrease at 24 weeks) G333E0.367 N348I1.000 V365I0.370 A371V0.047 A376S0.601 PSS0.0017

11. Multivariate Analysis: Factors Affecting Virologic Response P Value Baseline CD4 0.0226 Baseline Viral Load 0.0011 Effect of Drug Free Period 0.2744 Effect of Standard vs. Mega HAART 0.3257 PSS 1 0.2803 Y118I0.0282 G190S0.0485 T215F0.0314 Other RT and Connection Domain Mutations NS 1. The PSS incorporates CD and other mutations

12. Conclusions CD mutations are far more frequent in treatment-experienced populations than in untreated populations CD mutations are far more frequent in treatment-experienced populations than in untreated populations CD mutations are associated with primary RT mutations- CD mutations are associated with primary RT mutations- –Likely shared selection pressure (treatment history) –Functional dependency, i.e. compensatory mutations, is possible Additive effect of CD mutations above primary RT mutations in clinical practice is unknown Additive effect of CD mutations above primary RT mutations in clinical practice is unknown

13. Limitations Linkage of CD and primary ARV mutations cannot be directly established without clonal analysis Linkage of CD and primary ARV mutations cannot be directly established without clonal analysis Population sequencing underestimates the frequency of mutations present Population sequencing underestimates the frequency of mutations present RNase H mutations were not analyzed RNase H mutations were not analyzed The complicated background of mutations and suboptimal ARV treatment regimens made it hard to distinguish the effect of single mutations The complicated background of mutations and suboptimal ARV treatment regimens made it hard to distinguish the effect of single mutations Given extensive ARV resistance and limited treatment options, patients were unlikely to fully respond to any regimen, making it difficult to differentiate treatment response between groups of patients Given extensive ARV resistance and limited treatment options, patients were unlikely to fully respond to any regimen, making it difficult to differentiate treatment response between groups of patients

14. Future Directions Ultra deep sequencing Ultra deep sequencing Comparison of plasma vs. PBMC sample sequences Comparison of plasma vs. PBMC sample sequences Clonal analysis to establish linkage between CD mutations and primary ARV- associated HIV RT mutations Clonal analysis to establish linkage between CD mutations and primary ARV- associated HIV RT mutations

15. Acknowledgments Tri-National Trials Collaboration Tri-National Trials Collaboration –Canadian Institutes of Health Research (CIHR) –US Department of Veterans Affairs (VA) –Medical Research Council (MRC) of the United Kingdom (UK). Collaborators Collaborators –Dieter Ayers –Joel Singer –Richard Harrigan –Sheldon Brown –Tassos Kyriakides –Bill Cameron –Brian Angus –Mark Holodniy