How do you know if you almost cured someone of HIV?

Transcription

1 How do you know if you almost cured someone of HIV? PR 691: Identification of Biomarkers to Predict Time to Plasma HIV RNA Rebound and Post-Treatment Viral Control during an Intensely Monitored Antiretroviral Pause (MAP) Davey Smith, MD on behalf of the ACTG Monitored Antiretroviral Pause Working Group 1 How do you know if you cured someone of HIV? 2 1

2 How do you know if you cured someone of HIV? Virus does not return= CURE Stop ART 3 How do you know if you cured someone of HIV? Stop ART Virus returns = DAMN 4 2

3 How do you know if you cured someone of HIV? Stop ART Virus returns = DAMN Are we on the right track? 5 How do you know if you cured someone of HIV? Stop ART Virus returns = DAMN We need a MAP Are we on the right track? 6 3

4 How do you know if you cured someone of HIV? Stop ART Virus returns = DAMN You are here We need a MAP Are we on the right track? 7 What can we learn from MAPs? Where we are Where we are going How we know if we are on the right track 8 4

5 We need a MAP We Are Here HIV Cure 9 We Are Here HIV Cure 10 5

6 11/26/14 How do we know if we are on the right track? We Are Here HIV Cure Is there a biomarker that can predict incremental success of an HIV cure eradication? 11 What does a biomarker do? Diagnostic Able to diagnose disease when present and exclude disease when absent Predictive and Prognostic Able to predict future or course of disease Able to predict treatment response Common problem: associative vs. predictive -The Biomarkers Consortium Foundation of the NIH Courtesy of Scott Letendre 12 6

7 Proportion alive according to HIV RNA in copies /ml 1.0 < to 3,000 3,001 to 10,000 10,001 to 30,000 >30, Years since HIV RNA quantification (bdna) 13 Proportion alive according to HIV RNA in copies /ml If you have a biologically relevant biomarker, then you do not have to have a body count to assess if something worked. < to 3,000 3,001 to 10,000 10,001 to 30,000 >30, Years since HIV RNA quantification (bdna) 14 7

8 Proportion alive according to HIV RNA in copies /ml If a in biomarker does not correlate with outcome, (like time to rebound), then dump it. < to 3,000 3,001 to 10,000 10,001 to 30,000 >30, Years since HIV RNA quantification (bdna) 15 Proportion alive according to HIV RNA in copies /ml < to 3,000 3,001 to 10,000 10,001 to 30,000 >30,

9 All MAPs Are Not Created Equal 10^ SMART MAP for Cure Agenda Timing of ART Re-initiation CD4<250 VL > threshold Duration of Tx interrupt Prolonged Short Post-ATI Monitoring Monthly 2-3x per week Repeated Tx interrupt cycles? Yes No 18 9

10 MAPs Are Feasible and Can Be Performed Safely University of Minnesota Treatment Interruption Study(small) Viral load CD4 counts Brief STI in patients with preserved CD4 count and sustained virologic suppression is safe and well tolerated. 19 There is variability in time to rebound: ACTG Pooled Analysis 100 % Virologic Suppressed VL <1000 copies/ml VL <400 copies/ml VL <200 copies/ml (confirmed) Weeks Post-Treatment Interuption % Suppressed VL <1000 copies/ml: 39% 12% 6% VL <400 copies/ml: 35% 8% 5% VL <200 copies/ml: 33% 10% 6% Majority of participants lost viral suppression by week 4, but a subset had delayed viral rebound Jon Li et al

11 Can MAPs tell us we are going in the right direction? 21 pre-map HIV DNA predicts time to plasma viral load rebound 1,2 1 Hurst et al, CROI Personal communication, James Whitney 22 11

12 pre-map HIV DNA predicts time to plasma viral load rebound 1,2 Caveat: Boston Patients 1 Hurst et al, CROI Personal communication, James Whitney 3 Henrich T J et al. J Infect Dis. 2013;207: , Henrich T J et al. CROI The Berlin Patient SCT x 2 Chemo and Rad CCR5 32 GVHD CCR5 WT AML HIV CCR5 32 No AML No HIV Hütter G et al. N Engl J Med 2009;360:

13 CCR5-Modified CD4 T Cells during Treatment Interruption Did Not Decrease, Unlike Unmodified CD4 T Cells Tebas P et al. N Engl J Med 2014;370: Residual HIV RNA during ART is correlated with HIV DNA levels in CD4 T cells Chun T et al. J Infect Dis. 2011;204:

14 Time-scaled Bayesian phylogenetic trees Markov jump count density plot of viral migration between compartments HIV populations in blood can seed compartments and vice versa Chaillon A et al. J Infect Dis. 2014;209: At least in the setting of asymptomatic CMV shedding during ART, lymphocyte activation and proliferation are associated with HIV DNA levels. Gianella et al. J Viol. April 2014 in press 28 14

15 More CD4+ IFN cells, lower the viral load setpoint during ATI Schooley R T et al. J Infect Dis. 2010;202: Do something today your future self will thank you for No need to see the whole staircase to take the first step Biorepository Authors of quotes unknown

16 Post-Treatment Controllers (PTCs) PTCs may not be rare (15% of VISCONTI cohort) PTCs also identified in ACTG ATI studies of patients treated during acute and chronic infection What proportion of patients are PTCs? What is special about PTCs? 31 The Biomarker MAP Study Goals Evaluate virologic, immunologic, and pharmacologic biomarker predictors of HIV rebound; Develop a standardized MAP protocol for future studies; Describe time to rebound across broad patient groups; Identify potential mechanisms of HIV persistence; Delineate importance of anatomic and cellular reservoirs; Characterize rebounding virus so it can be targeted; Produce a biorepository to speed assay development; Determine frequency and predictors of post-treatment control

17 Proposed MAP Study 33 ART Re-Initiation Criteria Participants will be advised to restart ART if they meet any of the following criteria: HIV-1 RNA at or above 1,000 copies/ml on 2 consecutive measurements. A single HIV-1 RNA at or above 1,000 copies/ml and an HIV-1 RNA rise of 0.5 log 10 copies/ml per day. A single HIV-1 RNA at or above 10,000 copies/ml. CD4+ T-cell count below 350 cells/µl on 2 consecutive measurements. Clinical progression to CDC Category B or C disease. Diagnosis of Acute Retroviral Syndrome. Pregnancy Subject requests re-initiation of ART Subject unable or unwilling to use barrier protection with sexual partners 34 17

18 MAP Reviews 35 MAP Reviews 36 18

19 MAP Reviews 37 MAP Reviews 38 19

20 Should we go on this journey? 81% response rate

21 MAP Working Group Investigator Jonathan Li Davey Smith John Mellors Steve Deeks Rajesh Gandhi Jintanat Anaworanich Cara Wilson Sara Gianella-Weibel Ronald Bosch Courtney Fletcher Edward Acosta Nicolas Chomont James Whitney Andy Kaytes Institution Brigham and Women s Hospital University of California, San Diego University of Pittsburgh University of California, San Francisco Massachusetts General Hospital U.S. Military HIV Research Program University of Colorado University of California, San Diego Harvard School of Public Health University of Nebraska University of Alabama at Birmingham Vaccine and Gene Therapy Institute of Florida Beth Israel Deaconess Medical Center University of California, San Diego 41 Hypothesis In HIV-infected participants who initiated ART during either acute or chronic infection and who subsequently undergo a MAP, the time to confirmed HIV rebound will be predicted by pre- MAP biomarkers of HIV persistence. A subset of participants will maintain HIV suppression <1000 copies/ml during the MAP ( post-treatment controllers ), and pre-map biomarkers of this outcome can be identified

22 Should we go on this journey? Pros Biomarker identification and validation can: are critical steps toward achieving ART-free remission. provide common goals for interventions, speed assessment of interventions and progress towards a cure Cons 43 Other limitations of MAPs Identified biomarkers may be intervention specific e.g. gene therapy, enhanced HIV-specific immune function MAP in the context of interventional trials will identify specific biomarkers of success. No effective interventions exist, so need to move forward with identifying markers of success (delayed time to rebound) after ART alone (VISCONTI, rare non-rebounder following ART during acute or chronic HIV infection) 44 22

23 Primary Objectives Determine the safety of MAP with the proposed monitoring protocol Evaluate the association between pre-map biomarkers of HIV persistence and time to HIV rebound following MAP Assess the prevalence and predictors of HIV posttreatment control (HIV-1 RNA <1000 copies/ml at 24 and 48 weeks of MAP) in participants treated during early and chronic infection 45 Secondary Objectives To assess other immunologic, pharmacologic, and genetic biomarkers as predictors of time to viral rebound during MAP To assess whether virologic, immunologic and pharmacologic measures in non-blood compartments (genital secretions, CSF, GI tract) are associated with time to viral rebound To determine HIV-1 RNA and DNA dynamics and kinetics in blood plasma, cells, and anatomic compartments during MAP To evaluate changes in immune function and inflammatory biomarkers during the MAP 46 23

24 Inclusion Criteria: Cohort A Cohort A (ART initiated during chronic Infection; N = 51) Initiated ART during chronic infection (>6 months after estimated date of infection) and willing to have a MAP. Male and females aged > 18 years. Maintenance of plasma HIV RNA below the limit of detection of conventional assays for at least 24 months. Subjects who had isolated episodes of detectable but unconfirmed HIV RNA will remain eligible. Most recent (within 3 months) peripheral blood CD4+ count >400 cells/µl. Nadir CD4+ count >50 cells/µl. No history of AIDS defining illness. Ability and willingness to provide written informed consent. No documented HIV resistance or contraindication to standardized ART regimen of dolutegravir/abacavir/lamivudine. 47 Inclusion Criteria: Cohort B Cohort B (ART initiated in acute/early Infection [Fiebig I-V]; N = 51) Initiated ART during Fiebig stages I-V Male and females aged > 18 years Maintenance of plasma HIV RNA below the limit of detection of conventional assays for at least 24 months. Subjects who had isolated episodes of detectable but unconfirmed HIV-1 RNA will remain eligible Most recent (within 3 months) peripheral blood CD4+ count >400 cells/µl Nadir CD4+ count >50 cells/µl No history of AIDS defining illness Ability and willingness to provide written informed consent No documented HIV resistance or contraindication to standardized ART regimen of dolutegravir/abacavir/lamivudine

25 Staged Enrollment for Safety Cohort A participants will be enrolled first and we will conduct a safety review after 25% of participants have completed at least 12 weeks of Step 2 (or met ART re-initiation criteria) before opening enrollment to Group B. Once Cohort B begins enrollment, individuals who started ART during Fiebig I or II will not be enrolled until after an interim safety review of 10 subjects who started ART during Fiebig III-V have completed 8 weeks of observation (or met ART re-initiation criteria) during MAP. 49 Sample Size Considerations Cohorts A and B will be powered to see whether greater pre- MAP levels of cell-associated HIV RNA (CA-RNA) in CD4+ T cells are associated with shorter time to viral rebound during MAP. Sample size calculations were performed based on an ability to achieve 80% power and two-sided alpha=0.05 to detect a Spearman correlation between a pre-map marker and time to virologic rebound. We will be able to detect a biomarker correlation of 0.4 or greater with a sample size of 51 participants for each cohort. With 51 participants in Cohort B, this also provides power to exclude a post-treatment controller prevalence of 7% or higher, within the previously estimated rate of 15%

26 Response to Reviewer Questions/Comments Haven t there been prior ATI studies performed through the ACTG? These results are being analyzed as part of NWCS 371 and have been helpful in assessing study safety But participants were monitored relatively infrequently Limited stored samples for biomarker assessment 51 NWCS 371: Pooled Analysis of ATI Studies Jonathan Li, Evgenia Aga, Ron Bosch, Michael Para, Mary Kearney, Dan Kuritzkes, John Mellors, Raj Gandhi Inclusion Criteria: ParCcipant of an ACTG ATI study No immune- modulacng therapeuccs (e.g., vaccinacon, IL- 2) Pre- ATI viral load <50 copies/ml Interim Analysis CD4+ cell changes post- ATI ProporCon of subjects with viral suppression VL 200 copies/ml x 2 VL 400 copies/ml VL 1000 copies/ml StraCfied by Cming of ART or CD4+ count 52 26

27 Rate of CD4+ Decline and Loss of Viral Suppression: by Timing of ART *CD4+ loss at each week includes parccipants with virologic rebound 53 Rate of CD4+ Decline and Loss of Viral Suppression: by CD4+ Count *CD4+ loss at each week includes parccipants with virologic rebound 54 27

28 Response to Reviewer Questions/Comments This study will not prove a causation between the biomarker and outcome. Isn t there a risk that the biomarkers studied will not be the optimal measure of an effective therapy? Limitation of this study and any biomarker study Not feasible to perform MAP studies of all promising compounds - this is one step towards the systematic evaluation of biomarkers of viral rebound Biorepository will accelerate novel assay development What are the risks of MAP for acutely-treated patients? Good safety experience with Fiebig 3-5 participants through SPARTAC, ANRS PRIMO, ACTG studies Staggered enrollment for Fiebig 1-2 participants 55 Response to Reviewer Questions/Comments How will you assess the feasibility of enrolling acutelytreated participants? We plan to perform a feasibility survey How did you choose the CD4+ T cell thresholds for the inclusion and ART re-initiation criteria? Critical to enroll participants with broad pre-art characteristics to maximize range of time to HIV rebound Previous studies with participants at lower CD4 counts showed no obvious safety concerns CD4 re-initiation criteria still under consideration 56 28

29 Response to Reviewer Questions/Comments What clinical viral load assay will be used? Point of care testing ideal Roche Taqman assays performed at Quest 6 days/week What about participants who are unable to tolerate the new ART or have VL blips during Step 1? These participants will not undergo MAP and will be replaced Are there resources outside of the ACTG that will be used to fund this study? Funds have been requested from the Bill and Melinda Gates Foundation to offset most of the costs for this study 57 Why do a MAP? 1. Continued need for treatment interruption studies as part of the HIV Cure agenda 2. Bring everyone up to date on the modern design of treatment interruption trials and it's differences with the historical SMART type-studies and its problems 3. Need for biomarkers to accelerate the development of curative strategies

30 11/26/14 How do you know if you are on the right track? We Are Here HIV Cure 59 Models of HIV Treatment Interruption (TI) Studies Traditional Analytic Monitored Treatment Interruption Antiretroviral Pause (ATI) (MAP) Primary Viral Load Outcome Set Point Time to Rebound Duration of TI Limited by Post-TI Monitoring Time Viral load Weekly-Monthly 1-3x Weekly Months Days-to-Months Duration of TI Benefits of the Monitored Antiretroviral Pause approach: Decreased participant risk Shorter study duration 60 30

31 How do you know if you cured someone of HIV? Virus does not return= CURE Stop ART Virus does return= DAMN Did we almost cure someone of HIV? Are we even on the right track? Get a MAP

32 HIV Cure Interventions Success of interventions aimed at achieving HIV remission will be ultimately be judged by their ability to show clinically meaningful results in HIV treatment intervention trials Viral load Set point Delay in Rebound Time After Treatment Interruption 63 What the heck is a biomarker? A molecular, biological, or physical characteristic that indicates a specific, underlying physiologic state to identify risk for disease, to make a diagnosis, or to guide treatment. The Biomarkers Consortium Foundation of the National Institutes of Health; Courtesy of Scott Letendre 64 32

33 Proportion AIDS-free according to HIV RNA in copies /ml < to 3, ,001 to 10, ,001 to 30,000 >30, Years since HIV RNA quantification (bdna) 65 Basic Concepts of Biomarker Investigations Research objective is fundamentally important A diagnostic biomarker can be very different from a prognostic biomarker Disorder should be as well defined, homogeneous, and reliable as possible Many diseases, including HIV-associated neurocognitive disorder (HAND), are very heterogeneous Identifying surrogates can be very helpful Measurement method should also be precise and accurate 66 33

34 Precision and Accuracy in Measurement The target shooting analogy Random Error Bias Slide courtesy J. Allen McCutchan Types of Biomarkers Host Vital signs Weight, blood pressure Clinical labs Electrolytes, hemoglobin Cell types and numbers Total lymphocytes Lipids & carbohydrates Cholesterol, glucose Proteins and peptides Nucleic acid DNA genotypes RNA gene expression Neuroimaging Neuropsychological testing (Drug concentrations) 68 34

35 Types of Biomarkers Host Cell number e.g., CD4 and CD8 T lymphocyte counts and percent Cellular activation Surface markers: e.g., CD38, HLA-DR Soluble factors: e.g., soluble CD14 Cellular injury Neurons: e.g., neurofilament light, total tau More specific pathways of pathogenesis 69 Biomarker Objectives Associative vs. Predictive MSK ΔMSK ΔMSK CSF Associative Predictive Time (months) Bandaru et al, Neurology

36 Clinically Useful Biomarkers Predictive Ability to predict future disease Diagnostic Ability to diagnose disease when present and exclude it when absent Prognostic Ability to predict course of disease Important modifiers Easy to access body fluid Clinically available Cost-effective Screening vs. confirmatory tests Treated vs. untreated Many vs. few comorbidities 71 Study designs differ in their power to accurately identify biomarker associations Cross-sectional: Most HAND biomarker analyses to date Essential for determining diagnostic accuracy of a biomarker Many have been retrospective analyses of stored specimens High cost of case finding, careful characterization, and follow-up Interventional and non-interventional longitudinal studies: inherently more powerful than cross-sectional Longitudinal studies are essential for identifying predictive and prognostic biomarkers Interventional studies perturb relationships between biomarkers and outcomes, which can enable insights into pathogenesis Longitudinal studies can better account for inter-individual differences in time-linked variables Age, duration of HIV, duration of prior ART Nadir CD4+ cell count 72 36

37 Biomarker Investigations Issues to Consider How accurately can the biomarker be measured? Precision and accuracy Surrogate or parallel markers Specimen processing and assay sensitivity Can the biomarker be accurately measured in specimens that have been frozen? How do conditions other than the target condition at the time of sampling affect the levels? 73 Importance of biomarkers in other diseases Would give a little history about importance of biomarkers: plasma HIV RNA was critical for speeding ART development as was HCV RNA for DAA. Two step process: 1) validate as predictive marker (for HIV cure, time to rebound); 2) show that change in biomarker is associated with improved outcome (delayed time to rebound) New slide Delta Marker = or not= time to rebound? If not then move on to next marker. Use SAHA example- Archin Nature paper

38 Emphasize There are benefits for Group B since up to 14% of people can have PTC. Which biomarker to show success? SAHA Immune response- Merck therapeutic vaccine study. Clarify safety 75 What can we learn from a MAP? 76 38

39 Can MAPs tell us we are going in the right direction? For use in early phase studies Identify promising candidates for treatment interruption studies Avoid cost and risk of MAP studies for therapies unlikely to result in a delay of viral rebound Accelerate development of HIV curative strategies Lower the barrier of entry for drug development Is there a biomarker that can predict incremental success of an HIV cure eradication? 77 xxxxxx 78 39