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Deeks, MD University of California, San Francisco Pathogenesis of drug-resistant HIV: Pathogenesis of drug-resistant HIV Two linked biologically focused cohorts SCOPE: San Francisco, USA UARTO: Mbarara, Uganda Factors known to be predictive of outcome in setting of drug-resistant HIV include Change in viral load Regimen type Viral fitness Immune activation What role will these have in resource poor regions?Virologic responses to first HAART are comparable in South Africa and Switzerland: Virologic responses to first HAART are comparable in South Africa and Switzerland Egger et a; CROI 2007 Month after first VL < 500 0 6 12 18 24 0 20 40 60 80 100 % experienceing Virologic failure Switzerland South AfricaTime to treatment modification differs between regions, however: Time to treatment modification differs between regions, however Egger et a; CROI 2007 Months after starting ART 0 6 12 18 24 % with treatment change 0 20 40 60 Switzerland South Africa Regimen switches more common in Swiss Cohort, presumably because of greater number of options and aggressive management of failureSlide5: Risk of death during HAART higher in Sub-Saharan Africa compared to industrialized countries unadjusted HR adjusted HR (adjusted for cohort, age, sex, baseline CD4, ART-regimen, disease stage) Reference (industrialized countries) Months from starting ART 1 2 3-4 5-6 7-12 13-24 25-36 37-48 Relative mortality rate (95% CI) 0.5 1 2 4 8 16 Braitstein Lancet 2006 Egger et a; CROI 2007Why would clinical outcomes be so different in resource poor vs. resource rich regions (after controlling for baseline CD4)? : Why would clinical outcomes be so different in resource poor vs. resource rich regions (after controlling for baseline CD4)? Lack of access to medical care Higher prevalence of untreated/unrecognized co-infections Higher prevalence of sub-clinical infections (IRIS) Malnutrition Altered natural history of virologic failure?Slide7: - 2 . 5 - 2 - 1 . 5 - 1 - 0 . 5 0 0 . 5 1 1 . 5 2 2 . 5 0 2 4 4 8 7 2 9 6 - 2 0 0 - 1 5 0 - 1 0 0 - 5 0 0 5 0 1 0 0 1 5 0 2 0 0 JID 2000; 181:946-53 AIDS 2003; 17(13):1907-1915 SCOPE cohort (SF): Sustained CD4 gains during “complete” viral suppression CHANGE HIV RNA CHANGE CD4 Slide8: CD4+ Cells also rise in those with incomplete but still significant viral suppression (> 1 log) (n=74)Slide9: Transient virologic responses are associated with sustained (but blunted) CD4+ T Cell gains (n=49) Transient Virologic Responders: Transient decrease of > 1 log Will patients failing standard regimens in resource poor regions exhibit a sustained CD4+ T cell gains? : Will patients failing standard regimens in resource poor regions exhibit a sustained CD4+ T cell gains? Regimen type Inherent bias in all cohort studies Role of inflammation as a determinant of outcome in setting of partial viral suppressionSustained CD4+ T cell gains during long-term failure has largely been reported in protease-inhibitor treated cohorts: Sustained CD4+ T cell gains during long-term failure has largely been reported in protease-inhibitor treated cohorts Kaufman et al, Lancet 1999 Deeks et al, AIDS 1999, JID 2000 and AIDS 2002 Grabar et al, Annals Internal Medicine 2000 and JAIDS 2005 Lecossier et al, JID 2001 Piketty et al, JID 2001 Miller et al, JID 2002 Ledergerber et al, Lancet 2004 Stazewski et al, AIDS 1999 Raffanati et al, JAIDS 2004 Le Moing et al, JID 2002 Hirsch et al, JID 1999 Tuboi et al, JAIDS 2007 PLATO: CD4 decline slower with drug-resistant HIV compared to wild-type HIV, after controlling for level of viremia: PLATO: CD4 decline slower with drug-resistant HIV compared to wild-type HIV, after controlling for level of viremia Steady State Viral Load (copies/mL) CD4 Slope (cells/year) Lancet 2004Sustained CD4 gains during failure associated with protease inhibitor exposure: Univariate Multivariate Age (per 10 yr) -5.6 -6.7 Female 25 16 Current VL (per Log) -28 -25 Current CD4 (per 100) 8.2 NS # of drugs (per drug) 4.9 4.8 Boosted PI 33 (22 to 43) 18 (7 to 28) NNRTI -24 (-35 to –13) -23 (-35 to –11) Sustained CD4 gains during failure associated with protease inhibitor exposure Robust linear regression; 9173 observations from 1848 patients on-treatment with stable viral loadSlide14: Patients failing a PI-based regimen were more likely to have at least a 50 cell increase (n=343, month 6) CD4+ gains during virologic failure more common in PI than NNRTI-treated patients (ART LINC) Adapted from Tuboi et al; JAIDS 2006Slide15: Sustained CD4+ T cell gains in presence of MDR HIV: can prior studies be believed?Limitations of current literature: Limitations of current literature Observational studies have many limitations, including confounding by indication People doing well during virologic failure are more likely to remain on a stable regimen Ideal study: randomize patients to NNRTI versus protease inhibitor “failure” and follow them without modifying therapy Marginal Structural Models: Marginal Structural Models Marginal structural models Subjects are assigned a weight that is proportional to their probability of having received their treatment exposure given their covariates Regression model can then be fit, using weights to control for confounding Increasingly used in HIV studies in which randomization is not feasible Sterne Lancet 2005; Hogg et al Plos Med 2006; Peterson Am J Epidem 2007 Impact of delayed switching on mortality: Impact of delayed switching on mortality 2 cohorts of HIV-infected patients Johns Hopkins and Univ. North Carolina, Chapel Hill Baseline: date of first confirmed virologic failure (n=982 subjects, 3414 person-years follow-up) 742 failed a PI-based regimen 240 failed a non-PI based regimen Baseline and time-dependent confounders: HIV RNA, CD4, gender, age, race, intravenous drug use, calendar date, date of first antiretroviral therapy, prior NRTI exposure, AIDS events, HIV diagnosis dateSlide19: Risk of death among patients failing an NNRTI-based HAART regimen is increased by any delay in modification, with higher risk persisting even after a switch has occurred Switch < 6 months (early) vs. > 6 months (delayed)Slide20: Risk of death during and after protease inhibitor-based virologic failure is less dependent on time therapy is switched Switch < 6 months (early) vs. > 6 months (delayed) Petersen et al (unpublished)Why would outcomes be better during failure of a PI-based regimen compared to an NNRTI-based regimen?: Why would outcomes be better during failure of a PI-based regimen compared to an NNRTI-based regimen? NNRTI resistance is associated with a stronger risk of mortality compared to PI resistance HOMER Cohort (Hogg et al; PLoS Med 2006) FIRST (MacArthur et al; Lancet 2006) Impact of NNRTI vs. PI resistance on outcomes: Impact of NNRTI vs. PI resistance on outcomes PI resistance and not NNRTI resistance reduces viral “fitness” PI: many (reviewed in Clavel NEJM 2004 and Deeks Lancet 2003) NNRTI: Gerondelis, 1999, Koval, 2006, Bangsberg, 2006; Joly 2004 PI resistance associated with preserved thymic function Stoddart, Nature Med 2001; Lecossier JID 2001, others PI resistance and/or PI exposure associated with reduced levels of T cell activation and apoptosis Hunt AIDS 2006; Andre PNAS 1997; Phenix Blood 2001; Estaquier JV 2002; Sloand Blood 2002; Weichold J Human Virology 1999; Equils ACC 2004; Ikezoe Blood 2000; Weaver JCI 2005; Vlahakis Clin Pharm Ther 2007, othersSlide23: Role of chronic inflammation as a major determinant of immunologic and clinical outcomesSlide24: Blood, 2004 Higher CD8+ T cell Activation Associated with More Rapid CD4 Decline, independent of HIV RNA P=0.002Higher CD8 Activation Associated with Fewer CD4 Gains during HAART: Hunt et al, JID, 2003 Higher CD8 Activation Associated with Fewer CD4 Gains during HAARTProtease inhibitor resistance is independently associated with lower T cell activation: Protease inhibitor resistance is independently associated with lower T cell activation After adjustment for VL, nadir CD4, and HCV serostatus. Hunt et al, AIDS 2006Slide27: T cell activation measured by same lab; analysis controlled for HIV RNA and CD4 T cell activation higher in Uganda compared to SF, independent of other factorsSlide28: Ahuja and colleagues, Science 2005 and Nature Immunology (in press) CCL3L1 gene dose (and CCR5 promoter polymorphisms) predict CD4 outcomes independent of viral load, and varies between African and non-African populations Role of host genomicsConclusions: Conclusions Sustained clinical benefit during virologic failure commonly observed in North America/Europe may not occur in resource poor regions Sustained immunologic and clinical benefits in presence of MDR HIV consistently associated with PI-based therapy Fitness? T cell activation and inflammation are important determinants of outcome, and expected to be higher in resource poor compared to rich regions Failure of standard first HAART regimens in absence of HIV RNA monitoring will result in emergence and transmission of MDR HIV, negatively impacting both the individual and community levelSlide30: Implications Careful HIV RNA monitoring—which may be qualitative at level of ~ 200 to 500 copies—may be even more important in resource limited regions Aid for AIDS: Probability of virologic in a cohort of private sector patients followed in S. Africa (N=2821): Aid for AIDS: Probability of virologic in a cohort of private sector patients followed in S. Africa (N=2821) Nachega et al; Annals of Internal Medicine 2007 Virologic failure may be a late phenomenon, with its implications emerging laterSlide32: Acknowledgements UCSF/UC Berkeley Peter Hunt Elvin Geng Elizabeth Sinclair Mike McCune Diane Havlir Maya Petersen Mark van der Laan Huyen Cao David Bangsberg Jeff Martin UARTO/Mbarara Mbwesa Bwana Nneka Emenyou Irene Andia Sonia Napravnik Joseph Eron Richard Moore Mathias Egger Paula Braitstein Jean Nachega Sunil Ahuja Mauro Schechter David Moore Jonathan Shapiro You do not have the permission to view this presentation. In order to view it, please contact the author of the presentation.