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Impact of transmitted HIV-1 drug resistance on HIV plasma RNA and CD4 count over time

Impact of transmitted HIV-1 drug resistance on HIV plasma RNA and CD4 count over time. Vivek Jain, Eric M. Vittinghoff , Steven Deeks , and Frederick M. Hecht University of California, San Francisco, USA IAS Vienna, Austria July 21, 2010. Introduction / Background.

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Impact of transmitted HIV-1 drug resistance on HIV plasma RNA and CD4 count over time

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  1. Impact of transmitted HIV-1 drug resistance on HIV plasma RNA and CD4 count over time Vivek Jain, Eric M. Vittinghoff, Steven Deeks, and Frederick M. Hecht University of California, San Francisco, USA IAS Vienna, Austria July 21, 2010

  2. Introduction / Background • Clinical impact of transmitted drug resistance (TDR) on HIV plasma RNA and CD4 count in untreated patients is unclear Harrison et al., AIDS 2010; Booth et al., J AntimicrobChemother2007; Bannister et al., JAIDS 2008 • In vitro fitness costs documented for different mutations, but uncertain how well these fitness estimates translate into in vivo differences • Unclear whether viral load differences at presentation (acute HIV) persist into “set point” (chronic HIV)

  3. Hypotheses Lower initial viral load Lower viral load set point Larger effect for NRTI and PI mutations Lower effect for NNRTI mutations Hypothesis 1: Hypothesis 2: Fitness costs Lower RNA levels Acquired drug resistance Higher CD4 count set point

  4. Study Population • UCSF Options Project: • longitudinal cohort of individuals diagnosed with acute/early HIV (<6 mo.; <12 mo. before 2003) • Baseline HIV population sequence genotype performed within 90 days of study entry • ≥1 HIV-1 plasma RNA level and CD4 T cell count while ART-naïve • Analyzed all RNA and CD4 values measured while ART-naïve, up to 2 years

  5. Methods I Predictor: • TDR (presence of ≥1 drug resistance mutation from Shafer 2007 consensus list for epidemiologic studies) Shafer et al., AIDS 2007 Outcomes: • Modeled HIV-1 plasma RNA level over time • Modeled CD4 cell count over time • Key analyses: differences in VL and CD4 during • Acute HIV: 60 days post-infection • Chronic HIV “set-point”: 180 & 360 days post-infection

  6. Methods II How to model HIV viral load and CD4 over time, which exhibit non-linear trends during acute infection? • Spline: flexible curve  allows modeling of non-linear events • Restricted cubic splines used to flexibly model the non-linearity • Mixed models with random effects to account for repeated measures (within-subject correlations) • Also compared average viral load and CD4 levels during set point (6 months to 2 years) Viral Load Time

  7. Patient Characteristics

  8. Viral load is lower in TDR patients at 60 days. Differences diminish; not statistically significant at 6 mo. or 1 year 5.2 CUBIC SPLINE MODELS VL vs. time: overall drug resistance 5.0 4.8 -0.38 log p<0.001 4.6 Wild type Log(HIV Plasma RNA) 4.4 Drug resistance (any class) 4.2 4.0  3.8 0 90 180 270 360 450 540 630 720 Days Since HIV Infection

  9. Viral load is lower in TDR patients at 60 days. Differences diminish; not statistically significant at 6 mo. or 1 year 5.2 5.2 CUBIC SPLINE MODELS VL vs. time: overall drug resistance VL vs. time: NRTI resistance 5.0 5.0 -0.38 log 4.8 4.8 p<0.001 -0.32 log Wild type Wild type p=0.02 4.6 4.6 Log(HIV Plasma RNA) Log(HIV Plasma RNA) 4.4 4.4 Drug resistance (any class) 4.2 4.2 NRTI resistance   4.0 4.0 3.8 3.8 0 90 180 270 360 450 540 630 720 0 90 180 270 360 450 540 630 720 Days Since HIV Infection Days Since HIV Infection  5.2 VL vs. time: NNRTI resistance 5.0 5.2 VL vs. time: PI resistance NNRTI resistance 4.8 5.0 4.6 4.8 -0.56 log Log(HIV Plasma RNA) Wild type p=0.001 4.4 4.6 Log(HIV Plasma RNA) 4.2 4.4 Wild type 4.0 4.2 PI resistance 3.8 4.0  0 90 180 270 360 450 540 630 720 Days Since HIV Infection 3.8 0 90 180 270 360 450 540 630 720 Days Since HIV Infection

  10. CUBIC SPLINE MODELS CD4 vs. time: overall drug resistance CD4 counts are similar, both early and later, in patients with TDR vs. wild-type virus Drug resistance (any class) 700 Wild type 650 600 CD4 cell count (cells/uL) 550 500 450 400 0 90 180 270 360 450 540 630 720 Days Since HIV Infection

  11. CD4 counts are similar, both early and later, in patients with TDR vs. wild-type virus CUBIC SPLINE MODELS CD4 vs. time: overall drug resistance CD4 vs. time: NRTI resistance 700 700 650 650 600 600 Drug resistance (any class) CD4 cell count (cells/uL) CD4 cell count (cells/uL) 550 550 NRTI resistance 500 500 450 450 Wild type Wild type 400 400 0 90 180 270 360 450 540 630 720 0 90 180 270 360 450 540 630 720 Days Since HIV Infection Days Since HIV Infection CD4 vs. time: NNRTI resistance CD4 vs. time: PI resistance 700 700 650 650 600 600 CD4 cell count (cells/uL) CD4 cell count (cells/uL) 550 550 NNRTI resistance PI resistance 500 500 450 450 Wild type Wild type 400 400 0 90 180 270 360 450 540 630 720 0 90 180 270 360 450 540 630 720 Days Since HIV Infection Days Since HIV Infection

  12. Drug resistance wanes over time Next steps: • Analyze M184V mutations separately from NRTIs • Among patients with TDR, compare viral load and CD4 over time in patients with mutation replacement with wild-type vs. patients with mutation persistence Mutation Replacement By Mutation Group 100 75 Proportion Remaining Wild-Type, % NNRTI 50 PI NRTI 25 0 6 12 24 36 48 60 72 84 96 108 Months

  13. Conclusions In patients with TDR, viral load is lower during acute HIV • Early differences seen overall, and with NRTI and PIresistance, not with NNRTI resistance Early viral load differences wane over time • Waning of differences may be due to loss of drug resistance mutations affecting fitness or gain of “compensatory mutations” CD4 counts slightly higher with TDR, but difference is not statistically significant either early or later • Drug-resistant virus did not portend more rapid clinical progression Implications for HIV vaccines that would lower viral fitness • Possible early benefits, but may wane over time as virus adapts

  14. Acknowledgements

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