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, USAIAS Vienna, Austria

July 21, 2010

Introduction / Background• Clinical impact of transmitted drug resistance (TDR)

on HIV plasma RNA and CD4 count in untreated patients is unclearHarrison et al., AIDS 2010; Booth et al., J Antimicrob Chemother 2007; 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)

Hypotheses

Acquired drug

resistance

Lower initial viral loadLower viral load set point

Higher CD4 count set point

Fitness costs

Lower RNA

levels

Larger effect for NRTI and PI mutations

Lower effect for NNRTI mutations Hypothesis 2:

Hypothesis 1:

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

Methods IPredictor:• 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

Methods IIHow 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)

Time

ViralLoad

Patient CharacteristicsWild-type TDR

Total patients (n=556) 452 104 (19%)

Median Age 36 years 35 yearsMale 95% 96%MSM 93% 95%IVDU 7% 8%HIV duration 2.6 mo. 2.7 mo.

NRTI resistant --- 60 (11%)NNRTI resistant --- 38 (7%)PI resistant --- 38 (7%)

Viral load is lower in TDR patients at 60 days. Differences diminish; not statistically significant at

6 mo. or 1 year

3.8

4.0

4.2

4.4

4.6

4.8

5.0

5.2

Log(

HIV

Pla

sma

RN

A)

0 90 180 270 360 450 540 630 720

Days Since HIV Infection

Wild type

Drug resistance(any class)

CUBIC SPLINE MODELSVL vs. time: overall drug resistance

-0.38 logp<0.001

Viral load is lower in TDR patients at 60 days. Differences diminish; not statistically significant at

6 mo. or 1 year

3.8

4.0

4.2

4.4

4.6

4.8

5.0

5.2

Log(

HIV

Pla

sma

RN

A)

0 90 180 270 360 450 540 630 720Days Since HIV Infection

Wild type

Drug resistance(any class)

CUBIC SPLINE MODELSVL vs. time: overall drug resistance

-0.38 logp<0.001

Wild type

PI resistance

VL vs. time: PI resistance

-0.56 logp=0.001

3.8

4.0

4.2

4.4

4.6

4.8

5.0

5.2

Log(

HIV

Pla

sma

RN

A)

0 90 180 270 360 450 540 630 720Days Since HIV Infection

3.8

4.0

4.2

4.4

4.6

4.8

5.0

5.2

Log(

HIV

Pla

sma

RN

A)

0 90 180 270 360 450 540 630 720Days Since HIV Infection

Wild type

NRTI resistance

VL vs. time: NRTI resistance

-0.32 logp=0.02

3.8

4.0

4.2

4.4

4.6

4.8

5.0

5.2

Log(

HIV

Pla

sma

RN

A)

0 90 180 270 360 450 540 630 720Days Since HIV Infection

Wild type

NNRTI resistance

VL vs. time: NNRTI resistance

CD4 counts are similar, both early and later,in patients with TDR vs. wild-type virus

400

450

500

550

600

650

700

CD

4 ce

ll co

unt (

cells

/uL)

0 90 180 270 360 450 540 630 720

Days Since HIV Infection

Wild type

Drug resistance(any class)

CUBIC SPLINE MODELSCD4 vs. time: overall drug resistance

CD4 counts are similar, both early and later,in patients with TDR vs. wild-type virus

400

450

500

550

600

650

700

CD

4 ce

ll co

unt (

cells

/uL)

0 90 180 270 360 450 540 630 720Days Since HIV Infection

Wild type

NNRTI resistance

CD4 vs. time: NNRTI resistance

400

450

500

550

600

650

700

CD

4 ce

ll co

unt (

cells

/uL)

0 90 180 270 360 450 540 630 720Days Since HIV Infection

Wild type

Drug resistance(any class)

CUBIC SPLINE MODELSCD4 vs. time: overall drug resistance

400

450

500

550

600

650

700

CD

4 ce

ll co

unt (

cells

/uL)

0 90 180 270 360 450 540 630 720Days Since HIV Infection

Wild type

PI resistance

CD4 vs. time: PI resistance

400

450

500

550

600

650

700

CD

4 ce

ll co

unt (

cells

/uL)

0 90 180 270 360 450 540 630 720Days Since HIV Infection

Wild type

NRTI resistance

CD4 vs. time: NRTI resistance

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

025

5075

100

Pro

porti

on R

emai

ning

Wild

-Typ

e, %

6 12 24 36 48 60 72 84 96 108

Months

NRTIPINNRTI

Mutation Replacement By Mutation Group

ConclusionsIn patients with TDR, viral load is lower during

acute HIV– Early differences seen overall, and with NRTI and PI

resistance, 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

AcknowledgementsOptions Project, UCSFStudy participantsWendy Hartogensis, Lisa Loeb, Gerald Spotts, Lauren Poole, Lisa Harms, Ed Diaz

UCSF HIV/AIDS DivisionDiane Havlir, Chris Pilcher, Brad Hare

UCSF AIDS Research Institute / Laboratory for Core VirologyTeri Liegler, Jaqueline Javier, Timothy Schmidt

Funding SourcesUCSF/SFGH HIV/AIDS Division, UCSF Infectious Diseases Fellowship ProgramNIH: Ruth Kirschenstein NRSA FellowshipUCSF Center for AIDS Research (CFAR)