Virologic monitoring during ART

Virologic Monitoring during ART

Davey Smith, M.D., M.A.S.Associate Professor of Medicine

Director CFAR Translational Virology CoreUniversity of California San Diego

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Viral Load

CD4+ Cells

Time After Infection

Clinical Course of HIV Infection

Anti-retroviralTherapy

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Viral Load

CD4+ Cells

Time After Infection

Clinical Course of HIV Infection

Anti-retroviralTherapy

Treatment Failure

Consequences of HAART Failure

Virologic Rebound Increased Transmission End Organ Damage (Nervous system,

Cardiovascular, Liver, Renal, etc.) Immune System Deterioration

OIs, Cancers Drug Resistance

Transmitted Resistance

Tuyama A, et al. CROI 2008. Abstract 57; Letendre S. CROI 2008. Abstract 68. Little et al. NEJM 2002; Wawer, et al. JID 2005

Goals of Current HAART HAART really only works on the virus to

decrease or ‘stop’ replication Measured by viral loads

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Stopping viral replication hopefully stops or slows the destruction of CD4 cells, allowing them to rebound. Measured by CD4 counts

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Stopping viral replication hopefully stops or slows the destruction of CD4 cells, allowing them to rebound. Measured by CD4 counts

Rebounding CD4 counts restores immune function. Measured by symptoms or presence of OIs

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Monitoring Strategies Symptomatic

Immunologic (CD4 counts)

Virologic (Viral Loads)

8Bendavid, E. et al. Arch Intern Med 2008;168:1910-1918.

WHO criteria for ART Failure:

1) CD4 cell count falls below baseline in the absence of other concurrent infections,

2) CD4 cell count falls to less than 50% of peak CD4 levels without concurrent infections, or

3) CD4 cell count is ‘consistently’ below 100 cells/ml.

The association between a falling CD4 count and virological failure

Changes in CD4 count correlated well with detectable viral load but had very poor sensitivity.

“Thus, CD4 cell count measurements cannot be used as a substitute for early virological failure monitoring.”

10Badri, Lawn and Wood. BMC Infect Dis. 2008 Jul 4;8:89

Risk of Delayed HAART Switch

SCOPE cohort of ART-experienced subjects (n = 106)[1]

Stable HAART for 120 days HIV-1 RNA > 1000 c/mL 1 resistance mutation Resistance testing every 4 mos

until HAART modification

Emergence of new RAMs at 1 yr Any: 44% (95% CI: 33%-56%) NAMs: 23% (95% CI: 15%-34%) PI: 18% (95% CI: 9%-34%)

1. Hatano H, et al. CROI 2006. Abstract 615. 2. Lafeuillade A, et al. IAC 2004. Abstract WeOrB1293. 3. Margot NA, et al. JAIDS. 2003;33:15-21. 4. Napravnik S, et al. JAIDS. 2005;40:34-40. 5. Eron J. IAS Strategies for Antiretroviral Failure

Prop

ortio

n W

ithou

t N

ew M

utat

ion

1 new major PI mutation1 new NRTI mutation*Any new mutation

Number of available antiretrovirals from the following: ZDV, 3TC, ddI, ABC,TDF, EFV, IDV, NFV, SQV, RTV, APV, LPV

0

0.25

0.50

0.75

1.00

0 4 8 12 16 20 24Time (Mos)

Time to loss of 1 drug equivalent

Prop

ortio

n W

ithou

t Lo

ss o

f 1 D

rug

0 4 8 12 16 20 24

*PI-treated subjects (n = 71)0

0.25

0.50

0.75

1.00

Probability of Drug Resistance in First-line HAART Failure

Harrigan et al. J Infect Dis. 2005;191:339-347.

Disclosure

I am completely biased that virologic monitoring should be

used during HAART.

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Bendavid, E. et al. Arch Intern Med 2008;168:1910-1918.

CD4-based strategies resulted in higher life expectancy and were less costly than the symptom-based approaches.

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Bendavid, E. et al. Arch Intern Med 2008;168:1910-1918.

Adding viral load to CD4 count monitoring was associated with further increase in life expectancy. VL every 6 months was associated with a 2-month gain in life expectancy. VL was associated with an increased lifetime cost of $899 per person VL every 3 months vs. every 6 months was associated with modest increases in life

expectancy and significant increases in lifetime costs.

And the data keep coming in-Rawizza CID Dec. 2011

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Mermin et al. Utility of routine viral load, CD4 cell count, and clinical monitoring among adults with HIV receiving antiretroviral therapy in Uganda: randomised trial BMJ 2011

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Mermin et al. Utility of routine viral load, CD4 cell count, and clinical monitoring among adults with HIV receiving antiretroviral therapy in Uganda: randomised trial. BMJ 2011

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The issues are:•Morbidity lags behind both VL and CD4 •Does not take into account transmitted resistance

Projected costs of Viral Load Monitoring

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Annual Cost (US$)*

Percent of Total HIV Spending§

Low and Middle Income CountriesVL every 3 months (already on ART) $540,000,000 5.4%VL every 6 months (already on ART) $270,000,000 2.7%VL every 3 months + ART (all eligible) $3,667,741,801 36.7%VL every 6 months + ART (all eligible) $2,796,774,091 28%VL = viral load; *Based on estimated cost of US$45 per viral load assay (Bendavid et al.12); §Figures for global and Mozambican spending for 2007 and 2008 from UNAIDS11

and UNGASS14 respectively

From M. Tilghman 2011

If we are going to measure viral replication during HAART, can we do it efficiently?

Virologic MonitoringMethod Parameter Measured Expensive Needs Clinical

ValidationRoche Molecular

Systems (AmplicorMonitor, RealTime)

HIV-1 RNA X

Abbott RealTime HIV-1 RNA XBayer bDNA HIV-1 RNA X

Biomerieux (Nuclisens) HIV-1 RNA XPerkin Elmer p24 XCavidi ExaVir Reverse Transcriptase

ActivityX

Homebrew RealTime HIV-1 RNA XFlowcytometry HIV-1 RNA X

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Can we make currently validated methods cheaper?

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Pooling Methods Rationale Viral loads can identify people with acute infection. Because testing for HIV RNA in each blood

sample would be expensive, they pooled blood samples and performed one viral load assay on a pooled sample.

If the sample was negative, then most likely all individuals in the pool were negative.

Can be used with any quantitative viral load method.

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Methods: Quantitative Monitoring

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Eligibility: HIV-infected patients receiving ART x 6 months

Pooling: Minipools or Matrix of samples

Quantify: Standard viral loads of pools

Negative Positive

All samples with viral loads <500 copies/ml

At least one sample with viral load above threshold

Pooling AlgorithmNegative

Positive

Samples with virologic failure= deconvolute

Minipools

May et al. JAIDS 2009

Matrix Strategy Individual samples are

pooled (hexagons) vertically (A-J) and horizontally (1-10), and viral load tests are run first on the pooled samples only.

Ambiguous samples would then have to be resolved.

1 2 3 4 5 6 7 8 9 10ABCDEFGHIJ

Pools 1-10

Pools A-J

Example VL Output from a Pooled Matrix

Matrix Position

NAT Viral Load

Row 1= 0Row 2= 2700Row 3= 1500Row 4= 0Row 5= 0Row 6= 0Row 7= 0Row 8= 0Row 9= 0Row 10= 0

Column 1= 0Column 2= 0Column 3= 0Column 4= 2200Column 5= 0Column 6= 0Column 7= 2000Column 8= 0Column 9= 0Column 10= 0

1 2 3 4 5 6 7 8 9 10A B C D E F G H I J

Pools 1-10

Pools A-J

Ambiguous

samples in gray

Resolving the ambiguous samples is kind of like Sudoku….

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Matrix Formula (=If Ax1>0,”A+1”/2 based on the previous VL output)

Re-testIndividually test the samples with the largestestimated viral loads

Example VL Output from a Pooled Matrix

Matrix Position

NAT Viral Load



1 2 3 4 5 6 7 8 9 10A B C D E F G H I J

Pools 1-10

Pools A-J

Ambiguous

samples in gray

Resolve Ambiguities The ambiguous samples would be

A4, A7, B4, B7 Perform a viral load test on the

individual sample with the highest viral load in the matrix formula (A4)

The actual viral load for this sample is 700 copies/ml.

Resolve Ambiguities Now, subtract the

viral load value of A4 (700) from column A and row 4.

The output is now:

Matrix Position

NAT Viral Load

1= 02= 03= 04= 20005= 06= 07= 15008= 09= 010= 0

A= 1500B= 2000C= 0D= 0E= 0F= 0G= 0H= 0I= 0J= 0

Resolve Ambiguities The ambiguous samples would be

A7, B4, B7 Again, perform a viral load test on the

individual sample with the highest viral load in the matrix formula (B4)

The actual viral load for this sample is 2000 copies/ml.

Resolve Ambiguities

Now, subtract the viral load value of B4 (2000) from column B and row 4.

The output is now: Last VL of a sample is

1500.

Matrix Position

NAT Viral Load

1= 02= 03= 04= 05= 06= 07= 15008= 09= 010= 0

A= 1500B= 0C= 0D= 0E= 0F= 0G= 0H= 0I= 0J= 0

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Matrix Position

NAT Viral Load



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Re-Test Value-1

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Re-Test Value-2

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Re-Test Value-3

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40

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May et al. AIDS 2009.

Modeling Study

May et al. AIDS 2009.

Modeling Study

San Diego Pooled VL Study We compared performing viral loads on:

individual samples,10x10 matrix, minipools of 5 Pooling algorithm thresholds for detecting

HAART failure were set at: Minipools of 5 : <250 and <500 copies/mL 10x10 Matrix: <500 and <1500 copies/mL

150 individuals on HAART with a prevalence of HAART failure of 23%

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Smith et al. AIDS 2009.

San Diego Pooled Study

Turn Around Time

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Even though it took 17 days on average to completely resolve the matrix, 66% of all samples were resolved the first day

Mexico Pooled Study

47Tilghman et al. JAIDS 2010

Test characteristics of 10x10 matrix pooling platform compared to individual viral load testing

Negative Predictive Value (%)All Matrices

*VF = 500 c/mL>50 c/mL 90%>500 c/mL 90%>1,000 c/mL 90%>1,500 c/mL 91%

*VF = 1500 c/mL>50 c/mL 89%>500 c/mL 89%>1,000 c/mL 90%>1,500 c/mL 91%

•700 patients

•Unknown HAART use

•22% with detectable VL some with high VL

•One matrix identified with contamination

•Still with 33% cost savings ($14,000)

South Africa Pooled Study

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Method Prevalence of failure

(> 1000 copies/ml )

Negative

predictive value

Relative efficiency

(reduction in tests

needed)

Cost savings per

100 specimens

3 matrices

(n=300) 11% 98% 41% US$ 1640

80 minipools

(n=400) 9.50% 100% 30.5% US$ 1220

Van Zyl et al. CID 2011

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Issues

Viral loads are better to detect HAART failure than symptoms or CD4 trajectory but VLs are expensive

Unrecognized HAART failure can lead to drug resistance for the patient and the population

Clinically-relevant level of VL detection Prevalence of HAART failure in the local population Viral load assay variability

Haubrich R and Saag M. IAS Workshop 2008

Maybe a new test?

A Combined Screening Platform for HIV Treatment Failure and ResistanceTilghman et al. PLoS One 2012.

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Methods: Qualitative Monitoring

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Eligibility: HIV-infected patients receiving ART x 6 months

Pooling: Minipools of 5 samples O+O+O+O+O→ [O]

PCR: RT-PCR of HIV-1 RT in pool → cDNA → PCR of RT

Negative Positive

All samples with viral loads <500 copies/ml

At least one sample with viral load >500 copies/ml

PCR each sampleNegative

Positive

Samples with viral loads <500 copies/ml

Sequence PCR product for genotype

Table 1. Patient demographic data (for n = 171 patients).

Tilghman MW, May S, Pérez-Santiago J, Ignacio CC, et al. (2012) A Combined Screening Platform for HIV Treatment Failure and Resistance. PLoS ONE 7(4): e35401. doi:10.1371/journal.pone.0035401http://www.plosone.org/article/info:doi/10.1371/journal.pone.0035401

Table 2. Treatment and laboratory data for samples (n = 295)*.


Figure 1. Test characteristics of qualitative pooled RT assay in the detection of varying levels of virologic failure using first round of PCR only.


Considerations for Choosing a Strategy to Detect Virologic Failure

Factors Costs Clinical Considerations

Assay type and level of detection

Cost per assay Accuracy

Inherent error of assay type

Cost per assay Accuracy

Laboratory space to avoid contamination during processing

Space cost Quality assurance

Personnel availability Personnel cost Turnaround timePersonnel training Personnel cost Quality assuranceClinic population Size Cost per pool Turnaround timeRate of virologic failure Cost per pool AccuracyRate of screening Cost per pool Accuracy and Turnaround

time

Pooling Experiments

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Acknowledgements

Winston Tilghman Josué Pérez Santiago Connie Benson Richard Haubrich Susan Little Susanne May Douglas Richman Chip Schooley

Emilia Noormahomed(Mozambique)

KumarasamyNagalingeswaran (India)

Saravanan Shanmugam(India)

Gert van Zyl (South Africa) Don Diego Guerena (Mexico) Jun Yong Choi (South Korea)

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Virologic monitoring during ART

Health & Medicine