Virologic monitoring during antiretroviral therapy

1. Virologic Monitoring
during ART
Davey Smith, M.D., M.A.S.
Associate Professor of Medicine
Director CFAR Translational Virology Core
University of California San Diego

2. Clinical Course of HIV Infection
Anti-retroviral
Therapy
CD4+ Cells
Viral Load
Time After Infection
2

3. Clinical Course of HIV Infection
Anti-retroviral Treatment Failure
Therapy
CD4+ Cells
Viral Load
Time After Infection
3

4. 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

5. Goals of Current HAART
 HAART really only works on the virus to
decrease or ‘stop’ replication
 Measured by viral loads
5

6. Goals of Current HAART
 HAART really only works on the virus to
decrease or ‘stop’ replication
 Measured by viral loads
 Stopping viral replication hopefully stops or
slows the destruction of CD4 cells, allowing
them to rebound.
 Measured by CD4 counts
6

7. Goals of Current HAART
 HAART really only works on the virus to
decrease or ‘stop’ replication
 Measured by viral loads
 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
7

8. Monitoring Strategies
 Symptomatic
 Immunologic (CD4 counts)
 Virologic (Viral Loads)
Bendavid, E. et al. Arch Intern Med 2008;168:1910-1918.
8

9. 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.

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

11. Risk of Delayed HAART Switch
1 new major PI mutation
1 new NRTI mutation*
Proportion Without
1.00 Any new mutation
 SCOPE cohort of ART-experienced
New Mutation
subjects (n = 106)[1] 0.75
 Stable HAART for  120 days 0.50
 HIV-1 RNA > 1000 c/mL 0.25
  1 resistance mutation *PI-treated subjects (n = 71)
0
 Resistance testing every 4 mos 0 4 8 12 16 20 24
until HAART modification
Proportion Without
1.00
Loss of 1 Drug
 Emergence of new RAMs at 1 yr Time to loss of
0.75 1 drug equivalent
 Any: 44% (95% CI: 33%-56%)
0.50
 NAMs: 23% (95% CI: 15%-34%)
0.25 Number of available antiretrovirals from the following: ZDV,
 PI: 18% (95% CI: 9%-34%)
3TC, ddI, ABC,TDF, EFV, IDV, NFV, SQV, RTV, APV, LPV
0
0 4 8 12 16 20 24
Time (Mos)
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

12. Probability of Drug Resistance in
First-line HAART Failure
Harrigan et al. J Infect Dis. 2005;191:339-347.

13. Disclosure
I am completely biased that
virologic monitoring should be
used during HAART.

14. 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.
14

15. 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.
15

16. And the data keep coming in-
Rawizza CID Dec. 2011
16

17. 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
17

18. 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
The issues are:
•Morbidity lags behind both VL
and CD4
•Does not take into account
transmitted resistance
18

19. Projected costs of Viral Load
Monitoring
Annual Cost Percent of
(US$)* Total HIV
Spending§
Low and Middle Income Countries
VL 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 19

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

21. Virologic Monitoring
Method Parameter Measured Expensive Needs Clinical
Validation
Roche Molecular HIV-1 RNA X
Systems (Amplicor
Monitor, RealTime)
Abbott RealTime HIV-1 RNA X
Bayer bDNA HIV-1 RNA X
Biomerieux (Nuclisens) HIV-1 RNA X
Perkin Elmer p24 X
Cavidi ExaVir Reverse Transcriptase X
Activity
Homebrew RealTime HIV-1 RNA X
Flowcytometry HIV-1 RNA X
21

22. Can we make currently
validated methods cheaper?
22

23. 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.
23

24. Methods: Quantitative Monitoring
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
At least one sample with
with viral loads
viral load above threshold
<500 copies/ml
Negative Pooling Algorithm
Positive
Samples with virologic failure= deconvolute
24

25. Minipools
May et al. JAIDS 2009

26. Matrix Strategy
1 2 3 4 5 6 7 8 9 10
 Individual samples are A
pooled (hexagons) B
vertically (A-J) and C
horizontally (1-10), and D
viral load tests are run Pools A-J
E
first on the pooled
F
samples only.
G
 Ambiguous samples
would then have to be H
resolved. I
J
Pools 1-10

27. Example VL Output Matrix NAT Viral
Position Load
from a Pooled Matrix Row 1= 0
Row 2= 2700
Row 3= 1500
1 2 3 4 5 6 7 8 9 10 Row 4= 0
A Row 5= 0
Ambigu Row 6= 0
B
ous C
Row 7= 0
Row 8= 0
samples D Row 9= 0
in gray E
Pools A-J Row 10= 0
F
Column 1= 0
G Column 2= 0
H Column 3= 0
I Column 4= 2200
Column 5= 0
J Column 6= 0
Column 7= 2000
Column 8= 0
Column 9= 0
Pools 1-10
Column 10= 0

28. Resolving the ambiguous
samples is kind of like Sudoku….
Matrix Formula
(=If Ax1>0,”A+1”/2 based
on the previous VL output)
Re-test
Individually test the
samples with the largest
estimated viral loads
28

29. Example VL Output Matrix NAT Viral
Position Load
from a Pooled Matrix Row 1= 0
Row 2= 2700
Row 3= 1500
1 2 3 4 5 6 7 8 9 10 Row 4= 0
A Row 5= 0
Ambigu Row 6= 0
B
ous C
Row 7= 0
Row 8= 0
samples D Row 9= 0
in gray E
Pools A-J Row 10= 0
F
Column 1= 0
G Column 2= 0
H Column 3= 0
I Column 4= 2200
Column 5= 0
J Column 6= 0
Column 7= 2000
Column 8= 0
Column 9= 0
Pools 1-10
Column 10= 0

30. 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.

31. Resolve Matrix NAT Viral
Ambiguities Position
1=
Load
0
2= 0
3= 0
 Now, subtract the 4= 2000
5= 0
viral load value of 6= 0
7= 1500
A4 (700) from 8= 0
column A and row 4. 9= 0
10= 0
 The output is now:
A= 1500
B= 2000
C= 0
D= 0
E= 0
F= 0
G= 0
H= 0
I= 0
J= 0

32. 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.

33. Resolve Matrix NAT Viral
Ambiguities Position
1=
Load
0
2= 0
3= 0
4= 0
5= 0
 Now, subtract the viral 6= 0
7= 1500
load value of B4 (2000) 8= 0
from column B and row 4. 9=
10=
0
0
 The output is now: A= 1500
B= 0
 Last VL of a sample is C= 0
1500. D= 0
E= 0
F= 0
G= 0
H= 0
I= 0
J= 0

34. 34

35. Matrix NAT Viral
Position Load
Row 1= 0
Row 2= 0
Row 3= 0
Row 4= 2700
Row 5= 0
Row 6= 0
Row 7= 1500
Row 8= 0
Row 9= 0
Row 10= 0
Column 1= 2200
Column 2= 2000
Column 3= 0
Column 4= 0
Column 5= 0
Column 6= 0
Column 7= 0
Column 8= 0
Column 9= 0
Column 10= 0
35

36. Re-Test Value-1
36

37. Re-Test Value-2
37

38. Re-Test Value-3
38

39. 39

40. 40

41. 41

42. Modeling Study
May et al. AIDS 2009.

43. Modeling Study
May et al. AIDS 2009.

44. 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%
44

45. San Diego Pooled Study
Smith et al. AIDS 2009.

46. Turn Around Time
Even though it took 17
days on average to
completely resolve the
matrix, 66% of all
samples were resolved
the first day
46

47. Mexico Pooled Study
•700 patients
Test characteristics of 10x10 matrix pooling platform
compared to individual viral load testing
Negative Predictive Value (%) •Unknown HAART use
All Matrices
*VF = 500 c/mL
>50 c/mL 90% •22% with detectable VL
>500 c/mL 90%
>1,000 c/mL 90%
some with high VL
>1,500 c/mL 91%
*VF = 1500 c/mL
•One matrix identified
>50 c/mL 89% with contamination
>500 c/mL 89%
>1,000 c/mL 90%
>1,500 c/mL 91% •Still with 33% cost
savings ($14,000)
Tilghman et al. JAIDS 2010 47

48. South Africa Pooled Study
Method Prevalence of failure Negative Relative efficiency Cost savings per
(> 1000 copies/ml ) predictive value (reduction in tests 100 specimens
needed)
3 matrices
11% 98% 41% US$ 1640
(n=300)
80 minipools
9.50% 100% 30.5% US$ 1220
(n=400)
Van Zyl et al. CID 2011
48

49. 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 49

50. Maybe a new test?
A Combined Screening Platform for HIV
Treatment Failure and Resistance
Tilghman et al. PLoS One 2012.
50

51. Methods: Qualitative Monitoring
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
At least one sample with
with viral loads
viral load >500 copies/ml
<500 copies/ml
Negative PCR each sample
Positive
Sequence PCR
product for Samples with viral loads <500 copies/ml
genotype 51

52. 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.0035401
http://www.plosone.org/article/info:doi/10.1371/journal.pone.0035401

53. Table 2. Treatment and laboratory data for samples (n = 295)*.
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.0035401
http://www.plosone.org/article/info:doi/10.1371/journal.pone.0035401

54. Figure 1. Test characteristics of qualitative pooled RT assay in the detection of varying levels of
virologic failure using first round of PCR only.
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.0035401
http://www.plosone.org/article/info:doi/10.1371/journal.pone.0035401

55. Considerations for Choosing a Strategy
to Detect Virologic Failure
Factors Costs Clinical Considerations
Assay type and level of Cost per assay Accuracy
detection
Inherent error of assay Cost per assay Accuracy
type
Laboratory space to avoid Space cost Quality assurance
contamination during
processing
Personnel availability Personnel cost Turnaround time
Personnel training Personnel cost Quality assurance
Clinic population Size Cost per pool Turnaround time
Rate of virologic failure Cost per pool Accuracy
Rate of screening Cost per pool Accuracy and Turnaround
time

56. Pooling Experiments
56

57. Acknowledgements
 Winston Tilghman  Emilia Noormahomed
 Josué Pérez Santiago (Mozambique)
 Connie Benson  Kumarasamy
 Richard Haubrich Nagalingeswaran (India)
 Susan Little  Saravanan Shanmugam
(India)
 Susanne May
 Gert van Zyl (South Africa)
 Douglas Richman
 Don Diego Guerena (Mexico)
 Chip Schooley
 Jun Yong Choi (South Korea)