1. We Should Not Tailor Antiplatelet Therapy Based on Platelet Function Testing and Genotyping
CRT 2011, Washington DC
Sanjay Kaul, MD
Division of Cardiology
Cedars-Sinai Heart Institute Professor, Cedars-Sinai Medical Center &
Geffen School of Medicine at UCLA
Los Angeles, California

2. Disclosure Sanjay Kaul, MD No conflicts to disclose
Division of Cardiology
Cedars-Sinai Heart Institute Professor, Cedars-Sinai Medical Center &
Geffen School of Medicine at UCLA
Los Angeles, California
No conflicts to disclose

3. Clopidogrel Poor Metabolizers FDA Statement
FDA, March 2010 (clopidogrel boxed warning) “Clopidogrel may be less effective in people who are unable to metabolize the drug because of low CYP2C19 activity” “Be aware that tests are available to determine CYP2C19 genotype” “Consider use of other antiplatelet meds or alternative dosing strategies for clopidogrel”
No outcome study informs this recommendation!
Is it a rush to judgment? Is the Warning Actionable? 3

4. “The evidence base is insufficient to recommend either
genetic testing or platelet function testing at the present time”
Holmes, Dehmer, Kaul et al. JACC 2010

5. Platelet Phenotype and Genotype Evaluation RCT Post hoc/RCT
COGENT
GRAVITAS
Platelet Phenotype
and Genotype
Evaluation
Post hoc/RCT
Meta-analysis
TRITON
PLATO
CREDO
CURE
ACTIVE-A
CHARISMA
OASIS-7
Sofi et al.
Hulot et al.
Mega et al.
Observational,
&
PK/PD/Genotype
(Numerous)

6. Hierarchy of Evidence in Medicine
Randomized
Controlled
Trial
Meta-analysis - Clinical homogeneity
- Statistical homogeneity
Observational Trial - Case-control - Cohort
Observational dataset within RCT
- Candidate gene studies, GWAS
Laboratory study - PK/PD
- Genotyping

7. Impact of PPI on Cardiovascular Events in Clopidogrel-Treated Subjects Results of COGENT Trial
1.00
0.98
Placebo = 20/1885 (1.06%)
Treated = 23/1876 (1.23%)
MACE HR = % CI = 0.64; 2.10 P=0.63
HR = % CI = 0.68; 1.44 P=0.96
0.96
Survival Probability
0.94
Placebo = 54/1885 (5.7%)
0.92
Treated = 55/1876 (4.9%)
Adjustment through Cox Proportional Hazards Model Adjusted to Positive NSAID Use and Positive H. Pylori Status
0.90 30 60 90
120
150
180
210
240
270
300
330
360
390
Days
Bhatt et al, NEJM 2010 7

8. GRAVITAS Patient Flow 5429 patients screened with VerifyNow P2Y12
12-24 hours post-PCI
2214 (41%) with high residual platelet reactivity
(PRU ≥ 230)
3215 (59%) without high residual platelet reactivity
(PRU < 230)
We screened 5,429 patients with the VerifyNow test -- the largest cohort of patients to ever undergo platelet function testing in a single study.
41% of patients were categorized as having high residual reactivity, and these
2,214 patients were randomly assigned to study drug.
Random selection
Clopidogrel
Standard Dose
N=586
Clopidogrel
High Dose
N=1109
Clopidogrel
Standard Dose
N=1105
Non-Randomized Comparison 8

9. GRAVITAS Secondary Comparison High vs
GRAVITAS Secondary Comparison High vs. Not High Reactivity Treated with Clopidogrel 75-mg daily
Here are the results of this non-randomized comparison. The observed rate of the composite of cardiovascular death, MI, or stent thrombosis was 2.3% in patients with high reactivity compared with 1.4% in patients without high reactivity, representing a hazard ratio of 1.68.
The difference in event rates did not reach significance; note that the lower boundary of the 95% confidence interval is less than one.
The point estimate of this large hazard ratio appears consistent with previous studies documenting an association between residual reactivity and cardiovascular events after PCI.
Observed event rates are listed. P value by log-rank test. 9

10. GRAVITAS: VerifyNow and High Platelet Reactivity How Good is the Test?
HRPR
Positive
Negative
MACE
Present Absent
1055
578 25 8 33
1633
TOTAL
1080
586
Here are the results of this non-randomized comparison. The observed rate of the composite of cardiovascular death, MI, or stent thrombosis was 2.3% in patients with high reactivity compared with 1.4% in patients without high reactivity, representing a hazard ratio of 1.68.
The difference in event rates did not reach significance; note that the lower boundary of the 95% confidence interval is less than one.
The point estimate of this large hazard ratio appears consistent with previous studies documenting an association between residual reactivity and cardiovascular events after PCI. 10

11. GRAVITAS: VerifyNow and High Platelet Reactivity How Good is the Test?
0.2
0.4
0.6
0.8 1
Pre-Test Probability
Post-Test Probability
Test +
Test -
Sensitivity = 76%
Specificity = 35%
PPV = 2%
NPV = 99%
LR+ = 1.17
LR- = 0.69
AUC = 0.59
Here are the results of this non-randomized comparison. The observed rate of the composite of cardiovascular death, MI, or stent thrombosis was 2.3% in patients with high reactivity compared with 1.4% in patients without high reactivity, representing a hazard ratio of 1.68.
The difference in event rates did not reach significance; note that the lower boundary of the 95% confidence interval is less than one.
The point estimate of this large hazard ratio appears consistent with previous studies documenting an association between residual reactivity and cardiovascular events after PCI.
Impact on MACE risk prediction is tiny! 11

12. Predictive Performance of Platelet Function Testing and Genotyping

13. Shear-dependent tests
The POPular Study (Primary Endpoint)
Effect
AUC
Cutoff OR
(95% CI)
P value
Aggregation tests
LTA 5 μmol/L ADP
0.63
42.9%
2.09 ( )
0.009
LTA 20 μmol/L ADP
0.62
64.5%
2.05 ( )
0.001
VerifyNow® P2Y12
236 PRU
2.53 ( )
<0.001
Plateletworks®
0.61
80.5%
2.22 ( )
0.005
Shear-dependent tests
IMPACT-R
0.56
8.4% SC
1.34 ( )
0.21
IMPACT-R ADP
0.53
3.0% SC
1.11 ( )
0.68
PFA-100 COL/ADP
0.50
116s
0.77 ( )
0.31
INNOVANCE® PFA P2Y*  
299s
1.59 ( )
0.15
Breet NJ et al. JAMA 2010;303: 13

14. Predictive Performance of Platelet Function Test
Effect Sn Sp
PPV
NPV
LR+
LR-
AUC
LTA 20 μmol/L ADP
0.53
0.64
12%
94%
1.49
0.73
0.62
VerifyNow® P2Y12
0.59
0.63
13%
1.62
Plateletworks®
0.61
1.47
0.66
VASP
0.93
0.50
99%
1.86
0.14
0.85
Impact on risk prediction is tiny!
Breet NJ et al. JAMA 2010;303:

15. The POPular Study Predictive Model
Logistic regression modeling
Determine predictive value of addition novel risk factor: HPR
Identify independent correlates primary endpoint
Model predicting the primary endpoint
Model 1 Classic Risk factors  
Age, Gender, Hypertension, Hypercholesterolemia, Diabetes Mellitus,  
Current smoking, Family Hx of  CAD, LVEF<45%, Renal failure, Prior CABG
AUC  =  0.64
Model  2 Model  1 + Procedural Risk Factors
Total stent length, no. of lesions, no. of stents, LAD stenting, graft stenting, bifurcation lesion
AUC = 0.64   AUC = 0.72,  p=0.004
Model  3 Model  2 + HPR
AUC = 0.72   AUC = ,  p<0.01

16. Onset Maintenance Offset
The ONSET-OFFSET Study 
Last
Maintenance Dose
Loading Dose
Time (hours)
Onset Maintenance Offset
100 90 80 70 60 50 40 30 20 10
IPA %
Ticagrelor (n=54)
Clopidogrel (n=50) * ‡ †
20 µM ADP- Final Extent
IPA at 2hrs after first dose (loading): 88% ticagrelor vs. 38% clopidogrel, p<0.0001
weeks

17. All ACS Cohort in PLATO CVD/MI/Stroke: Time Course
Ticagrelor
Clopidogrel
Pinteraction = 0.313
RR 0.88 ( )
RR 0.80 ( )
CVD/MI/Stroke (%)
ARD=0.6%
ARD=1.3%
NNT=166
NNT=77
Randomization to 30d
>30d to end of follow-up
50% of total events (945/1878) occurred within 30 days
In the STEMI cohort, outcomes favored clopidogrel in day 0-30
FDA Briefing Document, July 28, 2010 17

18. Major Fatal/Life-Threatening Bleeding by Days from Last Dose of Treatment to CABG
100%
Ticagrelor
80%
Clopidogrel
60%
% Patients with Bleeding
post-CABG
40%
20% 0% 1 2 3 4 5 6 7
>8
Days
Bleeding differences favor ticagrelor >5 days post discontinuation

19. Metabolism of Clopidogrel
Topol et al, Nature Medicine 2011;17;40-41

20. How Much Does Carrier Status Matter?
Sensitivity 45%
Specificity 75%
Hochholzer, et al. J Am Coll Cardiol 2010

21. Clopidogrel Response Variability
Genetic Factors
Polymorphisms of CYP
Polymorphisms of PON1
Polymorphisms of ABCB1
Polymorphisms of P2Y12
Clopidogrel Response Variability
Clinical Factors
Failure to prescribe/poor compliance
Under-dosing
Poor absorption
Drug-drug interactions involving CYP3A4
Acute coronary syndrome/PCI
Diabetes mellitus/insulin resistance
Elevated body mass index
Cellular Factors
Accelerated platelet turnover
Reduced CYP3A metabolic activity
Increased ADP exposure
Up-regulation of the P2Y12 pathway
Up-regulation of the P2Y1 pathway
Up-regulation of P2Y–independent pathways
(collagen, epinephrine, TXA2, thrombin)
Adapted from Angiolillo DJ et al. J Am Coll Cardiol. 2007; 49: 21

22. EXCELSIOR: CYP2C19 and High Platelet Reactivity How Good is the Test?
0.2
0.4
0.6
0.8 1
Pre-Test Probability
Post-Test Probability
Sn = 45%
Sp = 75%
LR+ = 1.8
LR- = 0.7
AUC = 0.65
Test +
Test -
44%
24%
1.80
Hochholzer, et al. J Am Coll Cardiol 2010

23. CURE – Loss-of-Function Carrier Status
No heterogeneity for the first primary (P=0.84), second primary (P=0.87) or safety (P=0.74) endpoint

24. ACTIVE – Loss-of-Function Carrier Status
No heterogeneity for the primary (P=0.73) or safety (P=0.16) endpoints.

25. Primary Endpoint in the Clopidogrel Group in Relation to Any CYP2C19 LOF Allele12
Clopidogrel LOF
11.2 10
10.0
p=0.25 8
Clopidogrel No LOF
K-M estimate (%)
5.7 6 4
3.8
p=0.028 2
Days from randomization
No. at risk
Clopidogrel LOF
Clopidogrel No LOF
1, , , ,226 1,
3, , , ,186 2,691 2,123 1,757
Wallentin L et al Lancet 2010: 376, Online Aug 29, 2010

26. Predictive Performance of Platelet Genotyping
Effect Sn Sp
PPV
NPV
LR+
LR-
AUC
CYP2C19 for MACE
CURE
0.23
0.74 8%
91%
0.87
1.05
0.47
ACTIVE A
0.26
0.75
21%
80%
0.99
0.51
TRITON
0.36
12%
92%
1.36
0.57
PLATO
0.31
0.72
11%
1.11
0.96
0.52
Hulot meta-analysis
0.33
10%
1.17
0.93
0.54
Mega meta-analysis
1.16
0.94
ABCB1 for MACE
PLATO (CC, high)
0.27
0.73
90%
1.00
0.50
TRITON (TT, low)
0.39
13%
1.46
0.83
0.59

27. Predictive Performance of Platelet Genotyping
Effect Sn Sp
PPV
NPV
LR+
LR-
AUC
CYP2C19 for bleeding
CURE
0.21
0.74 3%
96%
0.80
1.07
0.45
ACTIVE A
0.44
0.76
10%
1.85
0.65
PLATO
0.57
0.49
11%
91%
1.12
0.88
0.54
ABCB1 for bleeding
TRITON
0.37
0.72 4%
98%
1.32
Risk prediction for bleeding: little to none!

28. Ultrasensitive Troponin (Sn = 88%, Sp = 85%)
Predictive Performance of Prognostic Tests Football, Banana, or a Carrot
Ultrasensitive Troponin (Sn = 88%, Sp = 85%)
0.2
0.4
0.6
0.8 1
Pre-Test Probability
VASP (Sn = 93%, Sp = 50%)
0.2
0.4
0.6
0.8 1
CYP2C19 (PAPI) (Sn = 47%, Sp = 73%)
Pre-Test Probability 1
0.8
Test +
Test -
0.6
Post-Test Probability
0.4
0.2
0.2
0.4
0.6
0.8 1
Pre-Test Probability
Football >> banana > carrot

29. Bouman et al, Nature Medicine 2011;17;110-116
PON1 Polymorphism and Impact on Nonfatal Stent Thrombosis A Case Cohort Elective PCI Study (41 cases, 71 noncases out of a cohort of 7719)
0.2
0.4
0.6
0.8 1
Sensitivity = 66%
Specificity = 65%
LR+ = 1.87
LR- = 0.53
PPV = 52%
NPV = 77%
AUC = 0.70
R2 = 0.725
Test +
Test -
52%
Post-Test Probability
23%
PON1 QQ vs QR/RR
27/52 (52%) vs 14/60 (23%)
OR 3.6 (1.6, 7.8); P=0.003
Pre-Test Probability
Bouman et al, Nature Medicine 2011;17;

30. Bouman et al, Nature Medicine 2011;17;110-116
PON1 Polymorphism and Impact on Nonfatal or Fatal Stent Thrombosis A Prospective PCI in ACS Study (N=1982) 1
Sensitivity = 70%
Specificity = 60%
LR+ = 1.76
LR- = 0.49
PPV = 4%
NPV = 99%
AUC = 0.70
Test +
Test -
0.8
0.6
Post-Test Probability
0.4
0.2
PON1 QQ vs QR/RR
31/808 (3.8%) vs 13/1174 (1.1%)
OR 3.6 (1.9, 6.9); P<0.001 4% 1%
0.2
0.4
0.6
0.8 1
Pre-Test Probability
Bouman et al, Nature Medicine 2011;17;

31. Predictive Performance of Prognostic Tests Impact of CYP2C19 Genotype: A Tale of Two Trials
Shuldiner et al
CYP2C19 (MACE) 14/67 vs 16/160; RR 2.4, p=0.02 (Sn = 47%, Sp = 73%)
Bouman et al
CYP2C19 (Stent thrombosis) 26/75 vs 15/37: RR 0.86, p=0.55
(Sn = 63%, Sp = 31%) 1
0.2
0.4
0.6
0.8 1
PPV =21%
NPV =90%
LR+ =1.73
LR- =0.73
AUC = 0.64
R2 =0.12
PPV =35%
NPV =59%
LR+ =0.92
LR- =1.18
AUC = 0.46
0.8
0.6
Post-Test Probability
Post-Test Probability 41
0.4 21 35
Test +
Test -
Test +
Test -
0.2 10
0.2
0.4
0.6
0.8 1
Pre-Test Probability
Pre-Test Probability
Shuldiner et al, JAMA 2009;302:
Bouman et al, Nature Medicine 2011:1:

32. Is Routine Testing of Platelet Genotype or Phenotype Reasonable?
Requirements of A Screening Tool
The test must be practical in the clinical setting.
The test must accurately characterize low- and
high-risk patients.
Identification of at-risk individuals should lead to
a treatment that improves outcome.
The process should be cost-effective.
Adapted from Miller et al, J Am Coll Cardiol 2006;48:761-4

33. Does Genetic Testing or Platelet Function Testing Fulfill the Criteria For a Screening Tool?
Practical clinical use
(POC)
Risk prediction accuracy
Improved outcomes
Cost effective
CYP2C19 No
PPV = 10-21%
NPV = 90-92% ?
LTA
PPV = 12%
NPV = 94%
VerifyNow
Yes
PPV = 13%
?/No
VASP
PPV = 10-12%
NPV = 96-99% 33

34. Prasugrel and Ticagrelor vs. Clopidogrel
Bleeding Outcomes
Trial
Active Rx
Control
RR, 95% CI
Major Bleeding (Study Criteria)
TRITON
2.4%
1.8%
1.32 (1.03 to 1.68)
PLATO
11.6%
11.2%
1.04 (0.95 to 1.13)
CABG TIMI Major bleeding
TRITON
13.4%
3.2%
4.74 (1.9 to )
PLATO
5.3%
5.8%
0.94 (0.82 to 1.07)
Non-CABG TIMI Major bleeding
TRITON
2.4%
1.8%
1.32 (1.03 to 1.68)
PLATO
2.8%
2.2%
1.25 (1.03 to 1.53)
TIMI Major or Minor Bleeding
TRITON
5.0%
3.8%
1.32 (1.11 to 1.56)
PLATO
11.4%
10.9%
1.05 (0.96 to 1.15)
Bleeding requiring transfusions
TRITON
4.0%
3.0%
1.34 (1.11 to 1.63)
PLATO
8.9%
8.9%
1.01 (0.91 to 1.11)
Fatal Bleeding
TRITON
0.4%
0.1%
4.19 (1.58 to )
PLATO
0.3%
0.3%
0.87 (0.48 to 1.59) 1 2 3 4 5
Relative Risk 34

35. Platelet Genotyping in Practice Challenges and Uncertainties
CYP2C19 is one of many polymorphisms
Point of care (POC) assay not currently available (3-7 days turnaround time)
Expensive ($500-$600)
Not reimbursed by insurance companies
Ischemic events not predicted consistently, low PPV
Bleeding not predicted
Holmes, Dehmer, Kaul et al. JACC 2010

36. Platelet Genotyping in Practice Challenges and Uncertainties
No prospective data for how to respond to genetic or functional testing - Dose (double dose, ? higher dose) - Additional agent (cilastozol) - Alternative agent (prasugrel, ticagrelor)
No prospective evidence that outcomes are improved
Remains a useful investigation tool at the current time
Numerous RCTs underway for both genotyping and phenotyping strategies to guide treatment
Holmes, Dehmer, Kaul et al. JACC 2010

37. Role of Platelet Function & Genetic Testing
A Case of Enthusiasm Exceeding the Evidence
Enthusiasm
Evidence

38. Tailoring Antiplatelet Therapy The Science of Medicine
Phenotype/
Genotype
Benefit
Risk
Modest prediction for ischemic outcomes
Little or no prediction for bleeding risk
Unfavorable logistics/cost
Insufficient evidence to recommend either
platelet function or genetic testing

39. Tailoring Antiplatelet Therapy The Art of Medicine
Phenotype/
Genotype
Benefit
Risk
Modest prediction for ischemic outcomes
Little or no prediction for bleeding risk
Unfavorable logistics/cost
May consider in patients with recurrent ischemic events on clopidogrel
May consider in high-risk intervention (complex lesion, diabetics, etc.)

40. Tailoring Antiplatelet Therapy Based on
Platelet Function Testing and Genotyping
Paul Gurbel, August 2010
"The bottom line is we have no prospective studies at this time that alteration of
therapy based on genotype or phenotype really affects patient outcomes”
White Paper, JACC 2010 (Bonello/Gurbel et al)
“However, until the results of large scale trials of personalized antiplatelet therapy are available, the routine use of platelet function measurements in the care of patients with cardiovascular disease cannot be recommended”
2010 ACCF/ACG/AHA Expert Consensus Document on the Concomitant Use of PPIs and Thienopyridines
"The role of either pharmacogenomic testing or platelet-function testing in managing therapy with thienopyridines and PPIs has not yet been established"