1. The COAG Trial: A Randomized Trial of a Pharmacogenetic versus a Clinical Algorithm for Warfarin Dosing Stephen E. Kimmel, MD, MSCE on behalf of the COAG Investigators American Heart Association November 19, 2013 C AG Clarification of Optimal Anticoagulation through Genetics O

2. CA GOBackgroundBackground  The need for clinical trials prior to widespread adoption of pharmacogenetic-based drug dosing and selection remains widely debated  Warfarin Model for pharmacogenetics Model for pharmacogenetics Hypothesis: Adding genetic to clinical information will improve anticoagulation control Hypothesis: Adding genetic to clinical information will improve anticoagulation control 2

3. CA GO The COAG Clinical Trial A multicenter, double-blinded, stratified RCT of 1,015 participants, comparing two approaches: 1) initiation of warfarin therapy based on algorithms using only clinical information (Clinical-guided dosing arm) 2) initiation of warfarin therapy based on algorithms using clinical information and an individual’s genotype (PGx-guided dosing arm) 3

4. CA GOInclusion/ExclusionInclusion/Exclusion  Inclusion/Exclusion Criteria included: New warfarin starters New warfarin starters Any indication for warfarin Any indication for warfarin Expected duration of treatment ≥ 1 month Expected duration of treatment ≥ 1 month Target INR 2–3 Target INR 2–3 If prior warfarin use, maintenance dose unknown If prior warfarin use, maintenance dose unknown Genetic variants unknown Genetic variants unknown Clinician opinion that no contraindications to using the dosing algorithms Clinician opinion that no contraindications to using the dosing algorithms 4 Am Heart J 2013;166:435-41

5. CA GO Intervention Period (Days 1-5) Dose Initiation Gage et al., CPT 2008 CPT 2008 Dose Revision Lenzini et al., CPT 2010 CPT 2010 Am Heart J 2013;166:435-41

6. CA GO Methods – Key Design Elements  Randomization stratified by site and race African American vs non-African American African American vs non-African American  Blinded to dose To maintain blinding to study arm To maintain blinding to study arm Isolate effects of genotyping from other post- randomization effects Isolate effects of genotyping from other post- randomization effects  Genotyped all participants at randomization To maintain blinding To maintain blinding  Pre-specified Subgroups Race Race Sex Sex Number of allelic variants Number of allelic variants 6 Am Heart J 2013;166:435-41

7. CA GOEndpointsEndpoints  Primary Outcome Percent time in therapeutic INR range (PTTR) at 28 days Percent time in therapeutic INR range (PTTR) at 28 days Co-primary analyses of PTTR Co-primary analyses of PTTR –In entire study population –In those with ≥1.0 mg/day absolute difference in initiation dose by the 2 algorithms  Principal secondary outcome INR ≥4 or serious clinical event (TE/Bleed) INR ≥4 or serious clinical event (TE/Bleed) 7 Trials 2010;11:108. Clinical Trials 2010;7:597-604

8. CA GO Sample Size and Power  Target sample size 1,022 to ensure >80% power –5.5% absolute difference in PTTR with a type-1 error rate of 0.04 among all participants –9% difference among the co-primary subgroup with a type-1 error rate of 0.01 8 Am Heart J 2013;166:435-41

9. CA GO Study Flow 9 1015 randomized* 514 assigned genotype-guided 501 assigned clinically guided Included in analysis - Primary: 484 ** - Safety: 514 29 withdrew - 1 SAE - 13 provider discretion - 15 patient decision 1 lost to follow-up 29 withdrew - 2 SAE - 13 provider discretion - 14 patient decision 1 became ineligible First patient enrolled: September 2009 Last patient enrolled: April 2013 All follow-up completed: July 2013 Included in analysis - Primary: 471 ** - Safety: 501 * Stratified by race and center ** INRs available on or after day 4/5

10. CA GODemographicsDemographics 10 † = Variable used in pharmacogenetic and/or clinical dose-initiation and/or dose-revision algorithm) PGx-Guided (n=514) Clinical-guided (n=501) Age, years, median† 5957 Female (%) 4751 African American (%)† 27 Diabetes (%)† 2324 Current smoker (%)† 1514 Stroke (%)† 76 BSA, m 2, median † 2.012.03 Amiodarone use (%) † 32 Fluvastatin use (%) † <1

11. CA GO Warfarin Initiation and Indication 11 PGx-guided (n=514) Clinical-guided (n=501) Inpatient initiation (%) 6866 Indication for warfarin therapy (%) DVT or PE only† 5660 Atrial fibrillation/flutter only 2321 Other indication only 11 Multiple indications 108 No indication given 11 † = Variable in used in pharmacogenetic and/or clinical dose- initiation and/or dose-revision algorithm)

12. CA GOGenotypeGenotype 12 Genetic variants PGx-Guided (n=514) Clinical-guided (n=501) CYP2C9*2 (%)† No variants8184 Heterozygous1814 Homozygous11 CYP2C9*3 (%)† No variants9290 Heterozygous710 Homozygous<10 VKORC1 (%)† No variants (GG)4947 Heterozygous (AG or GA)3940 Homozygous (AA)1112 Withdrew prior to genotyping: 1% PGx and <1% Clinical

13. CA GO Maintenance Dose Prediction 13 Pharmacogenetic- guided Clinical- guided Dose-initiation algorithm R 2 0.480.27 Dose-revision algorithm R 2 0.690.54 PGx demonstrated better maintenance dose prediction the clinical algorithm PGx demonstrated better maintenance dose prediction the clinical algorithm Dose prediction as expected based on prior studies Dose prediction as expected based on prior studies

14. CA GO Primary Outcome - PTTR at 4 Weeks 14 Genotype- guided dosing Clinical-guided dosing Mean difference (95% CI) *P-value Mean (SD) PTTR All participants (n=955)45.2 (26.6)45.4 (25.8)-0.18 (-3.4, 3.1)0.91 Algorithms’ Difference 0.63** ≥ 1mg/d (n=392)45.1 (25.5)46.5 (27.1)-1.1 (-6.2, 4.0)0.67 < 1 mg/d (n=563)45.2 (27.4)44.7 (24.8)0.52 (-3.7, 4.8)0.81 * Mean difference in PTTR between genotype-guided and clinical-guided dosing groups, estimated from multivariable linear regression models that adjusted for race and clinical center **Interaction P value to evaluate equality of mean difference between subgroups

15. CA GO Pre-Specified Subgroups: PTTR 4 Weeks 15 Genotype- guided dosing Clinical- guided dosing Mean difference (95% CI)P-value Race 0.003 * AA(n=255)35.2 (26.0)43.5 (26.5)-8.3 (-15, -2.0)0.010 Non AA(n=700)48.8 (25.9)46.1 (25.5)2.8 (-1.0, 6.6)0.15 Sex0.71 * Male(486)44.9 (26.9)45.5 (25.4)0.44 (-4.2, 5.1)0.85 Female(n=469)45.4 (26.3)45.3 (26.2)-0.81 (-5.5, 3.9)0.73 Total # variants 0.21 * 1 variant (n=343)48.1 (26.5)45.0 (23.7)2.6 (-2.9, 8.1)0.35 0 or >1 variant (n=612)43.6 (26.5)45.7 (27.0)-1.7 (-5.8, 2.4)0.41 * Interaction P value to evaluate equality of mean difference between subgroups

16. CA GO Pre-Specified Subgroups Primary Outcome 16

17. CA GO INR Over Time, By Race 17

18. CA GO Time to 1 st Therapeutic INR 18

19. CA GO Adverse Events at 4 Weeks 19 Outcome PGx- Guided (n=514) Clinical- guided (n=501) Hazard ratio (95% CI)P-value n (%) Any INR≥4, major bleeding, or TE*105 (20)103 (21)1.0 (0.77, 1.3)0.93 Any INR≥4100 (19)92 (18)1.1 (0.81, 1.4)0.59 Major bleeding4 (1)10 (2)0.41 (0.13, 1.3)0.13 Thromboembolism5 (1)4 (1)1.3 (0.34, 4.7)0.72 Clinically relevant non-major bleed13 (3)20 (4)0.62 (0.30, 1.3)**0.18 All-cause death2 (<1)1 (<1)2.1 (0.19, 23)0.55 * Principal secondary outcome ** Odds ratio

20. CA GOConclusionsConclusions  COAG trial does not support the hypothesis that adding genetic information to determine dosing for the first five days of warfarin therapy improves anticoagulation control compared to initiating warfarin using only clinical information 20

21. CA GOConclusionsConclusions  No effect of pharmacogenetic-based dosing in those expected to have benefit based on predicted dose differences  Effects varied by race Clinical-based dosing may be better than PGx- based dosing in African Americans Clinical-based dosing may be better than PGx- based dosing in African Americans  COAG highlights the importance of performing randomized trials for pharmacogenetics, particularly for complex medicine regimens such as warfarin 21

22. CA GO COAG Clinical Centers 22

23. CA GO COAG PIs and Sites Principal Investigator:Site: Thomas L. Ortel, MD, PhDDuke University Medical Center Jaspal Gujral, MBBS,FACP,FRCPGeorgia Regents Medical Center Vinay Shah, MDHenry Ford Hospital Emile R. Mohler III, MDHospital of the University of Pennsylvania Scott M. Stevens, MDIntermountain Medical Center Steven Yale, MD, FACPMarshfield Clinic Research Foundation Robert D. McBane, MDMayo Clinic College of Medicine Henny H. Billett MD, MScMontefiore Medical Center Robert J. Desnick, MD, PhDMount Sinai School of Medicine Patrice Delafontaine, MDTulane University Nita A. Limdi, PharmD, PhD, MSPHUniversity of Alabama at Birmingham 23

24. CA GO COAG PIs and Sites Principal Investigator:Site: Margaret C. Fang, MD, MPHUniversity of California, San Francisco Julie A. Johnson, PharmDUniversity of Florida Richard B. Horenstein, MDUniversity of Maryland School of Medicine Sherif Z. Abdel-Rahman, PhD University of Texas Medical Branch at Galveston Robert C. Pendleton, MDUniversity of Utah Health Care James A. S. Muldowney III, MD, FAACVanderbilt University Brian F. Gage, MD, MScWashington University School of Medicine Director:Central Lab: Charles S. Eby, MD, PhDWashington University School of Medicine Director:Investigational Drug Service: Kenneth Rockwell, Pharm DUniversity of Pennsylvania 24

25. CA GOAcknowledgmentsAcknowledgments  Funded by the NHLBI  Additional support Bristol-Meyers Squibb Bristol-Meyers Squibb GenMark Diagnostics GenMark Diagnostics AutoGenomics Inc. AutoGenomics Inc. 25

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