Alan Brennan 1 , Nick Bansback 1 , 1 ScHARR, University of Sheffield, England.

1. Methods to Analyse The Economic Benefits of a Pharmacogenetic (PGt) Test to Predict Response to Biologic Therapy in Rheumatoid Arthritis, and to Prioritise Further Research Alan Brennan1, Nick Bansback1, 1ScHARR, University of Sheffield, England. Kip Martha2, Marissa Peacock2, Kenneth Huttner2 2Interleukin Genetics, Inc.

2. “Biologics” Anakinra ($12,697), Etanercept ($18,850), Infliximab ($24,112)* *Costs include monitoring Anakinra 100mg Etanercept 25mg eow Infliximab 3mg/kg 8 weekly

3. “Biologics” Anakinra ($12,697), Etanercept ($18,850), Infliximab ($24,112)* Cytokines Interleukin 1 TNF alpha TNF Alpha *Costs include monitoring Anakinra 100mg Etanercept 25mg eow Infliximab 3mg/kg 8 weekly

4. “Biologics” Anakinra ($12,697), Etanercept ($18,850), Infliximab ($24,112)* Is Response Genetic? 91 patients, 150mg Anakinra, 24 week RCT1,2, gene = IL-1A +4845 Positive response = reduction of at least 50% in swollen joints 1 Camp et al. American Human Genetics Conf abstract 1088, 1999 2 Bresnihan Arthritis & Rheumatism, 1998 *Costs include monitoring Anakinra 100mg Etanercept 25mg eow Infliximab 3mg/kg 8 weekly

5. “Biologics” Anakinra ($12,697), Etanercept ($18,850), Infliximab ($24,112)* Is Response Genetic? 24 week RCT1,2 , 91 patients, 150mg Anakinra,, gene = IL-1A +4845 Defined response = reduction of at least 50% in swollen joints 1 Camp et al. American Human Genetics Conf abstract 1088, 1999 2 Bresnihan Arthritis & Rheumatism, 1998 *Costs include monitoring Anakinra 100mg Etanercept 25mg eow Infliximab 3mg/kg 8 weekly

6. “Biologics” Anakinra ($12,697), Etanercept ($18,850), Infliximab ($24,112)* Is Response Genetic? 91 patients, 150mg Anakinra, 24 week RCT1,2, gene = IL-1A +4845 Positive response = reduction of at least 50% in swollen joints 1 Camp et al. American Human Genetics Conf abstract 1088, 1999 2 Bresnihan Arthritis & Rheumatism, 1998 *Costs include monitoring Anakinra 100mg Etanercept 25mg eow Infliximab 3mg/kg 8 weekly 100% 50% 50%

7. Health Outcomes • ACR20 response -20% in swollen, and tender joints, and in 3 other measures

8. Health Outcomes • ACR20 response -20% in swollen, and tender joints, and in 3 other measures ACR20 = 0.88 * Swollen50 score (trial data)

9. Health Outcomes • ACR20 response -20% in swollen, and tender joints, and in 3 other measures ACR20 = 0.88 * Swollen50 score (trial data) Response ==> symptom relief and delayed progression long term

10. Health Outcomes • ACR20 response -20% in swollen, and tender joints, and in 3 other measures ACR20 = 0.88 * Swollen50 score (trial data) Response ==> symptom relief and delayed progression long term • “Years in ACR20 Response” = primary outcome 3 Kobelt et al. Economic Conseque of Progression of RA in Swe. A&R 1999

11. Health Outcomes • ACR20 response -20% in swollen, and tender joints, and in 3 other measures ACR20 = 0.88 * Swollen50 score (trial data) Response ==> symptom relief and delayed progression long term • “Years in ACR20 Response” = primary outcome • ACR 20 Response  0.8 reduction in HAQ (0 to 3 scale) • Utility  0.86 - 0.2 * HAQ 3 3 Kobelt et al. Economic Conseque of Progression of RA in Swe. A&R 1999

12. 50% Existing Uncertainty 50%

13. 2 Year Treatment Sequence Pathway • Initial Response Longer term discontinuation

14. A Pharmaco-Genetic Strategy Strategy 1 Strategy 2

15. 1 2 3 0 Strategy Sequences to Compare A Anakinra PGt Genetic E Etanercept I Infliximab - Maintenance

16. Existing Uncertainty (2)

17. Cost Assumptions • Drugs and Monitoring • Other Healthcare  HAQ$Cost pa = $1,084 + $1,636 * HAQ 4 ==> Responder = $ 2,400 pa Non Responder = $ 3,700 pa • PGt = $200 • Excluding :Nursing Home Care, Employer Costs • No uncertainty analysis 4 Yelin and Wanke . A&R 1999………...

18. 2 Level EVSI - Research Design4, 5 4 Brennan et al Poster SMDM 2002 5 Brennan et al Poster SMDM 2002

19. 2 Level EVSI - Research Design4, 5 0)Decision model, threshold, priors for uncertain parameters 4 Brennan et al Poster SMDM 2002 5 Brennan et al Poster SMDM 2002

20. 2 Level EVSI - Research Design4, 5 0)Decision model, threshold, priors for uncertain parameters 1) Simulate data collection: 4 Brennan et al Poster SMDM 2002 5 Brennan et al Poster SMDM 2002

21. 2 Level EVSI - Research Design4, 5 • 0)Decision model, threshold, priors for uncertain parameters • 1) Simulate data collection: • sample parameter(s) of interest once ~ prior • (1st level) 4 Brennan et al Poster SMDM 2002 5 Brennan et al Poster SMDM 2002

22. 2 Level EVSI - Research Design4, 5 • 0)Decision model, threshold, priors for uncertain parameters • 1) Simulate data collection: • sample parameter(s) of interest once ~ prior • decide on sample size (ni) (1st level) 4 Brennan et al Poster SMDM 2002 5 Brennan et al Poster SMDM 2002

23. 2 Level EVSI - Research Design4, 5 • 0)Decision model, threshold, priors for uncertain parameters • 1) Simulate data collection: • sample parameter(s) of interest once ~ prior • decide on sample size (ni) (1st level) • sample a mean value for the simulated data | parameter of interest 4 Brennan et al Poster SMDM 2002 5 Brennan et al Poster SMDM 2002

24. 2 Level EVSI - Research Design4, 5 • 0)Decision model, threshold, priors for uncertain parameters • 1) Simulate data collection: • sample parameter(s) of interest once ~ prior • decide on sample size (ni) (1st level) • sample a mean value for the simulated data | parameter of interest 4 Brennan et al Poster SMDM 2002 5 Brennan et al Poster SMDM 2002

25. 2 Level EVSI - Research Design4, 5 • 0)Decision model, threshold, priors for uncertain parameters • 1) Simulate data collection: • sample parameter(s) of interest once ~ prior • decide on sample size (ni) (1st level) • sample a mean value for the simulated data | parameter of interest • 2) combine prior + simulated data --> simulated posterior 4 Brennan et al Poster SMDM 2002 5 Brennan et al Poster SMDM 2002

26. 2 Level EVSI - Research Design4, 5 • 0)Decision model, threshold, priors for uncertain parameters • 1) Simulate data collection: • sample parameter(s) of interest once ~ prior • decide on sample size (ni) (1st level) • sample a mean value for the simulated data | parameter of interest • 2) combine prior + simulated data --> simulated posterior • 3) now simulate1000 times • parameters of interest ~ simulated posterior • unknown parameters ~ prior uncertainty(2nd level) 4 Brennan et al Poster SMDM 2002 5 Brennan et al Poster SMDM 2002

27. 2 Level EVSI - Research Design4, 5 • 0)Decision model, threshold, priors for uncertain parameters • 1) Simulate data collection: • sample parameter(s) of interest once ~ prior • decide on sample size (ni) (1st level) • sample a mean value for the simulated data | parameter of interest • 2) combine prior + simulated data --> simulated posterior • 3) now simulate1000 times • parameters of interest ~ simulated posterior • unknown parameters ~ prior uncertainty(2nd level) • 4) calculate best strategy = highest mean net benefit 4 Brennan et al Poster SMDM 2002 5 Brennan et al Poster SMDM 2002

28. 2 Level EVSI - Research Design4, 5 • 0)Decision model, threshold, priors for uncertain parameters • 1) Simulate data collection: • sample parameter(s) of interest once ~ prior • decide on sample size (ni) (1st level) • sample a mean value for the simulated data | parameter of interest • 2) combine prior + simulated data --> simulated posterior • 3) now simulate1000 times • parameters of interest ~ simulated posterior • unknown parameters ~ prior uncertainty(2nd level) • 4) calculate best strategy = highest mean net benefit • 5) Loop 1 to 4 say 1,000 times Calculate average net benefits 4 Brennan et al Poster SMDM 2002 5 Brennan et al Poster SMDM 2002

29. 2 Level EVSI - Research Design4, 5 • 0)Decision model, threshold, priors for uncertain parameters • 1) Simulate data collection: • sample parameter(s) of interest once ~ prior • decide on sample size (ni) (1st level) • sample a mean value for the simulated data | parameter of interest • 2) combine prior + simulated data --> simulated posterior • 3) now simulate1000 times • parameters of interest ~ simulated posterior • unknown parameters ~ prior uncertainty(2nd level) • 4) calculate best strategy = highest mean net benefit • 5) Loop 1 to 4 say 1,000 times Calculate average net benefits • 6) EVSI parameter set = (5) - (mean net benefit | current information) 4 Brennan et al Poster SMDM 2002 5 Brennan et al Poster SMDM 2002

30. 2 Level EVSI - Research Design4, 5 • 0)Decision model, threshold, priors for uncertain parameters • 1) Simulate data collection: • sample parameter(s) of interest once ~ prior • decide on sample size (ni) (1st level) • sample a mean value for the simulated data | parameter of interest • 2) combine prior + simulated data --> simulated posterior • 3) now simulate1000 times • parameters of interest ~ simulated posterior • unknown parameters ~ prior uncertainty(2nd level) • 4) calculate best strategy = highest mean net benefit • 5) Loop 1 to 4 say 1,000 times Calculate average net benefits • 6) EVSI parameter set = (5) - (mean net benefit | current information) 4 Brennan et al Poster SMDM 2002 5 Brennan et al Poster SMDM 2002

31. Results - 6 months 4 strategies: A, E, I and PGt

32. Results - 6 months 4 strategies: A, E, I and PGt

33. Results - 6 months 4 strategies: A, E, I and PGt

34. Base-case Results - 2 years 20 strategies: A, E, I and PGt sequences

35. Base-case Results - 2 years 20 strategies: A, E, I and PGt sequences Optimal Strategy Depends on Threshold: $10k ==> maintenance therapy (20) $20k ==> sequence of 2 biologics (11) $25k ==> PGt + 2 biologics (9) $30k ==> PGt + 3 biologics (19)

36. Base-case Results - 2 years 20 strategies: A, E, I and PGt sequences Optimal Strategy Prob Depends on Threshold: Optimal $10k ==> maintenance therapy (20) 100% $20k ==> sequence of 2 biologics (11) 42% $25k ==> PGt + 2 biologics (9) 18% $30k ==> PGt + 3 biologics (19) 43%

37. Incorporating Uncertainty • Assuming 25,000 per annum new patients starting biologics over next 5 years

38. Partial EVPI: Key Uncertainties

39. Partial EVPI: Key Uncertainties

40. Partial EVSI: PGt Research only Caveat: Small No.of Simulations on 1st Level

41. Interleukin Genetics Inc. TARGET RA program • Conceptual modelling identified key missing data and helped prioritise further primary data collection 1. PGt test performance (increased sample size). 2. Etanercept / Infliximab performance in gene subgroups 3. Anakinra response rate in anti-TNFα failures

42. Partial EVPI: TARGET RA Program

43. Conclusions • Early economic evaluation suggests potential for a cost-effective pharmacogenetic test.

44. Conclusions • Early economic evaluation suggests potential for a cost-effective pharmacogenetic test. • Expected value of information analysis has quantified the key research priorities.

45. Conclusions • Early economic evaluation suggests potential for a cost-effective pharmacogenetic test. • Expected value of information analysis has quantified the key research priorities. • EVSI can quantify the value of the specific research design