Methods to Analyse The Economic Benefits of a Pharmacogenetic (PGt) Test to Predict Response to Biologic Therapy in Rheumatoid Arthritis, and to Prioritise

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.

“Biologics”Anakinra ($12,697), Etanercept ($18,850), Infliximab ($24,112)*

*Costs include monitoringAnakinra 100mgEtanercept 25mg eowInfliximab 3mg/kg 8 weekly


Cytokines

Interleukin 1 TNF alpha TNF Alpha



Is Response Genetic?

91 patients, 150mg Anakinra, 24 week RCT1,2, gene = IL-1A +4845Positive 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




24 week RCT1,2 , 91 patients, 150mg Anakinra,, gene = IL-1A +4845Defined response = reduction of at least 50% in swollen joints



2 Bresnihan





91 patients, 150mg Anakinra, 24 week RCT1,2, gene = IL-1A +4845Positive response = reduction of at least 50% in swollen joints



2 Bresnihan


0%

20%

40%

60%

80%

100%

Placebo Anakinra Gene+ve

Gene -ve

% a

ch

iev

ing

"S

wo

llen

50

"

0 .0 %

1 0 .0 %

2 0 .0 %

3 0 .0 %

4 0 .0 %

5 0 .0 %

6 0 .0 %

7 0 .0 %

8 0 .0 %

9 0 .0 %

1 0 0 .0 %

*Costs include monitoringAnakinra 100mgEtanercept 25mg eowInfliximab 3mg/kg 8 weekly 50% 50%100%

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





• “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

Existing Uncertainty

0%

20%

40%

60%

80%

100%

Placeb

o

Anakin

ra

Gene

+ve

Gene

-ve

Etane

rcep

t

Inflix

imab

% a

chie

vin

g A

CR

20

0 .0 %

1 0 .0 %

2 0 .0 %

3 0 .0 %

4 0 .0 %

5 0 .0 %

6 0 .0 %

7 0 .0 %

8 0 .0 %

9 0 .0 %

1 0 0 .0 %

50% 50%

2 Year Treatment Sequence Pathway

• Initial Response Longer term discontinuation

AE or AE or lose efficacy?

Respond? No Drug 1 …..

Yes Drug 1

Yes Drug 2 …..

Drug 1

Respond?

Yes Drug 2 …..

No Drug 2

No Drug 3 …..

0 - 6 months 6 - 12 months 12-18 months

Respond ?

Gene +ve? Yes …..

Yes Anakinra

No …..

PGt

Respond?

Yes …..

No Etanercept

No …..

Respond?

Yes

Anakinra

No

Before 0 - 6 months

Before 0 - 6 months

A Pharmaco-Genetic Strategy

Strategy 1

Strategy 2

Strategy Sequences to Compare

A AnakinraPGt GeneticE EtanerceptI Infliximab-

Maintenance

1st 2nd 3rd 4thPGt sister strategy

1 PGt - - - 32 A - - -3 E - - -4 I - - -5 E A - -6 I A - -7 E I - -8 I E - -9 PGt E - - 7

10 PGt I - - 811 A E - -12 A I - -13 A E I -14 A I E -15 E I A -16 E A I -17 I E A -18 I A E -19 PGt E I - 720 - - - -

1

2

3

0

Existing Uncertainty (2)

0%

20%

40%

60%

80%

100%

Anakin

ra

Gene

+ve

Gene

-ve

Etane

rcep

t

Inflix

imab

E afte

r I

E afte

r A

I afte

r E

I afte

r A

A afte

r E

A afte

r I% a

ch

iev

ing

AC

R2

0

0 .0 %

1 0 .0 %

2 0 .0 %

3 0 .0 %

4 0 .0 %

5 0 .0 %

6 0 .0 %

7 0 .0 %

8 0 .0 %

9 0 .0 %

1 0 0 .0 %

Trials Sequence "priors"

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………...

2 Level EVSI - Research Design4, 5

4 Brennan et al Poster

SMDM 2002


SMDM 2002



SMDM 2002


SMDM 2002

0)Decision model, threshold, priors for uncertain parameters



SMDM 2002


SMDM 2002

0)Decision model, threshold, priors for uncertain parameters1) Simulate data collection:



SMDM 2002


SMDM 2002

0)Decision model, threshold, priors for uncertain parameters1) Simulate data collection: • sample parameter(s) of interest once ~ prior • (1st level)



SMDM 2002


SMDM 2002

0)Decision model, threshold, priors for uncertain parameters1) Simulate data collection: • sample parameter(s) of interest once ~ prior • decide on sample size (ni) (1st level)



SMDM 2002


SMDM 2002

0)Decision model, threshold, priors for uncertain parameters1) 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



SMDM 2002


SMDM 2002

0)Decision model, threshold, priors for uncertain parameters1) 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



SMDM 2002


SMDM 2002

0)Decision model, threshold, priors for uncertain parameters1) 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



SMDM 2002


SMDM 2002

0)Decision model, threshold, priors for uncertain parameters1) 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 simulate 1000 times

parameters of interest ~ simulated posteriorunknown parameters ~ prior uncertainty (2nd level)



SMDM 2002


SMDM 2002



4) calculate best strategy = highest mean net benefit



SMDM 2002


SMDM 2002



4) calculate best strategy = highest mean net benefit 5) Loop 1 to 4 say 1,000 times Calculate average net benefits



SMDM 2002


SMDM 2002



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)



SMDM 2002


SMDM 2002



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) )NB(d,max|)NB(d,max EEE

di

dX i

4 strategies: A, E, I and PGt

Results - 6 months


Results - 6 months

A PGt E I6 month Cost $6,349 $8,087 $9,425 $12,056 % Responder 43% 63% 71% 50%Cost per Resp $14,764 $12,755 $13,275 $24,112

Cost per Responder

$0

$5,000

$10,000

$15,000

$20,000

$25,000

$30,000

A PGt E I


Results - 6 months

A PGt E I6 month Cost $6,349 $8,087 $9,425 $12,056 % Responder 43% 63% 71% 50%Cost per Resp $14,764 $12,755 $13,275 $24,112

Cost per Responder

$0

$5,000

$10,000

$15,000

$20,000

$25,000

$30,000

A PGt E I

Incremental Cost per Responder Year Gained

$8,521 $10,987$17,608

$81,534

PGt DOM$0

$20,000

$40,000

$60,000

$80,000

$100,000

PGt v A

E v A

E v PGt

I v P

GtI v

A

20 strategies: A, E, I and PGt sequences

Base-case Results - 2 years


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)



Optimal Strategy ProbDepends 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%


Incorporating Uncertainty

• Assuming 25,000 per annum new patients starting biologics over next 5 years

Overall EVPI

$0 $0

$99$116

$138

$178

$0

$50

$100

$150

$200

$0 $10k $20k $30k $40k $50kCost per Responder Year Threshold

$million

Partial EVPI: Key Uncertainties

$0

$10

$20

$30

$40

$50

$10 $15 $20 $25 $30 $35 $40 $45 $50

Threshold ($ 000)

EV

PI

($m

)

PGt response

Partial EVPI: Key Uncertainties

$0

$10

$20

$30

$40

$50

$10 $15 $20 $25 $30 $35 $40 $45 $50

Threshold ($ 000)

EV

PI

($m

)

E,I after anybiologic

TNF afterAnakinra

1st Line E,I,A

TNF crossover

Anakinra afterTNF

Partial EVSI: PGt Research onlyEVSI for PGt Research only

(for threshold = $20,000 per responder year gained)

$0.0$5.0

$10.0$15.0$20.0$25.0$30.0

10 20 50 100

200

500

1000

2000

5000

Perfe

ct

Sample Size

EV

SI

$m

Caveat: Small No.of Simulations on 1st Level

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

subgroups3. Anakinra response rate in anti-TNFα failures

Partial EVPI: TARGET RA Program

$0

$10

$20

$30

$40

$50

$60

$10 $15 $20 $25 $30 $35 $40 $45 $50

Threshold ($ 000)

EV

PI

($m

)

TARGET RA

Conclusions• Early economic evaluation suggests potential for

a cost-effective pharmacogenetic test.

Conclusions• Early economic evaluation suggests potential for

a cost-effective pharmacogenetic test.

• Expected value of information analysis has quantified the key research priorities.

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

Documents

Methods to Analyse The Economic Benefits of a Pharmacogenetic (PGt) Test to Predict Response to Biologic Therapy in Rheumatoid Arthritis, and to Prioritise