Mechanism-based
pharmacoeconomic modelling
Professor Dyfrig Hughes
Centre for Health Economics & Medicines Evaluation
Empirical modelling
• Data collection
• Specify correlation structure between
variables
• Use a numerical technique to fin...
Mechanism-based modelling
• Use fundamental knowledge of the
interactions between variables to define the
model structure
...
Principles of pharmacology
• Pharmacokinetics
– the study of drug disposition in the human body

• Pharmacodynamics
– the ...
Pharmacokinetics (PK)
– Absorption
– Distribution
– Metabolism
– Excretion
Plasma concentration (mg/l)

Population PK

Predicted plasma concentration (mg/l)
Pharmacodynamics (PD)
Emax D
E
n
n
EC50  D
n





Sigmoidal Emax model that describes the ‘dose response curve’ de...
PK-PD model
Time (months)
Clin Pharmacokinet 2008; 47 (7): 417-448
Applications
• Dose selection
• Dosing schedules
• Dosing in special populations
– Renal impairment
– Genetic polymorphism...
Aims
• To develop the methods for linking PKPD
models with economic evaluations
• To conduct proof of concept studies
Case study 1: Rituximab

Lewis Sheiner Prize, PAGE 2011, Athens
Economic models
Integrate PKPD with PE models
• Population PK and PD models from Ternant
– Br J Clin Pharmacol 2009; 68: 561-73

PK equati...
Maintenance therapy

Trial-based results

Simulation results

First line therapy
Conclusions
Case study 2: NOACs
• Newer oral anticoagulants (dabigatran,
rivaroxaban, apixaban) set to revolutionise
management of pat...
Key results from pivotal trials
• Primary outcome of stroke or systemic embolism
– superiority of dabigatran (1.11% v 1.71...
Economic model - DES
Generate patient
characteristics: Age, risk
factors, prior event history

Duplicate patient across th...
Clinical data
• Where possible, clinical data were taken from the RE-LY study
and the FDA submission based on it:
– Initia...
Utility data
• Utility decrements related to AF and other clinical events
taken from a published index of utility scores:
...
Cost data
• Costs of outcome events are all taken from NHS reference cost
data, following the methodologies of previously ...
Results: simulated vs. (trial)
Event

Dabigatran 110mg Dabigatran 150mg

Warfarin

Non-disabling stroke

0.52 (0.50)

0....
Main results
Strategy

Warfarin

Stroke or systemic embolism

0.241

Dabigatran
110mg
0.208

MI
Major bleed
(including IC...
Cost effectiveness acceptability curve
Results: subgroups
Sub-group

ICER (£/QALY)

RE-LY population

23 082

CHADS2 score 2

20 207

CHADS2 score ≥3

15 895

Ce...
Summary
• Dabigatran 110mg bid appears to be inferior to 150mg bid in
almost all circumstances
• In our base case analysi...
Other NOACs
• Each pivotal trial compared NOAC with
clinically-dosed warfarin
• Adjusted, indirect comparison
– to assess ...
Methods
• Stroke risk profile of the US atrial fibrillation
population, in terms of CHADS2 scores
• Bucher method of adjus...
Probabilities of treatment with highest net benefit by patient subgroup
Main findings
• Apixaban achieved highest net benefit
(expressed as QALYs), followed by dabigatran,
rivaroxaban then warfa...
What about PGx warfarin?
• Variability in response to warfarin can be partly
explained by genetic polymorphisms in
– CYP2C...
Simulation structure
• Population PKPD model of warfarin used to predict
time below, in and above INR range based on a ran...
Population PKPD model
• From Hamberg et al, CPT 2010;87:727-34 which predicts
INR measurements based on dose, age and gene...
Dosing algorithms
• Loading phase
– To achieve correct INR range as quickly as possible
without over-anticoagulating

• Pr...
Algorithm selection - Example
• Loading dose: 10, 10, 5mg (days 1,2,3)
• Predicted maintenance dose: IWPC algorithms
– A c...
Population PKPD results – INR

time in range (INR 2-3)

time above range (INR >3)
time below range (INR <2)

Open symbols ...
Results
Life extension
(months)

∆QALYs
(95% CR)

PGx warfarin

0.003

0.0031 (0.1649, 0.1327)

Rivaroxaban

1.11

0.0957 ...
Sub-group analysis

Probability of each treatment accruing the largest number of QALYs
Economic results
• Rivaroxaban was dominated as a treatment
option by both dabigatran and apixaban
• Dabigatran was extend...
Simulation Time in INR range

Genotype-guided group
Control group
Potential applications
•
•
•
•
•

Early estimation of cost-effectiveness
Cost effectiveness estimates in specific populati...
A natural extension to MBDD

Disease model

Drug model

• Biology
• Biomarker / outcome
relationship
• Natural progression...
Acknowledgements
• MRC funding (NW HTMR)
• Munir Pirmohamed, Steven Lane (University
of Liverpool)
• Joshua Pink (formerly...
Dyfrig hughes che presentation 15 1 2014
Dyfrig hughes che presentation 15 1 2014
Dyfrig hughes che presentation 15 1 2014
Dyfrig hughes che presentation 15 1 2014
Dyfrig hughes che presentation 15 1 2014
Dyfrig hughes che presentation 15 1 2014
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Dyfrig hughes che presentation 15 1 2014

  1. 1. Mechanism-based pharmacoeconomic modelling Professor Dyfrig Hughes Centre for Health Economics & Medicines Evaluation
  2. 2. Empirical modelling • Data collection • Specify correlation structure between variables • Use a numerical technique to find parameters for the structure such that the correlation between the data is maximised • Validate the model with external data
  3. 3. Mechanism-based modelling • Use fundamental knowledge of the interactions between variables to define the model structure • Perform experiments to determine the parameters of the model • Collect data from the process to validate the model
  4. 4. Principles of pharmacology • Pharmacokinetics – the study of drug disposition in the human body • Pharmacodynamics – the study of the pharmacological effects of drugs and the mechanisms of their actions
  5. 5. Pharmacokinetics (PK) – Absorption – Distribution – Metabolism – Excretion
  6. 6. Plasma concentration (mg/l) Population PK Predicted plasma concentration (mg/l)
  7. 7. Pharmacodynamics (PD) Emax D E n n EC50  D n   Sigmoidal Emax model that describes the ‘dose response curve’ derived from the receptor occupancy theory
  8. 8. PK-PD model
  9. 9. Time (months)
  10. 10. Clin Pharmacokinet 2008; 47 (7): 417-448
  11. 11. Applications • Dose selection • Dosing schedules • Dosing in special populations – Renal impairment – Genetic polymorphism in metabolising enzymes – Children • Study of drug interactions • Model-based drug development – Clinical trial simulations – Informing decisions along the drug development pathway
  12. 12. Aims • To develop the methods for linking PKPD models with economic evaluations • To conduct proof of concept studies
  13. 13. Case study 1: Rituximab Lewis Sheiner Prize, PAGE 2011, Athens
  14. 14. Economic models
  15. 15. Integrate PKPD with PE models • Population PK and PD models from Ternant – Br J Clin Pharmacol 2009; 68: 561-73 PK equations PD equation
  16. 16. Maintenance therapy Trial-based results Simulation results First line therapy
  17. 17. Conclusions
  18. 18. Case study 2: NOACs • Newer oral anticoagulants (dabigatran, rivaroxaban, apixaban) set to revolutionise management of patients with atrial fibrillation • Meanwhile, pharmacogenetic-guided warfarin dosing is an improvement on clinically dosed warfarin • Aim to assess comparative cost-effectiveness
  19. 19. Key results from pivotal trials • Primary outcome of stroke or systemic embolism – superiority of dabigatran (1.11% v 1.71% /year; p<0.001) and – apixaban (1.27% v 1.60% /year; p=0.01) and – non-inferiority of rivaroxaban (2.1% v 2.4% /year; p=0.12) • Rates of major bleeding not significantly different between dabigatran 150mg and warfarin or between rivaroxaban and warfarin, but apixaban associated with a lower risk of major bleeding (2.13% v 3.09% per year; p<0.001)
  20. 20. Economic model - DES Generate patient characteristics: Age, risk factors, prior event history Duplicate patient across the arms of the evaluation Simulate next patient event (e.g. stroke, MI, discontinuation) Calculate costs, utilities for that event and the time between events Update patient characteristics, health state, current medication
  21. 21. Clinical data • Where possible, clinical data were taken from the RE-LY study and the FDA submission based on it: – Initial patient characteristics – Event rates for strokes, bleeds, PE, TIA, MI, CHF, vascular death, adverse events whilst on warfarin or dabigatran – Discontinuation rates • Mortality from non-vascular causes, and incidences of hypertension and diabetes taken from UK population data • Event rates for aspirin come from a published meta-analysis comparing warfarin and aspirin Ann Intern Med 2007;146:857-67
  22. 22. Utility data • Utility decrements related to AF and other clinical events taken from a published index of utility scores: – Strokes, bleeds, TIA, PE and MI all lead to a temporary loss of utility – Strokes, MI and ICH also lead to a permanent loss of utility • Utility losses from taking warfarin and aspirin come from a published cost-utility study comparing the two treatments, and an assumption was made that the utility loss associated with dabigatran was the same as that for aspirin Med Decis Making 2006;26:410-20
  23. 23. Cost data • Costs of outcome events are all taken from NHS reference cost data, following the methodologies of previously published HTA submissions • All events come with a one-off cost; strokes and MIs also have costs that continue over time • Costs for warfarin therapy were derived from a warfarin pharmacogenetics study • List price of dabigatran Pharmacogenet Genomics 2009;19:800-12
  24. 24. Results: simulated vs. (trial) Event Dabigatran 110mg Dabigatran 150mg Warfarin Non-disabling stroke 0.52 (0.50) 0.36 (0.37) 0.58 (0.58) Disabling stroke Myocardial Infarction Pulmonary embolism Major bleed 0.94 (0.94) 0.66 (0.66) 1.00 (1.00) 0.72 (0.72) 0.74 (0.74) 0.52 (0.53) 0.11 (0.12) 0.14 (0.15) 0.09 (0.09) 2.72 (2.71) 3.10 (3.11) 3.38 (3.36) Minor bleed Death – vascular cause 13.14 (13.16) 14.85 (14.84) 16.39 (16.37) 2.43 (2.43) 2.27 (2.28) 2.70 (2.69)
  25. 25. Main results Strategy Warfarin Stroke or systemic embolism 0.241 Dabigatran 110mg 0.208 MI Major bleed (including ICH) Life years 0.061 0.072 0.073 0.331 0.318 0.361 10.851 10.940 11.051 QALYs 6.416 6.484 6.536 Total cost £6,480 £10,529 £9,850 Dabigatran 110mg bid dominated by dabigatran 150mg bid Dabigatran 150mg versus warfarin Incremental cost-effectiveness ratio: £23,082 Dabigatran 150mg 0.174
  26. 26. Cost effectiveness acceptability curve
  27. 27. Results: subgroups Sub-group ICER (£/QALY) RE-LY population 23 082 CHADS2 score 2 20 207 CHADS2 score ≥3 15 895 Centres’ time in therapeutic range ≥65.5% 42 386 Centres’ time in therapeutic range <65.5% 20 396 Creatinine clearance <30-50 mL/min 18 647 Previous stroke or TIA 17 286 Vitamin K antagonist naive 22 517 Age ≥75 years 17 857
  28. 28. Summary • Dabigatran 110mg bid appears to be inferior to 150mg bid in almost all circumstances • In our base case analysis, dabigatran 150mg bid is approximately on the boundary of being cost-effective • Cost-effectiveness varies considerably based on the patients’ stroke risk and the quality of INR control • Uncertainty in stroke rates is the largest single source of uncertainty
  29. 29. Other NOACs • Each pivotal trial compared NOAC with clinically-dosed warfarin • Adjusted, indirect comparison – to assess relative benefits and harms, and help guide treatment selection
  30. 30. Methods • Stroke risk profile of the US atrial fibrillation population, in terms of CHADS2 scores • Bucher method of adjustment for indirect comparisons among trials • Probabilities of treatment discontinuation • Utility scores from the US Medical Expenditure Panel Survey of several thousand patients
  31. 31. Probabilities of treatment with highest net benefit by patient subgroup
  32. 32. Main findings • Apixaban achieved highest net benefit (expressed as QALYs), followed by dabigatran, rivaroxaban then warfarin • No subgroup in which the probability of apixaban being the most effective is below 50%, and none where the probability of warfarin being the most effective is above 5%
  33. 33. What about PGx warfarin? • Variability in response to warfarin can be partly explained by genetic polymorphisms in – CYP2C9 , VKORC1 • People with variant alleles are at an increased risk of over-anticoagulation and bleeding • Dosing algorithms based on PGx may result in better INR control, and hence better clinical outcomes • No RCTs comparing PGx-warfarin with NOACs • Multiple dosing algorithms possible
  34. 34. Simulation structure • Population PKPD model of warfarin used to predict time below, in and above INR range based on a range of algorithms (NONMEM) • Data from a systematic review used to link time in range to clinical endpoints • Health outcome model used to extrapolate to a lifetime horizon and compare different treatments in terms of QALYs accrued – Based on discrete event simulation described earlier
  35. 35. Population PKPD model • From Hamberg et al, CPT 2010;87:727-34 which predicts INR measurements based on dose, age and genetic information • Patient characteristics based on those of the UK atrial fibrillation population • Model allows for explicit incorporation of non-adherence
  36. 36. Dosing algorithms • Loading phase – To achieve correct INR range as quickly as possible without over-anticoagulating • Predicted maintenance dose – To predict the most likely dose to maintain a patient in range in the long term • Maintenance phase – Further dose adjustments are made based on INR at clinic visits • Genetic information can be used in each stage
  37. 37. Algorithm selection - Example • Loading dose: 10, 10, 5mg (days 1,2,3) • Predicted maintenance dose: IWPC algorithms – A clinical algorithm based on age, height, weight, ethnicity, use of amiodarone and enzyme inducers – A pharmacogenetic algorithm which uses all these variables and genetic information • Doses adjusted with the Fennerty algorithm
  38. 38. Population PKPD results – INR time in range (INR 2-3) time above range (INR >3) time below range (INR <2) Open symbols (dashed lines) clinical algorithms Filled symbols (solid lines) pharmacogenetic algorithm
  39. 39. Results Life extension (months) ∆QALYs (95% CR) PGx warfarin 0.003 0.0031 (0.1649, 0.1327) Rivaroxaban 1.11 0.0957 (-0.0510, 0.2431) Apixaban 2.06 0.1298 (-0.0290, 0.2638) Dabigatran 1.47 0.1065 (-0.0493, 0.2489) All compared with warfarin dosed according to clinical algorithm
  40. 40. Sub-group analysis Probability of each treatment accruing the largest number of QALYs
  41. 41. Economic results • Rivaroxaban was dominated as a treatment option by both dabigatran and apixaban • Dabigatran was extendedly dominated by apixaban • ICER for genotype guided warfarin versus clinical algorithm dosed warfarin was £13,226 per QALY gained, and the ICER for apixaban versus genotype guided warfarin therapy was £19,858 per QALY gained
  42. 42. Simulation Time in INR range Genotype-guided group Control group
  43. 43. Potential applications • • • • • Early estimation of cost-effectiveness Cost effectiveness estimates in specific populations Inform pricing decisions Inform pricing decisions for different doses Assessing impact of inter-patient variability and protocol deviations on cost-effectiveness • Inform protocol design – Value of information analysis to quantify the value of future research in reducing parameter uncertainty
  44. 44. A natural extension to MBDD Disease model Drug model • Biology • Biomarker / outcome relationship • Natural progression • Pharmacokinetics • Pharmacodynamics • Co-variate effects Population model Health outcomes modelling • Patient demographics • Drop-outs • Adherence • Health state utilities • Benefit-risk assessment • Economic appraisal Milligan et al, CPT 2013 doi:10.1038/clpt.2013.54
  45. 45. Acknowledgements • MRC funding (NW HTMR) • Munir Pirmohamed, Steven Lane (University of Liverpool) • Joshua Pink (formerly Bangor University)

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