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. 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. 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
12. 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
13. Aims
• To develop the methods for linking PKPD
models with economic evaluations
• To conduct proof of concept studies
14. Case study 1: Rituximab
Lewis Sheiner Prize, PAGE 2011, Athens
20. 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
21.
22. 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)
23. 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
24. 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
25. 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
26. 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
30. 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
31. 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
32. 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
33. 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
37. 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%
38. 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
39. 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
40. 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
41. 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
42. 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
43. 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
44. 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
47. 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
49. 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
50. 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
51. Acknowledgements
• MRC funding (NW HTMR)
• Munir Pirmohamed, Steven Lane (University
of Liverpool)
• Joshua Pink (formerly Bangor University)
