Transcript MS fda workshop MRoessner

Modeling and Simulation:
Tool for Optimized Drug
Development
Martin Roessner
Biostatistics sanofi aventis
Bridgewater, NJ
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Outline
Background
Modeling and Simulation (M&S) approach
Clinical Utility Index (CUI)
Example: SERM
Conclusion
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Industry challenge
Drug development process not much changed over the last 25
years
Drug development cost continue to increase ($802 Mill +)
Time to market, attrition rates and the number of late stage
failures remain unchanged
The industry needs to radically rethink the drug development
process to remain competitive
The industry needs to work smarter not harder
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Modeling and Simulation is a tool for quantitative
decision-making
It is a methodology that uses mathematical/statistical models
and simulations in a predictive manner
–
Preclinical Information
–
PK/PD data
–
Dose response information
–
Clinical outcome data
(safety/efficacy)
–
–
Prior information: Historical
data, information on related
compounds, SBOAs, EPARs,
etc.
Marketing and Financial
projections
M&S provides an integrated framework to use this information
to optimize the drug development process
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Implementation of M&S
Development and broad adoption of M&S will help
create value
Benefits
Optimized development strategies
Early termination of unpromising compounds
Reduction in late stage attrition
Shorter development time earlier to approval and launch
Increase number of drugs to market
Enhanced labeling
More accurate and dynamic risk assessment along the
development
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Integrated modeling and simulation can be used any time
there is an important question impacting project value
“Is it worth
developing a new
dosage form?”
“What’s the best
dose and
schedule?”
“What is the optimal
patient population for
this drug?”
“Is this treatment
likely to be as good as
the competitors?”
“What are the most
important attributes
of a 2nd generation
compound?”
“What’s the probability of
success in Phase 3?”
“Should we in-license
this compound?”
“Should we continue
this development
program?”
“Is there a clinical
trial design that will
show PoC and find
the best dose?”
“Which indication
should we go into first
to maximize the value of
the program?”
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A modeling approach to decision-making involves
integration of information from a number of sources
Clinical and Preclinical Data
Physician Market
Research
Exploratory Data
Analysis
Clinical Utility Model
Efficacy DoseResponse Model
Safety DoseResponse Model
Simulation
Integration
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A modeling approach to decision-making involves
integration of information from a number of sources
Clinical and Preclinical Data
Physician Market
Research
Exploratory Data
Analysis
Clinical Utility Model
Efficacy DoseResponse Model
Safety DoseResponse Model
Simulation
Integration
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Clinical Utility Index (CUI) - a metric for the benefit of
treatment to the patient (1)
Every drug has benefits and risks.
The relative importance of these characteristics
depend on the disease the drug is intended to treat
They also change with dosage, patient population,
etc.
Trade-offs must often be made among the drug
effects comprising the product profile, balancing the
benefits and risks.
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Clinical Utility Index (CUI) - a metric for the benefit of
treatment to the patient (2)
The CUI quantifies trade-offs by providing a single
metric for the multiple dimensions of benefit and
risk.
It is…
a systematic approach to understand subjective preferences
a transparent way of weighing tradeoffs
knowledge-driven; available data are used; if not available,
rely on expert opinion
closely related to the Target Product Profile
It is not …
an “objective” measure in the sense of a physiological
measurement
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The framework for the CUI is elicited from the project
team; when combined with models of response, it
provides a relative estimate of the patient benefit
Identify Metrics
and Relevant
Response
Levels for each
Attribute
Assign
Preference
Values for each
Response Level
CUI
Framework
CUI Distributions for
Competing Treatments
1
P(CUI < X)
Identify Critical
Treatment
Attributes and
Relative Weights
TreatmentResponse
Models
A
E(CUIA )
E(CUIB )
B
0
Probability of
Individual
Attribute Levels
Estimated
Product
Profile
CUI
Here, treatment B is
expected to be superior to A
Expert Opinion
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Example
SERM, a Selective Estrogen Receptor Modifier for the Treatment
of Osteoporosis in Post-Menopausal Women
Two Phase II studies:
1. Placebo, SERM (2.5mg, 10mg, 50mg) and Raloxifene, n=118
2. Placebo, SERM (0.5mg, 5mg) n=79
Primary efficacy endpoint was % change from baseline U-CTX
Included additional safety and activity endpoints
How does the efficacy, safety and tolerability of SERM compare with
its major competitor drug and at which dose
Explorative analysis
Clinical Utility Index (CUI)
Simulation results and sensitivity analysis
Is it worthwhile to continue development
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Possible responses and their clinical value for each
attribute were defined
Attribute
Responses
Preference Ratio
Worse than Raloxifene
Equivalent to Raloxifene
Better than Raloxifene
1
10
20
Endometrial
Proliferation
Worse than Raloxifene
The same or better than Ralox.
1
30
Endometrial Lining
Thickness
Worse than Raloxifene
The same or better than Ralox
1
5
Smaller effect on LDL than Ralox
Same or larger effect on LDL vs. Ralox.
Same effect on LDL + effect on HDL
1
7.5
15
…..
…..
Presence of food effect
Absence of food effect
1
2
Efficacy on Bone
Cardiovascular
…..
Food Effect on PK
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Important attributes were ranked and their importance
weighted
Attribute
Rank
Rating
Relative Weight
Efficacy on Bone
1
100
0.27
Endometrial Proliferation
1
100
0.27
Endometrial Lining Thickness
3
50
0.14
Thromboembolic Disease
4
40
0.11
Hot Flashes
5
30
0.08
Breast Tenderness
6
15
0.04
Cardiovascular
7
10
0.03
Muscle Cramps
7
10
0.03
Atrophic Vaginitis
7
10
0.03
Food Effect on PK
10
5
0.01
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Models of dose-response provided estimates of attribute
level and uncertainty in these estimates
Dose-Response for Urinary CTX
% Difference from Placebo
Baseline-adjusted week-12
(measure of bone turnover)

Clear dose response

Log-Linear model adequately describe
available data
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Major Result: There was no dose for which SERM was
expected to be considered equivalent or superior to
Raloxifene
60
CUI for Raloxifene
50
Clinical Utility Index
Based on CUI and
simulated drug response
40
30
20
10
0
0.25
0.5
1
2.5
5
10
SERM Dose (mg)
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What if…….
SERM
60
80
similar to Raloxifene
i.e. no endometrial
proliferation
20
40
Raloxifene
0
Clinical Utility Index
100
If SERM did not cause endometrial proliferation, available data support
effects of SERM would be similar or better at doses of 1 mg and higher
0.25 mg
0.5 mg
1 mg
2.5 mg
5 mg
10 mg
SERM Dose
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Impact: Further development of SERM was halted,
saving $50-100M in development costs
SERM fails to show equivalent clinical utility to
Raloxifene at all doses examined
“Based on that simulation, ‘we stopped funding development of the
compound,’ says Frank Douglas… the ratio between the therapeutic
benefit and the side effect demonstrated that this [compound] was not as
beneficial as Evista.’ … Douglas estimates that the … computer
model … saved the company $50 million to $100 million, the cost of
later-stage clinical trials. ‘We also avoided exposing a lot of women to a
drug that ultimately would have failed,’ he adds. ‘And we were able to
switch to another project with a greater chance of success.’ “
—Forbes 10/7/02
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Conclusion
Industry needs to operate smarter
M&S provides a framework to optimize drug
development at various levels
Clinical Utility Index can be used to assess the
potential success of a product in the market
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Acknowledgement
B. Korsan, K. Dykstra, T.J. Carrothers (Pharsight)
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