1 modeling and simulation: tool for optimized drug development martin roessner biostatistics sanofi...
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Modeling and Simulation:Modeling and Simulation: Tool for Optimized Drug Tool for Optimized Drug Development 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 harderThe 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
M&S provides an integrated framework to use this
information to optimize the drug development process
– 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
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Implementation of M&S
Development and broad adoption of M&S will help
create value
Benefits
Optimized development strategiesEarly termination of unpromising compoundsReduction in late stage attritionShorter development time earlier to approval and launchIncrease number of drugs to marketEnhanced labelingMore 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
“What’s the best dose and schedule?”
“Is it worth developing a new dosage form?”
“Is this treatment likely to be as good as the competitors?”
“What’s the probability of success in Phase 3?”
“Should we continue this development program?”
“What is the optimal patient population for this drug?” “Is there a clinical
trial design that will show PoC and find the best dose?”
“What are the most important attributes of a 2nd generation compound?”
“Which indication should we go into first to maximize the value of the program?”
“Should we in-license this compound?”
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A modeling approach to decision-making involves integration of information from a number of sources
Clinical and Preclinical Data
Exploratory Data Analysis
Safety Dose-Response Model
Efficacy Dose-Response Model
Simulation
Physician Market Research
Clinical Utility Model
Integration
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A modeling approach to decision-making involves integration of information from a number of sources
Clinical and Preclinical Data
Exploratory Data Analysis
Safety Dose-Response Model
Efficacy Dose-Response Model
Simulation
Physician Market Research
Clinical Utility Model
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 preferencesa transparent way of weighing tradeoffsknowledge-driven; available data are used; if not available, rely on expert opinionclosely 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
CUI0
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CUI Distributions forCompeting Treatments
A
B
E(CUI )B
E(CUI )A
Here, treatment B isexpected to be superior to A
P(C
UI
< X
)
Identify CriticalTreatment
Attributes andRelative Weights
Identify Metricsand Relevant
ResponseLevels for each
Attribute
AssignPreference
Values for eachResponse Level
CUIFramework
Treatment-ResponseModels
Probability ofIndividual
Attribute Levels
Expert Opinion
EstimatedProductProfile
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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 analysisClinical 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
Efficacy on Bone 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
Cardiovascular Smaller effect on LDL than Ralox
Same or larger effect on LDL vs. Ralox.
Same effect on LDL + effect on HDL
1
7.5
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….. ….. …..
Food Effect on PK Presence of food effect
Absence of food effect
1
2
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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
Bas
elin
e-ad
just
ed w
eek-
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% D
iffer
ence
from
Pla
cebo
Clear dose response Log-Linear model adequately describe
available data
Dose-Response for Urinary CTX(measure of bone turnover)
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Major Result: There was no dose for which SERM was expected to be considered equivalent or superior to Raloxifene
Based on CUI and simulated drug response
SERM Dose (mg)
Cli
nic
al
Uti
lity
In
dex
0.25 0.5 1 2.5 5 10
0
10
20
30
40
50
60
CUI for Raloxifene
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What if…….
0.25 mg 0.5 mg 1 mg 2.5 mg 5 mg 10 mg
02
04
06
08
01
00
SERM Dose
Cli
nic
al U
tili
ty I
nd
ex
Raloxifene
SERM
similar to Raloxifenei.e. no endometrial proliferation
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
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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)