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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-

12

% 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)