Modeling and Simulationin

Drug Development

Presented to the

Pharmaceutical Sciences Advisory Committee

Michael D. Hale, Ph.D.

Glaxo Wellcome

November 16, 2000

2

Knowledge Generation Cycle

Experiment Experiment Observe Observe Collect Data Collect Data

SummarizeSummarizeAnalyzeAnalyzeInterpretInterpret

ModelModelSimulateSimulatePredictPredict

PharmaceuticalCompaniesDo These

PharmaceuticalCompaniesDo These

FDA Reviews,Evaluates,

Approves orRejects

FDA Reviews,Evaluates,

Approves orRejects

The Grey Zone:Used mostly bypharmaceuticalcompanies forinternal decisions

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Modeling & Simulation

• Modeling and Simulation are a technical articulation of our understanding (current state of knowledge; beliefs; accepted science)

Within the context of our science / premises,

• Modeling is the framework / rationale for explaining existing data

• Simulation expresses our expectation of behavior where data are lacking (or sparse)

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Why Modeling & Simulation?

We believe they enable us to provide

• improved patient therapy– greater beneficial effect– better safety– dosing convenience & “robustness”

• more effective and efficient use of limited resources– reduce costs of producing innovative medicines– faster product introduction without compromise

Part I

Modeling & Simulation

in

Drug Development

6

Primary Areas of Use

• Pre- and Non-Clinical

• First time in Humans

• “Specialty” studies

• Proof of Concept

• Phase III

• Post Marketing

Basically, we use it about everywhere!

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Pre-Clinical & Non-Clinical

• blood clotting cascade model

• receptor-ligand-inhibitor interactions

• receptor multimeric state models

• viral resistance emergence• ADME

– library design– selection for in-vitro, in-vivo

testing or screening– property/physiochemical

relationships

• E. coli strain optimization• process capability /

product specifications• product quality (process

Vs product characteristics)• stability• analytical method

development

Note: usually considered Private and Proprietary, with notable exceptions

Molecular Descriptorse.g.size, charge, lipophilicity, hydrogen bonding, fragments, fingerprints

ADME Datae.g Pharmacokinetics, Absorption, Metabolism, Brain Penetration etc

BUILD ADME MODELS

Statistical & Mathematical e.g. Multiple linear regression,principal component and cluster analysis, cellular automata, calculus, decision trees

CHEMISTS / DRUG DISCOVERY PROJECTSLibrary Design/Understand Physchem Relationships etc

Information

GlobalDatabase

ADME In-silico Modelling Process

Thanks to Anne Hersey & team for this slide!

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“Early” Clinical Development

• Selecting drug dose– amount– frequency

• PK sampling schedule

• Expected beneficial effect & safety*

• Comparison with competitors (internal and external)

*Note: safety is generally much more difficult to model, as an “all comers” situation, whereas effectiveness is targeted & better defined

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Basic Concept: A Sample “Chaining” of Models

D oseD rugC onc

B ioM arker1

B ioM arker2

C lin ica lR esponse

M arketValue

Example: Concentration/Safety Relationship Early Evaluation

1 10 100 1000

0.8

1.0

1.2

1.4

1.6

1.8

2.0

2.2

2.4

2.6

2.8

3.0

3.2

3.4

Observed dose1 in man

Observed dose2 in man

Modeled effectcurve in man(previouslystudied drug)

Simulated effect curvein man from rat model(candidate drug)

Sa

fety

ma

rke

r

Conc (ng/mL)

Thanks to Misba Beerahee & team for this slide!

Undesirable Effect Level

Evaluation of Dose & Drug t1/2 for Effect

10 100 1000

0

20

40

60

80

100

Simulated Dose-Response curves for a drug attwo possible elimination half-lives in man

6h half-life12h half-life

% o

f Max

incr

ease

in E

ffect

Daily Drug dose (mg)

Thanks to Paul Mudd & team for this slide!

t1/2 impact

dose impact

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“Late” Clinical Development

• Dose optimization

• Extension to other populations

• Alternative dosing regimens

• Optimal times and measures for evaluation

• Individualization of therapy?

• Examination of statistical tests (particularly in complex situations)

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Example: Simulation comparing therapeutic drug monitoring with fixed dosing

Simulation indicated proportion of patients benefiting from TDM, and degree of benefit

(unpublished, done to support investigator trial)

Individual Patient Benefit via Hale TDMAlgorithm Compared to Fixed Dose

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

0% 10% 20% 30% 40% 50% 60% 70% 80%

Patient at Population Percentile for AUC

% R

isk

Re

du

cti

on

Algorithm

Fixed

Action Limits at 20%, 40%, 85%, 95%

Pro

bab

ilit

y o

f B

enef

icia

l E

ffe

ct

Part II

Why Might a Rational Person

be Skeptical About

Modeling and Simulation?

Issues, and a way forward...

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Why Not Simulate?

Three legitimate reasons to challenge a modeling and simulation project

• Assumptions (Premises)

• Implementation

• Interpretation

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Premises (Two Classes)

Verifiable

• Accepted theory & models

• Supporting data

• Compelling plausibility

Subject of a Simulation Study

• Study “factor”

• “Noise”

ExpertsAgree

ExpertsDisagree

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Premises (Two Classes)

Verifiable

• Accepted theory & models

• Supporting data

• Compelling plausibility

Subject of a Simulation Study

• Study “factor”

• “Noise”Migrate on Disagreement

MoreCertain

LessCertain

Gain Expert Agreement with more Data, Knowledge, Advancement of Science,

Experience, etc.

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Implementation

Primary Question:

Do the modeling & simulation plan and software faithfully embody the premises?

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Towards Confidence in Implementation

General Framework• “Standard” accepted

tools• Validated model

libraries• Traditional software

validation• Benchmark case

studies

Specific Application• Credibility of

implementers• Independent review

– peer presentation– 3rd party?– line-by-line?

i.e., was “validated” system used properly?

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Interpretation

• Requires collaboration of subject matter and statistical expertise (& simulation expertise?)

• Relevance

• Scope

• Interpolation or extrapolation?

• Precedence

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Summary: Why Not Simulate?

Legitimate reasons to challenge a modeling and simulation project

• Assumptions (Premises)

• Implementation

• Interpretation

Hale Claims:

– Manageable. Negotiate shareholder agreement“up front”. *Excessive rigor could dilute value

– Achievable, with intensive ongoing effort (standardized libraries will help)

– Perhaps no more difficult than interpretingclinical trials?

Part III

Published Perspectives

on

Modeling & Simulation

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Recent Clinical References

• Review “Simulation of Clinical Trials” – Holford, Kimko, Monteleone, & Peck in Ann. Rev.

Pharmacol. Toxicol 2000. 40:209-34

• Review “PK/PD Modeling in Drug Development” – Sheiner and Steimer in Ann. Rev. Pharmacol. Toxicol

2000. 40:67-95

• July 25, 2000 FDA Antiviral AC on PK/PD– see http://www.fda.gov/ohrms/dockets/ac/cder00.htm#Anti-Viral

• “Good Practices” conference & consensus paper – see http://www.dml.georgetown.edu/cdds/sddgp723.html

• FDA Guidance on Population Pharmacokinetics

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Relevant (?) FDA Guidance

The May 1998 “Providing Clinical Evidence of Effectiveness for Human Drug and Biological Products” has goals

– “to articulate its current thinking concerning the quantitative and qualitative standards for demonstrating effectiveness of drugs and biologics.”

– “to encourage the submission of supplemental applications to add new uses to the labeling of approved drugs.”

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Effectiveness Guidance, pt 2

• “Section 1 addresses situations in which effectiveness of a new use may be extrapolated entirely from existing efficacy studies.”

• “In certain cases, effectiveness of an approved drug product for a new indication, or effectiveness of a new product, may be adequately demonstrated without additional adequate and well-controlled clinical efficacy trials. Ordinarily, this will be because other types of data provide a way to apply the known effectiveness to a new population or a different dose, regimen or dosage form.”

• Examples: pediatric, bioequivalence, modified-release dosage forms, different doses, regimens, or dosage forms

Emphasis is mine

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Do we currently combine premise, data, & analysis for regulatory approval?

• Premise (“equivalent rate & extent” equivalent drug)

plus• Data

(one pharmacokinetic clinical trial)

plus• Statistical analysis

(including decision criteria)

Conclusion equivalent effect & safety(without clinical testing of safety & effectiveness)

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Conclusions

• Modeling and simulation are a natural and necessary part of advancing the scientific understanding of drug characteristics

• Many early uses will be private and proprietary (i.e., perceived as providing competitive advantage, without any effect on patient safety or beneficial effect)

• Combining premise, data, and analysis for agreed conclusions is not new