Joseph C. FleishakerVice President

Clinical ResearchPfizer

The OutlineWhy modeling and simulation?M&S in Drug Discovery

Target selection/validation Systems Pharmacology/Biology

Compound selection Potency ADME

M&S in DevelopmentPharmacokineticsPK/PDClinical Trial Simulation

Conclusions

Why M&S?“Use all the data, all the time, everywhere.” Roy

Bullingham, Clinical Pharmacology, Pharmacia“It’s all part of the same experiment.” Sandy

Allerheilgen, Merck, referring to any new drug development program.

Really about using all of the available data to informTarget selectionCompound SelectionDose selectionStudy design

Everybody brings their own model

The trick – using one modelModel provides

Common frame of referenceUtilizes everybody’s models and assumptionsPuts everything on the tableAllow all information to be combined, including new data

that’s generatedSimulation allows

Assumptions to be testedWhat if scenarios to be testedMultiple dry-runs to be done in silicoReduce animal experimentationReduce clinical study burden

Target Selection

Source: J.E. Dumont, Cross Signaling, Cell Specificity, and Physiology Am. J. Physiology, Cell. Physiology. Vol. 283, Issue 1, C2-C28, July 2002Source: J.E. Dumont, Cross Signaling, Cell Specificity, and Physiology Am. J. Physiology, Cell. Physiology. Vol. 283, Issue 1, C2-C28, July 2002

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Critical nodes in signaling pathways: insights into insulin action – 2006Cullen M. Taniguchi, Brice Emanuelli and C. Ronald KahnNATURE REVIEWS MOLECULAR CELL BIOLOGY FEBRUARY 2006

Assessing the Impact of Predictive Biosimulation on Drug Discovery and DevelopmentJournal of Bioinformatics and Computational ChemistryAuthor: S. Michelsonvol 1 (1): 169-177

The Roles of Cells and Mediators in a Computer Model of Chronic Asthma International Archives of Allergy ImmunologyAuthor: A.K. Lewis, T. Paterson, C.C. Leong, N. Defranoux, S.T. Holgate, C.L. Stokes Vol. 124:282-286

Uses of M&S for Target SelectionSynthesize available knowledgeGain disease insights

Lack of sole role for eosinophils in airway inflammation

Explained failure of IL-5 antibodyUtilize to assess role of other targets in treating

asthmaUpdate model when node in the pathway has

been assessedEnrich the collection of validated targets to

prosecute in drug development.

Compound SelectionReceptor BindingADME Properties

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Structural Interaction Fingerprints – Uses for Modeling and Simulation

Target-focused Virtual Library generation

J. Med. Chem.; 2006; 49(2); 490-500

14

Binned cHLMG_01_CLIA

x ≤ 20

20 < x ≤40

40 < x

x ≤ 20 20 < x ≤ 40 40 < x

80 31 1

198

14

2 12

290

Calculated HLM CLint

Mea

sure

d H

LM C

Lint

• 95% prediction of all unstable compounds, and• 80% for compounds not in training set

In Silico Model for HLM has been Useful in the Design of Program X Analogs

• 70% prediction of all stable compounds, and• 60% for compounds not in training set

• Compounds predicted to be stable were stable 95% of the time, and• 85% of the time for compounds not in training set

In training set

M&S in Compound SelectionIt’s about enriching the collection of

compounds that is likely to yield a successful compound

It’s about time; electrons move faster that lab scientists

It’s not about finding the oneUse all available information to inform model

PKPK/PD and Clinical Trial SimulationCommercial Assessment

PK – SimCyp Physiological based PK

modelUsed for

Predicting PK in humansSimulating drug-drug

interactionsSpecial population PKEffects of formulation on

PK

M&S PKUseful for final compound selectionInitial guide to dose selection in first in

human studiesGuide clinical studies needed in drug

development

Estimating Dose-Response in Humans Using Pre-Clinical DataExperience with Gabapentin, Pregabalin and Related Compounds(Lockwood et al. Pharm Res 2003;20:1752-9)

StepsStep 1: Estimate preclinical pregabalin/gabapentin

potency ratio

Step 2: Develop drug/disease model for gabapentin pain scores using previous Phase 2/3 trials

Step 3: Apply potency ratio and gabapentin drug disease model to pregabalin

Step 4: Simulate expected response for different clinical trial designs

Step 1: Estimate Relative Potency of Pregabalin and Gabapentin

UNCERTAINTY in Potency Ratio EstimateRange = 2-4, best guess = 3

Pre-Clinical Data

gabapentin/pregabalin

EC50

ED50

•Range of pre-clinical models

•Receptor binding data

945-295

Time (Days)

0 2 4 6 8 10 12 14 16 18 20 22 24 26 28 30 32 34 36 38 40 42 44 46

Me

an

Pa

in S

co

re

-2.6

-2.4

-2.2

-2.0

-1.8

-1.6

-1.4

-1.2

-1.0

-0.8

-0.6

-0.4

-0.2

0.0 Placebo (Observed)1800 mg Daily (Observed)2400 mg Daily (Observed)Placebo (Predicted)1800 mg Daily (Predicted)2400 mg Daily (Predicted)

945-295

Time (Days)

0 2 4 6 8 10 12 14 16 18 20 22 24 26 28 30 32 34 36 38 40 42 44 46

Me

an

Pa

in S

co

re

-2.6

-2.4

-2.2

-2.0

-1.8

-1.6

-1.4

-1.2

-1.0

-0.8

-0.6

-0.4

-0.2

0.0 Placebo (Observed)1800 mg Daily (Observed)2400 mg Daily (Observed)Placebo (Predicted)1800 mg Daily (Predicted)2400 mg Daily (Predicted)

)1()*(

V

Clt

avg eCl

DoseC

PK from Phase 1

• Relative potency

• Based on pre-clinical data

Assumption: Similar Concentration-Response Shape

BaseR

Baseline pain score

))exp(1(1( tkPlm pl

Placebo effect

)

50

max

nnavg

navg

ECC

CE

Drug effect

What Can We Do With This Model?Run “virtual trials” - determine if a given study

design is informative# doses, # subjects, etc

Test what happens if the assumptions we make are not correct (model uncertainties)?Assumptions about the biology

(potency of drug, toxicity, disease progression)Assumptions about the trial

(medication non-compliance, dropouts)What if…..A good study design will answer the key question(s)

even when our assumptions are not quite right

Impr

ovem

ent f

rom

pla

cebo

0 200 400 600 800

-0.5

0.0

0.5

1.0

1.5

2.0

2.5

Distribution of Model Predicted Trial Outcomes

0.050.10.2

0.5

0.80.90.95

Pregabalin dose (mg/day)

Impr

ovem

ent f

rom

pla

cebo

0 200 400 600 800

-0.5

0.0

0.5

1.0

1.5

2.0

2.5

Actual Trial Results and Predicted Outcome Distribution

0.050.10.2

0.50.80.90.95

Pregabalin dose (mg/day)

M&S PK/PD - Impact on Drug Development

Pre-clinical PK-PD models were “validated” by correctly predicting dose-efficacy relationship in clinical trialsvaluable information for pre-clinical

pharmacologyProvided more confidence in making

decisions based on PK-PD data with this drug class and disease

Combining diverse data to obtain overall view of drug value

Clinical Utility Index (CUI)CUI is an integrated measure of benefit/risk CUI is determined as a function of clinically/

commercially relevant endpoints for an optimal sleep compound

Weights assigned based on quantitative market research

CUI can be defined over entire dose range

Ouellet et al.,Clinical Pharmacology & Therapeutics (2009); 85, 3, 277–282

# Night Wakings

Stages III & IV

Total Sleep Time

L-T Tolerance

Morning Refreshment

Latency (subj)

WASORebound Insomnia

L-T IndicationLatency (obj)

Unscheduled

Daytime Vitality

Abuse Potential

Hangover

How Metabolized

Sleep Quality

Memory Impairment

Dizziness

Wt Gain

Nausea

Derived Importance

Sta

ted

Im

po

rtan

ceHigh

Low

Low High

Size of bubble reflects relative importance based on hybrid conjoint.

EfficacySafety/Side Effect

*

Hybrid Conjoint Model

Ouellet et al.,Clinical Pharmacology & Therapeutics (2009); 85, 3, 277–282

Based on team discussion, clinical difference (normalize different scales), weights determined based on desired attributes

Calculation of CUI

CUI - Attribute Clinical Diff Weight

Residual Effect (LEEDS) 5 points 35%

WASO 25 min 25%

Quality 20 points 17%

LPS 15 min 13%

Sleep Architecture (Stage 1, Stage 3-4) 5% 10%

Ouellet et al.,Clinical Pharmacology & Therapeutics (2009); 85, 3, 277–282

Median CUI and 80% CIMedian CUI and 80% CI

DOSE

CU

I

0 20 40 60 80 100

-0.4

-0.2

0.0

0.2

0.4

0.6

0.8

Ouellet et al.,Clinical Pharmacology & Therapeutics (2009); 85, 3, 277–282

PD-200390 vs PD-299685

PD 0299685 Dose (mg)

0 10 20 30 40 50

CU

I

-0.8

-0.6

-0.4

-0.2

0.0

0.2

0.4

0.6

0.8

PD 0200390 Dose (mg)

0 20 40 60 80 100

PD 0200390

PD 0299685

Ouellet et al.,Clinical Pharmacology & Therapeutics (2009); 85, 3, 277–282

M&S in Commercial AssessmentAssess the contribution of multiple factors in

compound attractivenessAllows decisions regarding compound

progression to Phase III

ConclusionsModeling and simulation are applicable to all

phases of drug developmentUsing all the data, all the time, everywhereEarly, it’s about enrichment

Target spaceCompound Space

Later, it’s aboutGuiding clinical developmentTrial designDecision making