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Joseph C. FleishakerVice President
Clinical ResearchPfizer
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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
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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
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Everybody brings their own model
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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
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Target Selection
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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
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Assessing the Impact of Predictive Biosimulation on Drug Discovery and DevelopmentJournal of Bioinformatics and Computational ChemistryAuthor: S. Michelsonvol 1 (1): 169-177
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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
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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.
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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
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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
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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
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PKPK/PD and Clinical Trial SimulationCommercial Assessment
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PK – SimCyp Physiological based PK
modelUsed for
Predicting PK in humansSimulating drug-drug
interactionsSpecial population PKEffects of formulation on
PK
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M&S PKUseful for final compound selectionInitial guide to dose selection in first in
human studiesGuide clinical studies needed in drug
development
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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
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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
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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)
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)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
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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
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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)
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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)
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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
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Combining diverse data to obtain overall view of drug value
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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
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# 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
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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
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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
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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
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M&S in Commercial AssessmentAssess the contribution of multiple factors in
compound attractivenessAllows decisions regarding compound
progression to Phase III
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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