A General Framework for Model-Based Drug Development Using Probability

Metrics for Quantitative Decision MakingKen Kowalski, Ann Arbor Pharmacometrics Group (A2PG)

2

Outline Population Models Basic Notation and Key Concepts

Basic Probabilistic Concepts General Framework for Model-Based Drug

Development (MBDD) Examples Final Remarks/Discussion Bibliography

PaSiPhIC 2012 A2PG

PaSiPhIC 20123

Population ModelsBasic Notation

A2PG

General Form of a Two-Level Hierarchical Mixed Effects Model:

Definitions:

,0~ ),or (e.g., ,

,0~ ,,

Neg

Nhfy

iiiiii

iiiii

i

effects random individual-intra ofmatrix covariance effects random individual-inter ofmatrix covariance

individualfor effects random individual-intra of vector individualfor effects random individual-inter of vector

parameters effects fixed of vector individualfor parameters specific-subject of vector

individualfor nsobservatio of vector

ii

iiy

i

i

i

i

PaSiPhIC 20124

Population ModelsKey Concepts

A2PG

Typical Individual Prediction:

Easy to compute, same functional form as f Population Mean Prediction:

Integral is often intractable when f is nonlinear Typically requires Monte-Carlo integration (simulation)

The typical individual and population mean predictions are not the same when f is nonlinear Cannot observe a ‘typical individual’ Can observe a sample mean

fyE ii 0

iiiii dpyEyE

PaSiPhIC 20125

Basic Probabilistic Concepts Statistical intervals (i.e., confidence and

prediction intervals) Statistical power Probability of achieving the target value (PTV) Probability of success (POS) Probability of correct decision (POCD)

A2PG

PaSiPhIC 20126

What’s the difference between a confidence interval and prediction interval?

A2PG

A confidence interval (CI) is used to make inference about the true (unknown) quantity (e.g., population mean) Reflects uncertainty in the parameter estimates Typically used to summarize the current state of

knowledge regarding the quantity of interest based on all available data used in the estimation of the quantity

A prediction interval (PI) is used to make inference for a future observation (or summary statistic of future observations) Reflects both uncertainty in the parameter estimates as

well as the sampling variation for the future observation

PaSiPhIC 20127

Relationship Between CIs and PIs

A2PG

Confidence Limits for

X

X

Prediction Limits Recognizing Uncertainty

in E( )X

Distribution of sampling variation

Prediction Limits if E( ) Located Here

X

Note: Prediction intervals are always wider than confidence intervals.

PaSiPhIC 20128

Confidence interval for the mean based on a sample of N observations

A2PG

NstX N 1,1 2

Sample mean (parameter estimate)

Standard error of the mean (parameter uncertainty)

PaSiPhIC 20129

Prediction interval for a single future observation

A2PG

22

1,1 2s

NstX N

Sample mean (parameter estimate)

Sample variance of the mean (parameter

uncertainty)

Sample variance of a future observation (sampling variation)

Note: The sample mean based on N previous observations is the best estimate for a single future observation.

PaSiPhIC 201210

Prediction interval for the mean of M future observations

A2PG

Ms

NstX N

22

1,1 2

Sample mean (parameter estimate)

Sample variance of the mean (parameter

uncertainty)

Sample variance of the mean of M future

observations (sampling variation)

Note 1: The sample mean based on N previous observations is the best estimate for the mean of M future observations.Note 2: A prediction interval for M=∞ future observations is equivalent to a confidence interval (see Slide 8). This will also be referred to as ‘averaging out’ the sampling variation.

PaSiPhIC 201211

A Conceptual Extension of Confidence and Prediction Intervals to Population Modeling

A2PG

Measure/Quantity Simple Mean Model Population Model

Parameters , , Ω, Prediction

Sampling VariationParameter Uncertainty*

Confidence Interval See Slide 8Stochastic

simulations with sufficiently large M

Prediction Interval See Slide 10Stochastic

simulations with finite M

X ˆ fy

s

ˆ,ˆ,Cov

I2ˆˆ,ˆ

NssX

* Note for the simple mean model the standard error of the mean does not take into account uncertainty in the sampling variation (s) whereas in population models we typically take into account the uncertainty in Ω and .

PaSiPhIC 201212

Quantifying Parameter Uncertainty in Population Models – Nonparametric Bootstrap

A2PG

Randomly sample with replacement subject data vectors to preserve within-subject correlations to construct bootstrap datasets

Re-estimate model parameters for each bootstrap dataset to obtain an empirical (posterior) distribution of the parameter estimates (, Ω, )

May require stratified-resampling procedure (by design covariates) for a pooled-analysis with very heterogeneous study designs E.g., limited data at a high dose in one study may

be critical to estimation of Emax

PaSiPhIC 201213

Quantifying Parameter Uncertainty in Population Models – Parametric Bootstrap

A2PG

Draw random samples from multivariate normal distribution with Mean vector = [ ] Covariance matrix = Cov( )

Obtained from Hessian or other procedure (e.g., COV step in NONMEM) Based on Fisher’s theory (Efron, 1982)

Assumes asymptotic theory (large sample size) that maximum likelihood estimates converge to a MVN distribution See Vonesh and Chinchilli (1997)

Based on Wald’s approximation that likelihood surface can be approximated by a quadratic model locally around the maximum likelihood estimates Approximations are dependent on parameterization Improved approximations may occur by estimating the natural

logarithm of the parameter for parameters that must be positive

ˆ,ˆ, ˆ,ˆ,

PaSiPhIC 201214

Non-parametric Versus Parametric Bootstrap Procedures

A2PG

The non-parametric bootstrap procedure is widely used in pharmacometrics Often used as a back-up procedure to quantify parameter

uncertainty when difficulties arise in estimating the covariance matrix (eg., NONMEM COV step failure) In this setting issues with a large number of convergence

failures in the bootstrap runs may call into question the validity of the confidence intervals (i.e., Do they have the right coverage probabilities?)

This form of parametric bootstrap procedure is less computationally intensive than other bootstrap procedures that require re-estimation Requires successful estimation of the covariance matrix

(NONMEM COV step) but can lead to random draws outside the feasible range of the parameters unless appropriate transformations are applied

PaSiPhIC 201215

Non-parametric Versus Parametric Bootstrap Procedures (2)

A2PG

Developing stable models that avoid extremely high pairwise correlations (>0.95) between parameter estimates and have low condition numbers (<1000) can help Ensure successful covariance matrix estimation Reduce convergence failures in non-parametric

bootstrap runs Choice of bootstrap procedure should focus on

the adequacy of the parametric assumption Random draws from MVN versus the more

computationally intensive re-estimation approaches (e.g., non-parametric bootstrap)

PaSiPhIC 201216

Simulation Procedure to Construct Statistical Intervals for Population Model Predictions

A2PG

Obtain random draw of , Ω, from bootstrap

procedure for kth trial

Simulate subject level dataYi | , Ω,

for M subjects

Summarize predictions (e.g.,

mean) stratified by

design (dose ,time, etc.)

Repeat for

k=1,…,K trials

Use percentile method to

obtain statistical interval from K

predictions

k<K

k=KNote 1: To construct confidence interval consider sufficiently large M (e.g., ≥2000 subjects) to average out sampling variation in Ω and . Note 2: For prediction intervals, M is chosen based on observed or planned sample size.

PaSiPhIC 201217

To describe other probabilistic concepts we need to define some additional quantities

A2PG

True (unknown) treatment effect or quantity ()

Target value (TV) A reference value for

Data-analytic decision rule (e.g., Go/No-Go criteria) Based on an observed treatment effect or quantity

(T)

PaSiPhIC 201218

Treatment Effect ()

A2PG

is the true (unknown) treatment effect Models provide a prediction of

Uncertainty in the parameter estimates of the model provides uncertainty in the prediction of P( ) denotes the distribution of predictions of

ˆ,ˆ,ˆˆ g

PaSiPhIC 201219

Example of Model-Predicted Dose-Response Model for Fasted Plasma Glucose (FPG)

A2PG

Semi-mechanistic model of inhibition of glucose production

tK

out

inoutin

outeDED

DRFPG

KKR ,FPGK

DEDDK

dtdFPG

11

1

500

050

Mean Model Fit of FPG Versus Dose

(integrates data across dose and time)

Delta

FPG

(mg/

dL)

Dose (mg)

Week 0 Week 2

Week 4

Week 8

Week 6

Week 12

Observed MeanTypical Individual Prediction (PRED)

Model-Predicted Placebo-Corrected FPG Versus Dose at

Week 12

Dose (mg)Plac

ebo-

Corre

cted

Del

ta F

PG (m

g/dL

)

Population Mean Prediction5th Percentile (90% LCL)95th Percentile (90% UCL)

PaSiPhIC 201220

Target Value (TV)

A2PG

Suppose we desire to develop a compound if the true unknown treatment effect () is greater than or equal to some target value (TV) TV may be chosen based on:

Target marketing profile Clinically important difference Competitor’s performance

If we knew truth we would make a Go/No-Go decision to develop the compound based on: Go: ≥ TV No-Go: < TV

PaSiPhIC 201221

Data-Analytic Decision Rule

A2PG

But we don’t know truth… So we conduct trials and collect data to obtain an

estimate of the treatment effect (T) T can be a point estimate or confidence limit on the

estimate or prediction of (e.g., placebo-corrected change from baseline FPG)

We might make a data-analytic Go/No-Go decision to advance the development of the compound if: Go: T ≥ TV No-Go: T < TV

PaSiPhIC 201222

Statistical Power

A2PG

Power is a conditional probability based on an assumed fixed value of the treatment effect () Power = P(T ≥ TV | ) where P(T ≥ TV | = TV) = (false positive)

TV=0 for statistical tests of a treatment effect Power is an operating characteristic of the

design based on a likely value of No formal assessment that the compound can

achieve the assumed value of

PaSiPhIC 201223

Simulation Procedure to Calculate Power Based on a Population Model-Predicted

A2PG

Use the same final estimates

(, Ω, ) for each

simulated trial

Simulate subject level dataYi | , Ω,

for M subjects

Analyze simulated data

as per SAP to test

Ho: = TVHa: TV

Repeat for

k=1,…,K trials

Power is calculated as the fraction of the K trials in

which Ho is rejected

k<K

k=K

Note 1: Typically TV=0 when assessing whether the compound has an overall treatment effect.Note 2: When using simulations to evaluate power it is good practice to also simulate data under the null (e.g., no treatment effect or placebo model) to verify that the Type 1 error () is maintained.

PaSiPhIC 201224

Probability of Achieving the Target Value (PTV)

A2PG

Probability of achieving the target value is defined as the proportion of trials where the true ≥ TV PTV = P( ≥ TV)

Does not depend on design or sample size Based only on prior information through the model(s)

and its assumptions PTV is a measure of confidence in the

compound at a given stage of development Can change as compound progresses through

development PTV can be calculated from the same set of

simulations used to construct confidence intervals of the predicted treatment effect ()

PaSiPhIC 201225

Simulation Procedure to Calculate PTV Based on Population Model Predictions

A2PG

Obtain random draw of , Ω, from bootstrap

procedure for kth trial

Simulate subject-level

data Yi | , Ω, for arbitrarily

large M

Summarize simulated data to obtain population mean predictions

of

Repeat for

k=1,…,K trials

Calculate PTV as proportion of K trials in which

≥ TV

k<K

k=KNote: To calculate PTV use an arbitrarily large M (e.g., ≥2000 subjects) to average out sampling variation in Ω and . PTV should only reflect the parameter uncertainty based on all available data used in the model estimation.

PaSiPhIC 201226

Probability of Success (POS)

A2PG

Probability of success is defined as the proportion of trials where a data-analytic Go decision is made POS = P(Go) = P(T ≥ TV)

POS is an operating characteristic that evaluates both the performance of the compound and the design In contrast to Power = P(T ≥ TV | ) which is an

operating characteristic of the performance of the design for a likely value of

POS is sometimes referred to as ‘average power’ where a Go decision is based on a statistical hypothesis test

dPTVTPTVTPGoP

PaSiPhIC 201227

Simulation Procedure to Calculate POS Based on a Population Model-Predicted

A2PG

Obtain random draw of , Ω, from bootstrap

procedure for kth trial

Simulate subject-level

data Yi | , Ω, for planned

sample size (M)

Summarize simulated data to obtain estimate of (T) and perform hypothesis test

Repeat for

k=1,…,K trials

Calculate POS as proportion of K trials in which

T ≥ TV

k<K

k=KNote: POS integrates the conditional probability of a significant result over the distribution of plausible values of reflected through the uncertainty in the parameter estimates for , Ω, and .

PaSiPhIC 201228

Probability of Correct Decision (POCD)

A2PG

A correct data-analytic Go decision is made when T ≥ TV and ≥ TV

A correct data-analytic No-Go decision is made when T < TV and < TV

Probability of a correct decision is calculated as the proportion of trials where correct decisions are made POCD = P(T ≥ TV and ≥ TV) + P(T < TV and <

TV) POCD can only be evaluated through

simulation where the underlying truth () is known based on the data-generation model used to simulate the data

29

Simulation Procedure to Calculate POCD Based on a Population Model-Predicted

A2PGPaSiPhIC 2012

Obtain random draw of , Ω, from bootstrap

procedure for kth trial

Simulate subject-level

data Yi | , Ω, for planned

sample size (M)

Summarize simulated data

to obtain estimate of

(T)

Repeat for

k=1,…,K trials

Calculate POS as proportion of K trials in which

T ≥ TV

k<K k=K

ClassifyGo: ≥TV

No Go: <TV Under Truth

ClassifyGo: T≥TV

No Go: T<TV Under Trial Data

Compare Truth Versus

Data-Analytic Decision

ClassifyGo: ≥TV

No Go: <TV Under Truth

Note: Classification of trial under truth is obtained from the PTV simulations.

PaSiPhIC 201230

General Framework for MBDD Basic assumptions of MBDD Six components of MBDD Clinical trial simulations (CTS) as a tool to

integrate MBDD activities Table of trial performance metrics Improving POCD Setting performance targets Comparing performance targets between

early and late stage clinical drug development

A2PG

PaSiPhIC 201231

Basic Assumptions of MBDD

A2PG

Predicated on the assumptions: That we can and should develop

predictive models That these models can be used in

CTS to predict trial outcomes Think of MBDD as a series of learn-

predict-confirm cycles Update models based on new data

(learn) Conduct CTS to predict trial outcomes

(predict) Conduct trial to obtain actual

outcomes and evaluate predictions (confirm)

Learn

Predict

Confirm

PaSiPhIC 201232

Six Components of MBDD

A2PG

MBDD

PK/PD & Disease Models

Meta-Analytic Models (Meta-Data from Public Domain)

Design & Trial Execution Models

Data-Analytic Models

Quantitative Decision Criteria

Trial Performance Metrics

Explicitly and quantitatively defined

criteria “draw line in the sand”

Leverage understanding of

pharmacology/disease – useful for

extrapolation

Understand competitive landscape from a dose-response

perspective

Evaluate designs and dose selection; incorporate trial

execution models such as dropout models

Implement SAP, evaluate alternative analysis methods – ANCOVA, MMRM, regression, NLME

Evaluate probability of achieving

target value (PTV), success (POS),

correct decisions (POCD)

PaSiPhIC 201233

Clinical Trial Simulations (CTS)

A2PG

Just as a clinical trial is the basic building block of a clinical drug development program, clinical trial simulations should be the cornerstone of an MBDD program

CTS allows us to assume (know) truth for a hypothetical trial Based on simulation model we know Mimic all relevant design features of a proposed clinical trial

Sample size, treatments (doses), covariate distributions, drop out rates, etc.

Analyze simulated data based on the proposed statistical analysis plan (SAP)

Calculate T (test statistic for treatment effect) and apply data-analytic decision rule

CTS should be distinguished from other forms of stochastic simulations E.g., CIs for dose predictions, PTV calculations, etc.

CTS can be used to integrate the components of MBDD and the various probabilistic concepts (including POS and POCD)

PaSiPhIC 201234

Table of Trial Performance Metrics

A2PG

Trial No Go Trial Go Total

“True” No Go

“True” Go

Total

Correct No Go

P(Trial Go)

Incorrect Go P(True No Go)

Incorrect No Go

P(Trial No Go) 1.0

P(True Go)Correct Go

PTVPOCD POS

PaSiPhIC 201235

Improving the probability of making correct decisions

A2PG

Change the design n/group Regression-based designs ( # of dose groups,

n/group) Consider other design constraints (cross-over,

titration, etc.) Change the data-analytic model Regression versus ANOVA Longitudinal versus landmark analysis

Change the data-analytic decision rule Alternative choices for T

Point estimate, confidence limit, etc. All of the above can be evaluated in a CTS

PaSiPhIC 201236

Setting Performance Targets

A2PG

PTV will change over time as model is refined and new data emerge Bring forward compounds/treatments with higher

PTV as compound moves through development PTV may be low in early development

Industry average Phase 3 failure rate is approximately 50% It is difficult to improve on this average unless we can

routinely quantify PTV Strive to achieve PTV>50% before entering Phase 3

Strive to achieve high POCD in decision-making throughout development

PaSiPhIC 201237

Comparing performance targets between early and late stage clinical drug development

A2PG

Low Low Low

Low High HighLow High 100%Tota

l

Total

True No Go

Trial No Go

True Go

Trial Go

Late Development POCD should be high

PTV should be high

Advance good compounds / treatments to registration

High Low High

Low Low LowLow Low 100%Tota

l

Total

Trial No Go

Trial Go

Early Development POCD should be high

PTV may be low

Kill poor compounds / treatments early

True No Go

True Go

PaSiPhIC 201238

Examples

A2PG

Rheumatoid Arthritis Example Phase 3 development decision

Urinary Incontinence Example Potency-scaling for back-up to by-pass Phase 2a

POC trial and proceed to a Phase 2b dose-ranging trial

Acute Pain Differentiation Case Study Decision to change development strategy to

pursue acute pain differentiation hypothesis

PaSiPhIC 201239

Example – Rheumatoid Arthritis

A2PG

Endpoints: DAS28 remission (DAS28 < 2.6) ACR20 response (20% improvement in ACR score)

Models developed based on Phase 2a study: Continuous DAS28 longitudinal PK/PD model with Emax direct-

effect drug model ACR20 logistic regression PK/PD model with Emax drug model

Both direct and indirect-response models evaluated Conducted clinical trial simulations for a 24-week Phase 2b

placebo-controlled dose-ranging study (placebo, low, medium and high doses) At Week 12 non-responders assigned to open label extension at

medium dose level Primary analysis at Week 24; Week 12 responses for non-

responders carried forward to Week 24 Evaluated No-Go/Hold/Go criteria for Phase 3 development

PaSiPhIC 201240

Example – Rheumatoid Arthritis (2)

A2PG

DAS28-CRP Remission (Difference from placebo)

ACR20 (Difference from placebo)

<20% 20-25% 25-30% 30%

<10% No Go No Go Hold Hold

10-16% No Go Hold Hold Hold

16-20% Hold Hold Hold Go

20% Hold Hold Go Go

No Go: Stop development Hold: Wait for results of separate Phase 2b active comparator

trial Go: Proceed with Phase 3 development without waiting for

results from comparator trial

PaSiPhIC 201241

Example – Rheumatoid Arthritis (3)

A2PG

TreatmentProbability (%)

No Go Hold GoLow Dose 96.1 3.9 0.0Medium Dose 28.1 62.9 9.0High Dose 18.4 65.6 16.0

CTS results suggest a high probability that the team will have to wait for results from the Phase 2b active comparator trial before making a decision to proceed to Phase 3. Very low probability of taking low dose into Phase 3.

PaSiPhIC 201242

Example – Urinary Incontinence

A2PG

Endpoint: Daily micturition (MIC) counts

Models developed: Longitudinal Poisson-Normal model developed for daily MIC counts

for lead compound Time-dependent Emax drug model using AUC0-24 as measure of exposure

Potency scaling for back-up based on: In vitro potency estimates for lead and back-up (back-up more potent than

lead) Equipotency assumption between lead and back-up

Conducted CTS to evaluate Phase 2b study designs for back-up compound (placebo and four active dose levels) Evaluated various dose scenarios of low (L), medium #1 (M1),

medium #2 (M2) and high (H) doses levels Implemented SAP (constrained MMRM analysis with step down trend

tests) Quantified POS for the L, M1, M2 and H doses for the various dose

scenarios and potency assumptions

PaSiPhIC 201243

Example – Urinary Incontinence (2)

A2PG

Note: Low (L) dose was selected to be a sub-therapeutic response. Design was not powered to detect a significant treatment effect at this dose.

Dose Scenario

L M1 M2 H Comment

1 1X 2.5X 12.5X 25X Doses selected favor in vitro potency assumption (i.e., back-up more potent than lead compound)

2 1X 2.5X 12.5X 37.5X

3 1X 5X 25X 50X4 2.5X 5X 25X 75X5 2.5X 12.5X 37.5X 75X Doses selected favor

equipotent assumption6 5X 12.5X 50X 100X

PaSiPhIC 201244

Example – Urinary Incontinence (3)

A2PG

CTS results: High POS (>95%) demonstrating statistical significance at the H

dose for all 6 dose scenarios Insensitive to potency assumptions

High POS (>88%) demonstrating statistical significance at the M2 dose for all 6 dose scenarios Insensitive to potency assumptions

POS varied substantially for demonstrating statistical significance of the M1 dose Depending on dose scenario and potency assumption

POS < 60% for demonstrating statistical significance at the L dose Except for dose scenarios 4 – 6 for the in vitro potency assumption

CTS results provided guidance to the team to select a range of doses that would have a high probability of demonstrating dose-response while being robust to the uncertainty in the relative potency between the back-up and lead compounds. Provided confidence to bypass POC and move directly to a Phase 2b trial for the back-up.

PaSiPhIC 201245

Case Study – Acute Pain DifferentiationBackground

A2PG

SC-75416 is a selective COX-2 inhibitor Capsule dental pain study conducted Poor pain response relative to active control (50 mg

rofecoxib) Lower than expected SC-75416 exposure with capsule

relative to oral solution evaluated in Phase 1 PK studies PK/PD models developed to assess whether greater

efficacy would have been obtained if exposures were more like that observed for the oral solution Pain relief scores (PR) modeled as an ordered-categorical

logistic normal model Dropouts due to rescue therapy modeled as a discrete

survival endpoint dependent on the patient’s last observed PR Assumes a missing at random (MAR) dropout mechanism

PaSiPhIC 201246

Case Study – Acute Pain DifferentiationBackground (2)

A2PG

PK/PD modeling predicted greater efficacy with oral solution relative to capsules A 6-fold higher SC-75416 dose (360 mg) than previously

studied predicted to have clinically relevant improvement in pain relief relative to active control (400 mg ibuprofen)

Model extrapolates from capsules to oral solution and leverages in-house data from other COX-2s and NSAIDs

Project team considers change in development strategy to pursue a high-dose efficacy differentiation hypothesis Original strategy was to determine an acute pain dose that

was equivalent to an active control and then scale down the dose for chronic pain (osteoarthritis) Based on well established relationships that chronic pain doses for

NSAIDs tend to be about half of the acute pain dose

PaSiPhIC 201247

Case Study – Acute Pain DifferentiationProposed POC Dental Pain Trial

A2PG

Proposed conducting a proof of concept oral solution dental pain study Demonstrate improvement in pain relief for 360 mg SC

relative to 400 mg ibuprofen Primary endpoint is TOTPAR6 (SC vs. ibuprofen) TOTPAR6 = 3 (TV) is considered clinically relevant

Perform ANOVA on observed LOCF-imputed TOTPAR6 response and calculate LS mean differences T = LS mean (SC) – LS mean (ibuprofen) LCL95 = 2-sided lower 95% confidence limit on T

Compound and data-analytic decision rule: Truth: Go if ≥3, No-Go if <3 Data: Go if T≥3 and LCL95>0, No-Go if T<3 or LCL95≤0

PaSiPhIC 201248

Case Study – Acute Pain DifferentiationSimulation Procedure to Calculate PTV

A2PG

Simulate PR Model

Parameters

(PR,2) ~ MVN

Simulate Dropout Model Parameters

DO ~ MVN

Simulate Dropout Times

M=2,000 patients

per treatment

Simulate PR Scores

M=2,000 patientsper treatment Perform LOCF

Imputation and Calculate

TOTPAR6

Calculate Population Mean

TOTPAR6 & TOTPAR6

Across M=2,000 pts

Determine True Decision

Go: 3No Go: <3

Summarize Distribution of TOTPAR6 ()

k=K Repeat for k = 1,

…,K=10,000 trials

k<K

PaSiPhIC 201249

Case Study – Acute Pain DifferentiationPosterior Distribution of TOTPAR6

A2PG

0

500

1000

1500

2000

2500

Freq

uenc

y

0 1 2 3 4 5Delta-TOTPAR6

360 mg SC-75416 vs 400 mg Ibuprofen360 mg SC-75416 vs 400 mg Ibuprofen

PTV = P( 3) = 67.2%

Mean Prediction = 3.2

PTV = 67.2% sufficiently high to warrant recommendation to conduct oral solution dental pain study to test efficacy differentiation hypothesis.

PaSiPhIC 201250

Case Study – Acute Pain DifferentiationCTS Procedure to Evaluate POC Trial Designs

A2PG

Simulate PR Scores for k-th

Trialn pts / treatment

Simulate Dropout Times

for k-th Trialn pts / treatment

Perform LOCF Imputation &

Calculate TOTPAR6

Calculate Mean TOTPAR6 (T),

SEM & 95% LCL

Apply Decision Rule

Go: LCL>0 and T 3

No Go: LCL 0 or T<3

Compare Truth vs. Data-Analytic

Decision

Calculate MetricsPOS

POCD

k=KRepeat for k=1,

…,K=10,000 trials

k<K

PaSiPhIC 201251

Case Study – Acute Pain DifferentiationCTS Trial Performance Metrics

A2PG

TrialTruth

Trial No GoLCL95 0 or T<3

Trial GoLCL95> 0 and T3 Total

<3 20.81% 11.99% 32.80%

3 17.29% 49.91% 67.20%

Total38.10% 61.90%

100%(out of 10,000 trials)

POCD = 70.72% POS = 61.90%

PTV = 67.20%

A sufficiently high POCD and POS supported the recommendation and approval to proceed with the oral solution dental pain study.

PaSiPhIC 201252

Case Study – Acute Pain DifferentiationComparison of Observed and Predicted (About 9 months later…)

A2PG

0

100

200

300

400

Freq

uenc

y

-12 -8 -4 0 4 8DELTA

-12 -8 -4 0 4 8DELTA

0

100

200

300

400

Freq

uenc

y

-12 -8 -4 0 4 8DELTA

-12 -8 -4 0 4 8DELTA

PlaceboPlacebo 60 mg SC-7541660 mg SC-75416

180 mg SC-75416180 mg SC-75416 360 mg SC-75416360 mg SC-75416

Pred = 3.2Pred =

2.0

Pred = -0.9Pred = -7.0Obs = -9.6

Obs = -1.8

Obs = 3.3Obs =

2.6

PaSiPhIC 201253

Case Study – Acute Pain DifferentiationSummary of Results

A2PG

360 mg SC-75416 met pre-defined Go decision criteria Confirmed model predictions Demonstrated statistically significant improvement relative to

400 mg ibuprofen MBDD approach provided rationale to pursue acute pain

differentiation strategy that might not have been pursued otherwise

Allowed progress to be made while reformulation of solid dosage form was done in parallel

Validation of model predictions provided confidence to pursue alternative pain settings for new formulations without repeating dental pain study Model could be used to provide predictions for new

formulations

PaSiPhIC 201254

Final Remarks/Discussion

A2PG

Some thoughts on implementing MBDD Challenges to implementing MBDD

PaSiPhIC 201255

Final Remarks/DiscussionSome thoughts on implementing MBDD

A2PG

We need to clearly define objectives What questions are we trying to address with our models?

We need explicit and quantitatively defined decision criteria It’s difficult to know how to apply the models if decision criteria

are ambiguous or ill-defined We need complete transparency in communicating model

assumptions Entertain different sets of plausible model assumptions Evaluate designs for robustness to competing assumptions

We need to routinely evaluate the predictive performance of the models on independent data Modeling results should be presented as ‘hypothesis generating’

requiring confirmation in subsequent independent studies

PaSiPhIC 201256

Final Remarks/DiscussionSome thoughts on implementing MBDD (2)

A2PG

Conduct CTS integrating information across disciplines Implement key features of the design and trial

execution (e.g., dropout) Implement statistical analysis plan (SAP)

Provide graphical summaries of CTS results for recommended design prior to the release of the actual trial results Perform quick assessment of predictive performance

when actual trial reads out Update models and quantification of PTV after

actual trial reads out i.e., Begin new learn-predict-confirm cycle

PaSiPhIC 201257

Final Remarks/DiscussionChallenges to implementing MBDD

A2PG

Focus on timelines of individual studies and a ‘go-fast-at-risk’ strategy (i.e., minimizing gaps between studies) can be counter-productive to a MBDD implementation M&S (learning phase) is a time-consuming effort

Integration of MBDD activities in project timelines will require focus on integration of information across studies Not just tracking of individual studies

May need processes to allow modelers to be un-blinded to interim results to begin modeling activities earlier to meet aggressive timelines

Insufficient scientific staff with programming skills to perform CTS Pharmacometricians and statisticians with such skills should be

identified CTS implementation often requires considerable customization to

address the project’s needs (i.e., no two projects are alike)

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Final Remarks/DiscussionChallenges to implementing MBDD (2)

A2PG

Insufficient modeling and simulation resources to implement MBDD on all projects

Reluctance to be explicit in defining decision rules (i.e., reluctance to ‘draw line in the sand’) Due to complexities and tradeoffs in making decisions Can be difficult to achieve consensus

http://www.ascpt.org/Portals/8/docs/Meetings/2012%20Annual%20Meeting/2012%20speaker%20presentations/ASOP%20TUE%20CHERRY%20BLOS%20SESSION%201.pdf

Reluctance to use assumption rich models We make numerous assumptions now when we make

decisions…we’re just not very explicit about them MBDD can facilitate open debate about explicit

assumptions

PaSiPhIC 201259

Bibliography

A2PG

Neter, J., and Wasserman, W. Applied Linear Statistical Models, Irwin Inc., IL, 1974, pp. 71-73.

Efron, B. The Jackknife, the Bootstrap, and Other Resampling Plans, Society for Industrial and Applied Mathematics, PA, 1982, pp. 29-30.

Vonesh, E.F., and Chinchilli, V.M. Linear and Nonlinear Models for the Analysis of Repeated Measurements, Marcel Dekker, Inc., NY, 1997, pp. 245-246.

Kowalski, K.G., Ewy, W., Hutmacher, M.M., Miller, R., and Krishnaswami, S. “Model-Based Drug Development – A New Paradigm for Efficient Drug Development”. Biopharmaceutical Report 2007;15:2-22.

Lalonde, R.L., et al. “Model-Based Drug Development”. Clin Pharm Ther 2007;82:21-32.

Chuang-Stein, C.J., et al. “A Quantitative Approach to Making Go/No Go Decisions in Drug Development”. DIJ 2011;45:187-202.

Smith, M.K., et al. “Decision-Making in Drug Development – Application of a Model-Based Framework for Assessing Trial Performance”. Book chapter in Clinical Trial Simulations: Applications and Trends, Kimko H.C. and Peck C.C. eds. , Springer Inc., NY, 2011, pp. 61-83.

Kowalski, K.G., Olson, S., Remmers, A.E., and Hutmacher, M.M. “Modeling and Simulation to Support Dose Selection and Clinical Development of SC-75416, a Selective COX-2 Inhibitor for the Treatment of Acute and Chronic Pain”. Clin Pharm Ther, 2008; 83: 857-866.