qt evaluation studies: pharmacometric considerations

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QT Evaluation Studies:Pharmacometric Considerations

Leslie Kenna, Peter Lee and Yaning Wang

Office of Clinical Pharmacology and BiopharmaceuticsCDER/FDA

Clinical Pharmacology Subcommittee MeetingNovember 17, 2003

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Outline

• Overarching question• Challenges• Methods: Clinical Trial Simulation• Preliminary results

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What do we want to know?

Drug effect on QT interval: “Worst-case” scenario

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Challenges

Variation in response > response of interest

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Challenges

Variation in response > response of interest• Wide intra-individual variability

e.g. #1: within day variability

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QT

cF (

mse

c)

Time (hr)

Historical Baseline QTc Data for Drug “X”Subject i: 10 ECGs / time

15 msec

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Challenges


• Wide intra-individual variabilitye.g. #1: within day variabilitye.g. #2: between day variability

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QT

CF

(m

sec)

Time (hr)

Smooth Day 1

Smooth Day 4

Smooth Day 3

Smooth Day 2

Smooth through Days 1:4

Intraindividual Variability in Baseline QTc: Subject i: 4 Days of Measurement

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Challenges


• Wide intra-individual variabilitye.g. #1: within day variabilitye.g. #2: between day variability

• Wide inter-individual variability

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QT

CF

(m

sec)

Time (hr)

Interindividual Variability in Baseline QTc

Subject i Subject k

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Observations in Recent Submissions

Diverse study designs: e.g. duration, timing, # replicates

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“Baseline” for Six QT Evaluation Studies

Drug Baseline

1 A single Day –14 ECG

2 Mean of 3 ECGs at t=0

3 Mean of 1 screening ECG & 1 follow-up ECG

4 Drug, placebo:Mean of 100 ECGs over 2 baseline days

Positive Control:Mean of 10 ECGs 10 min pre-dose

5 Mean of 108 ECGs before each treatment arm& 30 ECGs on placebo

6 Median of 9 ECGs on placebo

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Different response to same positive control• Case 1 Moxifloxacin 400 mg 8 msec QTcF• Case 2 Moxifloxacin 400 mg 13 msec QTcF

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Observed Response to Moxifloxacinin Two Recent QT Evaluation Studies

Case 1 Case 2

QTcF at Tmax

(95% CI)

8 msec

(6,9)

13 msec

(9, 17)

Demographics Healthy males Healthy males

Sampling time Covered tmax moxifloxacin

Covered tmax moxifloxacin

N 59 45

ECGS / day 3: baseline

4: on drug

1: baseline

11: on drug

replicates / time 6 3

What role did study design playin the discrepancy in response?

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Different observed response to same positive control• Case 1 Moxifloxacin 400 mg 8 msec QTcF• Case 2 Moxifloxacin 400 mg 13 msec QTcF

Observed response sensitive to analysis method• Mean vs. outlier analysis

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Mean vs. Outlier Analysis

Mean Response

QTcF at Tmax

vs placebo (90% CI)

Drug “X” 4 msec

(2,5)

(+) Control 9 msec

(8,11)

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Mean Response

QTcF at Tmax

vs placebo (90% CI)

Outliers

# (%) Subjects

QTcF > 30 msec

Drug “X” 4 msec

(2,5)

14 subjects

(15%)

(+) Control 9 msec

(8,11)

11 subjects

(16%)

Placebo 7 subjects

(8%)

Mean vs. Outlier Analysis

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Different observed response to same positive control• Case 1 Moxifloxacin 400 mg 8 msec QTcF• Case 2 Moxifloxacin 400 mg 13 msec QTcF

Observed response sensitive to analysis method• Mean vs. outlier analysis• Definition of baseline

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Definition of Baseline Influences Analysis(# Outliers)

Treatment – free

+

On - placebo

Placebo 1

1X Dose 2

5X Dose 4

“Baseline”

Dose-responseappears shallow

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Definition of Baseline Influences Analysis(# Outliers)

Treatment – free

+

On - placebo

Treatment – free

Placebo 1 2

1X Dose 2 2

5X Dose 4 8

Response directlyproportional to dose

“Baseline” “Baseline”

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Goal

Use available data to aid in the prospective design of QT studies

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Specific Aims

• Assemble a QT database from submissions

• Resample the data and use Clinical Trial Simulation to evaluate:

(a) clinical trial designs(b) data analysis methods

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Clinical Trial Simulation Approach

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Create true dataSample from historical baseline QT dataChoose models and parameters for study design, PK, PDAdd baseline response to simulated response to treatment

Sample true data according to study design

Estimate responseMetrics in Concept Paper, in submissions

Repeat “many” times at given set of study design parameters

Compute PerformancePower: Fraction of simulations yielding insignificant effect

Simulation Study Overview

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Step 1(a): Create True Data (Sample Baseline)1. Randomly pick a subject

from the database

3. Randomly pick n baseline observations / time point

380

390

400

410

420

0 2 4 6 8 10 12

Time (hours)

QTc

F (

mse

c)

201

2. Randomly pick a day of subject i’s baseline observations

Day 4

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Step 1 (b): Choose Simulation Conditions• 4 treatments evaluated: 2 doses of drug, placebo, active control

• Parameters to be varied

1. Crossover vs. parallel design

2. Single dose vs. steady state design

3. N

4. ECG sampling• Timing• # replicate ECGs (n): at baseline, on treatment• # days of measurement: at baseline, on treatment

5. PK/PD model for drug

• Delayed or direct response• EC50; parent, metabolite

6. PK model for drug• CL; parent, metabolite• ka

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Treatmenteffect

Time

QT

c

Time

QT

c

+

+

=

=

Step 1 (c): Simulate Drug Response

Baseline QTc Treatmentresponse

Drug

Time

QT

c

Placebo

Time

QT

c

Time

QT

c

Time

QT

c

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Step 2: Sample According to Study Design

e.g. SampledBaseline

e.g. SampledResponse

TimeQ

Tc

Time

QT

cTime

QT

c

Time

QT

c

Dru

gP

lace

bo

TrueTreatmentresponse

Time

QT

c

Time

QT

c

Dru

gP

lace

bo

Baseline QTc

Dru

g

Time

QT

c

Pla

ceb

o

Time

QT

c

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Step 3: Estimate Response

-

-

Estimated

Treatment

Effect

=

=

e.g. SampledBaseline

Time

QT

c

Time

QT

c

Examples of approaches to estimating treatment effectMean(sampled response): treatment - baselineMax(sampled response): treatment - baseline

# Subjects with outlying values: treatment - baseline

e.g. SampledResponse

Time

QT

c

Time

QT

c

Dru

gP

lace

bo

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Step 4: Repeat Many Times

Randomly pick baseline data

↓Simulate response to treatment

↓Estimate drug effect

QTcFStudy 1 5 msecStudy 2 8 msec…Study 1000 2 msec

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Step 5: Evaluate Performance

e.g. Power

Fraction of simulations in which Ho rejected

Ho: mean change from baseline for drug = placebo

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Preliminary Results

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QT baseline source dataResample from 72 hr QTc data in 45 subjects

Individual Trial DesignRandomized, parallel, with treatment and placebo arms24 hour placebo run in; 24 hours on treatmentHourly QT sampling from 1-24 hoursN: varied

Treatment characteristicsp.o. administrationDose = 100 mgPK: One compartment; ka: 1/hr, CL: 25 L/hr, V: 400 L, Tmax: 2-5 hoursPK/PD: Linear relationship, no effect delayDrug effect on QTc is additive on top of the baseline variationIntersubject variability of PK and PK/PD are 25%

Analysis24-hr max QTc during treatment – 24-hr max QTc for baseline24-hr mean QTc during treatment – 24-hr mean QTc for baseline24-hr max QTc during treatment – 24-hr average QTc during baseline24-hr max QTc during treatment (no baseline subtraction)

Study Results: 200 clinical trials simulated

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Time (hr)

log

(C

on

c n

g /

mL

))

Tmax: 2-5 hours

QT

c f

rom

Ba

sel

ine

(mse

c)

Time (hr)

Rmax ~ 16 msec

Treatment Characteristics

40

0%

20%

40%

60%

80%

100%

0 20 40 60 80

Number of Subjects

Po

we

r o

f S

tud

y

Max,t - Max,b

Mean,t - Mean,b

Max,t - Mean,b

Max,t absolute

Power to Detect a Difference Between Drug and Placebo with 95% Confidence

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0%

20%

40%

60%

80%

100%

0 20 40 60 80

Number of Subjects

Po

we

r o

f S

tud

y

Max,t - Max,b

Mean,t - Mean,b

Max,t - Mean,b

Max,t absolute

Several QTsamples at

baseline

One QTsample atbaseline 0%

5%

10%

15%

20%

0 20 40 60 80

Number of Subjects

Po

wer

of

Stu

dy

Max,t - predose

Mean,t - predose

Effect of Baseline Measurement on Power

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Questions for the committee

1. What additional study design points are recommendedfor consideration in the analysis of PK-QT data?

2. Comment on the case studies presented and the prosand cons of using clinical trial simulation approaches toevaluate PK-QT study design. Are there other methodsof analyzing PK-QT data that the FDA should consider?

3. What critical design elements influence the outcome ofa PK-QT study that has as its goal to identify a meaningfulchange in QT?

qt evaluation studies: pharmacometric considerations

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