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
Variation in response > response of interest
• 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
Variation in response > response of interest
• 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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Observations in Recent Submissions
Diverse study designs: e.g. duration, timing, # replicates
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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Observations in Recent Submissions
Diverse study designs: e.g. duration, timing, # replicates
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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Observations in Recent Submissions
Diverse study designs: e.g. duration, timing, # replicates
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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Simulation Study Overview
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
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Simulation Study Overview
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
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Simulation Study Overview
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
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Simulation Study Overview
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
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Simulation Study Overview
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
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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
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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
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?