# Presentation: 070469

## QT Evaluation Studies:

**Pharmacometric Considerations**

- Leslie Kenna, Peter Lee and Yaning Wang
- Office of Clinical Pharmacology and Biopharmaceutics
- CDER/FDA
- Clinical Pharmacology Subcommittee Meeting
- November 17, 2003

## Outline

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

## What do we want to know?

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

## Challenges

- Variation in response > response of interest

## Challenges

- Variation in response > response of interest
- • Wide intra-individual variability
- 	e.g. #1: within day variability

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

- QTcF (msec)

- Time (hr)

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

## Challenges

- Variation in response > response of interest
- • Wide intra-individual variability
- 	e.g. #1: within day variability
- 	e.g. #2: between day variability

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

- QTCF (msec)

- Time (hr)

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

## Challenges

- Variation in response > response of interest
- • Wide intra-individual variability
- 	e.g. #1: within day variability
- 	e.g. #2: between day variability
- • Wide inter-individual variability

## Interindividual Variability in Baseline QTc

- QTCF (msec)

- Time (hr)

**Interindividual Variability in Baseline QTc**

- Subject i

- Subject k

## Observations in Recent Submissions

**Observations in Recent Submissions**

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

## “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 |

## 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

## Observed Response to Moxifloxacin

**in 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 play**
**in the discrepancy in response?**

## 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

## Mean vs. Outlier Analysis

|  | Mean Response
QTcF at Tmax
vs placebo (90% CI) |
| --- | --- |
| Drug “X” | 4 msec
(2,5) |
| (+) Control | 9 msec
(8,11) |
|  |  |

## Mean vs. Outlier Analysis

|  | 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**

## 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

## Definition of Baseline Influences Analysis(# Outliers)

|  | Treatment – free
+
On - placebo |
| --- | --- |
| Placebo | 1 |
| 1X Dose | 2 |
| 5X Dose | 4 |

**“****Baseline”**

**Dose-response**
**appears shallow**

## Definition of Baseline Influences Analysis(# Outliers)

|  | Treatment – free
+
On - placebo | Treatment – free |
| --- | --- | --- |
| Placebo | 1 | 2 |
| 1X Dose | 2 | 2 |
| 5X Dose | 4 | 8 |

**Response directly**
**proportional to dose**

**“****Baseline”**

**“****Baseline”**

## Goal

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

## 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

## Clinical Trial Simulation Approach

## Create true data
	Sample from historical baseline QT data
	Choose models and parameters for study design, PK, PD
	Add baseline response to simulated response to treatment

Sample true data according to study design

Estimate response
	Metrics in Concept Paper, in submissions

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

Compute Performance
	Power: Fraction of simulations yielding insignificant effect

- 	Sample from historical baseline QT data
- 	Choose models and parameters for study design, PK, PD
*	*Add baseline response to simulated response to treatment
- Sample true data according to study design
- Estimate response
- 	Metrics in Concept Paper, in submissions
- Repeat “many” times at given set of study design parameters
- Compute Performance
- 	Power: Fraction of simulations yielding insignificant effect

**Simulation Study Overview**

## Simulation Study Overview

- Create true data
- 	Sample from historical baseline QT data
- 	Choose models and parameters for study design, PK, PD
*	*Add baseline response to simulated response to treatment
- Sample true data according to study design
- Estimate response
- 	Metrics in Concept Paper, in submissions
- Repeat “many” times at given set of study design parameters
- Compute Performance
- 	Power: Fraction of simulations yielding insignificant effect

## Simulation Study Overview

- Create true data
- 	Sample from historical baseline QT data
- 	Choose models and parameters for study design, PK, PD
*	*Add baseline response to simulated response to treatment
- Sample true data according to study design
- Estimate response
- 	Metrics in Concept Paper, in submissions
- Repeat “many” times at given set of study design parameters
- Compute Performance
- 	Power: Fraction of simulations yielding insignificant effect

## Simulation Study Overview

- Create true data
- 	Sample from historical baseline QT data
- 	Choose models and parameters for study design, PK, PD
*	*Add baseline response to simulated response to treatment
- Sample true data according to study design
- Estimate response
- 	Metrics in Concept Paper, in submissions
- Repeat “many” times at given set of study design parameters
- Compute Performance
- 	Power: Fraction of simulations yielding insignificant effect

## Simulation Study Overview

- Create true data
- 	Sample from historical baseline QT data
- 	Choose models and parameters for study design, PK, PD
*	*Add baseline response to simulated response to treatment
- Sample true data according to study design
- Estimate response
- 	Metrics in Concept Paper, in submissions
- Repeat “many” times at given set of study design parameters
- Compute Performance
- 	Power: Fraction of simulations yielding insignificant effect

## Simulation Study Overview

- Create true data
- 	Sample from historical baseline QT data
- 	Choose models and parameters for study design, PK, PD
*	*Add baseline response to simulated response to treatment
- Sample true data according to study design
- Estimate response
- 	Metrics in Concept Paper, in submissions
- Repeat “many” times at given set of study design parameters
- Compute Performance
- 	Power: Fraction of simulations yielding insignificant effect

## Step 1(a): Create True Data (Sample Baseline)

- 1. Randomly pick a subject from the database

- 3. Randomly pick **n** baseline
-   observations / time point

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

**Day 4**

## 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

## Step 1 (c): Simulate Drug Response

**Step 1 (c): Simulate Drug Response**

- Baseline QTc

- Treatment
- response

## Step 2: Sample According to Study Design

- e.g. Sampled
- Baseline

- e.g. Sampled
- Response

## Step 3: Estimate Response 

**Examples of approaches to estimating treatment effect**
- Mean(sampled response): treatment - baseline
- Max(sampled response): treatment - baseline
- # Subjects with outlying values: treatment - baseline

## Step 4: Repeat Many Times

- Randomly pick baseline data
**↓**
- Simulate response to treatment
- ↓
- Estimate drug effect

- 			******QTcF**
- Study 1		5 msec
- Study 2		8 msec
- ...
- Study 1000		2 msec

## Step 5: Evaluate Performance

- e.g. Power
- 	Fraction of simulations in which Ho rejected
- 	Ho: mean change from baseline for drug = placebo

## Preliminary Results

## Study Results: 200 clinical trials simulated

- Resample from 72 hr QTc data in 45 subjects
**Individual Trial Design**
- Randomized, parallel, with treatment and placebo arms
- 24 hour placebo run in; 24 hours on treatment
- Hourly QT sampling from 1-24 hours
- N: varied
**Treatment characteristics**
- p.o. administration
- Dose = 100 mg
- PK: One compartment; ka: 1/hr, CL: 25 L/hr, V: 400 L, Tmax: 2-5 hours
- PK/PD: Linear relationship, no effect delay
- Drug effect on QTc is additive on top of the baseline variation
- Intersubject variability of PK and PK/PD are 25%
**Analysis**
- 24-hr max QTc during treatment – 24-hr max QTc for baseline
- 24-hr mean QTc during treatment – 24-hr mean QTc for baseline
- 24-hr max QTc during treatment – 24-hr average QTc during baseline
- 24-hr max QTc during treatment (no baseline subtraction)

**Study Results: 200 clinical trials simulated**

## Treatment Characteristics

**Treatment Characteristics**

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

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

## Effect of Baseline Measurement on Power

- Several QT
- samples at
- baseline

- One QT
- sample at
- baseline

**Effect of Baseline Measurement on Power**

## Questions for the committee

- 1. What additional study design points are recommended
- for consideration in the analysis of PK-QT data?
- 2. Comment on the case studies presented and the pros
- and cons of using clinical trial simulation approaches to
- evaluate PK-QT study design. Are there other methods
- of analyzing PK-QT data that the FDA should consider?
- 3. What critical design elements influence the outcome of
- a PK-QT study that has as its goal to identify a meaningful
- change in QT?