1Clinical PK
Optimal design and QT-
prolongation detection in
oncology studies
Sylvain Fouliard & Marylore Chenel Department of clinical PK, Institut de Recherches Internationales Servier, Courbevoie, France
PODE Meeting – Berlin - 11th June 2010
2Clinical PK
• QT prolongation, a biomarker of Torsade de Pointes.
• QT measured on ECG, then corrected.• Circadian rythm in QT/QTc data
• Usually mandatory QT/QTC study performed in healthy volunteers at supratherapeutic dose
• Guidelines: mean QTc effect > 5ms
CONTEXT (1)
3Clinical PK
CONTEXT (2)• New anti-cancer drug in clinical
development- QTc-prolongation = class effect ?
• Development of anticancer drugs: patients only
• 2 phase I studies:– PK data available population PK model– No QT data available
• 2 ongoing phase I/II studies- QTc-prolongation assessment: ECG measurement times already decided without optimization (=empirical design)
• Internal QT database in HV (wo drug) population circadian QTc model available
4Clinical PK
D1 D2 D4 D14 D22Inclusion
Treatment No Treatment
ECG
Dose
• 2 phase I clinical trials: n = 60 + 40 (=100) patients
• Dose regimen : 14 days on / 7 days off, BID administration (4h apart)
• 14 ECG measurements per patient
• Same measurement times for all patients
CONTEXT (3)EMPIRICAL DESIGN
ECG times : Inclusion D1 D2 D4 D14 D22 0 0, 1.5, 0, 1.5h 0, 1.5h 0, 1.5h 0, 1.5h 4, 5.5, 8 h
5Clinical PK
OBJECTIVES
1. Evaluate the Empirical design for ECG Times.
2. Calculate the Power of detection of a QTc effect in the on going phase I/II studies.
3. Optimize the ECG Measurement Times for future studies.
6Clinical PK
1
3,2,10 2/24
cos1n
n
nnc
QTLtQTAQTMtQT
[1] Piotrovsky, V. “Pharmacokinetic-pharmacodynamic modeling in the data analysisand interpretation of drug-induced QT/QTc prolongation” (2005)
Assumption: same model to describe the circadian rhythm in QTc in HV and in
patients• Model building dataset: 2 thorough QT/QTc studies
- 62 + 87 (=149) healthy volunteers
- QT data without drug
- Fredericia correction: QTc = QT * HR-0.33
• Model characteristics
- poly-cosine model [1]
- IIV on all parameters
- Additive error model
• Software (estimation method):
- NONMEM VI (FOCEI)
• Criteria : LRT
• Evaluation: GOF, RSE, VPC
QTc(ms)
Time (h)
…Median
5% - 95 % CI
Observations
MATERIALS & METHODS (1) POPULATION QTc MODEL WITHOUT DRUG
7Clinical PK
Assumptions:
• Same model to describe the circadian rhythm in QTc in HV and in patients
• Concentration proportional drug effect on Mesor
• QTc-prolongation is measured by :
• Max QTc-prolongation at Cmax (PKPD model)
MATERIALS & METHODS (2) POPULATION QTc MODEL WITH DRUG EFFECT
1
3,2,1 2/24cos1)(
nn
nnc
QTLtQTAtQTMtQT
)(0 tCQTMtQTc
tCQTMtQTM 10
8Clinical PK
• Model building dataset: 2 phase 1 studies
- 14 patients, IV multiple doses, oral single dose
- 35 patients, oral multiple doses
• Model characteristics:
- 3-compartments model
- First order absorption and elimination
- IIV on Ka, F, CL, V1, V2
- Combined error model
• Software (estimation method): NONMEM VI (FOCEI)
• Criteria : LRT
• Evaluation: GOF, RSE, VPCmore
MATERIALS & METHODS (3) POPULATION PK MODEL
Periph. 1
(V2)Central
(V1)Periph. 2
(V3)
CL
FKa
Q3Q2
9Clinical PK
MATERIALS & METHODS (5)CALCULATION OF FISHER INFORMATION MATRIX
Sequential pop PKPD modellingPK model
PK parameters
(not estimated)QTc model without treatment
Mesor,
3 Cosine amplitude terms
3 Cosine Lagtime
(estimated)
Drug effect
γ(estimated)
QTc model under treatment
8 parameters + Additive error
QTM0, QTA1, QTA2, QTA3, QTL1, QTL2, QTL3, γ
10Clinical PK
Range of relevant γ values
[0.01, 1]
Range of relevant
QTc-prolongation values
[1 ms, 100ms]
MATERIALS & METHODS (6)EVALUATION OF THE EMPIRICAL DESIGN
To find the range of relevant γ values corresponding to a range of relevant QTc prolongations
• Calculation of the population Fisher Information Matrix
– Parameters of QTc model without drug– γ = {0. 01, 0.02, 0.05, 0.1, 0.15, 0.2, 0.3, 0.4, 0.5, 0.8, 1}– IIV on γ = 30 %
• Output results:– SE, RSE, DET (determinant of the population FIM)
)(0 tCQTMtQTc
11Clinical PK
00
%95
1
11
MATERIALS & METHODS (7)POWER DETECTION OF DRUG
EFFECT
For each value of γ, SE(γ) is computed from FIM
Wald test is performed, with a 5 % type I error.
- Null hypothesis H0 :
no QTc effect of the drug, 0 = 0
- Alternative hypothesis H1:
QTc effect of the drug, 0 > 0
Then power is computed from the type II error β.
Power = 1- β.
)1,(~)( 0
NSE
12Clinical PK
• Design characteristics :- 1 group of patients- = 0.05, 30 % IIV- Same days* & number of measurement per day* as the empirical design, design domain = [0-10h] for D1
= [0-8h] for each other ECG measurement day
• Output results :– Optimal ECG times– SE, RSE, DET (determinant of the population FIM)
MATERIALS & METHODS (8)ECG DESIGN OPTIMIZATION
* 5 ECG on D1, 2 ECG on D2, 2 ECG on D4, 2 ECG on D14, 2 ECG on D22
13Clinical PK
MATERIALS & METHODS (9)
• Software:
- PopDes [2], version 3.0 under MATLAB
• Design options:
-Local, Population, Univariate (design variable = ECG measurement time only, i.e. PK fixed)
• Optimisation method: Fedorov Exchange • Criteria : D-Optimality
[2] Gueorguieva, K. Ogungbenro, G. Graham, S. Glatt, and L. Aarons. A program for individual and population optimal design for univariate and multivariate response pharmacokinetic and pharmacodynamic models. Comput. Methods Programs Biomed. 86(1): 51-61 (2007)
14Clinical PK
RESULTS (1)
EMPIRICAL DESIGN EVALUATION (1)
Whatever the values (i.e. drug effect), there is low impact on the RSEs of baseline QTc model parameters.
SE() increases with ; RSE is below 20 % for > 0.05 (QTc-prolongation of 5 ms).
15Clinical PK
RESULTS (2)
EMPIRICAL DESIGN EVALUATION (2)
The RSEs of QTc model parameters are always lower than 20% for fixed effects, except for QTA1, for which there are around 25%.
RSE of QTc model parameters for a drug effect () of 0.05 (corresponding to a QTc prolongation of about 5 ms).
QTM0
(ms)
QTA1 QTA2 QTA3 QTL1
(hr)
QTL2
(hr)
QTL3
(hr)
Add_Err(ms)
RSE (%) 0.37 27.1 8.4 10.8 5.9 13.6 2.9 4.85 11.7
16Clinical PK
Power > 90 % for > 0.02, corresponding to a 2 ms average QTc-prolongation.
RESULTS (3)
POWER DETECTION OF DRUG
EFFECTPower of drug effect detection versus value (drug effect size)
17Clinical PK
RESULTS (4)
ECG TIME OPTIMIZATION (1)RSE comparison for each parameter of the empirical and the optimal
designs
The optimal design is better than the empirical one, especially for QTA1.
Optimal design (Det = 2.22 x 1064)
Empirical design (Det = 2.37 x 1040)
QTM0
(ms)
QTA1 QTA2 QTA3 QTL1
(hr)
QTL2
(hr)
QTL3
(hr)
Add_Err(ms)
RSE (%) 0.37 27.1 8.4 10.8 5.9 13.6 2.9 4.85 11.7
QTM0 QTA1 QTA2 QTA3 QTL1 QTL2 QTL3 Add_Err
RSE (%) 0.29 8.99 3.35 3.49 1.64 0.37 0.66 5.25 0.26
Sampling times : D1 D2 D4 D14 D22
Phase I/II design 0, 1.5, 4, 5.5, 8h 0, 1.5h 0, 1.5h 0, 1.5h 0, 1.5h
Optimized design 4, 8, 8.2, 8.8, 9.6h 1.5, 5.6h 3.8, 5.2h 0, 0.6h 1, 1.5h
18Clinical PK
CONCLUSIONS
This work reassured us on the capability of the empirical design to detect any potential drug effect.
The empirical design should allow an accurate estimation of the parameters of the QTc model under treatment.
INTERESTS & LIMITS
Several assumptions have been made clinicians not ready yet to have an adaptive design within a study.
19Clinical PK
• Assumptions made will be challenged with first clinical data coming.– PK model– QTc baseline model parameter values– Linear drug effect
• Optimization of the ECG measurement times with different clinical constraints (days, times, number of group, doses, number of measurements) for further studies.
• Interest in having an integrated tool for estimation and optimization.
NEXT STEPS
CONCLUSIONS (2)
20Clinical PK
ACKNOWLEDGMENT
Sylvain Fouliard pharmacometrician at Servier
France Mentré
21Clinical PK
BACK-UP
22Clinical PK
CL KA F V1 V2 V3 Q2 Q3 ErrA ErrP
Estimates
(RSE %)
54(10.1
)
0.74(12)
0.30(10.3
)
45(14.6
)
630(11.7
)
61(11.7
)
12(12.8
)
35(12.8
)
0.0092(32.2)
0.31(6.36
)
IIV(RSE %)
0.114(38.8
)
0.342(32.2
)
0.277(28)
0.202(35.5
)
. 0.143(46.9
)
. .
Back
RESULTSMODEL BUILDING
Population PK model
Parameter estimates and RSE of the population PK model
Parameter
23Clinical PK
Normal scale Log scale
Normalizeddose
Median
5% - 95 % CI
Observations… Back
Time (h)
RESULTSMODEL EVALUATION
Visual predictive checks
Population PK model
24Clinical PK
Observed Values compared to Simulated Confidence Interval
CI Obs below CI (%)
Obs in CI (%)
Obs above CI (%)
MEDIAN 61.1 . 38.8
[P1-P99] 1.7 97.6 0
[P5-P95] 6.1 91.7 2.2
[P10-P90] 10.4 85.3 4.3
[P25-P75] 26.4 60.2 13.4
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RESULTSMODEL EVALUATION
Numerical predictive checks
Population PK model
25Clinical PK
RESULTSMODEL BUILDING
QTM0
(ms)
QTA1 QTL1(hr)
QTA2 QTL2(hr)
QTA3 QTL3(hr)
ErrA(ms)
Estimates(RSE %)
400(0.214)
0.011(12)
12(1.98)
0.0103(7.75)
7.66(1.04)
0.0073(3.8)
5.73(0.61)
5.35(2.5)
IIV(RSE %)
0.0008(10.7)
0.084(32.3)
2.4(24.3)
0.029(43.2)
0.488(26.2)
0.047(40.9)
0.091(23.7)
.
Baseline poly-cosine QTc model
Parameter
Back
Parameter estimates and RSE of the baseline poly-cosine QTc model
26Clinical PK
QTc(ms)
Time (h)
…Median
5% - 95 % CI
Observations
Back
RESULTSMODEL EVALUATION
Baseline poly-cosine QTc model
Visual predictive checks
27Clinical PK
Baseline poly-cosine QTc model
Observed Values compared to Simulated Confidence Interval
CI Obs below CI (%)
Obs in CI(%) Obs above CI (%)
MEDIAN 49.2 . 50.8
[P1-P99] 0.6 97.6 1.8
[P5-P95] 3.8 90.6 5.6
[P10-P90] 9.4 80.6 10
[P25-P75] 23.3 51.9 24.8
Back
RESULTSMODEL EVALUATION
Numerical predictive checks
28Clinical PK
CONTEXT
(1’)
• P wave: auricular depolarisation• QRS complex: ventricular depolarisation• T wave: auricular repolarisation
29Clinical PK
CONTEXT
(1’’)
• Relationship between QT and RR
(=60/HR1000)
• Compare QT before and after treatment, once QT is corrected for HR (QTc)
QT versus RR AT BASELINE
ALL DATA
QT_
BA
SE
LIN
E (m
sec)
300
325
350
375
400
425
450
475
RR (msec)500 750 1000 1250 1500 1750
QT vs. RR QT CORRECTION AT BASELINE
ALL DATA
QTc
_BA
SE
LIN
E (m
sec)
300
325
350
375
400
425
450
RR (msec)500 750 1000 1250 1500 1750
QTc vs. RR