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

Back

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