1 Clinical 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 - 11 th June 2010

2 Clinical PK QT prolongation, a biomarker of Torsade de Pointes. QT measured on ECG, then corrected. Circadian rythm in QT/QTc data Usually mandatory QT/QT C study performed in healthy volunteers at supratherapeutic dose Guidelines: mean QTc effect > 5ms CONTEXT (1)

3 Clinical 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

4 Clinical PK D1 D2 D4 D14 D22 Inclusion TreatmentNo Treatment ECG Dose 2 phase I clinical trials: n = (=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

5 Clinical 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.

6 Clinical PK [1] Piotrovsky, V. “Pharmacokinetic-pharmacodynamic modeling in the data analysis and 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 (=149) healthy volunteers - QT data without drug - Fredericia correction: QTc = QT * HR 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

7 Clinical 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

8 Clinical 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, VPC more MATERIALS & METHODS (3) POPULATION PK MODEL Periph. 1 (V2) Central (V1) Periph. 2 (V3) CL F Ka Q3 Q2

9 Clinical PK MATERIALS & METHODS (5) CALCULATION OF FISHER INFORMATION MATRIX Sequential pop PKPD modelling PK 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 QTM 0, QTA 1, QTA 2, QTA 3, QTL 1, QTL 2, QTL 3, γ

10 Clinical 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)

11 Clinical PK 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 H 0 : no QTc effect of the drug,  0 = 0 - Alternative hypothesis H 1 : QTc effect of the drug,  0 > 0 Then power is computed from the type II error β. Power = 1- β.

12 Clinical 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

13 Clinical 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): (2007)

14 Clinical 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).

15 Clinical PK RESULTS (2) EMPIRICAL DESIGN EVALUATION (2)  The RSEs of QTc model parameters are always lower than 20% for fixed effects, except for QTA 1, 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). QTM 0 (ms) QTA 1 QTA 2 QTA 3 QTL 1 (hr) QTL 2 (hr) QTL 3 (hr) Add_Err (ms)  RSE (%)

16 Clinical PK  Power > 90 % for  > 0.02, corresponding to a 2 ms average QTc- prolongation. RESULTS (3) POWER DETECTION OF DRUG EFFECT Power of drug effect detection versus  value (drug effect size)

17 Clinical 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 QTA 1. Optimal design (Det = 2.22 x ) Empirical design (Det = 2.37 x ) QTM 0 (ms) QTA 1 QTA 2 QTA 3 QTL 1 (hr) QTL 2 (hr) QTL 3 (hr) Add_Err (ms)  RSE (%) QTM 0 QTA 1 QTA 2 QTA 3 QTL 1 QTL 2 QTL 3 Add_Err  RSE (%) Sampling times : D1 D2 D4 D14 D22 Phase I/II design0, 1.5, 4, 5.5, 8h 0, 1.5h 0, 1.5h 0, 1.5h0, 1.5h Optimized design4, 8, 8.2, 8.8, 9.6h 1.5, 5.6h 3.8, 5.2h 0, 0.6h1, 1.5h

18 Clinical 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.

19 Clinical 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)

20 Clinical PK ACKNOWLEDGMENT Sylvain Fouliard pharmacometrician at Servier France Mentré

21 Clinical PK BACK-UP

22 Clinical PK CLKAFV1V2V3Q2Q3ErrAErrP 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) (32.2) 0.31 (6.36) IIV (RSE %) (38.8) (32.2) (28) (35.5) (46.9).. Back RESULTS MODEL BUILDING Population PK model Parameter estimates and RSE of the population PK model Parameter

23 Clinical PK Normal scaleLog scale Normalized dose Median 5% - 95 % CI Observations … Back Time (h) RESULTS MODEL EVALUATION Visual predictive checks Population PK model

24 Clinical PK Observed Values compared to Simulated Confidence Interval CIObs below CI (%) Obs in CI (%) Obs above CI (%) MEDIAN [P1-P99] [P5-P95] [P10-P90] [P25-P75] Back RESULTS MODEL EVALUATION Numerical predictive checks Population PK model

25 Clinical PK RESULTS MODEL BUILDING QTM 0 (ms) QTA1QTL1 (hr) QTA2QTL2 (hr) QTA3QTL3 (hr) ErrA (ms) Estimates (RSE %) 400 (0.214) (12) 12 (1.98) (7.75) 7.66 (1.04) (3.8) 5.73 (0.61) 5.35 (2.5) IIV (RSE %) (10.7) (32.3) 2.4 (24.3) (43.2) (26.2) (40.9) (23.7). Baseline poly-cosine QTc model Parameter Back Parameter estimates and RSE of the baseline poly-cosine QTc model

26 Clinical PK QTc (ms) Time (h) … Median 5% - 95 % CI Observations Back RESULTS MODEL EVALUATION Baseline poly-cosine QTc model Visual predictive checks

27 Clinical PK Baseline poly-cosine QTc model Observed Values compared to Simulated Confidence Interval CI Obs below CI (%) Obs in CI (%)Obs above CI (%) MEDIAN [P1-P99] [P5-P95] [P10-P90] [P25-P75] Back RESULTS MODEL EVALUATION Numerical predictive checks

28 Clinical PK CONTEXT (1’) P wave: auricular depolarisation QRS complex: ventricular depolarisation T wave: auricular repolarisation

29 Clinical PK CONTEXT (1’’) Relationship between QT and RR (=60/HR  1000) Compare QT before and after treatment, once QT is corrected for HR (QTc) QT vs. RR QTc vs. RR