Population PK/PD modeling of abexinostat -induced thrombocytopenia...

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Population PK/PD modeling of abexinostat-induced thrombocytopenia in phase I -- Application for the determination of the dose/toxicity relationship Sylvain Fouliard 1 , Quentin Chalret du Rieu 1,2 , Mélanie White-Koning 2 , Etienne Chatelut 2 , Marylore Chenel 1 1 Clinical Pharmacokinetics Department, Institut de Recherches Internationales Servier, Suresnes, France 2 EA4553, Université Paul Sabatier and Institut Universitaire du Cancer Toulouse - Oncopole, Toulouse, France

Transcript of Population PK/PD modeling of abexinostat -induced thrombocytopenia...

Page 1: Population PK/PD modeling of abexinostat -induced thrombocytopenia …regist2.virology-education.com/2015/1stOnco_pk/24_Fouliard.pdf · Population PK/PD modeling of abexinostat -induced

Population PK/PD modeling of abexinostat-induced thrombocytopenia in phase I

-- Application for the determination of the dose/toxicity relationship

Sylvain Fouliard1, Quentin Chalret du Rieu1,2, Mélanie White-Koning2, Etienne Chatelut2, Marylore Chenel1

1 Clinical Pharmacokinetics Department, Institut de Recherches Internationales Servier, Suresnes, France

2 EA4553, Université Paul Sabatier and Institut Universitaire du Cancer Toulouse - Oncopole, Toulouse, France

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23-24 March 2015 Amsterdam 1st International Workshop on Dose optimization Strategies for Targeted Drugs 2

Abexinostat

• Histone deacetylase inhibitor (HDACi)

• Histone hyper-acetylation leads to over-expression of tumour suppressor genes

• Thrombocytopenia: principal dose limiting toxicity (DLT)

• Dose escalation trials: – Few patients per

schedule/dose/study – Opportunity for integrated

analyses

Silenced genes

Opening chromatin and tumor-suppressor genes expression

Abexinostat

DLT: Grade 4 Thrombocytopenia (CTCAE v4.0, Platelet count < 25x109/L) during the 1st treatment cycle.

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Our objectives

Based on the PK/PD relationship between drug concentration and platelet count:

– To determine the recommended Phase II dose (RP2D) – To evaluate and optimize the administration schedule

23-24 March 2015 Amsterdam 1st International Workshop on Dose optimization Strategies for Targeted Drugs 3

FIH, All cancer

US ph I Solid Tum. EU ph I Solid Tum Combi-therapy ph II Sol. Tum.

Mono-therapy ph II Hem. Mal.

Combi-therapy ph II Hem. Mal. EU ph I Hem. Mal.

US ph I Hem. Mal.

Clin. Dvt.

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Pharmacokinetic modelling: a first step

FIH, All cancer US ph I Solid Tum. EU ph I Solid Tum Combi-therapy ph II Sol. Tum.

Mono-therapy ph II Hem. Mal.

Combi-therapy ph II Hem. Mal.

Upcoming Completed Ongoing

EU ph I Hem. Mal.

US ph I Hem. Mal.

Clin. Dvt.

1st International Workshop on Dose optimization Strategies for Targeted Drugs 23-24 March 2015 Amsterdam

External model

Evaluation

Initial model building

Time (h)

Conc

entr

atio

n (n

g/m

l)

Fouliard et al, Eur J Cancer (2013)

PK model

Oral

Administration

Drug

Elimination

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PK/PD relationship assessed in patients with solid tumours

23-24 March 2015 Amsterdam 1st International Workshop on Dose optimization Strategies for Targeted Drugs 5

Prol T T T Circ

External model

Evaluation -

Time (d)

Obs

erve

d an

d sim

ulat

ed

Plat

elet

cou

nt (G

/L)

Chalret du Rieu et al, Pharm Res (2013)

FIH, All cancer US ph I Solid Tum. EU ph I Solid Tum Combi-therapy ph II Sol. Tum.

Mono-therapy ph II Hem. Mal.

Combi-therapy ph II Hem. Mal.

Upcoming Completed Ongoing

US ph I Hem. Mal.

Clin. Dvt.

Initial model building (n=35)

EU ph I Hem. Mal.

PK/PD model

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• bid dosing is similar as qd dosing • Treatment split within a week is safer

qd bid 4h apart bid 12h apart tid 4 h apart

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Simulations across various administration schedules:

Plat

elet

cou

nt (G

/L)

Time (d)

Plat

elet

cou

nt (G

/L)

Time (d)

Influence of the administration schedule over a treatment cycle (same daily dose)

Influence of the administration schedule over a day (same daily dose)

14on/7off 4on/3off*3 5on/2off*2 11on/10off

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23-24 March 2015 Amsterdam 1st International Workshop on Dose optimization Strategies for Targeted Drugs 7

US ph I Solid Tum. EU ph I Solid Tum Combi-therapy ph II Sol. Tum.

Mono-therapy ph II Hem. Mal.

Combi-therapy ph II Hem. Mal.

Upcoming Completed Completed

EU ph I Hem. Mal.

US ph I Hem. Mal.

Clin. Dvt.

Amendment in EU studies in patients with hematological malignancies and solid tumours, implementing the 4on/3off*3 administration schedule

Initial model building

FIH, All cancer

• Study amendment

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23-24 March 2015 Amsterdam 1st International Workshop on Dose optimization Strategies for Targeted Drugs 8

US ph I Solid Tum. EU ph I Solid Tum Combi-therapy ph II Sol. Tum.

Mono-therapy ph II Hem. Mal.

Combi-therapy ph II Hem. Mal.

Upcoming Completed Completed

EU ph I Hem. Mal.

US ph I Hem. Mal.

Clin. Dvt.

Amendment in EU studies in patients with hematological malignancies and solid tumours, implementing the 4on/3off*3 administration schedule

2 dose levels increase of the RP2D in solid tumours study (+30% dose intensity)

Initial model building

FIH, All cancer

• Study amendment -> MTD increase

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23-24 March 2015 Amsterdam 1st International Workshop on Dose optimization Strategies for Targeted Drugs 9

US ph I Solid Tum. EU ph I Solid Tum Combi-therapy ph II Sol. Tum.

Mono-therapy ph II Hem. Mal.

Combi-therapy ph II Hem. Mal.

Upcoming Completed Completed

EU ph I Hem. Mal.

US ph I Hem. Mal.

Clin. Dvt.

Amendment in EU studies in patients with hematological malignancies and solid tumours, implementing the 4on/3off*3 administration schedule

2 dose levels increase of the RP2D in solid tumours study (+30% dose intensity)

No increase of the RP2D in hem. mal. study

Initial model building

FIH, All cancer

• Study amendment -> MTD increase • But no change in hem. mal. studies

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23-24 March 2015 Amsterdam 1st International Workshop on Dose optimization Strategies for Targeted Drugs 10

US ph I Solid Tum. EU ph I Solid Tum Combi-therapy ph II Sol. Tum.

Mono-therapy ph II Hem. Mal.

Combi-therapy ph II Hem. Mal.

Upcoming Completed Completed

EU ph I Hem. Mal.

US ph I Hem. Mal.

Clin. Dvt.

Amendment in EU studies in patients with hematological malignancies and solid tumours, implementing the 4on/3off*3 administration schedule

2 dose levels increase of the RP2D in solid tumours study (+30% dose intensity)

No increase of the RP2D in hem. mal. study

Joint model in both populations Initial model building

FIH, All cancer

• Study amendment -> MTD increase • But no change in hem. mal. studies

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Internal evaluation

Time (d)

Obs

erve

d an

d pr

edic

ted

Plat

elet

cou

nt (G

/L)

Chalret du Rieu et al, Invest New Drugs (2014)

Joint model in both populations (n=125)

FIH, All cancer US ph I Solid Tum. EU ph I Solid Tum Combi-therapy ph II Sol. Tum.

Mono-therapy ph II Hem. Mal.

Combi-therapy ph II Hem. Mal.

Upcoming Completed Ongoing

US ph I Hem. Mal.

Clin. Dvt.

EU ph I Hem. Mal.

Prol T T T Circ

Base

Time

-

- PK/PD model

Disease

model

A different PK/PD relationship in a different pathology

Time (d)

Obs

erve

d an

d pr

edic

ted

Plat

elet

cou

nt (G

/L)

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23-24 March 2015 Amsterdam 1st International Workshop on Dose optimization Strategies for Targeted Drugs 12

0

10

20

30

40

50

60

% D

LT

Dose (mg/day)

+ 2 dose levels

+ 4 dose levels

Target Threshold

• 4Don/3Doff is the safest administration schedule in both populations

• RP2D in patients with solid tumours is more sensitive to administration schedule than RP2D in patients with hem. mal.

• Need for a statistically efficient dose-finding methodology

Simulation of individual platelet count-time profile and derivation of the % of patients undergoing a DLT:

A different dose/safety relationship in a different pathology

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23-24 March 2015 Amsterdam

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Bayesian designs are more sensitive than algorithm-based designs

Vong et al, PAGE (2014), & under submission

1st International Workshop on Dose optimization Strategies for Targeted Drugs

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• When the context is favorable for model-informed decision-making, opportunities should be seized

• Consistent PK/PD relationship across doses/administration schedules/indication, allowing robust simulations and save time

• Illustration of the learn/confirm paradigm, and the need to challenge model assumptions through the development

• Advocates the use of model-based approaches for RP2D determination, integrating all available data

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Take home message

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Acknowledgements – Quentin Chalret du Rieu – Mélanie White-Koning – Étienne Chatelut – Marylore Chenel – Camille Vong

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