DDMODEL00000090: Claret_2009_oncology_capecitabine_TGI

  public model
Short description:
Model-Based Prediction of Phase III Overall Survival in Colorectal Cancer on the Basis of Phase II Tumor Dynamics. Assessing and predicting anticancer drug effect by using longitudinal tumor size data gathered in phase II study. In the original paper DOSE is used as time dependent covariate. The uploaded model includes the K-PD approach using capecitabine exposure as input
PharmML (0.6.1)
  • Model-Based Prediction of Phase III Overall Survival in Colorectal Cancer on the basis of Phase II Tumor Dynamics
  • L. Claret, P. Girard, P. M. Hoff, E. V. Cutsem, K. P. Zuideveld, K. Jorga, J. Fagerberg, R. Bruno
  • Journal of Clinical Oncology, 9/2009, Volume 27, pages: 4103-4108
  • Pharsight, Marseille (France) Merck Serono, Epfl Innovation Park, Lausanne (Switzerland) Université de Sao Paolo / Centre d’oncologie à l’hopital de Sírio Libanês, (Brazil) University of Leuven, Leuven (Belgium) F. Hoffmann-La Roche Ltd., Pharma, Ba
  • Purpose Purpose We developed a drug-disease simulation model to predict antitumor response and overall survival in phase III studies from longitudinal tumor size data in phase II trials. Methods We developed a longitudinal exposure-response tumor-growth inhibition (TGI) model of drug effect (and resistance) using phase II data of capecitabine (n  34) and historical phase III data of fluorouracil (FU; n  252) in colorectal cancer (CRC); and we developed a parametric survival model that related change in tumor size and patient characteristics to survival time using historical phase III data (n  245). The models were validated in simulation of antitumor response and survival in an independent phase III study (n  1,000 replicates) of capecitabine versus FU in CRC. Results The TGI model provided a good fit of longitudinal tumor size data. A lognormal distribution best described the survival time, and baseline tumor size and change in tumor size from baseline at week 7 were predictors (P  .00001). Predicted change of tumor size and survival time distributions in the phase III study for both capecitabine and FU were consistent with observed values, for example, 431 days (90% prediction interval, 362 to 514 days) versus 401 days observed for survival in the capecitabine arm. A modest survival improvement of 39 days (90% prediction interval, 21 to 110 days) versus 35 days observed was predicted for capecitabine. Conclusion The modeling framework successfully predicted survival in a phase III trial on the basis of capecitabine phase II data in CRC. It is a useful tool to support end-of-phase II decisions and design of phase III studies.
Nadia Terranova, Kheizurane_ElMekki
Context of model development: Disease Progression model;
Discrepancy between implemented model and original publication: The uploaded model covers only the TGI model implementation and not the analysis on the overall survival. The K-PD approach is included in this implementation.;
Long technical model description: Longitudinal model describing tumor growth dynamics assuming exponential growth and killing effect depending on the dose of a cytotoxic drug and on a function accounting for progressive resistance. ;
Model compliance with original publication: No;
Model implementation requiring submitter’s additional knowledge: No;
Modelling context description: Assessing and predicting the anticancer drug effect by using longitudinal tumor size data gathered in phase II;
Modelling task in scope: estimation;
Nature of research: Approval phase/Registration trial (Phase III);
Therapeutic/disease area: Oncology;
Annotations are correct.
This model is not certified.
  • Model owner: Nadia Terranova
  • Submitted: Dec 11, 2015 5:10:41 PM
  • Last Modified: Jul 13, 2016 12:25:08 PM
Revisions
  • Version: 25 public model Download this version
    • Submitted on: Jul 13, 2016 12:25:08 PM
    • Submitted by: Nadia Terranova
    • With comment: Updated model annotations.
  • Version: 21 public model Download this version
    • Submitted on: Jul 13, 2016 12:13:22 PM
    • Submitted by: Nadia Terranova
    • With comment: Updated model annotations.
  • Version: 19 public model Download this version
    • Submitted on: Jul 12, 2016 2:40:25 PM
    • Submitted by: Kheizurane_ElMekki
    • With comment: Updated model annotations.
  • Version: 14 public model Download this version
    • Submitted on: Jun 3, 2016 2:04:23 PM
    • Submitted by: Kheizurane_ElMekki
    • With comment: Updated model annotations.
  • Version: 8 public model Download this version
    • Submitted on: Dec 11, 2015 5:10:41 PM
    • Submitted by: Nadia Terranova
    • With comment: Edited model metadata online.

Independent variable T

Function Definitions

combinedError1(additive,proportional,f)=(additive+(proportional ×f))

Structural Model sm

Variable definitions

RESISTANCE=exp((-LAMBDA ×T))
dCAPEdT=(-KE ×CAPE)
dTSdT=((KL ×TS)-(((KD ×RESISTANCE) ×CAPE) ×TS))

Initial conditions

CAPE=0
TS=TS0

Variability Model

Level Type

DV

 residualError

ID

 parameterVariability

Parameter Model

Parameters
POP_TS0;  POP_KL;  POP_KD;  POP_KE;  POP_LAMBDA;  PERR;  AERR;  OMEGA_TS0;  OMEGA_KL;  OMEGA_KD;  OMEGA_KE;  OMEGA_LAMBDA;  SIGMA_RES_TS; 
eta_TS0∼N(0.0,OMEGA_TS0) — ID
eta_KL∼N(0.0,OMEGA_KL) — ID
eta_KD∼N(0.0,OMEGA_KD) — ID
eta_KE∼N(0.0,OMEGA_KE) — ID
eta_LAMBDA∼N(0.0,OMEGA_LAMBDA) — ID
eps_RES_TS∼N(0.0,SIGMA_RES_TS) — DV
log(TS0)=(log(POP_TS0)+eta_TS0)
log(KL)=(log(POP_KL)+eta_KL)
log(KD)=(log(POP_KD)+eta_KD)
log(KE)=(log(POP_KE)+eta_KE)
log(LAMBDA)=(log(POP_LAMBDA)+eta_LAMBDA)

Observation Model

Observation Y
Continuous / Residual Data

Parameters
Y=(TS+(combinedError1(AERR,PERR,TS) ×eps_RES_TS))

Estimation Steps

Estimation Step estimStep_1

Estimation parameters

Fixed parameters

  • PERR=0
  • SIGMA_RES_TS=1

Initial estimates for non-fixed parameters

  • POP_TS0=71
  • POP_KL=0.021
  • POP_KD=0.025
  • POP_KE=8.4
  • POP_LAMBDA=0.053
  • AERR=12
  • OMEGA_TS0=0.4
  • OMEGA_KL=0.499
  • OMEGA_KD=0.388
  • OMEGA_KE=0.04
  • OMEGA_LAMBDA=1.26
Estimation operations
1) Estimate the population parameters
    Algorithm FOCEI

    Step Dependencies

    • estimStep_1
     
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