DDMODEL00000128: Ribba_2012_PCV

  public model
Short description:
The model is a tumor growth inhibition model for adult diffuse low-grade gliomas (LGGs). The model describes tumor size evolution in patients treated with PCV (procarbazine, 1-(2-chloroethyl)-3-cyclohexyl-l-nitrosourea (CCNU) and vincristine). The data set includes longitudinal mean tumor diameter data obtained from 21. The model is developed in a mixed-effects fashion and consists of non-linear ordinary differential equations. Parameters are fit using the SAEM algorithm implemented in Monolix. The PCV protocol consisted of up to 6 cycles of the following treatment, with intervals of 6 weeks between cycles: CCNU (110 mg/m2) administered on day 1, procarbazine (60 mg/m2) administered on days 8 to 21, and vincristine (1.4 mg/m2, max. 2 mg) administered on days 8 and 29.
PharmML (0.6.1)
  • A tumor growth inhibition model for low-grade glioma treated with chemotherapy or radiotherapy.
  • Ribba B, Kaloshi G, Peyre M, Ricard D, Calvez V, Tod M, Cajavec-Bernard B, Idbaih A, Psimaras D, Dainese L, Pallud J, Cartalat-Carel S, Delattre JY, Honnorat J, Grenier E, Ducray F
  • Clinical cancer research : an official journal of the American Association for Cancer Research, 9/2012, Volume 18, Issue 18, pages: 5071-5080
  • Ribba, INRIA, Project-team NUMED, Ecole Normale Superieure de Lyon, 46 allee d0Italie, 69007 Lyon Cedex 07, France. benjamin.ribba@inria.fr
  • PURPOSE: To develop a tumor growth inhibition model for adult diffuse low-grade gliomas (LGG) able to describe tumor size evolution in patients treated with chemotherapy or radiotherapy. EXPERIMENTAL DESIGN: Using longitudinal mean tumor diameter (MTD) data from 21 patients treated with first-line procarbazine, 1-(2-chloroethyl)-3-cyclohexyl-l-nitrosourea, and vincristine (PCV) chemotherapy, we formulated a model consisting of a system of differential equations, incorporating tumor-specific and treatment-related parameters that reflect the response of proliferative and quiescent tumor tissue to treatment. The model was then applied to the analysis of longitudinal tumor size data in 24 patients treated with first-line temozolomide (TMZ) chemotherapy and in 25 patients treated with first-line radiotherapy. RESULTS: The model successfully described the MTD dynamics of LGG before, during, and after PCV chemotherapy. Using the same model structure, we were also able to successfully describe the MTD dynamics in LGG patients treated with TMZ chemotherapy or radiotherapy. Tumor-specific parameters were found to be consistent across the three treatment modalities. The model is robust to sensitivity analysis, and preliminary results suggest that it can predict treatment response on the basis of pretreatment tumor size data. CONCLUSIONS: Using MTD data, we propose a tumor growth inhibition model able to describe LGG tumor size evolution in patients treated with chemotherapy or radiotherapy. In the future, this model might be used to predict treatment efficacy in LGG patients and could constitute a rational tool to conceive more effective chemotherapy schedules.
Christian Laveille
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  • Model owner: Christian Laveille
  • Submitted: Dec 16, 2015 8:06:02 AM
  • Last Modified: Dec 16, 2015 8:06:02 AM
Revisions
  • Version: 4 public model Download this version
    • Submitted on: Dec 16, 2015 8:06:02 AM
    • Submitted by: Christian Laveille
    • With comment: Edited model metadata online.

Independent variable TIME

Function Definitions

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

Structural Model sm

Variable definitions

K=100
C=C_m
PT=PT_m
Q=Q_m
QP=QP_m
DPSTAR=((PT+Q)+QP)
dC_mdTIME=(-KDE ×C)
dPT_mdTIME=(((((LAMBDAP ×PT) ×(1-DPSTARK))+(KQPP ×QP))-(KPQ ×PT))-(((GAMA ×PT) ×KDE) ×C))
dQ_mdTIME=((KPQ ×PT)-(((GAMA ×Q) ×KDE) ×C))
dQP_mdTIME=(((((GAMA ×Q) ×KDE) ×C)-(KQPP ×QP))-(DELTAQP ×QP))
EFF=((PT_m+Q_m)+QP_m)

Initial conditions

C_m=0
PT_m=PT0
Q_m=Q0
QP_m=0

Variability Model

Level Type

DV

 residualError

ID

 parameterVariability

Parameter Model

Parameters
TVPT0;  TVQ0;  TVLAMBDAP;  TVKPQ;  TVKQPP;  TVDELTAQP;  TVGAMA;  TVKDE;  SDADD;  SDPROP;  OMPT0;  OMQ0;  OMLAMBDAP;  OMKPQ;  OMKQPP;  OMDELTAQP;  OMGAMA;  OMKDE;  SIGMA; 
ETA_PT0∼N(0.0,OMPT0) — ID
ETA_Q0∼N(0.0,OMQ0) — ID
ETA_LAMBDAP∼N(0.0,OMLAMBDAP) — ID
ETA_KPQ∼N(0.0,OMKPQ) — ID
ETA_KQPP∼N(0.0,OMKQPP) — ID
ETA_DELTAQP∼N(0.0,OMDELTAQP) — ID
ETA_GAMA∼N(0.0,OMGAMA) — ID
ETA_KDE∼N(0.0,OMKDE) — ID
EPS_Y∼N(0.0,SIGMA) — DV
log(PT0)=(log(TVPT0)+ETA_PT0)
log(Q0)=(log(TVQ0)+ETA_Q0)
log(LAMBDAP)=(log(TVLAMBDAP)+ETA_LAMBDAP)
log(KPQ)=(log(TVKPQ)+ETA_KPQ)
log(KQPP)=(log(TVKQPP)+ETA_KQPP)
log(DELTAQP)=(log(TVDELTAQP)+ETA_DELTAQP)
log(GAMA)=(log(TVGAMA)+ETA_GAMA)
log(KDE)=(log(TVKDE)+ETA_KDE)

Observation Model

Observation Y
Continuous / Residual Data

Parameters
Y=(EFF+(combinedError1(SDADD,SDPROP,EFF) ×EPS_Y))

Estimation Steps

Estimation Step estimStep_1

Estimation parameters

Fixed parameters

  • SDPROP=0
  • OMKDE=0.49
  • SIGMA=1

Initial estimates for non-fixed parameters

  • TVPT0=10
  • TVQ0=50
  • TVLAMBDAP=0.15
  • TVKPQ=0.05
  • TVKQPP=0.005
  • TVDELTAQP=0.01
  • TVGAMA=1
  • TVKDE=0.3
  • SDADD=3
  • OMPT0=0.25
  • OMQ0=0.25
  • OMLAMBDAP=0.25
  • OMKPQ=0.25
  • OMKQPP=0.25
  • OMDELTAQP=0.25
  • OMGAMA=0.25
Estimation operations
1) Estimate the population parameters
    Algorithm SAEM

    Step Dependencies

    • estimStep_1
     
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