DDMODEL00000197: Biomarker model for Sunitinib Treatment in GIST

  public model
Short description:
A combined model for biomarkers -VEGF, VEGFR2, VEGFR3 and SKIT in sunitib treated GIST patients.
Original code
  • PKPD Modeling of VEGF, sVEGFR-2, sVEGFR-3, and sKIT as Predictors of Tumor Dynamics and Overall Survival Following Sunitinib Treatment in GIST.
  • Hansson EK, Amantea MA, Westwood P, Milligan PA, Houk BE, French J, Karlsson MO, Friberg LE
  • CPT: pharmacometrics & systems pharmacology, 1/2013, Volume 2, pages: e84
  • Department of Pharmaceutical Biosciences, Uppsala University, Uppsala, Sweden.
  • The predictive value of longitudinal biomarker data (vascular endothelial growth factor (VEGF), soluble VEGF receptor (sVEGFR)-2, sVEGFR-3, and soluble stem cell factor receptor (sKIT)) for tumor response and survival was assessed based on data from 303 patients with imatinib-resistant gastrointestinal stromal tumors (GIST) receiving sunitinib and/or placebo treatment. The longitudinal tumor size data were well characterized by a tumor growth inhibition model, which included, as significant descriptors of tumor size change, the model-predicted relative changes from baseline over time for sKIT (most significant) and sVEGFR-3, in addition to sunitinib exposure. Survival time was best described by a parametric time-to-event model with baseline tumor size and relative change in sVEGFR-3 over time as predictive factors. Based on the proposed modeling framework to link longitudinal biomarker data with overall survival using pharmacokinetic-pharmacodynamic models, sVEGFR-3 demonstrated the greatest predictive potential for overall survival following sunitinib treatment in GIST.CPT: Pharmacometrics & Systems Pharmacology (2013) 2, e84; doi:10.1038/psp.2013.61; advance online publication 20 November 2013.
Sreenath M Krishnan
Context of model development: Disease Progression model; Variability sources in PK and PD (CYP, Renal, Biomarkers); Mechanistic Understanding;
Long technical model description: Final PD model (predicting plasma concentrations) of biomarkers during sunitinib treatment of imatinib-resistant GIST. 4 biomarkers: VEGF, sVEGFR-2, sVEGFR-3 and sKIT and 4 compartments, 1 for each biomarker, initiated to estimated baselines at t=0. Log-transformed plasma concentrations (DV) and effect on dA/dt for each biomarker as indirect response models. Sigmoid Imax for VEGF/sVEGFR-2 (hill factor) and Imax for sVEGFR-3/sKIT. Inhibition of Kout for VEGF and Kin for sVEGFR-2, sVEGFR-3 and sKIT. Lin. disease progression model for VEGF and sKIT (otherwise baseline only). No covariates. IIV on baseline, MRT (1/Kout), IC50 and disease progression slope. Residual error: Additive (on log scale) on VEGF, sVEGFR-3 and sKIT, and Additive + proportional (on log scale) on sVEGFR-2. Estimation method: FOCE with INTERACTION (+ COVARIANCE STEP).;
Model compliance with original publication: Yes;
Model implementation requiring submitter’s additional knowledge: No;
Modelling context description: PD model (predicting plasma concentrations) of biomarkers during sunitinib treatment of imatinib-resistant GIST; ;
Modelling task in scope: estimation; simulation;
Nature of research: Clinical research & Therapeutic use;
Therapeutic/disease area: Oncology;
Annotations are correct.
This model is not certified.
  • Model owner: Sreenath M Krishnan
  • Submitted: Jun 3, 2016 12:41:56 PM
  • Last Modified: Jun 3, 2016 12:41:56 PM
  • Version: 5 public model Download this version
    • Submitted on: Jun 3, 2016 12:41:56 PM
    • Submitted by: Sreenath M Krishnan
    • With comment: Updated model annotations.