DDMODEL00000221: Schindler_2016_SLD_SUV_OS_GIST

  public model
Short description:
The model uses the PPP&D approach to evaluate the predictive ability of longitudinal model-predicted tumor size (SLD) and individual lesion tumor metabolic activity (SUVmax) on overall survival. The model is based on a clinical study in GIST patients treated with sunitinib. The time-to-event model for OS is described by a constant baseline hazard wna d includes the maximum relative change in SUVmax from baseline across lesions at week 1 as predictor. Files provided: Output_simulated_SLD_SUV_OS_GIST.lst, Output_real_SLD_SUV_OS_GIST.lst, Simulated_SLD_SUV_OS_GIST.csv, Executable_SLD_SUV_OS_GIST.mod, Command.txt
Original code
  • PK-PD modeling of individual lesion FDG-PET response to predict overall survival in patients with sunitinib-treated gastrointestinal stromal tumor.
  • Schindler E, Amantea MA, Karlsson MO, Friberg LE
  • CPT: pharmacometrics & systems pharmacology, 4/2016, Volume 5, Issue 4, pages: 173-181
  • Department of Pharmaceutical Biosciences, Uppsala University, Uppsala, Sweden.
  • Pharmacometric models were developed to characterize the relationships between lesion-level tumor metabolic activity, as assessed by the maximum standardized uptake value (SUVmax) obtained on [(18)F]-fluorodeoxyglucose (FDG) positron emission tomography (PET), tumor size, and overall survival (OS) in 66 patients with gastrointestinal stromal tumor (GIST) treated with intermittent sunitinib. An indirect response model in which sunitinib stimulates tumor loss best described the typically rapid decrease in SUVmax during on-treatment periods and the recovery during off-treatment periods. Substantial interindividual and interlesion variability were identified in SUVmax baseline and drug sensitivity. A parametric time-to-event model identified the relative change in SUVmax at one week for the lesion with the most pronounced response as a better predictor of OS than tumor size. Based on the proposed modeling framework, early changes in FDG-PET response may serve as predictor for long-term outcome in sunitinib-treated GIST.
Emilie Schindler
Context of model development: Clinical end-point; Variability sources in PK and PD (CYP, Renal, Biomarkers);
Discrepancy between implemented model and original publication: None;
Long technical model description: A tumor growth inhibition model describes the time-course of SLD. An indirect response model in which sunitinib stimulates tumor loss of SUVmax response best described the longitudinal SUVmax data, as assessed on FDG-PET scans. Inter-lesion variability in SUVmax is implemented similarly to inter-occasion variability. No disease progression was identified for SUVmax. A time-to-event model with a constant baseline hazard driven by the relative change in SUVmax from baseline for the lesion that responds the most was used to describe overall survival data.;
Model compliance with original publication: Yes;
Model implementation requiring submitter’s additional knowledge: No;
Modelling context description: Changes in FDG-PET standardized uptake values reflecting tumor metabolic activity was suggested as a predictor for long-term clinical outcome in GIST patients treated with anti-angiogenic drugs such as sunitinib. The model characterized the inter-individual and inter-lesion variability in SUVmax response and the predictive ability of SUVmax-related metrics on overall survival.;
Modelling task in scope: estimation;
Nature of research: Early clinical development (Phases I and II);
Therapeutic/disease area: Oncology;
Annotations are correct.
This model is not certified.
  • Model owner: Emilie Schindler
  • Submitted: Oct 12, 2016 10:56:58 AM
  • Last Modified: Oct 12, 2016 10:56:58 AM
  • Version: 9 public model Download this version
    • Submitted on: Oct 12, 2016 10:56:58 AM
    • Submitted by: Emilie Schindler
    • With comment: Edited model metadata online.