DDMODEL00000120: Hansson_2013_hypertension_sunitinib

  public model
Short description:
Population PD indirect response model to characterize the relationship between the exposure of sunitinib, a tyrosine kinase inhibitor, and hypertension.
PharmML 0.8.x (0.8.1)
  • PKPD Modeling of Predictors for Adverse Effects and Overall Survival in Sunitinib-Treated Patients With GIST.
  • Hansson EK, Ma G, Amantea MA, French J, Milligan PA, Friberg LE, Karlsson MO
  • CPT: pharmacometrics & systems pharmacology, 1/2013, Volume 2, pages: e85
  • Department of Pharmaceutical Biosciences, Uppsala University, Uppsala, Sweden.
  • A modeling framework relating exposure, biomarkers (vascular endothelial growth factor (VEGF), soluble vascular endothelial growth factor receptor (sVEGFR)-2, -3, soluble stem cell factor receptor (sKIT)), and tumor growth to overall survival (OS) was extended to include adverse effects (myelosuppression, hypertension, fatigue, and hand-foot syndrome (HFS)). Longitudinal pharmacokinetic-pharmacodynamic models of sunitinib were developed based on data from 303 patients with gastrointestinal stromal tumor. Myelosuppression was characterized by a semiphysiological model and hypertension with an indirect response model. Proportional odds models with a first-order Markov model described the incidence and severity of fatigue and HFS. Relative change in sVEGFR-3 was the most effective predictor of the occurrence and severity of myelosuppression, fatigue, and HFS. Hypertension was correlated best with sunitinib exposure. Baseline tumor size, time courses of neutropenia, and relative increase of diastolic blood pressure were identified as predictors of OS. The framework has potential to be used for early monitoring of adverse effects and clinical response, thereby facilitating dose individualization to maximize OS.CPT: Pharmacometrics & Systems Pharmacology (2013) 2, e85; doi:10.1038/psp.2013.62; advance online publication 4 December 2013.
Paolo Magni
Context of model development: Risk & Benefit Characterization, Outcome Prediction (Clinical & design Viability); Clinical end-point;
Model compliance with original publication: Yes;
Model implementation requiring submitter’s additional knowledge: No;
Modelling context description: A modeling framework relating exposure, biomarkers (vascular endothelial growth factor (VEGF), soluble vascular endothelial growth factor receptor (sVEGFR)-2, -3, soluble stem cell factor receptor (sKIT)), and tumor growth to overall survival (OS) was extended to include adverse effects (myelosuppression, hypertension, fatigue, and hand-foot syndrome (HFS)). Longitudinal pharmacokinetic-pharmacodynamic models of sunitinib were developed based on data from 303 patients with gastrointestinal stromal tumor. Myelosuppression was characterized by a semiphysiological model and hypertension with an indirect response model. Proportional odds models with a first-order Markov model described the incidence and severity of fatigue and HFS. Relative change in sVEGFR-3 was the most effective predictor of the occurrence and severity of myelosuppression, fatigue, and HFS. Hypertension was correlated best with sunitinib exposure. Baseline tumor size, time courses of neutropenia, and relative increase of diastolic blood pressure were identified as predictors of OS. The framework has potential to be used for early monitoring of adverse effects and clinical response, thereby facilitating dose individualization to maximize OS.;
Modelling task in scope: estimation;
Nature of research: Approval phase/Registration trial (Phase III); Early clinical development (Phases I and II);
Therapeutic/disease area: Oncology; Cardiovascular;
Annotations are correct.
This model is not certified.
  • Model owner: Paolo Magni
  • Submitted: Dec 12, 2015 2:25:49 PM
  • Last Modified: Oct 10, 2016 8:09:57 PM
Revisions
  • Version: 8 public model Download this version
    • Submitted on: Oct 10, 2016 8:09:57 PM
    • Submitted by: Paolo Magni
    • With comment: Edited model metadata online.
  • Version: 6 public model Download this version
    • Submitted on: Jul 16, 2016 5:52:55 PM
    • Submitted by: Paolo Magni
    • With comment: Model revised without commit message
  • Version: 2 public model Download this version
    • Submitted on: Dec 12, 2015 2:25:49 PM
    • Submitted by: Paolo Magni
    • With comment: Edited model metadata online.

Name

Generated from MDL. MOG ID: hansson_bp_mog

Independent Variables

T

Function Definitions

combinedError2:realadditive:realproportional:realf:real=proportional2+additive2f2

Covariate Model: cm

Continuous Covariates

DOS
CL
PLAC
WEEK

Parameter Model: pm

Random Variables

eps_RESvm_err.DV~Normal2mean=0var=pm.SIGMA_RES
ETA_BASEvm_mdl.ID~Normal2mean=0var=pm.PPV_BASE
ETA_SLOPEvm_mdl.ID~Normal2mean=0var=pm.PPV_SLOPE
ETA_MRTvm_mdl.ID~Normal2mean=0var=pm.PPV_MRT

Population Parameters

POP_BASE_TREAT
POP_MRT
POP_SLOPE
RUV_PROP
RUV_ADD
POP_BASE_PLAC
PPV_BASE
PPV_SLOPE
PPV_MRT
COV_BASE_SLOPE
SIGMA_RES
POP_BASE_GROUP={pm.POP_BASE_PLACifcm.PLAC=1pm.POP_BASE_TREATotherwise

Individual Parameters

BASE=pm.POP_BASE_GROUPpm.ETA_BASE
MRT=pm.POP_MRTpm.ETA_MRT
SLOPE=pm.POP_SLOPEpm.ETA_SLOPE

Random Variable Correlation

covETA_BASEETA_SLOPE=pm.COV_BASE_SLOPE

Structural Model: sm

Variables

AUC=cm.DOScm.CL
KOUT=1pm.MRT
KIN=pm.BASEsm.KOUT
EFF=pm.SLOPEsm.AUC
TA1=sm.KIN1+sm.EFF-sm.KOUTsm.A1A1T=0=pm.BASE

Observation Model: om1

Continuous Observation

Y=sm.A1+combinedError2additive=pm.RUV_ADDproportional=pm.RUV_PROPf=sm.A1+pm.eps_RES

External Dataset

OID
nm_ds
Tool Format
NONMEM

File Specification

Format
csv
Delimiter
comma
File Location
Simulated_Sutent_BP.csv

Column Definitions

Column ID Position Column Type Value Type
ID
1
id
int
CYCL
2
undefined
real
TIME
3
idv
real
TIMED
4
undefined
real
DOS
5
covariate
real
DV
6
dv
real
MDV
7
mdv
int
CL
8
covariate
real
PLAC
9
covariate
real
WEEK
10
covariate
real

Column Mappings

Column Ref Modelling Mapping
ID
vm_mdl.ID
TIME
T
DOS
cm.DOS
DV
om1.Y
CL
cm.CL
PLAC
cm.PLAC
WEEK
cm.WEEK

Estimation Step

OID
estimStep_1
Dataset Reference
nm_ds

Parameters To Estimate

Parameter Initial Value Fixed? Limits
pm.POP_BASE_TREAT
71.8
false
0
pm.POP_MRT
361
false
0
pm.POP_SLOPE
0.119
false
0
pm.RUV_PROP
0.0697
false
0
pm.RUV_ADD
6.24
false
0
pm.POP_BASE_PLAC
77.6
false
0
pm.PPV_BASE
0.0151
false
pm.PPV_SLOPE
0.416
false
pm.COV_BASE_SLOPE
-0.0515
false
pm.PPV_MRT
0.694
false
pm.SIGMA_RES
1
true

Operations

Operation: 1

Op Type
generic
Operation Properties
Name Value
algo
foce

Step Dependencies

Step OID Preceding Steps
estimStep_1
 
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