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Model Definition
Trial Design
Modelling Steps

Short description:

Population PD indirect response model to characterize the relationship between the exposure of sunitinib, a tyrosine kinase inhibitor, and hypertension.

Format:	PharmML 0.8.x (0.8.1)
Related Publication:	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 Affiliation: Department of Pharmaceutical Biosciences, Uppsala University, Uppsala, Sweden. Abstract: 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.
Contributors:	Paolo Magni

Context of model development:	Clinical end-point; Risk & Benefit Characterization, Outcome Prediction (Clinical & design Viability);
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:	Early clinical development (Phases I and II); Approval phase/Registration trial (Phase III);
Therapeutic/disease area:	Oncology; Cardiovascular;

Validation Status:	Annotations are correct.
Certification Comment:	This model is not certified.

Mandatory file
- Executable_Hansson_2013_blood_pressure_sunitinib_NOSTRUCTURED.xml

Additional Files

Model owner: Paolo Magni
Submitted: Dec 12, 2015 2:25:49 PM
Last Modified: Oct 10, 2016 8:09:57 PM

Revisions

Version: 8
- Submitted on: Oct 10, 2016 8:09:57 PM
- Submitted by: Paolo Magni
- With comment: Edited model metadata online.
Version: 6
- Submitted on: Jul 16, 2016 5:52:55 PM
- Submitted by: Paolo Magni
- With comment: Model revised without commit message
Version: 2
- 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

Function Definitions

combinedError2 : real (additive : real, proportional : real, f : real) = \sqrt{({proportional}^{2} + {additive}^{2} \cdot f^{2})}

Covariate Model: $cm$

Continuous Covariates

DOS

CL

PLAC

WEEK

Parameter Model: $pm$

Random Variables

{eps_RES}_{vm_err.DV} ~ Normal2 (mean = 0, var = pm.SIGMA_RES)

{ETA_BASE}_{vm_mdl.ID} ~ Normal2 (mean = 0, var = pm.PPV_BASE)

{ETA_SLOPE}_{vm_mdl.ID} ~ Normal2 (mean = 0, var = pm.PPV_SLOPE)

{ETA_MRT}_{vm_mdl.ID} ~ Normal2 (mean = 0, var = 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 = {\begin{cases} pm.POP_BASE_PLAC & if & cm.PLAC = 1 \\ pm.POP_BASE_TREAT & otherwise \end{cases}

Individual Parameters

BASE = pm.POP_BASE_GROUP \cdot ⅇ^{pm.ETA_BASE}

MRT = pm.POP_MRT \cdot ⅇ^{pm.ETA_MRT}

SLOPE = pm.POP_SLOPE \cdot ⅇ^{pm.ETA_SLOPE}

Random Variable Correlation

cov (ETA_BASE, ETA_SLOPE) = pm.COV_BASE_SLOPE

Structural Model: $sm$

Variables

AUC = \frac{cm.DOS}{cm.CL}

KOUT = \frac{1}{pm.MRT}

KIN = pm.BASE \cdot sm.KOUT

EFF = pm.SLOPE \cdot sm.AUC

\begin{matrix} \frac{ⅆ}{ⅆ T} A1 = sm.KIN \cdot (1 + sm.EFF) - sm.KOUT \cdot sm.A1 \\ A1 (T = 0) = pm.BASE \end{matrix}

Observation Model: $om1$

Continuous Observation

Y = sm.A1 + combinedError2 (additive = pm.RUV_ADD, proportional = pm.RUV_PROP, f = 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
$estimStep_1$

DDMODEL00000120: Hansson_2013_hypertension_sunitinib