DDMODEL00000103: Trefz_2015_metabolism_Kuvan_TurnoverKPD
Short description:
Tetrahydrobiopterin (BH4) responsiveness in neonates with hyperphenylalaninemia: A semimechanisticallybased, nonlinear mixedeffect modeling.
Assess the individual BH4 responsiveness in neonates suffering from phenylalanine hydroxylase (PAH).
It is a pharmacodynamic turnover model (stimulation of loss) of Phenylalanine in blood disposition. The KPD approach is used to describe BH4 kinetics. Mixture population approach is used to differentiate BH4 sensitive and nonsensitive patients.
PharmML (0.6.1) 



Nadia Terranova, Kheizurane_ElMekki

Context of model development:  Patient Population Selection and Bridging between Population (Pediatrics, Elderly, Obese); 
Discrepancy between implemented model and original publication:  The model implemented in the original publication is a mixture model, including two population: sensitive to BH4 ( the SLOP parameter is assumed lognormally distributed) and nonsensitive to BH4 (the SLOP parameter is assumed to be equal to zero).; 
Long technical model description:  Pharmacodynamic turnover model with stimulation of loss describing the Phenylalanine in blood (Phe) disposition. A KPD approach was used to describe the kinetics of BH4. The model includes a mixture approaches to distinguish BH4 sensitive to non sensitive patients.; 
Model compliance with original publication:  No; 
Model implementation requiring submitter’s additional knowledge:  No; 
Modelling context description:  Pharmacodynamic model to improve the description of individual sensitivity to BH4 in neonatal period; 
Modelling task in scope:  estimation; 
Nature of research:  Clinical research & Therapeutic use; 
Therapeutic/disease area:  Metabolism; 
Annotations are correct. 

This model is not certified. 
 Additional Files
 Output_simulated_SEEalpha_NM_Trefz_2015_noMixture.zip
 Output_simulated_SEEalpha_NM_Trefz_2015_noMixture.zip/Output_simulated_SEEalpha_NM_Trefz_2015_noMixture/Trefz_2015_noMixture.SO.xml
 Output_simulated_SEEalpha_NM_Trefz_2015_noMixture.zip/Output_simulated_SEEalpha_NM_Trefz_2015_noMixture/Trefz_2015_noMixture.ext
 Output_simulated_SEEalpha_NM_Trefz_2015_noMixture.zip/Output_simulated_SEEalpha_NM_Trefz_2015_noMixture/sdtab
 Output_simulated_SEEalpha_NM_Trefz_2015_noMixture.zip/Output_simulated_SEEalpha_NM_Trefz_2015_noMixture/patab
 Output_simulated_SEEalpha_NM_Trefz_2015_noMixture.zip/Output_simulated_SEEalpha_NM_Trefz_2015_noMixture/Output_simulated_Trefz_2015_noMixture.lst
 DDMODEL00000103.rdf
 Model_Accommodations.txt
 Output_simulated_Trefz_2015_noMixture.lst
 Simulated_Trefz_2015_noMixture.csv
 Command.txt
 Executable_Trefz_2015_noMixture.mdl
 Output_simulated_SEEalpha_NM_Trefz_2015_noMixture.zip
 Model owner: Nadia Terranova
 Submitted: Dec 11, 2015 4:59:26 PM
 Last Modified: Jul 18, 2016 12:07:40 PM
Revisions
Independent variable T
Function Definitions
$\mathrm{combinedError2}(\mathrm{additive},\mathrm{proportional},f)=\sqrt{({\mathrm{proportional}}^{2}+({\mathrm{additive}}^{2}\times {f}^{2}\left)\right)}$
Structural Model sm
Variable definitions
$\frac{\mathrm{dDEPOT}}{\mathrm{dT}}=(\mathrm{KDE}\times \mathrm{DEPOT})$
$\mathrm{EFF}=(\mathrm{SLOP}\times \mathrm{DEPOT})$
$\frac{\mathrm{dPD}}{\mathrm{dT}}=(\mathrm{KOUT}((\mathrm{KOUT}\times (1+\mathrm{EFF}\left)\right)\times \mathrm{PD}\left)\right)$
Initial conditions
$\mathrm{DEPOT}=0$
$\mathrm{PD}=1$
Variability Model
Level  Type 

DV 
residualError 
ID 
parameterVariability 
Parameter Model
Parameters$POP\_KOUT$;
$POP\_KDE$;
$POP\_SLOP$;
$PERR$;
$AERR$;
$OMEGA\_KOUT$;
$OMEGA\_KDE$;
$OMEGA\_SLOP$;
$\mathrm{ETA\_KOUT}\sim N(\mathrm{0.0},\mathrm{OMEGA\_KOUT})$ — ID
$\mathrm{ETA\_KDE}\sim N(\mathrm{0.0},\mathrm{OMEGA\_KDE})$ — ID
$\mathrm{ETA\_SLOP}\sim N(\mathrm{0.0},\mathrm{OMEGA\_SLOP})$ — ID
$\mathrm{EPS\_Y}\sim N(\mathrm{0.0},\mathrm{1.0})$ — DV
$log\left(\mathrm{KOUT}\right)=(log(\mathrm{POP\_KOUT})+\mathrm{ETA\_KOUT})$
$log\left(\mathrm{KDE}\right)=(log(\mathrm{POP\_KDE})+\mathrm{ETA\_KDE})$
$log\left(\mathrm{SLOP}\right)=(log(\mathrm{POP\_SLOP})+\mathrm{ETA\_SLOP})$
Observation Model
Observation Y
Continuous / Residual Data
Parameters $Y=(\mathrm{PD}+(combinedError2(\mathrm{AERR},\mathrm{PERR},\mathrm{PD})\times \mathrm{EPS\_Y}\left)\right)$
Estimation Steps
Estimation Step estimStep_1
Estimation parameters
Initial estimates for nonfixed parameters
 $\mathrm{POP\_KOUT}=\mathrm{0.0388}$
 $\mathrm{POP\_KDE}=\mathrm{0.067}$
 $\mathrm{POP\_SLOP}=\mathrm{0.205}$
 $\mathrm{PERR}=\mathrm{0.447}$
 $\mathrm{AERR}=1$
 $\mathrm{OMEGA\_KOUT}=\mathrm{0.2}$
 $\mathrm{OMEGA\_KDE}=\mathrm{0.2}$
 $\mathrm{OMEGA\_SLOP}=\mathrm{0.2}$
Estimation operations
1) Estimate the population parameters
Step Dependencies
 estimStep_1