Short description:
 Format: PharmML (0.6.1) Related Publication: .hiddenContent {display:none;} Model-based approaches for ivabradine development in paediatric population, part II: PK and PK/PD assessment. Peigné S, Fouliard S, Decourcelle S, Chenel M Journal of pharmacokinetics and pharmacodynamics, 11/2015 Affiliation: Clinical Pharmacokinetics and Pharmacometrics Department, Institut de Recherches Internationales Servier, Suresnes, France. sophie.peigne@servier.com. Abstract: The objectives of this work were first to describe the pharmacokinetic (PK) of ivabradine and its active metabolite in a paediatric patient population after repeated oral administration of ivabradine using a population PK approach, and secondly to assess whether the blood/plasma ratio and the pharmacokinetic/pharmacodynamic (PK/PD) relationship are preserved in the paediatric population in comparison to adult. PK data for 70 patients were obtained after blood sampling using dried blood spot and one plasma sample in order to assess the relationship between blood and plasma concentration. In order to describe ivabradine and its metabolite blood concentrations in children, a joint population PK model was developed taking into account weight & age effects on PK parameters. Plasma PK exposure parameters were calculated in children using plasma PK profiles. In order to assess the PK/PD relationship in children, an adult PK/PD model was used. The relationship between blood and plasma concentrations was described using linear mixed effect models. Two and one-compartment models best described parent and metabolite dispositions. Weight effects were fixed to the allometric values of ¾ on clearance (CL) and 1 on volume. A maturation function was added on metabolite formation clearance (CL PM ) reflecting enzyme maturation. Plasma exposure comparison indicated that higher dose/kg were necessary to achieve a similar exposure between younger and older children. No differences between age classes were observed in terms of range of exposure at the maintenance dose. The PK/PD relationship in adult patients is conserved in children. Contributors: Vincent Croixmarie
 Validation Status: Annotations have not been checked. Certification Comment: This model is not certified.
• Model owner: Vincent Croixmarie
• Submitted: Feb 12, 2016 5:16:12 PM
##### Revisions
• Version: 8
• Submitted on: Feb 12, 2016 5:16:12 PM
• Submitted by: Vincent Croixmarie
• With comment: Edited model metadata online.

Independent variable T

### Function Definitions

$proportionalError(proportional,f)=(proportional ×f)$
$combinedError1(additive,proportional,f)=(additive+(proportional ×f))$

### Structural Model sm

Variable definitions

$dDEP1dT=((-DEP1 ×ka1) ×((F1 ×(2-PER))+((F1 ×IOV_F1) ×(PER-1))))$
$dQCdT=((((DEP1 ×ka1) ×((F1 ×(2-PER))+((F1 ×IOV_F1) ×(PER-1))))-((((CLp+CLPM)+Q1) ×ALLOM_TRANS)(V1 ×ALLOM_VOL) ×QC))+((Q1 ×ALLOM_TRANS)(V3 ×ALLOM_VOL) ×QP))$
$dQPdT=(((Q1 ×ALLOM_TRANS)(V1 ×ALLOM_VOL) ×QC)-((Q1 ×ALLOM_TRANS)(V3 ×ALLOM_VOL) ×QP))$
$dDEP2dT=((-DEP2 ×ka2) ×F2)$
$dQMdT=((((DEP2 ×ka2) ×F2)+((CLPM ×ALLOM_MET)(V1 ×ALLOM_VOL) ×QC))-((CLm ×ALLOM_TRANS)((V1+V3) ×ALLOM_VOL) ×QM))$
$CC=QC(V1 ×ALLOM_VOL)$
$CM=QM((V1+V3) ×ALLOM_VOL)$

Initial conditions

$DEP1=0$
$QC=0$
$QP=0$
$DEP2=0$
$QM=0$

### Variability Model

Level Type

DV

residualError

ID

parameterVariability

### Covariate Model

Continuous covariate PER

Continuous covariate WT

Continuous covariate AGE

### Parameter Model

Parameters
$POP_ka1$ $POP_F1$ $DUMMY_PARAMETER$ $POP_ka2$ $POP_F2$ $POP_V1$ $POP_V3$ $POP_Q1$ $POP_CLPM$ $POP_CLp$ $POP_CLm$ $Y1_PROP$ $Y2_PROP$ $Y2_ADD$ $OMEGA_ka1$ $OMEGA_F1$ $OMEGA_IOV_F1$ $OMEGA_V1$ $OMEGA_V3$ $OMEGA_Q1$ $OMEGA_CLPM$ $OMEGA_CLp$ $OMEGA_CLm$ $ALLOM_TRANS=WT14.50.75$ $ALLOM_MET=AGE0.83(0.31+AGE0.83)$ $ALLOM_VOL=WT14.5$
$ETA_ka1∼N(0.0,OMEGA_ka1)$ — ID
$ETA_F1∼N(0.0,OMEGA_F1)$ — ID
$ETA_IOV_F1∼N(0.0,OMEGA_IOV_F1)$ — ID
$ETA_V1∼N(0.0,OMEGA_V1)$ — ID
$ETA_V3∼N(0.0,OMEGA_V3)$ — ID
$ETA_Q1∼N(0.0,OMEGA_Q1)$ — ID
$ETA_CLPM∼N(0.0,OMEGA_CLPM)$ — ID
$ETA_CLp∼N(0.0,OMEGA_CLp)$ — ID
$ETA_CLm∼N(0.0,OMEGA_CLm)$ — ID
$EPS_Y1∼N(0.0,1.0)$ — DV
$EPS_Y2∼N(0.0,1.0)$ — DV
$log(ka1)=(log(POP_ka1)+ETA_ka1)$
$ka2=POP_ka2$
$log(F1)=(log(POP_F1)+ETA_F1)$
$F2=POP_F2$
$log(IOV_F1)=(log(DUMMY_PARAMETER)+ETA_IOV_F1)$
$log(V1)=(log(POP_V1)+ETA_V1)$
$log(V3)=(log(POP_V3)+ETA_V3)$
$log(Q1)=(log(POP_Q1)+ETA_Q1)$
$log(CLPM)=(log(POP_CLPM)+ETA_CLPM)$
$log(CLp)=(log(POP_CLp)+ETA_CLp)$
$log(CLm)=(log(POP_CLm)+ETA_CLm)$

### Observation Model

#### Observation Y1Continuous / Residual Data

Parameters
$Y1=(CC+(proportionalError(Y1_PROP,CC) ×EPS_Y1))$

#### Observation Y2Continuous / Residual Data

Parameters
$Y2=(CM+(combinedError1(Y2_ADD,Y2_PROP,CM) ×EPS_Y2))$

### Estimation Steps

#### Estimation Step estimStep_1

##### Estimation parameters

Fixed parameters

• $POP_F1=0.38$
• $DUMMY_PARAMETER=1$
• $POP_ka2=0.51$
• $POP_F2=0.18$

Initial estimates for non-fixed parameters

• $POP_ka1=1.1$
• $POP_V1=10$
• $POP_V3=60$
• $POP_Q1=50$
• $POP_CLPM=39$
• $POP_CLp=7$
• $POP_CLm=40$
• $Y1_PROP=0.3$
• $Y2_PROP=0.3$
• $Y2_ADD=1$
• $OMEGA_ka1=1$
• $OMEGA_F1=1$
• $OMEGA_IOV_F1=1$
• $OMEGA_V1=1$
• $OMEGA_V3=1$
• $OMEGA_Q1=1$
• $OMEGA_CLPM=1$
• $OMEGA_CLp=1$
• $OMEGA_CLm=1$
##### Estimation operations
1) Estimate the population parameters
Algorithm SAEM

### Step Dependencies

• estimStep_1