DDMoRe Model Repository

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Model Definition
Estimation Steps

Short description:

Model comprised of files: Data_Accomodations.txt, Peigne_2015_Ivabradine.xml, Executable_Peigne_2015_Ivabradine_algorithms.xmlx, Simulated_Peigne_2015_Ivabradine.csv, Executable_Peigne_2015_Ivabradine.mlxtran, Output_simulated_Peigne_2015_Ivabradine.zip, Peigne_2015_Ivabradine.mdl, Model_Peigne_2015_Ivabradine.txt, Executable_Peigne_2015_Ivabradine_graphics.xmlx, Command.txt

Format:	PharmML (0.6.1)
Related Publication:	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.

Mandatory file
- Peigne_2015_Ivabradine.xml

Additional Files

Model owner: Vincent Croixmarie
Submitted: Feb 12, 2016 5:16:12 PM
Last Modified: 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 \times f)

combinedError1 (additive, proportional, f) = (additive + (proportional \times f))

Structural Model sm

Variable definitions

\frac{dDEP1}{dT} = ((- DEP1 \times ka1) \times ((F1 \times (2 - PER)) + ((F1 \times IOV_F1) \times (PER - 1))))

\frac{dQC}{dT} = ((((DEP1 \times ka1) \times ((F1 \times (2 - PER)) + ((F1 \times IOV_F1) \times (PER - 1)))) - (\frac{(((CLp + CLPM) + Q1) \times ALLOM_TRANS)}{(V1 \times ALLOM_VOL)} \times QC)) + (\frac{(Q1 \times ALLOM_TRANS)}{(V3 \times ALLOM_VOL)} \times QP))

\frac{dQP}{dT} = ((\frac{(Q1 \times ALLOM_TRANS)}{(V1 \times ALLOM_VOL)} \times QC) - (\frac{(Q1 \times ALLOM_TRANS)}{(V3 \times ALLOM_VOL)} \times QP))

\frac{dDEP2}{dT} = ((- DEP2 \times ka2) \times F2)

\frac{dQM}{dT} = ((((DEP2 \times ka2) \times F2) + (\frac{(CLPM \times ALLOM_MET)}{(V1 \times ALLOM_VOL)} \times QC)) - (\frac{(CLm \times ALLOM_TRANS)}{((V1 + V3) \times ALLOM_VOL)} \times QM))

CC = \frac{QC}{(V1 \times ALLOM_VOL)}

CM = \frac{QM}{((V1 + V3) \times 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 = {\frac{WT}{14.5}}^{0.75}

;

ALLOM_MET = \frac{{AGE}^{0.83}}{(0.31 + {AGE}^{0.83})}

;

ALLOM_VOL = \frac{WT}{14.5}

;

ETA_ka1 \sim N (0.0, OMEGA_ka1)

— ID

ETA_F1 \sim N (0.0, OMEGA_F1)

— ID

ETA_IOV_F1 \sim N (0.0, OMEGA_IOV_F1)

— ID

ETA_V1 \sim N (0.0, OMEGA_V1)

— ID

ETA_V3 \sim N (0.0, OMEGA_V3)

— ID

ETA_Q1 \sim N (0.0, OMEGA_Q1)

— ID

ETA_CLPM \sim N (0.0, OMEGA_CLPM)

— ID

ETA_CLp \sim N (0.0, OMEGA_CLp)

— ID

ETA_CLm \sim N (0.0, OMEGA_CLm)

— ID

EPS_Y1 \sim N (0.0, 1.0)

— DV

EPS_Y2 \sim 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 Y1
Continuous / Residual Data

Parameters

Y1 = (CC + (proportionalError (Y1_PROP, CC) \times EPS_Y1))

Observation Y2
Continuous / Residual Data

Parameters

Y2 = (CM + (combinedError1 (Y2_ADD, Y2_PROP, CM) \times 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

DDMODEL00000129: Peigne_2015_Ivabradine

Revisions

Function Definitions

Structural Model sm

Variability Model

Covariate Model

Parameter Model

Observation Model

Observation Y1Continuous / Residual Data

Observation Y2Continuous / Residual Data

Estimation Steps

Estimation Step estimStep_1

Estimation parameters

Estimation operations

Step Dependencies

Observation Y1
Continuous / Residual Data

Observation Y2
Continuous / Residual Data