DDMoRe Model Repository

Overview
Files
History
Model Definition
Estimation Steps

Short description:

A competitive agonist-antagonist interaction model for prolactin release after risperidone and paliperidone treatment administered to healthy and schizophrenic subjects

Format:	PharmML (0.6.1)
Related Publication:	An agonist-antagonist interaction model for prolactin release following risperidone and paliperidone treatment. Friberg LE, Vermeulen AM, Petersson KJ, Karlsson MO Clinical pharmacology and therapeutics, 4/2009, Volume 85, Issue 4, pages: 409-417 Affiliation: Department of Pharmaceutical Biosciences, Division of Pharmacokinetics and Drug Therapy, Uppsala University, Uppsala, Sweden. lena.friberg@farmbio.uu.se Abstract: A mechanistic pharmacokinetic/pharmacodynamic model is presented, characterizing the time course of prolactin in healthy as well as schizophrenic subjects following the administration of various doses and formulations of the antipsychotic drugs risperidone and paliperidone. Prolactin concentrations from nine studies (1,462 subjects) were analyzed in NONMEM. A competitive agonist-antagonist interaction model described the competition between these drugs and dopamine for the D(2) receptors that regulate prolactin release. Tolerance development was explained by a feedback loop with prolactin stimulating dopamine release, whereas models wherein tolerance is described in terms of depletion of a prolactin pool did not explain the data well. The diurnal prolactin rhythm was described by a two-period cosine function. Baseline prolactin was health-status dependent and higher in women than in men, although the drug-induced release was less than proportional to baseline. This quantitative mechanism-based model is the first to describe prolactin release in patients, and it confirms that paliperidone and risperidone have similar potencies for prolactin release.
Contributors:	Zinnia Parra-Guillen

Context of model development:	Mechanistic Understanding; Comparator/Standard of Care Differentiation (Clinical & Commercial Viability); Variability sources in PK and PD (CYP, Renal, Biomarkers); Disease Progression model;
Discrepancy between implemented model and original publication:	Same structural model but there are some minor differences regarding initialisation of compartments and encoding of lag times which have been explicitely encoded rather than using the key NONMEM words (F and ALAG);
Long technical model description:	The model consists of two indirect response models where one represent the turnover of (observed) prolactin and one the turnover of (unobserved) dopamine. The drugs interact with prolactin release with and agonist-antagonist interaction function by reducing the inhibition of release from dopamine. A feedback mechanism from prolactin to dopamine regulates the production of dopamine. Two cosine functions characterize the diurnal rhythm of prolactin;
Model compliance with original publication:	No;
Model implementation requiring submitter’s additional knowledge:	Yes;
Modelling context description:	To develop a mechanistic model that describes the concentration–time profile of prolactin after administration of different formulations of risperidone and paliperidone in patients and healthy volunteers in order to identify potential subgroups of patients with different prolactin responses and to compare the prolactin-increasing effects of paliperidone to those of risperidone;
Modelling task in scope:	estimation;
Nature of research:	Approval phase/Registration trial (Phase III); Early clinical development (Phases I and II);
Therapeutic/disease area:	CNS;

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

Mandatory file
- Executable_Friberg_2009_Schizophrenia_Prolactine.xml

Additional Files

Model owner: Zinnia Parra-Guillen
Submitted: Sep 25, 2014 5:53:46 PM
Last Modified: May 24, 2016 11:53:14 AM

Revisions

Version: 15
- Submitted on: May 24, 2016 11:53:14 AM
- Submitted by: Zinnia Parra-Guillen
- With comment: Edited model metadata online.
Version: 6
- Submitted on: Dec 12, 2015 2:07:02 PM
- Submitted by: Zinnia Parra-Guillen
- With comment: Edited model metadata online.
Version: 2
- Submitted on: Sep 25, 2014 5:53:46 PM
- Submitted by: Zinnia Parra-Guillen
- With comment: Harmonised PharmML, MDL and NM-TRAN files according to established nomenclature

Independent variable T

Function Definitions

combinedError2Log (additive, proportional, f) = \sqrt{((proportional \times proportional) + {\frac{additive}{f}}^{2})}

Structural Model sm

Variable definitions

DIU = ((AMP1 \times cos (\frac{((2 \times (1 \times Π)) \times (T - PHS1))}{24})) + (AMP2 \times cos (\frac{((2 \times (1 \times Π)) \times (T - PHS2))}{12})))

CP = \frac{(1000 \times AC)}{VC}

RSTR = {\begin{matrix} \frac{ADP}{((1 + ADP) + \frac{CP}{KI})} & if & (ADP > 0) \\ 1 & otherwise \end{matrix}

FEED = {\begin{matrix} {\frac{APR}{PRL0}}^{UPDA} & if & (APR > 0) \\ 1 & otherwise \end{matrix}

RATEIN = {\begin{matrix} (KS \times AD) & if & (T > ALAG) \\ 0 & otherwise \end{matrix}

K23 = \frac{QB}{VC}

K32 = \frac{QB}{VP}

K = \frac{CLR}{VC}

\frac{dAD}{dT} = - RATEIN

\frac{dAC}{dT} = (((RATEIN \times FR) - ((K23 + K) \times AC)) + (K32 \times AP))

\frac{dAP}{dT} = ((K23 \times AC) - (K32 \times AP))

\frac{dAPR}{dT} = (((KINM \times (1 - RSTR)) + (KINB \times DIU)) - (KOUT \times APR))

\frac{dADP}{dT} = (((KDA \times DOP0) \times FEED) - (KDA \times ADP))

Initial conditions

AD = 0

AC = 0

AP = 0

APR = PRL0

ADP = DOP0

Variability Model

Level	Type
DV	residualError
ID NPER, parent level: ID	parameterVariability

Level

Type

residualError

NPER, parent level: ID

parameterVariability

Covariate Model

Continuous covariate PAT

Continuous covariate SEX

Continuous covariate STU

Continuous covariate ALAG

Continuous covariate FR

Continuous covariate VC

Continuous covariate VP

Continuous covariate CLR

Continuous covariate QB

Continuous covariate KS

Parameter Model

Parameters

POP_PRL0HV

;

POP_PRL0PATM

;

POP_PRL0PATF

;

POP_PRL0ST101M

;

POP_PRL0ST101F

;

POP_KOUT

;

POP_AMP1

;

DPHS

;

PHS1_HV

;

AMP2

;

POP_PHS2_HV

;

POP_KI

;

KDA

;

UPDA

;

ERROR_HVM

;

ERROR_PATM

;

ERROR_F

;

RUV_PROP

;

OMEGA_PRL0

;

OMEGA_KI

;

OMEGA_KOUT

;

OMEGA_AMP1

;

OMEGA_PHS2

;

GAMMA_PRL0

;

SIGMA

;

POP_PRL0 = {\begin{matrix} POP_PRL0PATM & if & (((PAT = 1) \land (SEX = 0)) \land (STU \neq 101)) \\ POP_PRL0PATF & if & (((PAT = 1) \land (SEX = 1)) \land (STU \neq 101)) \\ POP_PRL0ST101M & if & (((PAT = 1) \land (SEX = 0)) \land (STU = 101)) \\ POP_PRL0ST101F & if & (((PAT = 1) \land (SEX = 1)) \land (STU = 101)) \\ POP_PRL0HV & otherwise \end{matrix}

;

DOP0 = 10000

;

CVERROR = {\begin{matrix} ERROR_HVM & if & ((PAT = 0) \land (SEX = 0)) \\ ERROR_PATM & if & ((PAT = 1) \land (SEX = 0)) \\ ERROR_F & otherwise \end{matrix}

;

ETA_PRL0 \sim N (0.0, OMEGA_PRL0)

— ID

ETA_KI \sim N (0.0, OMEGA_KI)

— ID

ETA_KOUT \sim N (0.0, OMEGA_KOUT)

— ID

ETA_AMP1 \sim N (0.0, OMEGA_AMP1)

— ID

ETA_PHS2 \sim N (0.0, OMEGA_PHS2)

— ID

ETA_BSV_PRL0 \sim N (0.0, GAMMA_PRL0)

— NPER

EPS_SIGMA \sim N (0.0, SIGMA)

— DV

KOUT = (POP_KOUT \times exp (ETA_KOUT))

PRL0 = (POP_PRL0 \times exp ((ETA_PRL0 + ETA_BSV_PRL0)))

KI = (POP_KI \times exp (ETA_KI))

AMP1 = (POP_AMP1 \times exp (ETA_AMP1))

PHS1 = (PHS1_HV + (DPHS \times PAT))

PHS2 = ((POP_PHS2_HV + (DPHS \times PAT)) + ETA_PHS2)

KINB = (KOUT \times PRL0)

KINM = (KINB \times (1 + DOP0))

Covariance matrix for level ID and random effects: ETA_PRL0, ETA_KI

(\begin{matrix} 1 & 0.758 \\ 0.758 & 1 \end{matrix})

Observation Model

Observation Y
Continuous / Residual Data

Parameters

Y = (APR + (combinedError2Log (APR, CVERROR, RUV_PROP) \times EPS_SIGMA))

Estimation Steps

Estimation Step estimStep_1

Estimation parameters

Fixed parameters

$RUV_PROP = 0$
$SIGMA = 1$

Initial estimates for non-fixed parameters

$POP_PRL0HV = 7.67$
$POP_PRL0PATM = 16.1$
$POP_PRL0PATF = 35.2$
$POP_PRL0ST101M = 11.3$
$POP_PRL0ST101F = 23.2$
$POP_KOUT = 0.664$
$POP_AMP1 = 0.532$
$DPHS = - 1.61$
$PHS1_HV = 20.1$
$AMP2 = - 0.314$
$POP_PHS2_HV = 13.7$
$POP_KI = 1.96$
$KDA = 0.156$
$UPDA = 1.44$
$ERROR_HVM = 0.29$
$ERROR_PATM = 0.422$
$ERROR_F = 0.571$
$OMEGA_PRL0 = 0.425$
$OMEGA_KI = 1.82$
$OMEGA_KOUT = 0.589$
$OMEGA_AMP1 = 0.0558$
$OMEGA_PHS2 = 1.8$
$GAMMA_PRL0 = 0.0657$

Estimation operations

1) Estimate the population parameters

Algorithm FOCEI

Step Dependencies

estimStep_1

DDMODEL00000001: Friberg_2009_Schizophrenia_Prolactin

Revisions

Function Definitions

Structural Model sm

Variability Model

Covariate Model

Parameter Model

Observation Model

Observation YContinuous / Residual Data

Estimation Steps

Estimation Step estimStep_1

Estimation parameters

Estimation operations

Step Dependencies

Observation Y
Continuous / Residual Data