DDMODEL00000001: Friberg_2009_Schizophrenia_Prolactin

  public model
Short description:
A competitive agonist-antagonist interaction model for prolactin release after risperidone and paliperidone treatment administered to healthy and schizophrenic subjects
PharmML (0.6.1)
  • 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
  • Department of Pharmaceutical Biosciences, Division of Pharmacokinetics and Drug Therapy, Uppsala University, Uppsala, Sweden. lena.friberg@farmbio.uu.se
  • 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.
Zinnia Parra-Guillen
Context of model development: Comparator/Standard of Care Differentiation (Clinical & Commercial Viability); Disease Progression model; Mechanistic Understanding; Variability sources in PK and PD (CYP, Renal, Biomarkers);
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: Early clinical development (Phases I and II); Approval phase/Registration trial (Phase III);
Therapeutic/disease area: CNS;
Annotations are correct.
This model is not certified.
  • 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 public model Download this version
    • Submitted on: May 24, 2016 11:53:14 AM
    • Submitted by: Zinnia Parra-Guillen
    • With comment: Edited model metadata online.
  • Version: 6 public model Download this version
    • Submitted on: Dec 12, 2015 2:07:02 PM
    • Submitted by: Zinnia Parra-Guillen
    • With comment: Edited model metadata online.
  • Version: 2 public model Download this version
    • 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)=((proportional ×proportional)+additivef2)

Structural Model sm

Variable definitions

DIU=((AMP1 ×cos(((2 ×(1 ×Π)) ×(T-PHS1))24))+(AMP2 ×cos(((2 ×(1 ×Π)) ×(T-PHS2))12)))
CP=(1000 ×AC)VC
RSTR={ADP((1+ADP)+CPKI)  if  (ADP>0)1  otherwise
FEED={APRPRL0UPDA  if  (APR>0)1  otherwise
RATEIN={(KS ×AD)  if  (T>ALAG)0  otherwise
K23=QBVC
K32=QBVP
K=CLRVC
dADdT=-RATEIN
dACdT=(((RATEIN ×FR)-((K23+K) ×AC))+(K32 ×AP))
dAPdT=((K23 ×AC)-(K32 ×AP))
dAPRdT=(((KINM ×(1-RSTR))+(KINB ×DIU))-(KOUT ×APR))
dADPdT=(((KDA ×DOP0) ×FEED)-(KDA ×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

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={POP_PRL0PATM  if  (((PAT=1)(SEX=0))(STU101))POP_PRL0PATF  if  (((PAT=1)(SEX=1))(STU101))POP_PRL0ST101M  if  (((PAT=1)(SEX=0))(STU=101))POP_PRL0ST101F  if  (((PAT=1)(SEX=1))(STU=101))POP_PRL0HV  otherwise DOP0=10000 CVERROR={ERROR_HVM  if  ((PAT=0)(SEX=0))ERROR_PATM  if  ((PAT=1)(SEX=0))ERROR_F  otherwise
ETA_PRL0N(0.0,OMEGA_PRL0) — ID
ETA_KIN(0.0,OMEGA_KI) — ID
ETA_KOUTN(0.0,OMEGA_KOUT) — ID
ETA_AMP1N(0.0,OMEGA_AMP1) — ID
ETA_PHS2N(0.0,OMEGA_PHS2) — ID
ETA_BSV_PRL0N(0.0,GAMMA_PRL0) — NPER
EPS_SIGMAN(0.0,SIGMA) — DV
KOUT=(POP_KOUT ×exp(ETA_KOUT))
PRL0=(POP_PRL0 ×exp((ETA_PRL0+ETA_BSV_PRL0)))
KI=(POP_KI ×exp(ETA_KI))
AMP1=(POP_AMP1 ×exp(ETA_AMP1))
PHS1=(PHS1_HV+(DPHS ×PAT))
PHS2=((POP_PHS2_HV+(DPHS ×PAT))+ETA_PHS2)
KINB=(KOUT ×PRL0)
KINM=(KINB ×(1+DOP0))
Covariance matrix for level ID and random effects: ETA_PRL0, ETA_KI
( 1 0.758 0.758 1 )

Observation Model

Observation Y
Continuous / Residual Data

Parameters
Y=(APR+(combinedError2Log(APR,CVERROR,RUV_PROP) ×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
     
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