# DDMODEL00000002: Friberg_2009_Schizophrenia_Asenapine_PANSS

Short description:
A efficacy dose response model to characterise the effect of sublingual asenapine in patients with schizophrenia accounting for placebo effect
 Format: PharmML (0.6.1) Related Publication: .hiddenContent {display:none;} Modeling and simulation of the time course of asenapine exposure response and dropout patterns in acute schizophrenia. Friberg LE, de Greef R, Kerbusch T, Karlsson MO Clinical pharmacology and therapeutics, 7/2009, Volume 86, Issue 1, pages: 84-91 Affiliation: Department of Pharmaceutical Biosciences, Uppsala University, Uppsala, Sweden. lena.friberg@farmbio.uu.se Abstract: Modeling and simulation were utilized to characterize the efficacy dose response of sublingual asenapine in patients with schizophrenia and to understand the outcomes of six placebo-controlled trials in which placebo responses and dropout rates varied. The time course of total Positive and Negative Syndrome Scale (PANSS) scores was characterized for placebo and asenapine treatments in a pharmacokinetic-pharmacodynamic model in which the asenapine effect was described by an E(max) model, increasing linearly over the 6-week study period. A logistic regression model described the time course of dropouts, with previous PANSS value being the most important predictor. The last observation carried forward (LOCF) time courses were well described in simulations from the combined PANSS + dropout model. The observed trial outcomes were successfully predicted for all the placebo arms and the majority of the treatment arms. Although simulations indicated that the post hoc probability of success of the performed trials was low to moderate, these analyses demonstrated that 5 and 10 mg twice-daily (b.i.d.) doses of asenapine have similar efficacy. Contributors: Zinnia Parra-Guillen
 Context of model development: Disease Progression model; Long technical model description: A non-linear mixed effects analysis was performed using NONMEM VI, in which PANSS score was treated as a continuous variable. The placebo effect on the PANSS score was modeled first with a Weibull model. The placebo effect was modeled as being proportional to the estimated baseline PANSS score. The AUC of asenapine was most predictive for the effect. An Emax-model characterized the effect of asenapine which was proportional to the PANSS score predicted by the placebo model. The rate of increase in maximum asenapine response was described by a linear function, with Emax reaching its maximum values at day 42. Model simulations were performed in NONMEM VI, while S-PLUS 6.2, R version 2.4.1 and Xpose were used for model diagnostics and graphical inspection of the results.; Model compliance with original publication: Yes; Model implementation requiring submitter’s additional knowledge: Yes; Modelling context description: Asenapine Exposure – Response of PANSS in Acute Schizophrenia.; 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;
 Validation Status: Annotations are correct. Certification Comment: This model is not certified.
• Model owner: Zinnia Parra-Guillen
• Submitted: Sep 25, 2014 5:58:08 PM
##### Revisions
• Version: 7
• Submitted on: May 18, 2016 7:42:15 PM
• Submitted by: Zinnia Parra-Guillen
• With comment: Updated model annotations.
• Version: 4
• Submitted on: Dec 10, 2015 4:53:00 PM
• Submitted by: Zinnia Parra-Guillen
• With comment: Edited model metadata online.
• Version: 2
• Submitted on: Sep 25, 2014 5:58:08 PM
• Submitted by: Zinnia Parra-Guillen
• With comment: Harmonised PharmML, MDL and NM-TRAN and used of established nomenclature

Independent variable TIME

### Function Definitions

$additiveError(additive)=additive$

### Structural Model sm

Variable definitions

$PMOD=(PMAX ×(1-exp(-TIMETDPOW)))$
$EFF=((EMAX ×AUC)(AUC50+AUC) ×FT)$
$PANSS_total=((PAN0 ×(1-PMOD)) ×(1-EMOD))$

### Variability Model

Level Type

DV

residualError

ID

parameterVariability

### Covariate Model

Continuous covariate DDUR

Continuous covariate STUD

Continuous covariate HOSP

Continuous covariate US

Continuous covariate AUC

### Parameter Model

Parameters
$PAN0_II$ $PAN0_III$ $PAN0_CHRON$ $TVPMAX$ $PMAX_PHASEIII$ $TD$ $POW$ $POP_AUC50$ $EMAX$ $THETA_HOSP$ $THETA_US$ $TVERROR$ $IIV_PAN0$ $IIV_PMAX$ $IIV_AUC50$ $IIV_ERROR$ $SIGMA$ $POP_PMAX=(TVPMAX ×(1+(PMAX_PHASEIII ×PHASE)))$ $POP_ERROR=(CHOSP ×(TVERROR ×(1+(THETA_US ×US))))$
$ETA_PAN0∼N(0.0,IIV_PAN0)$ — ID
$ETA_PMAX∼N(0.0,IIV_PMAX)$ — ID
$ETA_AUC50∼N(0.0,IIV_AUC50)$ — ID
$ETA_ERROR∼N(0.0,IIV_ERROR)$ — ID
$EPS_SIGMA∼N(0.0,SIGMA)$ — DV
$PAN0=(POP_PAN0+ETA_PAN0)$
$PMAX=(POP_PMAX+ETA_PMAX)$
$AUC50=(POP_AUC50 ×exp(ETA_AUC50))$
$ERROR=(POP_ERROR ×exp(ETA_ERROR))$
Covariance matrix for level ID and random effects: ETA_PAN0, ETA_PMAX
$( 1 -0.395 -0.395 1 )$

### Observation Model

#### Observation YContinuous / Residual Data

Parameters
$Y=(PANSS_total+(additiveError(ERROR) ×EPS_SIGMA))$

### Estimation Steps

#### Estimation Step estimStep_1

##### Estimation parameters

Fixed parameters

$SIGMA=1$

Initial estimates for non-fixed parameters

• $PAN0_II=94$
• $PAN0_III=90.5$
• $PAN0_CHRON=-0.0339$
• $TVPMAX=0.0859$
• $PMAX_PHASEIII=0.688$
• $TD=13.2$
• $POW=1.24$
• $POP_AUC50=82$
• $EMAX=0.191$
• $THETA_HOSP=-0.145$
• $THETA_US=0.623$
• $TVERROR=3.52$
• $IIV_PAN0=167$
• $IIV_PMAX=0.0249$
• $IIV_AUC50=21.7$
• $IIV_ERROR=0.196$
##### Estimation operations
1) Estimate the population parameters
Algorithm FOCEI

### Step Dependencies

• estimStep_1