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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:	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:	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_Asenapine_PANSS.xml

Additional Files

Model owner: Zinnia Parra-Guillen
Submitted: Sep 25, 2014 5:58:08 PM
Last Modified: May 18, 2016 7:42:15 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 \times (1 - exp ({- \frac{TIME}{TD}}^{POW})))

FT = {\begin{matrix} 1 & if & (TIME > 42) \\ \frac{TIME}{42} & otherwise \end{matrix}

EFF = (\frac{(EMAX \times AUC)}{(AUC50 + AUC)} \times FT)

EMOD = {\begin{matrix} EFF & if & ((TIME > 0) \land (AUC > 0)) \\ 0 & otherwise \end{matrix}

PANSS_total = ((PAN0 \times (1 - PMOD)) \times (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

;

DDU = {\begin{matrix} 1 & if & (DDUR > 2) \\ 0 & otherwise \end{matrix}

;

PHASE = {\begin{matrix} 1 & if & (STUD > 30) \\ 0 & otherwise \end{matrix}

;

POP_PAN0 = {\begin{matrix} (PAN0_II \times (1 + (PAN0_CHRON \times DDU))) & if & (PHASE = 0) \\ (PAN0_III \times (1 + (PAN0_CHRON \times DDU))) & if & (PHASE = 1) \end{matrix}

;

POP_PMAX = (TVPMAX \times (1 + (PMAX_PHASEIII \times PHASE)))

;

CHOSP = {\begin{matrix} (1 + THETA_HOSP) & if & (HOSP = 0) \\ 1 & otherwise \end{matrix}

;

POP_ERROR = (CHOSP \times (TVERROR \times (1 + (THETA_US \times US))))

;

ETA_PAN0 \sim N (0.0, IIV_PAN0)

— ID

ETA_PMAX \sim N (0.0, IIV_PMAX)

— ID

ETA_AUC50 \sim N (0.0, IIV_AUC50)

— ID

ETA_ERROR \sim N (0.0, IIV_ERROR)

— ID

EPS_SIGMA \sim N (0.0, SIGMA)

— DV

PAN0 = (POP_PAN0 + ETA_PAN0)

PMAX = (POP_PMAX + ETA_PMAX)

AUC50 = (POP_AUC50 \times exp (ETA_AUC50))

ERROR = (POP_ERROR \times exp (ETA_ERROR))

Covariance matrix for level ID and random effects: ETA_PAN0, ETA_PMAX

(\begin{matrix} 1 & - 0.395 \\ - 0.395 & 1 \end{matrix})

Observation Model

Observation Y
Continuous / Residual Data

Parameters

Y = (PANSS_total + (additiveError (ERROR) \times 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

DDMODEL00000002: Friberg_2009_Schizophrenia_Asenapine_PANSS

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