DDMoRe Model Repository

Overview
Files
History
Model Definition
Trial Design
Modelling Steps

Short description:

Bridging Clinical Outcomes of Canakinumab Treatment in Patients With Rheumatoid Arthritis With a Population Model of IL-1beta Kinetics

Format:	PharmML 0.8.x (0.8.1)
Related Publication:	Bridging Clinical Outcomes of Canakinumab Treatment in Patients With Rheumatoid Arthritis With a Population Model of IL-1? Kinetics. Ait-Oudhia S, Mager DE, Lowe P CPT: pharmacometrics & systems pharmacology, 1/2012, Volume 1, pages: e5 Affiliation: Department of Pharmaceutical Sciences, University at Buffalo, The State University of New York, Buffalo, New York, USA. Abstract: Canakinumab, an anti-interleukin-1? (IL-1?) monoclonal antibody, is approved for cryopyrin-associated periodic syndromes and is under investigation for the management of other inflammatory disorders. In this study, population-based pharmacokinetic-pharmacodynamic models were developed to understand responses to canakinumab in patients with rheumatoid arthritis (RA). Total canakinumab and total IL-1? concentrations were obtained from four clinical trials (n = 472). In contrast to traditional models, free IL-1? concentrations were calculated and used to link canakinumab to changes in C-reactive protein (CRP) concentrations and American College of Rheumatology (ACR) scores of 20, 50, and 70% improvement. Temporal patterns of total canakinumab, total IL-1?, CRP, and ACR scores were all well described. Simulations confirmed that 150?mg every 4 weeks improved ACR scores in patients with RA, but no additional benefit was provided by higher doses or more frequent administration. Integrating predicted endogenous free ligand concentrations with biomarkers and clinical outcomes could be extended to new therapies of anti-inflammatory diseases.CPT: Pharmacometrics & Systems Pharmacology (2012) 1, e5; doi:10.1038/psp.2012.6; advance online publication 26 September 2012.
Contributors:	Paolo Magni

Context of model development:	Disease Progression model;
Discrepancy between implemented model and original publication:	In the original publication, two PD models were considered, describing two different outcomes: a continuous biomarker and a categorical clinical outcome, namely C-reactive protein (CRP) and the American College of Rheumatology (ACRx), but this model only includes CRP.;
Model compliance with original publication:	Yes;
Model implementation requiring submitter’s additional knowledge:	No;
Modelling context description:	Canakinumab, an anti-interleukin-1? (IL-1?) monoclonal antibody, is approved for cryopyrin-associated periodic syndromes and is under investigation for the management of other inflammatory disorders. In this study, population-based pharmacokinetic-pharmacodynamic models were developed to understand responses to canakinumab in patients with rheumatoid arthritis (RA). Total canakinumab and total IL-1? concentrations were obtained from four clinical trials (n = 472). In contrast to traditional models, free IL-1? concentrations were calculated and used to link canakinumab to changes in C-reactive protein (CRP) concentrations and American College of Rheumatology (ACR) scores of 20, 50, and 70% improvement. Temporal patterns of total canakinumab, total IL-1?, CRP, and ACR scores were all well described. Simulations confirmed that 150?mg every 4 weeks improved ACR scores in patients with RA, but no additional benefit was provided by higher doses or more frequent administration. Integrating predicted endogenous free ligand concentrations with biomarkers and clinical outcomes could be extended to new therapies of anti-inflammatory diseases;
Modelling task in scope:	estimation;
Nature of research:	Clinical research & Therapeutic use;
Therapeutic/disease area:	Rheumatology;

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

Mandatory file
- Executable_AitOudhia_2012_CRP_canakinumab.xml

Additional Files

Model owner: Paolo Magni
Submitted: Dec 12, 2015 4:09:21 PM
Last Modified: Oct 13, 2016 6:30:37 PM

Revisions

Version: 11
- Submitted on: Oct 13, 2016 6:30:37 PM
- Submitted by: Paolo Magni
- With comment: Edited model metadata online.
Version: 9
- Submitted on: Jul 16, 2016 4:56:30 PM
- Submitted by: Paolo Magni
- With comment: Updated model annotations.
Version: 6
- Submitted on: Dec 12, 2015 4:09:21 PM
- Submitted by: Paolo Magni
- With comment: Edited model metadata online.

Name

Generated from MDL. MOG ID: ait2012_mog

Independent Variables

Function Definitions

additiveError : real (additive : real) = additive

Covariate Model: $cm$

Continuous Covariates

K_A

Vp

CLEARANCE

CLD

Kd

FR

Vcentral

CLL

Parameter Model: $pm$

Random Variables

{eta_CRP0}_{vm_mdl.ID} ~ Normal2 (mean = 0, var = pm.OMEGA_CRP0)

{eta_beta}_{vm_mdl.ID} ~ Normal2 (mean = 0, var = pm.OMEGA_beta)

{eta_kout}_{vm_mdl.ID} ~ Normal2 (mean = 0, var = pm.OMEGA_kout)

{eta_gama}_{vm_mdl.ID} ~ Normal2 (mean = 0, var = pm.OMEGA_gama)

{epsilon}_{vm_err.DV} ~ Normal2 (mean = 0, var = 1)

Population Parameters

POP_CRP0

POP_beta

POP_kout

POP_gama

RES

OMEGA_CRP0

OMEGA_beta

OMEGA_kout

OMEGA_gama

kdeg = \frac{cm.CLL}{cm.Vcentral}

R00 = \frac{0.42 \cdot cm.Vcentral}{17000}

ksyn = pm.R00 \cdot pm.kdeg

ILb_base = \frac{pm.R00}{cm.Vcentral}

Individual Parameters

\ln (CRP0) = \ln (pm.POP_CRP0) + pm.eta_CRP0

\ln (beta) = \ln (pm.POP_beta) + pm.eta_beta

\ln (kout) = \ln (pm.POP_kout) + pm.eta_kout

\ln (gama) = \ln (pm.POP_gama) + pm.eta_gama

kin = {(pm.CRP0 \cdot {pm.kout}^{pm.gama})}^{\frac{1}{pm.gama}}

CRP10 = \frac{pm.kin}{pm.kout}

CRP20 = \frac{pm.kin}{pm.kout}

CRP30 = {\frac{pm.kin}{pm.kout}}^{pm.gama}

Structural Model: $sm$

Variables

FREE = 0.5 \cdot (sm.Qc - sm.QL - cm.Kd \cdot cm.Vcentral + {({(sm.Qc - sm.QL - cm.Kd \cdot cm.Vcentral)}^{2} + 4 \cdot cm.Kd \cdot cm.Vcentral \cdot sm.Qc)}^{0.5})

ILb_total = \frac{sm.QL}{cm.Vcentral}

ILb_drug = \frac{sm.ILb_total \cdot \frac{sm.FREE}{cm.Vcentral}}{(cm.Kd + \frac{sm.FREE}{cm.Vcentral})}

ILb_free = sm.ILb_total - sm.ILb_drug

STIM = {\frac{sm.ILb_free}{pm.ILb_base}}^{pm.beta}

lnCRP3 = \ln (sm.CRP3)

\begin{matrix} \frac{ⅆ}{ⅆ T} Qs_F = - cm.K_A \cdot sm.Qs_F \\ Qs_F (T = 0) = 0 \end{matrix}

\begin{matrix} \frac{ⅆ}{ⅆ T} Qc = cm.K_A \cdot sm.Qs_F \cdot cm.FR - \frac{cm.CLEARANCE}{cm.Vcentral} \cdot sm.Qc - (\frac{cm.CLEARANCE}{cm.Vcentral} + \frac{cm.CLD}{cm.Vp} - \frac{cm.CLEARANCE}{cm.Vcentral}) \cdot sm.FREE + \frac{cm.CLD}{cm.Vp} \cdot sm.Qp \\ Qc (T = 0) = 0 \end{matrix}

\begin{matrix} \frac{ⅆ}{ⅆ T} Qp = \frac{cm.CLD}{cm.Vcentral} \cdot sm.FREE - \frac{cm.CLD}{cm.Vp} \cdot sm.Qp \\ Qp (T = 0) = 0 \end{matrix}

\begin{matrix} \frac{ⅆ}{ⅆ T} QL = pm.ksyn - (\frac{cm.CLEARANCE}{cm.Vcentral} - \frac{cm.CLL}{cm.Vcentral}) \cdot (sm.Qc - sm.FREE) - \frac{cm.CLL}{cm.Vcentral} \cdot sm.QL \\ QL (T = 0) = pm.R00 \end{matrix}

\begin{matrix} \frac{ⅆ}{ⅆ T} CRP1 = pm.kin \cdot sm.STIM - pm.kout \cdot sm.CRP1 \\ CRP1 (T = 0) = pm.CRP10 \end{matrix}

\begin{matrix} \frac{ⅆ}{ⅆ T} CRP2 = pm.kout \cdot (sm.CRP1 - sm.CRP2) \\ CRP2 (T = 0) = pm.CRP20 \end{matrix}

\begin{matrix} \frac{ⅆ}{ⅆ T} CRP3 = pm.kout \cdot ({sm.CRP2}^{pm.gama} - sm.CRP3) \\ CRP3 (T = 0) = pm.CRP30 \end{matrix}

Observation Model: $om1$

Continuous Observation

Y = sm.lnCRP3 + additiveError (additive = pm.RES) + pm.epsilon

External Dataset

OID	$nm_ds$
Tool Format	NONMEM

File Specification

Format	$csv$
Delimiter	comma
File Location	Simulated_ait2012_data_CRP.csv

Column Definitions

Column ID	Position	Column Type	Value Type
$ID$	$1$	$id$	$int$
$TIME$	$2$	$idv$	$real$
$DV$	$3$	$dv$	$real$
$AMT$	$4$	$dose$	$real$
$CMT$	$5$	$cmt$	$int$
$MDV$	$6$	$mdv$	$int$
$K_A$	$7$	$covariate$	$real$
$Vp$	$8$	$covariate$	$real$
$CLEARANCE$	$9$	$covariate$	$real$
$CLD$	$10$	$covariate$	$real$
$Kd$	$11$	$covariate$	$real$
$FR$	$12$	$covariate$	$real$
$Vcentral$	$13$	$covariate$	$real$
$CLL$	$14$	$covariate$	$real$

Column Mappings

Column Ref	Modelling Mapping
$ID$	$vm_mdl.ID$
$TIME$	$T$
$DV$	$om1.Y$
$AMT$	${\begin{cases} sm.Qs_F & if & CMT = 1 \land AMT > 0 \\ sm.Qc & if & CMT = 2 \land AMT > 0 \end{cases}$
$K_A$	$cm.K_A$
$Vp$	$cm.Vp$
$CLEARANCE$	$cm.CLEARANCE$
$CLD$	$cm.CLD$
$Kd$	$cm.Kd$
$FR$	$cm.FR$
$Vcentral$	$cm.Vcentral$
$CLL$	$cm.CLL$

Estimation Step

OID	$estimStep_1$
Dataset Reference	$nm_ds$

Parameters To Estimate

Parameter	Initial Value	Fixed?	Limits
pm.POP_CRP0	$8.44$	false	$()$
pm.POP_beta	$0.25$	false	$()$
pm.POP_kout	$1.06$	false	$()$
pm.POP_gama	$1.92$	false	$()$
pm.RES	$0.111$	false	$()$
pm.OMEGA_CRP0	$0.447$	false	$()$
pm.OMEGA_beta	$0.567$	false	$()$
pm.OMEGA_kout	$0.105$	false	$()$
pm.OMEGA_gama	$0.4$	false	$()$

Operations

Operation: $1$

Op Type

generic

Operation Properties

Name	Value
algo	$foce$

Step Dependencies

Step OID	Preceding Steps
$estimStep_1$

DDMODEL00000123: AitOudhia_2012_CRP_canakinumab