Second-generation TNFα turnover model for improved analysis of test compound interventions in LPS challenge studies
Artikel i vetenskaplig tidskrift, 2021

This study presents a non-linear mixed effects model describing tumour necrosis factor alpha (TNFα) release after lipopolysaccharide (LPS) provocations in absence or presence of anti-inflammatory test compounds. Inter-occasion variability and the pharmacokinetics of two test compounds have been added to this second-generation model, and the goal is to produce a framework of how to model TNFα response in LPS challenge studies in vivo and demonstrate its general applicability regardless of occasion or type of test compound. Model improvements based on experimental data were successfully implemented and provided a robust model for TNFα response after LPS provocation, as well as reliable estimates of the median pharmacodynamic parameters. The two test compounds, Test Compound A and roflumilast, showed 81.1% and 74.9% partial reduction of TNFα response, respectively, and the potency of Test Compound A was estimated to 0.166 µmol/L. Comparing this study with previously published work reveals that our model leads to biologically reasonable output, handles complex data pooled from different studies, and highlights the importance of accurately distinguishing the stimulatory effect of LPS from the inhibitory effect of the test compound.

Lipopolysaccharides

Non-linear mixed effects

Sprague-Dawley rats

Turnover models

LPS challenge studies

Tumor necrosis factor alpha

Författare

Julia Larsson

Chalmers, Matematiska vetenskaper, Tillämpad matematik och statistik

Stiftelsen Fraunhofer-Chalmers Centrum för Industrimatematik

Edmund Hoppe

Grünenthal GmbH

Michael Gautrois

Grünenthal GmbH

Marija Cvijovic

Chalmers, Matematiska vetenskaper, Tillämpad matematik och statistik

Göteborgs universitet

Mats Jirstrand

Stiftelsen Fraunhofer-Chalmers Centrum för Industrimatematik

European Journal of Pharmaceutical Sciences

0928-0987 (ISSN)

Vol. 165 105937

Ämneskategorier

Farmaceutisk vetenskap

Bioinformatik (beräkningsbiologi)

Sannolikhetsteori och statistik

DOI

10.1016/j.ejps.2021.105937

Mer information

Senast uppdaterat

2021-08-13