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Mechanisms of a sustained anti-inflammatory drug response in alveolar macrophages unraveled with mathematical modeling
Department of Biomedical Engineering, Linköping University, Linköping, Sweden.
Drug Metabolism and Pharmacokinetics, Research and Early Development, Respiratory & Inflammation, BioPharmaceuticals R&D, AstraZeneca, Gothenburg, Sweden.
Department of Biomedical Engineering, Linköping University, Linköping, Sweden; Department of Mathematics, Linköping University, Linköping, Sweden.
Department of Biomedical Engineering, Linköping University, Linköping, Sweden.
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2020 (English)In: CPT: Pharmacometrics and Systems Pharmacology (PSP), E-ISSN 2163-8306, Vol. 9, no 12, p. 707-717Article in journal (Refereed) Published
Abstract [en]

Both initiation and suppression of inflammation are hallmarks of the immune response. If not balanced, the inflammation may cause extensive tissue damage, which is associated with common diseases, e.g. asthma and atherosclerosis. Anti-inflammatory drugs come with side-effects which may be aggravated by high and fluctuating drug concentrations. To remedy this, an anti-inflammatory drug should have an appropriate pharmacokinetic half-life or better still: a sustained anti-inflammatory drug response. However, we still lack a quantitative mechanistic understanding of such sustained effects. Here, we study the anti-inflammatory response to a common glucocorticoid drug, Dexamethasone. We find a sustained response 22 hours after drug removal. With hypothesis testing using mathematical modeling, we unravel the underlying mechanism - a slow release of Dexamethasone from the receptor-drug complex. The developed model is in agreement with time-resolved training and testing data, and is used to simulate hypothetical treatment schemes. This work opens up for a more knowledge-driven drug development, to find sustained anti-inflammatory responses and fewer side effects.

Place, publisher, year, edition, pages
John Wiley & Sons, 2020. Vol. 9, no 12, p. 707-717
National Category
Pharmaceutical Sciences
Identifiers
URN: urn:nbn:se:oru:diva-87536DOI: 10.1002/psp4.12568ISI: 000598046400001PubMedID: 33217190Scopus ID: 2-s2.0-85097511676OAI: oai:DiVA.org:oru-87536DiVA, id: diva2:1503066
Funder
Swedish Research Council, 2019-03767Swedish Heart Lung FoundationEU, Horizon 2020
Note

Funding Agencies:

 CENIIT (Center for Industrial Information Technology)  

KAW and Sci Life Lab Covid-19 platform 

Available from: 2020-11-23 Created: 2020-11-23 Last updated: 2021-01-15Bibliographically approved

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Persson, AlexanderEklund, Daniel

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