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Quantitative predictive modelling approaches to understanding rheumatoid arthritis : a brief review

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cells_09_00074.pdf (2.296Mb)
Date
27/12/2019
Author
Macfarlane, Fiona Ruth
Chaplain, Mark Andrew Joseph
Eftimie, Raluca
Keywords
Rheumatoid arthritis
Mathematical models
Deterministic models
ODEs
PDEs
Probabilistic models
QA Mathematics
QH301 Biology
RC Internal medicine
T-NDAS
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Abstract
Rheumatoid arthritis is a chronic autoimmune disease that is a major public health challenge. The disease is characterised by inflammation of synovial joints and cartilage erosion, which lead to chronic pain, poor life quality and, in some cases, mortality. Understanding the biological mechanisms behind the progression of the disease, as well as developing new methods for quantitative predictions of disease progression in the presence/absence of various therapies is important for the success of therapeutic approaches. The aim of this study is to review various quantitative predictive modelling approaches for understanding rheumatoid arthritis. To this end, we start by briefly discussing the biology of this disease and some current treatment approaches, as well as emphasising some of the open problems in the field. Then, we review various mathematical mechanistic models derived to address some of these open problems. We discuss models that investigate the biological mechanisms behind the progression of the disease, as well as pharmacokinetic and pharmacodynamic models for various drug therapies. Furthermore, we highlight models aimed at optimising the costs of the treatments while taking into consideration the evolution of the disease and potential complications.
Citation
Macfarlane , F R , Chaplain , M A J & Eftimie , R 2019 , ' Quantitative predictive modelling approaches to understanding rheumatoid arthritis : a brief review ' , Cells , vol. 9 , no. 1 , 74 . https://doi.org/10.3390/cells9010074
Publication
Cells
Status
Peer reviewed
DOI
https://doi.org/10.3390/cells9010074
ISSN
2073-4409
Type
Journal item
Rights
Copyright © 2019 by the Author(s). This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited
Collections
  • University of St Andrews Research
URI
http://hdl.handle.net/10023/19230

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