Show simple item record

Files in this item

Thumbnail

Item metadata

dc.contributor.authorMacfarlane, Fiona Ruth
dc.contributor.authorChaplain, Mark Andrew Joseph
dc.contributor.authorEftimie, Raluca
dc.date.accessioned2020-01-06T16:30:04Z
dc.date.available2020-01-06T16:30:04Z
dc.date.issued2019-12-27
dc.identifier.citationMacfarlane , 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/cells9010074en
dc.identifier.issn2073-4409
dc.identifier.otherPURE: 265382690
dc.identifier.otherPURE UUID: 419e6de3-39ea-4da4-8c16-71005927b42e
dc.identifier.otherWOS: 000515398200074
dc.identifier.otherScopus: 85090491128
dc.identifier.urihttp://hdl.handle.net/10023/19230
dc.description.abstractRheumatoid 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.
dc.format.extent26
dc.language.isoeng
dc.relation.ispartofCellsen
dc.rightsCopyright © 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 citeden
dc.subjectRheumatoid arthritisen
dc.subjectMathematical modelsen
dc.subjectDeterministic modelsen
dc.subjectODEsen
dc.subjectPDEsen
dc.subjectProbabilistic modelsen
dc.subjectQA Mathematicsen
dc.subjectQH301 Biologyen
dc.subjectRC Internal medicineen
dc.subjectT-NDASen
dc.subject.lccQAen
dc.subject.lccQH301en
dc.subject.lccRCen
dc.titleQuantitative predictive modelling approaches to understanding rheumatoid arthritis : a brief reviewen
dc.typeJournal itemen
dc.description.versionPublisher PDFen
dc.contributor.institutionUniversity of St Andrews.School of Mathematics and Statisticsen
dc.contributor.institutionUniversity of St Andrews.Applied Mathematicsen
dc.identifier.doihttps://doi.org/10.3390/cells9010074
dc.description.statusPeer revieweden


This item appears in the following Collection(s)

Show simple item record