Quantitative Predictive Modelling Approaches to Understanding Rheumatoid Arthritis: A Brief Review

Fiona R. Macfarlane (Lead / Corresponding author), Mark A. J. Chaplain, Raluca Eftimie

Research output: Contribution to journalReview articlepeer-review

56 Downloads (Pure)

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.
Original languageEnglish
Article number74
Number of pages26
JournalCells
Volume9
Issue number1
Early online date27 Dec 2019
DOIs
Publication statusPublished - Jan 2020

Keywords

  • rheumatoid arthritis
  • mathematical models
  • deterministic models
  • ODEs
  • PDEs
  • probabilistic models

Fingerprint Dive into the research topics of 'Quantitative Predictive Modelling Approaches to Understanding Rheumatoid Arthritis: A Brief Review'. Together they form a unique fingerprint.

Cite this