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

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.

Quantifying the Role of Stochasticity in the Development of Autoimmune Disease

“…In the specific context of autoimmunity, stochasticity is known to play a number of important roles, controlling the balance of activation and inhibition of T cell regulation [73] and expression of autoantigens and peripheral tissue antigens [74,75], as well as many other gene regulatory events associated with immune response [76]. A very recent review by Macfarlane et al [77] has specifically highlighted an extremely important role played by stochasticity in triggering and mediating the progress of rheumatoid arthritis, a chronic autoimmune condition characterised by inflammation of joints, and suggested that stochastic models need to be developed in order to better understand the dynamics of this disease, and to optimise its management and potential treatments. In application to EAU dynamics, stochasticity manifests itself in the fact that, even though experiments are performed on genetically identical mice with exactly the same immunisation protocol, there is clinically significant variation in the time course of autoimmune disease between individual eyes.…”

Simulation Model for Hashimoto Autoimmune Thyroiditis Disease

“…Instead, the growth of TH17 and TH1 lymphocytes is represented by a functional response of the immune system ( 43 ), which in this model is assumed a Holling type II functional saturation response. This lymphocytic growth depends on the contribution of the differentiation process induced by the thyrocytes, with a maximum contribution of growth φ 1 and φ 2 , respectively.…”

Modelling rheumatoid arthritis: A hybrid modelling framework to describe pannus formation in a small joint

“…Mathematical modelling is a useful tool to aid in the understanding of biological process at multiple spatial and temporal scales. We have recently reviewed previous mathematical models of rheumatoid arthritis, [20], and refer the reader to that paper for further detail. The few models existent in the literature, and described in our review paper, are mainly deterministic, however it has been suggested that accounting for stochasticity within RA may be key in understanding the evolution of the disease [9,21] and predicting the success of RA treatments [22].…”