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

TL;DR

This review summarizes quantitative modelling approaches for rheumatoid arthritis, focusing on biological mechanisms, drug therapy models, and treatment optimization to improve understanding and management of the disease.

Contribution

It provides a comprehensive overview of mechanistic, pharmacokinetic, and optimization models addressing open problems in rheumatoid arthritis research.

Findings

Mechanistic models elucidate disease progression pathways.

Pharmacokinetic/pharmacodynamic models inform drug therapy strategies.

Optimization models aim to reduce treatment costs while managing disease evolution.

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 leads to chronic pain, poor life quality and, in some cases, premature 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 discussing briefly 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.