Analysis of a mathematical model of rheumatoid arthritis

Building a modular and multi-cellular virtual twin of the synovial joint in Rheumatoid Arthritis

Rheumatoid arthritis is a complex disease marked by joint pain, stiffness, swelling, and chronic synovitis, arising from the dysregulated interaction between synoviocytes and immune cells. Its unclear etiology makes finding a cure challenging. The concept of digital twins, used in engineering, can be applied to healthcare to improve diagnosis and treatment for complex diseases like rheumatoid arthritis. In this work, we pave the path towards a digital twin of the arthritic joint by building a large, modular biochemical reaction map of intra- and intercellular interactions. This network, featuring over 1000 biomolecules, is then converted to one of the largest executable Boolean models for biological systems to date. Validated through existing knowledge and gene expression data, our model is used to explore current treatments and identify new therapeutic targets for rheumatoid arthritis.

Multiscale, mechanistic model of Rheumatoid Arthritis to enable decision making in late stage drug development

Rheumatoid Arthritis (RA) is a chronic autoimmune inflammatory disease that affects about 0.1% to 2% of the population worldwide. Despite the development of several novel therapies, there is only limited benefit for many patients. Thus, there is room for new approaches to improve response to therapy, including designing better trials e.g., by identifying subpopulations that can benefit from specific classes of therapy and enabling reverse translation by analyzing completed clinical trials. We have developed an open-source, mechanistic multi-scale model of RA, which captures the interactions of key immune cells and mediators in an inflamed joint. The model consists of a treatment-naive Virtual Population (Vpop) that responds appropriately (i.e. as reported in clinical trials) to standard-of-care treatment options-Methotrexate (MTX) and Adalimumab (ADA, anti-TNF-α) and an MTX inadequate responder sub-population that responds appropriately to Tocilizumab (TCZ, anti-IL-6R) therapy. The clinical read-outs of interest are the American College of Rheumatology score (ACR score) and Disease Activity Score (DAS28-CRP), which is modeled to be dependent on the physiological variables in the model. Further, we have validated the Vpop by predicting the therapy response of TCZ on ADA Non-responders. This paper aims to share our approach, equations, and code to enable community evaluation and greater adoption of mechanistic models in drug development for autoimmune diseases.

Latest models for the discovery and development of rheumatoid arthritis drugs

INTRODUCTION

Rheumatoid arthritis (RA) is a chronic autoimmune disease that reduces the quality of life. The current speed of development of therapeutic agents against RA is not satisfactory. Models on which initial experiments are conducted do not fully reflect human pathogenesis. Overcoming this oversimplification might be a crucial step to accelerate studies on RA treatment.

AREAS COVERED

The current approaches to produce novel models or to improve currently available models for the development of RA drugs have been discussed. Advantages and drawbacks of two- and three-dimensional cell cultures and animal models have been described based on recently published results of the studies. Moreover, approaches such as tissue engineering or organ-on-a-chip have been reviewed.

EXPERT OPINION

The cell cultures and animal models used to date appear to be of limited value due to the complexity of the processes involved in RA. Current models in RA research should take into account the heterogeneity of patients in terms of disease subtypes, course, and activity. Several advanced models and tools using human cells and tissues have been developed, including three-dimensional tissues, liquid bioreactors, and more complex joint-on-a-chip devices. This may increase knowledge of the molecular mechanisms leading to disease development, to help identify new biomarkers for early detection, and to develop preventive strategies and more effective treatments.