Objective. Rheumatoid arthritis (RA) is characterized by inflammation and joint destruction, with the degree of damage varying greatly among patients. Prediction of disease severity using known clinical and serologic risk factors is inaccurate. This study was undertaken to identify new serologic markers for RA severity using an in silico model of the rheumatic joint.Methods. An in silico model of a prototypical rheumatic joint was used to predict candidate markers associated with erosiveness. The following 4 markers were chosen for validation: tartrate-resistant acid phosphatase 5b (TRAP-5b), N-telopeptide of type I collagen (NTX), angiopoietin 2 (Ang-2), and CXCL13. Serum from 74 RA patients was used to study whether radiologic joint destruction (total erosion score and total Sharp/van der Heijde score [SHS]) after 4 years of disease was associated with serum levels at the time of diagnosis. Serum marker levels were determined using enzyme-linked immunosorbent assays. For confirmation, baseline serum levels were analyzed for an association with progression of joint damage over 7 years of followup in a cohort of 155 patients with early RA.Results. Comparison of high and low quartiles of erosion score and SHS at 4 years showed a difference in baseline serum CXCL13 level (P ‫؍‬ 0.011 and P ‫؍‬ 0.018, respectively). In the confirmation cohort, elevated baseline CXCL13 levels were associated with increased rates of joint destruction during 7 years of followup (P < 0.001 unadjusted and P < 0.004 with adjustment for C-reactive protein level). Analyzing anti-CCP-2-positive and anti-CCP-2-negative RA separately yielded a significant result only in the anti-CCP-2-negative group (P < 0.001).Conclusion. Our findings indicate that CXCL13 is a novel serologic marker predictive of RA severity. This marker was identified with the help of an in silico model of the RA joint.

A large-scale mathematical model, the Entelos Rheumatoid Arthritis (RA) PhysioLab platform, has been developed to describe the inflammatory and erosive processes in afflicted joints of people suffering from RA. The platform represents the life cycle of inflammatory cells, endothelium, synovial fibroblasts, and chondrocytes, as well as their products and interactions. The interplay between these processes culminates in clinically relevant measures for inflammation and erosion. The simulation model is deterministic, which allows tracing back the mechanism of action for a particular simulation result. Different patient phenotypes are represented by different virtual patients. The RA PhysioLab platform has been used to systematically and quantitatively study the predicted therapeutic effect of modulating several molecular targets, which resulted in a ranking of putative drug targets and a workflow to confirm the simulations experimentally. In addition, critical pathways were identified that drive the predicted disease outcome. Within these pathways, targets were identified from public literature that were not previously associated with arthritis. The model provides insights into the biology of RA and can be used as a platform for hypothesis-driven research. Case studies of therapies directed against IL-12 and IL-15 illustrate the approach, with emphasis on the analysis of system dynamics.