PII-218 - QSP MODEL OF RHEUMATOID ARTHRITIS, CAPTURING RANGE OF RESPONSES TO METHOTREXATE, ADALIMUMAB AND TOCILIZUMAB THERAPIES

D. Bedathuru¹, A. Shaliban¹, T. Ray¹, M. Rengaswamy¹, P. Packrisamy¹, M. Channavazzala¹, R. Kumar²; ¹Vantage Research, ²AbbVie Inc, North Chicago, IL, US.

Background: Rheumatoid arthritis (RA) is the most common inflammatory systemic autoimmune disorder which affects about 0.45% of the global population. One of the key challenges in optimizing therapies for RA patients is to understand the factors that drive response to different therapies. Multi-scale Quantitative Systems Pharmacology (QSP) models integrate mechanistic understanding and clinical outcomes and can aid in interpreting existing data and predicting clinical response of novel therapies.

Methods: Model design, engineering, survey of published physiological and clinical data was carried out in accordance with standard QSP techniques. An average inflamed joint in an RA patient at steady-state (with no disease progression or episodic inflammation) is captured in the model. The model captures cellular lifecycle and interactions of Fibroblast like Synoviocytes (FLS), B cells, T cells and Macrophages among other relevant cell types and relevant pro and anti-inflammatory cytokines (e.g. IL-6, TNF-α, TGF-ꞵ). A virtual cohort was generated by varying select parameters of the model to capture the variability in the disease severity as well as the response to therapy. A virtual population was selected from the virtual cohort to capture the clinical outcomes observed in Phase 3 trials of Methotrexate, Adalimumab and Tocilizumab.

Results: The model captures the clinical outcomes observed in phase 3 clinical trials for Methotrexate, Adalimumab and Tocilizumab. We have validated the virtual population against a Tocilizumab phase 3 trial on an anti-TNFa non responder population. We have identified baseline levels of cytokines & Cells which may point to patients simultaneously being responders to Adalimumab/Non-responders to Toci or vice-versa.

Conclusion: The RA-QSP model captures the mechanistic and clinically relevant features of RA along with the response to three different therapies. The calibrated virtual population reasonably captures the response to tocilizumab in an anti-TNFa non responder population, building confidence in the model. Further, the utility of the model is showcased by the physiological insights derived into what drives efficacy for different therapies. Calibration to more therapies will enhance the scope and insights offered by the model.