2019 - Stockholm - Sweden

PAGE 2019: Drug/Disease modelling - Other topics
Vikram Prabhakar

QSP Model of Rheumatoid Arthritis, capturing range of clinical responses to Methotrexate and anti-TNF-a therapies

Tamara Ray, Madhav Channavazzala, Dinesh Bedathuru, Maithreye Rengaswamy, Rukmini Kumar

Vantage Research


Rheumatoid arthritis (RA) is an inflammatory, systemic autoimmune disorder affecting about 1-3% of global population, with over 2.3 million cases in the EU alone. QSP models are vital to understand patient response to existing and novel therapies. Multi-scale Quantitative Systems Pharmacology (QSP) models connect physiological mechanisms at the cellular and organ level, to responses at the patient and population level, and can therefore be used to predict clinical response of novel therapies.

We have developed an RA QSP model comprising multiple immune-cell types and cytokines of interest. The model simulates ACR & DAS-28 scores for an entrant population for two RA therapies – Methotrexate and anti-TNF-alpha therapies.


* Develop QSP model of RA, at appropriate physiological detail and scale, to address various questions of interest in drug development at both mechanistic and population level

* Use model to generate predictions of interest, such as: clinical outcomes for novel therapies, combinations of existing therapies, identifying sub-populations with greater response to therapies, and simulate and optimize novel trial designs

* Create a modular model design such that common immunological pathways are re-usable for auto immune diseases


Model design, engineering, survey of published physiological and clinical data was carried out in accordance with standard QSP approaches1.  An average, inflamed joint capturing the disease at steady-state (i.e., with no disease progression or episodic inflammation) is modelled.

Using Ordinary Differential Equations (ODEs), 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-alpha, TGF-beta). Reference virtual subjects are generated and calibrated to be average responders/non-responders to methotrexate (standard of care) and to anti-TNF-α therapy. Model parameters are constrained by clinical trial data (top down constraints) as well as by data from basic science literature (bottom up constraints), e.g. proliferation and apoptosis rates of cells, cytokine secretion rates. ACR and DAS-28 scores in Virtual Populations are calibrated to match seminal trials for Methotrexate and anti-TNF-alpha therapies, namely Premier Study2 and RA Beam3.

To enable model repurposing to other autoimmune diseases, modular design approaches are used in setting up common immunological subsystems such as cell life-cycles and cytokine effects. This common compartment structure is modelled to impact the site of inflammation in an appropriate way in each disease (e.g., site of inflammation is joint in RA, or Lamina Propria in Inflammatory Bowel Disease).


A QSP model capturing multiple physiological pathways of interest and response to specified therapies in RA was developed that can be used for clinical trial visualization, trial optimization, responder/non-responder identification etc. Reference virtual subjects in this model span a DAS-28 range from 5 to 7 at baseline and show a comparable response (reduction of disease severity) to the two therapies studied in the BEAM and Premier trials. Reference subjects corresponding to responder and non-responder to Methotrexate and anti-TNF-alpha therapies were generated. Sensitivity analysis of pathways was carried out to determine factors contributing to response.


The Vantage RA-QSP model captures the physiology and clinical outcomes of RA, including response to Methotrexate and anti-TNF-alpha therapies4. Modular model design allows the model to be extended to other autoimmune disorders with similar pathophysiology by re-use of the core immunological components. Future efforts will add therapeutic pathways including anti-IL-6, anti-IL-17, JAK-inhibitors and anti-IL-23.

[1] Gadkar K, Kirouac DC, Mager DE, van der Graaf PH, Ramanujan S. A Six-Stage Workflow for Robust Application of Systems Pharmacology. CPT pharmacometrics Syst Pharmacol. 2016;5(5):235-249. doi:10.1002/psp4.12071
[2] Breedveld FC, Weisman MH, Kavanaugh AF, et al. The PREMIER study: A multicenter, randomized, double-blind clinical trial of combination therapy with adalimumab plus methotrexate versus methotrexate alone or adalimumab alone in patients with early, aggressive rheumatoid arthritis who had not had previo. Arthritis Rheum. 2006;54(1):26-37. doi:10.1002/art.21519
[3] Beattie S, Ishii T, Schlichting D, et al. Baricitinib versus Placebo or Adalimumab in Rheumatoid Arthritis. N Engl J Med. 2017;376(7):652-662. doi:10.1056/nejmoa1608345
[4] Greef J van der. Systems biology for battling rheumatoid arthritis: Application of the Entelos PhysioLab platform. IEE Proc Syst Biol. 2005;152(4). doi:10.1049/ip-syb

Reference: PAGE 28 (2019) Abstr 9081 [www.page-meeting.org/?abstract=9081]
Poster: Drug/Disease modelling - Other topics
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