Anuraag Saini, Madhav Channavazzala, Dinesh Bedathuru, Maithreye Rengaswamy, Tamara Ray, Rukmini Kumar
Vantage Research, India
Introduction:
Rheumatoid arthritis (RA) is an inflammatory systemic autoimmune (AI) disorder affecting about 1% of global population. While the pathophysiology of the disease is reasonably well understood, multiple pathways are being investigated to alleviate the disease severity.
QSP models are an important tool to understand clinical outcomes of therapies. These models integrate physiological mechanisms at cellular and organ level to responses at patients and population level. They are used for their predictive capabilities for combination therapy, identification of subpopulation that is responsive to therapy and optimization of novel drug trial design.
We have developed an RA QSP model comprising multiple immune-cell types and cytokines of interest. The model outcome is calibrated to capture clinical readouts such as ACR & DAS-28 scores for an entrant population for three RA therapies – Methotrexate, anti-TNF-α (Adalimumab) and Tocilizumab therapies. This gives a predictive power to capture the combination therapies and protocol optimization.
Objectives:
- Develop QSP model of RA capturing mechanistic pathways to guide and support drug development process at both mechanistic and population level
- Develop a clinical trial visualization app for visualizing clinical trials with novel therapies, combination of therapies, identifying sub-populations with greater response to therapies, and simulate and optimize novel trial designs
Methods:
The Vantage RA QSP model1 depicts a prototypical inflamed RA joint at steady state without disease progression or flaring (episodic inflammation). An extensive survey of published physiological and clinical data was carried out in accordance with standard QSP approaches2 to finalize model design.
Using 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-α, TGF-β). 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-α therapies, namely Williams et al,19853 and OPTIMA4
To enable model repurposing to other AI 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).
Further we developed Vantage QSP Modelling tools to enable the process of creating phenotypes (or virtual population)5 and a clinical trial visualization app to visualize outcomes from phase III clinical trial simulations. The app can predict clinical trials outcomes involving novel therapies, can help visualize combination of therapies, can identify sub-populations (phenotypes) with greater response to therapies and can optimize trial designs.
Results:
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. Model was calibrated to post therapy effects on disease severity scores. Reference subjects corresponding to responder and non-responder to Methotrexate and anti-TNF-α therapies were generated. The model was tested for sensitivity of the parameters to determine factors contributing to response. Reference virtual subjects in this model span the 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 Williams et al, 1985, OPTIMA and ROSE trials.
Conclusions:
The Vantage RA-QSP model captures the physiology and clinical outcomes of RA, including response to Methotrexate, anti-TNF-α and anti-IL-6 therapies6. Modular model design allows the model to be extended to other AI disorders with similar pathophysiology by re-use of the core immunological components. Future efforts will add therapeutic pathways including JAK-inhibitors and anti-IL-23.
References:
[1] Tamara Ray, Madhav Channavazzala, Dinesh Bedathuru, Maithreye Rengaswamy, Rukmini Kumar. QSP Model of Rheumatoid Arthritis, capturing range of clinical responses to Methotrexate and anti-TNF-a therapies. Poster presented at: PAGE 28 (2019), Stockholm, Sweden.
[2] 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
[3] Williams, H. J., Willkens, R. F., Samuelson Jr, C. O., Alarcón, G. S., Guttadauria, M., Yarboro, C., & Dahl, S. L. (1985). Comparison of low-dose oral pulse methotrexate and placebo in the treatment of rheumatoid arthritis. A controlled clinical trial. Arthritis & Rheumatism: Official Journal of the American College of Rheumatology, 28(7), 721-730.
[4] Kavanaugh, A., Fleischmann, R. M., Emery, P., Kupper, H., Redden, L., Guerette, B., … & Smolen, J. S. (2013). Clinical, functional and radiographic consequences of achieving stable low disease activity and remission with adalimumab plus methotrexate or methotrexate alone in early rheumatoid arthritis: 26-week results from the randomised, controlled OPTIMA study. Annals of the rheumatic diseases, 72(1), 64-71.
[5] Madhav Channavazzala, Dinesh Bedathuru, Priyamvada Modak, Rukmini Kumar. Quantitative Systems Pharmacology (QSP) tools to aid in model development and communication: Vantage QSP Modelling Tools (VQMTools). Poster presented at: PAGE 28 (2019), Stockholm, Sweden.
[6] Rullmann, J.A.C., Struemper, H., Defranoux, N.A., Ramanujan, S., Meeuwisse, C.M.L. and Van Elsas, A., 2005. Systems biology for battling rheumatoid arthritis: application of the Entelos PhysioLab platform. IEE Proceedings-Systems Biology, 152(4), pp.256-262.
[7] Yazici Y, Curtis JR, Ince A, Baraf H, Malamet RL, Teng LL, Kavanaugh A (2010). Efficacy of tocilizumab in patients with moderate to severe active rheumatoid arthritis and a previous inadequate response to disease-modifying antirheumatic drugs: the ROSE study. Ann Rheum Dis. 2012 Feb;71(2):198-205. doi: 10.1136/ard.2010.148700.
Reference: PAGE () Abstr 9584 [www.page-meeting.org/?abstract=9584]
Poster: Drug/Disease Modelling - Other Topics