主催: 日本臨床薬理学会
Introduction: Rheumatoid arthritis (RA) is the most commoninflammatory systemic autoimmune disorder which affects about 0.45% of theglobal population[1]. One of the key challenges in optimizing therapies for RApatients is to understand the factors that drive response to differenttherapies. Multi-scale Quantitative Systems Pharmacology (QSP) models integratemechanistic understanding and clinical outcomes and can aid in interpretingexisting data and predicting clinical response of novel therapies. We havedeveloped an RA QSP model which consists of multiple cell types (immune andstructural) and cytokines of interest and simulates clinical scores such as ACR& DAS-28. Such a model can be used to predict clinical response to noveltherapies and combinations in different patient subpopulations of interest andalso to provide a mechanistic understanding of response to therapy.
Objectives: Develop, calibrate and validate a QSP model ofappropriate physiological detail and scale to address various questions ofinterest in drug development at both patient and population level in RA and usethe model to generate predictions of interest, such as: clinical outcomes fornovel therapies, combinations of existing therapies, identifying subpopulationswith greater response to therapies, test novel trial designs etc.
Methods: Model design, engineering, survey of publishedphysiological and clinical data was carried out in accordance with standard QSPtechniques[2]. An average inflamed joint in an RA patient at steady-state (withno disease progression or episodic inflammation) is captured in the model.Through Ordinary Differential Equations (ODEs), the model captures cellularlifecycle and interactions of Fibroblast like Synoviocytes (FLS), B cells, Tcells and Macrophages among other relevant cell types and relevant pro andanti-inflammatory cytokines (e.g. IL-6, TNF-α, TGF-ꞵ). The model was calibratedusing publicly available data. Using an in-house algorithm, a virtual cohort wasgenerated by varying select parameters of the model to capture the variabilityin the disease severity as well as the response to therapy. A virtual populationwas selected from the virtual cohort to capture the clinical outcomes observedin Phase 3 trials of Methotrexate[3], Adalimumab[4] and Tocilizumab[5].
Results: We have developed a model of stable RA disease, whichincludes key immune system cells and mediators in an inflamed joint. We havedeveloped a virtual population that captures the clinical outcomes observed inphase 3 clinical trials for Methotrexate, Adalimumab and Tocilizumab. We havevalidated the virtual population against a Tocilizumab phase 3 trial on ananti-TNFa non responder population[6].
Conclusions: The Vantage RA-QSP model captures the mechanistic andclinically relevant features of RA along with the response to three differenttherapies. The calibrated virtual population reasonably captures the response totocilizumab in an anti-TNFa non responder population, building confidence in themodel. Further, the utility of the model is showcased by the physiologicalinsights derived into what drives efficacy for different therapies. Calibrationto more therapies will enhance the scope and insights offered by the model.
