Early diagnosis, prediction of radiographic outcomes and development of rational, personalised treatment strategies to improve long-term outcomes in psoriatic arthritis

Topic description

Specific Challenge:

Psoriatic arthritis (PsA) is a chronic immune-mediated disease involving axial and peripheral joints, nails, skin and enthesis. Cutaneous manifestations often precede articular symptoms and it has been estimated that about 20-30 % of psoriatic patients develop arthritis or enthesitis over time. In fact, the precedence of cutaneous symptoms may give as much as about 7 years to predict, detect and potentially treat PsA.

Currently, it is felt that the earlier PsA can be diagnosed, the better the chances that treatment could influence the disease

There are still a large number of patients suffering from PsA that are diagnosed after several years of signs and symptoms (late diagnosis) and fail to respond to current standard of care treatments, or quickly relapse on, or following treatment.

The Group for Research and Assessment of Psoriasis and Psoriatic Arthritis (GRAPPA) identified the following major unmet medical needs:

• Early diagnosis of PsA either in psoriasis (PsO) patients or in patients without initial psoriasis skin manifestations. Significant delay in diagnosis contributes to poor clinical and radiographic outcome.
• Early identification of patients at risk of progression to PsA. Defining the predictors of progression to PsA in patients with skin PsO will enable earlier intervention and possibly even prevent development of PsA.
• Definition of the clinical, genetic, immune factors or protein biomarkers that predict disease progression in PsA patients at time of diagnosis.

Better prediction, at diagnosis, for prognosis and stratification by therapeutic needs.

The aim of this topic is to characterise the natural history of PsA from psoriasis to “early” PsA to “full-fledged” PsA, as diagnosed by the Classification Criteria for Psoriatic Arthritis (CASPAR). This characterisation will be based on discovering new biomarkers and endotypes, constructed on genetic, epigenetic, transcriptomic, proteomic and/or clinical markers. To identify those endotypes, artificial intelligence (AI) and machine learning (ML) processes will be needed.

In particular, the topic aims to achieve the following specific objectives:

• to enable rheumatologists, dermatologists and general practitioners to make an early diagnosis of PsA in patients with PsO and other rheumatic disorders;
• to identify early patients at risk of progression to PsA in order to enable earlier interventions and possibly prevent PsA development;
• to define the factors that predict disease progression in PsA patients, including early prediction of bone/joint damages, leading to the development of more adapted treatment strategies;
• to develop rational and personalised treatment strategies (e.g. select the optimal first line or second line treatment based on patient characteristics) with optimised outcomes in PsA patients and reduce the disease burden.

In their proposals, applicants should describe how the outputs of the project will contribute to the following impacts and include wherever possible baseline, targets and metrics to measure impact:

• “Early PsA” diagnosis and earlier personalised treatments for patients would impact the disease progression and ultimately prevent PsA development. AI would help identifying endotypes which could take into account the clinical and biological heterogeneities of PsA.
• Development of objective and sensitive functional measures would enable the early diagnosis of PsA in PsO patients and the early prediction of bone/joint damages in PsA patients, yielding long-lived reduction in disease and improvement of patients’ quality of life.
• Improved rates of treatment successes through better understanding of the relation between molecular characteristics of PsA and treatment responses would reduce costs to patients (side effects) and society (economics).

In their proposals, applicants should outline how the project plans to leverage the public-private partnership model to maximise impacts on innovation, research & development; regulatory, clinical and healthcare practices, as relevant. This could include a strategy for the engagement with patients, healthcare professional associations, healthcare providers, regulators, HTA (health technology assessment) agencies, payers, etc., where relevant.

In addition, applicants should describe how the project will impact on the competitiveness and growth of companies including SMEs.

In their proposals, applicants should outline how the project will:

• Manage research data including use of data standards¹.
• Disseminate, exploit, and sustain the project results. This may involve engaging with suitable biological and medical sciences research infrastructures².
• Communicate the project’s activities to relevant target audiences.

¹ Guidance on data management is available at http://ec.europa.eu/research/participants/docs/h2020-funding-guide/cross-cutting-issues/open-access-data-management/data-management_en.htm

² http://www.corbel-project.eu/about-corbel/research-infrastructures.html