Promote early diagnosis of PsA in patients with Psoriasis. Develop a tool to reliably diagnose patients early with features of PsA. This tool will likely include a variety of objectively measured blood markers (so-called ‘biomarkers’) and patient reported outcomes.
Identify indicators from patient characteristics such as age, sex or lifestyle (smoking, weight gain etc) which help identify PsA
Search for novel indicators present in the blood or other patient samples which differentiate PsA from other forms of arthritis
Develop more advanced imaging techniques (e.g. MRI) for detection of early signs of PsA
Combine these features using advanced statistical approaches with the aim of developing an algorithm for PsA diagnosis.
Develop a test to identify psoriasis patients at risk of progression to PsA. Discovering biomarkers that can identify psoriasis patients at risk of developing PsA. This will enable earlier interventions and possibly prevent the development of PsA.
Set up a large European study, HPOS, to collect biological and patient-relevant data.
Results from this study will potentially provide valuable data for all four Work Packages.
WP1 and 2 will both use methods within the field of Artificial Intelligence and machine learning.
Develop a methodology that allows us to select the drug that is most likely to best work for each individual patient. Utilize extensive samples from many European cohorts of PsA patients, including samples from relevant biobanks.
Test these samples at cellular and molecular levels, including relevant advanced DNA and RNA tests.
Analyse large volumes of data using artificial intelligence techniques, including machine learning, searching for which patients with which sets of characteristics respond best to particular classes of drug.