Overview of the PHARAO consortium strategy to enable precision treatment in psoriatic arthritis (PsA). A large, well-phenotyped PsA biobank is established to identify biomarkers predicting response to TNFα inhibitors and IL-17 inhibitors. Integrated multi-omics profiling — including whole-exome sequencing, circulating cell-free RNA transcriptomics, immunomics, and lipid/lipoprotein lipidomics — is combined with clinical and patient-reported data to develop a predictive algorithm. The resulting decision-support tool aims to improve individualized therapy selection and enhance treatment outcomes in PsA.

Psoriatic arthritis (PsA) is a chronic rheumatic disease that often co-occurs with psoriasis and is characterised by joint inflammation. Although targeted biological therapies — such as tumour necrosis factor α inhibitors (TNFi) and interleukin-17 inhibitors (IL-17i) — have significantly improved outcomes for many patients, a notable proportion continues to display suboptimal responses or complete therapeutic failure. This variability in treatment efficacy underscores the limitations of the current empirical, trial-and-error approach to therapy selection. Consequently, patients may experience prolonged disease activity, accrual of irreversible joint damage, diminished quality of life, and escalating healthcare costs. This underscores the unmet need of reliable biomarkers to predict individual therapeutic responses and advance the field of personalised medicine in PsA.

The PHARAO consortium aims to meet this challenge by developing a predictive decision-support tool for individualised treatment stratification in PsA, specifically targeting responses to TNFi and IL-17i therapies. To this end, a biobank comprising a large cohort of well-characterised PsA patients will be established, integrating standardised clinical data and patient-reported outcomes. A systems-level, multi-omics approach will be utilised to elucidate biomarkers of treatment response, integrating genomics (whole-exome sequencing), transcriptomics (circulating cell-free RNA profiling), immunomics (antibody profiling), and lipidomics (circulating lipid and lipoprotein profiling). These data will inform the development of a predictive algorithm, which will be integrated in a clinical decision-support tool that will guide precision treatment selection. This project will support personalised treatment selection, improving therapeutic efficacy and advancing stratified medicine in PsA.