Performance of PEAR 2.0 Software in Prescribing Biotherapy in Patients With Rheumatoid Arthritis

The management of rheumatoid arthritis is based on the prescription of disease-modifying anti-rheumatic drugs (DMARDs) to induce clinical and biological remission. If the first line of treatment (methotrexate) fails, a biotherapy may be prescribed. In daily practice, the initiation of a targeted therapy must therefore be based on the prescriber's expertise or qualification in terms of his or her level of experience in the diagnosis and management of chronic inflammatory rheumatic diseases such as rheumatoid arthritis. As the therapeutic arsenal has expanded, so has the question of choosing the right treatment for the right patient at the right time. At present, in daily practice, there is no tool to help clinicians predict treatment efficacy. The choice of biotherapy based on efficacy carries relatively little weight, firstly because this choice is made in relation to other biotherapies, and secondly because there are no superiority studies that have actually demonstrated greater efficacy in favor of one of the targeted therapies. In the age of Big Data, artificial intelligence can be used to develop algorithms for predicting treatment response. mYXpression has developed medical decision support software based on the integration of transcriptomic markers to assess the probability of response and/or non-response to biotherapies for each patient. The algorithm's performance was theoretically tested by retrospectively collecting transcriptomic data and clinical responses to 6 biotherapies from 992 patients included in 17 clinical trials or cohorts. The aim of this observational study is to demonstrate the value of PEAR 2.0 medical decision support software in the management of rheumatoid arthritis patients who are candidates for biotherapy.

NCT Number	NCT06186375
Lead Sponsor	CEN Biotech Collaborators: MYEXPRESION
Conditions	Polyarthritis; Rheumatoid
Enrollment	239 participants
Start Date	2023-05-05
Primary Completion	2025-11-30 (estimated)
Study Completion	2026-03-31 (estimated)
Updated on ClinicalTrials.gov	2025-08-06