Researchers at Queen Mary University of London have developed a promising rheumatoid arthritis biologic test using machine learning. It aims to help doctors select the best treatment for each patient.
The test successfully predicted the most effective biologic for nearly 80% of participants in trials. This could reduce the time patients spend suffering while trying different medications.
Biologics target the root cause of rheumatoid arthritis (RA), unlike traditional drugs that suppress the whole immune system. These treatments work by focusing on specific pathways that trigger joint inflammation.
Until now, choosing the right biologic was trial and error. About 40% of biologics fail because they don’t target the right immune cells. This new rheumatoid arthritis biologic test analyzes a patient’s joint tissue to match their gene activity with one of three biologics: etanercept, tocilizumab, or rituximab.
To develop the test, scientists studied gene patterns in patients who responded well to biologics. They identified 524 relevant genes and created a model to compare them against new patients.
Experts say prescribing the right biologic from the start can improve physical and emotional well-being. RA causes chronic pain and limits movement, so early relief is crucial. Many patients feel hopeless when medications fail repeatedly.
The researchers are now looking for commercial partners to bring the test to clinics. Clinical trials are ongoing, but results are promising. Scientists believe this personalized approach could change how autoimmune diseases like RA are treated.
As with any new medical tool, experts urge cautious optimism. More testing is needed before it becomes widely available. Still, this could mark a big step forward for precision medicine in arthritis care.
