According to a recent study, DNA may be able to predict how well an individual would respond to arthritis therapies.
A recent study from Queen Mary University of London suggests that pharmacological therapies for rheumatoid arthritis (RA) patients may be significantly impacted by the molecular profiling of sick joint tissue. On May 19th, 2022, the study was released in the journal Nature Medicine. The researchers also discovered certain genes linked to refractory disease—a condition that is resistant to the majority of current pharmacological therapies—which may hold the key to developing new, efficient treatments for these individuals.
Despite significant advancements in the treatment of arthritis over the last few decades, over 40% of patients do not react to specific pharmacological therapies, and between 5 and 20% of those with the illness are resistant to all forms of medication now available.
In a biopsy-based clinical investigation, the researchers examined the responses of 164 arthritis patients to the RA treatments rituximab and tocilizumab. The results of the initial study, which were reported in The Lancet in 2021, demonstrated that among patients with a low synovial B-cell molecular signature, just 12% responded to rituximab, a medicine that targets B cells, whereas 50% responded to an alternative drug (tocilizumab). When patients had high levels of this genetic signature, both medicines worked equally well.
The Queen Mary team also looked at the cases where patients did not respond to treatment via any of the drugs and discovered 1,277 genes that were unique to them specifically as part of the groundbreaking study, funded by the Efficacy and Mechanism Evaluation (EME) Programme, an MRC and NIHR partnership.
On the basis of this, the researchers used a data analysis method known as machine learning models to create computer algorithms that could forecast how each patient would respond to a particular medicine. When compared to a model that solely employed tissue pathology or clinical characteristics, machine learning algorithms that integrated gene profiling from biopsies performed noticeably better at predicting which medication would be most effective.
The findings makes a compelling argument for gene analysis of biopsies taken from arthritic joints before pricey so-called biologic targeted medicines are recommended. By avoiding potential adverse effects, joint injury, and poorer outcomes that are frequently experienced by patients, this might save the NHS and society a significant amount of time and money. Such testing might inform doctors about which patients would not react to any of the available treatments, which would highlight the need for alternative drug development in addition to impacting therapy prescription.
The findings makes a compelling argument for gene analysis of biopsies taken from arthritic joints before pricey so-called biologic targeted medicines are recommended. By avoiding potential adverse effects, joint injury, and poorer outcomes that are frequently experienced by patients, this might save the NHS and society a significant amount of time and money. Such testing might inform doctors about which patients would not react to any of the available treatments, which would highlight the need for alternative drug development in addition to impacting therapy prescription.
"Incorporating molecular information before prescribing arthritis therapies to patients might permanently revolutionize the way we treat the illness," said Professor Costantino Pitzalis, Versus Arthritis Professor of Rheumatology at Queen Mary University of London.Instead of the trial-and-error medicine prescription that is presently the norm, patients would benefit from a tailored strategy that has a much higher probability of success.
The area is still in its infancy and more confirmatory studies will be needed to fully achieve the promise of precision medicine in RA, but these results are tremendously exciting in that they show the potential at our fingertips.
The findings are crucial for developing treatments for those who, regrettably, do not now benefit from any. Knowing which precise molecular profiles have an influence on this and which disease-promoting pathways are still active in these individuals will aid in the development of novel medications that will produce better outcomes and provide much-needed pain and suffering alleviation.
To convert these discoveries into ordinary clinical treatment, it will be required to include these signatures in next diagnostic tests.
