Genes may determine how well people respond to arthritis therapies, according to a recent study.
According to a recent study from Queen Mary University of London, molecular profiling of sick joint tissue might have a significant impact on whether specific pharmacological therapies for rheumatoid arthritis (RA) patients are effective. On May 19th, 2022, the findings were published in Nature Medicine. The researchers also discovered genes linked to medication resistance, commonly known as refractory illness, which might lead to the discovery of new, successful treatments for these individuals.
While there has been significant progress in treating arthritis in recent decades, a huge number of people (about 40%) do not react to specific therapeutic therapies, and 5-20% of people with the illness are resistant to all types of medication.
The researchers tested 164 arthritis patients' responses to rituximab or tocilizumab, two drugs often used to treat RA, in a biopsy-based clinical trial. The results of the first experiment, published in The Lancet in 2021, indicated that only 12% of people with a low synovial B-cell molecular signature responded to a B-cell-targeting drug (rituximab), whereas 50% responded to a different drug (tocilizumab). When patients had high levels of this genetic signature, both medicines were equally beneficial.
The Queen Mary team looked at 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 first-of-its-kind study, which was funded by the Efficacy and Mechanism Evaluation (EME) Programme, an MRC and NIHR partnership.
Based on this, the researchers developed computer algorithms that might predict medication reactions in specific patients using a data analysis approach called machine learning models. When compared to a model that just employed tissue pathology or clinical characteristics, the machine learning algorithms that used gene profiling from biopsies performed significantly better at predicting which medication would work best.
The study backs up the idea of using samples from arthritic joints to undertake gene analysis before prescribing pricey biologic targeted therapy. This might save the NHS and society a lot of time and money, as well as assist patients avoid unfavorable side effects, joint injury, and worse results. Such testing might not only influence treatment prescriptions, but it could also reveal which persons may not react to any of the present pharmaceuticals on the market, underlining the need for new medications to be developed.
“Incorporating molecular information prior to prescribing arthritis treatments to patients could forever change the way we treat the condition. Patients would benefit from a personalized approach that has a far greater chance of success, rather than the trial-and-error drug prescription that is currently the norm," said Professor Costantino Pitzalis, Versus Arthritis Professor of Rheumatology at Queen Mary University of London.
These findings are extremely encouraging in terms of showcasing the potential at our fingertips; nevertheless, the discipline is still in its early stages, and more research is needed to fully achieve the promise of precision medicine in RA.
