Predicting the Response of Patients With Rheumatoid Arthritis to Treatment With Genetically Engineered Biological Drugs and Janus (JAK) Kinase Inhibitors

Rheumatoid arthritis (RA) is a chronic disease affecting about 1% of the worldwide population. RA is characterized by inflammation of the synovial membrane joints, which can lead to the destruction of the osteochondral structures of the joint and cause a number of systemic complications. RA represents a serious medical and social problem in the Russian Federation with a high level of disability. Recently, genetically engineered biological drugs (GIBPs) and Janus-kinase inhibitors (JAK-i) have become a popular component in the treatment of the severe RA, which is reflected in Russian and International clinical guidelines (1,2). Despite the widespread use of these drugs, many patients do not adequately respond to the therapy. According to the clinical guidelines, the assessment of treatment effectiveness is carried out in RA within 3 to 6 months from the start of treatment (1,2). Treatment for GIBPs and JAK-i is expensive. The cost of drugs without consideration of the medical personnel services cost is on average RUB 700,000 - 1,000,000 per year. Prescribing GIBP and JAK-i therapy to patients who do not respond well to the proposed drugs lead to significant costs for the national healthcare system. Thus, the development of effective approaches to predicting the response of patients to drugs from the GIBD and JAK-i groups is urgent. The search for molecular predictors of treatment response before drug exposure is a part of personalized medicine purposed at substantiating the most effective treatment strategies for a particular patient at a given time. "Big data" summarizing clinical, biochemical clinical indicators (metadata) in combination with molecular proteomic and metabolic results are characterized by a high diagnostic and prognostic value, and can provide the choice of effective treatment strategy for a particular patient. Up to nowadays, there are no practical methods for predicting the response to treatment with drugs from the GIBD and JAK groups in the cli