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 clinical practice of RA.

In the present study, it is proposed to develop a new approach to identify patients with the insufficiently expressed immunomodulatory effects of drugs from the GIBP and JAK groups and to recommend replacing them with a drug from another group. It is planned to study the response of patients to the most widely used RA therapy in clinical practice: 1) GIBPs from the group of tumor necrosis factor inhibitors (TNF-i) and 2) JAK inhibitors (JAK-i). These groups of drugs differ in their mechanisms of action on the immune system and are characterized by different therapeutic targets.

It is proposed to perform a dynamic scientific study of metabolomic-proteomic changes in blood samples from patients with RA with a follow-up period of 12 months. Monitoring of the molecular changes will be carried out within 7 temporary points of blood plasma sampling: before the appointment of treatment, after 2 weeks, and after 1, 3, 6, 9 and 12 months following the appointment of treatment. Two comparison groups will be investigated (GIBP from the TNF-i, and from the JAK-i group). Each comparison group will include 30 patients. Achievement/non-response to the treatment will be assessed using the CDAI index (≤10.0). Secondary evaluation points for the answer will be:

1. achieving remission of the disease according to the CDAI index (≤2.8);
2. achieving a low disease activity according to the DAS28-ESR index (≤3.2).
3. achievement of disease remission according to the DAS28-ESR index (≤2.6).
4. achievement of the minimum clinically significant improvement in the patient's function in daily life - a decrease in the HAQ index by ≥0.22 points.

The proposed novelty of the project is to study the molecular basis of the development of the response in RA patients to immunomodulatory drugs with different mechanisms of action, to create a mathematical model for choosing patients who respond to therapy with drugs of a specific group using mathematical algorithms and neural networks.

References

1. Nasonov E.L., Karateev D.E. Rheumatoid arthritis. In the book: Russian clinical guidelines. Rheumatology / Under. Ed. E. L. Nasonova - M .: GEOTAR-Media, 2020 .-- 448 p. - ISBN 978-5-9704-5398-8, p. 17-57.
2. G. Chatzidionysiou K., Dougados M., et al. EULAR recommendations for the management of rheumatoid arthritis with synthetic and biological disease-modifying antirheumatic drugs: 2016 update. Ann Rheum Dis. 2017; 76 (6): 960- 977.doi: 10.1136 / annrheumdis-2016-210715.