Computational evaluation of leflunomide and ticagrelor combination targeting the A2A receptor for the management of rheumatoid arthritis

Background: Rheumatoid arthritis (RA) is a chronic autoimmune disease characterized by progressive joint damage and inflammation, which leads to a reduction in patients' quality of life. Traditional therapies are inadequate to eradicate these illnesses. Therefore, cutting-edge therapeutic approaches are required to eradicate these challenges and improve patients' lives. Objectives: In the current research work, computational analysis of ticagrelor, a purinergic P2Y G protein-coupled receptor antagonist (P2Y12) that increases extracellular adenosine by blocking equilibrative nucleoside transporter-1 (ENT-1), combined with leflunomide, a traditional disease-modifying anti-rheumatic drug (DMARD), was investigated. Additionally, the study hypothesizes that increased levels of adenosine may activate anti-inflammatory adenosine receptor (A2A) and enhance leflunomide’s immunomodulatory effects. Methods: Molecular docking was performed using the Python Prescription (PyRx) tool, which integrates with AutoDock Vina and AutoDock 4, providing an optimal balance between computational speed and flexibility. Results: Molecular docking demonstrates strong affinities to A2AR Leflunomide -8.7Kcal/mol; and Ticagrelor -8.0 Kcal/mol. Both compounds showed low toxicity according to Absorption, Distribution, Metabolism, Excretion and Toxicity (ADMET) profiling using Swiss-ADME and ADMETSAR. Conclusion: Based on initial in-silico findings, the combination of leflunomide and ticagrelor appears to be a promising treatment approach for RA, which warrants further experimental confirmation.