Target Prediction for DMARDs as anti-Alzheimer’s Drugs

Abstract

Introduction: Chronic inflammation is a common feature in various autoimmune disorders and neurological diseases, particularly Alzheimer's disease. The role of anti-inflammatory agents, such as disease-modifying antirheumatic drugs (DMARDs), has garnered attention due to their potential to reduce neuroinflammation. New evidence suggests that patients undergoing treatment with DMARDs, especially those using biological agents, may have a reduced risk of developing dementia or Alzheimer's disease. This connection highlights the importance of exploring DMARDs as a therapeutic approach for neurological conditions. Objective: Target Prediction for DMARDs as anti-Alzheimer’s Drugs.Methods: Initially, a comprehensive database for DMARD compounds was created using the KEGG database and literature searches. Additionally, potential Alzheimer's targets were gathered from previous research and related databases. Target predictions were made using a developed ChEMBL model and a similarity-based fingerprinting method, with the Proba score calculated for each prediction using the Naive Bayes algorithm. Finally, predicted targets with a score higher than 0.9 were selected and ranked based on in vivo and in vitro experimental evidence regarding the relationship between the predicted target and the corresponding ligand, as well as similar results from online prediction systems. Higher-ranked targets were chosen for ligand-target interaction studies using molecular docking. Python scripts were developed to execute the target prediction process for the ligands.Results: From an initial set of 90 compounds in the DMARDs database, targets for 25 of them were predicted for various Alzheimer's targets. By combining evidence-based results with findings from online target prediction systems, the predicted targets were ranked. Ultimately, two targets, JNK3 and VEGFR-2, were selected for molecular docking studies.Conclusion: The results of this study support the potential repurposing of DMARDs in the treatment of neurodegenerative diseases such as Alzheimer's. Furthermore, the predictive model developed in this research offers a valuable tool for identifying potential Alzheimer's targets for additional lead compounds, paving the way for future exploration and therapeutic innovation in this critical area of medicine.