Learning good therapeutic targets in ALS, neurodegeneration, using observational studies

Analysis of real-world data (RWD) is attractive for its applicability to real-world scenarios but RWD is typically used for drug repurposing and not therapeutic target discovery. Repurposing studies have identified few effective options in neuroinflammatory diseases with relatively few patients such as amyotrophic lateral sclerosis (ALS), which is characterized by progressive muscle weakness and death with no disease-modifying treatments available. We previously reclassified drugs by their simulated effects on proteins downstream of drug targets and observed class-level effects in the EHR, implicating the downstream protein as the source of the effect. Here, we developed a novel ALS-focused pathways model using data from patient samples, the public domain, and consortia. With this model, we simulated drug effects on ALS and measured class effects on overall survival in retrospective EHR studies. We observed an increased but non-significant risk of death for patients taking drugs associated with the complement system downstream of their targets and experimentally validated drug effects on complement activation. We repeated this for six protein classes, three of which, including multiple chemokine receptors, were associated with a significant increased risk for death, suggesting that targeting proteins such as chemokine receptors could be advantageous for these patients. We recovered effects for drugs associated with complement activation and chemokine receptors in Parkinsons and Myasthenia Gravis patients. We demonstrated the utility of network medicine for testing novel therapeutic effects using RWD and believe this approach may accelerate target discovery in neuroinflammatory diseases, addressing the critical need for new therapeutic options.