PhenClust, a standalone tool for identifying trends within sets of biological phenotypes using semantic similarity and the Unified Medical Language System metathesaurus

Uncovering New Therapeutic Targets for Amyotrophic Lateral Sclerosis and Neurological Diseases Using Real-World Data

Although attractive for relevance to real-world scenarios, real-world data (RWD) is typically used for drug repurposing and not therapeutic target discovery. Repurposing studies have identified few effective options in neurological diseases such as the rare disease, amyotrophic lateral sclerosis (ALS), which has 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 network medicine 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 with complement system proteins 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 significantly increased risk for death, suggesting that targeting proteins such as CXCR5, CXCR3, chemokine signaling generally, or neuropeptide Y (NPY) could be advantageous therapeutic targets for these patients. We expanded our analysis to the neuroinflammatory condition, myasthenia gravis, and neurodegenerative disease, Parkinson's, and recovered similar effect sizes. We demonstrated the utility of network medicine for testing novel therapeutic effects using RWD and believe this approach may accelerate target discovery in neurological diseases, addressing the critical need for new therapeutic options.

Preclinical side effect prediction through pathway engineering of protein interaction network models

Modeling tools aim to predict potential drug side effects, although they suffer from imperfect performance. Specifically, protein-protein interaction models predict drug effects from proteins surrounding drug targets, but they tend to overpredict drug phenotypes and require well-defined pathway phenotypes. In this study, we used PathFX, a protein-protein interaction tool, to predict side effects for active ingredient-side effect pairs extracted from drug labels. We observed limited performance and defined new pathway phenotypes using pathway engineering strategies. We defined new pathway phenotypes using a network-based and gene expression-based approach. Overall, we discovered a trade-off between sensitivity and specificity values and demonstrated a way to limit overprediction for side effects with sufficient true positive examples. We compared our predictions to animal models and demonstrated similar performance metrics, suggesting that protein-protein interaction models do not need perfect evaluation metrics to be useful. Pathway engineering, through the inclusion of true positive examples and omics measurements, emerges as a promising approach to enhance the utility of protein interaction network models for drug effect prediction.

Beta-2 adrenergic receptor agonism alters astrocyte phagocytic activity and has potential applications to psychiatric disease

Schizophrenia is a debilitating condition necessitating more efficacious therapies. Previous studies suggested that schizophrenia development is associated with aberrant synaptic pruning by glial cells. We pursued an interdisciplinary approach to understand whether therapeutic reduction in glial cell-specifically astrocytic-phagocytosis might benefit neuropsychiatric patients. We discovered that beta-2 adrenergic receptor (ADRB2) agonists reduced phagocytosis using a high-throughput, phenotypic screen of over 3200 compounds in primary human fetal astrocytes. We used protein interaction pathways analysis to associate ADRB2, to schizophrenia and endocytosis. We demonstrated that patients with a pediatric exposure to salmeterol, an ADRB2 agonist, had reduced in-patient psychiatry visits using a novel observational study in the electronic health record. We used a mouse model of inflammatory neurodegenerative disease and measured changes in proteins associated with endocytosis and vesicle-mediated transport after ADRB2 agonism. These results provide substantial rationale for clinical consideration of ADRB2 agonists as possible therapies for patients with schizophrenia.