Abstract / Summary
The majority of antidepressant and antipsychotic medications are underscored by their common weight-related adverse drug events; medication-induced weight gain is a central hindrance to psychiatric treatments because it impacts patient compliance and results in a cascade of secondary comorbidities with negative health outcomes. Here, we use network pharmacology approaches to characterize the multiple effects of drugs and explore the mechanisms by which drug-molecule interactions lead to adverse drug events. We first construct a robust interactome containing 17,293 proteins (network vertices) and 408,883 weighted interactions (network edges) and identify communities of interconnected vertices using a modularity optimization clustering algorithm. We link 33 antidepressants and 26 antipsychotics into this network via their drug-protein interactions and implement network measures of cosine similarity, shortest path distance, and connectivity significance to determine which communities are most proximal to the drugs. We identify a subnetwork of 33 (out of 536) communities enriched for genes associated with adiposity traits to test for connectivity significance to the drug vertices. Through an additional random walk analysis, we triangulate consistent links between mental health drugs and molecular pathways, including GPCR, PI3K-Akt, leptin, and insulin signaling, as well as adipogenesis. We highlight specific components within these curated pathways and map the crosstalk between them. While we demonstrate our approach in the context of mental health drugs and their weight-related side effects, our study design is broadly applicable to a wide range of drug classes, drug events, and traits of interest.