Drug mechanism enrichment analysis improves prioritization of therapeutics for repurposing.

BACKGROUND: There is a pressing need for improved methods to identify effective therapeutics for diseases. Many computational approaches have been developed to repurpose existing drugs to meet this need. However, these tools often output long lists of candidate drugs that are difficult to interpret, and individual drug candidates may suffer from unknown off-target effects. We reasoned that an approach which aggregates information from multiple drugs that share a common mechanism of action (MOA) would increase on-target signal compared to evaluating drugs on an individual basis. In this study, we present drug mechanism enrichment analysis (DMEA), an adaptation of gene set enrichment analysis (GSEA), which groups drugs with shared MOAs to improve the prioritization of drug repurposing candidates. RESULTS: First, we tested DMEA on simulated data and showed that it can sensitively and robustly identify an enriched drug MOA. Next, we used DMEA on three types of rank-ordered drug lists: (1) perturbagen signatures based on gene expression data, (2) drug sensitivity scores based on high-throughput cancer cell line screening, and (3) molecular classification scores of intrinsic and acquired drug resistance. In each case, DMEA detected the expected MOA as well as other relevant MOAs. Furthermore, the rankings of MOAs generated by DMEA were better than the original single-drug rankings in all tested data sets. Finally, in a drug discovery experiment, we identified potential senescence-inducing and senolytic drug MOAs for primary human mammary epithelial cells and then experimentally validated the senolytic effects of EGFR inhibitors. CONCLUSIONS: DMEA is a versatile bioinformatic tool that can improve the prioritization of candidates for drug repurposing. By grouping drugs with a shared MOA, DMEA increases on-target signal and reduces off-target effects compared to analysis of individual drugs. DMEA is publicly available as both a web application and an R package at https://belindabgarana.github.io/DMEA .

Metabolomics paves the way for improved drug target identification.

Correctly identifying candidate drugs for protein targets is crucial for drug discovery. Despite the importance of this problem for the pharmaceutical industry, chemical screening remains a challenging task, and drug-target misidentification may contribute to failures in drug development. In their recent study, Sauer and colleagues (Holbrook-Smith et al, 2022) demonstrate proof-of-concept for a new way to identify drug-target interactions using high-throughput metabolomics, potentially paving the way towards a universal method for predicting drug-target relationships.

Design and Evaluation of Novel Bite Block for Invasive Imaging Procedures.

Commercially available bite blocks used for invasive imaging procedures have design limitations, including bulky profile, being made of hard plastic that may damage surrounding tissue, and tendency to dislodge. We designed a novel bite block to address these limitations and evaluated this bite block in 50 patients undergoing diagnostic or intraprocedural transesophageal echocardiography examinations. Nine of 11 (82%) imagers who used the redesigned bite block preferred it over the standard bite block used at our institution. The novel bite block is an alternative device to standard bite blocks that was redesigned to protect both the patient and probe.