A Network-Based Approach Exploiting Transcriptomics and Interactomics Data for Predicting Drug Repurposing Solutions Across Human Cancers

Background: Drug repurposing allows us to save time and money when looking for new therapeutic options. Providing a stronger foundation to support the drug proposals, it would result in faster and more reliable decision-making. Methods: We implemented a network-based algorithm, named PALADIN (Pathways Analyzer for off-LAbel inDIcatioNs), which combines a biological network of causal relationships, the modes of action, the target genes of the considered drugs, and transcriptomics data relative to various types of human cancers. Results: We identified several repurposable drug candidates, which we can understand at a deeper level thanks to the causal connections in the biological network. Conclusions: Many of the identified drug repurposing options are in accordance with the scientific literature about the drugs and their relationships with cancer, and by looking for the causal connections related to our top-rated drug–disease combination, we achieved a deeper understanding about the proposed candidates.