Discovery of drug mode of action and drug repositioning from transcriptional responses

Abstract

A bottleneck in drug discovery is the identification of the molecular targets of a compound (mode of action, MoA) and of its off-target effects. Previous approaches to elucidate drug MoA include analysis of chemical structures, transcriptional responses following treatment, and text mining. Methods based on transcriptional responses require the least amount of information and can be quickly applied to new compounds. Available methods are inefficient and are not able to support network pharmacology. We developed an automatic and robust approach that exploits similarity in gene expression profiles following drug treatment, across multiple cell lines and dosages, to predict similarities in drug effect and MoA. We constructed a "drug network" of 1,302 nodes (drugs) and 41,047 edges (indicating similarities between pair of drugs). We applied network theory, partitioning drugs into groups of densely interconnected nodes (i.e., communities). These communities are significantly enriched for compounds with similar MoA, or acting on the same pathway, and can be used to identify the compoundtargeted biological pathways. New compounds can be integrated into the network to predict their therapeutic and off-target effects. Using this network, we correctly predicted the MoA for nine anticancer compounds, and we were able to discover an unreported effect for a well-known drug. We verified an unexpected similarity between cyclin-dependent kinase 2 inhibitors and Topoisomerase inhibitors. We discovered that Fasudil (a Rho-kinase inhibitor) might be "repositioned" as an enhancer of cellular autophagy, potentially applicable to several neurodegenerative disorders. Our approach was implemented in a tool (Mode of Action by NeTwoRk Analysis, MANTRA, http://mantra.tigem.it).computational drug discovery | drug repurposing | systems biology | chemotherapy

A review of connectivity map and computational approaches in pharmacogenomics

“…For example, Iskar et al [18] performed a quantitative evaluation of CMap methods by applying a centered mean approach to normalize the gene expression intensity values in CMap to reduce batch-specific effects. Also, Iorio et al uses the pairwise druginduced gene expression profile similarity (DIPS) scores between drug pairs in CMap to calculate total enrichment score [4]. They used drug compounds with shared ATC classification, and high chemical similarities to discretize true positives in their approach.…”

“…For example, some study the drug-target structural relationships for specific drugs to discover new targets implicated in diseases, whereas others predict biochemical interactions of small molecules with their respective targets using, e.g. the Connectivity Map (CMap) approach [3][4][5]. However, for either type of investigations, machine learning [6] and biomedical text mining [7] approaches have been vital to uncover hidden relationships between drugs and potential new indications.…”

Constructing and characterizing a bioactive small molecule and microRNA association network for Alzheimer's disease

“…For example, Butte's group confirmed known effective drug-disease pairs and predicted new indications for already approved agents by comparing the expression profile similarity [25,26]. Iorio et al [27] constructed a drug network based on similar expression profiles perturbed by various compounds and identified drug communities to predict modes of action for compounds that are still being studied or to discover previously unreported modes of action for well-known drugs. Our previous research investigated the potential connections between small molecules and miRNAs across 23 human cancers based on transcriptional responses similarity.…”

Demonstration Study: A Protocol to Combine Online Tools and Databases for Identifying Potentially Repurposable Drugs

“…When a suitable drug is administered to treat a disease, it tends to correct the aberration by bringing the gene expression pattern back to its normal state. Thus, a negative correlation is generally revealed between the gene expression signatures of the drug and the disease [8,9]. A schematic diagram representing opposing gene expression patterns under a disease state and under its drug treatment is shown in Figure 1.…”