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 compound-targeted 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).

Fig. 1.

Methodology overview. (A) A distance value for each couple of drugs is computed. (B) Each drug is considered as a node in a network with weighted edges (proportional to distances) connecting pairs of drugs. Network communities are identified. (C) Ranked list of differentially expressed genes, following treatment with a previously undescribed drug X are merged together, and the distance d(X,Y) is computed for each drug Y in the reference dataset. X is connected to drugs whose distance is below a significant threshold.

Fig. 2.

The Drug network. Communities and rich clubs are highlighted. (Insets) Some communities are magnified, and the enriched Mode of Actions are provided.

Fig. 3.

Classification of drugs. Subnetworks connected to the tested compounds (cyan nodes) once they have been integrated in the drug network. For clarity we included only compounds whose distances from the tested compounds were less than 0.8 (A and C) or 0.72 (B). Edge thickness is inversely proportional to the distance between the drugs; edge and node colors indicate communities. Hexagonal-shaped nodes represent community exemplars. (A) HSP90 inhibitors; (B) Topo inhibitors; (C) CDK inhibitors.

Fig. 4.

Western blots (A) Western blot of total MCF7 cell lysates following 6-h treatment of MCF7 cells with Doxorubicin (Dx), SN38, and the CDK inhibitor PHA-793887 (887). Induction of p21 coupled to decreased phosphorylation of the CDK2 substrates Retinoblastoma (Rb) and Nucleophosmin (NPM) by the Topo inhibitors Dx and SN-38. (B) Evaluation of LC3 levels in human fibroblasts after treatment with drugs: (Rp, Rapamycin; HF, Fasudil; Tr, Trifluoperazine; 2D, 2-deoxy-D-glucose; NT, untreated). The experiments were performed in triplicate, and representative results are shown.