Systematic analyses of drugs and disease indications in RepurposeDB reveal pharmacological, biological and epidemiological factors influencing drug repositioning

Abstract

Increase in global population and growing disease burden due to the emergence of infectious diseases (Zika virus), multidrug-resistant pathogens, drug-resistant cancers (cisplatin-resistant ovarian cancer) and chronic diseases (arterial hypertension) necessitate effective therapies to improve health outcomes. However, the rapid increase in drug development cost demands innovative and sustainable drug discovery approaches. Drug repositioning, the discovery of new or improved therapies by reevaluation of approved or investigational compounds, solves a significant gap in the public health setting and improves the productivity of drug development. As the number of drug repurposing investigations increases, a new opportunity has emerged to understand factors driving drug repositioning through systematic analyses of drugs, drug targets and associated disease indications. However, such analyses have so far been hampered by the lack of a centralized knowledgebase, benchmarking data sets and reporting standards. To address these knowledge and clinical needs, here, we present RepurposeDB, a collection of repurposed drugs, drug targets and diseases, which was assembled, indexed and annotated from public data. RepurposeDB combines information on 253 drugs [small molecules (74.30%) and protein drugs (25.29%)] and 1125 diseases. Using RepurposeDB data, we identified pharmacological (chemical descriptors, physicochemical features and absorption, distribution, metabolism, excretion and toxicity properties), biological (protein domains, functional process, molecular mechanisms and pathway cross talks) and epidemiological (shared genetic architectures, disease comorbidities and clinical phenotype similarities) factors mediating drug repositioning. Collectively, RepurposeDB is developed as the reference database for drug repositioning investigations. The pharmacological, biological and epidemiological principles of drug repositioning identified from the meta-analyses could augment therapeutic development.

Figure 1

Curation, mapping and analytics strategy of RepurposeDB. (A) Biocuration strategy leveraged to develop RepurposeDB. (B) Terminology mapping strategy used to compile disease dictionaries. (C) Analytics framework for analyzing medications (small molecules and biotech), drug targets, diseases and networks (drug–target, seed functional target network, expanded functional target network, drug–drug and drug similarity network).

Figure 2

Database interface and features of RepurposeDB. (A) Web interface of RepurposeDB. (B) Plotting utility to compare and map various chemoinformatics features (n=112) and display on an interactive plot. (C) Web-based visualization to view drug–disease bipartite network. (D) Search service to compare a given small molecule in SMILE format across repositioned compounds in RepurposeDB using Tanimoto distance.

Figure 3

Biochemical composition of medications in RepurposeDB a) Approval status b) Molecular types of medications in RepurposeDB c) Super-Classes of small molecules in RepurposeDB d) Distribution of units by which repositioned drugs are marketed e) Mode of drug-target interactions in RepurposeDB.

Figure 4

Chemical, biological, pathway-level and phenomic correlates of drug repositioning a) Chemical properties of repurposed drugs: Compounds in RepurposeDB mapped to ChEBI ontology (structure and role merged terminologies) b) Molecular function of repurposed drug targets: reduced representation of molecular function terms enriched among drug targets of repositioned drugs c) Targets of repurposed drugs mapped to KEGG metabolic pathways d) Distribution of semantic similarity of indications in RepurposeDB using Disease Ontology, Human Phenotype Ontology and combined scores. e) Overlap of posthoc validation of drug repositioning investigations in RepurposeDB using disease-comorbidity analyses (EHR), shared genetic architectures (SGA) and pathway cross-talks (PCT).

Figure 5

Shared genetic architecture and pair-wise comorbidities of diseases targeted by repurposed drugs a) Shared genetic architecture of diseases targeted by same drug. Thickness of the lines between disease indicates number of shared genes across the diseases b) Distribution of semantic similarity of indications in RepurposeDB d) Overlap of validation of drug repositioning investigations in RepurposeDB using disease-comorbidity analyses, shared genetic architectures and pathway cross-talks b) Example of pair-wide disease comorbidity estimation: Thalidomide (Rx00233): 20 disease pairs were computed and the pairs significant after multiple testing correction are used to generate the plot. Disease pair #1=severe erythema nodosum leprosum and Crohn's disease; Disease pair #2=severe erythema nodosum leprosum and recurrent aphthous ulcers; Disease pair #3=moderate erythema nodosum leprosum and Crohn's disease and Disease pair #4=moderate erythema nodosum leprosum and recurrent aphthous ulcers c) Example of shared genetic architectures driving drug repurposing: Sildenafil (Rx00215): Three disease were associated with sildenafil (angina, erectile dysfunction and pulmonary hypertension). Reference database had 154 genomic associations for angina and 89 associations for pulmonary dysfunction; 26 genes were shared by both diseases suggesting the geneset as shared genetic architecture driving successful outcome of Sildenafil as a therapy for both diseases.

Figure 6

Chemical, biological and interaction networks compiled using data from RepurposeDB a) Chemical similarity network of small molecules in RepurposeDB: Histogram of Tanimoto similarity of small-molecules in RepurposeDB. Tanimoto similarity estimates the similarity of the two compounds based on the angle between the attribute vectors (fingerprint) of each compound. b) Tanimoto similarity network of small molecules in RepurposeDB was computed, and chemical similarity network was calculated and visualized using chemViz. Small molecule from RepurposeDB represents the nodes and edges are Tanimoto similarity (values between 0 to 1; threshold set at >=0.5 for visualization) and weighted by the Tanimoto similarity values. Inset highlights a section of the chemical similarity network of repositioned compounds and maximum common chemical substructures are indicated. c) Drug-target interaction network d) Drug-drug interaction network: Targets are colored according to the biochemical action (inhibitor, antagonist, agonist, potentiator and others) e) SFN: Seed Functional Network reconstructed using targets of repositioned drugs f) EFN: Expanded Functional Network reconstructed using targets of repositioned drugs as seed and adding 20% of genes shared by the nodes in SFN. Data to generate various networks and high-resolution versions of the network figures are provided in the Supplementary Data.