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.

Lost in translation: the valley of death across preclinical and clinical divide – identification of problems and overcoming obstacles

“…There are already several large-scale biobanks and population-based cohorts including the UK Biobank (500,000 participants), the All of Us initiative (1000,000+ participants), and the Million Veterans Program, now include linkage to EHRs [73]. Deep phenotyping by way of using EHR-linked biobank data has been used as a resource for the discovery of novel drug targets [74][75][76][77]. For example, Dewey et al [78] used the exome sequencing data linked to the EHR from 58,000 patients collected at the Geisinger Health System and found that loss-of-function mutations in ANGPTL3 were correlated with the development of coronary artery disease.…”

Prioritizing Small Molecule as Candidates for Drug Repositioning using Machine Learning

“…RepurposeDB (http://repurposedb.dudleylab.org) is a compendium of successful drug repositioning investigations that we previously developed to address a critical knowledge gap in computational drug discovery [9]. The motivation for developing RepurposeDB is to catalog…”

Identification of Drug-Disease Associations Using Information of Molecular Structures and Clinical Symptoms via Deep Convolutional Neural Network

“…By conservative estimates, it takes about 15 years and $0.8-1.5 billion to bring a drug to market (Dudley et al, 2011;Yu et al, 2015), and during the development stage, almost 90% of the small molecules cannot pass the Phase I clinical trial and finally be eliminated (Wu et al, 2019). On the other hand, disease burden is increasing globally due to the growth of population, outbreak of infectious disease and emergence of antibiotic resistance (Shameer et al, 2018). In order to circumvent this dilemma, drug repositioning has become a promising alternative strategy for drug research and development (Wu et al, 2017).…”