A systems-level analysis of drug–target–disease associations for drug repositioning

Abstract

Drug repositioning is the process of finding new therapeutic uses for existing, approved drugs-a process thathas value when considering the exorbitant costs of novel drug development. Several computational strategies exist as a way to predict these alternative applications. In this study, we used datasets on: (1) human biological drug targets and (2) disease-associated genes and, based on a direct functional interaction between them, searched for potential opportunities for drug repositioning. From the set of 1125 unique drug targets and their 88 490 interactions with disease-associated genes, 30 drug targets were analyzed and (3) discussed in detail for the purpose of this article. The current indications of the drugs thattarget them were validated through the interactions, and new opportunities for repositioning were predicted. Among the set of drugs for potential repositioning werebenzodiazepines for the treatment of autism spectrum disorders; nortriptyline for the treatment of melanoma, glioma and other cancers; and vitamin B6 in prevention of spontaneous abortions and cleft palate birth defects. Special emphasis was also placed on those new potential indications that pertained to orphan diseases-these are diseases whose rarity means that development of novel treatment is not financially viable. This computational drug repositioning approach uses existing information on drugs and drug targets, and insights into the genetic basis of disease, as a means to systematically generate the most probable new uses for the drugs on offer, and in this way harness their true therapeutic power.

Designing Data-Driven Learning Algorithms: A Necessity to Ensure Effective Post-Genomic Medicine and Biomedical Research

“…Setting appropriate diagnostic and effective therapeutic regimens is a critical clinical decision and essential for setting effective health measures and efficient strategies to control a disease. This process is limited by the lack of advanced diagnostic tools and approved therapy or vaccine against most existing and emerging diseases [40,41]. Moreover, despite undeniable advances made in understanding of human biology, etiology, and pathogenesis of several diseases, and emergence of advanced technologies, the translation of the existing biological knowledge toward effective new treatments and clinical interventions has not been as fast as expected or anticipated.…”

“…Moreover, Nemati et al [14] optimized a treatment dosing policy for intensive care patients using deep reinforcement learning and Wang et al [46] predicted drug-target binding site interactions using ANN with two hidden layers taking a drug and a target binding site as inputs. Finally, it is known that drug repositioning or re-purposing approach, which examines new therapeutic uses for approved drugs, represents an optimal model for suggesting new drugs using drug-target interactions [40,41]. Wang and Zeng [47] used a learning technique based on restricted Boltzmann machines to predict novel drug-target interactions directing to drug re-purposing.…”

It is time to apply biclustering: a comprehensive review of biclustering applications in biological and biomedical data

“…However, rigorous testing of safety and efficacy of novel drug makes drug development time-consuming, expensive and often unsuccessful. Alternatively, computational drug repositioning is termed as an efficient way to identify new applications for current medicines [153]. By the advancement of biotechnologies, a significant amount of gene expression data becomes a paramount component in characterizing the human responses to drugs.…”

An Automated Functional Annotation Pipeline That Rapidly Prioritizes Clinically Relevant Genes for Autism Spectrum Disorder

“…Notably, the biological functions of different genes implicated in ASD point to convergent mechanisms [ 25 , 26 , 27 ]. By identifying biological processes enriched for genes implicated in ASD and then focusing specifically on genes that influence these processes, genes of particular interest for functional follow-up and drug target repurposing or discovery can be readily classified [ 28 , 29 , 30 ]. Efforts to consistently describe gene products across databases, like the Gene Ontology (GO) Consortium [ 16 ], allow for identification of these larger, multi-gene processes.…”