A data-driven methodology towards evaluating the potential of drug repurposing hypotheses

Lucía Prieto Santamaría; Esther Ugarte Carro; Marina Díaz Uzquiano; Ernestina Menasalvas Ruiz; Yuliana Pérez Gallardo; Alejandro Rodríguez-González

doi:10.1016/j.csbj.2021.08.003

A data-driven methodology towards evaluating the potential of drug repurposing hypotheses

Comput Struct Biotechnol J. 2021 Aug 9:19:4559-4573. doi: 10.1016/j.csbj.2021.08.003. eCollection 2021.

Authors

Lucía Prieto Santamaría^{1

2

3}, Esther Ugarte Carro¹, Marina Díaz Uzquiano¹, Ernestina Menasalvas Ruiz^{1

2}, Yuliana Pérez Gallardo³, Alejandro Rodríguez-González^{1

2}

Affiliations

¹ Centro de Tecnología Biomédica, Universidad Politécnica de Madrid, 28660 Boadilla del Monte, Madrid, Spain.
² ETS Ingenieros Informáticos, Universidad Politécnica de Madrid, 28660 Boadilla del Monte, Madrid, Spain.
³ Ezeris Networks Global Services S.L., 28028 Madrid, Spain.

Abstract

Drug repurposing has become a widely used strategy to accelerate the process of finding treatments. While classical de novo drug development involves high costs, risks, and time-consuming paths, drug repurposing allows to reuse already-existing and approved drugs for new indications. Numerous research has been carried out in this field, both in vitro and in silico. Computational drug repurposing methods make use of modern heterogeneous biomedical data to identify and prioritize new indications for old drugs. In the current paper, we present a new complete methodology to evaluate new potentially repurposable drugs based on disease-gene and disease-phenotype associations, identifying significant differences between repurposing and non-repurposing data. We have collected a set of known successful drug repurposing case studies from the literature and we have analysed their dissimilarities with other biomedical data not necessarily participating in repurposing processes. The information used has been obtained from the DISNET platform. We have performed three analyses (at the genetical, phenotypical, and categorization levels), to conclude that there is a statistically significant difference between actual repurposing-related information and non-repurposing data. The insights obtained could be relevant when suggesting new potential drug repurposing hypotheses.

Keywords: ACE, Angiotensin I Converting Enzyme; AHR, Aryl Hydrocarbon Receptor; ALK, Anaplastic Lymphoma Kinase; API, Application Programming Interface; CMap, Connectivity Map; COX-2, Cyclooxygenase 2; CUI, Concept Unique Identifier; DISNET knowledge base; DR, Drug Repurposing or Drug Repositioning; DRD3, Dopamine Receptor D3; Data integration; Disease understanding; Drug repositioning; Drug repurposing; Drug-disease validation; ESR1, Estrogen Receptor 1; ESR2, Estrogen Receptor 2; FCGR2A, Fc Fragment Of IgG Receptor IIa; FCGR3A, Fc Fragment Of IgG Receptor IIIa; FCGR3B, Fc Fragment Of IgG Receptor IIIb; GDA, Gene Disease Association; ICD-10-CM, International Classification of Diseases, 10th revision, Clinical Modification; ID, Identifier; KDR, Kinase insert Domain Receptor; LTα, Lymphotoxin alpha; MeSH-PA, Medical Subject Headings – Pharmacological Action; ND, New Disease; NLM, National Library of Medicine; OD, Original Disease; PTGS2, Prostaglandin-endoperoxidase synthase 2; SM, Supplementary Material; SRD5A1, Steroid 5 Alpha-Reductase 1; SRD5A2, Steroid 5 Alpha-Reductase 2; TNFα, Tumour Necrosis Factor alpha; UMLS, Unified Medical Language System.