Applications of Genome-Wide Screening and Systems Biology Approaches in Drug Repositioning
Review article, 2020

Simple Summary Drug repurposing is an accelerated route for drug development and a promising approach for finding medications for orphan and common diseases. Here, we compiled databases that comprise both computationally- or experimentally-derived data, and categorized them based on quiddity and origin of data, further focusing on those that present high throughput omic data or drug screens. These databases were then contextualized with genome-wide screening methods such as CRISPR/Cas9 and RNA interference, as well as state of art systems biology approaches that enable systematic characterizations of multi-omic data to find new indications for approved drugs or those that reached the latest phases of clinical trials. Modern drug discovery through de novo drug discovery entails high financial costs, low success rates, and lengthy trial periods. Drug repositioning presents a suitable approach for overcoming these issues by re-evaluating biological targets and modes of action of approved drugs. Coupling high-throughput technologies with genome-wide essentiality screens, network analysis, genome-scale metabolic modeling, and machine learning techniques enables the proposal of new drug-target signatures and uncovers unanticipated modes of action for available drugs. Here, we discuss the current issues associated with drug repositioning in light of curated high-throughput multi-omic databases, genome-wide screening technologies, and their application in systems biology/medicine approaches.

systems medicine

systems pharmacology

genomic screens

machine learning

drug repositioning

Author

Elyas Mohammadi

Ferdowsi University of Mashhad

Royal Institute of Technology (KTH)

Rui Benfeitas

Stockholm University

Hasan Turkez

Atatürk University

Jan Boren

University of Gothenburg

Jens B Nielsen

Chalmers, Biology and Biological Engineering, Systems and Synthetic Biology

BioInnovation Institute

Mathias Uhlen

Royal Institute of Technology (KTH)

Adil Mardinoglu

Royal Institute of Technology (KTH)

King's College London

Cancers

2072-6694 (ISSN)

Vol. 12 9 2694

Subject Categories

Pharmaceutical Sciences

Bioinformatics (Computational Biology)

Social and Clinical Pharmacy

Areas of Advance

Health Engineering

DOI

10.3390/cancers12092694

PubMed

32967266

More information

Latest update

11/12/2020