Applications of Genome-Wide Screening and Systems Biology Approaches in Drug Repositioning

Mohammadi E., Benfeitas R., TÜRKEZ H., Boren J., Nielsen J., Uhlen M., ...Daha Fazla

CANCERS, cilt.12, sa.9, 2020 (SCI-Expanded) identifier identifier identifier

  • Yayın Türü: Makale / Derleme
  • Cilt numarası: 12 Sayı: 9
  • Basım Tarihi: 2020
  • Doi Numarası: 10.3390/cancers12092694
  • Dergi Adı: CANCERS
  • Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus, Academic Search Premier, BIOSIS, CAB Abstracts, CINAHL, EMBASE, Veterinary Science Database, Directory of Open Access Journals
  • Anahtar Kelimeler: drug repositioning, genomic screens, machine learning, systems pharmacology, systems medicine, SCALE CRISPR-CAS9 KNOCKOUT, RNAI SCREEN, INTERACTION NETWORKS, CONNECTIVITY MAP, GENE, DATABASE, EXPRESSION, GENERATION, DISCOVERY, CELLS
  • Atatürk Üniversitesi Adresli: Evet

Özet

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.