In silico drug repositioning: from large-scale transcriptome data to therapeutics

Area of Science:

• Pharmacology
• Genomics
• Drug Discovery

Background:

• Drug repositioning is a cost-effective alternative to traditional drug development, with ~30% of FDA-approved drugs utilizing this strategy.
• Existing drugs have established pharmacokinetic and safety profiles, accelerating clinical validation.
• Advances in next-generation sequencing have led to a rapid expansion of disease-related transcriptome data.

Purpose of the Study:

• To review publicly available transcriptome databases and tools for drug repositioning.
• To highlight recent approaches for discovering novel drug targets, indications, and mechanisms of action (MOA).
• To explore the potential of transcriptome data in understanding disease mechanisms and enabling personalized pharmacotherapy.

Main Methods:

• Literature review of drug repositioning strategies.
• Analysis of publicly available large-scale transcriptome databases.
• Examination of recent computational and experimental approaches in drug repositioning.

Main Results:

• Identification of key transcriptome databases and computational tools relevant to drug repositioning.
• Overview of innovative methods for discovering new drug indications and targets.
• Discussion of approaches for identifying drug response biomarkers and elucidating drug MOA.

Conclusions:

• Harnessing transcriptome data is crucial for advancing drug repositioning efforts.
• Drug repositioning, supported by omics data, accelerates the development of novel therapeutics.
• This strategy holds significant promise for personalized medicine and efficient disease treatment.