Vol. 14, Issue 6 (2025)

Author(s):
Naga Mownica KP, Nagendra Babu Mennuru and Narahari KV

Abstract:
The pharmaceutical industry faces significant challenges in developing new medications, including exorbitant costs, lengthy timelines, and high failure rates, prompting the need for alternative strategies. Drug repurposing has emerged as a promising solution, offering a faster and more cost-effective pathway to therapeutic development by identifying new uses for existing drugs beyond their original indications. Also known as drug repositioning or reprofiling, this approach capitalizes on established safety and pharmacokinetic data, significantly reducing the risks and expenses associated with traditional drug discovery. The advent of advanced computational technologies, including artificial intelligence, machine learning, and big data analytics, has revolutionized drug repurposing efforts by enabling systematic analysis of vast datasets to uncover novel drug-disease relationships. These computational methods employ various techniques such as molecular docking, signature matching, and network pharmacology to predict potential therapeutic applications for approved or discontinued drugs. Successful examples include thalidomide's transformation from a withdrawn sedative to a treatment for multiple myeloma, and sildenafil's repurposing from hypertension to erectile dysfunction therapy. Beyond economic benefits, drug repurposing provides opportunities to rescue failed compounds and address unmet medical needs, particularly for rare and neglected diseases. As the pharmaceutical landscape evolves, integrating multi-omics data and fostering collaborative research networks will be crucial to maximizing the potential of drug repurposing, ultimately accelerating the delivery of safe and effective treatments to patients worldwide while optimizing resource allocation in drug development.