Review On Drug Repurposing

Drug repurposing, also known as drug repositioning, represents a strategic approach to identify new therapeutic uses for existing pharmacological agents. Unlike traditional de novo drug development, which is often hampered by high costs, extended timelines, and significant attrition rates, repurposing leverages previously approved drugs with well-characterized safety profiles, thereby accelerating the development pipeline. This review offers a comprehensive examination of methodologies employed in drug repurposing, including computational techniques such as signature matching, genetic association, pathway mapping, and machine learning, as well as experimental strategies like binding assays and phenotypic screening. Case studies of successfully repurposed drugs - such as sildenafil, thalidomide, and minoxidil ¬- demonstrate the clinical and commercial potential of this paradigm. The review also addresses critical challenges, including intellectual property limitations, regulatory and economic barriers, and data access constraints. Moreover, it highlights the role of collaborative models, personalized medicine, and systems pharmacology in advancing repurposing initiatives, especially for rare and neglected diseases. Despite its inherent challenges, drug repurposing stands as a promising avenue for expanding therapeutic options and optimizing healthcare outcomes in a cost-effective and timely manner.

Publication Details

Journal
Zenodo (CERN European Organization for Nuclear Research)
Published
2025-06-22
DOI
https://doi.org/10.5281/zenodo.15716253
Primary Topic
Computational Drug Discovery Methods
Type
article
Field-Weighted Citation Impact
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article

Review On Drug Repurposing

Zenodo (CERN European Organization for Nuclear Research)
Computational Drug Discovery Methods
article

Review On Drug Repurposing

article en

Abstract

Drug repurposing, also known as drug repositioning, represents a strategic approach to identify new therapeutic uses for existing pharmacological agents. Unlike traditional de novo drug development, which is often hampered by high costs, extended timelines, and significant attrition rates, repurposing leverages previously approved drugs with well-characterized safety profiles, thereby accelerating the development pipeline. This review offers a comprehensive examination of methodologies employed in drug repurposing, including computational techniques such as signature matching, genetic association, pathway mapping, and machine learning, as well as experimental strategies like binding assays and phenotypic screening. Case studies of successfully repurposed drugs - such as sildenafil, thalidomide, and minoxidil ¬- demonstrate the clinical and commercial potential of this paradigm. The review also addresses critical challenges, including intellectual property limitations, regulatory and economic barriers, and data access constraints. Moreover, it highlights the role of collaborative models, personalized medicine, and systems pharmacology in advancing repurposing initiatives, especially for rare and neglected diseases. Despite its inherent challenges, drug repurposing stands as a promising avenue for expanding therapeutic options and optimizing healthcare outcomes in a cost-effective and timely manner.

Zenodo (CERN European Organization for Nuclear Research)
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Computational Drug Discovery Methods
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