Drug repurposing is a strategy that seeks new medical uses for existing drugs, including both approved medicines and experimental compounds that failed for their original indication. Because these molecules already have known safety, pharmacokinetic and manufacturing profiles, repurposing can bypass early high risk stages of drug development, reducing cost and time to the clinic.

Two main approaches are used. The first is hypothesis driven, where knowledge of disease biology and a drug’s mechanism of action suggests a new indication. For example, an anti inflammatory drug might be tested in neurodegenerative disease if shared pathways are identified. The second is data driven, which uses large scale omics data, real world clinical records and computational methods to match drugs to diseases. Signature reversion is a common concept here. A disease is characterized by a gene expression pattern, then researchers look for drugs that induce an opposite pattern in cells, predicting that they might reverse the disease state.

Modern repurposing relies heavily on high throughput screening and artificial intelligence. Machine learning models integrate chemical structures, targets, side effect profiles, genetic data and clinical outcomes to predict new drug disease links. Network based methods treat diseases, genes and drugs as interconnected systems and search for promising paths within these networks.

Despite its promise, drug repurposing faces challenges. Intellectual property protection is weaker, which can reduce commercial incentives. Optimal dose, formulation and safety for the new indication must still be tested in rigorous clinical trials. Nevertheless, repurposed drugs have already transformed treatment in fields such as oncology, infectious disease and neurology, demonstrating that existing pharmacological space still contains many untapped therapeutic opportunities.