Computational Drug Repositioning Method Applied to Lung Cancer

Abstract

Lung cancer is the most common cancer affecting men and women worldwide. Among different types of lung cancer (LC), two primary forms stand out: small-cell lung cancer (SCLC) and non-small-cell lung cancer (NSCLC). NSCLC, the more prevalent variety, accounts for around 85–90% of all lung cancer cases. Drug repurposing, also known as drug repositioning, is the investigation of using existing drugs for new therapeutic purposes. This approach can lead to the formulation of effective treatments for diseases that might not have been adequately addressed by traditional drug discovery methods. The importance of drug repositioning in lung cancer research cannot be overstated. Traditional drug discovery is often a lengthy and costly process, requiring years of research and substantial investment. In contrast, repurposing existing medications can enhance the pre-clinical journey of developing new drugs. High-performance computing capabilities enable the efficient processing of these extensive datasets, which may include biological, biomedical, and electronic health-related information. This data-driven approach has dramatically accelerated the development of computational methods for drug repositioning. The strategy of drug repositioning offers a promising pathway for researchers, significantly shortening the timeline and reducing the costs associated with new drug development.