Drug Discovery: Computational Chemistry-Based Drug Repurposing

Author(s): Maria Messina*

Despite increasing investments and an improved understanding of disease, the pharmaceutical industry has failed to translate these into credible therapeutic outputs. This has resulted in a need for innovative approaches like drug repurposing to treat both common and rare diseases. Some of the earliest examples of repurposing relied on serendipity and retrospective clinical experience, leading to the successful repurposing of previously failed drugs such as thalidomide and sildenafil in multiple disease conditions. However, modern repurposing approaches tap into an ever-increasing wealth of drug- and disease-related data, computationally driven hypothesis generation and high throughput screening methods for the identification of newer uses for existing drugs. This book discusses some of the most widely used approaches in drug repurposing and the major stakeholders involved; it also highlights various challenges and suggests innovative solutions to take forward. Because of the high risk, substantial cost and slow pace of new drug discovery and development, drug repurposing is becoming an attractive and economically viable strategy for identifying new indications for approved or investigational drugs. Great successes have been witnessed in recent years. Particularly, some existing drugs have shown promise for treating SARS-CoV-2 infection and are currently undergoing clinical investigation. For example, remdesivir, a drug candidate for the treatment of Ebola developed by Gilead Sciences, is currently being assessed for treating SARS-CoV-2 infection and has also received approval for emergency use in some regions of the world.