Antibiotic Drug Development Methods

Antimicrobial resistance (AMR) is one of the biggest health-care challenges in recent times. With the rising incidence of infections caused by drug-resistant pathogens and the declining antibiotic discovery pipeline, we are staring at what is being called a “post-antibiotic era.” The antimicrobial resistance problem is also referred to as the silent pandemic that requires urgent intervention. Traditionally, antibiotic discovery is based on finding natural compounds from a variety of microorganisms in nature and optimizing their chemical properties for clinical use. However, with the rapid progress in genomics and proteomics methods, the entire process of drug development has seen an overhaul. In this approach, finding the suitable drug target is the most significant step against which synthetic or semisynthetic compounds could be developed and validated using bioinformatics tools before proceeding with the in vitro characterization. Moreover, with the rapid progress in artificial intelligence and machine learning tools, drug development methods have taken a leap forward that may significantly reduce the time required from discovery to the development process. In this chapter, we have discussed the traditional antibiotic discovery and development methods and have highlighted the recent achievements in the area aided by omics, artificial intelligence, and machine learning methods.