In Silico Approaches for Prioritizing Drug Targets in Pathogens

Abstract

Antimicrobial resistance is a natural evolutionary process in response to antimicrobial exposure; however, the indiscriminate use of antimicrobials is accelerating this progression. The development of resistance happens when microorganisms evolve mechanism to evade damage caused by the contact with antimicrobial drugs, such as antibiotics, antifungals, antivirals, antimalarials, and anthelmintics, which involves genetic changes. Infections with resistant pathogens also prompt a higher health care cost (estimated budget of $20 billion annually in the United States) compared to non-resistant infections due to longer duration of illness/hospitalization, additional tests and use of more expensive drugs.The comparative genomics associated with Pan-genomics, subtractive genomics, structural bioinformatics, and metabolic pathways analysis approaches are currently applied to reach the development of new antibiotics and fight antimicrobial resistance. Targeted drug development retains major challenges from candidate selection to in vitro and in vivo experiments and clinical trials. Yet, the advances in scientific knowledge and research and development, the advent of omics approaches for example, genomics, transcriptomics, proteomics, and bioinformatics breakthroughs conduct to a ‘big-data era’ that improved identification of putative targets via the application of in silico tools that shortened the timeline in a cost-efficient manner. In this chapter, we are focusing on different bioinformatics strategies for prioritizing drug targets in pathogens.