Frontiers in Pharmacology (Jun 2021)

From Genome to Drugs: New Approaches in Antimicrobial Discovery

  • Federico Serral,
  • Florencia A. Castello,
  • Ezequiel J. Sosa,
  • Ezequiel J. Sosa,
  • Agustín M. Pardo,
  • Miranda Clara Palumbo,
  • Carlos Modenutti,
  • Carlos Modenutti,
  • María Mercedes Palomino,
  • María Mercedes Palomino,
  • Alberto Lazarowski,
  • Jerónimo Auzmendi,
  • Jerónimo Auzmendi,
  • Pablo Ivan P. Ramos,
  • Marisa F. Nicolás,
  • Adrián G. Turjanski,
  • Adrián G. Turjanski,
  • Marcelo A. Martí,
  • Marcelo A. Martí,
  • Darío Fernández Do Porto,
  • Darío Fernández Do Porto

DOI
https://doi.org/10.3389/fphar.2021.647060
Journal volume & issue
Vol. 12

Abstract

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Decades of successful use of antibiotics is currently challenged by the emergence of increasingly resistant bacterial strains. Novel drugs are urgently required but, in a scenario where private investment in the development of new antimicrobials is declining, efforts to combat drug-resistant infections become a worldwide public health problem. Reasons behind unsuccessful new antimicrobial development projects range from inadequate selection of the molecular targets to a lack of innovation. In this context, increasingly available omics data for multiple pathogens has created new drug discovery and development opportunities to fight infectious diseases. Identification of an appropriate molecular target is currently accepted as a critical step of the drug discovery process. Here, we review how diverse layers of multi-omics data in conjunction with structural/functional analysis and systems biology can be used to prioritize the best candidate proteins. Once the target is selected, virtual screening can be used as a robust methodology to explore molecular scaffolds that could act as inhibitors, guiding the development of new drug lead compounds. This review focuses on how the advent of omics and the development and application of bioinformatics strategies conduct a “big-data era” that improves target selection and lead compound identification in a cost-effective and shortened timeline.

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