An Overview of Biological and Computational Methods for Designing Mechanism-Informed Anti-biofilm Agents

Andy Y. An, Ka Yee Grace Choi, Arjun S. Baghela, Robert E.W. Hancock

Research output: Contribution to journalReview articlepeer-review

Abstract

Bacterial biofilms are complex and highly antibiotic-resistant aggregates of microbes that form on surfaces in the environment and body including medical devices. They are key contributors to the growing antibiotic resistance crisis and account for two-thirds of all infections. Thus, there is a critical need to develop anti-biofilm specific therapeutics. Here we discuss mechanisms of biofilm formation, current anti-biofilm agents, and strategies for developing, discovering, and testing new anti-biofilm agents. Biofilm formation involves many factors and is broadly regulated by the stringent response, quorum sensing, and c-di-GMP signaling, processes that have been targeted by anti-biofilm agents. Developing new anti-biofilm agents requires a comprehensive systems-level understanding of these mechanisms, as well as the discovery of new mechanisms. This can be accomplished through omics approaches such as transcriptomics, metabolomics, and proteomics, which can also be integrated to better understand biofilm biology. Guided by mechanistic understanding, in silico techniques such as virtual screening and machine learning can discover small molecules that can inhibit key biofilm regulators. To increase the likelihood that these candidate agents selected from in silico approaches are efficacious in humans, they must be tested in biologically relevant biofilm models. We discuss the benefits and drawbacks of in vitro and in vivo biofilm models and highlight organoids as a new biofilm model. This review offers a comprehensive guide of current and future biological and computational approaches of anti-biofilm therapeutic discovery for investigators to utilize to combat the antibiotic resistance crisis.

Original languageEnglish
Article number640787
JournalFrontiers in Microbiology
Volume12
DOIs
Publication statusPublished or Issued - 13 Apr 2021

Keywords

  • anti-biofilm agents
  • antibiotic resistance
  • biofilm models
  • biofilms
  • machine learning
  • organoids
  • systems biology
  • virtual screening

ASJC Scopus subject areas

  • Microbiology
  • Microbiology (medical)

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