TY - JOUR
T1 - An Overview of Biological and Computational Methods for Designing Mechanism-Informed Anti-biofilm Agents
AU - An, Andy Y.
AU - Choi, Ka Yee Grace
AU - Baghela, Arjun S.
AU - Hancock, Robert E.W.
N1 - Funding Information:
Funding. We acknowledge funding from a Canadian Institutes for Health Research grant FDN-154287 to RH. The content is solely the responsibility of the authors and does not necessarily represent the official views of the Canadian Institutes for Health Research. RH holds a Canada Research Chair in Health and Genomics and a UBC Killam Professorship. AA was supported by the Canadian Institutes of Health Research Frederick Banting and Charles Best Canada Graduate Scholarship and the Centre for Blood Research Graduate Student Award. K-YC was supported by the Michael Smith Foundation for Health Research and Lotte & John Hecht Memorial Foundation Research Trainee Award.
PY - 2021/4/13
Y1 - 2021/4/13
N2 - 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.
AB - 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.
KW - anti-biofilm agents
KW - antibiotic resistance
KW - biofilm models
KW - biofilms
KW - machine learning
KW - organoids
KW - systems biology
KW - virtual screening
UR - http://www.scopus.com/inward/record.url?scp=85104939433&partnerID=8YFLogxK
U2 - 10.3389/fmicb.2021.640787
DO - 10.3389/fmicb.2021.640787
M3 - Review article
AN - SCOPUS:85104939433
VL - 12
JO - Frontiers in Microbiology
JF - Frontiers in Microbiology
SN - 1664-302X
M1 - 640787
ER -