Abstract
Phage therapy, the therapeutic usage of viruses to treat bacterial infections, has many theoretical benefits in the ‘post antibiotic era.’ Nevertheless, there are currently no approved mainstream phage therapies. One reason for this is a lack of understanding of the complex interactions between bacteriophage, bacteria and eukaryotic hosts. These three-component interactions are complex, with non-linear or synergistic relationships, anatomical barriers and genetic or phenotypic heterogeneity all leading to disparity between performance and efficacy in in vivo versus in vitro environments. Realistic computer or mathematical models of these complex environments are a potential route to improve the predictive power of in vitro studies for the in vivo environment, and to streamline lab work. Here, we introduce and review the current status of mathematical modeling and highlight that data on genetic heterogeneity and mutational stochasticity, time delays and population densities could be critical in the development of realistic phage therapy models in the future. With this in mind, we aim to inform and encourage the collaboration and sharing of knowledge and expertise between microbiologists and theoretical modelers, synergising skills and smoothing the road to regulatory approval and widespread use of phage therapy.
Highlights
Antimicrobial ResistanceAccording to the World Health Organization (WHO), only three communicable diseases were in the top ten killers globally in 2016, a drop on previous reviews (Ghebreyesus et al, 2018)
It is clear that a number of factors have been key to creating realistic mathematical models of phage therapy, including the synergy between the immune system and bacteriophages (Tiwari et al, 2011; Górski et al, 2017; Roach et al, 2017; Van Belleghem et al, 2018), the mammalian host-versus-phage (MHvP) immune response (Merril et al, 1996; Hodyra-Stefaniak et al, 2015; Majewska et al, 2015; Van Belleghem et al, 2018), threshold densities of phages and bacteria (Payne and Jansen, 2000; Cairns et al, 2009) and the development of phage resistant bacterial populations (Cairns et al, 2009; Leung and Weitz, 2017; Roach et al, 2017)
There are a number of knowledge gaps and limitations with current two- and three-component mathematical models that still need to be addressed in order for phage therapy models to accurately predict experimental outcomes a priori, in a clinical scenario
Summary
Antimicrobial ResistanceAccording to the World Health Organization (WHO), only three communicable diseases (lower respiratory infections, diarrheal diseases and tuberculosis) were in the top ten killers globally in 2016, a drop on previous reviews (Ghebreyesus et al, 2018). Antibiotics kill multiple bacterial strains indiscriminately (they are Mathematically Modeling Bacteriophages broad spectrum) and allow for treatment with limited pressure on diagnostic sensitivity and specificity. This broad-spectrum capability is a two-edged sword and can lead to over-prescription, disruption of the gut microbiome, allergic reactions (Lin et al, 2017) and unwanted side effects (e.g., the nephrotoxicity of colistin (Moulin et al, 2016)). By far the most significant problem, connected with antibiotic use is antimicrobial resistance (AMR) (O’Neill, 2016) In spite of their current relatively low global incidence, communicable diseases are certainly not a phenomenon of the past (U.S Department of Health and Human Services and U.S Centers for Disease Control and Prevention, 2019). Of the options for alternative treatments, the therapeutic use of bacteriophages (phage therapy) is a attractive choice for the treatment of drug-resistant pathogenic bacteria, partially due to their abundance in nature
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
