Roberts_et_al_2018_b.pdf (5.32 MB)
Predictive modelling of a novel anti-adhesion therapy to combat bacterial colonisation of burn wounds
journal contribution
posted on 2023-06-09, 17:13 authored by Paul Roberts, Ryan M Huebinger, Emma Keen, Anne-Marie Krachler, Sara JabbariAs the development of new classes of antibiotics slows, bacterial resistance to existing antibiotics is becoming an increasing problem. A potential solution is to develop treatment strategies with an alternative mode of action. We consider one such strategy: anti-adhesion therapy. Whereas antibiotics act directly upon bacteria, either killing them or inhibiting their growth, anti-adhesion therapy impedes the binding of bacteria to host cells. This prevents bacteria from deploying their arsenal of virulence mechanisms, while simultaneously rendering them more susceptible to natural and artificial clearance. In this paper, we consider a particular form of anti-adhesion therapy, involving biomimetic multivalent adhesion molecule 7 coupled polystyrene microbeads, which competitively inhibit the binding of bacteria to host cells. We develop a mathematical model, formulated as a system of ordinary differential equations, to describe inhibitor treatment of a Pseudomonas aeruginosa burn wound infection in the rat. Benchmarking our model against in vivo data from an ongoing experimental programme, we use the model to explain bacteria population dynamics and to predict the efficacy of a range of treatment strategies, with the aim of improving treatment outcome. The model consists of two physical compartments: the host cells and the exudate. It is found that, when effective in reducing the bacterial burden, inhibitor treatment operates both by preventing bacteria from binding to the host cells and by reducing the flux of daughter cells from the host cells into the exudate. Our model predicts that inhibitor treatment cannot eliminate the bacterial burden when used in isolation; however, when combined with regular or continuous debridement of the exudate, elimination is theoretically possible. Lastly, we present ways to improve therapeutic efficacy, as predicted by our mathematical model.
History
Publication status
- Published
File Version
- Published version
Journal
PLoS Computational BiologyISSN
1553-734XPublisher
Public Library of ScienceExternal DOI
Issue
5Volume
14Page range
1-28Article number
e1006071Department affiliated with
- Neuroscience Publications
Research groups affiliated with
- Sussex Neuroscience Publications
Full text available
- Yes
Peer reviewed?
- Yes
Legacy Posted Date
2019-03-12First Open Access (FOA) Date
2019-03-12First Compliant Deposit (FCD) Date
2019-03-11Usage metrics
Categories
No categories selectedKeywords
Licence
Exports
RefWorks
BibTeX
Ref. manager
Endnote
DataCite
NLM
DC