| Autor: |
Blee JA; School of Engineering Mathematics and Technology, University of Bristol, Ada Lovelace Building, Tankard's Close, Bristol BS8 1TW, UK.; School of Biological Sciences, University of Bristol, Life Sciences Building, 24 Tyndall Avenue, Bristol BS8 1TQ, UK., Gorochowski TE; School of Biological Sciences, University of Bristol, Life Sciences Building, 24 Tyndall Avenue, Bristol BS8 1TQ, UK.; BrisEngBio, School of Chemistry, University of Bristol, Cantock's Close, Bristol BS8 1TS, UK., Hauert S; School of Engineering Mathematics and Technology, University of Bristol, Ada Lovelace Building, Tankard's Close, Bristol BS8 1TW, UK.; BrisEngBio, School of Chemistry, University of Bristol, Cantock's Close, Bristol BS8 1TS, UK. |
| Abstrakt: |
Biofilms are responsible for most chronic infections and are highly resistant to antibiotic treatments. Previous studies have demonstrated that periodic dosing of antibiotics can help sensitize persistent subpopulations and reduce the overall dosage required for treatment. Because the dynamics and mechanisms of biofilm growth and the formation of persister cells are diverse and are affected by environmental conditions, it remains a challenge to design optimal periodic dosing regimens. Here, we develop a computational agent-based model to streamline this process and determine key parameters for effective treatment. We used our model to test a broad range of persistence switching dynamics and found that if periodic antibiotic dosing was tuned to biofilm dynamics, the dose required for effective treatment could be reduced by nearly 77%. The biofilm architecture and its response to antibiotics were found to depend on the dynamics of persister cells. Despite some differences in the response of biofilm governed by different persister switching rates, we found that a general optimized periodic treatment was still effective in significantly reducing the required antibiotic dose. As persistence becomes better quantified and understood, our model has the potential to act as a foundation for more effective strategies to target bacterial infections. |
