The formation of biofilms in response to locally administered antimicrobial therapy #

Parna Mandal, Nigel J Mottram, Sean McGinty

13:30 Tuesday in 3Q68.

Part of the Biofilms and pattern formation session.

Abstract #

Bacteria can be free-floating or adhering to a surface, producing biofilm communities. Biofilm formation is a multi-stage dynamical process with initial attachment of the proliferative bacteria, production of an extra polymeric substance (EPS) that helps to stabilize the biofilm, maturation where we see interchange between different bacterial phenotypes such as proliferative, persister and finally due to various reasons we get dead bacteria which for some time can act as a nutrient source, and possibly, detachment. The majority of natural settings contain biofilms made up of different species, whereas single-species biofilms are much less prevalent and typically only occur in specialized infection sites, including the surfaces of medical implants. Implant infection is a serious clinical problem, with treatment usually involving systemic delivery of antibiotics. However, due to the ability of bacteria within biofilms to survive antibiotic dosages that would ordinarily kill free-swimming proliferative bacteria, biofilm infections are extremely difficult to eradicate. Antibiotic resistance and tolerance confound the problem, often associated with nutrient insufficiency, hypoxia in the deeper layers of biofilm and antibiotic concentration at levels above the Minimum Inhibitory Concentration (MIC). An alternative approach is to delivery antibiotics locally in a sustained manner. In this study, we present a mathematical model of biofilm growth subject to antibiotic delivery, with the aim of understanding how the biofilm growth and composition depends on the drug dose and release rate. We have formulated a 1D biofilm growth model in which we introduce controlled antibiotic release directly from the implant. Infection can take hold if the antibiotic release is insufficient to stop bacterial growth, but excessive drug release could hinder the healing of healthy tissue around the implant. This is an example of a delicate balance that can be investigated and optimized by mathematical modelling. A more effective biofilm prevention technique might be achieved by modelling the growth of the biofilm while concurrently optimizing the antibiotic dose and release rate. The model consists of different bacterial phenotypes, self-produced extra cellular polymeric substance (EPS), nutrient concentration, water volume fraction in the biofilm pores, growth of the biofilm and a porous implant filled with antibiotic. We gained a better knowledge of drug delivery for the prevention of infection through the simulation of the two moving boundaries in the model, which were the drug level inside the implant’s pores and the thickness of the biofilm, respectively. Additionally, we have run simulations to see how certain model factors, such as nutrient content, affect the development of various bacterial phenotypes. The composition and time-course of biofilm formation are impacted by various antibiotic-release techniques from the nanoporous implant. This model takes into account both natural and antibiotic-induced mortality of living organisms. As would be predicted, the density of proliferating bacteria reduces with increasing antibiotic dose and increases as one moves away from the implant, where the antibiotic is being supplied from. Since the proliferative bacteria undergo a phenotypic change into the persister bacteria in order to survive the antibiotic dosage, persister bacteria, one of the primary causes of antibiotic resilience, rises with increasing antibiotic dose. As the thickness of the biofilm and the number of proliferating bacteria cells decrease with increasing antibiotic dosage, our model shows that carefully regulating the antibiotic release could aid in preventing biofilm growth for implant-associated infection. The model is able to depict experimentally observed resilience to antibiotic shown by persister cells. Our next step would be to identify the best antibiotic administration arrangement so that the infection and persister.