Predictive modelling of a novel anti-adhesion therapy to combat bacterial colonisation of burn wounds.
Predictive modelling of a novel anti-adhesion therapy to combat bacterial colonisation of burn wounds.
复制标题
DOI：
10.1371/journal.pcbi.1006071
复制
发表时间：
2018-05
影响因子：
4.3
通讯作者：
Jabbari S
中科院分区：
文献类型：
--
作者：
Roberts PA;Huebinger RM;Keen E;Krachler AM;Jabbari S
随着新型抗生素的开发放缓，细菌对现有抗生素的耐药性正在成为一个日益严重的问题。一个潜在的解决方案是开发具有替代作用模式的治疗策略。我们认为这样的策略之一：抗粘连治疗。抗生素直接作用于细菌，杀死它们或抑制它们的生长，而抗粘附疗法阻碍细菌与宿主细胞的结合。这阻止了细菌部署其毒力机制，同时使它们更容易受到自然和人工清除的影响。在本文中，我们考虑了一种特殊形式的抗粘附治疗，涉及仿生多价粘附分子7耦合聚苯乙烯微珠，竞争性抑制细菌与宿主细胞的结合。我们开发了一个数学模型，制定为一个系统的常微分方程，描述抑制剂治疗铜绿假单胞菌烧伤创面感染的大鼠。根据正在进行的实验项目的体内数据对我们的模型进行基准测试，我们使用该模型来解释细菌种群动态并预测一系列治疗策略的疗效，目的是改善治疗结果。该模型由两个物理隔室组成：宿主细胞和渗出液。据发现，当有效地减少细菌负荷时，抑制剂治疗通过防止细菌与宿主细胞结合和通过减少子细胞从宿主细胞进入渗出液的通量来起作用。我们的模型预测，当单独使用时，抑制剂治疗不能消除细菌负荷;然而，当与渗出液的定期或连续清创相结合时，消除在理论上是可能的。最后，我们提出了提高治疗效果的方法，正如我们的数学模型所预测的那样。人类正在进行一场军备竞赛，我们有失败的危险。自从第一种抗生素的开发和应用以来，耐药菌株不断出现。随着新抗生素发现速度的放缓，威胁也在增加。目前，全球每年有70万人死于抗生素耐药性，除非采取新的行动，否则预计到2050年，这一数字将上升到每年1000万人。因此，重要的是，我们要探索替代治疗策略，以取代或补充传统的抗菌药物。在这里，我们使用数学模型来解释和预测一种新的抗粘连治疗应用于感染的烧伤创面的影响。这种理论上的耐药治疗通过阻断宿主细胞结合位点来阻止细菌与宿主细胞结合来起作用。这会阻止细菌获得营养物质，并使它们容易受到人工清除。将我们的模型拟合到实验数据，我们确定了一些有效的参数集，并预测了治疗对每组有效的条件。这些预测是实验测试，并可用于指导抗粘连治疗在临床环境中的开发和应用。
As 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. Humankind is engaged in an arms race; one we are in danger of losing. Since the development and application of the first antibiotics, resistant strains of bacteria have steadily emerged. As the rate of discovery of new antibiotics slows, the threat increases. At present, 700,000 individuals globally die each year due to antimicrobial resistance and this number is predicted to rise to 10 million per year by 2050 unless fresh action is taken. It is important, therefore, that we explore alternative treatment strategies to replace or complement traditional antimicrobials. Here we use mathematical models to explain and predict the effects of a novel anti-adhesion therapy applied to infected burn wounds. This theoretically resistance-proof therapy operates by impeding bacteria from binding to host cells by blocking the host cell binding sites. This prevents bacteria from accessing nutrients and renders them susceptible to artificial clearance. Fitting our model to experimental data, we identify a number of valid parameter sets, and predict the conditions under which treatment will be effective for each set. These predictions are experimentally testable, and could be used to guide the development and application of anti-adhesion treatments in a clinical setting.