Maths-AIM: A mathematical and experimental approach for the rational assessment of bacterial Adhesion Inhibitor Materials in vivo

利用数学建模与实验数据相结合，这项研究将加速细菌感染新疗法的开发。这些治疗方法将被设计为最大限度地减少细菌对其产生耐药性的可能性。自世纪初青霉素被发现以来，抗生素一直是治疗细菌感染的有效药物。然而，细菌是有弹性的生物体，可以在单一感染过程中进化。如果在感染过程中，一个细菌细胞产生具有随机基因突变的后代，使其能够逃避抗生素的作用，它将在治疗过程中存活下来，同时也产生基因相同的后代。因此，一种耐药的细菌菌株可以迅速出现。因此，存在对多种抗生素具有抗性的细菌菌株，包括对所有已知抗生素具有抗性的一些菌株。这给人类和动物健康带来了巨大的问题，也给医疗保健系统、兽医服务和农业带来了经济负担。如果不进行干预，情况将继续恶化。必须作为紧急事项探索新型感染治疗方法，并制定防止细菌对这些治疗方法产生耐药性的战略。我们的方法不是杀死细菌（如抗生素），而是防止细菌引起感染。这将削弱它们的能力，使其能够被免疫系统清除，而不会为耐药菌株的繁殖提供环境。细菌采用大量的机制来引起感染，但所有细菌都有一种感染所必需的能力，即与动物、人类或植物宿主细胞上的蛋白质结合的能力。这意味着抑制与宿主细胞的粘附应该可以防止感染。分子可以被设计成模仿宿主细胞上的蛋白质（因此细菌错误地结合它们而不是宿主细胞）或细菌细胞上的蛋白质（因此分子结合宿主细胞并阻止细菌结合那里）。我们称之为粘附抑制剂材料（AIM）。如果细菌对AIM产生耐药性，它们也会失去与宿主细胞结合的能力，使它们无法引起感染。因此，与抗生素不同，对AIM产生耐药性不应该使细菌受益，应该防止耐药性的出现。AIM已被证明可以削弱感染，但尚未证明AIM可以成功治疗感染（以及预防感染）以及它们是否真的可以避免耐药性。治疗方法的开发是漫长而昂贵的，必须探索所有途径来加速它。我们将使用数学模型在计算机上研究AIM。使用铜绿假单胞菌（一种存在于自然界的传染性细菌）和在我们的初步实验研究中显示出希望的原型AIM收集的数据，我们将开发一组方程来准确模拟感染中细菌和药物的动力学。这将使我们能够测试AIM在治疗各种类型感染方面的成功程度，并确定细菌对其产生耐药性的可能性。重要的是，将从该模型中设计出许多策略，以提高治疗效果并防止细菌对AIM和抗生素的耐药性的出现（通过改变其设计和给药方案）并在实验室中进行测试。这种结合建模和实验的方法将有助于在计算机上优化新的治疗方法，然后再大规模开发用于试验测试。最终，这项工作将有助于未来抗菌治疗的发展，这将在国家和世界范围内对抗耐药性的上升。

Using mathematical modelling combined with experimental data, this research will accelerate the development of novel treatments for bacterial infections. These treatments will be designed to minimise the likelihood that bacteria develop resistance to them. Since the discovery of penicillin in the early 20th century, antibiotics have been effective drugs for the treatment of bacterial infections. However, bacteria are resilient organisms that can evolve during the course of a single infection. If, during an infection, one bacterial cell produces offspring with a random genetic mutation enabling it to evade the action of antibiotics, it will survive the course of treatment while also producing genetically identical offspring. Thus, an antibiotic-resistant strain of bacteria can rapidly emerge. Consequently, strains of bacteria exist that are resistant to multiple antibiotics, including some that are resistant to all known antibiotics. This presents a huge problem for human and animal health and an economic burden upon the healthcare system, veterinary services and agricultural industries. Without intervention the situation will continue to deteriorate.New types of treatment for infections must be explored as a matter of urgency and strategies to prevent bacteria from developing resistance to these treatments put in place. Rather than kill bacteria (as antibiotics do), our approach is to prevent bacteria from being able to cause infection. This should weaken them sufficiently to allow clearance by the immune system without providing an environment in which a resistant strain can flourish.Bacteria employ a vast array of mechanisms to cause infection but one that is universal to all bacteria and absolutely required for infection is an ability to bind to proteins on host cells in animals, humans or plants. This means that inhibiting adhesion to host cells should prevent infection. Molecules can be designed that mimic either the proteins on the host cells (so that bacteria mistakenly bind them instead of host cells) or proteins on the bacterial cells (so that the molecules bind the host cells and prevent the bacteria from binding there). We call these Adhesion Inhibitor Materials (AIMs). If bacteria develop resistance to AIMs they should also lose the ability to bind to host cells, rendering them unable to cause an infection. Thus, unlike with antibiotics, developing resistance to AIMs should not benefit the bacteria and the emergence of drug-resistance should be prevented.AIMs have been shown to weaken infections, but it is not yet proven that AIMs can be successful in treating infections (as well as preventing them) and whether they really would circumvent drug resistance. Treatment development is lengthy and costly and all avenues must be explored to accelerate it. We will employ mathematical modelling to investigate AIMs on a computer. Using data collected on Pseudomonas aeruginosa (an infectious bacteria present throughout nature) and a prototype AIM that has shown promise in our preliminary experimental studies, we will develop a set of equations to accurately simulate the dynamics of bacteria and drugs in an infection. This will enable us to test how successful the AIM would be in treating various types of infection and determine the likelihood of the bacteria to develop resistance to it. Importantly, numerous strategies will be devised from the model to improve the efficacy of the treatment and to prevent the emergence of bacterial resistance both to the AIM and to antibiotics (through alterations to its design and dosing regimens) and tested in the laboratory.This combined modelling and experimental approach will facilitate the optimisation of a new treatment on a computer prior to it being developed on a grander scale for testing in trials. Ultimately this work will contribute to the development of future antibacterial treatments that will combat the rise in drug-resistance on a national and worldwide scale.