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

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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.