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Designing new antibiotic treatment regimens that mitigate against poor patient compliance


   Department of Computing Science and Mathematics

  Dr Andrew Hoyle  Applications accepted all year round  Self-Funded PhD Students Only

About the Project

Leading organisations including the NHS, G20, WHO and NIHR recognise antibiotic resistance as an international strategic priority, putting an increasing pressure on our health service, with an estimated 33100 deaths across the EU/EEA in 2015. This problem needs to be addressed now, rather than leaving it to future generations when it is too late. This problem is exacerbated by poor patient compliance with, for example, only 32.6% of people reportedly adhering correctly to a twice-daily prescription. Hospital re-admissions associated with non-adherence are nearly 5%, with almost all of these considered preventable. All of this is putting a strain on health providers in the UK and across the globe at a time when resources are stretched more than ever before.

Conventional antibiotic treatment regimens are not personalised, but standardised for populations, applying a constant daily dosage, e.g. 250mg of penicillin every 12 hours for 5 days. These treatments are only loosely based on a patient's characteristics and are decades behind treatments such as chemotherapy, which is highly personalised. The aim of this project is to combine mathematical modelling, Artificial Intelligence (AI) and experimental data to design and test novel antibiotic treatment regimens.

 This project will generate its own data through biological experiments. This data will be used by the student to build and parameterise novel mathematical models of a bacterial infection. By applying AI search algorithms, the student will predict the optimal antibiotic treatment regimen against a range of competing objectives, including mitigating against the risks of poor patient compliance. The results will be validated throughout the project by means of targeted biological experiments.


Funding Notes

This is a self-funded PhD, and will take you a minimum of three years to complete. As a PhD student, you will be eligible to carry out paid work such as tutoring at the University and we encourage students to engage with the pedagogical side of university life.

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