Round 3

PI: Dr Lisa Hill. Co-Is: Dr James Andrews, Dr Marie-Christine Jones, Dr Paul Roberts (SMQB).

Improving drug delivery to the back of the eye

Background: Diseases of the retina, the light sensing tissue at the back of the eye, cause blindness. Patients with retinal disease would prefer eyedrops instead of injections directly into the eye. We have made significant progress in developing eyedrops for retinal disease but we are still relying solely on use of rats and mice eyes to help us understand how drugs are delivered.

What we are hoping to find out: We would like to use mathematics to help predict how a drug would be distributed within the human eye.

How we will investigate: The features inside the eye will be represented using equations to describe how a drug moves within the eye. Our mathematical model will then be compared to biological experiments which we have performed in the laboratory.

Wider impact of this research: Our model will help to prioritise new treatments to prevent vision loss and bring them into patient use sooner.  Importantly, our new mathematical model will also help us reduce the number of animals used in vision research.

PI: Professor Niki Karavitaki. Co-Is: Dr Christian Ludwig, Dr Leandro Junges (SMQB)

Pituitary tumours are lumps that develop in the pituitary gland (which sits at the base of the brain). They can cause many problems, e.g., loss of eyesight, headaches, problems with becoming pregnant or fathering a child, reduced ability of the body to cope with stress, high blood sugar and blood pressure, thin bones. They can also lead to early death if not successfully managed.

Available treatments (surgery, X-rays, drugs) often are not successful and frequently the tumour comes back. Therefore, we clearly need to understand what makes these tumours occur or regrow. We will try to answer this by studying metabolism in tumours removed by surgery. Metabolism is the chemical reactions important for release or use of energy, and its products are called metabolites. We plan to measure metabolites with scientific methods. This will give us information on what went wrong that may have led to the development or progress of the tumour and which metabolites can be used to inform us that the tumour will be difficult to manage.

This research is novel and it could allow the development of new treatments and improve the way we manage pituitary tumours with a positive impact on patients’ lives.

PI: Dr Onaedo Ilozumba. Co-Is: Sopna Choudhury, Professor Iain Styles

Leprosy is a bacterial disease that affects many lives in low- and middle-income countries like India, Nepal and Nigeria. Although Leprosy can be treated with existing medications it frequently leads to loss of sensation in specific parts of the body like feet and hands. If left untreated leprosy can lead to the development of ulcers which are sores of varying sizes and depths on surfaces like the sole of the foot. People often have delays in receiving care for leprosy and such ulcers. These can be due to leprosy associated stigma, a lack of money for travel and drugs or health systems which cannot provide the right care.

In our project, we will explore the possible use of computer learning to move healthcare closer to the people in these remote communities. First, we will learn from over 3,000 pictures of ulcers that have been taken in India, Nepal and Nigeria. We hope to teach a computer to be able to analyse an ulcer from a picture taken on a mobile phone. Next, we will develop a mobile application, which will give community health workers tailored advice to provide care in remote areas where people affected by leprosy leave. Community health workers, are generally members of the community who receive some medical training but not equivalent to a nurse or doctor and provide basic healthcare in their communities.

PI: Dr Sara Jabbari. Co-Is: Dr Jessica Blair, Dr Daniel Galvis (SMQB) 

Bacteria cause millions of infections every year. While some are minor and treatable with antibiotics, others can be long-lasting and caused by bacteria that can evade antibiotics; these are called resistant infections.

For antibiotics to work, the drug must build up inside the bacterial cells. One key way that bacteria use to become resistant to antibiotics is to simply throw the antibiotic out of the cell using what’s called an efflux pump. The bacterial cells respond to antibiotic inside the cell by making more copies of their efflux pumps.

In this project we will develop a computer model of a bacterial cell that enables us to see how efflux activity changes and ultimately work out the best way to disrupt efflux pumps. By inactivating efflux pumps at the correct time in an infection, we should be able to ensure that antibiotic resistance is lost and the antibiotics can successfully kill the cells and cure an infection.