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UK funding (£195,846): Detecting Antibiotic Resistance Proteins in Clinical Samples Using Proteomics Ukri13 Apr 2016 UK Research and Innovation, United Kingdom
Overview
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Detecting Antibiotic Resistance Proteins in Clinical Samples Using Proteomics
| Abstract | Approximately 40,000 people die in the UK every year as a result of Sepsis, which is a medical condition usually triggered by the body's reaction to bacteria in the blood. When bacteria are present in the blood this is called bacteraemia. The bacteria can come from all sorts of places and can be of many different species. So a diagnosis of Sepsis doesn't tell a doctor what bacterium is responsible. Antibiotics kill bacteria, and so antibiotic therapy is absolutely critical to treating Sepsis. Without removing the underlying cause - the bacteraemia - treatment of Sepsis is unlikely to succeed. The dilemma that doctors face is that because they don't know the identity of the bacterium they want to kill, they are not certain what antibiotics to use. The rise of antibiotic resistance in bacteria makes this choice even more difficult. One way of dealing with this is to use "empiric therapy": to try a particular antibiotic, wait to see if the patient improves and if they don't, try another. But in the meantime, the patient may be getting more and more ill. The alternative approach is that the doctor may start treatment with the latest, most broad acting antibiotic they can find to give them the best chance of killing the bacteria. This means that this "last resort" drug might have been used when it wasn't really needed. Inappropriate use of a last resort drug is the primary driver for antibiotic resistance and will inevitably shorten its useful life. What we really need is to give doctors information about the identity of the bacterium infecting a patient's blood and, more importantly, what antibiotics it is susceptible to. Then they can make informed antibiotic prescribing choices. At the moment, from the time a blood sample is taken from a patient where bacteraemia is suspected it can take 48 hours just to prove there are any bacteria present. Using new MALDI-TOF machines it is possible to identify the bacterium a few hours later, but that doesn't tell you anything about antibiotic susceptibility. It may take another 24 h to find out what antibiotics can be used. This means that patients can be on the wrong antibiotic for up to 72 hours. If that's a non-effective antibiotic, the patient's life is in danger, if it is an inappropriately used last resort antibiotic, the antibiotic's useful life is being shortened. Everyone agrees that reducing the time it takes to get antibiotic susceptibility data to doctors is the key, not just for the treatment of patients, but also to better protect our dwindling supply of useful antibiotics. We feel that it may be possible to achieve this by identifying antibiotic resistance proteins - the tools bacteria employ to resist antibiotics - directly in bacteria isolated from patients' blood. If a particular resistance protein is present, the doctor would know not to use a particular drug. To test our hypothesis we want to test whether we can identify resistance proteins in bacteria in blood samples that have been cultured and processed exactly as they would be in hospital diagnostic labs. We will find out whether it is possible to use existing MALDI-TOF machines to identify at least some antibiotic resistance proteins 24 h earlier than is currently the case. We will also test whether it is possible to use more specialised LC-MS/MS machines to reduce the time to get antibiotic sensitivity data by up to 60 hours, giving a positive indication of antibiotic susceptibility about 12-15 h after sampling. It is not necessary to provide a diagnostic test that works minutes after sampling to have real clinical benefit. For severe Sepsis, each hour without working antibiotics gives a 6% increase in patient mortality, so even shaving tens of hours off the current minimum time it takes to predict antibiotic susceptibility would transform patient care. |
| Category | Research Grant |
| Reference | MR/N013646/1 |
| Status | Closed |
| Funded period start | 13/04/2016 |
| Funded period end | 12/10/2017 |
| Funded value | £195,846.00 |
| Source | https://gtr.ukri.org/projects?ref=MR%2FN013646%2F1 |
Participating Organisations
| University of Bristol |
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