SputaGen
Research type
Research Study
Full title
Predicting Outcomes from a Range of Intravenous Antibiotics for Sputum Producing Exacerbations of Chronic Lung Disease, Using Microbial Genomics and Artificial Intelligence
IRAS ID
345064
Contact name
David Abelson
Contact email
Sponsor organisation
Royal Papworth Hospital Foundation Trust
Clinicaltrials.gov Identifier
Pending, Pending
Duration of Study in the UK
5 years, 2 months, 1 days
Research summary
Chronic airway diseases, including cystic fibrosis, chronic obstructive pulmonary disease and bronchiectasis, are characterized by recurrent pulmonary exacerbations during which patients produce thick, coloured (purulent) sputum. Exacerbations significantly worsen patient morbidity, prognosis and healthcare resources.
Although treatment with antibiotics is first line (often intravenously), antibiotics frequently fail (20-50% of the time). Antibiotics are usually chosen based on the sputum bacterial culture and antibiotic sensitivity testing. Unfortunately, evidence indicates this poorly predicts clinical response, leading trial-and-error approaches which delay effective treatment, cause progressive lung damage, and increase the risk of antimicrobial resistance in both patients and the community. Patients urgently need a point of care test that can predict if antibiotics are likely to be effective and which will maximise clinical response and minimise resistance.
Advancements in genomic technologies have revolutionized our understanding of the lung microbiome, revealing its complexity and the concurrent presence of multiple bacterial populations. These technologies allow for detailed analysis of microbial communities, predicting their abundance, antibiotic sensitivity, and resistance profiles individually.
By analysing the genetic information of these strains, along with sputum proteins and metabolites, we aim to understand how specific antibiotics lead to successful treatment outcomes.
In this study, we will observe patients at the Cambridge Centre for Lung Infection (CCLI) who are receiving intravenous (IV) antibiotics for infection-related flare-ups of their chronic lung diseases. We will collect sputum samples and detailed clinical information from these patients to analyse using genetic sequencing and artificial intelligence.
The primary goal is to develop a new test that can more accurately predict the best antibiotic treatment for each patient, based on their sputum. This could lead to faster, more effective treatments and improve patients' quality of life.
REC name
South West - Frenchay Research Ethics Committee
REC reference
24/SW/0122
Date of REC Opinion
23 Oct 2024
REC opinion
Further Information Favourable Opinion