About the Project
Chronic and allergic pulmonary aspergillosis syndromes complicate asthma, COPD and bronchiectasis, leading to symptoms, admissions and mortality. Antifungal treatment with azoles is the mainstay of treatment, however response rates are suboptimal, eg 60% in CPA suggesting that concomitant patient of microbial factors may have an impact. Several phenotypes of allergic bronchopulmonary aspergillosis (ABPA) and chronic pulmonary aspergillosis (CPA) exist, yet treatment recommendations are not…
individualised. Atopy, bronchiectasis, COPD, or concomitant infection may affect antifungal treatment response resulting in deterioration, hospital admissions and mortality. In addition, patients with chronic lung disease may not have the full criteria for diagnosis of ABPA or CPA, but may have evidence of Aspergillus lung colonisation; some evidence indicates that these patients have worse outcomes, however no treatment recommendation exists.
This project will explore the potential for a personalised approach to management of aspergillosis in chronic lung disease via 2 distinct workstreams.
Workstream 1: Real-world large scale data analysis: By accessing data from a large primary care database, this workstream will explore the effect of antifungal treatment on the outcomes of patients with chronic lung disease, when prescribed for indications outside well defined aspergillosis infection syndromes. The student will use advanced statistical methods and will be supported by supervisors experienced in such analyses.
Workstream 2: Prospective precision medicine trial: This workstream will explore the role of patient factors and biomarkers in the response to antifungal treatment in ABPA and CPA. Antifungals have a role in ABPA and CPA, although several phenotypes of these diseases exist, justifying the need for personalised treatment in these patients. A prospective trial of assessment of antifungal treatment response in patients with ABPA and CPA will be undertaken. The use of biomarkers such as aspergillus serology, FeNO, peripheral eosinophil count, volatile organic compounds will be explored as potential predictors of treatment response.
Candidates are expected to hold (or be about to obtain) a minimum 2:1 Bachelors Degree with Honours (or equivalent) in a related area/subject. Candidates with a clinical background, such as in respiratory medicine or infectious disease, a laboratory background in microbiology or chemistry and/or with experience in mathematics or statistics are encouraged to apply.
Eligibility
Applicants must have obtained or be about to obtain a minimum Upper Second class UK honours degree, or the equivalent qualifications gained outside the UK, in a relevant discipline.
Before you Apply
Applicants must make direct contact with preferred supervisors before applying. It is your responsibility to make arrangements to meet with potential supervisors, prior to submitting a formal online application.
How to Apply
To be considered for this project you MUST submit a formal online application form – on the application form select PhD Infectious Diseases Programme. Full details on how to apply can be found on the Website: How to apply for postgraduate research at The University of Manchester
If you have any queries regarding making an application please contact our admissions team FBMH.doctoralacademy.admissions@manchester.ac.uk
Equality, Diversity and Inclusion
Equality, diversity and inclusion is fundamental to the success of The University of Manchester, and is at the heart of all of our activities. The full Equality, diversity and inclusion statement can be found on the website: Equality, diversity and inclusion (EDI | Postgraduate Research | Biology, Medicine and Health | University of Manchester)
Funding Notes
Applications are invited from self-funded students. This project has a Band 2 (med) fee. Details of our different fee bands can be found on our website View Website
References
Kosmidis C, Smith H, Mollett G, Harris C, Akili S, Bazaz R. Predictive factors for treatment response and mortality in chronic pulmonary aspergillosis. Mycoses. 2023 Nov;66(11):960-968. doi: 10.1111/myc.13641. Epub 2023 Aug 8. PMID: 37553558.
Sengupta A, Ray A, Upadhyay AD, Izumikawa K, Tashiro M, Kimura Y, Bongomin F, Su X, Maitre T, Cadranel J, de Oliveira VF, Iqbal N, Irfan M, Uzunhan Y, Aguilar-Company J, Munteanu O, Beardsley J, Furuuchi K, Takazono T, Ito A, Kosmidis C, Denning DW. Mortality in chronic pulmonary aspergillosis: a systematic review and individual patient data meta-analysis. Lancet Infect Dis. 2025 Mar;25(3):312-324. doi: 10.1016/S1473-3099(24)00567-X. Epub 2024 Nov 29. Erratum in: Lancet Infect Dis. 2025 Mar;25(3):e137. doi: 10.1016/S1473-3099(25)00098-2. PMID: 39617023.
Training/techniques to be provided (maximum of 150 words)
The student will obtain a dual skill-set in data science and clinical research. Training will be provided in data analysis using SQL,R and causal inference methods. Access to a multidisciplinary clinical team of physicians, nurses, physiotherapists and patient representatives will be given. Through integration with the clinical team, the student will gain understanding of fungal lung disease, the use of biomarkers and the evaluation of treatment outcomes. Obtaining samples and chemical analysis of volatile organic compounds will be performed after appropriate training. The student will experience designing a biomarker-driven clinical study, from ethics to dissemination. Finally, opportunities to present at respiratory and infectious diseases meetings and authorship in peer reviewed journals are expected.
This is a Preview Listing…
You must sign in to see the full job description, and to apply.
Manage / Upgrade this job to a Full Job Listing.
Find Your Best Opportunity
Tell them AcademicJobs.com sent you!