| EPSRC Reference: |
EP/Y018842/1 |
| Title: |
Infection-AID: AI assisted genomic profiling to inform the Diagnosis, personalised treatment and control of infections |
| Principal Investigator: |
Clark, Professor T |
| Other Investigators: |
|
| Researcher Co-Investigators: |
|
| Project Partners: |
|
| Department: |
Infectious and Tropical Diseases |
| Organisation: |
London Sch of Hygiene & Tropic. Medicine |
| Scheme: |
Standard Research - NR1 |
| Starts: |
02 October 2023 |
Ends: |
01 April 2025 |
Value (£): |
518,745
|
| EPSRC Research Topic Classifications: |
|
| EPSRC Industrial Sector Classifications: |
|
| Related Grants: |
|
| Panel History: |
|
|
Summary on Grant Application Form |
Characterising the genetic code ("genome") of an organism can inform on its ability to survive, tolerate drugs and treatments, and its likely geographical source. Researchers can investigate the genome of an organism, and its important mutations (genome "spelling mistakes"), through applying sequencing technologies to its DNA. Cost-effective and rapid sequencing technologies are now being rolled-out in hospitals and clinics to identify important mutations, and thereby prevent disease, diagnose, and personalise treatment of patients. Genome sequencing has become an important diagnostic tool in infectious disease settings, including to identify microorganisms causing infections ("pathogens") and their resistance to drugs, and to track outbreaks. Such knowledge is revolutionizing clinical decision making, public health surveillance and infection control; as demonstrated during the COVID-19 pandemic, where rapid sequencing of the causal SARS-CoV-2 viral genomes has assisted the detection of clinically important mutations (e.g., omicron variants) and informed on their geographical spread ("transmission patterns"). To assist the analysis of the large datasets arising from the sequencing of pathogens, it is important to identify key mutations linked to (severe) patient outcomes, drug resistance, likely geographical source, and other important "barcoding" information that can provide a "profile" of the pathogen underlying any infection. Computer software tools have been developed (e.g., our TB-Profiler and Malaria-Profiler software) that can rapidly analyse sequence data to provide such pathogen profiles, for easy interpretation by medical doctors and infection control specialists.
With the increasing use of sequencing technologies in hospitals and clinics, there is a need for Artificial Intelligence (AI) computational methods to analyse the resulting "big data" in real time, including to update the lists of barcoding genetic mutations and to identify if the pathogen genome
is related to those previously sequenced i.e., it is being transmitted. We have previously applied AI methods to identify known and novel genetic mutations linked to drug resistance and transmission, as well as created computing repositories (e.g., TB-ML) where the underlying software can be stored, allowing comparisons between statistical models and AI approaches. Our proposed project will integrate these AI-based tools into our profiling software to reveal drug resistance mutation and transmission patterns, and generate informative reports for clinical and infection control decision making. Working within established collaborations involving The UK Health Security Agency and Health ministries in Asia (Bangladesh, Philippines, Thailand, Vietnam), which are routinely using sequencing technologies to inform clinical diagnosis, we will attempt to implement the resulting AI systems software in the UK and overseas settings endemic for infectious diseases. We will initially focus on three main infectious diseases of high global burden, tuberculosis, malaria and Klebsiella infections, with the potential to extend the work to other infections. All sequence data and software developed will be made publicly accessible, leading to their use by other biomedical researchers and healthcare stakeholders. Ultimately, the implementation of such AI-based tools will reduce the burden of infectious diseases, leading to healthier populations and associated economic benefits.
|
|
Key Findings |
|
This information can now be found on Gateway to Research (GtR) http://gtr.rcuk.ac.uk
|
|
Potential use in non-academic contexts |
|
This information can now be found on Gateway to Research (GtR) http://gtr.rcuk.ac.uk
|
|
Impacts |
|
| Description |
This information can now be found on Gateway to Research (GtR) http://gtr.rcuk.ac.uk |
| Summary |
|
| Date Materialised |
|
|
|
|
Sectors submitted by the Researcher |
|
This information can now be found on Gateway to Research (GtR) http://gtr.rcuk.ac.uk
|
| Project URL: |
|
| Further Information: |
|
| Organisation Website: |
|