To develop a virtual platform that models infection and the host response to pathogen assault for basic research and enhances new target development in infectious diseases.
Control of infectious diseases remains a key priority in human and veterinary medicine. The World Health Organisation (WHO) estimates that respiratory and diarrhoeal infections, HIV, tuberculosis, malaria and measles are responsible for 90% of human deaths and a plethora of animal pathogens threaten food security at a time of fast accelerating demand.
Many different species, from non-vertebrates to rodents and non-human primates, are used to study infection, host response and efficacy of drugs and vaccines. Typically, animals are infected with an infectious agent to test therapeutic efficacy, resulting in symptoms of differing severity. A reliable in silico model of infection and the host response would result in the reduced use of animals. Ideally such a model would provide the foundation for future models which would help predict the efficacy of drugs, vaccines and other treatments.
There is a focus across the biosciences on using the availability of large datasets to exploit computational tools to generate results that are not obvious from single experiments. ‘Virtual’ data for infectious diseases includes diverse information relating to the:
- Repertoire, sequence, transcription, translation, regulation and function of pathogen and host genes.
- Host immune response.
- Impact of modulation of pathogen and host functions by genetic modification, drugs or vaccines on the outcome of infection.
- Temporal and spatial migration, interactions and activities of pathogen and host cells or their products.
The challenge is how to use this data to develop testable predictions, particularly when such data often reflect an average within a tissue at a specific time interval and may therefore not fully reflect events at a finer spatial and temporal level.
While complex mathematical models are being used to model spread of infection and immune response throughout human populations, it is less common for models to be used to study within-host dynamics of infection and response. The focus of this Challenge is on using in silico methods to model infection and the host response in an individual animal. The aims are to employ in silico methods to:
- Reduce the use of animals.
- Model in vivo pathogenesis and protection.
- Model the potential impact of drugs or vaccines to accelerate the development of treatments to infectious diseases.
Animal use in a typical rodent efficacy study for new antibiotics or vaccines can involve approximately 100 animals per candidate. The animals are infected with the pathogen after vaccination or treated with the drug of interest. Untreated controls are always used. The resulting disease in control animals and those in whom the vaccine or drug are ineffective can cause severe suffering. The use of in silico approaches to study disease biology and predict efficacy would reduce the number of animals used.
Phase 1 winners
Project teams led by:
- Professor Tom Freeman,University of Edinburgh, £84,561.
- Professor Paul Kaye, University of York,£100,000.
Phase 2 winner
Project team led by:
- Professor Paul Kaye, University of York, £996,464.
Full Challenge information
- NC3Rs blog: Building a community for innovation