Address
St. Mary’s Hospital
Praed St., London
London, W2 1NY
United Kingdom
Contact
Leonid Chindelevitch+447389896780
lchindel@ic.ac.uk
Website
Predict antibiotic resistance from genome sequence
Our validated models can accurately predict the antibiotic resistance profile of bacterial pathogens
The use of genome sequence data to determine antibiotic resistance profiles can save you time (relative to laboratory protocols) and increase accuracy
Technology:
Microorganisms:
Application:
Development stage:
- Antimicrobial stewardship
- Microbial diagnostics
- Infection prevention
- Antimicrobial compound/strategy
- Removal antibiotics/bacteria
Microorganisms:
- Bacteria
- Viruses
- Fungi
- Yeasts
- Parasites
Application:
- Human
- Veterinary
- Environmental
- AgriFood
- Other
Development stage:
- Research
- Development
- Validation
- Market entry
- Marketed product
Organization:
Partnering:
Funding organisation:
Infectious disease area:
Geographic origin:
- Academia
- Company
- Institute
- NGO
- Government
Partnering:
- License
- Co-develop
- Outsource
- Joint Venture
- Sell
Funding organisation:
- FIND
- CARB-X
- GARDP
- REPAIR
- OTHER / NA
Infectious disease area:
- UTI
- STI
- RTI
- GII
- BSI
- SSTI
- CNSI
- IAI
- SSI
Geographic origin:
- Eurasia
- North America
- South America
- Africa
- Oceania
N.A.
The Department of Infectious Disease Epidemiology is a leading academic group focused on infectious diseases worldwide.
We are currently extending our models to A. baumannii, E. coli, N. gonorrhoeae and S. pneumoniae, among other bacterial pathogens. We welcome collaborations with organisations that have genotype data labelled with drug resistance profiles available.
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