Brief Lay Background
Microbial keratitis (MK) is an infection of the cornea (the transparent window of the eye surface). In a healthy eye, these pathogens do not affect the cornea. However, if the eye surface is damaged by trauma or contact-lens related irritation, pathogens can attach and invade into the cornea. This triggers host responses such as an influx of immune cells.
While these cells attempt to clear the infection, they concurrently destroy adjacent tissue which results in scarring of the cornea. Consequently, the patient’s vision is affected even if the pathogen is successfully killed during treatment.
Over 50% of patients remain with impaired vision, making MK the second leading cause of single-eye vision loss.
What problem/knowledge gap does it help address?
Standard MK treatment targets the pathogen. If successful, this may stop the infection but does not modulate the immune reaction, despite its tremendous impact on vision. Preventing and treating the visual impairment caused by immune reactions remains an unresolved clinical challenge.
Existing drugs modulating immune cells like neutrophils (which make up 80-90% of the immune cell response in MK) could potentially be repurposed from other inflammatory diseases. However, their beneficial effect in MK and safe application to the eye needs to be investigated before testing in patients. Therefore, models of the disease that include the immune response are needed. The development of a laboratory-based model would allow for direct manipulation and evaluation of drug efficacy without animal involvement.
Aim of the research project
The aim is to develop a dynamic MK model encompassing immune responses, as a platform for high-throughput screening of immunomodulatory drugs to reduce tissue destruction in MK
Potential impact on people with sight loss
MK affects millions of people per year. Global rates are increasing as a result of increased contact lens wear, climate change and antimicrobial resistance, and deterioration or loss of vision despite treatment remains a challenge.
Tools to screen libraries of drug candidates efficiently and robustly for MK are lacking. This project will hopefully address this unmet need.
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