Development of a Probability-Based In Vitro Eye Irritation Screening Platform.

Area of Science:

• Ophthalmology
• Toxicology
• Biotechnology

Background:

• Traditional eye irritation tests using animal models or ex vivo tissues present ethical and throughput limitations.
• Existing in vitro methods struggle to balance high throughput with accurate prediction of irritation potential due to complex mechanisms.
• Simple cell models lack mechanistic depth, while complex tissue models are not scalable for high-throughput screening.

Purpose of the Study:

• To develop a novel strategy for enhancing the predictive accuracy of simple cell models in eye irritation testing.
• To integrate multiple mechanistic readouts into a single index for reliable eye irritation potential assessment.
• To create a high-throughput screening-compatible method for eye irritation testing.

Main Methods:

• Utilized micropatterned chips to create numerous discrete small populations of human corneal epithelial cells.
• Combined results from two in vitro assays: cell apoptosis and nociceptor (TRPV1) activation.
• Measured apoptosis and nociceptor activation responses after compound exposure and analyzed large datasets for statistical fitting.

Main Results:

• Developed a mathematical probability model based on binarized and statistically fitted data from cell micropatterns.
• Enabled the amalgamation of multiple mechanistic readouts into a singular index.
• Demonstrated a method amenable to high-throughput screening for eye irritation potential.

Conclusions:

• The novel strategy enhances the predictive accuracy of screening-compatible simple cell models for eye irritation.
• This approach offers a more accurate and reliable prediction of eye irritation potential in a high-throughput format.
• The method successfully integrates multiple mechanistic insights for improved eye safety assessments.