Computational Model-Assisted Development of a Nonenzymatic Fluorescent Glucose-Sensing Assay

Area of Science:

• Biomedical Engineering
• Analytical Chemistry
• Fluorescence Spectroscopy

Background:

• Current glucose monitoring methods often lack nonenzymatic approaches and deep-red fluorescence for enhanced skin penetration.
• Developing injectable biosensors with improved optical properties is crucial for continuous glucose monitoring, especially in diverse populations.

Purpose of the Study:

• To develop a novel, fully injectable, nonenzymatic glucose biosensor utilizing deep-red fluorescence.
• To optimize assay conditions for sensitive glucose detection within the physiological range, considering skin optical properties.

Main Methods:

• Synthesis of Cy5.5 labeled mannose conjugates (mannobiose, mannotriose, mannotetraose) as fluorescent competing ligands.
• Utilizing Concanavalin A as the recognition molecule in a competitive binding assay.
• Employing fluorescence anisotropy (FA) and a computational model to determine optimal assay component concentrations.

Main Results:

• Cy5.5-mannotetraose demonstrated the most sensitive glucose response across the physiological range via FA.
• A computational model successfully predicted optimal concentrations for sensitive glucose measurements.
• The assay design allows for sensitive glucose detection with improved light penetration for darker skin tones.

Conclusions:

• The developed nonenzymatic glucose biosensor with deep-red fluorescence offers a promising approach for continuous glucose monitoring.
• The method is adaptable to various fluorescent dyes and nonenzymatic assay formats.
• This technology has the potential to improve glucose management, particularly for individuals with darker skin tones.