Worm Generator: A System for High-Throughput in Vivo Screening.

Area of Science:

• Biomedical Engineering
• Pharmacology
• Bioinformatics

Background:

• Preclinical drug development requires efficient and cost-effective screening tools.
• Traditional methods for monitoring animal behavior in vivo are often labor-intensive and expensive.

Purpose of the Study:

• To develop a novel in vivo screening strategy for high-throughput pharmacological evaluation.
• To combine biohybrid triboelectric nanogenerators (HB-TENGs) with computational bioinformatics for drug discovery.

Main Methods:

• Hierarchically structured HB-TENG arrays with micropillars were designed to convert organism behavior into electrical signals.
• Triboelectric signals were recorded and analyzed to extract bioinformation for each screened compound.
• Machine learning algorithms, specifically multiple-Gaussian-kernels, were employed to predict drug identity from electrical readouts.

Main Results:

• The HB-TENGs successfully converted organism behaviors into measurable electrical outputs.
• Extracted bioinformation from triboelectric signals proved sufficient for predicting drug identity.
• The developed strategy demonstrated high-throughput and cost-effectiveness compared to traditional methods.

Conclusions:

• The proposed HB-TENGs-based strategy offers a powerful tool for in vivo pharmacological screening.
• This approach accelerates the identification of novel therapeutics by enabling rapid analysis of organism responses.
• The method is versatile and applicable to various fields requiring in vivo biological evaluations.