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Biosensor-based High Throughput Biopanning and Bioinformatics Analysis Strategy for the Global Validation of Drug-protein Interactions

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Published on: December 1, 2020

Engineered Nucleotide Chemicapacitive Microsensor Array Augmented with Physics-Guided Machine Learning for

Stephanie Hui Kit Yap¹, Jieming Pan¹, Dao Viet Linh^2,3

¹Department of Electrical and Computer Engineering, National University of Singapore, 4 Engineering Drive 3, Singapore, 117583, Singapore.

Small (Weinheim an Der Bergstrasse, Germany)

|May 6, 2022

Summary

This summary is machine-generated.

A new Aptamer-based BioSenSing System (ABS3) offers a rapid, automated, and cost-effective method for classifying cannabidiol (CBD) concentrations. This high-throughput platform significantly improves upon traditional screening for pharmaceutical development.

Keywords:

aptamers biosensing cannabidiol interdigitated electrodes physics-guided machine learning

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Published on: June 1, 2011

Area of Science:

Biotechnology and Bioengineering
Chemical and Bioanalytical Sciences
Pharmaceutical Development

Background:

Growing interest in microbial production of medicinal cannabidiol (CBD) for neurological conditions like epilepsy.
Conventional screening methods for CBD are time-consuming, labor-intensive, and costly, hindering efficient drug development.

Purpose of the Study:

To introduce a portable, high-throughput Aptamer-based BioSenSing System (ABS3) for label-free, automated CBD classification.
To address the limitations of conventional screening platforms in terms of speed, cost, and labor.

Main Methods:

Development of the ABS3 system featuring an array of interdigitated microelectrode sensors functionalized with engineered aptamers.
Utilizing unique electrochemical features from each sensor, synergized via physics-guided multidimensional analysis.
Employing an optimized random forest (RF) model for data analysis and classification.

Main Results:

Achieved highly accurate CBD concentration classification with a prediction accuracy of 99.98%.
Demonstrated a remarkably fast testing time of 22 µs per sample.
Successfully classified CBD concentrations in complex biological environments.

Conclusions:

The ABS3 system provides a low-cost, fully automated, and highly accurate solution for CBD classification.
This technology has the potential to accelerate the transformation of fundamental research into miniaturized diagnostic systems.
Envisioned applications include disease biomarker diagnostics and drug development in chemical and bioanalytics.