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Accelerating the optimization of vertical flow assay performance guided by a rational systematic model-based

Dousabel M Y Tay1, Seunghyeon Kim2, Yining Hao2

  • 1Department of Chemical Engineering, Massachusetts Institute of Technology, Cambridge, MA, 02139, USA; Research Laboratory of Electronics, Massachusetts Institute of Technology, Cambridge, MA, 02139, USA; Microsystems Technology Laboratory, Massachusetts Institute of Technology, Cambridge, MA, 02139, USA.

Biosensors & Bioelectronics
|December 14, 2022
PubMed
Summary

This study introduces a simplified model to accelerate the optimization of rapid diagnostic tests (RDTs). The framework uses minimal data to predict assay performance, improving sensitivity and reducing development time.

Keywords:
Affinity proteinAssay optimizationCelluloseEnd-to-end theoretical modelingEnzyme-linked immunosorbent assayRapid diagnostic tests

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Area of Science:

  • Biomedical Engineering
  • Assay Development
  • Diagnostic Technologies

Background:

  • Rapid diagnostic tests (RDTs) are crucial for healthcare but face development inefficiencies.
  • Current modeling approaches for RDTs are often complex and limited in scope.
  • Optimizing RDT sensitivity typically involves laborious empirical methods.

Purpose of the Study:

  • To develop a streamlined, model-based framework for accelerating RDT assay optimization.
  • To enable prediction of assay performance metrics using minimal experimental data.
  • To provide a versatile tool for assay developers at any stage.

Main Methods:

  • Proposed a simplified model-based framework for RDT optimization.
  • Calibrated models using a minimal experimental dataset.
  • Focused on predicting both specific and background interactions.

Main Results:

  • Models successfully recapitulated experimental data across various RDT formats and conditions.
  • The framework accurately predicted key performance metrics like limit-of-detection and signal-to-noise ratio.
  • Demonstrated the utility of the model for estimating assay sensitivity and difference.

Conclusions:

  • The proposed framework offers a facile and efficient approach to RDT assay optimization.
  • This model-based workflow can significantly reduce development time and resources.
  • The approach is valuable for assay developers seeking to improve RDT performance.