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E.P.A.S.S: Electroanalytical Pillbox Assessment Sensor System, A Case Study Using Metformin Hydrochloride.

Anirban Paul1, Vikram Narayanan Dhamu1, Sriram Muthukumar2

  • 1Department of Bioengineering, University of Texas at Dallas, Richardson, Texas 75080, United States.

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A novel electrochemical sensor detects trace adulteration in metformin, a vital diabetes drug. This IoT-enabled device uses graphene oxide and machine learning for reliable drug safety screening.

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Area of Science:

  • Electrochemistry
  • Materials Science
  • Analytical Chemistry

Background:

  • Medication adulteration poses a significant global health risk, challenging clinical and forensic toxicology.
  • Screening for drug adulterants is a critical concern for regulatory bodies worldwide.
  • Metformin hydrochloride, a widely used diabetes medication, is frequently found to be adulterated.

Purpose of the Study:

  • To develop a novel electrochemical sensor for detecting trace-level adulteration in metformin hydrochloride.
  • To create an Internet of Things (IoT)-enabled platform for real-time drug adulteration analysis.
  • To utilize advanced materials and machine learning for enhanced sensing capabilities.

Main Methods:

  • Fabrication of exfoliated graphene oxide (GO)-Nafion-modified gold screen-printed electrodes (SENCE).
  • Characterization of the GO-Nafion-SPE interface using techniques like XRD, XPS, SEM, EDX, and FTIR.
  • Development of an electrochemical adsorptive stripping voltammetry method with differential pulse voltammetry for metformin detection.
  • Application of statistical analysis and machine learning for interpreting sensing data and classifying adulteration levels.

Main Results:

  • A sensitive electrochemical sensor (SENCE) functionalized with GO nanoparticles was successfully developed.
  • The sensor demonstrated a linear dose response for metformin with a low limit of detection (10 ppm).
  • The IoT-enabled platform, integrated with machine learning, accurately classified drug adulteration as low, medium, or high.

Conclusions:

  • The developed GO-Nafion-SPE based SENCE offers a promising, first-of-its-kind solution for detecting metformin adulteration.
  • This IoT-enabled electrochemical sensor and analysis platform provides a reliable tool for ensuring drug safety and quality.
  • The integration of advanced materials, electrochemical techniques, and machine learning represents a significant advancement in combating pharmaceutical adulteration.