4.8 Article

EPASS: Electroanalytical Pillbox Assessment Sensor System, A Case Study Using Metformin Hydrochloride

Journal

ANALYTICAL CHEMISTRY
Volume 94, Issue 30, Pages 10617-10625

Publisher

AMER CHEMICAL SOC
DOI: 10.1021/acs.analchem.2c00611

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Adulteration of medications presents a significant threat to human health. A novel electrochemical biomedical device utilizing graphene oxide-modified gold screen-printed electrodes has been developed to detect trace level adulteration in metformin, an important drug for diabetes treatment.
Adulteration of medications is an emerging and significant threat to human health and well-being, even though adulterants are still often not considered seriously in clinical or forensic toxicology. Screening of drug adulterations is a major challenge and concern for regulatory authorities worldwide. Metformin hydro-chloride, an important drug to treat diabetes, is found to be adulterated worldwide and a major reason to worry about the health and safety procedure. We have demonstrated a first-of-a-kind electrochemical biomedical device utilizing exfoliated graphene oxide (GO)-Nafion-modified customized gold screen-printed electrodes (spiral electrochemical notification-coupled electrode, SENCE), driven by electro-chemical adsorptive stripping voltammetry, to identify the trace level adulteration in metformin. The GO-Nafion-SPE interface has been characterized by powder X-ray diffraction, X-ray photoelectron spectroscopy, scanning electron microscopy, energy-dispersive X-ray analysis, and Fourier transform infrared. Custom-made screen-printed SENCEs have been functionalized with GO nanoparticles (transducer) to obtain a fingerprint signal response of metformin using differential pulse voltammetry. A linear calibrated dose response has been obtained with n = 3 repetitions with a low limit of detection of 10 ppm for metformin. We have used the sensing response as a function of adulteration, and it is extensively supported by rigorous statistical analysis along with the help of the machine learning tool. This is a first-of-its-kind IoT-enabled electrochemical sensor and analysis platform that can detect drug adulteration as a low, medium, and high output.

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