ISSN print edition: 0366-6352 ISSN electronic edition: 1336-9075 Registr. No.: MK SR 9/7 Published monthly

Experimental development of gold nanoparticle-based colorimetric sensor for simultaneous detection of triptan drugs in biological samples

Rafid Jihad Albadr, Waam Mohammedtaher, Mariem Alwan, Mahmood Jasem Jawad, Hiba Mushtaq, Monthar Kedhim, Aseel Smerat, and Nadia Msarhan

Ahl Al Bayt University, Kerbala, Iraq

E-mail: dr.rafid.jk@gmail.com

Received: 19 January 2025  Accepted: 5 May 2025

Abstract:

Naratriptan (NRT), zolmitriptan (ZMT), and eletriptan (ELT) are triptan drugs commonly used for the acute treatment of migraines due to their selective serotonin receptor agonist activity, which induces cranial vasoconstriction and inhibits neuropeptide release. This study introduces a colorimetric nanosensor based on unmodified gold nanoparticles (AuNPs) for the simultaneous detection of these three drugs. The nanosensor operates through a drug-induced aggregation mechanism, where the interaction between AuNPs and the drugs causes visible color changes detectable via UV–Vis spectroscopy. Principal Component Analysis was employed to simplify complex spectral data, while an Artificial Neural Network was trained to accurately predict the concentrations of NRT, ZMT, and ELT in complex mixtures. The method demonstrated excellent sensitivity and accuracy, achieving near-complete recovery rates (~ 98–102%) when tested in plasma and urine samples. This simple, cost-effective, and rapid detection platform holds significant promise for pharmaceutical analysis and therapeutic monitoring in biological samples. This study presents the first experimental development of a gold nanoparticle-based colorimetric sensor for the simultaneous detection of triptan drugs in biological samples.

Keywords: Bionanoelectronics; Chemical Diagnostics; Nanomaterial; Nanomedicine and Nanotoxicology; Nanoparticle Synthesis; Nanosensors; Colorimetric nanosensor; Gold nanoparticles (AuNPs); Naratriptan (NRT); Simultaneous drug detection; Artificial neural network

Full paper is available at www.springerlink.com.

DOI: 10.1007/s11696-025-04118-0

Chemical Papers 79 (8) 5197–5218 (2025)