Issue 1, 2023

Molecularly imprinted polymers as effective capturing receptors in a pseudo-ELISA immunoassay for procalcitonin detection in veterinary species

Abstract

In this study, a new sandwich-type immunoenzymatic assay, based on a molecularly imprinted polymer (MIP) as an artificial antibody (pseudo-ELISA), was developed for the determination of procalcitonin (PCT) in veterinary species. The quantification of PCT in human medicine represents the state of the art for the diagnosis of sepsis; instead the clinical studies on the relevance of PCT as a sepsis predictor in veterinary patients are few, likely due to the total absence of validated assays. MIPs have been widely used as antibody mimics for important applications, and MIP-based sandwich assays have emerged as promising analytical tools for the detection of disease biomarkers. Herein, a polynorepinephrine (PNE)-based imprinted film was directly synthesized on the well surface of a 96-well plate. Subsequently, based on a commercial ELISA kit, the PCT quantification was accomplished via a colorimetric sandwich assay by replacing the capture antibody of the kit with the PNE-based MIP. This method was performed to detect canine and equine PCT in buffer and in plasma samples. Under optimal conditions, the results obtained in plasma samples showed a limit of detection (LOD) of 5.87 ng mL−1 and a reproducibility (CVav%) of 10.0% for canine samples, while a LOD = 4.46 ng mL−1 and CVav% = 7.61% were obtained for equine samples.

Graphical abstract: Molecularly imprinted polymers as effective capturing receptors in a pseudo-ELISA immunoassay for procalcitonin detection in veterinary species

Supplementary files

Article information

Article type
Paper
Submitted
21 Jūl. 2022
Accepted
28 Nov. 2022
First published
29 Nov. 2022
This article is Open Access
Creative Commons BY-NC license

Anal. Methods, 2023,15, 27-35

Molecularly imprinted polymers as effective capturing receptors in a pseudo-ELISA immunoassay for procalcitonin detection in veterinary species

F. Battaglia, F. Bonelli, M. Sgorbini, L. Intorre, M. Minunni, S. Scarano and V. Meucci, Anal. Methods, 2023, 15, 27 DOI: 10.1039/D2AY01175A

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