Recombinant Peptide Mimetic NanoLuc Tracer for Sensitive Immunodetection of Mycophenolic Acid

Álvaro Luque-Uriá, Riikka Peltomaa, Tarja K. Nevanen*, Henri O. Arola, Kristiina Iljin, Elena Benito-Peña*, Mariá C. Moreno-Bondi*

*Corresponding author for this work

Research output: Contribution to journalArticleScientificpeer-review

8 Citations (Scopus)

Abstract

Mycophenolic acid (MPA) is an immunosuppressant drug commonly used to prevent organ rejection in transplanted patients. MPA monitoring is of great interest due to its small therapeutic window. In this work, a phage-displayed peptide library was used to select cyclic peptides that bind to the MPA-specific recombinant antibody fragment (Fab) and mimic the behavior of MPA. After biopanning, several phage-displayed peptides were isolated and tested to confirm their epitope-mimicking nature in phage-based competitive immunoassays. After identifying the best MPA mimetic (ACEGLYAHWC with a disulfide constrained loop), several immunoassay approaches were tested, and a recombinant fusion protein containing the peptide sequence with a bioluminescent enzyme, NanoLuc, was developed. The recombinant fusion enabled its direct use as the tracer in competitive immunoassays without the need for secondary antibodies or further labeling. A bioluminescent sensor, using streptavidin-coupled magnetic beads for the immobilization of the biotinylated Fab antibody, enabled the detection of MPA with a detection limit of 0.26 ng mL-1 and an IC50 of 2.9 ± 0.5 ng mL-1. The biosensor showed good selectivity toward MPA and was applied to the analysis of the immunosuppressive drug in clinical samples, of both healthy and MPA-treated patients, followed by validation by liquid chromatography coupled to diode array detection.

Original languageEnglish
Pages (from-to)10358–10364
JournalAnalytical Chemistry
Volume93
Issue number29
DOIs
Publication statusPublished - 27 Jul 2021
MoE publication typeA1 Journal article-refereed

Funding

This study was supported by the Spanish Ministry of Science and Innovation (MICINN, RTI2018-096410-B-C21). A.L.-U. acknowledges MICINN for a predoctoral grant (BES-2016-078137).

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