Browse by author
Lookup NU author(s): Amy Dann, Professor Katarina NovakovicORCiD, Dr Jake McClementsORCiD, Dr Shayan SeyedinORCiD, Dr James DawsonORCiD
This work is licensed under a Creative Commons Attribution 4.0 International License (CC BY 4.0).
© 2026 The AuthorsCurrent estimates see 25.2 million people living with from Parkinson's disease (PD) worldwide by 2050, with no cure close to being available. With therapeutic LDp (LDp) use being the mainstay treatment for a vast proportion of individuals with PD, an effective protocol for managing medication in real-time, is not only essential but long overdue. Presented hereafter is a highly reproducible polymeric electrochemical detection platform with an economically viable production process that can specifically and selectively detect LDp at the relevant physiological range. Computational modelling of target-monomer interactions is employed to direct monomer selection and polymer synthesis. Testing the sensor platform within a dynamic range (5–50 μM) of LDp and its metabolite Dp (Dp) in a range of different sample media affords a 42% higher response of current change upon binding to LDp compared to Dp despite high structural similarity between the compounds. Furthermore, the sensor shows no significant difference when tested in different sample media, allowing this electrochemical sensor to operate across a range of different sample sources, further enhancing its adaptability and applicability in an ever-changing landscape of medical technology.
Author(s): Jamieson O, Dann A, Liu X, Oliveira Abib T, Novakovic K, McClements J, Seyedin S, Gruber J, Crapnell RD, Snyder H, Banks CE, Dawson JA, Peeters M
Publication type: Article
Publication status: Published
Journal: Analytica Chimica Acta
Year: 2026
Volume: 1393
Print publication date: 01/04/2026
Online publication date: 30/01/2026
Acceptance date: 29/01/2026
Date deposited: 16/02/2026
ISSN (print): 0003-2670
ISSN (electronic): 1873-4324
Publisher: Elsevier B.V.
URL: https://doi.org/10.1016/j.aca.2026.345174
DOI: 10.1016/j.aca.2026.345174
Data Access Statement: Data will be made available on request.
Altmetrics provided by Altmetric
