A wearable patch for continuous levodopa monitoring in sweat: Towards exertion and power-free pharmacodynamic assessment in Parkinson's disease.

Authors

Tamoghna Saha, Muhammad Inam Khan, Katherine Longardner, Barak Sabbagh, Kaiwen Zheng, Hugo de Mendoza, Gaoyuan Ji, Bumsik Choi, Zongnan Wang, Rosie Pham, Michael Skipworth, Eshita Shah, Maria Reynoso, Chochanon Moonla, Abdulhameed Abdal, Debika Datta, Samar Singh Sandhu, Ponnusamy Nandhakumar, Artur Jedrzak, Shichao Ding, Lu Yin, Irene Litvan, Joseph Wang

Published in

Proceedings of the National Academy of Sciences of the United States of America. Volume 123. Issue 32. Pages e2610453123. Aug 11, 2026. Epub Jul 27, 2026.

Abstract

Precision management of Parkinson's disease (PD) requires frequent levodopa (L-dopa) dose adjustments, yet current monitoring relies on subjective symptom reporting and infrequent blood testing. Here, we present a soft, fingertip-mounted wearable platform for continuous, noninvasive L-dopa monitoring. By combining osmotically harvested passive sweat with soft hydrogels, a potentiometric sensing strategy, and individualized calibration, the platform estimates blood L-dopa information from sweat without external power or iontophoresis. Strong correlations between sweat and high-performance liquid chromatography (HPLC)-measured blood L-dopa concentrations were observed in healthy ([Formula: see text]) and PD subjects ([Formula: see text]) following a single immediate-release L-dopa/carbidopa dose. Low motor symptom scores aligned with peak L-dopa levels, confirming pharmacodynamic relevance. L-dopa cleared faster in PD patients despite similar bioavailability to healthy subjects, while recorded hemodynamic responses showed short hypotensive trends for both groups. Machine learning identified sweat and blood pressure as key contributors toward accurate estimation of blood L-dopa levels (mean absolute error = 2.02 µM vs. ground truth). Overall, our easy-to-use, energy-efficient wearable supports real-time, stimulation-free monitoring, potentially enabling at-home dosage adjustments and paving the way for future autonomous closed-loop L-dopa therapeutic system development.

PMID:
42507913
Bibliographic data and abstract were imported from PubMed on 28 Jul 2026.

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