A pH compensation and peak identification algorithm for voltammetric measurement of therapeutic drugs with sweat sensors

Abstract

The current approach of Therapeutic Drug Monitoring (TDM) relies on blood analysis to closely monitor drugs with a narrow therapeutic window. This method is uncomfortable for the patient and time-consuming and therefore challenging for frequent monitoring. Electrochemical analysis in sweat is a promising alternative, as sweat sensors are non-invasive and can continuously measure drug concentrations. This study explores novel techniques to improve the analytical performance of voltammetric sensors for TDM in a sweat matrix. Methotrexate (MTX) is selected as the model analyte as it is a widely used therapeutic drug for treatment of cancer, rheumatoid arthritis, among other disorders. Changes in pH and interference from amino acids originating from sweat have been shown to impact the measurement of target drugs such as MTX. Herein, an algorithm is developed to compensate for potential pH fluctuations in sweat by using the relation between the pH level and the peak potential of the electro-oxidized analyte to estimate the pH and calculate the concentration of the analyte. Additionally, an algorithm was developed to separate peaks of distinct amino acids with a similar oxidation potential as MTX. The algorithm uses Gaussian fitting for subtracting and linear discriminant analysis (LDA) to identify the peak related to the analyte. The results demonstrate that the algorithms are effective for the detection of MTX and present an approach to compensating for sweat matrix-related interferences in wearable sweat sensors, driving development for low-cost continuous therapeutic drug monitoring.