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    Device Technologies and Biomedical Robotics

    (J-393) Point-of-care diagnostic tools for safe management of mood stabilizers

    Saturday, October 14, 2023
    9:30 AM – 10:30 AM PDT
    Location: Exhibit Hall - Row J - Poster # 393
    Introduction::

    The advances in point-of-care care diagnostic tools have exponentially amplified our capability to detect and quantify various biochemical entities with high sensitivity and precision. Considering the impact of point-of-care diagnostic tools on society's life quality, preventing unnecessary hospital visits, and making personalized healthcare more accessible, we decided to look into developing diagnostic tools for the safe monitoring of mood stabilizers.  Sodium valproate is one of the most commonly prescribed anticonvulsant and mood-stabilizing drugs, where accurately detecting sodium valproate levels is paramount. This medication, primarily used in the treatment of epilepsy, mental health disorders, and migraine headaches, was prescribed for more than six million and necessitates close compliance monitoring due to having a narrow therapeutic index and the potential for solemn adverse side effects, including hepatic toxicity, teratogenicity, and blood dyscrasias. Moreover, interpatient variability in drug absorption, metabolism, and elimination can significantly impact plasma concentrations, adding further complexity (1,2). Current sensing methods such as fluorescence polarization immunoassays and mass spectrometry are expensive and require trained personnel, creating barriers to care for low-resource settings (3). Therefore, there is an imperative need for robust, reliable, and rapid detection methods for Sodium Valproate. Here, we devised an inexpensive, yarn-based electrochemical diagnostic tool for safe drug compliance and management of sodium valproate as a commonly prescribed mood stabilizer in socioeconomically challenged regions.


     


    Materials and Methods:: To fabricate our yarn-based sensors, we started with creating a conductive ink by utilizing commercially accessible carbon black powder. The ink was prepared by blending the carbon black powder with poly(vinyl chloride), and o-nitrophenyl-octyl-ether (polymer and plasticizer). We applied the conductive ink on precut cotton yarns to fabricate an inexpensive electrode. The sodium valproate sensing mechanism was created by utilizing the permselectivity of tridodecylmethylammonium chloride (an ionic salt) and dispersing it in a polymeric matrix consisting of PVC and plasticizer forming sodium valproate-selective precursor. We dipped the yarn-based electrodes into the sodium valproate-selective precursor to prepare our sensors (Figure 1A). We performed all measurements with a 16-channel potentiometer (Lawson labs.) and commercial double junction reference electrode. Our sensor was validated in solutions and artificial saliva based on physiologically relevant conditions.

    Results, Conclusions, and Discussions:: The sensing mechanism exhibited accurate sensitivity for sodium valproate with a slope of -56.9 mV/decade comparable to the theoretical value (-59.2 mV/decade). The detection limit was 75.56 uM, encompassing the therapeutic window of sodium valproate therapy (50-100 mcg/mL) (Figure 1C). A selectivity assessment indicated that the sodium valproate sensors’ selectivity against interfering ions such as bicarbonate (0.87) and phosphate (1.16). Moreover, we validated the performance of our sodium-valproate sensors in artificial saliva and serum to recapitulate the feasibility of the developed system, granting a 92% and 94% recovery, respectively. The obtained recovery values fall within the FDA-accepting tolerance range. This sensing mechanism demonstrated reliability and accuracy for sensing valproate in artificial fluids; however, further work should be taken to increase accuracy in real human biofluids. Due to its inexpensive fabrication cost, this mechanism can be a valuable tool for point-of-care diagnostics in socioeconomically challenged settings to improve personalized drug management.

     

     


    Acknowledgements (Optional): :

    References (Optional): : 1. Kane SP. Valproate, ClinCalc DrugStats Database, Version 2022.08. ClinCalc: https://clincalc.com/DrugStats/Drugs/Valproate. Updated August 24, 2022. Accessed July 23, 2023.

    2. Damegunta, S. R. (2014). Time matters!: When is the right time to estimate serum valproic acid levels?. Indian Journal of Psychological Medicine, 36(3), 349-350.

    3. Pascali, J. P., Giorgetti, A., Barone, R., Pelletti, G., & Fais, P. (2023). Valproic acid determination by liquid chromatography coupled to mass spectrometry (LC-MS/MS) in whole blood for forensic purposes. Drug testing and analysis, 15(1), 128–133. https://doi.org/10.1002/dta.3362