Solid-phase extraction coupled to automated centrifugal microfluidics SERS: Improving quantification of therapeutic drugs in human serum

Surface-enhanced Raman spectroscopy (SERS) is a powerful method in analytical chemistry, but its application in real-life medical settings has been limited due to technical challenges. In this work, we introduce an innovative approach that is meant to advance the automation of microfluidics SERS to improve reproducibility and label- free quantification of two widely used therapeutic drugs, methotrexate (MTX) and lamotrigine (LTG), in human serum. Our methodology involves a miniaturized solid-phase extraction (mu -SPE) method coupled to a centrifugal microfluidics disc with incorporated SERS substrates (CD-SERS). The CD-SERS platform enables simultaneous controlled sample wetting and accurate SERS mapping. Together with the assay we implemented a machine learning method based on Partial Least Squares Regression (PLSR) for robust data analysis and drug quantification. The results indicate that combining mu -SPE with CD-SERS (mu -SPE to CD-SERS) led to a substantial improvement in the signal-to-noise ratio compared to combining CD-SERS with ultrafiltration or protein precipitation. The PLSR model enabled us to obtain the limit of detection and quantification for MTX as 2.90 and 8.92 mu M, respectively, and for LTG as 10.76 and 32.29 mu M. We also validated our mu -SPE to CD-SERS method for MTX against HPLC and immunoassay (p-value <0.05), using patient samples undergoing MTX therapy. In addition, we achieved a satisfactory recovery rate (80%) for LTG when quantifying it in patient samples. Our results show the potential of this newly developed approach as a strategy for therapeutic drugs in point-of-care clinical settings and highlight the benefits of automating label-free SERS assays.