Label-free therapeutic drug monitoring in human serum by the 3-step surface enhanced Raman spectroscopy and multivariate analysis

Abstract

Surface enhanced Raman spectroscopy (SERS) is widely used in drug molecular detection. However, SERS detections of drug molecules in serum with high sensitivity and reproducibility remains extremely challenging due to signal interference of complex constituents of serum. The latter presents a high SERS background noise that buries the signals of the drug molecules. Here, we report a 3-step method to make SERS system of silver nanoparticle clusters to overcome the interference and achieve quantitative SERS analysis of drugs in serum by 1) proteins removal from serum; 2) enhanced drug adsorption on the nanoparticles; and 3) background suppression by internal standard in nanoparticle aggregation. By careful selection of the aggregation agents and internal standard, clear SERS peaks of the internal standard and six different drug analytes were observed for pesticide identification in human serum. Significantly, the SERS peak ratio of the internal stardard and drug analytes has achieved univariate quantitative monitoring of drug metabolism in mice serum, which is in agreement with analysis by the multivariate curve resolution-alternating least squares method. Our method shows great clinical application potential in therapeutic drug monitoring and personalized medicine.