Raman-based detection of antibiotics and metabolites in pharmaceutical formulations and clinical-relevant matrices

Raman spectroscopy is a powerful tool for bioanalytical detection methods due to providing molecular-specific fingerprint information. Furthermore, the inherently weak Raman signal can be enhanced by several orders of magnitude via the application of plasmonic-active nanostructured surfaces, i.e., using the SERS technique. Consequently, analyte molecules in the µM range or lower can be detected with high specificity. However, due to the different complexity of the sample matrix, especially the strong interference of the highly complex matrix, customizing the detection strategy targeting less interference by background ingredients becomes more necessary for SERS application in future, which is also the theme of this thesis discussion. The aim of this thesis is, to develop Raman- and SERS-based detection schemes for sample matrices of different complexity, i.e., from pharmaceutical formulations to clinical matrices. For interfering substances in pharmaceutical formulations, the SERS signal of the analyte can be significantly enhanced simply by the dilution method, while protein precipitation and microdialysis can be used in protein-rich body fluids to avoid the blocking effect of protein corona, and for the interference of highly concentrated metabolites, it has become possible to use metal-organic frameworks to enrich analytes. These three solutions are facing gradually increasing complexity scenarios to provide innovative SERS strategies in the pharmaceutical industry, therapeutic drug monitoring and rapid detection of antituberculosis drug susceptibility tests.

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