SERS-Driven Multivariate Analysis and Kinetic Profiling of Omadacycline in Human Serum Using a Hybrid Microgel with In-Silico Insight

Achieving robust and sensitive quantification of drug release in complex biological matrices remains a critical challenge in quantitative bioanalysis. In this work, a surface-enhanced Raman scattering (SERS)-driven multivariate analytical platform was developed for monitoring in-vitro release kinetics of omadacycline (OMC) in human blood serum using a poly(N-isopropylacrylamide)/Ag/Fe2O3 hybrid microgel system. The OMC released in serum was quantified by SERS using silver nanoparticles with results validated by spectrophotometry. Validation in phosphate-buffered saline (pH 7.4, 37 °C) also confirmed consistency with serum-based measurements. Time-resolved SERS measurements (3–33 h at 3 h intervals) were analyzed using principal component analysis, hierarchical cluster analysis, and partial least squares regression (PLSR), demonstrating clear temporal discrimination and precise quantitative performance. The PLSR model exhibited excellent linearity (R2 = 0.982) with low prediction error (RMSEP = 0.0832), achieving a 0.51 ppm limit of detection and 1.11 ppm limit of quantification over a working range of 1.44–5.0 ppm. Kinetic modeling revealed that the release process is best described by the Korsmeyer–Peppas model (R2 ≈ 0.995), indicating diffusion-controlled release. Furthermore, in-silico screening with human serum albumin protein (PDB: 1AO6) provided molecular-level insight into drug–protein interactions influencing release dynamics. This approach provides a sensitive, reproducible, and mechanistically informative platform for drug monitoring and controlled delivery in complex biological systems.