Fluidized bed granulation is a widely applied wet granulation technique in the pharmaceutical industry to produce solid dosage forms. The process involves the spraying of a binder liquid onto fluidizing powder particles. As a result, the (wetted) particles collide with each other and form larger permanent aggregates (granules). After spraying the required amount of granulation liquid, the wet granules are rapidly dried in the fluid bed granulator. Since the FDA launched its Process Analytical Technology initiative (and even before), a wide range of analytical process sensors has been used for real-time monitoring and control of fluid bed granulation processes. By applying various data analysis techniques to the multitude of data collected from the process analyzers implemented in fluid bed granulators, a deeper understanding of the process has been achieved. This review gives an overview of the process analytical technologies used during fluid bed granulation to monitor and control the process. The fundamentals of the mechanisms contributing to wet granule growth and the characteristics of fluid bed granulation processing are briefly discussed. This is followed by a detailed overview of the in-line applied process analyzers, contributing to improved fluid bed granulation understanding, modeling, control, and endpoint detection. Analysis and modeling tools enabling the extraction of the relevant information from the complex data collected during granulation and the control of the process are highlighted.

Exosome-like vesicles (ELVs) are nanovectors released by cells that are endowed with a variety of molecules, including proteins, nucleic acids, and chemicals that reflect the molecular signature of the producing cell. Given their presence in many biofluids, they form an easily accessible biomarker for early disease detection. Previously we demonstrated the possibility of identifying individual ELVs by analyzing their molecular signatures with surface-enhanced Raman scattering (SERS) after functionalization of ELVs with 4-(dimethylamino)­pyridine (DMAP)-stabilized gold nanoparticles (AuNP). Although this strategy was capable of distinguishing ELVs from different cellular origins, the quality of the SERS spectra was suboptimal due to high background coming from the DMAP stabilizing molecules at the AuNP surface. In this study we demonstrate that it is possible to eliminate interfering SERS signals from stabilizing molecules at the AuNP surface by overgrowing in situ the ELV-attached AuNPs with a silver layer so as to form a core–shell nanoparticle (Au@AgNPs) directly at the ELV surface. As such it represents the first known strategy to generate clear SERS spectral fingerprints of delicate biological structures without interference of linker molecules that are needed to ensure colloidal stability of the plasmonic NP and to allow them to associate to the ELV surface. This new strategy using core–shell plasmonic NPs as SERS substrate showed higher near-field enhancements than previous approaches, which resulted in SERS spectra with improved signal-to-noise ratio. This allowed us to discriminate individual vesicles derived from B16F10 melanoma cells and red blood cells (RBC) with an unprecedented sensitivity and specificity >90%. Importantly, thanks to the higher near field enhancement the acquisition time could be reduced by 20-fold in comparison to previously reported strategies, paving the way toward high-throughput label-free single ELV identification.

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Analysis of protein glycation in human fingernail clippings with NIR spectroscopy could be an alternative affordable technique for the diagnosis of diabetes mellitus.