Use of Raman spectroscopy and PCA for quality evaluation and out-of-specification identification in biopharmaceutical products.

Over the past three decades, there was a remarkable growth in the approval of antibody-based biopharmaceutical products. These molecules are notably susceptible to the stresses occurring during drug manufacturing, often leading to structural alterations. A key concern is thus the ability to detect and comprehend these alterations caused by processes, such as aggregation, fragmentation, oxidation levels, as well as the change in protein concentration throughout the process steps, potentially resulting in out-of-spec products. In the present study, Raman spectroscopy, coupled with Principal Component Analysis (PCA), has proven to be an excellent tool for characterizing protein-based products. Notably, it offers the advantages of being minimally invasive, rapid and relatively insensitive to water. Therefore, it was successfully employed to discriminate between various stresses impacting a monoclonal antibody (mAb). The molecule used in this study is a fully human IgG1 fusion protein. Thermal stress was induced by incubating the samples at 50 °C for one month, while oxidative stress was induced by introducing hydrogen peroxide. Additionally, dilutions were performed to explore a broader range of protein concentrations. Specific key bands were identified in the Raman spectra, which facilitated the PCA classification and allowed for their association with distinct changes in the secondary and tertiary structures of the protein. Notably, it was observed that signals corresponding to amino acids exhibited a decrease in intensity with increasing levels of thermal stress, while other alterations were noted in the amide bands. It was shown that changes in the range 2800-3000 cm-1 pertains to the dilution process, while specific peaks of C-H stretching were essential for the discrimination between the oxidative-stressed samples and the thermal and diluted counterparts. Furthermore, the model calibrated on the mAb demonstrated remarkable performance when used to evaluate a different product, e.g. a hormone.

Use of machine learning tools and NIR spectra to estimate residual moisture in freeze-dried products.

Residual Moisture (RM) in freeze-dried products is one of the most important critical quality attributes (CQAs) to monitor, since it affects the stability of the active pharmaceutical ingredient (API). The standard experimental method adopted for the measurements of RM is the Karl-Fischer (KF) titration, that is a destructive and time-consuming technique. Therefore, Near-Infrared (NIR) spectroscopy was widely investigated in the last decades as an alternative tool to quantify the RM. In the present paper, a novel method was developed based on NIR spectroscopy combined with machine learning tools for the prediction of RM in freeze-dried products. Two different types of models were used: a linear regression model and a neural network based one. The architecture of the neural network was chosen so as to optimize the prediction of the residual moisture, by minimizing the root mean square error with the dataset used in the learning step. Moreover, the parity plots and the absolute error plots were reported, allowing a visual evaluation of the results. Different factors were considered when developing the model, namely the range of wavelengths considered, the shape of the spectra and the type of model. The possibility of developing the model using a smaller dataset, obtained with just one product, that could be then applied to a wider range of products was investigated, as well as the performance of a model developed for a dataset encompassing several products. Different formulations were analyzed: the main part of the dataset was characterized by a different percentage of sucrose in solution (3%, 6% and 9% specifically); a smaller part was made up of sucrose-arginine mixtures at different percentages and only one formulation was characterized by another excipient, the trehalose. The product-specific model for the 6% sucrose mixture was found consistent for the prediction of RM in other sucrose containing mixtures and in the one containing trehalose, while failed for the dataset with higher percentage of arginine. Therefore, a global model was developed by including a certain percentage of all the available dataset in the calibration phase. Results presented and discussed in this paper demonstrate the higher accuracy and robustness of the machine learning based model with respect to the linear models.

Integration of binding peptide selection and multifunctional particles as tool-box for capture of soluble proteins in serum.

In this paper, we report on a general approach for the detection of a specific tumoural biomarker directly in serum. Such detection is made possible using a protein-binding peptide selected through an improved phage display technique and then conjugated to engineered microparticles (MPs). Protein biomarkers represent an unlimited source of information for non-invasive diagnostic and prognostic tests; MP-based assays are becoming largely used in manipulation of soluble biomarkers, but their direct use in serum is hampered by the complex biomolecular environment. Our technique overcomes the current limitations as it produces a selective MP--engineered with an antifouling layer--that 'captures' the relevant protein staying impervious to the background. Our system succeeds in fishing-out the human tumour necrosis factor alpha directly in serum with a high selectivity degree. Our method could have great impact in soluble protein manipulation and detection for a wide variety of diagnostic applications.

PLGA microspheres by Supercritical Emulsion Extraction: a study on insulin release in myoblast culture.

Supercritical Emulsion Extraction in a Continuous operation layout is proposed for the production of poly-lactic-co-glycolic acid (PLGA) microspheres loaded with insulin, selected as a model of bioactive signal. Microspheres with different mean sizes of 2 µm (±0.9 µm) and 3 µm (±2.2 µm) and insulin loadings of 3 and 6 mg/g were obtained by processing different water-oil-water emulsions; an encapsulation efficiency of about 60% w/w was measured in all cases. Insulin release profiles from PLGA microspheres were also characterized in two different media (Phosphate-Buffered Saline and Dulbecco's Modified Eagle Medium) and kinetic constants were estimated by using a model proposed in literature. The produced microspheres were, then, used for the cultivation of rat embryonic ventricular myoblasts in a serum-free medium to monitor the biological effect of the released insulin. The best cell viability and proliferation, supported by released insulin, was monitored when microspheres with mean size of 3 µm loaded with 3 mg/g of insulin were added.

Injectable PLGA/hydrocortisone formulation produced by continuous supercritical emulsion extraction.

The objective of the present study was to develop an anti-inflammatory prolonged action formulation for local injection in prefilled syringes. Hydrocortisone acetate (HA) was selected as a model corticosteroid drug to be incorporated in poly(lactic-co-glycolic) (PLGA) microspheres. The formulation was obtained by supercritical emulsion extraction in continuous operation layout (SEE-C) to test the process robustness for a continuous industrial production. PLGA/HA microspheres with mean sizes between 1 µm (SD±0.20) and 5 µm (SD±1.45) were obtained when operating at 80 bar and 38 °C with a L/G ratio of 0.1 in the counter-current tower. The produced microdevices showed excellent encapsulation efficiencies between 75% and 80%, depending on the emulsion formulations tested, and different sustained release in the range of 6-15 days. In dependence of the different emulsion (single or double) processed by SEE-C, different products can be obtained according to the therapeutic requests. SEE-C confirms to be an innovative and flexible technology for biopolymer microdevices production, coupling the efficiency of continuous operation to the easy process scalability.

PLGA microdevices for retinoids sustained release produced by supercritical emulsion extraction: continuous versus batch operation layouts.

Retinyl acetate (RA) was selected as a model compound to be entrapped in poly(lactic-co-glycolic)acid (PLGA) microspheres using supercritical emulsion extraction (SEE). Several oil-in-water emulsions prepared using acetone and aqueous glycerol (80% glycerol, 20% water) were processed using supercritical carbon dioxide (SC-CO2 ) to extract the oily phase and to induce microspheres formation. The characteristics of the microspheres obtained by conventional liquid emulsion extraction and SEE were also compared: SEE produced spherical and free flowing microspheres, whereas the conventional liquid-liquid extraction showed large intraparticles aggregation. Emulsion extraction by SC-CO2 technology was tested using two different operation layouts: batch (SEE-B) and continuous (SEE-C). SEE-C was performed using a packed tower to produce emulsion/SC-CO2 contact in countercurrent mode, allowing higher microsphere recovery and process efficiencies. Operating at 80 bar and 36°C, SEE-C produced PLGA/RA microspheres with mean sizes between 3.3 and 4.5 µm with an excellent encapsulation efficiency of 80%-90%. Almost all the drug was released in about 6 days when charged at 2.7% (w/w), whereas only 40% and 10% of RA were released in the same period of time when the charge was 5.2% and 8.8% (w/w), respectively. Release kinetics constants calculated from the experimental data, using a mathematical model, were also proposed and discussed.