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.

A New Model-Based Approach for the Development of Freeze-Drying Cycles Using a Small-Scale Freeze-Dryer.

This paper presents a model-based approach for the design of the primary drying stage of a freeze-drying process using a small-scale freeze-dryer (MicroFD® by Millrock Technology Inc.). Gravimetric tests, coupled with a model of the heat transfer to the product in the vials that account also for the heat exchange between the edge vials and the central vials, are used to infer the heat transfer coefficient from the shelf to the product in the vial (Kv), that is expected to be (almost) the same in different freeze-dryers. Differently from other approaches previously proposed, the operating conditions in MicroFD® are not chosen to mimic the dynamics of another freeze-dryer: this allows saving time and resources as no experiments are needed in the large-scale unit, and no additional tests in the small-scale unit, apart from the three gravimetric tests usually needed to assess the effect of chamber pressure on Kv. With respect to the other model parameter, Rp, the resistance of the dried cake to mass transfer, it is not influenced by the equipment and, thus values obtained in a freeze-dryer may be used to simulate the drying in a different unit, provided the same filling conditions are used, as well as the same operating conditions in the freezing stage, and cake collapse (or shrinkage) is avoided. The method was validated considering ice sublimation in two types of vials (2R and 6R) and at different operating conditions (6.7, 13.3 and 26.7 Pa), with the freeze-drying of a 5% w/w sucrose solution as a test case. An accurate estimate for both Kv and Rp was obtained with respect to the values obtained in a pilot-scale equipment, determined through independent tests for validation purposes. Simulation of the product temperature and drying time in a different unit was then possible, and results were validated experimentally.

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.