Anhydrous milk fat (AMF) and its fractions are used as ingredients in a wide range of food applications. Obtaining the appropriate solid fat content (SFC) is essential to achieve the desired product texture. At present, in-line monitoring techniques to control milk fat crystallization and melting are largely unavailable. The thermal behaviour of milk fat (AMF and four of its fractions) was monitored in a temperature-controlled vessel using an in-line Raman analyser and compared with thermograms generated using differential scanning calorimetry (DSC). The major stages of milk fat crystallization and melting were identified using the in-line Raman analyser. Thermal data from DSC showed excellent linear correlations with Raman spectral data (R2 value of 0.97 for the onset of milk fat crystallisation). Partial least squares regression (PLSR) models were developed using Raman spectra to predict SFC with coefficient of determination (R2Cs) from 0.929 to 0.992 and root mean standard error of calibration (RMSECs) ranging from 3.20 to 10.36%. Results demonstrated Raman spectroscopy has significant potential as a way of monitoring milk fat crystallization and melting processes.

Process Analytical Technology (PAT) has revolutionized pharmaceutical manufacturing by providing real-time monitoring and control capabilities throughout the production process. This review paper comprehensively examines the application of PAT methodologies specifically in the production of solid active pharmaceutical ingredients (APIs). Beginning with an overview of PAT principles and objectives, the paper explores the integration of advanced analytical techniques such as spectroscopy, imaging modalities and others into solid API substance production processes. Novel developments in in-line monitoring at academic level are also discussed. Emphasis is placed on the role of PAT in ensuring product quality, consistency, and compliance with regulatory requirements. Examples from existing literature illustrate the practical implementation of PAT in solid API substance production, including work-up, crystallization, filtration, and drying processes. The review addresses the quality and reliability of the measurement technologies, aspects of process implementation and handling, the integration of data treatment algorithms and current challenges. Overall, this review provides valuable insights into the transformative impact of PAT on enhancing pharmaceutical manufacturing processes for solid API substances.

Total sialic acid content (TSA) in biotherapeutic proteins is often a critical quality attribute as it impacts the drug efficacy. Traditional wet chemical assays to quantify TSA in biotherapeutic proteins during cell culture typically takes several hours or longer due to the complexity of the assay which involves isolation of sialic acid from the protein of interest, followed by sample preparation and chromatographic based separation for analysis. Here, we developed a machine learning model-based technology to rapidly predict TSA during cell culture by using typically measured process parameters. The technology features a user interface, where the users only have to upload cell culture process parameters as input variables and TSA values are instantly displayed on a dashboard platform based on the model predictions. In this study, multiple machine learning algorithms were assessed on our dataset, with the Random Forest model being identified as the most promising model. Feature importance analysis from the Random Forest model revealed that attributes like viable cell density (VCD), glutamate, ammonium, phosphate, and basal medium type are critical for predictions. Notably, while the model demonstrated strong predictability by Day 14 of observation, challenges remain in forecasting TSA values at the edges of the calibration range. This research not only emphasizes the transformative power of machine learning and soft sensors in bioprocessing but also introduces a rapid and efficient tool for sialic acid prediction, signaling significant advancements in bioprocessing. Future endeavors may focus on data augmentation to further enhance model precision and exploration of process control capabilities.

High shear wet granulation (HSWG) is widely used in tablet manufacturing mainly because of its advantages in improving flowability, powder handling, process run time, size distribution, and preventing segregation. In line process analytical technology measurements are essential in capturing detailed particle dynamics and presenting real-time data to uncover the complexity of the HSWG process and ultimately for process control. This study presents an opportunity to predict the properties of the granules and tablets through torque measurement of the granulation bowl and the force exerted on a novel force probe within the powder bed. Inline force measurements are found to be more sensitive than torque measurements to the granulation process. The characteristic force profiles present the overall fingerprint of the high shear wet granulation, in which the evolution of the granule formation can improve our understanding of the granulation process. This provides rich information relating to the properties of the granules, identification of the even distribution of the binder liquid, and potential granulation end point. Data were obtained from an experimental high shear mixer across a range of key process parameters using a face-centred surface response design of experiment (DoE). A closed-form analytical model was developed from the DOE matrix using the discovery of evolutionary equations. The model is able to provide a strong predictive indication of the expected tablet tensile strength based only on the data in-line. The use of a closed form mathematical equation carries notable advantages over other AI methodologies such as artificial neural networks, notably improved interpretability/interrogability, and minimal inference costs, thus allowing the model to be used for real-time decision making and process control. The capability of accurately predicting, in real time, the required compaction force required to achieve the desired tablet tensile strength from upstream data carries the potential to ensure compression machine settings rapidly reach and are maintained at optimal values, thus maximising efficiency and minimising waste.

Pharmaceutical manufacturing is reliant upon bioprocessing approaches to generate the range of therapeutic products that are available today. The high cost of production, susceptibility to process failure, and requirement to achieve consistent, high-quality product means that process monitoring is paramount during manufacturing. Process analytic technologies (PAT) are key to ensuring high quality product is produced at all stages of development. Spectroscopy-based technologies are well suited as PAT approaches as they are non-destructive and require minimum sample preparation. This study explored the use of a novel attenuated total reflection Fourier transform infrared (ATR-FTIR) spectroscopy platform, which utilises disposable internal reflection elements (IREs), as a method of upstream bioprocess monitoring. The platform was used to characterise organism health and to quantify cellular metabolites in growth media using quantification models to predict glucose and lactic acid levels both singularly and combined. Separation of the healthy and nutrient deficient cells within PC space was clearly apparent, indicating this technique could be used to characterise these classes. For the metabolite quantification, the binary models yielded R 2 values of 0.969 for glucose, 0.976 for lactic acid. When quantifying the metabolites in tandem using a multi-output partial least squares model, the corresponding R 2 value was 0.980. This initial study highlights the suitability of the platform for bioprocess monitoring and paves the way for future in-line developments.

The purpose of this study was to investigate the mechanical stresses and strains acting on pharmaceutical glass tubing vials during freezing and thawing of model pharmaceutical formulations. Strain measurements were conducted inside of a laboratory-scale freeze-dryer using a custom wireless sensor. In both sucrose and trehalose formulations at concentrations between 5 % and 20 % w/v, the strain measurements initially increased before peaking in magnitude at temperatures close to the respective glass transition temperatures of the maximally freeze concentrated solutes, Tg\'. We attribute this behavior to a shift in the mechanical properties of the frozen system from a purely elastic glass below Tg\' to a viscoelastic rubber-like material above Tg\'. That is, when the interstitial region becomes mechanically compliant at temperature above Tg\'. The outputs were less predictable below 5 % w/v and tended to exhibit two separate peaks in strain output, one near the equilibrium melting temperature of pure ice and the other near Tg\'. The peaks merged at concentrations between 4 and 5 % w/v where the largest strain magnitude was observed. The strain on primary packaging has traditionally been applied to evaluate the risk of damage or breakage due to, for example, crystallization of excipients. However, data collected during this study suggest there may be utility in formulation design or as a process analytical technology to minimize potentially destabilizing stresses and strains in the frozen formulation.

Protein crystallization as opposed to well-established chromatography processes has the benefits to reduce production costs while reaching a comparable high purity. However, monitoring crystallization processes remains a challenge as the produced crystals may interfere with analytical measurements. Especially for capturing proteins from complex feedstock containing various impurities, establishing reliable process analytical technology (PAT) to monitor protein crystallization processes can be complicated. In heterogeneous mixtures, important product characteristics can be found by multivariate analysis and chemometrics, thus contributing to the development of a thorough process understanding. In this project, an analytical set-up is established combining offline analytics, on-line ultraviolet visible light (UV/Vis) spectroscopy, and in-line Raman spectroscopy to monitor a stirred-batch crystallization process with multiple phases and species being present. As an example process, the enzyme Lactobacillus kefir alcohol dehydrogenase (LkADH) was crystallized from clarified Escherichia coli (E. coli) lysate on a 300 mL scale in five distinct experiments, with the experimental conditions changing in terms of the initial lysate solution preparation method and precipitant concentration. Since UV/Vis spectroscopy is sensitive to particles, a cross-flow filtration (cross-flow filtration)-based bypass enabled the on-line analysis of the liquid phase providing information on the lysate composition regarding the nucleic acid to protein ratio. A principal component analysis (PCA) of in situ Raman spectra supported the identification of spectra and wavenumber ranges associated with productspecific information and revealed that the experiments followed a comparable, spectral trend when crystals were present. Based on preprocessed Raman spectra, a partial least squares (PLS) regression model was optimized to monitor the target molecule concentration in real-time. The off-line sample analysis provided information on the crystal number and crystal geometry by automated image analysis as well as the concentration of LkADH and host cell proteins (HCPs) In spite of a complex lysate suspension containing scattering crystals and various impurities, it was possible to monitor the target molecule concentration in a heterogeneous, multi-phase process using spectroscopic methods. With the presented analytical set-up of off-line, particle-sensitive on-line, and in-line analyzers, a crystallization capture process can be characterized better in terms of the geometry, yield, and purity of the crystals.

Process in-line monitoring and control are crucial to optimize the productivity of bioprocesses. A frequently applied Process Analytical Technology (PAT) tool for bioprocess in-line monitoring is Raman spectroscopy. However, evaluating bioprocess Raman spectra is complex and calibrating state-of-the-art statistical evaluation models is effortful. To overcome this challenge, we developed an Indirect Hard Modeling (IHM) prediction model in a previous study. The combination of Raman spectroscopy and the IHM prediction model enables non-invasive in-line monitoring of glucose and ethanol mass fractions during yeast fermentations with significantly less calibration effort than comparable approaches based on statistical models. In this study, we advance this IHM-based approach and successfully demonstrate that the combination of Raman spectroscopy and IHM is capable of not only bioprocess monitoring but also bioprocess control. For this purpose, we used this combination\'s in-line information as input of a simple on-off glucose controller to control the glucose mass fraction in Saccharomyces cerevisiae fermentations. When we performed two of these fermentations with different predefined glucose set points, we achieved similar process control quality as approaches using statistical models, despite considerably smaller calibration effort. Therefore, this study reaffirms that the combination of Raman spectroscopy and IHM is a powerful PAT tool for bioprocesses.

Continuous manufacturing of pharmaceuticals offers several benefits, such as increased production efficiency, enhanced product quality control, and lower environmental footprint. To fully exploit these benefits, standard operation mode (production processes with no or minimal disturbance mitigation measures) should be supported by adopting novel quality-by-control (QbC) methodologies. The paper at hand is the first part of a study focused on developing QbC algorithms for optimizing twin-screw wet granulation in the industrial manufacturing line ConsiGmaTM-25, specifically addressing granule composition. This work relies on previously established process-analytical-technology (PAT) equipment for real-time monitoring of the granule composition, i.e., the active pharmaceutical ingredient (API) and liquid content in wet granules. The developed control platform integrates model-based process control algorithms that aim to keep the API- and liquid content at target values through real-time adjustments of the process parameters. Furthermore, the platform integrates a digital operator assistant, which aims to detect and classify granulation disturbances and provides messages and instructions for the plant operator. The present manuscript systematically outlines all design steps from the development phase in the simulation environment to the final real system application and validation. The control platform\'s performance is demonstrated through selected test scenarios on the ConsiGmaTM-25 manufacturing line. The obtained results indicate improved disturbance robustness and an increase in intermediate/final product quality (compared to conventional operating modes): The process control algorithms successfully maintained the API- and liquid content at target values despite process disturbances. Furthermore, realistic disturbances (feeder, pump, and material) were accurately detected and classified by the digital assistant algorithm. The information was provided through a user interface, offering real-time support for plant personnel.

Inclusion bodies (IBs) are protein aggregates formed as a result of overexpression of recombinant protein in E. coli. The formation of IBs is a valuable strategy of recombinant protein production despite the need for additional processing steps, i.e., isolation, solubilization and refolding. Industrial process development of protein refolding is a labor-intensive task based largely on empirical approaches rather than knowledge-driven strategies. A prerequisite for knowledge-driven process development is a reliable monitoring strategy. This work explores the potential of intrinsic tryptophan and tyrosine fluorescence for real-time and in situ monitoring of protein refolding. In contrast to commonly established process analytical technology (PAT), this technique showed high sensitivity with reproducible measurements for protein concentrations down to 0.01 g L - 1 . The change of protein conformation during refolding is reflected as a shift in the position of the maxima of the tryptophan and tyrosine fluorescence spectra as well as change in the signal intensity. The shift in the peak position, expressed as average emission wavelength of a spectrum, was correlated to the amount of folding intermediates whereas the intensity integral correlates to the extent of aggregation. These correlations were implemented as an observation function into a mechanistic model. The versatility and transferability of the technique were demonstrated on the refolding of three different proteins with varying structural complexity. The technique was also successfully applied to detect the effect of additives and process mode on the refolding process efficiency. Thus, the methodology presented poses a generic and reliable PAT tool enabling real-time process monitoring of protein refolding.