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This paper presents novel measurement methods, where deep learning was used to detect tableting defects and determine the crushing strength and disintegration time of tablets on images captured by machine vision. Five different classes of defects were used and the accuracy of the real-time defect recognition performed with the deep learning algorithm YOLOv5 was 99.2 %. The system can already match the production capability of tablet presses, with still further room left for improvement. The YOLOv5 algorithm was also used to determine the disintegration time and crushing strength of tablets produced at different compression force settings based on their surface texture. With these accurate, low-cost methods, the 100 % screening of the produced tablets could be carried out, resulting in the improvement of quality control and effectiveness of pharmaceutical production., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 The Author(s). Published by Elsevier B.V. All rights reserved.)

In this decade, one of the major trends in the pharmaceutical industry is the adoption of continuous manufacturing. This requires the development of continuous equivalents of essential pharmaceutical processes such as film coating. The process of film coating is the last step of the processing of solid dosage forms and is critical because it determines the visual appearance of the end product, along with ensuring its stability and possibly even defining the rate of drug release. Several manufacturers advertise continuous solutions for film coating, these include semi-continuous and fully continuous appliances. State-of-the-art continuous coaters can match the throughput of continuous manufacturing lines, because largest appliances have a capacity of 1200-1500 kg/h. The paper also describes the main challenges related to continuous film coating including waste production at the beginning and end of the process and the problem caused by elastic recovery of the tablets when film coating is performed immediately after tablet compression. Lastly, we give an overview of the in-line sensors that can be used to monitor the quality of the film coated tablets, enabling real-time quality control of the process. Near-infrared and Raman spectroscopy can measure the mass gain of the tablets, while terahertz pulsed imaging and optical coherence tomography enable coating thickness measurement of individual tablets and even the characterization of intra-tablet coating thickness variability. UV imaging and machine vision can also measure coating thickness, and they are also excellent for detecting tablets with defective coating., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 The Author(s). Published by Elsevier B.V. All rights reserved.)

This paper presents a precursor of a novel, high-throughput, in-line system, which utilizes ultraviolet (UV) imaging in order to predict the active pharmaceutical ingredient (API) content of tablets in real-time, non-destructive manner. Pimobendan, cardiovascular drug used in veterinary medicine was chosen as a fluorescent model API. Two experiments were carried out using different measurement setups, where the tablets were moving at different speeds. The blue colour components of the images were used to predict the pimobendan content of the tablets, and as a reference method, traditional UV spectroscopy was used to measure the API content of the dissolved tablets. In the case of the first, slower experiment (with a conveyor belt speed of 83 mm/s), a second order polynomial was fitted to the calibration tablets containing a nominal dose of 1.25, 5 and 10 mg of pimobendan and it was used to predict the API content. The RMSEP obtained was 0.428 mg for the validation tablets with a relative error as low as 7 % for the target level. For the second, faster experiment (1000 mm/s) the same polynomial was used to predict the pimobendan concentration of a different set of tablets, achieving a relative error of 2.03 %. Finally, the throughput of both systems was calculated to assess their applicability to meet the requirements of a pharmaceutical manufacturing line. The first system could inspect up to 93375 tablets per hour, while the second was able to process up to 360000 tablets in an hour, making it suitable for industrial application. By using these developed systems, the API content of all produced tablets could be determined non-destructively, which can greatly improve patient safety., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024. Published by Elsevier B.V.)

In the pharmaceutical industry, filtration is traditionally carried out in batch mode. However, with the spread of continuous technologies, there is an increasing demand for robust continuous filtration strategies suitable for processing suspensions produced in continuous crystallizers. Accordingly, this study aimed to investigate a lab-scale horizontal conveyor belt filtration approach for pharmaceutical separation purposes for the first time. The newly developed continuous horizontal belt filter (CHBF) was tested under different systems (microcrystalline cellulose (MCC)/water, lactose/ethanol and acetylsalicylic acid (ASA)/water) and diverse conditions. Filtration was robust using a well-defined unimodal particle size distribution MCC in water system, where the residual moisture content varied within narrow limits of 45-52% independently from the process conditions. Besides, the residual moisture content highly depended on the applied solvent and particle size. It could be reduced to below 2% by processing the suspensions of either a volatile solvent (lactose in ethanol) or an aqueous slurry of a large particle size ASA. Finally, the CHBF was connected to a mixed suspension mixed product removal (MSMPR) or a plug flow crystallizer (PFC). The residual moisture content of the CHBF-filtered ASA product and operation characteristics (onset of steady-state) were evaluated in both continuous crystallizer-filter systems. The MSMPR-CHBF system operated with a longer startup period. The size of the in situ-produced crystals was of a similar order magnitude in both systems, resulting in a similar residual moisture content (around 20%). Overall, the tested continuous filter was robust, did not modify the crystal morphology in the examined experimental range, and could be effectively integrated with continuous crystallizers., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 The Author(s). Published by Elsevier B.V. All rights reserved.)

Electrospinning (ES) is a promising continuous formulation strategy to produce amorphous solid dispersions (ASDs) and thereby improve the dissolution of poorly water-soluble drugs. However, processing the electrospun material into solid dosage forms (e.g. tablets) is challenging due to the poor flow properties. In this research, continuous twin-screw melt granulation was applied to improve the flowability of the fibers and therefore ease the further processing steps. During this work, two ASD compositions were investigated: one containing 60 % poly-vinylpyrrolidone-vinyl acetate 6:4 copolymer and 40 % itraconazole (ITR), and another one containing hydroxypropyl methylcellulose (HPMC) and ITR in the same ratio. Both fiber compositions were granulated with polyethene glycol as the binder material, while the effects of the process parameters were examined. The application of higher granulation temperature and screw configurations with increased shear forces compromised the fibrous structure, induced crystallization of the ASD, and decreased the dissolution. However, the stability of the ITR-HPMC fibers proved to be higher as their granulation at 60 °C led to granules with adequate flow properties and dissolution. Moreover, tablets with fewer excipients were pressed from them, resulting in a 34 % reduction in weight. Consequently, this process can complement ES technology and facilitate its industrial implementation., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024. Published by Elsevier B.V.)

In this study, a novel quality assurance system was developed utilizing Process analytical technology (PAT) tools and artificial intelligence (AI). Our goal was to monitor the critical quality attributes (CQAs) like drug concentration, morphology and fiber diameter of electrospun amorphous solid dispersion (ASD) formulations with fast at-line techniques. Doxycycline-hyclate (DOX), a tetracycline-type antibiotic was used as a model drug with 2-hydroxypropyl-β-cyclodextrin (HP-β-CD) as the matrix excipient. The water-based formulations were electrospun with high-speed electrospinning (HSES). Raman and NIR sensors and machine vision-based color measurement techniques were employed to accurately determine the drug concentration. Given that morphology can influence the solubility of the drug, a convolutional neural network (CNN)-based AI model was developed to examine this property and detect manufacturing defects. Additionally, the diameter of electrospun fibrous samples was measured using camera images and a trained AI model, enabling rapid analysis of fiber diameter with results similar to that of scanning electron microscopy (SEM). These methods and models demonstrate potential in-line analytical tools, offering rapid, cheap and non-destructive analysis of ASD formulations., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 The Author(s). Published by Elsevier B.V. All rights reserved.)

Gastroretentive dosage forms are recommended for several active substances because it is often necessary for the drug to be released from the carrier system into the stomach over an extended period. Among gastroretentive dosage forms, floating tablets are a very popular pharmaceutical technology. In this study, it was investigated whether a rapid, nondestructive method can be used to characterize the floating properties of a tablet. To accomplish our objective, the same composition was compressed, and varied compression forces were applied to achieve the desired tablet. In addition to physical examinations, digital microscopic images of the tablets were captured and analyzed using image analysis techniques, allowing the investigation of the floatability of the dosage form. Image processing algorithms and artificial neural networks (ANNs) were utilized to classify the samples based on their strength and floatability. The input dataset consisted solely of the acquired images. It has been shown by our research that visible imaging coupled with pattern recognition neural networks is an efficient way to categorize these samples based on their floatability. Rapid and non-destructive digital imaging of tablet surfaces is facilitated by this method, offering insights into both crushing strength and floating properties., (Copyright © 2024 The Author(s). Published by Elsevier B.V. All rights reserved.)

Twin-screw wet granulation (TWSG) is a promising continuous alternative of pharmaceutical wet granulation. One of its benefits is that the components dissolved in the granulation liquid are distributed homogeneously in the granules. This provides an elegant way to manufacture products with ultralow drug doses. Near-infrared (NIR) and Raman spectroscopy are well-established process analytical technology (PAT) tools that can be used for the in-line monitoring of TSWG. However, their detection limit does not enable the measurement of components in the ultralow (i.e., ppm) range. In this paper, an indirect approach is presented that enables the real-time determination of the concentration of a drug in concentrations between 40 and 100 ppm by using the signal of an excipient, in this case, the polyvinylpyrrolidone (PVP). This component is also dissolved in the granulation liquid; therefore, it is distributed in the same way as the active ingredient. Results of HPLC measurements have proved that the models trained to quantify the concentration of PVP in real-time gave an accurate determination for the active ingredient as well (root mean squared error was 7.07 ppm for Raman and 5.31 ppm for NIR spectroscopy, respectively). These findings imply that it is possible to indirectly predict the concentration of ultralow dose drugs with in-line analytical techniques based on the concentration of an excipient., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 The Author(s). Published by Elsevier B.V. All rights reserved.)

The paper provides a demonstration of how UV/VIS imaging can be employed to evaluate the crushing strength, friability, disintegration time and dissolution profile of tablets comprised of solely white components. The samples were produced using different levels of compression force and API content of anhydrous caffeine. Images were acquired from both sides of the samples using UV illumination for the API content prediction, while the other parameters were assessed using VIS illumination. Based on the color histograms of the UV images, API content was predicted with 5.6 % relative error. Textural analysis of the VIS images yielded crushing strength predictions under 10 % relative error. Regarding friability, three groups were established according to the weight loss of the samples. Likewise, the evaluation of disintegration time led to the identification of three groups: <10 s, 11-35 s, and over 36 s. Successful classification of the samples was achieved with machine learning algorithms. Finally, immediate release dissolution profiles were accurately predicted under 5 % of RMSE with an artificial neural network. The 50 ms exposition time during image acquisition and the resulting outcomes underscore the practicality of machine vision for real-time quality control in solid dosage forms, regardless of the color of the API., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 The Author(s). Published by Elsevier B.V. All rights reserved.)

Due to the continuously increasing Cost of Goods Sold, the pharmaceutical industry has faced several challenges, and the Right First-Time principle with data-driven decision-making has become more pressing to sustain competitiveness. Thus, in this work, three different types of artificial neural network (ANN) models were developed, compared, and interpreted by analyzing an open-access dataset from a real pharmaceutical tableting production process. First, the multilayer perceptron (MLP) model was used to describe the total waste based on 20 raw material properties and 25 statistical descriptors of the time series data collected throughout the tableting (e.g., tableting speed and compression force). Then using 10 process time series data in addition to the raw material properties, the cumulative waste, during manufacturing was also predicted by long short-term memory (LSTM) and bidirectional LSTM (biLSTM) recurrent neural networks (RNN). The LSTM network was used to forecast the waste production profile to allow preventive actions. The results showed that RNNs were able to predict the waste trajectory, the best model resulting in 1096 and 2174 tablets training and testing root mean squared errors, respectively. For a better understanding of the process, and the models and to help the decision-support systems and control strategies, interpretation methods were implemented for all ANNs, which increased the process understanding by identifying the most influential material attributes and process parameters. The presented methodology is applicable to various critical quality attributes in several fields of pharmaceutics and therefore is a useful tool for realizing the Pharma 4.0 concept., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 The Author(s). Published by Elsevier B.V. All rights reserved.)

We present a systematic and automatic approach for integrating tableting reduced-order models with upstream unit operations. The approach not only identifies the upstream critical material attributes and process parameters that describe the coupling to the first order and, possibly, the second order, but it also selects the mathematical form of such coupling and estimates its parameters. Specifically, we propose that the coupling can be generally described by normalized bivariate rational functions. We demonstrate this approach for dry granulation, a unit operation commonly used to enhance the flowability of pharmaceutical powders by increasing granule size distribution, which, inevitably, negatively impacts tabletability by reducing the particle porosity and imparting plastic work. Granules of different densities and size distributions are made with a 10% w / w acetaminophen and 90% w / w microcrystalline cellulose formulation, and tablets with a wide range of relative densities are fabricated. This approach is based on product and process understanding, and, in turn, it is not only essential to enabling the end-to-end integration, control, and optimization of dry granulation and tableting processes, but it also offers insight into the granule properties that have a dominant effect on each of the four stages of powder compaction, namely die filling, compaction, unloading, and ejection.

Continuous manufacturing is gaining increasing interest in the pharmaceutical industry, also requiring real-time and non-destructive quality monitoring. Multiple studies have already addressed the possibility of surrogate in vitro dissolution testing, but the utilization has rarely been demonstrated in real-time. Therefore, in this work, the in-line applicability of an artificial intelligence-based dissolution surrogate model is developed the first time. NIR spectroscopy-based partial least squares regression and artificial neural networks were developed and tested in-line and at-line to assess the blend uniformity and dissolution of encapsulated acetylsalicylic acid (ASA) - microcrystalline cellulose (MCC) powder blends in a continuous blending process. The studied blend is related to a previously published end-to-end manufacturing line, where the varying size of the ASA crystals obtained from a continuous crystallization significantly affected the dissolution of the final product. The in-line monitoring was suitable for detecting the variations in the ASA content and dissolution caused by the feeding of ASA with different particle sizes, and the at-line predictions agreed well with the measured validation dissolution curves (f 2  = 80.5). The results were further validated using machine vision-based particle size analysis. Consequently, this work could contribute to the advancement of RTRT in continuous end-to-end processes., (Copyright © 2024 The Author(s). Published by Elsevier B.V. All rights reserved.)

This research shows the detailed comparison of Raman and near-infrared (NIR) spectroscopy as Process Analytical Technology tools for the real-time monitoring of a protein purification process. A comprehensive investigation of the application and model development of Raman and NIR spectroscopy was carried out for the real-time monitoring of a process-related impurity, imidazole, during the tangential flow filtration of Receptor-Binding Domain (RBD) of the SARS-CoV-2 Spike protein. The fast development of Raman and NIR spectroscopy-based calibration models was achieved using offline calibration data, resulting in low calibration and cross-validation errors. Raman model had an RMSEC of 1.53 mM, and an RMSECV of 1.78 mM, and the NIR model had an RMSEC of 1.87 mM and an RMSECV of 2.97 mM. Furthermore, Raman models had good robustness when applied in an inline measurement system, but on the contrary NIR spectroscopy was sensitive to the changes in the measurement environment. By utilizing the developed models, inline Raman and NIR spectroscopy were successfully applied for the real-time monitoring of a process-related impurity during the membrane filtration of a recombinant protein. The results enhance the importance of implementing real-time monitoring approaches for the broader field of diagnostic and therapeutic protein purification and underscore its potential to revolutionize the rapid development of biological products., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 The Author(s). Published by Elsevier B.V. All rights reserved.)

Recently, concerns have been raised about the safety of titanium dioxide (TiO 2 ), a commonly used component of pharmaceutical film coatings. The European Union has recently prohibited the application of this material in the food industry, and it is anticipated that the same will happen in the pharmaceutical industry. For this reason, pharmaceutical manufacturers have to consider the possible impact of removing TiO 2 from the film coating of tablets. In this paper, we present a case study of a commercially produced tablet where the film coating containing TiO 2 was replaced with a coating using calcium carbonate (CaCO 3 ) or with a transparent coating. The performance of the coatings was compared by measuring the moisture absorption rate and the dissolution profile of the tablets. In these regards, there were negligible differences between the coating types. The tablets contained a highly photosensitive drug, the ability of the coatings to protect the drug was evaluated through environmental stability and photostability measurements. The HPLC results showed that the inclusion of TiO 2 does not provide additional benefits, when humidity and thermal stress is applied, however its role was vital in protecting the drug from external light. There were several decomposition products which appeared in large quantities when TiO 2 was missing from the coating. These results imply that photosensitivity is an issue, replacing TiO 2 will be challenging, though its absence can be tolerated when the drug does not need to be protected from light., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 The Authors. Published by Elsevier B.V. All rights reserved.)

Surface powder sticking in pharmaceutical mixing vessels poses a risk to the uniformity and quality of drug formulations. This study explores methods for evaluating the amount of pharmaceutical powder mixtures adhering to the metallic surfaces. Binary powder blends consisting of amlodipine and microcrystalline cellulose (MCC) were used to investigate the effect of the mixing order on the adherence to the vessel wall. Elevated API concentrations were measured on the wall and within the dislodged material from the surface, regardless of the mixing order of the components. UV imaging was used to determine the particle size and the distribution of the API on the metallic surface. The results were compared to chemical maps obtained by Raman chemical imaging. The combination of UV and VIS imaging enabled the rapid acquisition of chemical maps, covering a substantially large area representative of the analysed sample. UV imaging was also applied in tablet inspection to detect tablets that fail to meet the content uniformity criteria. The results present powder adherence as a possible source of poor content uniformity, highlighting the need for 100% inspection of pharmaceutical products to ensure product quality and safety., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 The Authors. Published by Elsevier B.V. All rights reserved.)

Cell culture media are essential for large-scale recombinant protein production using mammalian cell cultures. The composition and quality of media significantly impact cell growth and product formation. Analyzing media poses challenges due to complex compositions and undisclosed exact compositions. Traditional methods like NMR and chromatography offer sensitivity but require time-consuming sample preparation and lack spatial information. Raman chemical mapping characterizes solids, but its use in cell culture media analysis is limited so far. We present a chemometric evaluation for Raman maps to qualify and quantify media components, evaluate powder homogeneity, and perform lot-to-lot comparisons. Three lots of a marketed cell culture media powder were measured with Raman mapping technique. Chemometrics techniques have outlined a strategy to extract information from complex data. First, a spectral library has been structured. In addition to the 23 spectra for presumed ingredients, we obtained another 9 pure components with Multivariate Curve Resolution-Alternating Least Squares (MCR-ALS). Then the Spectral Angle Mapper-Orthogonal Projection (SAM-OP) algorithm revealed whether references actually occur in the mapped media powders. Finally, a quantification was provided by Classical Least Squares (CLS) modelling. Quantities of 18 significant amino acids mostly correlated with the reference method. The proposed method can be generally applied even for such complicated samples. Leveraging Raman mapping and innovative chemometric methods enhance recombinant protein production by improving the understanding of the spatial distribution and composition of cell culture media in mammalian cell cultivations., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 The Author(s). Published by Elsevier B.V. All rights reserved.)

In this work dynamic models of the continuous crystallization, filtration, deliquoring, washing, and drying steps are introduced, which are developed in the open-source pharmaceutical modeling tool PharmaPy. These models enable the simulation and digital design of an integrated continuous two-stage crystallization and filtration-drying carousel system. The carousel offers an intensified process that can manufacture products with tailored properties through optimal design and control. Results show that improved crystallization design enhances overall process efficiency by improving critical material attributes of the crystal slurry for downstream filtration and drying operations. The digital design of the integrated process achieves enhanced productivity while satisfying multiple design and product quality constraints. Additionally, the impact of model uncertainty on the optimal operating conditions is investigated. The findings demonstrate the systematic process development potential of PharmaPy, providing improved process understanding, design space identification, and optimized robust operation.

In this work, the feasibility of implementing a process analytical technology (PAT) platform consisting of Near Infrared Spectroscopy (NIR) and particle size distribution (PSD) analysis was evaluated for the prediction of granule downstream processability. A Design of Experiments-based calibration set was prepared using a fluid bed melt granulation process by varying the binder content, granulation time, and granulation temperature. The granule samples were characterized using PAT tools and a compaction simulator in the 100-500 kg load range. Comparing the systematic variability in NIR and PSD data, their complementarity was demonstrated by identifying joint and unique sources of variation. These particularities of the data explained some differences in the performance of individual models. Regarding the fusion of data sources, the input data structure for partial least squares (PLS) based models did not significantly impact the predictive performance, as the root mean squared error of prediction (RMSEP) values were similar. Comparing PLS and artificial neural network (ANN) models, it was observed that the ANNs systematically provided superior model performance. For example, the best tensile strength, ejection stress, and detachment stress prediction with ANN resulted in an RMSEP of 0.119, 0.256, and 0.293 as opposed to the 0.180, 0.395, and 0.430 RMSEPs of the PLS models, respectively. Finally, the robustness of the developed models was assessed., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2023 The Authors. Published by Elsevier B.V. All rights reserved.)

This work presents a system, where deep learning was used on images captured with a digital camera to simultaneously determine the API concentration and the particle size distribution (PSD) of two components of a powder blend. The blend consisted of acetylsalicylic acid (ASA) and calcium hydrogen phosphate (CHP), and the predicted API concentration was found corresponding with the HPLC measurements. The PSDs determined with the method corresponded with those measured with laser diffraction particle size analysis. This novel method provides fast and simple measurements and could be suitable for detecting segregation in the powder. By examining the powders discharged from a batch blender, the API concentrations at the top and bottom of the container could be measured, yielding information about the adequacy of the blending and improving the quality control of the manufacturing process., Competing Interests: Declaration of competing interest The authors declare that they have no conflicts of interest., (Copyright © 2023 The Authors. Published by Elsevier B.V. All rights reserved.)

The use of digital tools in pharmaceutical manufacturing has gained traction over the past two decades. Whether supporting regulatory filings or attempting to modernize manufacturing processes to adopt new and quickly evolving Industry 4.0 standards, engineers entering the workforce must exhibit proficiency in modeling, simulation, optimization, data processing, and other digital analysis techniques. In this work, a course that addresses digital tools in pharmaceutical manufacturing for chemical engineers was adjusted to utilize a new tool, PharmaPy, instead of traditional chemical engineering simulation tools. Jupyter Notebook was utilized as an instructional and interactive environment to teach students to use PharmaPy, a new, open-source pharmaceutical manufacturing process simulator. Students were then surveyed to see if PharmaPy was able to meet the learning objectives of the course. During the semester, PharmaPy's model library was used to simulate both individual unit operations as well as multiunit pharmaceutical processes. Through the initial survey results, students indicated that: (i) through Jupyter Notebook, learning Python and PharmaPy was approachable from varied coding experience backgrounds and (ii) PharmaPy strengthened their understanding of pharmaceutical manufacturing through active pharmaceutical ingredient process design and development.

This paper presents a machine learning-based image analysis method to monitor the particle size distribution of fluidized granules. The key components of the direct imaging system are a rigid fiber-optic endoscope, a light source and a high-speed camera, which allow for real-time monitoring of the granules. The system was implemented into a custom-made 3D-printed device that could reproduce the particle movement characteristic in a fluidized-bed granulator. The suitability of the method was evaluated by determining the particle size distribution (PSD) of various granule mixtures within the 100-2000 μm size range. The convolutional neural network-based software was able to successfully detect the granules that were in focus despite the dense flow of the particles. The volumetric PSDs were compared with off-line reference measurements obtained by dynamic image analysis and laser diffraction. Similar trends were observed across the PSDs acquired with all three methods. The results of this study demonstrate the feasibility of performing real-time particle size analysis using machine vision as an in-line process analytical technology (PAT) tool., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2023. Published by Elsevier B.V.)

Research Background: Protein A affinity chromatography is a well-established method currently used in the pharmaceutical industry. However, the high costs usually associated with chromatographic separation of protein A and the difficulties in continuous operation make the investigation of alternative purification methods very important., Experimental Approach: In this study, extraction/back-extraction and precipitation/dissolution methods were developed and optimised. They were compared with protein A and cation exchange chromatography separations in terms of yield of monoclonal antibody (mAb) and amount of residual impurities, such as DNA and host cell proteins, and amount of mAb aggregates. For a comprehensive comparison of the different methods, experiments were carried out with the same cell-free fermentation broth containing adalimumab., Results and Conclusions: Protein A and cation exchange chromatographic separations resulted in high yield and purity of adalimumab. The precipitation-based process resulted in high yield but with lower purity. The extraction-based purification resulted in low yield and purity. Thus, the precipitation-based method proved to be more promising than the extraction-based method for direct purification of adalimumab from harvested cell culture fluid., Novelty and Scientific Contribution: Although alternative purification methods may offer the advantages of simplicity and low-cost operation, further significant improvements are required to compete with the performance of chromatographic separations of adalimumab from true fermentation broth., Competing Interests: CONFLICT OF INTEREST The authors have declared no conflict of interest., (Authors.)

In this work, the performance of two fast chemical imaging techniques, Raman and near-infrared (NIR) imaging is compared by utilizing these methods to predict the rate of drug release from sustained-release tablets. Sustained release is provided by adding hydroxypropyl methylcellulose (HPMC), as its concentration and particle size determine the dissolution rate of the drug. The chemical images were processed using classical least squares; afterwards, a convolutional neural network was applied to extract information regarding the particle size of HPMC. The chemical images were reduced to an average HPMC concentration and a predicted particle size value; these were used as inputs in an artificial neural network with a single hidden layer to predict the dissolution profile of the tablets. Both NIR and Raman imaging yielded accurate predictions. As the instrumentation of NIR imaging allows faster measurements than Raman imaging, this technique is a better candidate for implementing a real-time technique. The introduction of chemical imaging in the routine quality control of pharmaceutical products would profoundly change quality assurance in the pharmaceutical industry.

In the last decades, continuous manufacturing (CM) has become a research priority in the pharmaceutical industry. However, significantly fewer scientific researches address the investigation of integrated, continuous systems, a field that needs further exploration to facilitate the implementation of CM lines. This research outlines the development and optimization of an integrated, polyethylene glycol aided melt granulation-based powder-to-tablet line that operates fully continuously. The flowability and tabletability of a caffeine-containing powder mixture were improved through twin-screw melt granulation resulting in the production of tablets with improved breaking force (from 15 N to over 80 N), excellent friability, and immediate release dissolution. The system was also conveniently scaleable: the production speed could be increased from 0.5 kg/h to 8 kg/h with only minimal changes in the process parameters and using the same equipment. Thereby the frequent challenges of scale-up can be avoided, such as the need for new equipment and separate optimization., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2023 The Author(s). Published by Elsevier B.V. All rights reserved.)

This paper presents a case study on the first in-line application of AI-based image analysis for real-time pharmaceutical particle size measurement in a continuous milling process. An AI-based imaging system, which utilises a rigid endoscope, was tested for the real-time particle size measurement of solid NaCl powder used as a model API in the range of 200-1000 µm. After creating a dataset containing annotated images of NaCl particles, it was used to train an AI model for detecting particles and measuring their size. The developed system could analyse overlapping particles without dispersing air, thus broadening its applicability. The performance of the system was evaluated by measuring pre-sifted NaCl samples with the imaging tool, after which it was installed into a continuous mill for in-line particle size measurement of a milling process. By analysing ∼100 particles/s, the system was able to accurately measure the particle size of sifted NaCl samples and detect particle size reduction when applied in the milling process. The Dv50 values and PSDs measured real-time with the AI-based system correlated well with the reference laser diffraction measurements (<6% mean absolute difference over the measured samples). The AI-based imaging system shows great potential for in-line particle size analysis, which, in line with the latest pharmaceutical QC trends, can provide valuable information for process development and control., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2023 The Author(s). Published by Elsevier B.V. All rights reserved.)

In this work, the capabilities of a state-of-the-art fast Raman imaging apparatus are exploited to gain information about the concentration and particle size of hydroxypropyl methylcellulose (HPMC) in sustained release tablets. The extracted information is utilized to predict the in vitro dissolution profile of the tablets. For the first time, convolutional neural networks (CNNs) are used for the processing of the chemical images of HPMC distribution and to directly predict the dissolution profile based on the image. This new method is compared to wavelet analysis, which gives a quantification of the texture of HPMC distribution, carrying information regarding both concentration and particle size. A total of 112 training and 32 validation tablets were used, when a CNN was used to characterize the particle size of HPMC, the dissolution profile of the validation tablets was predicted with an average f 2 similarity value of 62.95. Direct prediction based on the image had an f 2 value of 54.2, this demonstrates that the CNN is capable of recognizing the patterns in the data on its own. The presented methods can facilitate a better understanding of the manufacturing processes, as detailed information becomes available with fast measurements., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2023 The Author(s). Published by Elsevier B.V. All rights reserved.)

Current technologies to measure granule flowability involve at-line methods that can take hours to perform. This is problematic for a continuous dry granulation tableting line, where the quality assurance and control of the final tablet products depend on real-time monitoring and control of powder flowability. Hence, a real-time alternative is needed for measuring the flowability of the granular products coming out of the roller compactor, which is the unit operation immediately preceding the tablet press. Since particle analyzers have the potential to take inline measurements of the size and shape of granules, they can potentially serve as real-time flowability sensors, given that the size and shape measurements can be used to reliably predict flowability measurements. This paper reports on the use of Partial Least Squares (PLS) regression to utilize distributions of size and shape measurements in predicting the output of three different types of flowability measurements: rotary drum flow, orifice flow, and tapped density analysis. The prediction performance of PLS had a coefficient of determination ranging from 0.80 to 0.97, which is the best reported performance in the literature. This is attributed to the ability of PLS to handle high collinearity in the datasets and the inclusion of multiple shape characteristics-eccentricity, form factor, and elliptical form factor-into the model. The latter calls for a change in industry perspective, which normally dismisses the importance of shape in favor of size; and the former suggests the use of PLS as a better way to reduce the dimensionality of distribution datasets, instead of the widely used practice of pre-selecting distribution percentiles., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2023 American Pharmacists Association. Published by Elsevier Inc. All rights reserved.)

The pharmaceutical manufacturing sector needs to rapidly evolve to absorb the next wave of disruptive industrial innovations - Industry 4.0. This involves incorporating technologies like artificial intelligence, smart factories and 3D printing to automate, miniaturize and personalize the production processes. The goal of this study is to build a formulation and process design (FPD) framework for a pharmaceutical 3D printing technique called drop-on-demand (DoD) printing. FPD can automate the determination of formulation properties and printing conditions (input conditions) for DoD operation that can guarantee production of drug products with desired functional attributes. This study proposes to build the FPD framework in two parts: the first part involves building a machine learning model to simulate the forward problem - predicting DoD operation based on input conditions and the second part seeks to solve and experimentally validate the inverse problem - predicting input conditions that can yield desired DoD operation.

Continuous crystallization in the presence of polymer additives is a promising method to omit some drug formulation steps by improving the technological and also pharmacological properties of crystalline active ingredients. Accordingly, this study focuses on developing an additive-assisted continuous crystallization process using polyvinylpyrrolidone in a connected ultrasonicated plug flow crystallizer and an overflow mixed suspension mixed product removal (MSMPR) crystallizer system. We aimed to improve the flowability characteristics of small, columnar primary plug flow crystallizer-produced acetylsalicylic acid crystals as a model drug by promoting their agglomeration in MSMPR crystallizer with polyvinylpyrrolidone. The impact of the cooling antisolvent crystallization process parameters (temperature, polymer amount, total flow rate) on product quality and quantity was investigated. Finally, a spatially segmented antisolvent dosing method was also evaluated. The developed technology enabled the manufacture of purified, constant quality products in a short startup period, even with an 85% yield. We found that a higher polymer amount (7.5-14%) could facilitate agglomeration resulting in "good" flowability without altering the favorable dissolution characteristics of the primary particles., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2023 The Author(s). Published by Elsevier B.V. All rights reserved.)

The aim of this study was to develop antisense oligonucleotide tablet formulations using high-speed electrospinning. Hydroxypropyl-beta-cyclodextrin (HPβCD) was used as a stabilizer and as an electrospinning matrix. In order to optimize the morphology of the fibers, electrospinning of various formulations was carried out using water, methanol/water (1:1), and methanol as solvents. The results showed that using methanol could be advantageous due to the lower viscosity threshold for fiber formation enabling higher potential drug loadings by using less excipient. To increase the productivity of electrospinning, high-speed electrospinning technology was utilized and HPβCD fibers containing 9.1% antisense oligonucleotide were prepared at a rate of ~330 g/h. Furthermore, to increase the drug content of the fibers, a formulation with a 50% drug loading was developed. The fibers had excellent grindability but poor flowability. The ground fibrous powder was mixed with excipients to improve its flowability, which enabled the automatic tableting of the mixture by direct compression. The fibrous HPβCD-antisense oligonucleotide formulations showed no sign of physical or chemical degradation over the 1-year stability study, which also shows the suitability of the HPβCD matrix for the formulation of biopharmaceuticals. The obtained results demonstrate possible solutions for the challenges of electrospinning such as scale-up and downstream processing of the fibers.

As the pharmaceutical industry increasingly adopts the Pharma 4.0. concept, there is a growing need to effectively predict the product quality based on manufacturing or in-process data. Although artificial neural networks (ANNs) have emerged as powerful tools in data-rich environments, their implementation in pharmaceutical manufacturing is hindered by their black-box nature. In this work, ANNs were developed and interpreted to demonstrate their applicability to increase process understanding by retrospective analysis of developmental or manufacturing data. The in vitro dissolution and hardness of extended-release, directly compressed tablets were predicted from manufacturing and spectroscopic data of pilot-scale development. The ANNs using material attributes and operational parameters provided better results than using NIR or Raman spectra as predictors. ANNs were interpreted by sensitivity analysis, helping to identify the root cause of the batch-to-batch variability, e.g., the variability in particle size, grade, or substitution of the hydroxypropyl methylcellulose excipient. An ANN-based control strategy was also successfully utilized to mitigate the batch-to-batch variability by flexibly operating the tableting process. The presented methodology can be adapted to arbitrary data-rich manufacturing steps from active substance synthesis to formulation to predict the quality from manufacturing or development data and gain process understanding and consistent product quality., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2023 The Authors. Published by Elsevier B.V. All rights reserved.)

This study details the development of simulation-aided design, development, and successful operation of a continuous liquid-liquid extraction platform made with 1.5 mm tubing for the extraction of 2-chloroethyl isocyanate, an important reagent in the synthesis of cancer drugs. Preliminary solvent screening was carried out with partition coefficient calculations to determine solvents of interest. Next, batch and flow extraction experiments of 2-chloroethyl isocyanate in 2-methyl tetrahydrofuran and water were conducted to estimate extraction parameters. Following parameter estimation, experimental and model values for K L a were determined in the range of 1.13×10 -3 to 36.0×10 -3 s -1 . Simulations of the extraction of 2-chloroethyl isocyanate were found to agree with experimental data resulting in a maximum efficiency of 77% and percent extraction of 69% for the continuous platform. Finally, model selection and discrimination was implemented for design space generation with experimental and model determined K L a values to guide lab-scale operation., Competing Interests: The authors declare no competing financial interest.

Continuous manufacturing and closed-loop quality control are emerging technologies that are pivotal for next-generation pharmaceutical modernization. We develop a process control framework for a continuous carousel for integrated filtration-drying of crystallization slurries. The proposed control system includes model-based monitoring and control routines, such as state estimation and real-time optimization, implemented in a hierarchical, three-layer quality-by-control (QbC) framework. We implement the control system in ContCarSim, a publicly available carousel simulator. We benchmark the proposed control system against simpler methods, comprising a reduced subset of the elements of the overall control system, and against open-loop operation (the current standard in pharmaceutical manufacturing). The proposed control system demonstrates superior performance in terms of higher consistency in product quality and increased productivity, proving the benefits of closed-loop control and of model-based techniques in pharmaceutical manufacturing. This study represents a step forward toward end-to-end continuous pharmaceutical processing, and in the evolution of quality-by-design toward quality-by-control.

Increased interest in the pharmaceutical industry to transition from batch to continuouos manufacturing motivates the use of digital frameworks that allow systematic comparison of candidate process configurations. This paper evaluates the technical and economic feasibility of different end-to-end optimal process configurations, viz . batch, hybrid and continuous, for small-scale manufacturing of an active pharmaceutical ingredient. Production campaigns were analyzed for those configurations containing continuous equipment, where significant start-up effects are expected given the relatively short campaign times considered. Hybrid operating mode was found to be the most attractive process configuration at intermediate and large annual production targets, which stems from combining continuous reactors and semi-batch vaporization equipment. Continuous operation was found to be more costly, due to long stabilization times of continuous crystallization, and thermodynamic limitations of flash vaporization. Our work reveals the benefits of systematic digital evaluation of process configurations that operate under feasible conditions and compliant product quality attributes.

The aim of this research was to investigate three thermoanalytical techniques from the glass transition temperature (T g ) determination point of view. In addition, the examination of the correlation between the measured T g values and the stability of the amorphous solid dispersions (ASDs) was also an important part of the work. The results showed that a similar tendency of the T g can be observed in the case of the applied methods. However, T g values measured by thermally stimulated depolarization currents showed higher deviation from the theoretical calculations than the values measured by modulated differential scanning calorimetry, referring better to the drug-polymer interactions. Indeed, the investigations after the stress stability tests revealed that micro-thermal analysis can indicate the most sensitive changes in the T g values, better indicating the instability of the samples. In addition to confirming that the active pharmaceutical ingredient content is a crucial factor in the stability of ASDs containing naproxen and poly(vinylpyrrolidone-co-vinyl acetate), it is worthwhile applying orthogonal techniques to better understand the behavior of ASDs. The development of stable ASDs can be facilitated via mapping the molecular mobilities with suitable thermoanalytical methods.

In this paper, continuous crystallization of Atorvastatin calcium (ASC) using a continuous oscillatory baffled crystallizer (COBC) has been investigated. Like most API manufacturing, ASC is manufactured batchwise and the pure API is recovered via batch combined cooling and antisolvent crystallization (CCAC) process, which has the challenges of low productivity, wide crystal size distribution (CSD) and sometimes polymorphic form contamination. To overcome the limitations of the batch crystallization, continuous crystallization of ASC was studied in a NiTech (United Kingdom) DN15 COBC, manufactured by Alconbury Weston Ltd. (AWL, United Kingdom), with the aim to improve productivity and CSD of the desired polymorph. The COBC has the advantage of high heat transfer rates and improved mixing that significantly reduces the crystallization time. It also has the advantage of spatial temperature distribution and multiple addition ports to control supersaturation and hence the crystallization process. This work uses an array of process analytical technology (PAT) tools to assess key process parameters that affect the polymorphic outcome and CSD. Two parameters were found to have significant impact on the polymorph, they are ratio of solvent to antisolvent at the point of mixing of the two streams and presence of seeds. The splitting of antisolvent into two addition ports in the COBC was found to give the desired form. The CCAC of ASC in COBC was found to be -30-fold more productive than the batch CCAC process. The cycle time for generating 100 g of desired polymorphic form of ASC also significantly reduced from 22 h in batch process to 12 min in the COBC. The crystals obtained using a CCAC process in a COBC had a narrower CSD compared to that from a batch crystallization process., Competing Interests: Declaration of Competing Interest The authors declare the following financial interests/personal relationships which may be considered as potential competing interests: Shivani Kshirsagar reports financial support was provided by Dr Reddy’s Laboratories Ltd. Zoltan K. Nagy reports financial support was provided by Dr Reddy’s Laboratories Ltd. Naga Lakshmi Ramana Susarla reports financial support was provided by Dr Reddy’s Laboratories Ltd. Srividya Ramakrishnan reports financial support was provided by Dr Reddy’s Laboratories Ltd. Zoltan K. Nagy reports financial support was provided by US Food and Drug Administration. Shivani Kshirsagar reports financial support was provided by US Food and Drug Administration., (Copyright © 2022 Elsevier B.V. All rights reserved.)

Glucose is widely used in both the food and pharmaceutical industry. However, the application of industrially crystallized glucose in solid dosage forms is challenged by its poor flowability and tabletability. To improve these characteristics continuous twin-screw granulation was tested, which has the potential to be integrated into the continuous production of solid glucose from corn starch. A completely continuous manufacturing line (including drying and milling) was developed and the different production steps were examined and synchronized. Our line was supplemented with an in-line applicable near-infrared spectroscopic probe to monitor the moisture content of the milled granules in real-time. The flowability and tabletability of the powder improved significantly, and tablets with acceptable breaking force (greater than 100 N) could be prepared from the granules. The developed continuous line can be easily installed into the industrial solid glucose production process resulting in pure glucose granules with adequate flow properties and tabletability in a simple, continuous and efficient way., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2022 The Author(s). Published by Elsevier B.V. All rights reserved.)

In this study, a concentration predicting soft sensor was achieved based on the Residence Time Distribution (RTD) of an integrated, three-step pharmaceutical formulation line. The RTD was investigated with color-based tracer experiments using image analysis. Twin-screw wet granulation (TSWG) was directly coupled with a horizontal fluid bed dryer and an oscillating mill. Based on integrated measurement, we proved that it is also possible to couple the unit operations in silico. Three surrogate tracers were produced with a coloring agent to investigate the separated unit operations and the solid and liquid inputs of the TSWG. The soft sensor's prediction was compared to validating experiments of a 0.05 mg/g (15% of the nominal) concentration change with High-Performance Liquid Chromatography (HPLC) reference measurements of the active ingredient proving the adequacy of the soft sensor (RMSE < 4%)., (Copyright © 2022 The Authors. Published by Elsevier B.V. All rights reserved.)

The pharmaceutical industry has traditionally relied on mass manufacturing to make its products. This has created multiple problems in the drug supply network, including long production times, inflexible and sluggish manufacturing and lack of personalized dosing. The industry is gradually adapting to these challenges and is developing novel technologies to address them. Continuous manufacturing and 3D printing are two promising techniques that can revolutionize pharmaceutical manufacturing. However, most research studies into these methods tend to treat them separately. This study seeks to develop a new processing route to continuously integrate a 3D printing platform (Drop-on-Demand, DoD, printing) with crystallization that is generally the final step of the active ingredient manufacturing. Accomplishing this integration would enable harnessing the benefits of each method- personalized dosing of 3D printing and flexibility and speed of continuous manufacturing. A novel unit operation, three-phase settling (TPS), is developed to integrate DoD with the upstream crystallizer. To ensure on-spec production of each printed dosage, two process analytical technology tools are incorporated in the printer to monitor drug loading in manufactured drug products in real time. Experimental demonstration of this system is carried out via two case studies: the first study uses an active ingredient celecoxib to test the standalone operation of TPS; the second study demonstrates the operation of the integrated system (crystallizer - TPS - DoD) to continuously make drug products for the active ingredient- lomustine. A dissolution test is also performed on the manufactured and commercial lomustine drug products to compare their dissolution behavior., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2022 American Pharmacists Association. Published by Elsevier Inc. All rights reserved.)

This paper presents a system, where images acquired with a digital camera are coupled with image analysis and deep learning to identify and categorize film coating defects and to measure the film coating thickness of tablets. There were 5 different classes of defective tablets, and the YOLOv5 algorithm was utilized to recognize defects, the accuracy of the classification was 98.2%. In order to characterize coating thickness, the diameter of the tablets in pixels was measured, which was used to measure the coating thickness of the tablets. The proposed system can be easily scaled up to match the production capability of continuous film coaters. With the developed technique, the complete screening of the produced tablets can be achieved in real-time resulting in the improvement of quality control., (Copyright © 2022. Published by Elsevier B.V.)

This article introduces ContCarSim, a benchmark simulator for the development and testing of quality-by-design and quality-by-control strategies in the continuous intensified filtration-drying of paracetamol/ethanol slurries on a novel carousel technology, developed by Alconbury Weston Ltd (United Kingdom). The simulator is based on a detailed mechanistic mathematical modeling framework, and has been validated with filtration and drying experiments on a prototype equipment. A set of design- and control-relevant challenges to be addressed through ContCarSim are proposed. A case study is developed, to demonstrate the features of the simulator and its suitability to design, test and optimize the unit operation. ContCarSim is expected to promote the transition to end-to-end continuous pharmaceutical manufacturing and the adoption of closed-loop quality control by the pharmaceutical industry. The simulator can also be employed as a benchmark for data analytics and process monitoring studies.

Industry 4.0 has started to transform the manufacturing industries by embracing digitalization, automation, and big data, aiming for interconnected systems, autonomous decisions, and smart factories. Machine learning techniques, such as artificial neural networks (ANN), have emerged as potent tools to address the related computational tasks. These advancements have also reached the pharmaceutical industry, where the Process Analytical Technology (PAT) initiative has already paved the way for the real-time analysis of the processes and the science- and risk-based flexible production. This paper aims to assess the potential of ANNs within the PAT concept to aid the modernization of pharmaceutical manufacturing. The current state of ANNs is systematically reviewed for the most common manufacturing steps of solid pharmaceutical products, and possible research gaps and future directions are identified. In this way, this review could aid the further development of machine learning techniques for pharmaceutical production and eventually contribute to the implementation of intelligent manufacturing lines with automated quality assurance., (© 2022. The Author(s).)

The potential of machine vision systems has not currently been exploited for pharmaceutical applications, although expected to provide revolutionary solutions for in-process and final product testing. The presented paper aimed to analyze the particle size of meloxicam, a yellow model active pharmaceutical ingredient, in intact tablets by a digital UV/VIS imaging-based machine vision system. Two image processing algorithms were developed and coupled with pattern recognition neural networks for UV and VIS images for particle size-based classification of the prepared tablets. The developed method can identify tablets containing finer or larger particles than the target with more than 97% accuracy. Two algorithms were developed for UV and VIS images for particle size analysis of the prepared tablets. According to the applied statistical tests, the obtained particle size distributions were similar to the results of the laser diffraction-based reference method. Digital UV/VIS imaging combined with multivariate data analysis can provide a new non-destructive, rapid, in-line tool for particle size analysis in tablets., (Copyright © 2022 The Author(s). Published by Elsevier B.V. All rights reserved.)

An innovative, Raman spectroscopy-based monitoring and control system is introduced in this paper for designing dynamic feeding strategies that allow the maintenance of key cellular nutrients at an ideal level in Chinese hamster ovary cell culture. The Partial Least Squares calibration models built for glucose, lactate and 16 (out of 20) individual amino acids had very good predictive power with low root mean square errors values and high square correlation coefficients. The developed models used for real-time measurement of nutrient and by-product concentrations allowed us to gain better insight into the metabolic behavior and nutritional consumption of cells. To establish a more beneficial nutritional environment for the cells, two types of dynamic feeding strategies were used to control the delivery of two-part multi-component feed media according to the prediction of Raman models (glucose or arginine). As a result, instead of high fluctuations, the nutrients (glucose together with amino acids) were maintained at the desired level providing a more balanced environment for the cells. Moreover, the use of amino acid-based feeding control enabled to prevent the excessive nutrient replenishment and was economically beneficial by significantly reducing the amount of supplied feed medium compared to the glucose-based dynamic fed culture., (© 2022 The Authors. Biotechnology Journal published by Wiley-VCH GmbH.)

In this paper, the applicability of Raman chemical imaging for the non-destructive prediction of the in vitro dissolution profile of sustained-release tablets is demonstrated for the first time. Raman chemical maps contain a plethora of information about the spatial distribution and the particle size of the components, compression force and even polymorphism. With proper data analysis techniques, this can be converted into simple numerical information which can be used as input in a machine learning model. In our work, sustained-release tablets using hydroxypropyl methylcellulose (HPMC) as matrix polymer are prepared, the concentration and particle size of this component varied between samples. Chemical maps of HPMC are converted into histograms with two different methods, an approach based on discretizing concentration values and a wavelet analysis technique. These histograms are then subjected to Principal Component Analysis, the score value of the first two principal components was found to represent HPMC content and particle size. These values are used as input in Artificial Neural Networks which are trained to predict the dissolution profile of the tablets. As a result, accurate predictions were obtained for the test tablets (the average f 2 similarity value is higher than 59 with both methods). The presented methodology lays the foundations of the analysis of far more extensive datasets acquired with the emerging fast Raman imaging technology., (Copyright © 2022 The Authors. Published by Elsevier B.V. All rights reserved.)

The present paper reports the powder filling of milled electrospun materials in vials, which contained voriconazole and sulfobutylether-β-cyclodextrin. High-speed electrospinning was used for the production of the fibrous sample, which was divided into 6 parts. Each portion was milled using different milling methods and sizes of sieves to investigate whether the milling influences the powder and filling properties. Bulk and tapped density tests, laser diffraction and angle of repose measurements were applied to characterize the milled powders, while a vibratory feeder was used for the feeding experiments. The correlation between the material property descriptors and the feeding responses was investigated by multivariate data analysis. Based on the results, three samples were chosen for the vial filling, which was accomplished with 3400 mg electrospun material containing 200 mg voriconazole, representative of the commercial product. The feed rate was set to fit the 240 g/h production rate of the electrospinning and the relative standard deviation of three repeated vial filling was determined to see the accuracy of the process. This research shows that by applying a suitable milling method it is possible to process electrospun fibers to a powder, which can be filled into vials and used as reconstitution dosage forms., (Copyright © 2021 The Authors. Published by Elsevier B.V. All rights reserved.)

The present paper serves as a demonstration how an in-line PAT tool can be used for rapid and efficient process development in a fully continuous powder to granule line consisting of an interconnected twin-screw wet granulator, vibrational fluid bed dryer, and a regranulating mill. A new method was investigated for the periodic in-line particle size measurement of high mass flow materials to obtain real-time particle size data of the regranulated product. The system utilises a vibratory feeder with periodically altered feeding intensity in order to temporarily reduce the mass flow of the material passing in front of the camera. This results in the drastic reduction of particle overlapping in the images, making image analysis a viable tool for the in-line particle size measurement of high mass-flow materials. To evaluate the performance of the imaging system, the effect of several milling settings and the liquid-to-solid ratio was investigated on the product's particle size in the span of a few hours. The particle sizes measured with the in-line system were in accordance with the expected trends as well as with the results of the off-line reference particle size measurements. Based on the results, the in-line imaging system can serve as a PAT tool to obtain valuable real-time information for rapid process development or quality assurance., (Copyright © 2021 The Authors. Published by Elsevier B.V. All rights reserved.)

Curcuminoids (CUs) of antitumor and various other potential biological activities have extremely low water solubility therefore special formulation was elaborated. New fast dissolving reconstitution dosage forms of four CUs were prepared as fibrous form of 2-hydroxypropyl-β-cyclodextin (HP-β-CD). In the electrospinning process HP-β-CD could act both as solubilizer and fiber-forming agent. The solubilization efficiency of the CU-HP-β-CD systems was determined with phase-solubility measurements. The electrospun CUs were amorphous and uniformly distributed in the fibers according to XRD analysis and Raman mappings. The fibrous final products had fast (<5 min) and complete dissolution. In typical iv. infusion reconstitution volume (20 mL) fibers containing 40-80 mg of CU could be dissolved, which is similar to the currently proposed dose (<120 mg/m 2 ). The in vitro cytostatic effect data showed that the antitumor activity of the CU-HP-β-CD complexes was similar or better compared to the free APIs., (Copyright © 2021 The Authors. Published by Elsevier B.V. All rights reserved.)

The work aimed to develop the Absorption Driven Drug Formulation (ADDF) concept, which is a new approach in formulation development to ensure that the drug product meets the expected absorption rate. The concept is built on the solubility-permeability interplay and the rate of supersaturation as the driving force of absorption. This paper presents the first case study using the ADDF concept where not only dissolution and solubility but also permeation of the drug is considered in every step of the formulation development. For that reason, parallel artificial membrane permeability assay (PAMPA) was used for excipient selection, small volume dissolution-permeation apparatus was used for testing amorphous solid dispersions (ASDs), and large volume dissolution-permeation tests were carried out to characterize the final dosage forms. The API-excipient interaction studies on PAMPA indicated differences when different fillers or surfactants were studied. These differences were then confirmed with small volume dissolution-permeation assays where the addition of Tween 80 to the ASDs decreased the flux dramatically. Also, the early indication of sorbitol's advantage over mannitol by PAMPA has been confirmed in the investigation of the final dosage forms by large-scale dissolution-permeation tests. This difference between the fillers was observed in vivo as well. The presented case study demonstrated that the ADDF concept opens a new perspective in generic formulation development using fast and cost-effective flux-based screening methods in order to meet the bioequivalence criteria. Graphical Abstract., (© 2022. The Author(s).)