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Fermentation monitoring is a powerful tool for bioprocess development and optimization. On‐line metabolomics is a technology that is starting to gain attention as a bioprocess monitoring tool, allowing the direct measurement of many compounds in the fermentation broth at a very high time resolution. In this work, targeted on‐line metabolomics was used to monitor 40 metabolites of interest during three Escherichia coli succinate production fermentation experiments every 5 min with a triple quadrupole mass spectrometer. This allowed capturing high‐time resolution biological data that can provide critical information for process optimization. For nine of these metabolites, simple univariate regression models were used to model compound concentration from their on‐line mass spectrometry peak area. These on‐line metabolomics univariate models performed comparably to vibrational spectroscopy multivariate partial least squares regressions models reported in the literature, which typically are much more complex and time consuming to build. In conclusion, this work shows how on‐line metabolomics can be used to directly monitor many bioprocess compounds of interest and obtain rich biological and bioprocess data. [ABSTRACT FROM AUTHOR]

Pharmaceutical 3D printing (3DP) is one of the emerging enabling technologies of personalised medicines as it affords the ability to fabricate highly versatile dosage forms. In the past 2 years, national medicines regulatory authorities have held consultations with external stakeholders to adapt regulatory frameworks to embrace point-of-care manufacturing. The proposed concept of decentralized manufacturing (DM) involves the provision of feedstock intermediates (pharma-inks) prepared by pharmaceutical companies to DM sites for manufacturing into the final medicine. In this study, we examine the feasibility of this model, with respect to both manufacturing and quality control. Efavirenz-loaded granulates (0–35%w/w) were produced by a manufacturing partner and shipped to a 3DP site in a different country. Direct powder extrusion (DPE) 3DP was subsequently used to prepare printlets (3D printed tablets), with mass ranging 266–371 mg. All printlets released more than 80% drug load within the first 60 min of the in vitro drug release test. An in-line near-infrared spectroscopy system was used as a process analytical technology (PAT) to quantify the printlets' drug load. Calibration models were developed using partial least squares regression, which showed excellent linearity (R2 = 0.9833) and accuracy (RMSE = 1.0662). Overall, this work is the first to report the use of an in-line NIR system to perform real-time analysis of printlets prepared using pharma-inks produced by a pharmaceutical company. By demonstrating the feasibility of the proposed distribution model through this proof-of-concept study, this work paves the way for investigation of further PAT tools for quality control in 3DP point-of-care manufacturing.

Volatile organic compound (VOC) emissions were measured from Chinese Hamster Ovary (CHO) cell and T cell bioreactor gas exhaust lines with the goal of non-invasively metabolically profiling the expansion process. Measurements of cellular 'breath' were made directly from the gas exhaust lines using polydimethylsiloxane (PDMS)-coated magnetic stir bars, which underwent subsequent thermal desorption-gas chromatography-mass spectrometry (TD-GC-MS) analysis. Baseline VOC profiles were observed from bioreactors filled with only liquid media. After inoculation, unique VOC profiles correlated to cell expansion over the course of 8 d. Partial least squares (PLS) regression models were built to predict cell culture density based on VOC profiles of CHO and T cells (R 2 = 0.671 and R 2 = 0.769, respectively, based on a validation data set). T cell runs resulted in 47 compounds relevant to expansion while CHO cell runs resulted in 45 compounds; the 20 most relevant compounds of each cell type were putatively identified. On the final experimental days, sorbent-covered stir bars were placed directly into cell-inoculated media and into media controls. Liquid-based measurements from spent media containing cells could be distinguished from media-only controls, indicating soluble VOCs excreted by the cells during expansion. A PLS-discriminate analysis (PLS-DA) was performed, and 96 compounds differed between T cell-inoculated media and media controls with 72 compounds for CHO cells; the 20 most relevant compounds of each cell line were putatively identified. This work demonstrates that the volatilome of cell cultures can be exploited by chemical detectors in bioreactor gas and liquid waste lines to non-invasively monitor cellular health and could possibly be used to optimize cell expansion conditions 'on-the-fly' with appropriate control loop systems. Although the basis for statistical models included compounds without certain identification, this work provides a foundation for future research of bioreactor emissions. Future studies must move towards identifying relevant compounds for understanding of underlying biochemistry.

Compared with traditional membrane separation methods such as distillation and chromatography, nanofiltration (NF) affords decreased waste generation and energy consumption. Despite the multiple advantages of NF and materials available for NF membranes, the industrial applicability of this process requires improvement. To address these challenges, we propose four important pillars for the future of membrane materials and process development. These four pillars are digitalization, structure–property analysis, miniaturization, and automation. We fill gaps in the development of NF membranes and processes by fostering the most promising contemporary technologies, e.g., the integration of process analytical technologies and the development of a parallel artificial nanofiltration permeability assay (PANPA) or large online databases. Moreover, we propose the extensive use of density functional theory-aided structure–property relationship methods to understand solute transport process at a molecular level. Realizing an inverse design would allow researchers and industrial scientists to develop custom membranes for specific applications using optimized properties.

Due to high perishability and poor distribution management, strawberry is one of the most frequently discarded fruits. The aim of this study was to develop a non-destructive system for accurate estimation of shelf-life of strawberries using hyperspectral imaging technology. Harvested strawberries were stored for 9 day at five different temperatures. Shelf-life was calculated based on subjective visual evaluation of appearance attributes (colour, shrivelling, and decay) using a rating scale. Hyperspectral images of strawberries were obtained during cold storage, using a novel handheld push broom line-scanning hyperspectral camera. The model developed by partial least square regression (PLSR) with selected spectra was used to predict appearance scores of strawberries with a coefficient of determination of prediction (R2p) of 0.97 and root mean square error of prediction (RMSEp) of 0.17. The appearance scores from the PLSR model were used to develop a model based on first-order kinetics and Arrhenius equations to predict the remaining shelf-life of the strawberries. The models predicted remaining shelf-life with R2p of 0.86 and RMSEp of 1.4 days. Prediction models were also developed for other quality attributes and biochemical properties (weight loss, ascorbic acid, and soluble solids). The results from this study support the development a non-destructive system for the accurate estimation of shelf-life of strawberries. A system that could potentially offer objective quality assessment as product moves through the supply chain, aiding decision making and facilitating proactive actions to be taken with the aim of minimising loss/waste; benefiting food supply chain stakeholders across the globe and supporting the drive for zero waste. • Establishing a non-destructive system for shelf-life estimation of strawberries. • Remaining shelf-life of strawberries determined along the supply chain. • Novel system supports for reducing waste along strawberry supply chain. [ABSTRACT FROM AUTHOR]

Automated chemical synthesizers have become more common in recent years but struggle to apply rigid procedures to broad substrate scopes. We have developed an adaptive auto‐synthesizer that uses online HPLC and FTIR measurements to adapt to the changing reactivities of different substrates, allowing precise control of reaction conditions. To do so, we designed a flexibly‐timed procedure consisting of specific actions performed by our platform when specific reaction‐monitoring checkpoints are met. Online HPLC allowed our system to autonomously separate, label and quantify most reaction components, with orthogonal FTIR enabling non‐UV active species to be additionally tracked. We tested our platform with CDI‐mediated multistep amidation reactions using a variety of different acid and amine substrates. To demonstrate the high reproducibility and control afforded by our system, we determined the relative rates of both acid activation and subsequent amidation, providing insight into substrate reactivities and the reaction mechanism. [ABSTRACT FROM AUTHOR]

The ability to take non-invasive Raman measurements presents a unique opportunity to use one Raman probe across multiple vessels in parallel, reducing costs but making measurements infrequent. Under these conditions, infrequent and irregular feedback signals can result in poor closed-loop control performance. This study addressed the issue of infrequent and irregular Raman measurements using a linear dynamic model developed from interpolated data to predict more frequent measurements of the controlled variable. The simulated monoclonal antibody production was sampled hourly with white noise added to the simulated glucose concentration to replicate real Raman measurements. The hourly samples were interpolated into 15 min intervals and a linear dynamic model was developed to predict the glucose concentration at 15 min intervals. These predicted values were then used in a feedback control loop by using model predictive control or a conventional proportional and integral controller to control the glucose concentration at 15 min sampling intervals. For setpoint tracking, the model predictive control reduced the integral of absolute errors to 14,600 from 15,900 (with a 1 h sampling time) or 8.2% reduction. With adaptive model predictive control, the integral of absolute errors was reduced from 14,500 (1 h sampling time) to 14,200 for setpoint tracking and from 13,500 (1 h sampling time) to 13,300 for disturbance rejection. A final comparison demonstrated that the proposed method can also cope with random variations in the sampling time. [ABSTRACT FROM AUTHOR]

Purpose: In continuous manufacturing of pharmaceuticals, dry granulation is of interest because of its large throughput capacity and energy efficiency. In order to manufacture solid oral dosage forms continuously, valid control strategies for critical quality attributes should be established. To this date, there are no published control strategies for granule size distribution in continuous dry granulation. Methods: In-line laser diffraction was used to determine the size of granules in a continuous roll compaction/dry granulation line (QbCon® dry). Different process parameters were evaluated regarding their influences on granule size. The identified critical process parameters were then incorporated into control strategies. The uncontrolled and the controlled processes were compared based on the resulting granule size. In both processes, a process parameter was changed to induce a shift in median particle size and the controller had to counteract this shift. Results: In principle, all process parameters that affect the median particle size could also be used to control the particle size in a dry granulation process. The sieve impeller speed was found to be well suited to control the median particle size as it reacts fast and can be controlled independently of the throughput or material. Conclusion: The median particle size in continuous roll compaction can be controlled by adjusting process parameters depending on real-time granule size measurements. The method has to be validated and explored further to identify critical requirements to the material and environmental conditions. [ABSTRACT FROM AUTHOR]

Machine learning algorithms, and artificial intelligence in general, have a wide range of applications in the field of pharmaceutical technology. Starting from the formulation development, through a great potential for integration within the Quality by design framework, these data science tools provide a better understanding of the pharmaceutical formulations and respective processing. Machine learning algorithms can be especially helpful with the analysis of the large volume of data generated by the Process analytical technologies. This paper provides a brief explanation of the artificial neural networks, as one of the most frequently used machine learning algorithms. The process of the network training and testing is described and accompanied with illustrative examples of machine learning tools applied in the context of pharmaceutical formulation development and related technologies, as well as an overview of the future trends. Recently published studies on more sophisticated methods, such as deep neural networks and light gradient boosting machine algorithm, have been described. The interested reader is also referred to several official documents (guidelines) that pave the way for a more structured representation of the machine learning models in their prospective submissions to the regulatory bodies.

[Display omitted] Wireless sensor networks have become prolific in a wide range of industrial processes and offer several key advantages over their wired counterparts in terms of positioning flexibility, modularity, interconnectivity, and data routing. We demonstrate their utility in pharmaceutical lyophilization by developing a series of wireless devices to measure spatial variations in gas pressure and temperature during primary drying. The influence of shelf temperature, chamber pressure, excipient concentration, and dryer configuration are explored for various representative cycles using a laboratory-scale pharmaceutical lyophilizer. Pressure and temperature variations across the shelf for these cases are shown to vary up to 1.2 Pa and 10 °C, respectively. Experimental measurements are supported by computational fluid dynamics simulations to reveal the mechanisms driving the vapor flow. The measurements and simulation data are then combined to estimate the shelf-wise sublimation rate in the inverse sense to within a deviation of 3% based on comparison with gravimetric data. We then apply the sublimation rate profile to obtain the vial heat transfer coefficient and product mass transfer resistance for a 5% w/v mannitol formulation. Finally, these parameters are applied to a one-dimensional quasi-steady heat transfer model to predict the evolution of the product temperature over the course of primary drying. Thermocouple measurements of product temperature are compared directly to the simulated data and demonstrate accuracy comparable to existing published one-dimensional models. [ABSTRACT FROM AUTHOR]

Freeze‐dried injectable drugs based on gemcitabine are mainly used as anticancer drugs for pancreatic cancer. High‐performance liquid chromatography (HPLC) analysis is mainly used to analyze the active pharmaceutical ingredient (API) content in the pharmaceuticals prepared in this way. While the HPLC method has high accuracy, it has the disadvantage that it cannot measure all of the large numbers of products produced due to the long measurement time. To solve this problem, a Raman spectrometer was installed in the pharmaceutical production process to measure the API content of all products. After installing a Raman spectrometer in the pharmaceutical production process, the verified quantitative model was used to measure API content in real time. A total of 4500 vials from three batches were tested, and the averages of each of the three batches were 100.0%, 100.2%, and 99.7%. [ABSTRACT FROM AUTHOR]

Lentiviral vectors (LVs) are a popular gene delivery tool in cell and gene therapy and they are a primary tool for ex vivo transduction of T cells for expression of chimeric antigen receptor (CAR) in CAR-T cell therapies. Extensive process and product characterization are required in manufacturing virus-based gene vectors to better control batch-to-batch variability. However, it has been an ongoing challenge to make quantitative assessments of LV product because current analytical tools often are low throughput and lack robustness and standardization is still required. This paper presents a high-throughput and robust physico-chemical characterization method that directly assesses total LV particles. With simple sample preparation and fast elution time (6.24 min) of the LV peak in 440 mM NaCl (in 20 mM Tris-HCl [pH 7.5]), this ion exchange high-performance liquid chromatography (IEX-HPLC) method is ideal for routine in-process monitoring to facilitate the development of scalable and robust LV manufacturing processes. Furthermore, this HPLC method is suitable for the analysis of all in-process samples, from crude samples such as LV supernatants to final purified products. The linearity range of the standard curve is 3.13 × 108 to 1.0 × 1010 total particles/mL, and both the intra- and inter-assay variabilities are less than 5%.

Process Analytical Technologies (PAT) used to monitor and control manufacturing processes are crucial for efficient and automated bioprocessing, which is in congruence with lights-off-manufacturing and Industry 4.0 initiatives. As biomanufacturing seeks to realize more high-throughput and automated operation, an increasing need for multimodal analysis of process metrics becomes essential. Herein, we detail a series of methods for analyzing product yield from a bioreactor and how to conduct cross-method comparisons. We employ a model system of Escherichia coli ( E. coli ) expression of green fluorescent protein (GFP), which is a simple, cost effective model for students and educators to replicate at different scales. GFP is an ideal analytical marker as it is easy to visualize due to its fluorescence which indicates cellular protein expression, cell localization and physiological changes of the cell population. In this study, samples from a 300 L bioreactor with GFP-expressing E. coli are analyzed to improve product yield and bioprocessing efficiency. Utilizing a fed-batch process for enhanced cell density and product titer, this bioreactor runs on a 24-hour schedule from inoculation to GFP induction and final harvest. To reliably quantify relative GFP expression and E. coli proliferation, we provide simple protocols and example results for comparing three different analytical methods: (1) in-line bioreactor measurements, (2) plate reader assays, and (3) microscopy. The GFP and cell density results follow similar trends based on the various inline and offline analytical methods and show a peak of GFP expression and cell density between 12.5 and 18 hours post inoculation.

Purpose: Roll compaction/dry granulation is established in manufacturing of solid oral dosage forms and, within the context of continuous manufacturing, it has sparked interest as material is fed, processed, and ejected continuously while also providing large possible throughputs. However, this amount of material has to be adequately controlled in real time to assure quality. Methods: This research aimed at monitoring the critical quality attribute granule size distribution in continuous roll compaction/dry granulation (QbCon®; L.B. Bohle, Ennigerloh, Germany) using in-line and on-line laser diffraction. The influence of varying process parameters and excipient formulations was studied and evaluated with the prospect of using this technique to develop control loops. For this purpose, residence time parameters were assessed. In- and on-line data was compared with off-line laser diffraction and dynamic image analysis data. Results: The system successfully monitored the granule size distribution in a variety of process parameters and throughputs (up to 27.5 kg/h). It was sensitive to changes in process parameters and changes in material blends, which could pose a potential threat to the final drug products' quality. Average event propagation time from the compaction zone to the laser diffraction system of 17.7 s demonstrates the systems' fast reaction time. Conclusion: Results highlight laser diffraction as a valuable method of in- and on-line size determination and allow for the development of a control strategy using this principle. [ABSTRACT FROM AUTHOR]

In multistep continuous flow chemistry, studying complex reaction mixtures in real time is a significant challenge, but provides an opportunity to enhance reaction understanding and control. We report the integration of four complementary process analytical technology tools (NMR, UV/Vis, IR and UHPLC) in the multistep synthesis of an active pharmaceutical ingredient, mesalazine. This synthetic route exploits flow processing for nitration, high temperature hydrolysis and hydrogenation reactions, as well as three inline separations. Advanced data analysis models were developed (indirect hard modeling, deep learning and partial least squares regression), to quantify the desired products, intermediates and impurities in real time, at multiple points along the synthetic pathway. The capabilities of the system have been demonstrated by operating both steady state and dynamic experiments and represents a significant step forward in data‐driven continuous flow synthesis. [ABSTRACT FROM AUTHOR]

A key quality attribute for solid oral dosage forms is their hardness and ability to withstand breaking or grinding. If the product is to be manufactured continuously, it can be of interest to monitor the hardness of the material at different stages of manufacturing. Using the controlled process parameters of roll compaction/dry granulation specific compaction force, roll speed and gap width, hardness of the resulting ribbons and granules can be predicted. For the first time, in this study two yield variables (corrected torque of the granulation unit and throughput of material) are used to predict the granules failure load. The increase in granule hardness was monitored in-line when the specific compaction force was increased during the compaction process. This opens the way for in-line control of material hardness, and its use for feedback and feedforward control loops for future continuous manufacturing processes.

• The PAT tools in solid oral dosage manufacture were summarized and compared. • The entire critical units for solid oral dosage manufacture were analyzed. • The trends of PAT tools in solid oral dosage manufacture were evaluated. Quality is the lifeline for all pharmaceuticals. Process analytical technology (PAT), introduced by FDA in 2004, providing significant opportunities for improving pharmaceutical development, manufacturing, and quality assurance. Therefore, in this review various PAT tools were introduced for solid oral dosage manufacture quality monitor and control. Throughout this review, we would like to provide more information to both researchers and manufacturers in order to improve the drug quality. [ABSTRACT FROM AUTHOR]

• A NIR method was provided to monitor the reaction process of Fmoc-D-Ala-OH in SPPS. • The chemometric method of SEPA was used for model establishment. • The predicted values were in good correlation with the values measured by HPLC. Solid phase peptide synthesis (SPPS) has received increasing attention from research laboratories and industry, and a variety of peptides have been successfully constructed in recent years. In this paper, near-infrared spectroscopy was applied to monitor SPPS process, and an analytical model of Fmoc-D-Ala-OH in SPPS solution was established. With the established model the Fmoc-D-Ala-OH content could be predicted rapidly at any time during the reaction. With a help of chemometric method of sampling error profile analysis (SEPA), some pretreatment methods were carefully compared, and the partial least squares method was used to build a model between NIR spectrum and Fmoc-D-Ala-OH content measured by high performance liquid chromatography (HPLC). The results showed that the model had a satisfactory performance, the determination coefficient, root mean square error of the calibration set were 0.9677 and 0.9284 mg/mL, respectively, and the determination coefficient, root mean square error of the prediction set were 0.9561, 1.1208 mg/mL, respectively. These results clearly showed that the model had a good predictive ability. Therefore, it had great potential in the monitoring of SPPS in pharmaceutical production. [ABSTRACT FROM AUTHOR]

The ability to monitor the status of cells during nutrient limitation is important for optimizing bioprocess growth conditions in batch and fed‐batch cultures. The activity level of Na+/K+ ATPase pumps and cytoplasm ionic concentrations are directly influenced by the nutrient level, and thus, cytoplasm conductivity can be used as a markerless indicator of cell status. In this work, we monitored the change in cytoplasm conductivity of Chinese hamster ovary (CHO) cells during nutrient deprivation and reintroduction. Employing single cell dielectrophoresis, the change in cytoplasm conductivity was measured over a 48‐hr period. The conditions under which the cytoplasm conductivity would recover to a normal level after nutrient reintroduction was determined. In addition, numerical simulations of cell ion flux, for different levels of Na+/K+ ATPase pump inhibition, were used to predict the minimum conductivity expected for nutrient‐deprived CHO cells. This predicted value is close to the minimum observed experimental cytoplasm conductivity for CHO cells that maintain the ability to restore the cytoplasm conductivity to the normal viable levels when nutrients are reintroduced. The recovery of starved cells was verified by reintroducing them to nutrient for 36 hr and measuring their proliferation using trypan blue exclusion assay. We conclude that cytoplasm conductivity can be used as a marker to indicate whether cells are in a recoverable state, such that the reintroduction of nutrients results in cells returning to a normal healthy state. [ABSTRACT FROM AUTHOR]

Clean-in-Place is an autonomous technique used to clean the internal surfaces of processing equipment in the food and drink sector. However, these systems clean for a longer time than required with negative economic and environmental impacts. In this work, an ultrasonic sensor system was developed to monitor the cleaning of different food fouling materials at laboratory scale. The fouling removal of three different food materials was also studied at different cleaning fluid temperatures. The three food materials had different cleaning mechanisms, which could be monitored successfully with the ultrasonic system. Tomato paste and gravy appeared to be cleaned by mechanical forces whereas malt extract dissolved into the cleaning water. The results yielded from the cleaning of the malt was found to be repeatable whereas the tomato and gravy were more variable between repeat experiments. It was found that changes in recorded ultrasonic signals were mainly affected by the area of fouling that covered the transducer's active element. • Ultrasonic method used to monitor the cleaning of different fouling material relevant to food sector. • Ultrasonic technique delivers repeatable results for a range of materials and two different temperatures. • Tomato paste and gravy clean by mechanical removal whereas malt cleans predominantly by dissolution. • Ultrasonic technique more sensitive to the area of fouling covering the transducer footprint than the fouling thickness. [ABSTRACT FROM AUTHOR]

Polyhydroxyalkanoates (PHAs) are biodegradable plastic-like materials with versatile properties. Plant oils are excellent carbon sources for a cost-effective PHA production, due to their high carbon content, large availability, and comparatively low prices. Additionally, efficient process development and control is required for competitive PHA production, which can be facilitated by on-line or in-line monitoring devices. To this end, we have evaluated photon density wave (PDW) spectroscopy as a new process analytical technology for Ralstonia eutropha (Cupriavidus necator) H16 plant oil cultivations producing polyhydroxybutyrate (PHB) as an intracellular polymer. PDW spectroscopy was used for in-line recording of the reduced scattering coefficient µs' and the absorption coefficient µa at 638 nm. A correlation of µs' with the cell dry weight (CDW) and µa with the residual cell dry weight (RCDW) was observed during growth, PHB accumulation, and PHB degradation phases in batch and pulse feed cultivations. The correlation was used to predict CDW, RCDW, and PHB formation in a high-cell-density fed-batch cultivation with a productivity of 1.65 gPHB•L-1•h-1 and a final biomass of 106 g•L-1 containing 73 wt% PHB. The new method applied in this study allows in-line monitoring of CDW, RCDW, and PHA formation. [ABSTRACT FROM AUTHOR]

Computer vision and near infrared spectroscopy are fast and non-invasive techniques currently available for processing control in the meat industry. These techniques can be used, either separately or combined, for on-line assessment of meat quality parameters. This study aimed to compare a portable near-infrared (NIR) spectrometer, near infrared hyperspectral imaging (NIR-HSI) and red, green and blue imaging (RGB-I) to differentiate ground samples from beef, pork and chicken meat; and to quantify amounts of each in mixtures. Chicken breast meat was adulterated with either pork leg meat or beef round meat from 0 to 50% (w/w). Partial Least Squares regression (PLSR) models were performed using full spectra and after selecting most important wavelengths. The best results were obtained with NIR-HSI, with coefficient of prediction (R P 2 ) of 0.83 and 0.94, ratio performance to deviation (RPD) of 1.96 and 3.56, and ratio of error range (RER) of 10.0 and 18.1, for samples of chicken adulterated with pork and beef, respectively. In addition, the results obtained using NIR spectroscopy and RGB-I confirm that these techniques provide an alternative for rapid, on-line inspection of ground meat in the food industry. • Rapid techniques used for identification of ground meat from chicken, pork and beef. • NIR spectroscopy, hyperspectral imaging and colour (RGB) imaging were compared. • Multivariate analyses were used to test the data obtained from three techniques. • NIR-HSI and RGB-I with PLSR detected the level of adulteration in chicken. [ABSTRACT FROM AUTHOR]

Abstract The Process Analytical Technology (PAT) initiative published by the Food and Drug administration (FDA) describes PAT systems as important tools to optimize pharmaceutical processes in order to ensure the final product quality. Real-time particle size measurements provide a promising approach for process automation of fluid bed granulation processes. This work focuses on the introduction and evaluation of a new time-based buffer approach for in-line spatial filtering technique (SFT) in order to provide a minimum time delay of the real-time measurement. Comparing the new buffer system to the conventionally used number-based approach, a reduced influence of the particle rate and a constant response time was found. A performance qualification of the Parsum probe IPP 80 using this buffer approach according to the ICH guideline Q2 was performed to investigate whether common variations of process parameters influence the real-time particle size measurement. Focussing on the main requirements for process control, hereby a good precision and specificity as well as an adequate robustness was found. Summarized, this study identified the time-based buffer system as more suitable buffer system for process automation. Graphical abstract Unlabelled Image [ABSTRACT FROM AUTHOR]

Highlights • At-line, off-line , in-line / on-line , in-situ applications of process analytical tools (PAT) are presented. • The combination of tools, incl. data analysis and correlation methods is described. • The importance of in-situ, multi-parameter, and -position techniques is discussed. • A tool for mobile multi-parameter measurements in the large-scale is presented. Abstract Recent advances in sensor development and miniaturization offer new possibilities to monitor and control bioprocesses. Specific requirements for anaerobic processes in terms of low costs and high robustness against insoluble fractions and impurities in media led to a decelerated penetration of new technology in this field. Since no regulatory framework demands for process monitoring and documentation like in the pharmaceutical industry, the implementation of new sensors beyond long-established methods is not conducted as intensively. Nevertheless, many attempts have been made in recent years to adopt sensors and show their applicability in anaerobic fermentation processes. New possibilities arise for improved monitoring, control and faster process development through digitalization. This review aims to provide an overview over these recent attempts with the focus on the liquid phase in the upstream part of pure culture bioprocesses for anaerobic bulk compound, food, and beverage production. In particular, methods that monitor the viability, metabolic activity or related parameters are discussed, like electrochemical, impedance and spectroscopy probes, and methods related to fluid flow and gradient formation, like acoustic and mobile sensors. [ABSTRACT FROM AUTHOR]

The statistical framework to systematically detect mean stationarity in the context of continuous manufacturing is described in this article. The methods presented in this article use econometric and financial time‐series analysis concepts in the form of unit‐root and stationarity hypothesis tests. The tests under discussion are the augmented Dickey‐Fuller, Philips‐Perron, Leybourne‐McCabe, and Kwiatkowski‐Phillips‐Schmidt‐Shin. These hypothesis tests are evaluated on data generated by a focused‐beam reflectance measurement sensor implemented on‐line in a continuous plug‐flow crystallizer. This contribution has shown that the hypothesis tests can be used to detect steady‐state conditions on‐line in a plug‐flow crystallizer. Furthermore, this econometric framework can be used as a mean stationarity “certificate” of collected samples to document that the process was mean stationary during the sampling. The statistical framework described in this article can be applied to any continuously operated unit operation or sensor measurement. © 2018 American Institute of Chemical Engineers AIChE J, 64: 2426–2437, 2018 [ABSTRACT FROM AUTHOR]

One major advantage of continuous pharmaceutical manufacturing over traditional batch manufacturing is the possibility of enhanced in-process control, reducing out-of-specification and waste material by appropriate discharge strategies. The decision on material discharge can be based on the measurement of active pharmaceutical ingredient (API) concentration at specific locations in the production line via process analytic technology (PAT), e.g. near-infrared (NIR) spectrometers. The implementation of the PAT instruments is associated with monetary investment and the long term operation requires techniques avoiding sensor drifts. Therefore, our paper proposes a soft sensor approach for predicting the API concentration from the feeder data. In addition, this information can be used to detect sensor drift, or serve as a replacement/supplement of specific PAT equipment. The paper presents the experimental determination of the residence time distribution of selected unit operations in three different continuous processing lines (hot melt extrusion, direct compaction, wet granulation). The mathematical models describing the soft sensor are developed and parameterized. Finally, the suggested soft sensor approach is validated on the three mentioned, different continuous processing lines, demonstrating its versatility. [ABSTRACT FROM AUTHOR]

Determining amylose content in rice with near infrared (NIR) spectroscopy, associated with a suitable multivariate regression method, is both feasible and relevant for the rice business to enable Process Analytical Technology applications for this critical factor, but it has not been fully exploited. Due to it being time-consuming and prone to experimental errors, it is urgent to develop a low-cost, nondestructive and ‘on-line’ method able to provide high accuracy and reproducibility. Different rice varieties and specific chemometrics tools, such as partial least squares (PLS), interval-PLS, synergy interval-PLS and moving windows-PLS, were applied to develop an optimal regression model for rice amylose determination. The model performance was evaluated by the root mean square error of prediction (RMSEP) and the correlation coefficient (R). The high performance of the siPLS method (R = 0.94; RMSEP = 1.938; 8941–8194 cm −1 ; 5592–5045 cm −1 ; and 4683–4335 cm −1 ) shows the feasibility of NIR technology for determination of the amylose with high accuracy. [ABSTRACT FROM AUTHOR]

Two new multi-component molecular complexes of cytosine and 5-fluorocytosine with 4-chloro-3,5-dinitrobenzoic acid are presented. Materials synthesis was achieved initially by evaporative crystallisation and the crystal structures determined. The process was then successfully transferred into a controlled small scale cooling crystallisation environment, with bulk samples shown to be representative of the multi-component product phase, by powder X-ray diffraction (PXRD) and differential scanning calorimetry (DSC) methods. Turbidity measurements are shown to be a valuable process analytical technology probe for characterising the initial stages of molecular complex formation in solution. The significance of these findings for scale-up of crystallisation of multi-component molecular materials and for future transfer into continuous cooling crystallisation is discussed.

Polyhydroxyalkanoates (PHAs) are biodegradable plastic-like materials with versatile properties. Plant oils are excellent carbon sources for a cost-effective PHA production, due to their high carbon content, large availability, and comparatively low prices. Additionally, efficient process development and control is required for competitive PHA production, which can be facilitated by on-line or in-line monitoring devices. To this end, we have evaluated photon density wave (PDW) spectroscopy as a new process analytical technology for Ralstonia eutropha (Cupriavidus necator) H16 plant oil cultivations producing polyhydroxybutyrate (PHB) as an intracellular polymer. PDW spectroscopy was used for in-line recording of the reduced scattering coefficient µs’ and the absorption coefficient µa at 638 nm. A correlation of µs’ with the cell dry weight (CDW) and µa with the residual cell dry weight (RCDW) was observed during growth, PHB accumulation, and PHB degradation phases in batch and pulse feed cultivations. The correlation was used to predict CDW, RCDW, and PHB formation in a high-cell-density fed-batch cultivation with a productivity of 1.65 gPHB·L−1·h−1 and a final biomass of 106 g·L−1 containing 73 wt% PHB. The new method applied in this study allows in-line monitoring of CDW, RCDW, and PHA formation.

Application of in-line real-time process monitoring using a process analytical technology for granule size distribution can enable quality-by-design development of a drug product and enable attribute-based monitoring and control strategies. In this study, an in-line laser focused beam reflectance measurement (FBRM) C35 probe was used to investigate the effect of formulation and process parameters on the granule growth profile over time during the high shear wet granulation of a high drug load formulation of brivanib alaninate. The probe quantitatively captured changes in the granule chord length distribution (CLD) with the progress of granulation and delineated the impact of water concentration used during granulation. The results correlated well with offline particle size distribution measured by nested sieve analyses. An end point indication algorithmwas developed that was able to successfully track the process time needed to reach the target CLD. Testing of the brivanib alaninate granulation through 25-fold scaleup of the batch process indicated that the FBRM CLD profile can provide a scale-independent granule attribute-based process fingerprint. These studies highlight the ability of FBRM to quantitate a granule attribute of interest during wet granulation that can be used as an attribute-based scale-up and process monitoring and control parameter. [ABSTRACT FROM AUTHOR]

Real-time process monitoring using a process analytical technology for granule size distribution can enable quality-by-design in drug product manufacturing. In this study, the resolution and sensitivity of chord length distribution (CLD) measured inline inside a high shear granulator using focused beam reflectance measurement (FBRM) C35 probe was investigated using different particle size grades of microcrystalline cellulose (MCC). In addition, the impact of water and impeller tip speed on the measurement accuracy as well as correlation with offline particle sizing techniques (FBRM, laser diffraction [Malvern Mastersizer ® ], microscopy [Sympatec QicPic ® ], and nested sieve analysis) was studied. Inline FBRM resolved size differences between different MCC grades, and the data correlated well with offline analyses. Impeller tip speed changed the number density of inline CLD measurements while addition of water reduced the CLD of dry MCC, likely due to deagglomeration of primary particles. In summary, inline FBRM CLD measurement in high shear granulator provides adequate resolution and reproducible measurements in the pharmaceutically relevant size range both in the presence and in the absence of water. Therefore, inline FBRM can be a valuable tool for the monitoring of high shear wet granulation. [ABSTRACT FROM AUTHOR]

Pharmaceutical 3D printing (3DP) is one of the emerging enabling technologies of personalised medicines as it affords the ability to fabricate highly versatile dosage forms. In the past 2 years, national medicines regulatory authorities have held consultations with external stakeholders to adapt regulatory frameworks to embrace point-of-care manufacturing. The proposed concept of decentralized manufacturing (DM) involves the provision of feedstock intermediates (pharma-inks) prepared by pharmaceutical companies to DM sites for manufacturing into the final medicine. In this study, we examine the feasibility of this model, with respect to both manufacturing and quality control. Efavirenz-loaded granulates (0-35% w /w) were produced by a manufacturing partner and shipped to a 3DP site in a different country. Direct powder extrusion (DPE) 3DP was subsequently used to prepare printlets (3D printed tablets), with mass ranging 266-371 mg. All printlets released more than 80% drug load within the first 60 min of the in vitro drug release test. An in-line near-infrared spectroscopy system was used as a process analytical technology (PAT) to quantify the printlets' drug load. Calibration models were developed using partial least squares regression, which showed excellent linearity (R 2  = 0.9833) and accuracy (RMSE = 1.0662). Overall, this work is the first to report the use of an in-line NIR system to perform real-time analysis of printlets prepared using pharma-inks produced by a pharmaceutical company. By demonstrating the feasibility of the proposed distribution model through this proof-of-concept study, this work paves the way for investigation of further PAT tools for quality control in 3DP point-of-care manufacturing., Competing Interests: 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., (© 2023 The Authors.)