The Financial Returns on Investments in Process Analytical Technology and Lean Manufacturing: Benchmarks and Case Study

This report with recommendations is the result of an expert panel meeting on PAT applications in food industry that was organized by the M3C Section of the European Society of Biochemical Engineering Science (ESBES) at the 10th ESBES Symposium. The aim of the panel was to provide an update on the present status of the subject and to identify critical needs and issues for wider applications of PAT in food industry. A brief description of the current state-of-the-art and industrial uptake of the methodology is provided in this report. It concludes with a number of recommendations to facilitate further developments and a wider application of PAT in food industry. Process Analytical Technologies (PAT) [1] (European Medicines Agency EMA-FDA pilot program for parallel assessment of Quality by Design applications; Document EMA/172347/2011) have been extensively discussed in literature, particularly with respect to (bio)pharmaceutical process modelling, monitoring and control [2]. Table 1 provides an historic overview of the PAT development in the context of food applications. Although successful applications within food industry are increasingly being reported [3, 4], the session on PAT in food industries at the ESBES-IFIBiop 2014 in Lille highlighted significant challenges and opportunities for further development in this area. This position paper briefly reviews the current state-of-the-art, industrial needs and opportunities as well as scientific challenges to be addressed in order to extend the use of PAT methodology in the food industries. Currently, quality and safety control are still based mainly on discontinuous analysis with traditional analytical methods in the lab or, at best, at-line measurements. This is no longer sufficient to fulfill the needs of the food industry. Due to higher safety and quality standards and demands and high throughput of production facilities, the number of samples to be analyzed is increasing. Rapid analysis methods and PAT are required to address these needs along the complete production chain leading to a better understanding and control of raw materials, intermediate products in the production process as well as the final products to be packaged and delivered. The goal is to achieve real time analysis in order: • to avoid usage of any out-of-specification raw material and to detect adulteration, substitution, tampering and counterfeiting leading to non-authentic products; • to be able to intervene and stop/change processes in order to secure the target quality; • to assess the final quality to avoid out-of-specification products being packaged and shipped, thus leading to undesirable customer dissatisfaction and costs associated with the resolution of complaints. Over a hundred years ago (on May 22nd, 1913) the first patent on a PAT device ("Pfeiffenanalysator" for measuring the ratio of H2 and N2 gas for ammonia synthesis) was granted to Paul Gmelin from Badische Anilin- und Soda-Fabrik, BASF (Patentschrift Nr. 281157, Klasse 42/. Gruppe 4). Since then, PAT found broad application in chemical industry, which is dominated by highly automated, continuous processes. Today in chemical processes, such as the synthesis of isocyanates, e.g. hexamethylendiisocyanate (HDI), typically 60-130 PAT measurements are collected. In contrast, in sectors like (bio)pharmaceutical or food industry, the application of PAT is significantly less frequent today, especially, in terms of on-line analytics, where the measurement takes place in or close to the production step (or unit operation). One reason for this is the reduced degree of automation of the processes in these industries, which are dominated by unit operations, single production steps consecutively executed after each other, resulting in production batches. Analytics of a production batch takes place mostly off-line in analytical laboratories during hold-up times of the process intermediate between the different unit operations. Regulatory requirements represent an equally important driver for extensive online analytics. With the 2004 FDA's Quality by Design initiative, regulatory agencies demand systematic risk based process development and understanding of the processing space. This also allows for flexible process adjustments within the explored space for producing the desired product quality. In order to achieve the required understanding, more extensive analytical data is required, ideally obtained in-time on raw materials, process intermediates and ideally also on product quality attributes. In food industry, where typically high product titers are obtained, product analytics is sometimes possible by in-line measurements, such as spectroscopy (section 4). In biopharmaceutical processes, product titers are usually very low, the products are proteins with high molecular weight, and quality attributes are challenging to analyse. In these situations, online sampling with automated sample preparation and analytics may open up a solution [5]. But, identifying and implementing analytical methods, which are fast enough for delivering results on product quality attributes (e.g. protein glycosylation) in time, during the unit operation for allowing process modulation, remains a challenge. The overall typical goals in industry are high safety, high and stable product quality, high yield, low consumption of resources (materials, energy, room, time, and people), reduced influence of variability in raw materials as well as an increased shelf life of products which holds also in food processes. However, compared to the chemical and biopharmaceutical industries, food industry has to deal with certain characteristics, which renders PAT a formidable challenge in this context. The characteristic features in food industry are: • Raw materials are not pure substances: they are complex combinations of pure substances with varying compositions • Raw materials are soft, variable size, fragile and slippery • Physical properties of raw materials depend heavily on temperature, pressure, moisture and harvesting, and storage conditions • Raw materials undergo usually a phase transformation during processing and hence change their physical properties during processing • Micro processes (physical, (bio)-chemical, microbiological) are frequently not known • Highly perishable products. Here the challenge is to produce good quality, which maintains for a long time (shelf life) • High demand for hygiene These characteristics lead to a challenge for the application of sensors in the food industry. For important quality and process variables, such as sensory assessment, micro flora or spoilage, reliable and robust sensors are not yet available. Moreover, the sensors which are available are typically used only in isolated applications and frequently provide insufficient reliability. They are often not integrated in a common data management infrastructure. The materials of construction do not always consider suitability of contact with food and the solutions developed within advanced research projects frequently demand high care and maintenance. One reason why PAT is not as common as in other areas is that in food industry knowledge from various subject areas is necessary (such as physics, chemistry, biology, mathematics, informatics, engineering, nutritional science). For unit operations mathematical models are available in principle, but frequently they are too complicated to adapt. One of the biggest challenges in food industries is the dynamic nature of the processes. Changes in geometry, porosity, microstructure, solubility as well as mass and energy transfer coefficients must be addressed. Mostly gradients of temperature and moisture have to be considered as well as changes in kinetic parameters during process run. End product qualities like color, smell and 'brokens' also strongly influence the process. Therefore, food processes can be considered to be significantly more complex then chemical and bio-pharmaceutical processes. To achieve more stable processes PAT applications are necessary to check the quality of the raw and processed materials and their relationship to each other. If the quality is changing, then control actions resulting in online parameter changes are required in order to maintain constant product quality. Measurement systems are required, which guarantee that the process is in accordance with recipe and formulation. Furthermore, disturbances must be compensated for using control actions to reduce process variability and to conform to food regulations. As Glassey [6] argues, PAT methodology can aid in product design and testing as well as in ensuring full compliance with the HACCP and ISO 22000:2005 requirements during processing. As demonstrated below, PAT methodologies have the potential to aid the identification of critical control points and their critical limits, their effective monitoring and control, but also effective communication with suppliers and customers. Documentation which requires measurements of important variables is especially important so that traceability can be guaranteed. Here PAT has to deliver the corresponding measurement systems. Clearly a high demand for PAT in food industry is evident as is the need for further developments in the science and technology that help address the specific challenges posed by the characteristics of the food industry highlighted above. The complexity of raw materials that are typically soft and easily damageable is arguably one of the major scientific and technological challenges in food industry. During storage and processing the quality of such raw materials can decline due to oxidation processes, pressure and temperature effects. In addition, the visual impression of the final product (i.e. its appearance) is much more important than in other industries due to the fact that this will influence the purchase decision of the consumer. During the whole processing from raw material to the storage of the final product, hygiene is of utmost importance. Therefore non-invasive sensor systems are required in these applications. Measurement systems based on optical principles are particularly suitable from this perspective. Such measurement systems, including near infrared (NIR), Raman and fluorescence spectroscopy as well as computer-assisted image-based systems, potentially have a number of advantages in food process supervision and automation. Spectroscopic methods and imaging devices are well suited for PAT purposes because they are fast, non-destructive, provide multiple chemical information, allowing remote in-process analysis via fiber optics or instruments mounted directly on-line. Fluorescence spectroscopy is the most sensitive spectroscopic technique. Recently, many applications have been developed using fluorescence techniques [3, 7, 8]. Raw materials, the supervision of processing as well as product quality and the contamination of the equipment can be monitored by fluorescence. For example, Everard et al. [3] presented a method for detection of fecal contamination on spinach leaves. They coupled three hyperspectral imaging (HSI) configurations with two multivariate image analysis techniques and compared fluorescence imaging in the visible region with ultra violet and violet excitation sources, and reflectance imaging in the visible to near-infrared regions. They showed, that both fluorescence configurations had 100% detection rates for fecal contamination up to 1:10 dilution level and violet HSI had 99% and 87% detection rates for 1:20 and 1:30 levels, respectively. Everard et al. emphasized that on-line detection of fecal contamination on leaves has the potential to reduce the cases of food borne illnesses and their associated costs. A similar approach is presented by Lee et al. [7] where bovine faeces on Romaine lettuce and baby spinach leaves were investigated. They pointed out that two-band ratios using bands at 665.6 nm and 680.0 nm for lettuce and at 660.8 nm and 680.0 nm for spinach effectively differentiated all contamination spots applied. Grote et al. [8] described a fluorescence measurement technique to monitor a sourdough fermentation process. For the prediction of pH value and acidity during rye sourdough fermentations they applied partial least squares regression and principal component regression models for prediction and compared them with an evaluation where principal component analysis was combined with artificial neural networks. Depending on process operation and evaluation technique the average percentage root mean square errors of prediction for pH values were between 2.5 and 5.1%. For the prediction of the acidity level, the best results were between 6.0 and 8.1%. Liu et al. [9] used the Hoffman reaction to convert acrylamide to a compound which shows strong fluorescence emission at 480 nm. They showed good correlation of acrylamide in the range of 0.015 μg/mL to 20 μg/mL. Using this technique the food security will be increased. A fluorescence imaging device to detect deli residues on deli slicers were used by Beck et al. [10] processing four cheeses and four processed meats. The authors suggested that a fluorescence imaging device can be applied for routine use even in delicatessens. Fig. 1 clearly shows that the application of fluorescence for the monitoring of food processes increased from less than 10 before 2003 to more than 60 a year since 2013. This figure includes all papers from a search containing the key words "Food" and "Fluorescence". Although most of these papers describe laboratory applications rather than industrial PAT applications, they indicate potential future applications in this technology in food industry. Number of published papers obtained from the Scopus database searching for the words "Food" and "Image analysis" or "Raman" or "Near infrared spectroscopy" or "Fluorescence spectroscopy". Raman spectroscopy can only be applied if no fluorescence occurs in the corresponding excitation range. He et al. [11] applied the surface enhanced Raman scattering spectroscopy to detect banned food additives, such as Sudan I dye and Rhodamine B in food, Malachite green residues in aquaculture fish. They concluded, that Raman spectroscopy and chemometric evaluation techniques can be used to identify banned food additives to ensure food safety. Ilaslan et al. [12] presented a method based on Raman spectroscopy to provide a rapid method for evaluating the quantitative analysis of glucose, fructose, and sucrose in soft drinks. Wang et al. [13] applied a Raman spectrometer as a process analyzer to monitor the wine fermentation. They demonstrated that sugar, ethanol and glycerol can be measured on-line with high correlation (higher than 0.98) to the HPLC reference measurements. Nache et al. [14] investigated Raman spectra from pork meat to monitor the early postmortem lactate accumulation and pH decline. They suggested that the locally weighted regression applied to the standard normal variate (SNV) normalized Raman spectra provide one of the most accurate and robust models with a cross-validated coefficient of determination (r2cv) of 0.97 for pH and lactate, a cross-validated root mean square error (RMSECV) of 4.5 mmol/kg for the lactate prediction and 0.06 pH-units for the pH prediction. These results demonstrate significant potential of combining chemometrics and Raman spectroscopy for on-line meat quality control applications. Fig. 1 shows the number of papers recorded in Scopus (search words "Food" and "Raman"). Compared to fluorescence, the number of papers describing Raman spectroscopy is twice as high in recent years with a significant rate of increase, demonstrating the increasing interest in this method for food process monitoring. Computer vision systems enable one of the main aspects of consumer preference – the appearance of a product – to be inherently considered. Therefore, computer vision systems for the supervision of food processing also gained importance over the years. An overview of several examples is given by Sun [15]. Especially the supervision of food drying processes is discussed by Aghbashlo at al. [16]. They pointed out that there is a large unexploited potential in the image data captured during various food processing operations. More informative feature extraction algorithms or novel pattern recognition procedures have to be developed. Paquet-Durand et al. [4] described a system for the supervision of the baking process. They demonstrated, by using the Viola–Jones-algorithm as well as neural networks, that the pastry can be identified and the volume increase as well as color development can be monitored. To detect defective apples Zhang et al. [17] used a computer vision system, which was combined with an automatic lightness correction system. For 160 samples they showed a 95% overall detection accuracy. For the evaluation they used a weighted relevance vector machine classifier. Fig. 1 shows the number of papers over time from Scopus searching for "Food" and "Image analysis". Here the applications start earlier compared to the spectroscopic methods, the number of applications per year is higher than in the case of spectroscopic applications; except in the year 2014, where more reports of Raman applications were published. Moreover imaging systems using visible and NIR region are available to perform quality checks based on the spectroscopic information derived from each point in the image. One example is the on-line analysis of the widely varying fat distribution in salmon for sorting purposes [18]. Near infrared (NIR) is a well-established method for rapid analysis of food raw materials and products [18], either on-/in-line, at-line or off-line. In the lab or at-line multiple components such as fat, protein, moisture and many more, can be analyzed without any sample preparation in all kinds of liquid, solid and semi-solid samples. Raw materials can be verified for identity and further characterized regarding composition already in the goods reception. The composition, freshness and adulteration of edible oils [19], e.g. olive oil [20], can be analyzed rapidly before a truck is unloaded. Many other sample types like meat, grains, flour, dairy products [21] and others can be analyzed. Even mixtures like vitamin premixes can be analyzed to make sure that the correct material was delivered and can be used in production. There are several approaches and technologies for PAT available, ranging from simple filter based devices over dispersive diode array spectrometers to Fourier-Transform (FT)-NIR instruments. Samples can be analyzed in-line by fiber optic probes or by contactless systems (Fig. 2). Smaller instruments can be directly attached to pipes, chutes or in other installations. A) NIR reflection probe in a fluid bed dryer. (B) Probe head for contactless measurement of grain and other solids. Applications cover a broad range from a simple monitoring of moisture content of a product on a conveyor belt to more complex control situations. In dairy industry there are demands to control milk powder production by monitoring the feed of the spray tower and the powder after fluid bed drying. Other important production steps to control are the standardization of milk, cream, whey and concentrates [22]. In frying processes with big volumes of oil an in-line monitoring of the oil quality with regards to acid value, anisidine value and the content of polar and polymerized components [23] are of particular interest. Another example of high volume processes where monitoring of protein, moisture and ash is important is the milling of grain to flour and the production of cereals of any kind. Finally there are more and more fermentation processes controlled by in-line NIR to optimize the conditions and follow the feeding and consumption cycles during biomass buildup [24]. In 2013 roughly 120 papers were published, however the recent increase is not as steep as in the case of Raman spectroscopy (Fig. 1). The economic benefits due to PAT can be attributed to higher product quality and yield as well as decreased product variation. This will reduce the overall production costs significantly and increase competitiveness. Furthermore, the knowledge and understanding of the process will increase. However, the level of automation in industrial food processes is significantly lower compared to the chemical and pharmaceutical processes. This is partly due to the complexity of food processes. On the other hand, the lack of reliable sensor systems to determine important process parameters and variables contributed to this in the past. Sensors represent a fundamentalpart of all automation systems, although they only represent a part of the requirements for wider application of PAT in food industry. The areas of further development and recommendations enabling more extensive use of the PAT within this industry also include: • Further industrial case studies and wider dissemination of the positive impact of reliable sensor technology in raw material and product quality as well as process monitoring. • Operational and maintenance requirements of sensor technologies will remain a significant aspect of future sensor technology uptake in this industry. • Data management and analysis software will play a significant role in extending the PAT application within the food industry. Platform solutions, preferably supporting wireless data transfer, at competitive pricing levels are required. • Cost benefit analysis studies are particularly important given the business drivers of this industry. • The impact of PAT is likely to be more pronounced in the new processes, although it is important to continue to encourage the application of PAT approaches within established food processes to enhance the process economics and compliance with the standards ensuring safe food supply chains. • PAT can significantly contribute to the stringent traceability and documentation requirements, although issues of compatibility and data standardization will impact upon data management systems employed by the companies within the supply chain. The authors declare no financial or commercial conflict of interest.

Process analytical technology (PAT), though a viable field for several decades, is finally com ing of age in the pharmaceuti cal industry. The uS FDA’s PAT initiative of 2004 has led to the rebirth of a field that has proven itself in other industries with the potential of gaining manufacturing efficien cies and better regulation for pharmaceu tical manufacturing processes. When one looks at the role of spectroscopy in PAT, it is clear that there are huge opportunities for increasing process understanding by use of such tools throughout the development of processes and on into manufacturing, where the development knowledge can be applied to provide process verification and control. In this article, examples of how NIR spectroscopy is used in various areas of the pharmaceutical industry will be discussed. however, since PAT has yet to be widely adopted in the pharmaceutical industry, there is still a stage of NIR education and development for such applications, though the science may be well understood. As NIR instrumentation and software development continues to progress, the applications and implementations of NIR in these areas are bound to increase. In the manufacture of active pharmaceuti cal ingredients (APIs), there are many exam ples of spectroscopy deployment and these have been well described.

Adoption of Quality by Design (QbD) principles, regulatory support of QbD, process analytical technology (PAT), and continuous manufacturing are major factors effecting new approaches to pharmaceutical manufacturing and bioprocessing. In this review, we highlight new technology developments, data analysis models, and applications of Raman spectroscopy, which have expanded the scope of Raman spectroscopy as a process analytical technology. Emerging technologies such as transmission and enhanced reflection Raman, and new approaches to using available technologies, expand the scope of Raman spectroscopy in pharmaceutical manufacturing, and now Raman spectroscopy is successfully integrated into real-time release testing, continuous manufacturing, and statistical process control. Since the last major review of Raman as a pharmaceutical PAT in 2010, many new Raman applications in bioprocessing have emerged. Exciting reports of in situ Raman spectroscopy in bioprocesses complement a growing scientific field of biological and biomedical Raman spectroscopy. Raman spectroscopy has made a positive impact as a process analytical and control tool for pharmaceutical manufacturing and bioprocessing, with demonstrated scientific and financial benefits throughout a product’s lifecycle.

Chapter 9 - Applications of MVDA and PAT for Drug Product Development and Manufacturing

Purpose The purpose of this study is to analyze the relationship between lean manufacturing and corporate environmental performance. Scholarly literature has extensively addressed the relationship between those two areas but empirical papers present mixed and inconsistent results, calling for further analysis to establish a clearer understanding of the actual relationship and to identify the causes of conflicting findings across studies. Given the importance of sustainable practices in the current business landscape, this paper aims to provide a comprehensive overview of this relationship through a meta-analysis of previous research, with a focus on integrating quantitative findings to shed light on the potential impact of lean manufacturing on environmental performance and report its intensity with Pearson’s correlation coefficient. Design/methodology/approach This paper analyzes the data from 29 primary studies published between 2001 and 2022 that have empirically measured the relationship between lean manufacturing and corporate environmental performance and that have been identified in the Web of Science and SCOPUS databases through an exhaustive review of the literature. To integrate previous empirical results and evaluate the evidence for the lean manufacturing’s influence on environmental performance, a meta-analytic methodology was adopted through the Hedges–Olkin random effect approach, based on correlations. Findings Main findings support the notion that a significant, positive, rather moderated, relationship exists between lean manufacturing and environmental performance, with an overall correlation coefficient r¯ = 0.25. The result seems robust, as several tests confirm that publication bias is unlikely. Overall, various lean practices are correlated to varying degrees with different measures of environmental performance. The environmental efficiency of external lean practices is higher than that observed for internal practices and quality management has a more pronounced effect than other internal lean practices. The 2008 global crisis did not change these relationships and the impact of lean manufacturing on environmental performance does not seem to be moderated by the degree of environmental awareness of the country where applied. Research limitations/implications The results conclude that a significant, positive relationship exists between lean manufacturing and environmental performance (r¯ = 0.25). This study tests certain factors that exhibit varying effect sizes and moderate the overall outcome, highlighting that the environmental efficiency of external lean practices is higher than that observed for internal practices. In addition, it suggests a more pronounced effect of quality management among other internal lean practices. Practical implications This study provides companies with an opportunity to align their operational strategies with environmental sustainability goals. Understanding that various lean practices exhibit diverse levels of correlation with multiple measures of environmental performance, decision-makers can prioritize their efforts and apply the lean practices that have a stronger effect on the desired environmental outcomes to improve their environmental impact. Conversely, managers are aware that certain lean practices have a week relationship with some environmental performance so they can avoid overestimating environmental benefits of lean manufacturing. Finally, results underscore the importance of organizational commitment to environmental sustainability. Originality/value It is, to the best of the authors’ knowledge, the first meta-analytic study to investigate the strength of the association between lean manufacturing and environmental performance and to test whether various lean practices are correlated to different measures of environmental performance. It fills this gap in the literature and therefore it represents a valuable contribution to the field. In addition, this paper explores certain factors that moderate the overall outcome.

A review on the modernization of pharmaceutical development and manufacturing – Trends, perspectives, and the role of mathematical modeling

Introduction: Manufacturing companies operating within dynamic and intensely competitive markets have a mindset that concentrates on continuous improvement processes. The management efforts have focused on enhancing quality, flexibility, and customer response time through the application of Lean thinking principles. For an organisation to realise its full potential of lean application, lean manufacturing organisation must embrace a comprehensive business strategy, rather than being confined to a singular focus within operations. The lean manufacturing organisation necessitates the incorporation of lean approaches throughout operations and various business functions. Management accounting practices (MAP), and lean accounting serve as crucial elements in attaining higher performance improvement and align with the requirements and goals of lean organisations. This study aims to investigate the relationship between lean accounting components and the manufacturing performance of Malaysian manufacturing companies. Methods: The survey data collected from 282 Malaysian manufacturing firms was utilised and develop a structural equation model of lean accounting and manufacturing performance. Results: The findings show that the implementation of lean accounting is directly related to manufacturing performance. Specifically, the elimination of transactions (ELT), performance measurement (PFM), financial benefits (FNB), and value stream management (VSM) have direct effects, while value stream costing (VSC) indirectly impacts manufacturing performance in Malaysia. Conclusion: The results of our research align with the perspective that lean thinking constitutes a comprehensive business strategy. To achieve the most significant effect on performance, our findings suggest that operations management must across the departments and areas to maximizse its efficiency. Operations and accounting personnel must collaborate to ensure that lean accounting is strategically integrated into the lean manufacturing organisation. This study assists both researchers and practitioners in comprehending how lean accounting can facilitate operations personnel in their internal decision-making processes, as well as support operations executives and business leaders in their goal of enhancing lean operations performance within the framework of a comprehensive lean manufacturing strategy.

<div class="htmlview paragraph">High Speed Machining, or better yet High Velocity Machining is often chosen especially for new investments in manufacturing, at least when machining non-ferrous metals. This makes sense given the progress in machine spindle technology and the velocity with which the machine axes can travel.</div> <div class="htmlview paragraph">This in conjunction with advanced cutting material, particularly polycrystalline diamonds, substantially reduces main machining and non-production time.</div> <div class="htmlview paragraph">Different is the situation when machining ferrous metal. Cast iron, Carbon and allow steels as well as super alloys; all have their specific characteristics making it difficult to apply real high cutting speeds.</div> <div class="htmlview paragraph">Since it is either technologically not feasible and/or impractical because of an unfavorable price/performance ratio to apply cutting speeds close to those applicable for non-ferrous metals, other avenues have to be taken.</div> <div class="htmlview paragraph">Besides the cutting speed, attention has to be paid to minimizing machining time and optimizing the entire machining process. This is what is called “High Performance Machining (HPM)”.</div> <div class="htmlview paragraph">HPM does not necessarily require the acquisition of new machinery; the efforts go toward improving upon existing processes. The most promising and intriguing part of the process is the cutting tools systems, for they can easiest be adapted to specific applications. Furthermore, when the application drives the process it is the new cutting tool system that mostly determines the level of machining and manufacturing performance.</div>

Chapter 35 - Process Analytical Technology in Solid Dosage Development and Manufacturing

Contributors

After the Food and Drug Association in the USA published guidelines on the enhanced use of process analytical technology (PAT) and continuous manufacturing, many studies regarding PAT and continuous manufacturing have been published. This paper describes a case study involving granulation and coating steps with ethenzamide to investigate interference for PAT model construction and model management. We investigated what factors should be considered and addressed when PAT is implemented for continuous manufacturing and how predictive models should be constructed. The product qualities that were monitored were moisture content and particle size in the granulation step and tablet weight and moisture content in the coating step. We have constructed models for the granulation step and validated the predictive capability of the models against an external dataset. A partial least squares (PLS) model with manual wavelength selection had the best predictive accuracy for loss on drying against the external validation set. We found that the prediction of loss on drying was accurate, but the prediction of particle size was not sufficiently accurate. In the coating step, because of the small amount of data, we performed three-fold cross-validation and y-scrambling 10 times, to select the optimal hyper-parameters and to check if the models were fitted to chance correlations. We confirmed that the coating agent weights, tablet weights, and water content could be accurately predicted based on the mean of the R2 score for cross-validation. Addition of other variables, as well as the absorbance, slightly improved the predictive accuracy.

A study on the applicability of multiple process analysers in the production of coated pellets

Global regulatory agencies have encouraged the use of process analytical technology (PAT) to assure quality in the pharmaceutical industry. A frequently cited obstacle to the implementation of spectroscopy-based PAT methods is the difficulty associated with directly transferring calibration models between PAT instruments. The goal of this study was to compare model transfer strategies for method transfer between two transmission Raman spectroscopy (TRS) instruments. The calibration and test samples were pharmaceutical compacts of acetaminophen and excipients. The experimental design was a 3 factor by 5 level circumscribed central composite design of active pharmaceutical ingredient, lactose, and microcrystalline cellulose concentrations. The calibration and test data were collected using two instruments. Quantitative models were constructed using partial least squares regression. Global calibration modeling and direct model transfer were compared to evaluate opportunities for situations involving method transfer, calibration update, and line extension. Models were compared using a t test-based method to evaluate performance statistics. Statistical analysis demonstrated equivalent performance of the global modeling and direct transfer methods. This work demonstrated that a quantitative transmission Raman model could be directly transferred across instruments, thus avoiding the challenges and resources necessary when creating global models.

Chapter 8 Modelling of pan-coating processes for pharmaceutical dosage forms

Deep learning for continuous manufacturing of pharmaceutical solid dosage form