A Review of PAT Strategies in Secondary Solid Oral Dosage Manufacturing of Small Molecules

Application of artificial intelligence combined with near infrared spectroscopy in the continuous counter-current extraction process of Angelica dahurica formula granules

The establishment of near infrared (NIR) spectroscopy model mostly relies on chemometrics, and spectral analysis combined with artificial intelligence (AI) provides a new way of thinking for pharmaceutical quality inspection, new algorithms such as back propagation artificial neural networks (BP-ANN) and swarm intelligence optimization algorithms such as sparrow search algorithm (SSA) provide core technical support. In order to explore the application of AI in the pharmaceutical field, in this study, Angelica dahurica formula granules with a relatively complex system were selected as the research object. Quantitative analysis models were established by using partial least squares regression (PLSR) with a micro-NIR spectrometer, and BP-ANN modeling results were compared. For the best PLSR models of six characteristic components in the continuous counter-current extract of Angelica dahurica, R2v of imperatorin was lower than 0.90, and the RPD values of imperatorin, phellopterin, and isoimperatorin were even lower than 1. When the prediction model established by SSA-BP-ANN was used for quantitative analysis, R2v of six components were all higher than 0.92, and the RPD values all higher than 1.5, which proved that the BP-ANN method was better than PLSR. This study confirmed that in the continuous counter-current extraction progress of Angelica dahurica formula granules, the use of micro-NIR spectrometer combined with AI could realize the rapid prediction of the contents of six characteristic components. The comparison results provided a scientific reference for the process analysis and on-line monitoring in the production process of traditional Chinese medicine by micro-NIR spectrometer combined with AI.

Real-time prediction of dissolution profiles of coated oral dosage forms

Optical coherence tomography (OCT) has emerged as an in-line monitoring technique for pharmaceutical coating processes based on a representative number of samples. In this study, an approach was developed to correlate the coating thickness measured in-line via OCT with the resultant tablet dissolution profile. This strategy enables prediction of the dissolution profile of coated oral dosage forms for each individual state of the coating process in real-time. Correlation models were developed for a tablet pan coating process and for a pellet fluid-bed coating process. The feasibility of the correlation models was tested using different process parameters and types of coating formulations. This work demonstrated that using the OCT data to predict dissolution could possibly form a unique way of assuring drug product quality and establishing a control strategy within the real-time release testing (RTRT) concept.

Process analytical technology in Downstream-Processing of Drug Substances- A review

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

Prediction of in-vitro dissolution and tablet hardness from optical porosity measurements

Advanced manufacturing technologies such as continuous processing require fast information on the quality of intermediates and products. Process analytical technologies (PAT) to monitor many critical quality attributes (CQAs) have been developed and successfully implemented in pharmaceutical industry. However, there are some CQAs, which still have to be measured off-line with significant effort due to the lack of suitable PAT sensors. Two prominent examples are the in-vitro dissolution and the tablet hardness. Both are obtained via destructive measurement, and the dissolution is tedious and time-consuming to determine. In this study, these two CQAs were predicted via correlation with the optical porosity of tablets. The optical porosity was measured via a novel combination of gas in scattering media absorption spectroscopy (GASMAS) and photon time of flight spectroscopy (pTOFS) with a SpectraPore instrument. The approach was tested in a continuous tableting line and showed promising results in predicting the amount of drug released after specific dissolution times as well as the tablet hardness. This indicates that the measurement of optical porosity can support control strategies within the real-time release testing (RTRT) concept.

Hot-Melt Extrusion: from Theory to Application in Pharmaceutical Formulation-Where Are We Now?

Hot-melt extrusion (HME) is a globally recognized, robust, effective technology that enhances the bioavailability of poorly soluble active pharmaceutical ingredients and offers an efficient continuous manufacturing process. The twin-screw extruder (TSE) offers an extremely resourceful customizable mixer that is used for continuous compounding and granulation by using different combinations of conveying elements, kneading elements (forward and reverse configuration), and distributive mixing elements. TSE is thus efficiently utilized for dry, wet, or melt granulation not only to manufacture dosage forms such as tablets, capsules, or granule-filled sachets, but also for designing novel formulations such as dry powder inhalers, drying units for granules, nanoextrusion, 3D printing, complexation, and amorphous solid dispersions. Over the past decades, combined academic and pharmaceutical industry collaborations have driven novel innovations for HME technology, which has resulted in a substantial increase in published articles and patents. This article summarizes the challenges and models for executing HME scale-up. Additionally, it covers the benefits of continuous manufacturing, process analytical technology (PAT) considerations, and regulatory requirements. In summary, this well-designed review builds upon our earlier publication, probing deeper into the potential of twin-screw extruders (TSE) for various new applications.

Solid-state analysis for pharmaceuticals: Pathways to feasible and meaningful analysis

The solid state of matter is the preferred starting point for designing a pharmaceutical product. This is driven by both patient preferences and the relative ease of supplying a solid pharmaceutical product with desired quality and performance. Solid form diversity is increasingly prevalent as a crucial element in designing these products, which underpins the importance of solid-state analytical methods. This paper provides a critical analysis of challenges related to solid-state analytics, as well as considerations and suggestions for feasible and meaningful pharmaceutical analysis.

Optimal quantification of residence time distribution profiles from a quality assurance perspective

Residence time distribution (RTD) has been widely applied across various fields of chemical engineering, including pharmaceutical manufacturing, for applications such as material traceability, quality assurance, system health monitoring, and fault detection. Determination of a representative RTD, in principle, requires an accurate process analytical technology (PAT) procedure capturing the entire range of tracer concentrations from zero to maximum. Such a wide concentration range creates at least two problems: i) decreased accuracy of the model across the entire range of concentrations, relating to limit of quantification, and ii) ambiguity associated with the detection of the tracer for low concentration levels, relating to limit of detection (LOD). These problems affect not only the RTD profile itself, but also RTD-based applications, which can potentially lead to erroneous conclusions. This article seeks to minimize the impact of these problems by understanding the relative importance of different features of RTD on the detection of out-of-specification (OOS) products. In this work, the RTD obtained experimentally was truncated at different levels, to investigate the impact of the truncation of RTD on funnel plots for OOS detection. The main finding is that the tail of the RTD can be truncated with no loss of accuracy in the determination of exclusion intervals. This enables the manufacturing scientist to focus entirely on the peak region, maximizing the accuracy of chemometric models.

Towards a real-time release of blends and tablets using NIR and Raman spectroscopy at commercial scales

Near Infrared and Raman spectroscopy-based Process Analytical Technology tools were used for monitoring blend uniformity (BU) and content uniformity (CU) for solid oral formulations. A quantitative Partial Least Square model was developed to monitor BU as real-time release testing at a commercial scale. The model having the R2, and root mean square error of 0.9724 and 2.2047, respectively can predict the target concentration of 100% with a 95% confidence interval of 101.85-102.68% even after one year. The tablets from the same blends were investigated for CU using NIR and Raman techniques both in reflection and transmission mode. Raman reflection technique was found to be the best and the PLS model was developed using tablets compressed at different concentrations, hardness, and speed. The model with R2 and RMSE of 0.9766 and 1.9259, respectively was used for the quantification of CU. Both the BU and CU models were validated for accuracy, precision, specificity, linearity, and robustness. The accuracy was proved against the HPLC method with a relative standard deviation of less than 3%. The equivalency for BU by NIR and CU by Raman was evaluated using Schuirmann's Two One-sided tests and found equivalent to HPLC within a 2% acceptable limit.

Interpretable artificial neural networks for retrospective QbD of pharmaceutical tablet manufacturing based on a pilot-scale developmental dataset

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.

Terahertz frequency-domain sensing combined with quantitative multivariate analysis for pharmaceutical tablet inspection

Near infrared (NIR) and Raman spectroscopy combined with multivariate analysis are established techniques for the identification and quantification of chemical properties of pharmaceutical tablets like the concentration of active pharmaceutical ingredients (API). However, these techniques suffer from a high sensitivity to particle size variations and are not ideal for the characterization of physical properties of tablets such as tablet density. In this work, we have explored the feasibility of terahertz frequency-domain spectroscopy, with the advantage of low scattering effects, combined with multivariate analysis to quantify API concentration and tablet density. We studied 33 tablets, consisting of Ibuprofen, Mannitol, and a lubricant with API concentration and filler particle size as the design factors. The terahertz signal was measured in transmission mode across the frequency range 750 GHz to 1.5 THz using a vector network analyzer, frequency extenders, horn antennas, and four off-axis parabolic mirrors. The attenuation spectral data were pre-processed and orthogonal partial least square (OPLS) regression was applied to the spectral data to obtain quantitative prediction models for API concentration and tablet density. The performance of the models was assessed using test sets. While a fair model was obtained for API concentration, a high-quality model was demonstrated for tablet density. The coefficient of determination (R²) for the calibration set was 0.97 for tablet density and 0.98 for API concentration, while the relative prediction errors for the test set were 0.7% and 6% for tablet density and API concentration models, respectively. In conclusion, terahertz spectroscopy demonstrated to be a complementary technique to Raman and NIR spectroscopy, which enables the characterization of physical properties of tablets like tablet density, and the characterization of API concentration with the advantage of low scattering effects.

NIR Spectroscopy as an Online PAT Tool for a Narrow Therapeutic Index Drug: Toward a Platform Approach Across Lab and Pilot Scales for Development of a Powder Blending Monitoring Method and Endpoint Determination

An online near-infrared (NIR) spectroscopy platform system for real-time powder blending monitoring and blend endpoint determination was tested for a phenytoin sodium formulation. The study utilized robust experimental design and multiple sensors to investigate multivariate data acquisition, model development, and model scale-up from lab to manufacturing. The impact of the selection of various blend endpoint algorithms on predicted blend endpoint (i.e., mixing time) was explored. Spectral data collected at two process scales using two NIR spectrometers was incorporated in a single (global) calibration model. Unique endpoints were obtained with different algorithms based on standard deviation, average, and distributions of concentration prediction for major components of the formulation. Control over phenytoin sodium's distribution was considered critical due to its narrow therapeutic index nature. It was found that algorithms sensitive to deviation from target concentration offered the simplest interpretation and consistent trends. In contrast, algorithms sensitive to global homogeneity of active and excipients yielded the longest mixing time to achieve blending endpoint. However, they were potentially more sensitive to subtle uniformity variations. Qualitative algorithms using principal component analysis (PCA) of spectral data yielded the prediction of shortest mixing time for blending endpoint. The hybrid approach of combining NIR data from different scales presents several advantages. It enables simplifying the chemometrics model building process and reduces the cost of model building compared to the approach of using data solely from commercial scale. Success of such a hybrid approach depends on the spectroscopic variability captured at different scales and their relative contributions in the final NIR model.

Soft sensor for content prediction in an integrated continuous pharmaceutical formulation line based on the residence time distribution of unit operations

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%).

Development of Inline Near-Infrared Spectroscopy Method for Real-Time Monitoring of Blend Uniformity of Direct Compression and Granulation-Based Products at Commercial Scales

Blending is a critical intermediate unit operation for all solid oral formulations. For blend uniformity testing, API content in the blend must be quantified precisely. A detailed study was conducted to demonstrate the suitability of inline NIR (near-infrared) spectroscopy for blend uniformity testing of two solid oral formulations: existing direct compression (DC) product with a multistep blending process and granulation-based product with API granules. Both qualitative and quantitative methods were developed at a laboratory scale using statistical moving block standard deviation (MBSD) and multivariate data analysis such as principal component analysis (PCA) and partial least squares (PLS) regression. The qualitative MBSD method demonstrated that there was no need for multiple steps for the existing DC product. Hence, a simplified single-step process was developed for blending. Quantitative PLS models for blending processes of both the products were developed, validated, and successfully implemented at a commercial scale for the real-time release of blends. Results obtained from the validated model were in good agreement with the current method of sampling and chromatography.

Challenges and Opportunities of Implementing Data Fusion in Process Analytical Technology-A Review

The release of the FDA's guidance on Process Analytical Technology has motivated and supported the pharmaceutical industry to deliver consistent quality medicine by acquiring a deeper understanding of the product performance and process interplay. The technical opportunities to reach this high-level control have considerably evolved since 2004 due to the development of advanced analytical sensors and chemometric tools. However, their transfer to the highly regulated pharmaceutical sector has been limited. To this respect, data fusion strategies have been extensively applied in different sectors, such as food or chemical, to provide a more robust performance of the analytical platforms. This survey evaluates the challenges and opportunities of implementing data fusion within the PAT concept by identifying transfer opportunities from other sectors. Special attention is given to the data types available from pharmaceutical manufacturing and their compatibility with data fusion strategies. Furthermore, the integration into Pharma 4.0 is discussed.

In-line fluorescence spectroscopy for quantification of low amount of active pharmaceutical ingredient

The pharmaceutical industry is currently implementing new manufacturing principles and modernizing the related processing solutions. A key element in this development is implementation of process analytical technologies (PAT) for measuring product quality in a real-time mode, ideally for a continuously operating processing line. Near-infrared (NIR) spectroscopy is widely used for this purpose, but has limited use for low concentration formulations, due to its inherent detection limit. Light-induced fluorescence (LIF) spectroscopy is a PAT tool that can be used to quantify low concentrations of active pharmaceutical ingredient, and recent development of instrumentation has made it available for in-line applications. In this study, the content of tryptophan in a dynamic powder flow could be measured as low as 0.10 w/w % with LIF spectroscopy with good accuracy of RMSEP = 0.008 w/w %. Both partial least squares regression and support vector machines (SVM) were investigated, but we found SVM to be the better option due to non-linearities between the calibration test and the in-line measurements. With the use of SVM, LIF spectroscopy is a promising candidate for low concentration applications where NIR is not suitable.

Challenges, opportunities and recent advances in near infrared spectroscopy applications for monitoring blend uniformity in the continuous manufacturing of solid oral dosage forms

Near infrared (NIR) spectroscopy has been widely recognized as a powerful PAT tool for monitoring blend uniformity in continuous manufacturing (CM) processes. However, the dynamic nature of the powder stream and the fast rate at which it moves, compared to batch processes, introduces challenges to NIR quantitative methods for monitoring blend uniformity. For instance, defining the effective sample size interrogated by NIR, selecting the best sampling location for blend monitoring, and ensuring NIR model robustness against influential sources of variability are challenges commonly reported for NIR applications in CM. This article reviews the NIR applications for powder blend monitoring in the continuous manufacturing of solid oral dosage forms, with a particular focus on the challenges, opportunities for method optimization and recent advances with respect three main aspects: effective sample size measured by NIR, probe location and method robustness.

PAT implementation for advanced process control in solid dosage manufacturing - A practical guide

The implementation of continuous pharmaceutical manufacturing requires advanced control strategies rather than traditional end product testing or an operation within a small range of controlled parameters. A high level of automation based on process models and hierarchical control concepts is desired. The relevant tools that have been developed and successfully tested in academic and industrial environments in recent years are now ready for utilization on the commercial scale. To date, the focus in Process Analytical Technology (PAT) has mainly been on achieving process understanding and quality control with the ultimate goal of real-time release testing (RTRT). This work describes the workflow for the development of an in-line monitoring strategy to support PAT-based real-time control actions and its integration into solid dosage manufacturing. All stages are discussed in this paper, from process analysis and definition of the monitoring task to technology assessment and selection, its process integration and the development of data acquisition.

Using residence time distribution in pharmaceutical solid dose manufacturing - A critical review

While continuous manufacturing (CM) of pharmaceutical solid-based drug products has been shown to be advantageous for improving the product quality and process efficiency in alignment with FDA's support of the quality-by-design paradigm (Lee, 2015; Ierapetritou et al., 2016; Plumb, 2005; Schaber, 2011), it is critical to enable full utilization of CM technology for robust production and commercialization (Schaber, 2011; Byrn, 2015). To do so, an important prerequisite is to obtain a detailed understanding of overall process characteristics to develop cost-effective and accurate predictive models for unit operations and process flowsheets. These models are utilized to predict product quality and maintain desired manufacturing efficiency (Ierapetritou et al., 2016). Residence time distribution (RTD) has been a widely used tool to characterize the extent of mixing in pharmaceutical unit operations (Vanhoorne, 2020; Rogers and Ierapetritou, 2015; Teżyk et al., 2015) and manufacturing lines and develop computationally cheap predictive models. These models developed using RTD have been demonstrated to be crucial for various flowsheet applications (Kruisz, 2017; Martinetz, 2018; Tian, 2021). Though extensively used in the literature (Gao et al., 2012), the implementation, execution, evaluation, and assessment of RTD studies has not been standardized by regulatory agencies and can thus lead to ambiguity regarding their accurate implementation. To address this issue and subsequently prevent unforeseen errors in RTD implementation, the presented article aims to aid in developing standardized guidelines through a detailed review and critical discussion of RTD studies in the pharmaceutical manufacturing literature. The review article is divided into two main sections - 1) determination of RTD including different steps for RTD evaluation including experimental approach, data acquisition and pre-treatment, RTD modeling, and RTD metrics and, 2) applications of RTD for solid dose manufacturing. Critical considerations, pertaining to the limitations of RTDs for solid dose manufacturing, are also examined along with a perspective discussion of future avenues of improvement.

Quality-by-design in pharmaceutical development: From current perspectives to practical applications

Current pharmaceutical research directions tend to follow a systematic approach in the field of applied research and development. The concept of quality-by-design (QbD) has been the focus of the current progress of pharmaceutical sciences. It is based on, but not limited, to risk assessment, design of experiments and other computational methods and process analytical technology. These tools offer a well-organized methodology, both to identify and analyse the hazards that should be handled as critical, and are therefore applicable in the control strategy. Once implemented, the QbD approach will augment the comprehension of experts concerning the developed analytical technique or manufacturing process. The main activities are oriented towards the identification of the quality target product profiles, along with the critical quality attributes, the risk management of these and their analysis through in silico aided methods. This review aims to offer an overview of the current standpoints and general applications of QbD methods in pharmaceutical development.

Feasibility of In-line monitoring of critical coating quality attributes via OCT: Thickness, variability, film homogeneity and roughness

The feasibility of Optical Coherence Tomography (OCT) for in-line monitoring of pharmaceutical film coating processes has recently been demonstrated. OCT enables real-time acquisition of high-resolution cross-sectional images of coating layers and computation of coating thickness. In addition, coating quality attributes can be computed based on in-line data. This study assesses the in-line applicability of OCT to various coating functionalities and formulations. Several types of commercial film-coated tablets containing the most common ingredients were investigated. To that end, the tablets were placed into a miniaturized perforated drum. An in-line OCT system was used to monitor the tablet bed. This set-up resembles the final stage of an industrial pan coating process. All investigated coatings were measured, and the coating thickness, homogeneity and roughness were computed. The rotation rate was varied in a range comparable to large-scale coating operations, and no influence on the outcome was observed. The results indicate that OCT can be used to determine end-point and establish in-process control for a wide range of coating formulations. The real-time computation of coating homogeneity and roughness can support process optimization and formulation development.

Additive manufacturing in drug delivery: Innovative drug product design and opportunities for industrial application

Additive manufacturing (AM) or 3D printing is enabling new directions in product design. The adoption of AM in various industrial sectors has led to major transformations. Similarly, AM presents new opportunities in the field of drug delivery, opening new avenues for improved patient care. In this review, we discuss AM as an innovative tool for drug product design. We provide a brief overview of the different AM processes and their respective impact on the design of drug delivery systems. We highlight several enabling features of AM, including unconventional release, customization, and miniaturization, and discuss several applications of AM for the fabrication of drug products. This includes products that have been approved or are in development. As the field matures, there are also several new challenges to broad implementation in the pharmaceutical landscape. We discuss several of these from the regulatory and industrial perspectives and provide an outlook for how these issues may be addressed. The introduction of AM into the field of drug delivery is an enabling technology and many new drug products can be created through productive collaboration of engineers, materials scientists, pharmaceutical scientists, and industrial partners.

In-line monitoring and endpoint determination of percolation process of herbal medicine using ultraviolet spectroscopy combined with convolutional neural network

OBJECTIVES

As a common step in the herbal medicine production process, percolation usually lacks effective process monitoring methods and is often conducted with fixed process parameters. In this study, an in-line ultraviolet (UV) spectroscopy was used for monitoring the Caulis Sinomenii percolation process.

METHODS

The spectra and concentration data of 156 percolation samples from five batches were collected. Convolutional neural networks (CNNs) were used to develop quantitative calibration models. The mean squared error (MSE), mean absolute percentage error (MAPE) and mean absolute error (MAE) were compared to select the proper loss function for developing the CNN models. Meanwhile, partial least square regression (PLSR) was also used to develop calibration models for performance comparison.

KEY FINDINGS

The CNN models with MAPE or MAE as the loss function could provide accurate predictions for all samples. However, CNN models adopting MSE as the loss function tended not to predict low-concentration samples accurately. The CNN models mostly achieved satisfactory results without any preprocessing techniques and surpassed PLSR models in all the performance metrics.

CONCLUSIONS

An in-line UV spectroscopy system combining the CNN algorithm was implemented to monitor the percolation process of Caulis Sinomenii. The system can accurately determine the endpoint of the percolation process.

Advances in Twin-Screw Granulation Processing

Twin-screw granulation (TSG) is a pharmaceutical process that has gained increased interest from the pharmaceutical industry for its potential for the development of oral dosage forms. The technology has evolved rapidly due to the flexibility of the equipment design, the selection of the process variables and the wide range of processed materials. Most importantly, TSG offers the benefits of both batch and continuous manufacturing for pharmaceutical products, accompanied by excellent process control, high product quality which can be achieved through the implementation of Quality by Design (QbD) approaches and the integration of Process Analytical Tools (PAT). Here, we present basic concepts of the various twin-screw granulation techniques and present in detail their advantages and disadvantages. In addition, we discuss the detail of the instrumentation used for TSG and how the critical processing paraments (CPP) affect the critical quality attributes (CQA) of the produced granules. Finally, we present recent advances in TSG continuous manufacturing including the paradigms of modelling of continuous granulation process, QbD approaches coupled with PAT monitoring for granule optimization and process understanding.

Multivariate Spectroscopic Method Lifecycle Management as Part of the Quality Management System

Multivariate model based spectroscopic methods require model maintenance through their lifecycle. A survey conducted by the International Consortium for Innovation and Quality in Pharmaceutical Development (IQ) in 2019 showed that regulatory reporting categories for the model related changes can be a hurdle for the routine use of these types of methods. This article introduces industry best practices on multivariate method and model lifecycle management within the Pharmaceutical Quality System. Case studies are provided to demonstrate how the Established Conditions and Post-Approval Change Management Protocol concepts may be leveraged to allow regulatory flexibility for change management and to encourage the use of these techniques for the development and commercialization of pharmaceutical products.

Integrating sensors for monitoring blend content in a pharmaceutical continuous manufacturing plant

In a pharmaceutical manufacturing process, Critical Quality Attributes (CQAs) need to be monitored not only for the final product but also for intermediates. Blend uniformity of powders is one such attribute that needs to be measured to ensure the quality of the final product. Multiple in-line sensors were implemented within a Direct Compaction (DC) continuous tablet manufacturing line to monitor the blend content of the powders. In most cases, since the primary ingredient of interest is the active pharmaceutical ingredient (API), the concentration (potency) of the API was monitored/predicted over the course of manufacturing. For the calibration model building process, a unique setup involving dynamic powder spectral acquisition method was used. This setup was aimed at mimicking the powder flow characteristics within the manufacturing line, while at the same time utilizing a relatively small amount of powder. A Raman probe and a portable NIR were used concurrently at the exit of the blending process before the tableting stage. The performance of the two sensors and their respective models were evaluated in terms of accuracy, precision, operating range, measurement frequency, placement, reliability, robustness, and compared to predictions using gravimetric feed rates. Additionally, their performances were validated by off-line traditional analytical measurements.

Pharmaceutical application of multivariate modelling techniques: a review on the manufacturing of tablets

The tablet manufacturing process is a complex system, especially in continuous manufacturing (CM). It includes multiple unit operations, such as mixing, granulation, and tableting. In tablet manufacturing, critical quality attributes are influenced by multiple factorial relationships between material properties, process variables, and interactions. Moreover, the variation in raw material attributes and manufacturing processes is an inherent characteristic and seriously affects the quality of pharmaceutical products. To deepen our understanding of the tablet manufacturing process, multivariable modeling techniques can replace univariate analysis to investigate tablet manufacturing. In this review, the roles of the most prominent multivariate modeling techniques in the tablet manufacturing process are discussed. The review mainly focuses on applying multivariate modeling techniques to process understanding, optimization, process monitoring, and process control within multiple unit operations. To minimize the errors in the process of modeling, good modeling practice (GMoP) was introduced into the pharmaceutical process. Furthermore, current progress in the continuous manufacturing of tablets and the role of multivariate modeling techniques in continuous manufacturing are introduced. In this review, information is provided to both researchers and manufacturers to improve tablet quality.

Residence time distribution modelling and in line monitoring of drug concentration in a tablet press feed frame containing dead zones

The presence of a 'significant dead zone' in any continuous manufacturing equipment may affect the product quality and need to be investigated systematically. Dead zone will affect the residence time distribution (RTD) of continuous manufacturing and thus the mixing and product quality. Tablet press (feed frame) is one of unit operations that directly influence the critical quality attributes (CQA's). However, currently no systematic methods and tools are available to characterize and model the feed frame dead zone. In this manuscript, the RTD of the tablet press feed frame containing dead zone is investigated. Step-change experiments revealed that the feed frame could be expressed as a traditional continuous stirred tank model. The volume fractions of the dead zones are determined experimentally as well as using RTD model. In addition, an in-line NIR method for drug concentration monitoring inside the feed frame is also developed. The developed NIR calibration model enables to monitor the drug concentration precisely and detect the variation immediately with the probe positioned right above the left paddle. It is also found that the feed frame paddle speed slightly affects the predictive accuracy of NIR, while the die disc speed has no significant effect.

Design of condition-based maintenance framework for process operations management in pharmaceutical continuous manufacturing

Continuous manufacturing, an emerging technology in the pharmaceutical industry, has the potential to increase the efficiency, and agility of pharmaceutical manufacturing processes. To realize these potential benefits of continuous operations, effectively managing materials, equipment, analyzers, and data is vital. Developments for continuous pharmaceutical manufacturing have led to novel technologies and methods for processing material, designing and configuring individual equipment and process analyzers, as well as implementing strategies for active process control. However, limited work has been reported on managing abnormal conditions during operations to prevent unplanned deviations and downtime and sustain system capabilities. Moreover, although the sourcing, analysis, and management of real-time data have received growing attention, limited discussion exists on the continued verification of the infrastructure for ensuring reliable operations. Hence, this work introduces condition-based maintenance (CBM) as a general strategy for continually verifying and sustaining advanced pharmaceutical manufacturing systems, with a focus on the continuous manufacture of oral solid drug products (OSD-CM). Frameworks, such as CBM, benefit unified efforts towards continued verification and operational excellence by leveraging process knowledge and the availability of real-time data. A vital implementation consideration for manufacturing operations management applications, such as CBM, is a systems architecture and an enabling infrastructure. This work outlines the systems architecture design for CBM in OSD-CM and highlights sample fault scenarios involving equipment and process analyzers. For illustrative purposes, this work also describes the infrastructure implemented on an OSD-CM testbed, which uses commercially available automation systems and leverages enterprise architecture standards. With the increasing digitalization of manufacturing operations in the pharmaceutical industry, proactively using process data towards modernizing maintenance practices is relevant to a single unit operation as well as to a series of physically integrated unit operations.

Effect of Polymers and Storage Relative Humidity on Amorphous Rebamipide and Its Solid Dispersion Transformation: Multiple Spectra Chemometrics of Powder X-Ray Diffraction and Near-Infrared Spectroscopy

This study aimed to investigate the effect of polymers and storage relative humidity on amorphous rebamipide (RB) and its solid dispersion phase transformation using chemometrics based on multiple datasets. The amorphous RB was prepared using particle mixture and grinding methods with hydroxypropyl cellulose, polyvinylpyrrolidone, and sodium dodecyl sulfate. Prepared amorphous RB and solid dispersion samples were stored under a relative humidity of 30% and 75% for four weeks. Infrared spectra of the dispersion samples suggested that the hydrogen bond network was constructed among quinolinone, carbonyl acid, and amide of RB and other polymers. The dataset combining near-infrared (NIR) spectra and powder X-ray diffractograms were applied to principal component analysis (PCA). The relationship between diffractograms and NIR spectra was evaluated using loadings and the PCA score. The multiple spectra analysis is useful for evaluating model amorphous active pharmaceutical ingredients without a standard sample.

Development and Evaluation of an In-line and On-line Monitoring System for Granule Size Distributions in Continuous Roll Compaction/Dry Granulation Based on Laser Diffraction

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.

A critical review of recent trends, and a future perspective of optical spectroscopy as PAT in biopharmaceutical downstream processing

As competition in the biopharmaceutical market gets keener due to the market entry of biosimilars, process analytical technologies (PATs) play an important role for process automation and cost reduction. This article will give a general overview and address the recent innovations and applications of spectroscopic methods as PAT tools in the downstream processing of biologics. As data analysis strategies are a crucial part of PAT, the review discusses frequently used data analysis techniques and addresses data fusion methodologies as the combination of several sensors is moving forward in the field. The last chapter will give an outlook on the application of spectroscopic methods in combination with chemometrics and model predictive control (MPC) for downstream processes. Graphical abstract.

Recent progress in continuous manufacturing of oral solid dosage forms

Continuous drug product manufacturing is slowly being implemented in the pharmaceutical industry. Although the benefits related to the quality and cost of continuous manufacturing are widely recognized, several challenges hampered the widespread introduction of continuous manufacturing of drug products. Current review presents an overview of state-of-the art research, equipment, process analytical technology implementations and advanced control strategies. Additionally, guidelines and regulatory viewpoints on implementation of continuous manufacturing in the pharmaceutical industry are discussed.

Feeding of particle-based materials in continuous solid dosage manufacturing: a material science perspective

The pharmaceutical industry today is experiencing a paradigm shift from batch to continuous manufacturing, which promises greater flexibility to target diverse populations, as well as more-consistent product quality to ensure best efficacy. However, shifting to continuous processing means that even basic process steps, such as feeding, can become unexpected but are crucially important. In this review, we will present the fundamental differences between dispensing (batch) and feeding (continuous) and how they impact the formulation design space. We will further outline our rational development approach, applicable to continuous unit operations in general, which includes standardized material and process characterization, as well as predictive modeling based on advanced, multidomain simulation tools.

Towards a novel continuous HME-Tableting line: Process development and control concept

The objective of this study was to develop a novel closed-loop controlled continuous tablet manufacturing line, which first uses hot melt extrusion (HME) to produce pellets based on API and a polymer matrix. Such systems can be used to make complex pharmaceutical formulations, e.g., amorphous solid dispersions of poorly soluble APIs. The pellets are then fed to a direct compaction (DC) line blended with an external phase and tableted continuously. Fully-automated processing requires advanced control strategies, e.g., for reacting to raw material variations and process events. While many tools have been proposed for in-line process monitoring and real-time data acquisition, establishing real-time automated feedback control based on in-process control strategies remains a challenge. Control loops were implemented to assess the quality attributes of intermediates and product and to coordinate the mass flow rate between the unit operations. Feedback control for the blend concentration, strand temperature and pellet thickness was accomplished via proportional integral derivative (PID) controllers. The tablet press hopper level was controlled using a model predictive controller. To control the mass flow rates in all unit operations, several concepts were developed, with the tablet press, the extruder or none assigned to be the master unit of the line, and compared via the simulation.

Why should the pharmaceutical industry claim for the implementation of second-order chemometric models-A critical review

Today, pharmaceutical products are submitted to a large number of analytical tests, planned to either ensure or construct their quality. The official methods of analysis used to perform these determinations are very different in nature, but almost all demand the intensive use of reagents and manpower as major drawbacks. Thus, analytical development is continuously evolving to find fast and smart approaches. First-order chemometric models are well-known in the pharmaceutical industry, and are extensively used in many fields. Such is the impact of chemometric models that regulatory agencies include them in guidelines and compendia. However, the mention or practical application of higher-order models in the pharmaceutical industry is rather scarce. Herein, we try to bring a brief introduction to chemometric models and useful literature references, focusing on higher-order chemometric models (HOCM) applied to reduce manpower, reagent consumption, and time of analysis, without sacrificing accuracy or precision, while gaining selectivity and sensitivity. The advantages and drawbacks of HOCM are also discussed, and the comparison to first-order chemometric models is also analyzed. Along the work, HOCM are evidenced as a powerful tool for the pharmaceutical industry; moreover, its implementation is shown during several steps of production, such as identification, purity test and assay, and other applications as homogeneity of API distribution, Process Analytical Technology (PAT), Quality by Design (QbD) or natural product fingerprinting. Among these topics, qualitative and quantitative applications were covered. Experimental approaches of chemometrics coupled to several analytical techniques such as UV-vis, fluorescence and vibrational spectroscopies (NIR, MIR and Raman), and other techniques as hyphenated-chromatography and electrochemical techniques applied to production and analysis are discussed throughout this work.

Benefits of Fractal Approaches in Solid Dosage Form Development

Pharmaceutical formulations are complex systems consisting of active pharmaceutical ingredient(s) and a number of excipients selected to provide the intended performance of the product. The understanding of materials' properties and technological processes is a requirement for building quality into pharmaceutical products. Such understanding is gained mostly from empirical correlations of material and process factors with quality attributes of the final product. However, it seems also important to gain knowledge based on mechanistic considerations. Promising is here to study morphological and/or topological characteristics of particles and their aggregates. These geometric aspects must be taken into account to better understand how product attributes emerge from raw materials, which includes, for example, mechanical tablet properties, disintegration or dissolution behavior. Regulatory agencies worldwide are promoting the use of physical models in pharmaceutics to design quality into a final product. This review deals with pharmaceutical applications of theoretical models, focusing on percolation theory, fractal, and multifractal geometry. The use of these so-called fractal approaches improves the understanding of different aspects in the development of solid dosage forms, for example by identifying critical drug and excipient concentrations, as well as to study effects of heterogeneity on dosage form performance. The aim is to link micro- and macrostructure to the emerging quality attributes of the pharmaceutical solid dosage forms as a strategy to enhance mechanistic understanding and to advance pharmaceutical development and manufacturing processes.

Continuous Twin Screw Wet Granulation and Drying-Control Strategy for Drug Product Manufacturing

The use of continuous manufacturing has been increasing within the pharmaceutical industry over the last few years. Continuous direct compression has been the focus of publications on the topic to date. The use of wet granulation can improve segregation resistance, uniformity, enhance density, and flow properties for improved tabletability, or improve stability of products that cannot be manufactured by using a direction compression process. This article focuses on development of appropriate control strategies for continuous wet granulation (especially twin screw wet granulation) through equipment design, material properties and manufacturing process along with areas where additional understanding is required. The article also discusses the use of process analytical technologies as part of the control and automation approach to ensure a higher assurance of product quality. Increased understanding of continuous wet granulation should result in increased utilization of the technique, thereby allowing for an increase in diversity of products manufactured by continuous manufacturing and the benefits that comes with a more complex process such as wet granulation compared with direct compression process.

Integrated continuous manufacturing in pharmaceutical industry: current evolutionary steps toward revolutionary future

Continuous manufacturing (CM) has the potential to provide pharmaceutical products with better quality, improved yield and with reduced cost and time. Moreover, ease of scale-up, small manufacturing footprint and on-line/in-line monitoring and control of the process are other merits for CM. Regulating authorities are supporting the adoption of CM by pharmaceutical manufacturers through issuing proper guidelines. However, implementation of this technology in pharmaceutical industry is encountered by a number of challenges regarding the process development and quality assurance. This article provides a background on the implementation of CM in pharmaceutical industry, literature survey of the most recent state-of-the-art technologies and critically discussing the encountered challenges and its future prospective in pharmaceutical industry.

The development of an inline Raman spectroscopic analysis method as a quality control tool for hot melt extruded ramipril fixed-dose combination products

Currently in the pharmaceutical industry, continuous manufacturing is an area of significant interest. In particular, hot-melt extrusion (HME) offers many advantages and has been shown to significantly reduce the number of processing steps relative to a conventional product manufacturing line. To control product quality during HME without process interruption, integration of inline analytical technology is critical. Vibrational spectroscopy (Raman, NIR and FT-IR) is often employed and used for real-time measurements because of the non-destructive and rapid nature of these analytical techniques. However, the establishment of reliable Process Analytical Technology (PAT) tools for HME of thermolabile drugs is challenging. Indeed, the Raman effect is inherently weak and might be subject to interference. Moreover, during HME, heating and photodecomposition can occur and disrupt spectra acquisition. The aim of this research article was to explore the use of inline Raman spectroscopy to characterise a thermolabile drug, ramipril (RMP), during continuous HME processing. Offline measurements by HPLC, LC-MS and Raman spectroscopy were used to characterise RMP and its main degradation product, ramipril-diketopiperazine (RMP-DKP, impurity K). A set of HME experiments together with inline Raman spectroscopic analyses were performed. The feasibility of implementing inline Raman spectroscopic analysis to quantify the level of RMP and RMP-DKP in the extrudate was addressed. Two regions in the Raman spectrum were selected to differentiate RMP and RMP-DKP. When regions were combined, a principle component analysis (PCA) model defined by these two main components (PC 1 = 50.1% and PC 2 = 45%) was established. Using HPLC analyses, we were able to confirm that the PC 1 score was attributed to the level of RMP-DKP, and the PC 2 score was related to the RMP drug content. Investigation of the PCA scatterplot indicated that HME processing temperature was not the only factor causing RMP degradation. Additionally, the plasticiser content, feeding speed and screw rotating speed contributed to RMP degradation during HME processing.

Continuous Manufacturing in Pharmaceutical Process Development and Manufacturing

The pharmaceutical industry has found new applications for the use of continuous processing for the manufacture of new therapies currently in development. The transformation has been encouraged by regulatory bodies as well as driven by cost reduction, decreased development cycles, access to new chemistries not practical in batch, improved safety, flexible manufacturing platforms, and improved product quality assurance. The transformation from batch to continuous manufacturing processing is the focus of this review. The review is limited to small, chemically synthesized organic molecules and encompasses the manufacture of both active pharmaceutical ingredients (APIs) and the subsequent drug product. Continuous drug product is currently used in approved processes. A few examples of production of APIs under current good manufacturing practice conditions using continuous processing steps have been published in the past five years, but they are lagging behind continuous drug product with respect to regulatory filings.

Optimisation of an in-line Raman spectroscopic method for continuous API quantification during twin-screw wet granulation and its application for process characterisation

In a previous publication, the development of an in-line Raman spectroscopic method for continuous API quantification during twin-screw wet granulation was presented. An in-line method was developed successfully and the developed method showed an acceptable prediction error. A disadvantage of the developed method was that a measurement was only possible in the dark since light influenced the Raman spectra and made a data interpretation impossible. Therefore, the measurement setup for the implementation of the Raman probe was optimised in the present study to allow a measurement in interior light and to further improve the predictive performance. With the optimised setup, two different calibration models were developed and compared. For the first calibration model, spectra were collected in the dark as before and for the second in interior light. The dark calibration model was able to predict the API content with an RMSEP of 0.31% and the light model with an RMSEP of 0.29%. Thus, both PLS models showed prediction errors in the same order. Consequently, it was possible to evaluate Raman spectra which were collected in interior light. Further, the previous prediction error of 0.60% could be clearly decreased. The optimised Raman method was applicable to evaluate the mixing efficiency of the twin-screw granulator during a split feeding process. The quality of the mixture was monitored behind different barrel sections by Raman spectroscopy and the corresponding API concentrations were predicted by the developed calibration model. For a screw length of 40 D and a screw configuration with two kneading blocks a good mixing ability was observed. For a screw length of 20 D and one kneading block the mixing efficiency was largely acceptable whereas a broad scattering of the API content was observed when no kneading blocks were used. In a second part, an experimental design was performed for each screw configuration to evaluate the influence of the barrel-fill level and screw speed on the mixing efficiency. The quality of the mixture using the entire barrel length was minimally influenced by the fill-level. For the other two positions, the screw speed influenced the quality of the mixture slightly. Thus, for an appropriate mixing, a certain barrel length and a screw configuration with two kneading blocks were necessary.

Inline Spectroscopy: From Concept to Function

The field of applied spectroscopy is strongly dominated by publications presenting proof-of-concepts, lab set-ups, and demonstrations. In contrast, the corresponding number of commercial successes of inline spectroscopy is surprisingly lower. This article discusses inline spectroscopy from an instrumentation perspective. It is the authors' firm belief that the success of inline spectroscopy lies in the understanding of how the design and implementation of the optical instrumentation affects the data quality, and how this in turn will limit or enhance the performance of the prediction model. This article emphasizes the need for a strong, multidisciplinary design team, whose design process is rooted in first principles, to bridge the technology "valley of death" and convert research in applied spectroscopy into commercially successful solutions.