Advanced process design and understanding of continuous twin-screw granulation via implementation of in-line process analytical technologies

Statistical data treatment for residence time distribution studies in pharmaceutical manufacturing

Residence time distribution (RTD) method has been widely used in the pharmaceutical manufacturing for understanding powder dynamics within unit operations and continuous integrated manufacturing lines. The dynamics thus captured is then used to develop predictive models for unit operations and important RTD-based applications ensuring product quality assurance. Despite thorough efforts in tracer selection, data acquisition, and calibration model development to obtain tracer concentration profiles for RTD studies, there can exist significant noise in these profiles. This noise can make it challenging to identify the underlying signal and get a representative RTD of the system under study. Such concerns have previously indicated the importance of noise handling for RTD measurements in literature. However, the literature does not provide sufficient information on noise handling or data treatment strategies for RTD studies. To this end, we investigate the impact of varying levels of noise using different tracers on measurement of RTD profile and its applications. We quantify the impact of different denoising methods (time and frequency averaging methods). Through this investigation, we see that Savitsky Golay filtering turns out to a good method for denoising RTD profiles despite varying noise levels. The investigation is performed such that the key features of the RTD profile (which are important for RTD based applications) are preserved. Subsequently, we also investigate the impact of denoising on RTD-based applications such as out-of-specification (OOS) analysis and RTD modeling. The results show that the degree of noise levels considered in this work do not significantly impact the RTD-based applications.

Optimizing Energy Efficiency of a Twin-Screw Granulation Process in Real-Time Using a Long Short-Term Memory (LSTM) Network

Traditional pharmaceutical manufacturing processes for solid oral dosage forms can be inefficient and have been known to produce a large amount of undesired product. With the progressing trend of achieving carbon neutrality, there is an impetus to increase the energy efficiency of these manufacturing processes while maintaining the critical quality attributes of the product. One of the important steps in downstream pharmaceutical manufacturing is wet granulation, and within that, twin screw granulation (TSG) is a popular continuous manufacturing technique. In this study, the energy efficiency of the TSG process was maximized by combining a long-term memory (LSTM) model with an optimization algorithm. The LSTM model was trained on time-series process data obtained from the TSG experimental runs. The optimization process, with the objective of maximizing energy efficiency, was performed using a stochastic optimization algorithm, and constraints were enforced on the process parameter design space. Experimental runs at the optimal process parameters were conducted on the TSG equipment with updates occurring at predefined intervals depending on the optimization scenarios. The purpose of these experimental runs was to validate the capability of increasing the overall process energy efficiency when operating at the optimized process parameters. A maximum increase of 27% was obtained between two tested optimization scenarios while maintaining the yield of the granules at the end of the twin-screw granulation process.

Pharmaceutical Application of Process Understanding and Optimization Techniques: A Review on the Continuous Twin-Screw Wet Granulation

Twin-screw wet granulation (TSWG) is a method of continuous pharmaceutical manufacturing and a potential alternative method to batch granulation processes. It has attracted more and more interest nowadays due to its high efficiency, robustness, and applications. To improve both the product quality and process efficiency, the process understanding is critical. This article reviews the recent work in process understanding and optimization for TSWG. Various aspects of the progress in TSWG like process model construction, process monitoring method development, and the strategy of process control for TSWG have been thoroughly analyzed and discussed. The process modeling technique including the empirical model, the mechanistic model, and the hybrid model in the TSWG process are presented to increase the knowledge of the granulation process, and the influence of process parameters involved in granulation process on granule properties by experimental study are highlighted. The study analyzed several process monitoring tools and the associated technologies used to monitor granule attributes. In addition, control strategies based on process analytical technology (PAT) are presented as a reference to enhance product quality and ensure the applicability and capability of continuous manufacturing (CM) processes. Furthermore, this article aims to review the current research progress in an effort to make recommendations for further research in process understanding and development of TSWG.

AI-based Analysis of In-line Process Endoscope images for Real-time Particle Size Measurement in a Continuous Pharmaceutical Milling Process

This paper presents a case study on the first in-line application of AI-based image analysis for real-time pharmaceutical particle size measurement in a continuous milling process. An AI-based imaging system, which utilises a rigid endoscope, was tested for the real-time particle size measurement of solid NaCl powder used as a model API in the range of 200-1000µm. After creating a dataset containing annotated images of NaCl particles, it was used to train an AI model for detecting particles and measuring their size. The developed system could analyse overlapping particles without dispersing air, thus broadening its applicability. The performance of the system was evaluated by measuring pre-sifted NaCl samples with the imaging tool, after which it was installed into a continuous mill for in-line particle size measurement of a milling process. By analysing ∼100 particles/s, the system was able to accurately measure the particle size of sifted NaCl samples and detect particle size reduction when applied in the milling process. The Dv50 values and PSDs measured real-time with the AI-based system correlated well with the reference laser diffraction measurements (<6% mean absolute difference over the measured samples). The AI-based imaging system shows great potential for in-line particle size analysis, which, in line with the latest pharmaceutical QC trends, can provide valuable information for process development and control.

Advances in numerical simulation of unit operations for tablet preparation

Recently, there has been an increase in the use of numerical simulation technology in pharmaceutical preparation processes. Numerical simulation can contribute to a better understanding of processes, reduce experimental costs, optimize preparation processes, and improve product quality. The intermediate material of most dosage forms is powder or granules, especially in the case of solid preparations. The macroscopic behavior of particle materials is controlled by the interactions of individual particles with each other and surrounding fluids. Therefore, it is very important to analyze and control the microscopic details of the preparation process for solid preparations. Since tablets are one of the most widely used oral solid preparations, and the preparation process is relatively complex and involves numerous units of operation, it is especially important to analyze and control the tablet production process. The present paper discusses recent advances in numerical simulation technology for the preparation of tablets, including drying, mixing, granulation, tableting, and coating. It covers computational fluid dynamics (CFD), discrete element method (DEM), population balance model (PBM), finite element method (FEM), Lattice-Boltzmann model (LBM), and Monte Carlo model (MC). The application and deficiencies of these models in tablet preparation unit operations are discussed. Furthermore, the paper provides a systematic reference for the control and analysis of the tablet preparation process and provides insight into the future direction of numerical simulation technology in the pharmaceutical industry.

Risk Assessment for a Twin-Screw Granulation Process Using a Supervised Physics-Constrained Auto-encoder and Support Vector Machine Framework

Quality risk management is an important task when it pertains to the pharmaceutical industry, as this is directly related to product performance. With the ICH Q9 guidelines, several regulatory bodies have encouraged the pharmaceutical industry to implement risk management plans using scientific and systemic approaches such as quality-by-design to asses product quality. However, the implementation of such methods has been challenging as assessment of risks requires accurate quantitative models to predict changes in quality when variations occur. This study describes a framework that quantitatively assesses risk for a twin screw wet granulation process. This framework consists of a physics-constrained autoencoder system, whose outputs are constrained using physics-based boundary conditions. The latent variables obtained from the auto-encoder are used in a support vector machine-based classifier to understand the granule growth behavior occurring within the system. This framework is able to predict the process outcomes with 86% accuracy and classify the granule growth regimes with a true positive rate of 0.73. Based on the classification the risk associated with the process can be estimated.

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.

Indirect monitoring of ultralow dose API content in continuous wet granulation and tableting by machine vision

This paper presents new machine vision-based methods for indirect real-time quantification of ultralow drug content during continuous twin-screw wet granulation and tableting. Granulation was performed with a solution containing carvedilol (CAR) as API in the ultralow dose range (0.05w/w% in the granule) and the addition of riboflavin (RI) as a coloured tracer. An in-line calibration in the range of 0.047-0.058 w/w% was prepared for the measurement of CAR concentration using colour analysis (CA) and particle size analysis (PSA), and the validation with HPLC resulted in respective relative errors of 2.62% and 2.30% showing great accuracy. To improve the technique, a second in-line calibration was conducted in a broader CAR concentration range of 0.039-0.063 w/w% utilizing only half the amount of RI (0.045 w/w%), while doubling the output of the granulation line to 2 kg/h, producing a relative error of 4.51% and 4.29%, respectively. Finally, it was shown that the CA technique can also be carried on to monitor the CAR content of tablets in the 42-62 μg dose range with a relative error of 5.20%. Machine vision was proven to be a potent indirect method for the in-line, determination and monitoring of ultralow API content during continuous manufacturing.

Analysis of the Effects of Process Parameters on Start-Up Operation in Continuous Wet Granulation

Toward further implementation of continuous tablet manufacturing, one key issue is the time needed for start-up operation because it could lead to lower product yield and reduced economic performance. The behavior of the start-up operation is not well understood; moreover, the definition of the start-up time is still unclear. This work investigates the effects of process parameters on the start-up operation in continuous wet granulation, which is a critical unit operation in solid drug manufacturing. The profiles of torque and granule size distribution were monitored and measured for the first hour of operation, including the start-up phase. We analyzed the impact of process parameters based on design of experiments and performed an economic assessment to see the effects of the start-up operation. The torque profiles indicated that liquid-to-solid ratio and screw speed would affect the start-up operation, whereas different start-up behavior resulted in different granule size. Depending on the indicator used to define the start-up operation, the economic optimal point was significantly different. The results of this study stress that the start-up time differs according to the process parameters and used definition, e.g., indicators and criteria. This aspect should be considered for the further study and regulation of continuous manufacturing.

Binder-free twin-screw melt granulation: An effective approach to manufacture high-dose API formulations

This study investigates the use of twin-screw binder-free melt granulation (BFMG) in the development of high-dose solid dose formulations for low melting point thermally stable drugs. Both ibuprofen and guaifenesin are examined. By granulating pure API powder, it is shown that BFMG can successfully be used to produce granules that contain 100% API. A design of experiments (DoE) response surface methodology was used to establish the design space for the end-product. The effects of the most relevant process variables (barrel operating temperature, powder feed rate, screw speed and screw configuration) on granule properties (outlet temperature, size distribution, morphology, flowability, compressibility, porosity) and tablet attributes (tensile strength and in-vitro dissolution) were thoroughly studied. Barrel temperature (alone or in interactions with the other variables) represented the most significant variable for both drugs since it governs the formation of granules by partial melting and subsequent agglomeration of the fed powder. Interestingly, the shear action originated by screw speed and screw configuration resulted in various significant responses depending on the drug substance, indicating that it can also be affected by the nature of the processed molecule. Flow properties were improved (i.e., lower Hausner ratio) for both drugs after formation of granules. Tabletability was also tested by preparing 600 mg tablets for all samples. Surprisingly, the resulting granules were highly compactible, requiring only 1% lubricant to form strong tablets containing 96% API and 3% disintegrant. The results also showed that tablets become harder as the granule size increased, especially for guaifenesin. As expected, in-vitro dissolution results indicated that tablets and capsules showed slightly slower dissolution rates than the granules.

Process Analytical Technology Tools for Monitoring Pharmaceutical Unit Operations: A Control Strategy for Continuous Process Verification

Various frameworks and methods, such as quality by design (QbD), real time release test (RTRT), and continuous process verification (CPV), have been introduced to improve drug product quality in the pharmaceutical industry. The methods recognize that an appropriate combination of process controls and predefined material attributes and intermediate quality attributes (IQAs) during processing may provide greater assurance of product quality than end-product testing. The efficient analysis method to monitor the relationship between process and quality should be used. Process analytical technology (PAT) was introduced to analyze IQAs during the process of establishing regulatory specifications and facilitating continuous manufacturing improvement. Although PAT was introduced in the pharmaceutical industry in the early 21st century, new PAT tools have been introduced during the last 20 years. In this review, we present the recent pharmaceutical PAT tools and their application in pharmaceutical unit operations. Based on unit operations, the significant IQAs monitored by PAT are presented to establish a control strategy for CPV and real time release testing (RTRT). In addition, the equipment type used in unit operation, PAT tools, multivariate statistical tools, and mathematical preprocessing are introduced, along with relevant literature. This review suggests that various PAT tools are rapidly advancing, and various IQAs are efficiently and precisely monitored in the pharmaceutical industry. Therefore, PAT could be a fundamental tool for the present QbD and CPV to improve drug product quality.

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.

Digital Twins in Pharmaceutical and Biopharmaceutical Manufacturing: A Literature Review

The development and application of emerging technologies of Industry 4.0 enable the realization of digital twins (DT), which facilitates the transformation of the manufacturing sector to a more agile and intelligent one. DTs are virtual constructs of physical systems that mirror the behavior and dynamics of such physical systems. A fully developed DT consists of physical components, virtual components, and information communications between the two. Integrated DTs are being applied in various processes and product industries. Although the pharmaceutical industry has evolved recently to adopt Quality-by-Design (QbD) initiatives and is undergoing a paradigm shift of digitalization to embrace Industry 4.0, there has not been a full DT application in pharmaceutical manufacturing. Therefore, there is a critical need to examine the progress of the pharmaceutical industry towards implementing DT solutions. The aim of this narrative literature review is to give an overview of the current status of DT development and its application in pharmaceutical and biopharmaceutical manufacturing. State-of-the-art Process Analytical Technology (PAT) developments, process modeling approaches, and data integration studies are reviewed. Challenges and opportunities for future research in this field are also discussed.

Model-Based Scale-Up Methodologies for Pharmaceutical Granulation

In the pharmaceutical industry, it is a major challenge to maintain consistent quality of drug products when the batch scale of a process is changed from a laboratory scale to a pilot or commercial scale. Generally, a pharmaceutical manufacturing process involves various unit operations, such as blending, granulation, milling, tableting and coating and the process parameters of a unit operation have significant effects on the quality of the drug product. Depending on the change in batch scale, various process parameters should be strategically controlled to ensure consistent quality attributes of a drug product. In particular, the granulation may be significantly influenced by scale variation as a result of changes in various process parameters and equipment geometry. In this study, model-based scale-up methodologies for pharmaceutical granulation are presented, along with data from various related reports. The first is an engineering-based modeling method that uses dimensionless numbers based on process similarity. The second is a process analytical technology-based modeling method that maintains the desired quality attributes through flexible adjustment of process parameters by monitoring the quality attributes of process products in real time. The third is a physics-based modeling method that involves a process simulation that understands and predicts drug quality through calculation of the behavior of the process using physics related to the process. The applications of these three scale-up methods are summarized according to granulation mechanisms, such as wet granulation and dry granulation. This review shows that these model-based scale-up methodologies provide a systematic process strategy that can ensure the quality of drug products in the pharmaceutical industry.

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.

Determining key parameters of continuous wet granulation for tablet quality and productivity: A case in ethenzamide

This paper aims to determine key parameters that affect tablet quality and productivity in continuous tablet manufacturing. Experiments were performed based on design of experiments using a continuous high-shear granulator and ethenzamide as the active pharmaceutical ingredient. To guide a systematic and comprehensive parameter analysis, a parameter framework was defined that comprised five input parameters on raw material properties and process parameters, 11 intermediate parameters on granule properties, and 11 output parameters on tablet quality and productivity. The interrelationships were analyzed statistically and were described as matrix functions. The liquid/solid ratio was the key parameter that affected circularity, density, and flowability as the granule properties, and disintegration and dissolution as the tablet quality. The maximum acceptable manufacturing rate that governs productivity was also affected by the liquid/solid ratio. Circularity was found to affect disintegration and dissolution. This result was specific to the setup of the study, but suggested development opportunities for a new process analytical technology system/quality-by-design application based on circularity. In addition, practical findings were obtained as follows: (1) high-speed manufacturing favored a lower liquid/solid ratio, and (2) high circularity slowed down disintegration/dissolution. This obtained knowledge will enhance the applicability of continuous technology in an actual manufacturing environment.

Characterization of NIR interfaces for the feeding and in-line monitoring of a continuous granulation process

This work describes the characterization of three NIR interfaces intended to monitor a continuous granulation process. Two interfaces (i.e. a barrel interface and a rotating paddle interface) were evaluated to monitor the API concentration at the entrance of the granulator, and a third interface (i.e. an outlet interface), was evaluated to examine the quality of the resulting outlet granules. The barrel interface provided an assessment of the API concentration during the feeding process by scanning the material conveyed by the screws of the loss-in-weight feeder. The rotating paddle interface analyzed discrete amounts of powder upon exiting the feeder via the accumulation of material on the paddles. Partial Least Squares (PLS) calibration models were developed using the same powder blends for the two inlet interfaces and using the outlet granules for the outlet interface. Five independent batches were used to evaluate the prediction performance of each inlet calibration model. The outlet interface produced the lowest error of prediction due to the homogeneity of the granules. The barrel interface produced lower errors of prediction than the rotating paddle interface. However, powder density affected only the barrel interface, producing deviations in the predicted values. Therefore, powder density is a factor that should be considered in the calibration sample design for spectroscopic measurements when using this type of interface. A variographic analysis demonstrated that the continuous 1-dimensional motion in the barrel and outlet interfaces produced representative measurements of each batch during calibration and test experiments, generating a low minimum practical error (MPE).