Simulation of a tablet coating process at different scales using DEM

Ascertain a minimum coating thickness for acid protection of enteric coatings by means of optical coherence tomography

Enteric coatings are designed to protect active pharmaceutical ingredients (APIs) against untimely release in the stomach. Acid protection of such coatings depends on the coating layer thickness and integrity, which must be determined in an accurate and reliable way to ensure the final product's desired performance. Our work addresses the use of optical coherence tomography (OCT) for characterizing the coating thickness and variability of an enteric-coated drug product and linking them to resistance against gastric fluid. In this study, three batches of enteric-coated tablets drawn during the manufacturing process were investigated. An industrial OCT system was used to establish the coating thickness variability of single tablets (intra-tablet), all tablets in a batch (inter-tablet) and between the batches (inter-batch). Based on the large amount of OCT data, we calculated a critical coating thickness for the investigated film coating, which was found to be 27.4 µm. The corresponding distribution has a mean coating thickness of 44.3 µm ± 7.8 µm. The final coated product has a final mean coating thickness of 63.4 µm ± 8.7 µm, guaranteeing that all tablets meet the quality criterion (i.e., acid protection). Based on the measured thickness distributions, already known distribution functions were considered and an additional, new function was proposed for characterizing the coating thickness distributions in the early stages of industrial coating processes. The proposed approach can be transferred to in-line monitoring of the tablet coating processes, which could drastically improve the production efficiency by ultimately allowing real-time release testing (RTRT).

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.

Modeling the coating layer thickness in a pharmaceutical coating process

Although mechanistic numerical simulations can offer great insights into a process, they are limited with respect to resolved process time. While statistical models provide long-term predictability, determining the underlying probability distributions is often challenging. In this work, detailed CFD-DEM simulations of a pharmaceutical Wurster coating process for microspheres are used to evaluate the input parameters for a novel Monte-Carlo simulation approach. The combined strengths of both modeling approaches make it possible to predict the coating mass and thickness distributions over the entire process time. It was observed that smaller beads receive a thicker coating layer since they pass the spray zone closer to the nozzle. Moreover, it was established that, in contrast to the airflow rate, the spray rate has a great impact on the inter-particle coating variability. A stochastic model was developed to investigate the relative contribution of coating layer variability and fill weight variability to the product non-uniformity in a capsule filling process of Multiple Unit Pellet Systems (MUPS).

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.

Validating a Numerical Simulation of the ConsiGma(R) Coater

Continuous manufacturing is increasingly used in the pharmaceutical industry, as it promises to deliver better product quality while simultaneously increasing production flexibility. GEA developed a semi-continuous tablet coater which can be integrated into a continuous tableting line, accelerating the switch from traditional batch production to the continuous mode of operation. The latter offers certain advantages over batch production, e.g., operational flexibility, increased process/product quality, and decreased cost. However, process understanding is the key element for process control. In this regard, computational tools can improve the fundamental understanding and process performance, especially those related to new processes, such as continuous tablet coating where process mechanics remain unclear. The discrete element method (DEM) and computational fluid dynamics (CFD) are two methods that allow transition from empirical process design to a mechanistic understanding of the individual process units. The developed coupling model allows to track the heat, mass, and momentum exchange between the tablet and fluid phase. The goal of this work was to develop and validate a high-fidelity CFD-DEM simulation model of the tablet coating process in the GEA ConsiGma® coater. After the model development, simulation results for the tablet movement, coating quality, and heat and mass transfer during the coating process were validated and compared to the experimental outcomes. The experimental and simulation results agreed well on all accounts measured, indicating that the model can be used in further studies to investigate the operating space of the continuous tablet coating process.

Image-Based Artificial Intelligence Methods for Product Control of Tablet Coating Quality

Mimicking the human decision-making process is challenging. Especially, many process control situations during the manufacturing of pharmaceuticals are based on visual observations and related experience-based actions. The aim of the present work was to investigate the use of image analysis to classify the quality of coated tablets. Tablets with an increasing amount of coating solution were imaged by fast scanning using a conventional office scanner. A segmentation routine was implemented to the images, allowing the extraction of numeric image-based information from individual tablets. The image preprocessing was performed prior to utilization of four different classification techniques for the individual tablet images. The support vector machine (SVM) technique performed superior compared to a convolutional neural network (CNN) in relation to computational time, and this approach was also slightly better at classifying the tablets correctly. The fastest multivariate method was partial least squares (PLS) regression, but this method was hampered by the inferior classification accuracy of the tablets. Finally, it was possible to create a numerical threshold classification model with an accuracy comparable to the SVM approach, so it is evident that there exist multiple valid options for classifying coated tablets.

Pharmaceutical Application of Tablet Film Coating

Tablet film coating is a common but critical process providing various functionalities to tablets, thereby meeting diverse clinical needs and increasing the value of oral solid dosage forms. Tablet film coating is a technology-driven process and the evolution of coated dosage forms relies on advancements in coating technology, equipment, analytical techniques, and coating materials. Although multiple coating techniques are developed for solvent-based or solvent-free coating processes, each method has advantages and disadvantages that may require continuous technical refinement. In the film coating process, intra- and inter-batch coating uniformity of tablets is critical to ensure the quality of the final product, especially for active film coating containing active pharmaceutical ingredients in the coating layer. In addition to experimental evaluation, computational modeling is also actively pursued to predict the influence of operation parameters on the quality of the final product and optimize process variables of tablet film coating. The concerted efforts of experiments and computational modeling can save time and cost in optimizing the tablet coating process. This review provides a brief overview of tablet film coating technology and modeling approaches with a focus on recent advancements in pharmaceutical applications.

Developing HME-Based Drug Products Using Emerging Science: a Fast-Track Roadmap from Concept to Clinical Batch

This paper presents a rational workflow for developing enabling formulations, such as amorphous solid dispersions, via hot-melt extrusion in less than a year. First, our approach to an integrated product and process development framework is described, including state-of-the-art theoretical concepts, modeling, and experimental characterization described in the literature and developed by us. Next, lab-scale extruder setups are designed (processing conditions and screw design) based on a rational, model-based framework that takes into account the thermal load required, the mixing capabilities, and the thermo-mechanical degradation. The predicted optimal process setup can be validated quickly in the pilot plant. Lastly, a transfer of the process to any GMP-certified manufacturing site can be performed in silico for any extruder based on our validated computational framework. In summary, the proposed workflow massively reduces the risk in product and process development and shortens the drug-to-market time for enabling formulations.

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.

Application of the Discrete Element Method for Manufacturing Process Simulation in the Pharmaceutical Industry

Process simulation using mathematical modeling tools is becoming more common in the pharmaceutical industry. A mechanistic model is a mathematical modeling tool that can enhance process understanding, reduce experimentation cost and improve product quality. A commonly used mechanistic modeling approach for powder is the discrete element method (DEM). Most pharmaceutical materials have powder or granular material. Therefore, DEM might be widely applied in the pharmaceutical industry. This review focused on the basic elements of DEM and its implementations in pharmaceutical manufacturing simulation. Contact models and input parameters are essential elements in DEM simulation. Contact models computed contact forces acting on the particle-particle and particle-geometry interactions. Input parameters were divided into two types-material properties and interaction parameters. Various calibration methods were presented to define the interaction parameters of pharmaceutical materials. Several applications of DEM simulation in pharmaceutical manufacturing processes, such as milling, blending, granulation and coating, were categorized and summarized. Based on this review, DEM simulation might provide a systematic process understanding and process control to ensure the quality of a drug product.

Study of the capsule-filling dosator process via calibrated DEM simulations

Capsule filling is frequently accomplished via the dosator process. Controlling the main quality attributes, i.e., the fill weight and the fill weight variability, requires excellent process understanding. For the investigation of critical process parameters of low-dose capsule filling, DEM simulations were used. Two contact models (Hertz model and Luding model) were calibrated and validated to represent the lactose powder Lactohale 100. Both models gave good results, yet the Luding model resulted in better predictions. Since the dosator process is a volume-based approach, the dosator geometry is an important factor. We provide evidence that not only the volume, but also the proportions of the dosator affect the capsule fill weight. Slim nozzle chambers cause lower mass due to increased wall friction impact. The process is not volumetric, as a fill weight to volume correlation does not exist. Furthermore, the fill weight increases with higher powder beds and smaller gaps between the nozzle tip and the container bottom due to higher densification of the powder. Fill weight variations due to bed inhomogeneity were investigated by varying the bulk density of the initial powder bed.