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

Mathematical models of dissolution testing: Challenges and opportunities toward real-time release testing

Real-time release testing (RTRt) of tablet dissolution can significantly improve manufacturing efficiency along with the adoption of continuous manufacturing in the pharmaceutical industry. To assure product quality without destructive testing, models for RTRt should be sufficiently reliable and robust. Whereas mechanistic models have merits of broader applicability and interpretability, data-driven models have been common approaches due to computational speed. This paper discusses challenges and opportunities in the application of mechanistic models for dissolution testing to enable RTRt of solid dosage. After a comprehensive literature review on mechanistic dissolution models and RTRt, the potential benefits and challenges of mechanistic models are presented. Compared to data-driven models, mechanistic models require less experimental data that can reduce time and cost for RTRt development. However, to enable the implementation of mechanistic models in RTRt, computational time should be short either by using a simple mechanistic model or by applying surrogate models.

Detailed accounts of segregation mechanisms and the evolution of pharmaceutical blend segregation analysis: A review

Segregation refers to the separation of components in a powder mixture, resulting in potential issues related to concentration inhomogeneity. Any well-mixed blend that undergoes secondary processing is inherently susceptible to segregation which, if unmitigated, will lead to active compound concentration variance and poorer product quality. The consequences range from adverse financial impact to manufacturers with product failures to the detrimental health effects to product users. Hence, the topic of segregation is of paramount importance to the industry, requiring it to be dissected and scrutinized intensively by scientists worldwide. This review provides a well-crafted theoretical framework designed to understand the common segregation mechanisms that manufacturing facilities face, followed by the efforts to gauge the degree of segregation. To minimize segregation in blends, various approaches - mathematical modeling, empirical experiments, and empirical methods with modeling consideration - have been utilized in segregation research and are covered in this review. The past segregation studies from many fields are discussed, with particular emphasis on pharmaceuticals. The review also discusses the evolution and advances in mixing technology and storage systems implemented by the pharmaceutical industry to prevent segregation. In the conclusion, the authors articulated their perspectives on potential mitigation measures, including suggestions for improvements and future studies.

Leveraging Numerical Simulation Technology to Advance Drug Preparation: A Comprehensive Review of Application Scenarios and Cases

BACKGROUND/OBJECTIVES

Numerical simulation plays an important role in pharmaceutical preparation recently. Mechanistic models, as a type of numerical model, are widely used in the study of pharmaceutical preparations. Mechanistic models are based on a priori knowledge, i.e., laws of physics, chemistry, and biology. However, due to interdisciplinary reasons, pharmacy researchers have greater difficulties in using computer models.

METHODS

In this paper, we highlight the application scenarios and examples of mechanistic modelling in pharmacy research and provide a reference for drug researchers to get started.

RESULTS

By establishing a suitable model and inputting preparation parameters, researchers can analyze the drug preparation process. Therefore, mechanistic models are effective tools to optimize the preparation parameters and predict potential quality problems of the product. With product quality parameters as the ultimate goal, the experiment design is optimized by mechanistic models. This process emphasizes the concept of quality by design.

CONCLUSIONS

The use of numerical simulation saves experimental cost and time, and speeds up the experimental process. In pharmacy experiments, part of the physical information and the change processes are difficult to obtain, such as the mechanical phenomena during tablet compression and the airflow details in the nasal cavity. Therefore, it is necessary to predict the information and guide the formulation with the help of mechanistic models.

Investigating screw-agitator speed ratio impact on feeding performance in pharmaceutical manufacturing using discrete element method

In continuous powder handling processes, precise and consistent feeding is crucial for ensuring the quality of the final product. The intermixing effect caused by agitators, which alters the powder's bulk density, flow rate, and flow patterns, plays a significant role in this process, yet it is often overlooked. This study combines discrete element method (DEM) modeling and experiments using a commercial-scale feeder to propose a Digital Twin (DT) framework. The DEM model accurately captures key flow features, such as bypass trajectories, stagnant zones, and preferential flow patterns, while providing quantitative predictions for the feed factor and zones prone to material accumulation. Scenario analysis is performed to identify the most favorable operating ranges of the screw-agitator ratio and screw speed, considering the cohesive properties of the powder. The study demonstrates that powders with poor flow characteristics require tighter operational constraints, as the screw-agitator ratio is susceptible to variations in mass feed rate. This contribution highlights the importance of selecting an appropriate screw-agitator ratio instead of maintaining a fixed value. Properly choosing this ratio helps determine an optimal operation window, which aims to achieve a minimum agitation level needed to induce unhindered flow and reduce variability in the mass flow rate.

Modeling Mechanochemical Depolymerization of PET in Ball-Mill Reactors Using DEM Simulations

Developing efficient and sustainable chemical recycling pathways for consumer plastics is critical for mitigating the negative environmental implications associated with their end-of-life management. Mechanochemical depolymerization reactions have recently garnered great attention, as they are recognized as a promising solution for solvent-free transformation of polymers to monomers in the solid state. To this end, physics-based models that accurately describe the phenomena within ball mills are necessary to facilitate the exploration of operating conditions that would lead to optimal performance. Motivated by this, in this paper we develop a mathematical model that couples results from discrete element method (DEM) simulations and experiments to study mechanically-induced depolymerization. The DEM model was calibrated and validated via video experimental data and computer vision algorithms. A systematic study on the influence of the ball-mill operating parameters revealed a direct relationship between the operating conditions of the vibrating milling vessel and the total energy supplied to the system. Moreover, we propose a linear correlation between the high-fidelity DEM simulation results and experimental monomer yield data for poly(ethylene terephthalate) depolymerization, linking mechanical and energetic variables. Finally, we train a reduced-order model to address the high computational cost associated with DEM simulations. The predicted working variables are used as inputs to the proposed mathematical expression which allows for the fast estimation of monomer yields.

Evolution characteristics of micromechanics provides insights into the microstructure of pharmaceutical tablets fabricated by bimodal mixtures

This research focuses on the evolution of mechanical behavior of bimodal mixtures undergoing compaction and diametrical compression. The clusters were built and discrete element method (DEM) was used to investigate the densification process and micromechanics of bimodal mixtures. Additionally, a more comprehensive investigate of the respective breakage of the bimodal mixtures has been carried out. On this basis, qualitative and quantitative analysis of the compressive force, force chain, contact bonds and density field evolution characteristics of the clusters are investigated during the compression process. The entire loading process of the clusters is divided into three stages: rearrangement, breakage and elastic-plastic deformation. Additionally, there are differences in the evolution of micromechanics behavior of different particles in the bimodal mixture, with pregelatinized starch breakage and deformation occurring before microcrystalline cellulose. With the tablet deformation, the fragmentation process of the tablet started at the point of contact and extended toward the center, and the curvature of the force chain increased. This approach may potentially hold a valuable new information relevant to important transformation forms batch manufacturing to advanced manufacturing for the oral solid dosage form.

Emerging Process Modeling Capabilities for Dry Powder Operations for Inhaled Formulations

Dry powder inhaler (DPI) products are commonly formulated as a mixture of micronized drug particles and large carrier particles, with or without additional fine particle excipients, followed by final powder filling into dose containment systems such as capsules, blisters, or reservoirs. DPI product manufacturing consists of a series of unit operations, including particle size reduction, blending, and filling. This review provides an overview of the relevant critical process parameters used for jet milling, high-shear blending, and dosator/drum capsule filling operations across commonly utilized instruments. Further, this review describes the recent achievements regarding the application of empirical and mechanistic models, especially discrete element method (DEM) simulation, in DPI process development. Although to date only limited modeling/simulation work has been accomplished, in the authors' perspective, process design and development are destined to be more modeling/simulation driven with the emphasis on evaluating the impact of material attributes/process parameters on process performance. The advancement of computational power is expected to enable modeling/simulation approaches to tackle more complex problems with better accuracy when dealing with real-world DPI process operations.

Validation of model-based design of experiments for continuous wet granulation and drying

This paper presents an application case of model-based design of experiments for the continuous twin-screw wet granulation and fluid-bed drying sequence. The proposed framework consists of three previously developed models. Here, we are testing the applicability of previously published unit operation models in this specific part of the production line to a new active pharmaceutical ingredient. Firstly, a T-shaped partial least squares regression model predicts d-values of granules after wet granulation with different process settings. Then, a high-resolution full granule size distribution is computed by a hybrid population balance and partial least squares regression model. Lastly, a mechanistic model of fluid-bed drying simulates drying time and energy efficiency, using the outputs of the first two models as a part of the inputs. In the application case, good operating conditions were calculated based on material and formulation properties as well as the developed process models. The framework was validated by comparing the simulation results with three experimental results. Overall, the proposed framework enables a process designer to find appropriate process settings with a less experimental workload. The framework combined with process knowledge reduced 73.2% of material consumption and 72.3% of time, especially in the early process development phase.

How can machine learning and multiscale modeling benefit ocular drug development?

The eyes possess sophisticated physiological structures, diverse disease targets, limited drug delivery space, distinctive barriers, and complicated biomechanical processes, requiring a more in-depth understanding of the interactions between drug delivery systems and biological systems for ocular formulation development. However, the tiny size of the eyes makes sampling difficult and invasive studies costly and ethically constrained. Developing ocular formulations following conventional trial-and-error formulation and manufacturing process screening procedures is inefficient. Along with the popularity of computational pharmaceutics, non-invasive in silico modeling & simulation offer new opportunities for the paradigm shift of ocular formulation development. The current work first systematically reviews the theoretical underpinnings, advanced applications, and unique advantages of data-driven machine learning and multiscale simulation approaches represented by molecular simulation, mathematical modeling, and pharmacokinetic (PK)/pharmacodynamic (PD) modeling for ocular drug development. Following this, a new computer-driven framework for rational pharmaceutical formulation design is proposed, inspired by the potential of in silico explorations in understanding drug delivery details and facilitating drug formulation design. Lastly, to promote the paradigm shift, integrated in silico methodologies were highlighted, and discussions on data challenges, model practicality, personalized modeling, regulatory science, interdisciplinary collaboration, and talent training were conducted in detail with a view to achieving more efficient objective-oriented pharmaceutical formulation design.

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.

Simulation of Non-Carious Cervical Lesions by Computational Toothbrush Model: A Novel Three-Dimensional Discrete Element Method

Non-carious cervical lesions (NCCLs) are saucer-shaped abrasions of a tooth. NCCLs can form due to various etiologies, including toothbrushing wear, acid erosion, and mechanical stress. Owing to this complex interplay, the mechanism of NCCLs in tooth abrasion has not been established. This study aims to develop a numerical method using a computational toothbrush to simulate NCCLs. The forces acting on the teeth and the amount of abrasion generated were evaluated. The discrete element method using in-house code, connected particle model, and Archard wear model were applied for brushing. In the toothbrush model, 42 acrylic tufts were fixed into a toothbrush head. The teeth models with enamel properties comprised four flat plates and two grooves to simulate the anterior teeth and NCCLs. The brushing speed and depth for one cycle were established as simulation parameters. The force applied within the ununiform plane was concentrated on several bristles as the toothbrush passed through the interproximal space. The brushing force (depth) had a greater effect on tooth abrasion than the brushing speed. Toothbrushing abrasion was mainly concentrated in the interproximal space. Therefore, forceful tooth brushing can cause NCCLs from the interproximal space to the cervical area of the tooth.

An efficient multiscale bi-directional PBM-DEM coupling framework to simulate one-dimensional aggregation mechanisms

The mesoscale population balance modelling (PBM) technique is widely used in predicting aggregation processes. The accuracy and efficiency of PBM depend on the formulation of its kernels. A model of the volume- and time-dependent one-dimensional aggregation kernel is developed for predicting the temporal evolution of the considered particulate system. To make the developed model physically relevant, the PBM model needs three unknown parameters as input: volume-dependency in collisions, collision frequency per particle and aggregation probability. For this, the microscale discrete element model (DEM) is used. The system’s collision frequency is extracted periodically using a novel collision detection algorithm that detects and ignores duplicate collisions.

Finally, a multiscale bi-directional PBM–DEM coupling framework is presented to simulate the aggregation mechanism. PBM and DEM simulations take place periodically to update the particle size distribution (PSD) and extract the collision-frequency, respectively. The coupling framework successfully explains the dependence between the PSD and the collision frequency. Additionally, computational cost of the algorithm is optimized while maintaining the accuracy of the results. Lastly, the accuracy and efficiency of the developed framework are verified using two different test cases. In one of the examples, a simple aggregation is simulated directly inside the DEM for the first time.

A comprehensive review of the application of DEM in the investigation of batch solid mixers

Powder mixing is a vital operation in a wide range of industries, such as food, pharmaceutical, and cosmetics. Despite the common use of mixing systems in various industries, often due to the complex nature of mixing systems, the effects of operating and design parameters on the mixers’ performance and final blend are not fully known, and therefore optimal parameters are selected through experience or trial and error. Experimental and numerical techniques have been widely used to analyze mixing systems and to gain a detailed understanding of mixing processes. The limitations associated with experimental techniques, however, have made discrete element method (DEM) a valuable complementary tool to obtain comprehensive particle level information about mixing systems. In the present study, the fundamentals of solid-solid mixing, segregation, and characteristics of different types of batch solid mixers are briefly reviewed. Previously published papers related to the application of DEM in studying mixing quality and assessing the influence of operating and design parameters on the mixing performance of various batch mixing systems are summarized in detail. The challenges with regards to the DEM simulation of mixing systems, the available solutions to address those challenges and our recommendations for future simulations of solid mixing are also presented and discussed.

A Novel Computational Approach Coupled with Machine Learning to Predict the Extent of Agglomeration in Particulate Processes

Solid particle agglomeration is a prevalent phenomenon in various processes across the chemical, food, and pharmaceutical industries. In pharmaceutical manufacturing, agglomeration is both desired in unit operations like wet granulation and undesired in unit operations such as agitated filter drying of highly potent active pharmaceutical ingredients (API). Agglomeration needs to be controlled for optimal physical properties of the API powder. Even after decades of work in the field, there is still very limited understanding of how to quantify, predict, and control the extent of agglomeration, owing to the complex interaction between the solvent and the solid particles and stochasticity imparted by mixing. Furthermore, a large size of industrial scale particulate process systems makes it computationally intractable. To overcome these challenges, we present a novel theory and computational methodology to predict the agglomeration extent by coupling the experimental measurements of agglomeration risk zone or "sticky zone" with discrete element method. The proposed model shows good agreement with experiments. Further, a machine learning model was built to predict agglomeration extent as a function of input variables, such as material properties and processing conditions, in order to build a digital twin of the unit operation. While the focus of the present study is the agglomeration of particles during industrial drying processes, the proposed methodology can be readily applied to numerous other particulate processes where agglomeration is either desired or undesired.

Continuous mixing technology: Validation of a DEM model

Continuous powder mixing is an important technology used in the development and manufacturing of solid oral dosage forms. Since critical quality attributes of the final product greatly depend on the performance of the mixing step, an analysis of such a process using the Discrete Element Method (DEM) is of crucial importance. On one hand, the number of expensive experimental runs can be reduced dramatically. On the other hand, numerical simulations can provide information that is very difficult to obtain experimentally. In order to apply such a simulation technology in product development and to replace experimental runs, an intensive model validation step is required. This paper presents a DEM model of the vertical continuous mixing device termed CMT (continuous mixing technology) and an extensive validation workflow. First, a cohesive contact model was calibrated in two small-scale characterization experiments: a compression test with spring-back and a shear cell test. An improved, quicker calibration procedure utilizing the previously calibrated contact models is presented. The calibration procedure is able to differentiate between the blend properties caused by different API particle sizes in the same formulation. Second, DEM simulations of the CMT were carried out to determine the residence time distribution (RTD) of the material inside the mixer. After that, the predicted RTDs were compared with the results of tracer spike experiments conducted with two blend material properties at two mass throughputs of 15 kg/h and 30 kg/h. Additionally, three hold-up masses (500, 730 and 850 g) and three impeller speeds (400, 440 and 650 rpms) were considered. Finally, both RTD datasets from DEM and tracer experiments were used to predict the damping behavior of incoming feeder fluctuations and the funnel of maximum duration and magnitude of incoming deviations that do not require a control action. The results for both tools in terms of enabling a control strategy (the fluctuation damping and the funnel plot) are in excellent agreement, indicating that DEM simulations are well suited to replace process-scale tracer spike experiments to determine the RTD.

Computational pharmaceutics - A new paradigm of drug delivery

In recent decades pharmaceutics and drug delivery have become increasingly critical in the pharmaceutical industry due to longer time, higher cost, and less productivity of new molecular entities (NMEs). However, current formulation development still relies on traditional trial-and-error experiments, which are time-consuming, costly, and unpredictable. With the exponential growth of computing capability and algorithms, in recent ten years, a new discipline named "computational pharmaceutics" integrates with big data, artificial intelligence, and multi-scale modeling techniques into pharmaceutics, which offered great potential to shift the paradigm of drug delivery. Computational pharmaceutics can provide multi-scale lenses to pharmaceutical scientists, revealing physical, chemical, mathematical, and data-driven details ranging across pre-formulation studies, formulation screening, in vivo prediction in the human body, and precision medicine in the clinic. The present paper provides a comprehensive and detailed review in all areas of computational pharmaceutics and "Pharma 4.0", including artificial intelligence and machine learning algorithms, molecular modeling, mathematical modeling, process simulation, and physiologically based pharmacokinetic (PBPK) modeling. We not only summarized the theories and progress of these technologies but also discussed the regulatory requirements, current challenges, and future perspectives in the area, such as talent training and a culture change in the future pharmaceutical industry.

Predictive Approach to Understand and Eliminate Tablet Breakage During Film Coating

Pharmaceutical tablets can be susceptible to damage such as edge chipping or erosion of the core during the tablet coating process. The intersection of certain process parameters, equipment design, and tablet properties may induce more significant tablet damage such as complete tablet fracture. In this work, a hybrid predictive approach was developed using discrete element method (DEM) modeling and lab-based tablet impact experiments to identify conditions that may lead to tablet breakage events. The approach was extended to examine potential modifications to the coating equipment and process conditions in silico to mitigate the likelihood of tablet breakage during future batches. The approach is shown to enhance process understanding, identify optimal process conditions within development constraints, and de-risk the manufacture of future tablet coating batches.

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.

Control Strategy for Process Development of High-Shear Wet Granulation and Roller Compaction to Prepare a Combination Drug Using Integrated Quality by Design

In this study, we developed a control strategy for a drug product prepared by high-shear wet granulation and roller compaction using integrated quality by design (QbD). During the first and second stages, we optimized the process parameters through the design of experiments and identified the intermediate quality attributes (IQAs) and critical quality attributes (CQAs) relationship, respectively. In the first stage, we conducted an initial risk assessment by selecting critical process parameters with high impact on IQAs and CQAs and confirmed the correlation between control and response factors. Additionally, we performed Monte Carlo simulations by optimizing the process parameters to deriving and building a robust design space. In the second stage, we identified the IQAs and CQAs relationship for the control strategy, using multivariate analysis (MVA). Based on MVA, in the metformin layer, dissolution at 1 h was significantly correlated with intrinsic dissolution rate and granule size, and dissolution at 3 h was significantly correlated with bulk density and granule size. In dapagliflozin layer, dissolution at 10 min and 15 min was significantly correlated with granule size. Our results suggest that the desired drug quality may result through IQAs monitoring during the process and that the integrated QbD approach utilizing MVA can be used to develop a control strategy for producing high-quality drug products.