Cracking the code: Spatial heterogeneity as the missing piece for modeling granular fluidized bed drying.

Pharmaceutical twin-screw wet granulation is a multifaceted and intricate process pivotal to drug product development. Accurate modeling of this process is indispensable for optimizing manufacturing parameters and ensuring product quality. The fluid bed dryer, an integral component of this granulation process, significantly influences the granular critical quality attributes. This study builds upon prior research by integrating experimental findings on granule segregation during fluid bed drying into an existing compartmental model, enhancing its predictive capabilities. An additional model layer on granule segregation behavior is composed and integrated into the existing model structure in this study. The added model compartment describes probability distributions on the vertical position of granules within each granule size class considered. To beware of overfitting, predictions of both the moisture content after drying and the granule bed temperature throughout drying are discussed in this study relative to experimental data from earlier published studies. These independent analyses demonstrated a marked improvement in prediction accuracy compared to earlier published model structures. The refined model accurately predicts the residual moisture content after drying for an untrained formulation. Moreover, it simultaneously makes accurate predictions of the granular bed temperature, which emboldens its structural correctness. This advancement makes it a powerful tool for predicting the behavior of the pharmaceutical fluid bed drying, which holds significant promise to facilitate pharmaceutical product development.

A framework for the in silico assessment of the robustness of an MPC in a CDC line in function of process variability.

The shift from batch manufacturing towards continuous manufacturing for the production of oral solid dosages requires the development and implementation of process models and process control. Previous work focused mainly on developing deterministic models for the investigated system. Furthermore, the in silico tuning and analysis of a control strategy are mostly done based on deterministic models. This deterministic approach could lead to wrong actions in diversion strategies and poor transferability of the controller performance if the system behaves differently than the deterministic model. This work introduces a framework that explicitly includes the process variability which is characteristic of powder handling processes and tests it on a novel continuous feeding-blending unit (i.e., the FE continuous processing system (CPS)), followed by a tablet press (i.e., the FE 55). It employs a stochastic model by allowing the model parameters to have a probability distribution. The performance of a model predictive control (MPC), steering the feed rate of the main excipient feeder to compensate for the feed rate deviations of the active pharmaceutical ingredient (API) feeder to keep the API concentration close to the desired value, is evaluated and the impact of process variability is assessed in a Monte Carlo (MC) analysis. Next to the process variability, a model for the prediction error of the chemometric model and realistic feed rate disturbances were included to increase the transferability of the results to the real system. The obtained results show that process variability is inherently present and that wrong conclusions can be drawn if it is not taken into account in the in silico analysis.

A full-scale operational digital twin for a water resource recovery facility-A case study of Eindhoven Water Resource Recovery Facility.

Digital transformation for the water sector has gained momentum in recent years, and many water resource recovery facilities modelers have already started transitioning from developing traditional models to digital twin (DT) applications. DTs simulate the operation of treatment plants in near real time and provide a powerful tool to the operators and process engineers for real-time scenario analysis and calamity mitigation, online process optimization, predictive maintenance, model-based control, and so forth. So far, only a few mature examples of full-scale DT implementations can be found in the literature, which only address some of the key requirements of a DT. This paper presents the development of a full-scale operational DT for the Eindhoven water resource recovery facility in The Netherlands, which includes a fully automated data-pipeline combined with a detailed mechanistic full-plant process model and a user interface co-created with the plant's operators. The automated data preprocessing pipeline provides continuous access to validated data, an influent generator provides dynamic predictions of influent composition data and allows forecasting 48 h into the future, and an advanced compartmental model of the aeration and anoxic bioreactors ensures high predictive power. The DT runs near real-time simulations every 2 h. Visualization and interaction with the DT is facilitated by the cloud-based TwinPlant technology, which was developed in close interaction with the plant's operators. A set of predefined handles are made available, allowing users to simulate hypothetical scenarios such as process and equipment failures and changes in controller settings. The combination of the advanced data pipeline and process model development used in the Eindhoven DT and the active involvement of the operators/process engineers/managers in the development process makes the twin a valuable asset for decision making with long-term reliability. PRACTITIONER POINTS: A full-scale digital twin (DT) has been developed for the Eindhoven WRRF. The Eindhoven DT includes an automated continuous data preprocessing and reconciliation pipeline. A full-plant mechanistic compartmental process model of the plant has been developed based on hydrodynamic studies. The interactive user interface of the Eindhoven DT allows operators to perform what-if scenarios on various operational settings and process inputs. Plant operators were actively involved in the DT development process to make a reliable and relevant tool with the expected added value.

Analysis of the effect of formulation properties and process parameters on granule formation in twin-screw wet granulation.

In the last few years, twin-screw wet granulation (TSWG) has become one of the key continuous pharmaceutical unit operations. Despite the many studies that have been performed, only little is known about the effect of the starting material properties on the stepwise granule formation along the length of the twin-screw granulator (TSG) barrel. Hence, this study obtained a detailed understanding of the effect of formulation properties (i.e., Active Pharmaceutical Ingredient (API) properties, formulation blend particle size distribution and formulation drug load) and process settings on granule formation in TSWG. An experimental set-up was used allowing the collection of granules at the different TSG compartments. Granules were characterized in terms of granule size, shape, binder liquid and API distributions. Liquid-to-solid (L/S) ratio was the only TSG process parameter impacting the granule size and shape evolution. Particle size and flow properties (e.g., flow rate index) had an important effect on the granule size and shape changes whereas water-related properties (e.g., water binding capacity and solubility) became influential at the last TSG compartments. The API solubility and L/S ratio were found to have a major impact on the distribution of binder liquid over the different granule size fractions. In the first TSG compartment (i.e., wetting compartment), the distribution of the API in the granules was influenced by its solubility in the granulation liquid.

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.

Mechanistic modeling of semicontinuous fluidized bed drying of pharmaceutical granules by incorporating single particle and bulk drying kinetics.

In this work, a mechanistic fluidized bed drying model computing the granule moisture content in function of granule size, drying time, process settings and formulation properties is developed. Modeling the moisture content distribution concerning the granule size is essential for tabletability and drug product quality. This work combines a mechanistic bulk model and a single-particle drying kinetics model in a semicontinuous mode. The added model complexity allows physical approximations of drying phenomena at both the drying system level and the granular level. This includes quantifying the variations in moisture content by taking into account the specific dryer design and the variations in granule size. The model performance was quantified through industrially relevant case studies. It was revealed that the proposed model structure accurately predicts the drying behavior of the yield fraction. However, systematic model biases were observed for the fine and coarse fractions of the granule size distribution. In addition, discrepancies in the predicted outgoing air properties (relative air humidity and air temperature) were obtained. Further enhancement of the model complexity, e.g. complete incorporation of fluidization and segregation phenomena, is likely to improve the model performance. Notwithstanding, the developed model forms a step towards a formulation-generic fluidized bed drying model as interacting mechanisms on different levels of the drying system are considered.

Exploring the effect of raw material properties on continuous twin-screw wet granulation manufacturability.

Twin-screw wet granulation (TSWG) stands out as a promising continuous alternative to conventional batch fluid bed- and high shear wet granulation techniques. Despite its potential, the impact of raw material properties on TSWG processability remains inadequately explored. Furthermore, the absence of supportive models for TSWG process development with new active pharmaceutical ingredients (APIs) adds to the challenge. This study tackles these gaps by introducing four partial least squares (PLS) models that approximate both the applicable liquid-to-solid (L/S) ratio range and resulting granule attributes (i.e., granule size and friability) based on initial material properties. The first two PLS models link the lowest and highest applicable L/S ratio for TSWG, respectively, with the formulation blend properties. The third and fourth PLS models predict the granule size and friability, respectively, from the starting API properties and applied L/S ratio for twin-screw wet granulation. By analysing the developed PLS models, water-related material properties (e.g., solubility, wettability, dissolution rate), as well as density and flow-related properties (e.g., flow function coefficient), were found to be impacting the TSWG processability. In addition, the applicability of the developed PLS models was evaluated by using them to propose suitable L/S ratio ranges (i.e., resulting in granules with the desired properties) for three new APIs and related formulations followed by an experimental validation thereof. Overall, this study helped to better understand the effect of raw material properties upon TSWG processability. Moreover, the developed PLS models can be used to propose suitable TSWG process settings for new APIs and hence reduce the experimental effort during process development.

Quantifying the hydrodynamic stress for bioprocesses.

Hydrodynamic stress is an influential physical parameter for various bioprocesses, affecting the performance and viability of the living organisms. However, different approaches are in use in various computational and experimental studies to calculate this parameter (including its normal and shear subcomponents) from velocity fields without a consensus on which one is the most representative of its effect on living cells. In this letter, we investigate these different methods with clear definitions and provide our suggested approach which relies on the principal stress values providing a maximal distinction between the shear and normal components. Furthermore, a numerical comparison is presented using the computational fluid dynamics simulation of a stirred and sparged bioreactor. It is demonstrated that for this specific bioreactor, some of these methods exhibit quite similar patterns throughout the bioreactor-therefore can be considered equivalent-whereas some of them differ significantly.

Partial least squares regression to calculate population balance model parameters from material properties in continuous twin-screw wet granulation.

In the pharmaceutical industry, twin-screw wet granulation has become a realistic option for the continuous manufacturing of solid drug products. Towards the efficient design, population balance models (PBMs) have been recognized as a tool to compute granule size distribution and understand physical phenomena. However, the missing link between material properties and the model parameters limits the swift applicability and generalization of new active pharmaceutical ingredients (APIs). This paper proposes partial least squares (PLS) regression models to assess the impact of material properties on PBM parameters. The parameters of the compartmental one-dimensional PBMs were derived for ten formulations with varying liquid-to-solid ratios and connected with material properties and liquid-to-solid ratios by PLS models. As a result, key material properties were identified in order to calculate it with the necessary accuracy. Size- and moisture-related properties were influential in the wetting zone whereas density-related properties were more dominant in the kneading zones.

Evaluation of the influence of material properties and process parameters on granule porosity in twin-screw wet granulation.

In recent years, continuous twin-screw wet granulation (TSWG) is gaining increasing interest from the pharmaceutical industry. Despite the many publications on TSWG, only a limited number of studies focused on granule porosity, which was found to be an important granule property affecting the final tablet quality attributes, e.g. dissolution. In current study, the granule porosity along the length of the twin-screw granulator (TSG) barrel was evaluated. An experimental set-up was used allowing the collection of granules at the different TSG compartments. The effect of active pharmaceutical ingredient (API) properties on granule porosity was evaluated by using six formulations with a fixed composition but containing APIs with different physical-chemical properties. Furthermore, the importance of TSWG process parameters liquid-to-solid (L/S) ratio, mass feed rate and screw speed for the granule porosity was evaluated. Several water-related properties as well as particle size, density and flow properties of the API were found to have an important effect on granule porosity. While the L/S ratio was confirmed to be the dictating TSWG process parameter, granulator screw speed was also found to be an important process variable affecting granule porosity. This study obtained crucial information on the effect of material properties and process parameters on granule porosity (and granule formation) which can be used to accelerate TSWG process and formulation development.

A Colourful Way to Unravel the Important Fluidization-Related Granule Size Effect on Semi-Continuous Drying.

Continuous twin screw wet granulation (TSWG) systems are possible pathways for oral solid dosage manufacturing in the pharmaceutical industry. TSWG requires a drying step after granulation before the tableting process. Typically, semi-continuous fluidized bed dryers (FBDs) are used for this purpose. At the same time, the pharmaceutical sector is interested in mathematical prediction models to save resources during the early drug product development (DPD) stage or to control manufacturing. Several authors have already developed prediction models for semi-continuous drying processes. However, these model structures reported systematic prediction offsets, which could be related to the incomplete implementation of fluidization and granule segregation phenomena. This study evaluates the complex fluidization behavior of wet granules in industrially relevant semi-continuous FBDs. A transparent perspex version of the dryer was used for the analysis of bed height, pressure drop, porosity, segregation, and spatial heating patterns at varying process settings. The investigated behaviors of the fluidizing bed will be helpful to derive phenomenological (sub)models for the detailed description of segregation in the semi-continuous fluidized bed system. In this study, it was found that semi-continuous FBDs are characterized by a change in fluidization regime from plug flow to a bubbling bed at the moment that the granule bed slumps. Secondly, the presence of size-based vertical segregation phenomena as well as spatial temperature differences were proven. The experimental results suggest that larger granules are dried under more intense drying conditions than smaller granules.

An extended 3-compartment model for describing step change experiments in pharmaceutical twin-screw feeders at different refill regimes.

Residence time distributions (RTDs) are a valuable tool for product tracking in the unit operations of a continuous line for manufacturing pharmaceutical oral solid dosage (OSD) and the integrated system itself. The first unit operation in such a continuous line in which extended intermixing can occur, is typically a feeder. The RTD of a feeder can be obtained by performing tracer experiments with a tracer material. A physical interpretation can be given to the observed tracer concentration responses by fitting a tanks-in-series (TIS) or compartmental model to it. Consequently, the internal mixing behaviour inside the feeder hopper can be rationalized. However, typically, a constant volume is assumed for the tanks or compartments in these models. This has led to several publications where the experimental set-up does not violate the constant volume assumption, i.e. one performs refills at a high hopper fill level. Here, we step away from this assumption and develop a set of differential equations for a 3-compartment model in order to account for a non-constant volume of the compartments. Moreover, the model distinguishes between a bypass trajectory formed by the agitator inside the feeder and an inner mixing volume, in which the tracer concentration lags on the tracer concentration in the bypass volume. This compartmentalization was inspired by the results obtained in a previous study using a spatial sampling method to assess the tracer concentration throughout the feeder hopper for different experimental runtimes. The developed model successfully describes the step responses for different refill regimes: the standard smooth first order plus dead time response (FOPDT) for a high refill regime and the more complex step response, including dips in the rising phase of the curve, for the low refill regime. As a consequence, a more thorough understanding of the complex mixing behaviour inside the feeder is obtained, which allows for an improved traceability. Next to that, the model delivers enhanced knowledge on the interaction between the residence time and the refill regime. The developed model was fitted to a data set, containing step change experiments for different pharmaceutical materials (Tablettose 80 (T80), Microcelac 100 (MCL), and Avicel PH101 (MCC)), different mass flow rates, and refill regimes. The experimentally observed phenomena could be reliably described by the proposed model. The model showed an improved transferability compared to typical TIS models.

The transition of WRRF models to digital twin applications.

Digital Twins (DTs) are on the rise as innovative, powerful technologies to harness the power of digitalisation in the WRRF sector. The lack of consensus and understanding when it comes to the definition, perceived benefits and technological needs of DTs is hampering their widespread development and application. Transitioning from traditional WRRF modelling practice into DT applications raises a number of important questions: When is a model's predictive power acceptable for a DT? Which modelling frameworks are most suited for DT applications? Which data structures are needed to efficiently feed data to a DT? How do we keep the DT up to date and relevant? Who will be the main users of DTs and how to get them involved? How do DTs push the water sector to evolve? This paper provides an overview of the state-of-the-art, challenges, good practices, development needs and transformative capacity of DTs for WRRF applications.

Hybrid differential equations: Integrating mechanistic and data-driven techniques for modelling of water systems.

Mathematical modelling is increasingly used to improve the design, understanding, and operation of water systems. Two modelling paradigms, i.e., mechanistic and data-driven modelling, are dominant in the water sector, both with their advantages and drawbacks. Hybrid modelling aims to combine the strengths of both paradigms. Here, we introduce a novel framework that incorporates a data-driven component into an existing activated sludge model of a water resource recovery facility. In contrast to previous efforts, we tightly integrate both models by incorporating a neural differential equation into an existing mechanistic ODE model. This machine learning component fills in the knowledge gaps of the mechanistic model. We show that this approach improves the predictive capabilities of the mechanistic model and is able to extrapolate to unseen conditions, a problematic task for data-driven models. This approach holds tremendous potential for systems that are difficult to model using the mechanistic paradigm only.

A hybrid model for multipoint real time potency observation in continuous direct compression manufacturing operations.

The ongoing transition from batch to continuous manufacturing offers both challenges and opportunities in the field of oral solid dosage form production. In turn, Process Analytical Technology (PAT) offers a path towards the successful deployment of continuous tablet manufacturing in rotary tablet presses. One promising PAT tool for this endeavour is the NIR-derived potency measurement. However, the high degree of noise in the data may hamper the extraction of useful information. For this reason, this work focused on the implementation of an adaptive Kalman filter algorithm that incorporates and reconciles the potency prediction given by one or more NIR probes with those of a semi-mechanistic compartmental model developed for the application at hand. This approach allowed for more robust concentration estimations. Furthermore, it was observed that potency levels in multiple locations in the studied tablet press (including those in the finished tablets) could be appropriately inferred using a single in-line measurement data stream. This methodology thus opens the door to advanced process control applications.

Assessing the effects of non-pharmaceutical interventions on SARS-CoV-2 transmission in Belgium by means of an extended SEIQRD model and public mobility data.

We present a compartmental extended SEIQRD metapopulation model for SARS-CoV-2 spread in Belgium. We demonstrate the robustness of the calibration procedure by calibrating the model using incrementally larger datasets and dissect the model results by computing the effective reproduction number at home, in workplaces, in schools, and during leisure activities. We find that schools and home contacts are important transmission pathways for SARS-CoV-2 under lockdown measures. School reopening has the potential to increase the effective reproduction number from Re=0.66±0.04 (95 % CI) to Re=1.09±0.05 (95 % CI) under lockdown measures. The model accounts for the main characteristics of SARS-CoV-2 transmission and COVID-19 disease and features a detailed representation of hospitals with parameters derived from a dataset consisting of 22 136 hospitalized patients. Social contact during the pandemic is modeled by scaling pre-pandemic contact matrices with Google Community Mobility data and with effectivity-of-contact parameters inferred from hospitalization data. The calibrated social contact model with its publically available mobility data, although coarse-grained, is a cheap and readily available alternative to social-epidemiological contact studies under lockdown measures, which were not available at the start of the pandemic.

Cost-Effectiveness of COVID-19 Policy Measures: A Systematic Review.

OBJECTIVES: The COVID-19 pandemic has had a major impact on our society, with drastic policy restrictions being implemented to contain the spread of the severe acute respiratory syndrome coronavirus 2. This study aimed to provide an overview of the available evidence on the cost-effectiveness of various coronavirus disease 2019 policy measures. METHODS: A systematic literature search was conducted in PubMed, Embase, and Web of Science. Health economic evaluations considering both costs and outcomes were included. Their quality was comprehensively assessed using the Consensus Health Economic Criteria checklist. Next, the quality of the epidemiological models was evaluated. RESULTS: A total of 3688 articles were identified (March 2021), of which 23 were included. The studies were heterogeneous with regard to methodological quality, contextual factors, strategies' content, adopted perspective, applied models, and outcomes used. Overall, testing/screening, social distancing, personal protective equipment, quarantine/isolation, and hygienic measures were found to be cost-effective. Furthermore, the most optimal choice and combination of strategies depended on the reproduction number and context. With a rising reproduction number, extending the testing strategy and early implementation of combined multiple restriction measures are most efficient. CONCLUSIONS: The quality assessment highlighted numerous flaws and limitations in the study approaches; hence, their results should be interpreted with caution because the specific context (country, target group, etc) is a key driver for cost-effectiveness. Finally, including a societal perspective in future evaluations is key because this pandemic has an indirect impact on the onset and treatment of other conditions and on our global economy.

Particle-Scale Modeling to Understand Liquid Distribution in Twin-Screw Wet Granulation.

Experimental characterization of solid-liquid mixing for a high shear wet granulation process in a twin-screw granulator (TSG) is very challenging. This is due to the opacity of the multiphase system and high-speed processing. In this study, discrete element method (DEM) based simulations are performed for a short quasi-two-dimensional simulation domain, incorporating models for liquid bridge formation, rupture, and the effect of the bridges on inter-particular forces. Based on the knowledge gained from these simulations, the kneading section of a twin-screw wet granulation process was simulated. The time evolution of particle flow and liquid distribution between particles, leading to the formation of agglomerates, was analyzed. The study showed that agglomeration is a rather delayed process that takes place once the free liquid on the particle surface is well distributed.

Improvement of a 1D Population Balance Model for Twin-Screw Wet Granulation by Using Identifiability Analysis.

Recently, the pharmaceutical industry has undergone changes in the production of solid oral dosages from traditional inefficient and expensive batch production to continuous manufacturing. The latest advancements include increased use of continuous twin-screw wet granulation and application of advanced modeling tools such as Population Balance Models (PBMs). However, improved understanding of the physical process within the granulator and improvement of current population balance models are necessary for the continuous production process to be successful in practice. In this study, an existing compartmental one-dimensional PBM of a twin-screw granulation process was improved by altering the original aggregation kernel in the wetting zone as a result of an identifiability analysis. In addition, a strategy was successfully applied to reduce the number of model parameters to be calibrated in both the wetting zone and kneading zones. It was found that the new aggregation kernel in the wetting zone is capable of reproducing the particle size distribution that is experimentally observed at different process conditions as well as different types of formulations, varying in hydrophilicity and API concentration. Finally, it was observed that model parameters could be linked not only to the material properties but also to the liquid to solid ratio, paving the way to create a generic PBM to predict the particle size distribution of a new formulation.

Integrated mechanistic and data-driven modeling for risk assessment of greenhouse gas production in an urbanized river system.

Surrounded by intense anthropogenic activities, urban polluted rivers have increasingly been reported as a significant source of greenhouse gases (GHGs). However, unlike pollution and climate change, no integrated urban water models have investigated the GHG production in urban rivers due to system complexity. In this study, we proposed a novel integrated framework of mechanistic and data-driven models to qualitatively assess the risks of GHG accumulation in an urban river system in different water management interventions. Particularly, the mechanistic model delivered elaborated insights into river states in four intervention scenarios in which the installation of a new wastewater treatment plant using two different technologies, together with new sewage systems and additional retention tanks, were assessed during dry and rainy seasons. From the insights, we applied fuzzy rule-based models as a decision support tool to predict the GHG accumulation risks and identify their driving factors in the scenarios. The obtained results indicated the important role of new discharge connection and additional storage capacity in decreasing pollutant concentrations, consequently, reducing the risks. Moreover, among the major variables explaining the GHG accumulation in the rivers, DO level was considerably affected by the reaeration capacity of the rivers that was strongly dependent on river slope and flow. Furthermore, river water quality emerged as the most critical variable explaining the pCO2 and N2O accumulation that implied that the more polluted and anaerobic the sites were, the higher were their GHG accumulation. Given its simplicity and transparency, the proposed modeling framework can be applied to other river basins as a decision support tool in setting up integrated urban water management plans.