Characterizing the Exponential Profile of W' Recovery Following Partial Depletion.

PURPOSE: The aim of this study was to characterize W' recovery kinetics in response to a partial W' depletion. We hypothesized that W' recovery following partial depletion would be better described by a biexponential than by a monoexponential model. METHODS: Nine healthy men performed a ramp incremental exercise test, three to five constant load trials to determine critical power and W', and ten experimental trials to quantify W' depletion. Each experimental trial consisted of two constant load work bouts (WB1 + WB2) interspersed by a recovery interval. WB1 was designed to evoke a 25% or 75% W' depletion (DEP 25% + DEP 75% ). Subsequently, participants recovered for 30, 60, 120, 300 or 600 s, and then performed WB2 to exhaustion in order to calculate the observed W' recovery (W' OBS ). W' OBS data were fitted using monoexponential and biexponential models, both with a variable and a fixed model amplitude. Root mean square error (RMSE) and Akaike information criterion (AIC c ) were calculated to evaluate the models' goodness-of-fit. RESULTS: The biexponential model fits were associated with overall lower RMSE values (0.4-5.0%) compared to the monoexponential models (2.9-8.0%). However, ΔAIC c resulted in negative values (-15.5 and -23.3) for the model fits where the amplitude was free, thereby favoring the use of a monoexponential model for both depletion conditions. For the model fits where the amplitude was fixed at 100%, ΔAIC c was negative for DEP 25% (-15.0), but positive for DEP 75% (11.2). W' OBS values were strongly correlated between both depletion conditions ( r = 0.92), and positively associated with V̇O 2peak , CP and GET ( r = 0.67-0.77). CONCLUSIONS: The present study results did not provide evidence in favor of a biexponential modeling technique to characterize W' recovery following partial depletion. Moreover, we demonstrated that fixed t values were insufficient to model W' recovery across different depletion levels, and that W' recovery was positively associated with aerobic fitness. These findings underline the importance of employing variable and individualized t values in future predictive W' models.

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.

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.

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.

The Fitness-Fatigue Model: What's in the Numbers?

PURPOSE: The purpose of this commentary is to outline some of the pitfalls when using the fitness-fatigue model to unravel the interaction between training load and performance. By doing so, we encourage sport scientists and coaches to interpret the parameters from the model with some extra caution. CONCLUSIONS: Caution is needed when interpreting the fitness-fatigue model since the parameter values are influenced by the starting parameter values, the modeling technique, and the input of the model. Also, the use of general constants should be avoided since they do not account for interindividual differences and differences between training-load methods. Therefore, we advise sport scientists and coaches to use the model as a way to work more data-informed rather than working data-driven.

Can filaments, pellets and powder be used as feedstock to produce highly drug-loaded ethylene-vinyl acetate 3D printed tablets using extrusion-based additive manufacturing?

Personalized medicine, produced through 3D printing, is a promising approach for delivering the required drug dose based on the patient's profile. The primary purpose of this study was to investigate the potential of two different extrusion-based additive manufacturing techniques - fused filament fabrication (FFF) and screw-based 3D printing, also known as direct extrusion additive manufacturing (DEAM). Different ethylene-vinyl acetate (EVA) copolymers (9 %VA, 12 %VA, 16 %VA, 18 %VA, 25 %VA, 28 %VA, and 40 %VA) were selected and loaded with 50% (w/w) metoprolol tartrate (MPT). Hot-melt extrusion was performed to produce the drug-loaded filaments. These filaments were used for FFF in which the mechanical and rheological properties were rate-limiting steps. The drug-loaded filament based on the 18 %VA polymer was the only printable formulation due to its appropriate mechanical and rheological properties. As for the highest VA content (40 %VA), the feeding pinch rolls cause buckling of the filaments due to insufficient stiffness, while other filaments were successfully feedable towards the extrusion nozzle. However, poor flowability out of the extrusion nozzle due to the rheological limitation excluded these formulations from the initial printing trials. Filaments were also pelletized and used for pellets-DEAM. This method showed freedom in formulation selection because the screw rotation drives the material flow with less dependence on their mechanical properties. All drug-loaded pellets were successfully printed via DEAM, as sufficient pressure was built up towards the nozzle due to single screw extrusion processing method. In contrast, filaments were used as a piston to build up the pressure required for extrusion in filament-based printing, which highly depends on the filament's mechanical properties. Moreover, printing trials using a physical mixture in powder form were also investigated and showed promising results. In vitro drug release showed similar release patterns for MPT-loaded 3D printed tablets regardless of the printing technique. Additionally, pellets-DEAM enabled the production of tablets with the highest VA content, which failed in FFF 3D printing but showed an interesting delayed release profile.

The Influence of Different Training Load Quantification Methods on the Fitness-Fatigue Model.

PURPOSE: Numerous methods exist to quantify training load (TL). However, the relationship with performance is not fully understood. Therefore the purpose of this study was to investigate the influence of the existing TL quantification methods on performance modeling and the outcome parameters of the fitness-fatigue model. METHODS: During a period of 8 weeks, 9 subjects performed 3 interval training sessions per week. Performance was monitored weekly by means of a 3-km time trial on a cycle ergometer. After this training period, subjects stopped training for 3 weeks but still performed a weekly time trial. For all training sessions, Banister training impulse (TRIMP), Lucia TRIMP, Edwards TRIMP, training stress score, and session rating of perceived exertion were calculated. The fitness-fatigue model was fitted for all subjects and for all TL methods. RESULTS: The error in relating TL to performance was similar for all methods (Banister TRIMP: 618 [422], Lucia TRIMP: 625 [436], Edwards TRIMP: 643 [465], training stress score: 639 [448], session rating of perceived exertion: 558 [395], and kilojoules: 596 [505]). However, the TL methods evolved differently over time, which was reflected in the differences between the methods in the calculation of the day before performance on which training has the biggest positive influence (range of 19.6 d). CONCLUSIONS: The authors concluded that TL methods cannot be used interchangeably because they evolve differently.

The Influence of Equipment Design and Process Parameters on Granule Breakage in a Semi-Continuous Fluid Bed Dryer after Continuous Twin-Screw Wet Granulation.

The drying unit of a continuous from-powder-to-tablet manufacturing line based on twin-screw granulation (TSG) is a crucial intermediate process step to achieve the desired tablet quality. Understanding the size reduction of pharmaceutical granules before, during, and after the fluid bed drying process is, however, still lacking. A first major goal was to investigate the breakage and attrition phenomena during transport of wet and dry granules, the filling phase, and drying phase on a ConsiGma-25 system (C25). Pneumatic transport of the wet granules after TSG towards the dryer induced extensive breakage, whereas the turbulent filling and drying phase of the drying cells caused rather moderate breakage and attrition. Subsequently, the dry transfer line was responsible for additional extensive breakage and attrition. The second major goal was to compare the influence of drying air temperature and drying time on granule size and moisture content for granules processed with a commercial-scale ConsiGma-25 system and with the R&D-scale ConsiGma-1 (C1) system. Generally, the granule quality obtained after drying with C1 was not predictive for the C25, making it challenging during process development with the C1 to obtain representative granules for the C25.

Two-dimensional moisture content and size measurement of pharmaceutical granules after fluid bed drying using near-infrared chemical imaging.

In pharmaceutical wet granulation, drying is a critical step in terms of energy and material consumption, whereas granule moisture content and size are important process outcomes that determine tabletting performance. The drying process is, however, very complex due to the multitude of interacting mechanisms on different scales. Building robust physical models of this process therefore requires detailed data. Current data collection methods only succeed in measuring the average moisture content of a size fraction of granules, whereas this property rather follows a distribution that, moreover, contains information on the drying patterns. Therefore, a measurement method is devised to simultaneously characterise the moisture content and size of individual pharmaceutical granules. A setup with near-infrared chemical imaging (NIR-CI) is used to capture an image of a number of granules, in which the absorbance spectra are used for deriving the moisture content of the material and the size of the granules is estimated based on the amount of pixels containing pharmaceutical material. The quantification of moisture content based on absorption spectra is performed with two different regression methods, Partial Least Squares regression (PLSR) and Elastic Net Regression (ENR). The method is validated with particle size data for size determination, loss-on-drying (LOD) data of average moisture contents of granule samples and, finally, batch fluid bed experiments in which the results are compared to the most detailed method to date. The individual granule moisture contents confirmed again that granule size is an important factor in the drying process. The measurement method can be used to gain more detailed experimental insight in different fluidisation and particulate processes, which will allow building of robust process models.

Modeling of Semicontinuous Fluid Bed Drying of Pharmaceutical Granules With Respect to Granule Size.

In the transition of the pharmaceutical industry from batchwise to continuous drug product manufacturing, the drying process has proven challenging to control and understand. In a semicontinuous fluid bed dryer, part of the ConsiGma™ wet granulation line, the aforementioned production methods converge. Previous research has shown that the evolution of moisture content of the material in this system shows strong variation in function of the granule size, making the accurate prediction of this pharmaceutical critical quality attribute a complex case. In this work, the evolution of moisture content of the material in the system is modeled by a bottom-up approach. A single granule drying kinetics model is used to predict the moisture content evolution of a batch of material of a heterogeneous particle size, where it is the first time that the single granule drying mechanism is validated for different granule sizes. The batch approach was validated when the continuous material inflow rate and filling time of the dryer cell are constant. The original single granule drying kinetics model has been extended to capture the granules' equilibrium moisture content. Finally, the influence of drying air temperature is captured well with a droplet energy balance for the granules.

Model development and prediction of particle size distribution, density and friability of a comilling operation in a continuous pharmaceutical manufacturing process.

The comilling process plays an important role in solid oral dosage manufacturing. In this process, the granulated products are comminuted to the required size distribution through collisions created from a rotating impeller. In addition to predicting particle size distribution, there is a need to predict other critical quality attributes (CQAs) such as bulk density and tapped density, as these impact tablet compaction behavior. A comprehensive modeling approach to predict the CQAs is needed to aid continuous process modeling in order to simulate interaction with the tablet press operation. In the current work, a full factorial experiment design is implemented to understand the influence of granule strength, impeller speed and residual moisture content on the CQAs. A population balance modeling approach is applied to predict milled particle size distribution and a partial least squares modeling approach is used to predict bulk and tapped density of the milled granule product. Good agreement between predicted and experimental CQAs is achieved. An R2 value of 0.9787 and 0.7633 is obtained when fitting the mean particle diameters of the milled product and the time required to mill the granulated material respectively.