Surrogate-Based Correlation Models in View of Real-Time Control of Ozonation of Secondary Treated Municipal Wastewater-Model Development and Dynamic Validation.

New robust correlation models for real-time monitoring and control of trace organic contaminant (TrOC) removal by ozonation are presented, based on UVA254 and fluorescence surrogates, and developed considering kinetic information. The abatement patterns of TrOCs had inflected shapes, controlled by the reactivity of TrOCs toward ozone and HO• radicals. These novel and generic correlation models will be of importance for WRRF operators to reduce operational costs and minimize byproduct formation. Both UVA254 and fluorescence surrogates could be used to control ΔTrOC, although fluorescence measurements indicated a slightly better reproducibility and an enlarged control range. The generic framework was validated for several WRRFs and correlations for any compound with known kinetic information could be developed solely using the second order reaction rate constant with ozone (kO3). Two distinct reaction phases were defined for which separate linear correlations were obtained. The first was mainly ozone controlled, while the second phase was more related to HO• reactions. Furthermore, parallel factor analysis of the fluorescence spectra enabled monitoring of multiple types of organic matter with different O3 and HO• reactivity. This knowledge is of value for kinetic modeling frameworks and for achieving a better understanding of the occurring changes of organic matter during ozonation.

The use of Rheology Combined with Differential Scanning Calorimetry to Elucidate the Granulation Mechanism of an Immiscible Formulation During Continuous Twin-Screw Melt Granulation.

PURPOSE: Twin screw hot melt granulation (TS HMG) is a valuable, but still unexplored alternative to continuous granulation of moisture sensitive drugs. However, knowledge of the material behavior during TS HMG is crucial to optimize the formulation, process and resulting granule properties. The aim of this study was to evaluate the agglomeration mechanism during TS HMG using a rheometer in combination with differential scanning calorimetry (DSC). METHODS: An immiscible drug-binder formulation (caffeine-Soluplus(®)) was granulated via TS HMG in combination with thermal and rheological analysis (conventional and Rheoscope), granule characterization and Near Infrared chemical imaging (NIR-CI). RESULTS: A thin binder layer with restricted mobility was formed on the surface of the drug particles during granulation and is covered by a second layer with improved mobility when the Soluplus(®) concentration exceeded 15% (w/w). The formation of this second layer was facilitated at elevated granulation temperatures and resulted in smaller and more spherical granules. CONCLUSION: The combination of thermal and rheological analysis and NIR-CI images was advantageous to develop in-depth understanding of the agglomeration mechanism during continuous TS HMG and provided insight in the granule properties as function of process temperature and binder concentration.

Global Sensitivity Analysis as Good Modelling Practices tool for the identification of the most influential process parameters of the primary drying step during freeze-drying.

Pharmaceutical batch freeze-drying is commonly used to improve the stability of biological therapeutics. The primary drying step is regulated by the dynamic settings of the adaptable process variables, shelf temperature Ts and chamber pressure Pc. Mechanistic modelling of the primary drying step leads to the optimal dynamic combination of these adaptable process variables in function of time. According to Good Modelling Practices, a Global Sensitivity Analysis (GSA) is essential for appropriate model building. In this study, both a regression-based and variance-based GSA were conducted on a validated mechanistic primary drying model to estimate the impact of several model input parameters on two output variables, the product temperature at the sublimation front Ti and the sublimation rate msub. Ts was identified as most influential parameter on both Ti and msub, followed by Pc and the dried product mass transfer resistance αRp for Ti and msub, respectively. The GSA findings were experimentally validated for msub via a Design of Experiments (DoE) approach. The results indicated that GSA is a very useful tool for the evaluation of the impact of different process variables on the model outcome, leading to essential process knowledge, without the need for time-consuming experiments (e.g., DoE).

Developing a framework to model the primary drying step of a continuous freeze-drying process based on infrared radiation.

The continuous freeze-drying concept based on spinning the vials during freezing and on non-contact energy transfer via infrared (IR) radiation during drying, improves process efficiency and product quality (uniformity) compared to conventional batch freeze-drying. Automated control of this process requires the fundamental mechanistic modelling of each individual process step. Therefore, a framework is presented for the modelling and control of the continuous primary drying step based on non-contact IR radiation. The IR radiation emitted by the radiator filaments passes through various materials before finally reaching the spin frozen vial. The energy transfer was computed by combining physical laws with Monte Carlo simulations and was verified with experimental data. The influence of the transmission properties of various materials on the emitted IR radiation profile was evaluated. These results assist in the selection of proper materials which could serve as IR window in the continuous freeze-drying prototype. The modelling framework presented in this paper fits the model-based design approach used for the development of this prototype and shows the potential benefits of this design strategy by establishing the desired engineering parameters and by enabling the engineer to assess mechanical tolerances and material options.

The relevance of shear, sedimentation and diffusion during spin freezing, as potential first step of a continuous freeze-drying process for unit doses.

Recently, a continuous freeze-drying process for the production of unit doses was presented and evaluated. In this concept, the freezing step is modified compared to traditional batch freeze-drying, as glass vials filled with a liquid formulation, are rotated around their longitudinal axis while cooled and frozen with a cold, sterile and inert gas (i.e. spin freezing). Finally, a thin frozen product layer spread over the entire vial wall is achieved. The aim of this paper is twofold: firstly, the relation between the rotation velocity and the relative difference between top and bottom of the frozen product layer thickness was determined for different vial types. Secondly, the impact of shear and centrifugal forces generated during spinning was examined, to find out whether they might cause pharmaceutical instability and sedimentation, respectively. Mechanistic and experimental evaluation showed that shear has no effect on proteins. Calculations showed that the sedimentation and diffusion velocity is too low to cause inhomogeneity in the product layer. In addition, Global Sensitivity Analysis (GSA) and Uncertainty Analysis (UA) were performed in order to account for the uncertainty of the used mechanistic model.

Mechanistic modelling of infrared mediated energy transfer during the primary drying step of a continuous freeze-drying process.

Conventional pharmaceutical freeze-drying is an inefficient and expensive batch-wise process, associated with several disadvantages leading to an uncontrolled end product variability. The proposed continuous alternative, based on spinning the vials during freezing and on optimal energy supply during drying, strongly increases process efficiency and improves product quality (uniformity). The heat transfer during continuous drying of the spin frozen vials is provided via non-contact infrared (IR) radiation. The energy transfer to the spin frozen vials should be optimised to maximise the drying efficiency while avoiding cake collapse. Therefore, a mechanistic model was developed which allows computing the optimal, dynamic IR heater temperature in function of the primary drying progress and which, hence, also allows predicting the primary drying endpoint based on the applied dynamic IR heater temperature. The model was validated by drying spin frozen vials containing the model formulation (3.9mL in 10R vials) according to the computed IR heater temperature profile. In total, 6 validation experiments were conducted. The primary drying endpoint was experimentally determined via in-line near-infrared (NIR) spectroscopy and compared with the endpoint predicted by the model (50min). The mean ratio of the experimental drying time to the predicted value was 0.91, indicating a good agreement between the model predictions and the experimental data. The end product had an elegant product appearance (visual inspection) and an acceptable residual moisture content (Karl Fischer).

Quantitative risk assessment via uncertainty analysis in combination with error propagation for the determination of the dynamic Design Space of the primary drying step during freeze-drying.

Traditional pharmaceutical freeze-drying is an inefficient batch process often applied to improve the stability of biopharmaceutical drug products. The freeze-drying process is regulated by the (dynamic) settings of the adaptable process parameters shelf temperature Ts and chamber pressure Pc. Mechanistic modelling of the primary drying step allows the computation of the optimal combination of Ts and Pc in function of the primary drying time. In this study, an uncertainty analysis was performed on the mechanistic primary drying model to construct the dynamic Design Space for the primary drying step of a freeze-drying process, allowing to quantitatively estimate and control the risk of cake collapse (i.e., the Risk of Failure (RoF)). The propagation of the error on the estimation of the thickness of the dried layer Ldried as function of primary drying time was included in the uncertainty analysis. The constructed dynamic Design Space and the predicted primary drying endpoint were experimentally verified for different RoF acceptance levels (1%, 25%, 50% and 99% RoF), defined as the chance of macroscopic cake collapse in one or more vials. An acceptable cake structure was only obtained for the verification runs with a preset RoF of 1% and 25%. The run with the nominal values for the input variables (RoF of 50%) led to collapse in a significant number of vials. For each RoF acceptance level, the experimentally determined primary drying endpoint was situated below the computed endpoint, with a certainty of 99%, ensuring sublimation was finished before secondary drying was started. The uncertainty on the model input parameters demonstrates the need of the uncertainty analysis for the determination of the dynamic Design Space to quantitatively estimate the risk of batch rejection due to cake collapse.

Modelling the primary drying step for the determination of the optimal dynamic heating pad temperature in a continuous pharmaceutical freeze-drying process for unit doses.

In the pharmaceutical industry, traditional freeze-drying of unit doses is a batch-wise process associated with many disadvantages. To overcome these disadvantages and to guarantee a uniform product quality and high process efficiency, a continuous freeze-drying process is developed and evaluated. The main differences between the proposed continuous freeze-drying process and traditional freeze-drying can be found firstly in the freezing step during which the vials are rotated around their longitudinal axis (spin freezing), and secondly in the drying step during which the energy for sublimation and desorption is provided through the vial wall by conduction via an electrical heating pad. To obtain a more efficient drying process, the energy transfer has to be optimised without exceeding the product and process limits (e.g. cake collapse, choked flow). Therefore, a mechanistic model describing primary drying during continuous lyophilisation of unit doses based on conduction via heating pads was developed allowing the prediction of the optimal dynamic power input and temperature output of the electric heating pads. The model was verified by experimentally testing the optimal dynamic primary drying conditions calculated for a model formulation. The primary drying endpoint of the model formulation was determined via in-line NIR spectroscopy. This endpoint was then compared with the predicted model based endpoint. The mean ratio between the experimental and model based predicted drying time for six verification runs was 1.05±0.07, indicating a good accordance between the model and the experimental data.

Uncertainty analysis as essential step in the establishment of the dynamic Design Space of primary drying during freeze-drying.

Large molecules, such as biopharmaceuticals, are considered the key driver of growth for the pharmaceutical industry. Freeze-drying is the preferred way to stabilise these products when needed. However, it is an expensive, inefficient, time- and energy-consuming process. During freeze-drying, there are only two main process variables to be set, i.e. the shelf temperature and the chamber pressure, however preferably in a dynamic way. This manuscript focuses on the essential use of uncertainty analysis for the determination and experimental verification of the dynamic primary drying Design Space for pharmaceutical freeze-drying. Traditionally, the chamber pressure and shelf temperature are kept constant during primary drying, leading to less optimal process conditions. In this paper it is demonstrated how a mechanistic model of the primary drying step gives the opportunity to determine the optimal dynamic values for both process variables during processing, resulting in a dynamic Design Space with a well-known risk of failure. This allows running the primary drying process step as time efficient as possible, hereby guaranteeing that the temperature at the sublimation front does not exceed the collapse temperature. The Design Space is the multidimensional combination and interaction of input variables and process parameters leading to the expected product specifications with a controlled (i.e., high) probability. Therefore, inclusion of parameter uncertainty is an essential part in the definition of the Design Space, although it is often neglected. To quantitatively assess the inherent uncertainty on the parameters of the mechanistic model, an uncertainty analysis was performed to establish the borders of the dynamic Design Space, i.e. a time-varying shelf temperature and chamber pressure, associated with a specific risk of failure. A risk of failure acceptance level of 0.01%, i.e. a 'zero-failure' situation, results in an increased primary drying process time compared to the deterministic dynamic Design Space; however, the risk of failure is under control. Experimental verification revealed that only a risk of failure acceptance level of 0.01% yielded a guaranteed zero-defect quality end-product. The computed process settings with a risk of failure acceptance level of 0.01% resulted in a decrease of more than half of the primary drying time in comparison with a regular, conservative cycle with fixed settings.

The use of rheology to elucidate the granulation mechanisms of a miscible and immiscible system during continuous twin-screw melt granulation.

Twin-screw hot melt granulation (TS HMG) is a valuable, but still unexplored alternative to granulate temperature and moisture sensitive drugs in a continuous way. Recently, the material behavior of an immiscible drug-binder blend during TS HMG was unraveled by using a rheometer and differential scanning calorimetry (DSC). Additionally, vibrational spectroscopic techniques proved the link between TS HMG and rheology since equal interactions at molecular level did occur in both processes. This allowed to use a rheometer to gain knowledge of the material behavior during hot melt processing of an immiscible drug-binder blend. However, miscibility of a drug-binder formulation and drug-binder interactions appear to influence the rheological properties and, hence conceivably also the granulation mechanism. The aim of this research was to examine if the TS HMG process of a miscible formulation system is comparable with the mechanism of an immiscible system and to evaluate whether rheology still serves as a useful tool to understand and optimize the hot melt granulation (HMG) process. The executed research (thermal analysis, rheological parameters and spectroscopic data) demonstrated the occurrence of a high and broad tan(δ) curve without a loss peak during the rheological temperature ramp which implies a higher material deformability without movement of the softened single polymer chains. Spectroscopic analysis revealed drug-polymer interactions which constrain the polymer to flow independently. As a result, the binder distribution step, which generally follows the immersion step, was hindered. This insight assisted the understanding of the granule properties. Inhomogeneous granules were produced due to large initial nuclei or adhesion of multiple smaller nuclei. Consequently, a higher granulation temperature was required in order to get the binder more homogeneously distributed within the granules.

Analysing drying unit performance in a continuous pharmaceutical manufacturing line by means of mass--energy balances.

The current trend in the pharmaceutical industry to move from batch-wise to continuous production processes strengthens the need for monitoring and controlling the process in-line. The ConsiGma™ continuous tableting line collects data of the different subunits in real-time, but these are not really used. In this paper the data of the six-segmented fluidized bed dryer in the line are used for the development and evaluation of a mass and energy balance. The objectives are multiple: (1) prediction of the moisture content of the granules leaving the dryer solely based on the currently logged data and (2) prediction of the gas outlet temperature to check the mass balances. Once a validated system is established the gas temperature in different horizontal sections of the drying unit can be predicted. Calculations are also used to identify errors in the system and to propose alternative sensor locations. A calibration is performed in order to predict the evaporation rate. The balances were able to predict both the moisture content of the granules at the end of the drying process and the gas outlet temperature quite accurately. Combining the gathered information with the height of the bed in the fluidized bed can be used to predict the gas temperature in different horizontal sections of the dryer. An extra sensor measuring the gas temperature and the humidity at the wet transfer line would increase the accuracy of the calculations. An extra gas velocity sensor at the outlet would be useful to incorporate an extra supervision of the calculations.

Moisture and drug solid-state monitoring during a continuous drying process using empirical and mass balance models.

Classically, the end point detection during fluid bed drying has been performed using indirect parameters, such as the product temperature or the humidity of the outlet drying air. This paper aims at comparing those classic methods to both in-line moisture and solid-state determination by means of Process Analytical Technology (PAT) tools (Raman and NIR spectroscopy) and a mass balance approach. The six-segmented fluid bed drying system being part of a fully continuous from-powder-to-tablet production line (ConsiGma™-25) was used for this study. A theophylline:lactose:PVP (30:67.5:2.5) blend was chosen as model formulation. For the development of the NIR-based moisture determination model, 15 calibration experiments in the fluid bed dryer were performed. Six test experiments were conducted afterwards, and the product was monitored in-line with NIR and Raman spectroscopy during drying. The results (drying endpoint and residual moisture) obtained via the NIR-based moisture determination model, the classical approach by means of indirect parameters and the mass balance model were then compared. Our conclusion is that the PAT-based method is most suited for use in a production set-up. Secondly, the different size fractions of the dried granules obtained during different experiments (fines, yield and oversized granules) were compared separately, revealing differences in both solid state of theophylline and moisture content between the different granule size fractions.

A GLUE uncertainty analysis of a drying model of pharmaceutical granules.

A shift from batch processing towards continuous processing is of interest in the pharmaceutical industry. However, this transition requires detailed knowledge and process understanding of all consecutive unit operations in a continuous manufacturing line to design adequate control strategies. This can be facilitated by developing mechanistic models of the multi-phase systems in the process. Since modelling efforts only started recently in this field, uncertainties about the model predictions are generally neglected. However, model predictions have an inherent uncertainty (i.e. prediction uncertainty) originating from uncertainty in input data, model parameters, model structure, boundary conditions and software. In this paper, the model prediction uncertainty is evaluated for a model describing the continuous drying of single pharmaceutical wet granules in a six-segmented fluidized bed drying unit, which is part of the full continuous from-powder-to-tablet manufacturing line (Consigma™, GEA Pharma Systems). A validated model describing the drying behaviour of a single pharmaceutical granule in two consecutive phases is used. First of all, the effect of the assumptions at the particle level on the prediction uncertainty is assessed. Secondly, the paper focuses on the influence of the most sensitive parameters in the model. Finally, a combined analysis (particle level plus most sensitive parameters) is performed and discussed. To propagate the uncertainty originating from the parameter uncertainty to the model output, the Generalized Likelihood Uncertainty Estimation (GLUE) method is used. This method enables a modeller to incorporate the information obtained from the experimental data in the assessment of the uncertain model predictions and to find a balance between model performance and data precision. A detailed evaluation of the obtained uncertainty analysis results is made with respect to the model structure, interactions between parameters and uncertainty boundaries.

Mechanistic modelling of the drying behaviour of single pharmaceutical granules.

The trend to move towards continuous production processes in pharmaceutical applications enhances the necessity to develop mechanistic models to understand and control these processes. This work focuses on the drying behaviour of a single wet granule before tabletting, using a six-segmented fluidised bed drying system, which is part of a fully continuous from-powder-to-tablet manufacturing line. The drying model is based on a model described by Mezhericher et al. and consists of two submodels. In the first drying phase (submodel 1), the surface water evaporates, while in the second drying phase (submodel 2), the water inside the granule evaporates. The second submodel contains an empirical power coefficient, ß. A sensitivity analysis was performed to study the influence of parameters on the moisture content of single pharmaceutical granules, which clearly points towards the importance of ß on the drying behaviour. Experimental data with the six-segmented fluidised bed dryer were collected to calibrate ß. An exponential dependence on the drying air temperature was found. Independent experiments were done for the validation of the drying model.

Mechanistic modelling of fluidized bed drying processes of wet porous granules: a review.

Fluidized bed dryers are frequently used in industrial applications and also in the pharmaceutical industry. The general incentives to develop mechanistic models for pharmaceutical processes are listed, and our vision on how this can particularly be done for fluidized bed drying processes of wet granules is given. This review provides a basis for future mechanistic model development for the drying process of wet granules in pharmaceutical processes. It is intended for a broad audience with a varying level of knowledge on pharmaceutical processes and mathematical modelling. Mathematical models are powerful tools to gain process insight and eventually develop well-controlled processes. The level of detail embedded in such a model depends on the goal of the model. Several models have therefore been proposed in the literature and are reviewed here. The drying behaviour of one single granule, a porous particle, can be described using the continuum approach, the pore network modelling method and the shrinkage of the diameter of the wet core approach. As several granules dry at a drying rate dependent on the gas temperature, gas velocity, porosity, etc., the moisture content of a batch of granules will reside in a certain interval. Population Balance Model (ling) (PBM) offers a tool to describe the distribution of particle properties which can be of interest for the application. PBM formulation and solution methods are therefore reviewed. In a fluidized bed, the granules show a fluidization pattern depending on the geometry of the gas inlet, the gas velocity, characteristics of the particles, the dryer design, etc. Computational Fluid Dynamics (CFD) allows to model this behaviour. Moreover, turbulence can be modelled using several approaches: Reynolds-averaged Navier-Stokes Equations (RANS) or Large Eddy Simulation (LES). Another important aspect of CFD is the choice between the Eulerian-Lagrangian and the Eulerian-Eulerian approach. Finally, the PBM and CFD frameworks can be integrated, to describe the evolution of the moisture content of granules during fluidized bed drying.