Orthogonal Redundant Monitoring of a New Continuous Fluid-Bed Dryer for Pharmaceutical Processing by Means of Mass and Energy Balance Calculations and Spectroscopic Techniques

Proof of a LOD Prediction Model with Orthogonal PAT Methods in Continuous Wet Granulation and Drying

Real-time monitoring of critical quality attributes, such as residual water in granules after drying which can be determined through loss-on-drying (LOD), during wet granulation and drying is essential in continuous manufacturing. Near-infrared (NIR) spectroscopy has been widely used as process analytical technology (PAT) for in-line LOD monitoring. This study aims to develop and apply a model for predicting the LOD based on process parameters. Additionally, the efficacy of an orthogonal PAT approach using NIR and mass balance (MB) for a vibrating fluidized bed dryer (VFBD) is demonstrated. An in-house-built, cost-effective NIR sensor was utilized for measurements and exhibited good correlation compared to standard method via infrared drying. The combination of NIR and MB, as independent methods, has demonstrated their applicability. A good correlation, with a Pearson r above 0.99, was observed for LOD up to 16 % (w/w). The use of an orthogonal PAT method mitigated the risk of false process adaption. In some experiments where the NIR sensor might have been covered by powder and therefore did not measure accurately, LOD monitoring via MB remained feasible. The developed model effectively predicted LOD or process parameters, resulting in an R2 of 0.882 and a RMSE of 0.475 between predicted and measured LOD using the standard method.

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.

Drying capacity of a continuous vibrated fluid bed dryer - Statistical and mechanistic model development

The drying capacity of a continuous vibrated fluid bed dryer was studied using a DoE by varying microcrystalline cellulose content in the formulation, water amount in the twin-screw granulation, inlet air temperature, air flow rate and the acceleration of the horizontal fluid-bed. Temperature and humidity profiles were measured along the dryer using wireless sensors. For the parameter space explored in this study, acceleration was the most influential process parameter of the dryer regarding the resulting granule moisture content. An empirical model was developed that allowed for fast and accurate moisture content prediction that could be incorporated into an enhanced control strategy. In addition, a mechanistic model was formulated that allow for prediction of temperature and moisture profiles, and most importantly the moisture content of the granules inside the dryer. The mechanistic model can be integrated to other unit operation models to provide overall understanding of an integrated continuous process line. The mechanistic model also makes it possible to define the equipment design requirements (e.g., length of the dryer) to meet the specific needs in terms of drying capacity, temperature and moisture profile.

Real-time in-line prediction of drug loading and release rate in the coating process of diclofenac sodium spheres based on near infrared spectroscopy

The preparation of diclofenac sodium spheres by fluidized bed is a common production mode for the pharmaceutical preparations at present, but the critical material attributes in the production process is mostly analyzed off-line, which is time-consuming and laborious, and the analysis results lag behind. In this paper, the real-time in-line prediction of drug loading of diclofenac sodium and the release rate during the coating process was realized by using near infrared spectroscopy. For the best near infrared spectroscopy (NIRS) model of drug loading, R²_cv, R²_p, RMSECV, RMSEP were 0.9874, 0.9973, 0.002549 mg/g, 0.001515 mg/g respectively. For the best NIRS model of three release time points, the R²_cv, R²_p, RMSECV and RMSEP were 0.9755, 0.9823, 3.233%, 4.500%; 0.9358, 0.9965, 2.598%, 0.7939% and 0.9867, 0.9927, 0.4085%, 0.4726% respectively. And the analytical ability of these model was verified. The organic combination of these two parts of work constituted an important basis for ensuring the safety and effectiveness of diclofenac sodium spheres from the perspective of production process.

Heat Transfer Model and Soft Sensing for Segmented Fluidized Bed Dryer

The aim of this work is to evaluate thermal behaviors and develop a soft sensor for online prediction of LOD (loss-on-drying) in the segmented fluidized bed dryer (Seg-FBD) in the ConsiGma25 line, which is regarded as the intermediate critical quality attribute for the final drug product. Preheating and drying experiments are performed and heat transfers and conductions among the Seg-FBD are evaluated based on the temperature measurements from sensors and an infrared thermal camera. A temperature distribution in dryer cells and high heat conductions in walls are found. Considerable heat transfers between the neighboring dryer cells are determined, which equal approximately 7% of the energy provided from the heated air. The cell-to-cell heat transfers are implemented into the heat transfer and drying models of the Seg-FBD. The models are calibrated successively in gPROMS Formulated Products (gFP) and the temperature and LOD errors are less than 2 °C and 0.5 wt.%, respectively. Subsequently, a soft sensor is established by combining data sources, a real-time data communication method, and the developed drying model, and it shows the capability of predicting real-time LOD, where the error of end-point LOD is within 0.5 wt.%. The work provides detailed steps and applicable tools for developing a soft sensor, and the online deployment of the soft sensor could support continuous production in the Seg-FBD by enabling visualization of process status and determination of process end point.

Integrated twin-screw wet granulation, continuous vibrational fluid drying and milling: A fully continuous powder to granule line

Highly homogeneous low-dose (50 μg) tablets were produced incorporating perfectly free-flowing granules prepared by a fully integrated Continuous Manufacturing (CM) line. The adopted CM equipment consisted of a Twin-Screw Wet Granulator (TSWG), a Continuous Fluid Bed Dryer (CFBD) and a Continuous Sieving (CS) unit. Throughout the experiments a pre-blend of lactose-monohydrate and corn starch was gravimetrically dosed with 1 kg/h into the TSWG, where they were successfully granulated with the drug containing water-based PVPK30 solution. The wet mass was subsequently dried in the CFBD on a vibratory conveyor belt and finally sieved in the milling unit. Granule production efficiency was maximized by determining the minimal Liquid-to-Solid (L/S) ratio (0.11). Design of Experiments (DoE) were carried out in order to evaluate the influence of the drying process parameters of the CFBD on the Loss-on-Drying (LOD) results. The manufactured granules were compressed into tablets by an industrial tablet rotary press with excellent API homogeneity (RSD < 3%). Significant scale-up was realized with the CM line by increasing the throughput rate to 10 kg/h. The manufactured granules yielded very similar results to the previous small-scale granulation runs. API homogeneity was demonstrated (RSD < 2%) with Blend Uniformity Analysis (BUA). The efficiency of TSWG granulation was compared to High-Shear Granulation (HSG) with the same L/S ratio. The final results have demonstrated that both the liquid distribution and more importantly API homogeneity was better in case of the TSWG granulation (RSD 1.3% vs. 4.5%).

Continuous twin screw granulation and fluid bed drying: A mechanistic scaling approach focusing optimal tablet properties

This study provides the results of investigation on scaling approaches for three differently-sized continuous granulation lines, each consisting of a twin screw wet granulation process and a continuous fluid bed drying process. To check the initial scaling approach with regard to granule and tablet properties, a process parameter Design of experiment (DoE) was performed on each of the three equipment scales. The processed formulation did not contain cellulose to allow a high overall flowrate through the directly connected granulation and drying sections. Enhanced scaling aspects showed the influence of Froude number [-] at different twin screw granulator scales and screw speeds on the overgranulated particle fraction [% (V/V] as well as on the scale-dependent drying performance of the continuous fluid bed dryers. Scale-independent, specification limits of the two granule material attributes particle fine fraction [%] and residual water content [%] could be defined, resulting in high tableting performance in terms of tabletability and compressibility. Based on these specification limits and the statistical evaluation of the process parameter DoE, a process design space for the continuous granulation and drying process for each scale was calculated. It came up, that this process design space was decreasing in range with increasing equipment scale. The applicability of the presented scaling approach in terms of granule and tablet properties could successfully be demonstrated by three control experiments performed on the different equipment scales. In sum, this work delivers a basis for a smooth transition of scales within process development on the investigated continuous twin screw granulation and drying lines.

From powder to tablets: Investigation of residence time distributions in a continuous manufacturing process train as basis for continuous process verification

The essence of Continuous Manufacturing (CM) resides in the fact that continuous process units are directly connected to each other forming a continuous process train. The thorough understanding of material flow in this train based on suitable sensors, including on-line process analytical technologies and other sensors, is key in understanding the time-domain behavior of the system and the process. This real-time monitoring correlated with the time domain material flow behavior could be used to close control-loops. In practical terms, the implementation of such a control strategy is only feasible, if the overlying control system knows precisely what material is when and where at all times. Consequently, thorough knowledge of the residence time distribution (RTD) of the material throughout the whole manufacturing network needs to be established early on in development. Once RTD is well understood, its constant observation could also be used for continuous process verification purposes hinging on the argument that the flow pattern of the material is unchanged. As continuous processes that run over extended periods of time are susceptible to unforeseen incidents like equipment wear-out or clogging, drifts or shifts in RTD could indicate such issues early on. The presented work aims to demonstrate this proposed concept for an integrated wet-granulation CM process. To achieve this aim, three steps were completed: First, thorough RTD knowledge was generated, by inducing endogenous step-tests in active pharmaceutical ingredient (API) content in the range of ±30% at varying process conditions, and analyzing the material RTDs via NIRS analysis at four different locations in the line. Second, it was demonstrated that also low-level step tests of ±5% and even ±3% are sufficient for accurate RTD determination. This validated the possibility of continuous RTD assessment during (pre-)validation trials or even commercial manufacturing, as the drug product would comply with required quality characteristics (content uniformity, assay). In the third step, it was then demonstrated that recurring low-level step testing during routine manufacturing could be used as a way to determine the current system health, as observed changes in RTD indicated blockages and accidental material hold-up in the line. While deliberate changes in API content during commercial production might seem counter intuitive, they would actually aid in ensuring the production of quality product in a better way, than running at constant process settings over an extended period of time without the constant assessment of system health.

End-Point Prediction of Granule Moisture in a ConsiGma™-25 Segmented Fluid Bed Dryer

Continuously operated pharmaceutical manufacturing lines often consist of a wet granulation unit operation, followed by a (semi-) continuous dryer. The operating conditions of the dryer are crucial for obtaining a desired final granule moisture. Commercially available dryers lack of a thorough online measurement of granule moisture during the drying process. However, this information could improve the operation of the equipment considerably, yielding a granule moisture close to the desired value (e.g., by drying time and process parameter adjustments in real-time). The paper at hand proposes a process model, which can be parameterized from a very limited number of experiments and then be used as a so-called soft sensor for predicting granule moisture. It utilizes available process measurements for the estimation of the granule moisture. The development of the model as well as parameter identification and validation experiments are provided. The proposed model paves the way for the application of sophisticated observer concepts. Possible future activities on that topic are outlined in the paper.

Recent progress in continuous manufacturing of oral solid dosage forms

Continuous drug product manufacturing is slowly being implemented in the pharmaceutical industry. Although the benefits related to the quality and cost of continuous manufacturing are widely recognized, several challenges hampered the widespread introduction of continuous manufacturing of drug products. Current review presents an overview of state-of-the art research, equipment, process analytical technology implementations and advanced control strategies. Additionally, guidelines and regulatory viewpoints on implementation of continuous manufacturing in the pharmaceutical industry are discussed.

Predictive Model-Based Process Start-Up in Pharmaceutical Continuous Granulation and Drying

Continuous manufacturing (CM) is a promising strategy to achieve various benefits in the context of quality, flexibility, safety and cost in pharmaceutical production. One of the main technical challenges of CM is that the process needs to handle transient conditions such as the start-up phase before state of control operation is reached, which can potentially cause out-of-specification (OOS) material. In this context, the presented paper aims to demonstrate that suitable process control strategies during start-up of a continuous granulation and drying operation can limit or even avoid OOS material production and hence can ensure that the provided benefits of CM are not compromised by poor production yields. In detail, heat-up of the drying chamber prior the start of production can lead to thermal energy being stored inside of the stainless-steel housing, acting as an energy buffer that is known to cause over-dried granules in the first few minutes of the drying process. To compensate this issue, an automatic ramping procedure of dryer rotation speed (and hence drying time) was introduced into the plant's process control system, which counteracts the excessive drying capacity during start-up. As a result, dry granules exiting the dryer complied with the targeted intermediate critical quality attribute loss-on-drying (LOD) from the very beginning of production.

Industrial application of heat- and mass balance model for fluid-bed granulation for technology transfer and design space exploration

This work demonstrates the application of state-of-the-art modeling techniques in pharmaceutical manufacturing for fluid bed granulation at varying scales to successfully predict process conditions and ultimately replace experiments during a technology transfer of five products. We describe a mathematical model able to simulate the time-dependent moisture profile in a fluid bed granulation process. The applicability of this model is then demonstrated by calibrating and validating it over a range of operating conditions, manufacturing scales, and formulations. The inherent capability of the moisture profile to serve as a simple, scale-independent surrogate is shown by the large number of successful scale-ups and transfers. A methodology to use this 'digital twin' to systematically explore the effects of uncertainty inherent in the process input and model parameter spaces and their impact on the process outputs is described. Two case studies exemplifying the utilization of the model in industrial practice to assess process robustness are provided. Lastly, a pathway to leverage model results to establish proven acceptable ranges for individual parameters is outlined.

Real-time monitoring of particle size distribution in a continuous granulation and drying process by near infrared spectroscopy

In continuous granulation, it can be important to control granules particle size distribution (PSD), as it may affect final product quality. Near infrared spectroscopy (NIRS) is already a routine analytical procedure within pharmaceutical continuous manufacturing for the in-line analysis of chemical material-characteristics. Consequently, the extraction of additional information related to granules' physical properties like particle size distribution is tempting, as it would enhance process knowledge without the need for new capital investments. Three in-line NIRS methods were developed via partial least squares regression, to predict dried granules PSD-fractions X10, X50, and X90 within a GMP-qualified continuous twin-screw wet granulation and fluid-bed drying process. Methods were developed for the size range of 20-234 µm (X10), 98-1017 µm (X50), and 748-2297 µm (X90) and assessed with one internal and three external validation datasets in agreement with current guidelines on NIRS. Internal validation indicated root mean square error of predictions (RMSEPs) of 17 µm, 97 µm, and 174 µm, for PSD X10, X50, and X90 respectively, with acceptable linearity, slope, and bias. Furthermore, the ratio of prediction to deviation (RPD), the ratio of prediction error to laboratory error (PRL), and the range error ratio (RER) were evaluated, with all values within the acceptance range for adequate to good NIR methods (1.75 > RPD < 3, PRL ≤ 2, RER ≥ 10). Methods applicability to in-line processes and their robustness towards water content and active pharmaceutical ingredient content was further demonstrated with three independent in-line datasets in real-time, showing good agreement between predicted and reference values. In summary, methods demonstrated to be sufficient for their intended purpose to monitor trends and sudden changes in dried granules PSD during continuous granulation and drying. Because of their fast response time, they are unique tools to characterize the dynamic behavior and navigate the agglomeration state of the material in static and transient process conditions during continuous granulation and drying.