Development and Application of Control Concepts for Twin-Screw Wet Granulation in the ConsiGmaTM-25: Part 2 Granule Size.

Traditional operation modes, such as running the production processes at constant process settings or within a narrow design space, do not fully exploit the advantages of continuous pharmaceutical manufacturing. Integrating Quality by Control (QbC) algorithms as a standard component of production processes can mitigate the effect of diverse process disturbances and enhance process efficiency, particularly in terms of production costs and environmental footprint. This paper explores the potential of QbC algorithms for optimizing twin-screw wet granulation in the ConsiGmaTM-25 manufacturing line, specifically addressing granule size. It represents the second part of a study (Celikovic et al. (2024)) focused on granule composition. The concepts proposed in this work rely on process analytical technology (PAT) equipment for real-time monitoring of the granulation CQAs and a dynamic process model linking the granulation process parameters and the monitored CQAs. The granule size model identified via the local-linear-model-tree (LoLiMoT) algorithm is used to develop both a model predictive controller (MPC) and a granule size soft sensor. The MPC employs this model as a core component for selecting optimal granulation parameters to ensure the production of granules with target size. A digital operator assistant is developed to address disturbances that cannot be mitigated via MPC but can be eliminated by the plant operators. This study systematically outlines a workflow, starting from conceptualization, moving through simulation development, and finally ending with real-world application on a production line. In this final step, all proposed concepts are transferred to the ConsiGmaTM-25 manufacturing line, where their performance is validated through selected disturbance scenarios.

Development and Application of Control Concepts for Twin-Screw Wet Granulation in the ConsiGmaTM-25: Part 1 Granule Composition.

Continuous manufacturing of pharmaceuticals offers several benefits, such as increased production efficiency, enhanced product quality control, and lower environmental footprint. To fully exploit these benefits, standard operation mode (production processes with no or minimal disturbance mitigation measures) should be supported by adopting novel quality-by-control (QbC) methodologies. The paper at hand is the first part of a study focused on developing QbC algorithms for optimizing twin-screw wet granulation in the industrial manufacturing line ConsiGmaTM-25, specifically addressing granule composition. This work relies on previously established process-analytical-technology (PAT) equipment for real-time monitoring of the granule composition, i.e., the active pharmaceutical ingredient (API) and liquid content in wet granules. The developed control platform integrates model-based process control algorithms that aim to keep the API- and liquid content at target values through real-time adjustments of the process parameters. Furthermore, the platform integrates a digital operator assistant, which aims to detect and classify granulation disturbances and provides messages and instructions for the plant operator. The present manuscript systematically outlines all design steps from the development phase in the simulation environment to the final real system application and validation. The control platform's performance is demonstrated through selected test scenarios on the ConsiGmaTM-25 manufacturing line. The obtained results indicate improved disturbance robustness and an increase in intermediate/final product quality (compared to conventional operating modes): The process control algorithms successfully maintained the API- and liquid content at target values despite process disturbances. Furthermore, realistic disturbances (feeder, pump, and material) were accurately detected and classified by the digital assistant algorithm. The information was provided through a user interface, offering real-time support for plant personnel.

Prediction of dissolution performance of uncoated solid oral dosage forms via optical coherence tomography.

Real-time prediction of the dissolution behavior of solid oral dosage forms is an important research topic. Although methods such as Terahertz and Raman can provide measurements that can be linked to the dissolution performance, they typically require a longer time off-line for analysis. In this paper, we present a novel strategy for analyzing uncoated compressed tablets by means of optical coherence tomography (OCT). Using OCT, which is fast and in-line capable, makes it possible to predict the dissolution behavior of tablets based on images. In our study, OCT images were obtained of individual tablets from differently produced batches. Differences between tablets or batches in these images were hardly visible to the human eye. Advanced image analysis metrics were developed to quantify the light scattering behavior captured by the OCT probe and depicted in the OCT images. Detailed investigations assured the repeatability and robustness of the measurements. A correlation between these measurements and the dissolution behavior was established. A tree-based machine learning model was used to predict the amount of dissolved active pharmaceutical ingredient (API) at certain time points for each immediate-release tablet. Our results indicate that OCT, which is a non-destructive and real-time technology, can be used for in-line monitoring of tableting processes.

Control oriented modeling of twin-screw granulation in the ConsiGmaTM-25 production plant.

ConsiGmaTM-25 is a continuous production plant integrating a twin-screw granulation, fluid bed drying, granule conditioning, and a tableting unit. The particle size distribution (PSD), active pharmaceutical ingredient (API) content, and liquid content of wet granules after twin-screw granulation affect the quality of intermediate and final products. This paper proposes methods for real-time monitoring of these quantities and control-oriented modeling of the granulator. The PSD of wet granules is monitored via an in-line process analytical technology (PAT) probe based on the spatial velocimetry principle. The algorithm for signal processing and evaluation of PSD characteristics is developed and applied to the acquired PSD data. A dynamic process model predicting PSD characteristics from granulation parameters is trained via the local linear model tree (LoLiMoT) approach. The experimental data required for the model training are collected via systematically designed excitation runs. Finally, the performance of the identified model is examined and verified by means of a new set of validation runs. Furthermore, an in-line PAT probe based on Raman spectroscopy is developed and integrated after the granulator. The API- and liquid content of produced wet granules are evaluated from the spectral data by means of chemometric modeling, and chemometric models are validated on a separate set of experimental data. The solutions proposed in this research can be used as a reliable (and necessary) basis for the development of advanced quality-by-design control concepts (e.g., PSD process control). Such concepts would ultimately improve the ConsiGmaTM-25 process performance in terms of robustness against disturbances and quality of intermediate and final products.

PAT implementation for advanced process control in solid dosage manufacturing - A practical guide.

The implementation of continuous pharmaceutical manufacturing requires advanced control strategies rather than traditional end product testing or an operation within a small range of controlled parameters. A high level of automation based on process models and hierarchical control concepts is desired. The relevant tools that have been developed and successfully tested in academic and industrial environments in recent years are now ready for utilization on the commercial scale. To date, the focus in Process Analytical Technology (PAT) has mainly been on achieving process understanding and quality control with the ultimate goal of real-time release testing (RTRT). This work describes the workflow for the development of an in-line monitoring strategy to support PAT-based real-time control actions and its integration into solid dosage manufacturing. All stages are discussed in this paper, from process analysis and definition of the monitoring task to technology assessment and selection, its process integration and the development of data acquisition.

Development of a Controlled Continuous Low-Dose Feeding Process.

This paper proposes a feed rate control strategy for a novel volumetric micro-feeder, which can accomplish low-dose feeding of pharmaceutical raw materials with significantly different powder properties. The developed feed-forward control strategy enables a constant feed rate with a minimum deviation from the set-point, even for materials that are typically difficult to accurately feed (e.g., due to high cohesion or low density) using conventional continuous feeders. Density variations observed during the feeding process were characterized via a displacement feed factor profile for each powder. The characterized effective displacement density profile was applied in the micro-feeder system to proactively control the feed rate by manipulating the powder displacement rate (i.e., computing the feed rate from the powder displacement rate). Based on the displacement feed factor profile, the feed rate can be predicted during the feeding process and at any feed rate set-point. Three pharmaceutically relevant materials were used for the micro-feeder evaluation: di-calcium phosphate (large-particle system, high density), croscarmellose sodium (small-particle system, medium density), and barium sulfate (very small-particle <10 µm, high density). A significant improvement in the feeding performance was achieved for all investigated materials. The feed rate deviation from the set-point and its relative standard deviation were minimal compared to operations without the control strategy.

Fluidization characterization in the ConSigma 25 dryer via process data - A method of advanced quality assurance in continuous manufacturing.

Wet granulation lines in pharmaceutical manufacturing facilities typically comprise a dryer that removes the excess moisture content after wet granulation. In this study, a semi-continuous dryer installed in the ConsiGma 25 wet granulation line was investigated. The goal was to highlight specific characteristics of this type of dryer, utilizing the available process data and the corresponding data obtained via material characterization. This paper addresses typical effects and issues associated with the dryer's setup and operation (e.g., unexpected cell temperature profiles, the effects of air flow and temperature on the granule properties, variations in the granule moisture between the dryer cells). Since in many situations the liquid-to-solid ratio is based on the properties of granules after the granulation step, the selection of inlet air flow rate and the inlet air temperature of the dryer as well as the overall line throughput (affecting the cell fill level in the dryer) are of particular interest from a practical point of view. This paper discusses these issues and provides suggestions on how to address them when setting up the process. A novel approach for characterizing the fluidization inside the dryer by means of quantifying the "smoothness" of the temperature profile is proposed. The paper should be viewed as a hands-on guideline, which highlights possible pitfalls during the process setup and offers solutions.

Advanced Real-Time Process Analytics for Multistep Synthesis in Continuous Flow*.

In multistep continuous flow chemistry, studying complex reaction mixtures in real time is a significant challenge, but provides an opportunity to enhance reaction understanding and control. We report the integration of four complementary process analytical technology tools (NMR, UV/Vis, IR and UHPLC) in the multistep synthesis of an active pharmaceutical ingredient, mesalazine. This synthetic route exploits flow processing for nitration, high temperature hydrolysis and hydrogenation reactions, as well as three inline separations. Advanced data analysis models were developed (indirect hard modeling, deep learning and partial least squares regression), to quantify the desired products, intermediates and impurities in real time, at multiple points along the synthetic pathway. The capabilities of the system have been demonstrated by operating both steady state and dynamic experiments and represents a significant step forward in data-driven continuous flow synthesis.

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.

Filling of lactose-based formulations in a tamping-pin capsule filler.

We studied three lactose-based formulations in terms of bulk powder properties and capsule-filling behavior in a tamping-pin capsule filling system, to which several mechanical adaptions were made for process optimization in light of future continuous production. The model formulations were thoroughly characterized and filled into size 1 capsules according a well-defined design of experiments (DoE). Overall, the three entirely different formulations were successfully filled within the selected design space. The fill weight and fill weight variability can be adjusted by fine-tuning the process settings, like the pin immersion depth and the maximum compaction pressure (pneumatic or spring-controlled), and by using the appropriate powder bed height and mechanical adaptions. This study demonstrated that selection of process parameters and mechanical adaptions could enhance the filling performance, especially in continuous production, since they reduce the powder volume in the process. Moreover, we showed that a tamping-pin system is capable of successfully filling a broad range of powders with various material characteristics and can potentially be used in a continuous production mode.

Towards a novel continuous HME-Tableting line: Process development and control concept.

The objective of this study was to develop a novel closed-loop controlled continuous tablet manufacturing line, which first uses hot melt extrusion (HME) to produce pellets based on API and a polymer matrix. Such systems can be used to make complex pharmaceutical formulations, e.g., amorphous solid dispersions of poorly soluble APIs. The pellets are then fed to a direct compaction (DC) line blended with an external phase and tableted continuously. Fully-automated processing requires advanced control strategies, e.g., for reacting to raw material variations and process events. While many tools have been proposed for in-line process monitoring and real-time data acquisition, establishing real-time automated feedback control based on in-process control strategies remains a challenge. Control loops were implemented to assess the quality attributes of intermediates and product and to coordinate the mass flow rate between the unit operations. Feedback control for the blend concentration, strand temperature and pellet thickness was accomplished via proportional integral derivative (PID) controllers. The tablet press hopper level was controlled using a model predictive controller. To control the mass flow rates in all unit operations, several concepts were developed, with the tablet press, the extruder or none assigned to be the master unit of the line, and compared via the simulation.

Ensuring tablet quality via model-based control of a continuous direct compaction process.

Switching from batch to continuous pharmaceutical production offers several advantages, such as an increased productivity, a steady product quality, and decreased costs. This paper presents a control strategy for direct compaction on a continuous tablet production line consisting of two feeders, one blender, and a tablet press (TP). A data-driven, linear modeling approach is applied in order to develop a Smith predictor for active pharmaceutical ingredient concentration control and a model predictive controller responsible for the TP hopper level. Additionally, in case of severe concentration variations out-of-specification material can be discarded before it enters the TP. The effectiveness of the control concept is tested not only in simulations but also by implementing it on a real pilot plant.

Material tracking in a continuous direct capsule-filling process via residence time distribution measurements.

Continuous production of pharmaceuticals requires traceability from the raw material to the final dosage form. With that regard, understanding the residence time distribution (RTD) of the whole process and its unit operations is crucial. This work describes a structured approach to characterizing and modelling of RTDs in a continuous blender and a tamping pin capsule filling machine, including insights into data processing. The parametrized RTD models were interconnected to model a continuous direct capsule-filling process, showing the batch transition as well as the propagation of a 2 min feed disturbance throughout the process. Various control strategies were investigated in-silico, aiding in the selection of optimal material diversion point to minimize the material waste. Additionally, the RTD models can facilitate process design and optimization. In this work, adaptions to the capsule filling machine were made and their influence on the RTD was examined to achieve an optimal machine setup.

Residence time distribution of a continuously-operated capsule filling machine: Development of a measurement technique and comparison of three volume-reducing inserts.

This paper presents the measurement and analysis of the residence time distribution (RTD) of a tamping-pin capsule filling machine. The tamping speed and the amount of material inside the powder bowl proved to have a significant effect on the RTD. Various inserts into the powder bowl that reduce the volume and alter mixing and transport in the bowl were experimentally investigated. To obtain the RTD, a tracer-based measurement method was applied and a sophisticated data processing strategy was developed. The tracer-based method also allowed investigations of stagnant zones in the powder bowl, another important aspect in continuous manufacturing (CM). The suitability of tracer material was assessed based on a detailed characterization of bulk and tracer material. Characteristic parameters of the RTD were extracted and compared, proposing a systematic strategy for selection of a suitable insert.

Control of three different continuous pharmaceutical manufacturing processes: Use of soft sensors.

One major advantage of continuous pharmaceutical manufacturing over traditional batch manufacturing is the possibility of enhanced in-process control, reducing out-of-specification and waste material by appropriate discharge strategies. The decision on material discharge can be based on the measurement of active pharmaceutical ingredient (API) concentration at specific locations in the production line via process analytic technology (PAT), e.g. near-infrared (NIR) spectrometers. The implementation of the PAT instruments is associated with monetary investment and the long term operation requires techniques avoiding sensor drifts. Therefore, our paper proposes a soft sensor approach for predicting the API concentration from the feeder data. In addition, this information can be used to detect sensor drift, or serve as a replacement/supplement of specific PAT equipment. The paper presents the experimental determination of the residence time distribution of selected unit operations in three different continuous processing lines (hot melt extrusion, direct compaction, wet granulation). The mathematical models describing the soft sensor are developed and parameterized. Finally, the suggested soft sensor approach is validated on the three mentioned, different continuous processing lines, demonstrating its versatility.

RTD modeling of a continuous dry granulation process for process control and materials diversion.

Disturbance propagation during continuous manufacturing processes can be predicted by evaluating the residence time distribution (RTD) of the specific unit operations. In this work, a dry granulation process was modelled and four scenarios of feeding events were simulated. We performed characterization of the feeders and developed RTD models for the blender and the roller compactor based on impulse-response measurements via color tracers. Out-of-specification material was defined based on the active pharmaceutical ingredient (API) concentration. We calculated the amount of waste material at various diversion points, considering four feeder-related process-upset scenarios and formulated considerations for the development of a control concept. The developed RTD models allow material tracking of materials that may be used for following the spread contaminants within the process and for batch definition. The results show that RTD modeling is a valuable tool for process development and design, as well as for process monitoring and material tracking.

Sensitivity analysis of a pharmaceutical tablet production process from the control engineering perspective.

This paper presents a sensitivity analysis of a pharmaceutical direct compaction process. Sensitivity analysis is an important tool for gaining valuable process insights and designing a process control concept. Examining its results in a systematic manner makes it possible to assign actuating signals to controlled variables. This paper presents mathematical models for individual unit operations, on which the sensitivity analysis is based. Two sensitivity analysis methods are outlined: (i) based on the so-called Sobol indices and (ii) based on the steady-state gains and the frequency response of the proposed plant model.

Optimized continuous pharmaceutical manufacturing via model-predictive control.

This paper demonstrates the application of model-predictive control to a feeding blending unit used in continuous pharmaceutical manufacturing. The goal of this contribution is, on the one hand, to highlight the advantages of the proposed concept compared to conventional PI-controllers, and, on the other hand, to present a step-by-step guide for controller synthesis. The derivation of the required mathematical plant model is given in detail and all the steps required to develop a model-predictive controller are shown. Compared to conventional concepts, the proposed approach allows to conveniently consider constraints (e.g. mass hold-up in the blender) and offers a straightforward, easy to tune controller setup. The concept is implemented in a simulation environment. In order to realize it on a real system, additional aspects (e.g., state estimation, measurement equipment) will have to be investigated.