Development and implementation of an advanced model predictive control system into continuous pharmaceutical tablet compaction process

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

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

Evaluation of the effect of granule size of raw tableting materials on critical quality attributes of tablets during the continuous tablet manufacturing process using near-infrared spectroscopy

OBJECTIVE

The effects of granule size of raw materials on tablet hardness (TH) and weight (TW) in the continuous tablet manufacturing process (CTMP) were investigated using near-infrared spectroscopy (NIRS).

METHODS

Granule materials of different sizes were prepared by extrusion granulation from a standard granule formula powder containing lactose/starch and 4.5% acetaminophen. Large-, small-, and medium-sized granules were sequentially filled in a hopper, and tablets were produced continuously using a single-shot tableting machine. After arranging approximately 500 tablets in order, the tablets were subjected to NIRS. A total of 450 NIRS datasets were divided into three groups of 150 each (calibration, validation 1, and validation 2 datasets).

RESULTS

The best fitted calibration models for predicting TH and TW were obtained, with sufficient accuracy, based on NIRS using the partial least squares regression, and comprised both physical and chemical information. The regression and loading vectors of the calibration models suggested that the models used to predict TH and TW involve physical information based on geometrical factors of the tablet and chemical information related to binder-related intermolecular interactions.

CONCLUSIONS

The changes in the predicted value profiles of TH and TW using NIRS reflected the changes in the measured values depending on the raw granule size during CTMP.

Data-Knowledge-Driven Modeling and Operational Adjustment for the Pharmaceutical Tablet Manufacturing Process via Wet Granulation

In the context of Pharma 4.0, pharmaceutical quality control (PQC) is beset by issues such as uncertainties from ever-changing critical material attributes and strong coupling between variables in the multi-unit pharmaceutical tablet manufacturing process (PTMP), and how to timely adjust the operational variables to deal with such challenges has become a key problem in PQC. In this study, we propose a novel data-knowledge-driven modeling and operational adjustment framework for PTMP by integrating Bayesian network (BN) and case-based reasoning (CBR). At the modeling level, first, a distributed concept is introduced, i.e., the BN model for each subunit of PTMP is established in accordance with the operation process sequence, and the transition variables are given by the BN model established first and retrieved as the new query for the next unit. Once the BN models of all subunits are built, they are integrated into a global BN model. At the operational adjustment level, by taking the expected critical quality attributes (CQAs) and related prior information as evidence, the operational adjustment is achieved through global BN reasoning. Finally, the case study in a sprayed fluidized-bed granulation-based PTMP demonstrates the feasibility and effectiveness in improving the terminal CQAs of the proposed method, which is also compared with other methods to showcase its efficacy and merits.

An integrated data management and informatics framework for continuous drug product manufacturing processes: A case study on two pilot plants

The pharmaceutical industry continuously looks for ways to improve its development and manufacturing efficiency. In recent years, such efforts have been driven by the transition from batch to continuous manufacturing and digitalization in process development. To facilitate this transition, integrated data management and informatics tools need to be developed and implemented within the framework of Industry 4.0 technology. In this regard, the work aims to guide the data integration development of continuous pharmaceutical manufacturing processes under the Industry 4.0 framework, improving digital maturity and enabling the development of digital twins. This paper demonstrates two instances where a data integration framework has been successfully employed in academic continuous pharmaceutical manufacturing pilot plants. Details of the integration structure and information flows are comprehensively showcased. Approaches to mitigate concerns in incorporating complex data streams, including integrating multiple process analytical technology tools and legacy equipment, connecting cloud data and simulation models, and safeguarding cyber-physical security, are discussed. Critical challenges and opportunities for practical considerations are highlighted.

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.

Experimental investigation and modelling of tensile strength of pharmaceutical tablets based on shear force applied by feed frame paddles

The feed frame is an essential device used in a rotary tablet press and it improves the performance of the powder filling process into dies. However, the feed frame affects critical quality attributes such as a tensile strength and a dissolution negatively due to a shear applied to powders from feed frame paddles, leading to over-lubrication. This effects may be significant for shear sensitive materials. The work focuses on the effect of tablet press parameters (die disk speed and feed frame speed) and mixture composition (amount of magnesium stearate) on the tensile strength and the prediction of the tensile strength by considering the extent of shear. It is found that within the investigated range of tablet press parameters and the amount of magnesium stearate, the feed frame speed and the amount of magnesium stearate have an impact on the tensile strength. Furthermore, a lubrication model based on the extent of shear is presented to predict the decreasing trend of the tensile strength of tablets during tableting process and the results demonstrate that the prediction of tensile strength is in good agreement with experimental measurements.

Performance assessment of linear iterative optimization technology (IOT) for Raman chemical mapping of pharmaceutical tablets

Raman chemical mapping is an inherently slow analysis tool. Accurate and robust multivariate analysis algorithms, which require least amount of time and effort in method development are desirable. Calibration-free regression and resolution approaches such as classical least squares (CLS) and multivariate curve resolution using alternating least squares (MCR-ALS), respectively, help in reducing the resources required for method development. However, conventional CLS does not consider appropriate constraints, which may result in negative and/or greater than 100 % Raman concentration scores, while MCR-ALS may not always be as accurate as regression-based algorithms. Linear iterative optimization technology (IOT) is another calibration-free algorithm, which with appropriate constraints has previously shown promise in online and offline pharmaceutical mixture composition determination. This paper aims to evaluate the performance of the linear IOT algorithm for Raman chemical mapping of the active pharmaceutical ingredient (API), diluent, and lubricant in pharmaceutical tablets. Two pre-processing strategies were applied to the raw Raman mapping spectra. The results were compared with CLS (current reference method) and MCR-ALS. Special emphasis was given to mapping at low Raman exposure times to enable feasible total acquisition times (< 5 h). The quality of IOT/CLS/MCR-ALS estimated Raman concentration predictions were assessed by calculating a correlation factor between the spectrum corresponding to the maximum predicted concentration (or resolved spectra) of a component for IOT/CLS (or MCR-ALS) and the pure powder component spectrum. The Raman chemical maps were visualized, and the average Raman concentrations scores were compared. The results demonstrated the utility of IOT in Raman chemical mapping of pharmaceutical tablets. The diluent (lactose) and API (semi-fine APAP) used in this study were reliably estimated by IOT at relatively short Raman exposure times. On the other hand, as expected, the lubricant (magnesium stearate) could not be detected in any of the cases investigated here, irrespective of the algorithm used. Overall, for the API and diluent used in this formulation as well as the chemical mapping conditions, linear IOT seemed to better estimate the pure spectrum intensities and the average Raman scores (closer to CLS) in comparison to MCR-ALS. Moreover, application of appropriate constraints in linear IOT avoided the presence of negative and/or greater than 100 % Raman concentration scores, as observed in CLS-based Raman chemical maps.

Model predictive control in pharmaceutical continuous manufacturing: A review from a user's perspective

Pharmaceutical continuous manufacturing is considered as an emerging technology by the regulatory agencies, which have defined a framework guided by an effective quality risk management. With the understanding of process dynamics and the appropriate control strategy, pharmaceutical continuous manufacturing is able to tackle the Quality-by-Design paradigm that paves the way to the future smart manufacturing described by Quality-by-Control. The introduction of soft sensors seems to be a helpful tool to reach smart manufacturing. In fact, soft sensors have the ability to keep the quality attributes of the final drug product as close as possible to their references set by regulatory agencies and to mitigate the undesired events by potentially discard out of specification products. Within this review, challenges related to implementing these technologies are discussed. Then, automation control strategies for pharmaceutical continuous manufacturing are presented and discussed: current control tools such as the proportional integral derivative controllers are compared to advanced control techniques like model predictive control, which holds promise to be an advanced automation concept for pharmaceutical continuous manufacturing. Finally, industrial applications of model predictive control in pharmaceutical continuous manufacturing are outlined. Simulations studies as well as real implementation on pharmaceutical plant are gathered from the control of one single operation unit such as the tablet press to the control of a full direct compaction line. Model predictive control is a key to enable the industrial revolution or Industry 4.0.

De-risking excipient particle size distribution variability with automated robust mixing: Integrating quality by design and process analytical technology

BACKGROUND

Particle size distribution (PSD) variability in excipients affects mixing. In response, manufacturers rely on raw material control and rigidly defined process parameters to achieve quality. However, this status quo is costly; and diverges from regulatory exceptions for process robustness. Although robustness improves cost and material usage efficiency, it remains under-adopted.

METHOD

To address this gap, a robust batch mixing operation that mitigated the impact of PSD variability was evaluated, with blends comprising chlorpheniramine, microcrystalline cellulose and lactose. PSD of lactose was varied to simulate commercially-relevant variability. Due to PSD-induced rheological variations, the blends had different optimal mixing speeds. For the automation study, near infrared (NIR) spectroscopy; process optimization and endpoint detection algorithms; and control hardware were integrated within a cluster of software environments. NIR spectroscopy was employed for in-line PSD characterization and blend monitoring, to modulate mixing speed and detect endpoint (feedforward and feedback control).

RESULTS

NIR spectroscopy rapidly detected PSD variations by the 6th-9th rotations, to activate feedforward control, which mitigated the effect of PSD variability and reduced the mixing time by 13-34%. Endpoints were correctly detected. PSD variations and blend homogeneity were accurately predicted (relative standard error of prediction ≤ 2%).

CONCLUSION

The automated robust mixing operation was successful. Pertinently, NIR spectrometer can be adopted for multimodal sensing. Its applicability for production-driven characterization of raw materials in batch and continuous pharmaceutical processing should be further explored. Lastly, this study laid the groundwork for end-to-end implementation of process analytical technology in robust batch processing.

Determining key parameters of continuous wet granulation for tablet quality and productivity: A case in ethenzamide

This paper aims to determine key parameters that affect tablet quality and productivity in continuous tablet manufacturing. Experiments were performed based on design of experiments using a continuous high-shear granulator and ethenzamide as the active pharmaceutical ingredient. To guide a systematic and comprehensive parameter analysis, a parameter framework was defined that comprised five input parameters on raw material properties and process parameters, 11 intermediate parameters on granule properties, and 11 output parameters on tablet quality and productivity. The interrelationships were analyzed statistically and were described as matrix functions. The liquid/solid ratio was the key parameter that affected circularity, density, and flowability as the granule properties, and disintegration and dissolution as the tablet quality. The maximum acceptable manufacturing rate that governs productivity was also affected by the liquid/solid ratio. Circularity was found to affect disintegration and dissolution. This result was specific to the setup of the study, but suggested development opportunities for a new process analytical technology system/quality-by-design application based on circularity. In addition, practical findings were obtained as follows: (1) high-speed manufacturing favored a lower liquid/solid ratio, and (2) high circularity slowed down disintegration/dissolution. This obtained knowledge will enhance the applicability of continuous technology in an actual manufacturing environment.

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.

A perspective on Quality-by-Control (QbC) in pharmaceutical continuous manufacturing

The Quality-by-Design (QbD) guidance issued by the US Food and Drug Administration (FDA) has catalyzed the modernization of pharmaceutical manufacturing practices including the adoption of continuous manufacturing. Active process control was highlighted recently as a means to improve the QbD implementation. This advance has since been evolving into the concept of Quality-by-Control (QbC). In this study, the concept of QbC is discussed, including a definition of QbC, a review of the recent developments towards the QbC, and a perspective on the challenges of QbC implementation in continuous manufacturing. The QbC concept is demonstrated using a rotary tablet press, integrated into a pilot scale continuous direct compaction process. The results conclusively showed that active process control, based on product and process knowledge and advanced model-based techniques, including data reconciliation, model predictive control (MPC), and risk analysis, is indispensable to comprehensive QbC implementation, and ensures robustness and efficiency.

Data reconciliation in the Quality-by-Design (QbD) implementation of pharmaceutical continuous tablet manufacturing

Data provided by in situ sensors is always affected by some level of impreciseness as well as uncertainty in the measurements due to process operation disturbance or material property variance. In-process data precision and reliability should be considered when implementing active product quality control and real-time process decision making in pharmaceutical continuous manufacturing. Data reconciliation is an important strategy to address such imperfections effectively, and to exploit the data redundancy and data correlation based on process understanding. In this study, a correlation between tablet weight and main compression force in a rotary tablet press was characterized by the classical Kawakita equation. A load cell, situated at the exit of the tablet press chute, was also designed to measure the tablet production rate as well as the tablet weight. A novel data reconciliation strategy was proposed to reconcile the tablet weight measurement subject to the correlation between tablet weight and main compression force, in such, the imperfect tablet weight measurement can be reconciled with the much more precise main compression force measurement. Special features of the Welsch robust estimator to reject the measurement gross errors and the Kawakita model parameter estimation to monitor the material property variance were also discussed. The proposed data reconciliation strategy was first evaluated with process control open-loop and closed-loop experimental data and then integrated into the process control system in a continuous tablet manufacturing line. Specifically, the real-time reconciled tablet weight measurements were independently verified with an at-line Sotax Auto Test 4 tablet weight measurements every five minutes. Promising and reliable performance of the reconciled tablet weight measurement was demonstrated in achieving process automation and quality control of tablet weight in pilot production runs.

Assessment of blend uniformity in a continuous tablet manufacturing process

Blend uniformity was monitored throughout a continuous manufacturing (CM) process by near infrared (NIR) spectroscopic measurements of flowing blends and compared to the drug concentration in the tablets. The NIR spectra were obtained through the chute after the blender and within the feed frame, while transmission spectra were obtained for the tablets. The CM process was performed with semi-fine acetaminophen blends at 10.0% (w/w). The blender was operated at 250 RPM, for best performance, and 106 and 495 rpm where a lower mixing efficiency was expected. The variation in blender RPM increased the variation in drug concentration at the chute but not at the feed frame. Statistical results show that the drug concentration of tablets can be predicted, with great accuracy, from blends within the feed frame. This study demonstrated a mixing effect within the feed frame, which contribute to a 60% decrease in the relative standard deviation of the drug concentration, when compared to the chute. Variographic analysis showed that the minimum sampling and analytical error was five times less in the feed frame than the chute. This study demonstrates that the feed frame is an ideal location for monitoring the drug concentration of powder blends for CM processes.

Variation and Risk Analysis in Tablet Press Control for Continuous Manufacturing of Solid Dosage via Direct Compaction

A continuous rotary tablet press is a multi-stage process with many punch stations running in parallel, in which each punch undergoes the following steps: die filling and metering, pre-compaction, main-compaction, tablet ejection, and tablet take-off from lower punch. Process uncertainties or disturbances within a punch station or among stations in the tablet press are a major source of variation in final product quality attributes, e.g., hardness, weight, etc., which in turn imposes challenges for the real-time release in pharmaceutical continuous manufacturing of solid dosage. In this study, the direct compression line at Purdue University was investigated and a Natoli BLP-16 tablet press was used to characterize powder compressibility, system dynamics and variation, as well as the interaction effects on process control development. The compressibility of tablets made from a blend of Acetaminophen (API), Avicel Microcrystalline Cellulose PH-200 (excipient), and SiO₂ (lubricant) was found to be largely independent of tableting speed. By contrast, filling depth or dosing level, turret speed, feed-frame speed, and compression force were interacting and significantly affected the die-filling process and the final product quality attributes. Thus, the design of the process control structure plays an important role in reducing process and product quality variations. A hierarchical three-level control design was proposed and evaluated, consisting of Level 0 Natoli built-in control, Level 1 decoupled Proportional Integral Derivative (PID) cascaded control loops for tablet weight and production rate control, and Level 2 advanced model predictive control. Process variations, e.g., in powder bulk density changes, during continuous steady-state operation were also investigated. Finally, a risk analysis of the effects of the process dynamics on variation on the product quality control was briefly discussed and summarized.