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

Kirchengast, Martin; Celikovic, Selma; Rehrl, Jakob; Sacher, Stephan; Kruisz, Julia; Khinast, Johannes G.; Horn, Martin

doi:10.1016/j.ijpharm.2019.118457

Cited by 20 publications

(13 citation statements)

References 9 publications

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“…As elaborated previously, the goal of state estimation is to obtain a 'true state' value by utilizing the information from both measurements and process models. The 'true state' can be either measurable, e.g., API concentration at the blender exit using NIR sensors [55][56][57], or unmeasured, e.g., powder holdup in the blender. Through the updating of uncertain model parameters, which have changes due to upstream disturbances, MHE enables the handling of plant-model mismatch, thus allowing the controller to receive estimated output variables (ŷ) with less uncertainty.…”

Section: Moving Horizon Estimation-based Nonlinear Model Predictive Control (Mhe-nmpc) Frameworkmentioning

confidence: 99%

“…The NMPC control algorithm then minimizes the error between setpoints y sp and estimated output variables (ŷ) by deciding the optimal control move (u) for the process to reach both setpoint tracking and disturbance rejection, i.e., the control objectives, while updating the model parameter (θ k ) and median of the error distribution in the past time window (ζ). e.g., API concentration at the blender exit using NIR sensors [55][56][57], or unmeasured powder holdup in the blender. Through the updating of uncertain model param which have changes due to upstream disturbances, MHE enables the handling of p model mismatch, thus allowing the controller to receive estimated output variable with less uncertainty.…”

Section: Moving Horizon Estimation-based Nonlinear Model Predictive Control (Mhe-nmpc) Frameworkmentioning

confidence: 99%

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Evaluation of a Combined MHE-NMPC Approach to Handle Plant-Model Mismatch in a Rotary Tablet Press

et al. 2021

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The transition from batch to continuous processes in the pharmaceutical industry has been driven by the potential improvement in process controllability, product quality homogeneity, and reduction of material inventory. A quality-by-control (QbC) approach has been implemented in a variety of pharmaceutical product manufacturing modalities to increase product quality through a three-level hierarchical control structure. In the implementation of the QbC approach it is common practice to simplify control algorithms by utilizing linearized models with constant model parameters. Nonlinear model predictive control (NMPC) can effectively deliver control functionality for highly sensitive variations and nonlinear multiple-input-multiple-output (MIMO) systems, which is essential for the highly regulated pharmaceutical manufacturing industry. This work focuses on developing and implementing NMPC in continuous manufacturing of solid dosage forms. To mitigate control degradation caused by plant-model mismatch, careful monitoring and continuous improvement strategies are studied. When moving horizon estimation (MHE) is integrated with NMPC, historical data in the past time window together with real-time data from the sensor network enable state estimation and accurate tracking of the highly sensitive model parameters. The adaptive model used in the NMPC strategy can compensate for process uncertainties, further reducing plant-model mismatch effects. The nonlinear mechanistic model used in both MHE and NMPC can predict the essential but complex powder properties and provide physical interpretation of abnormal events. The adaptive NMPC implementation and its real-time control performance analysis and practical applicability are demonstrated through a series of illustrative examples that highlight the effectiveness of the proposed approach for different scenarios of plant-model mismatch, while also incorporating glidant effects.

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Section: Moving Horizon Estimation-based Nonlinear Model Predictive Control (Mhe-nmpc) Frameworkmentioning

confidence: 99%

Section: Moving Horizon Estimation-based Nonlinear Model Predictive Control (Mhe-nmpc) Frameworkmentioning

confidence: 99%

Evaluation of a Combined MHE-NMPC Approach to Handle Plant-Model Mismatch in a Rotary Tablet Press

et al. 2021

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“…Lastly, we established a continuous HME-tableting line, with HME used to produce an ASD and nano-based formulation. The strand is cooled and cut into small pellets that are fed to a continuous direct compaction line consisting of loss-in-weight feeders, a blender, and a tablet press (114,115). Moreover, a sophisticated model-based control concept was developed that allows the continuous manufacturing process to remain in a state of control while combining various production steps.…”

Section: Experimental Verification Of Process Setup and Scale-upmentioning

confidence: 99%

Developing HME-Based Drug Products Using Emerging Science: a Fast-Track Roadmap from Concept to Clinical Batch

et al. 2020

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This paper presents a rational workflow for developing enabling formulations, such as amorphous solid dispersions, via hot-melt extrusion in less than a year. First, our approach to an integrated product and process development framework is described, including state-ofthe-art theoretical concepts, modeling, and experimental characterization described in the literature and developed by us. Next, lab-scale extruder setups are designed (processing conditions and screw design) based on a rational, model-based framework that takes into account the thermal load required, the mixing capabilities, and the thermo-mechanical degradation. The predicted optimal process setup can be validated quickly in the pilot plant. Lastly, a transfer of the process to any GMP-certified manufacturing site can be performed in silico for any extruder based on our validated computational framework. In summary, the proposed workflow massively reduces the risk in product and process development and shortens the drug-to-market time for enabling formulations.

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“…Switching from batch to continuous pharmaceutical mass production offers several advantages, such as increased productivity, steady product quality and decreased costs. Kirchengast et al [7] presented a control strategy for direct compaction on a continuous tablet production line consisting of two feeders, one blender and a tablet press (TP). They also applied a data-driven, linear modelling approach to develop a Smith predictor for active pharmaceutical ingredient concentration control and a model predictive controller responsible for the TP hopper level.…”

Section: Quality Management During Mass Productionmentioning

confidence: 99%

Quality Control Perspectives during Mass Production with a Focus on the Chemical Industry

Oduoza¹

2020

Mass Production Processes

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Mass production was part of the industrial revolution in 1870 and, with it, a huge step change in manufacturing processes. Its impact was ground breaking and became even more remarkable with automation in a business production environment. The chemical industry is one of the manufacturing sectors that has benefited from the technology of mass production achieved through automating the business process. In this era of industry 4.0 and with the associated advanced technologies of smart manufacturing, cloud computing, cyber physical systems and internet of things, mass production has been revolutionised but still faced issues such as quality control of the production process which was affected by supply chain management, customised production of commodity and specialty chemicals and huge demand from other chemical industry manufacturers. This chapter has reviewed the evolution of mass production during traditional manufacturing to the present day and carried out a risk assessment to quality of production in a mass production environment with a view to recommending adequate quality control of the production process. The chapter also included a case study for mass production of a pharmaceutical drug-Amoxicillin which was partly batch produced into dry powder and then mass produced using tableting and encapsulating machine, highlighting sources of contamination and inconsistency in tablet weight if adequate control measures were not put in place.

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

Cited by 20 publications

References 9 publications

Evaluation of a Combined MHE-NMPC Approach to Handle Plant-Model Mismatch in a Rotary Tablet Press

Evaluation of a Combined MHE-NMPC Approach to Handle Plant-Model Mismatch in a Rotary Tablet Press

Developing HME-Based Drug Products Using Emerging Science: a Fast-Track Roadmap from Concept to Clinical Batch

Quality Control Perspectives during Mass Production with a Focus on the Chemical Industry

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