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Lyytikäinen J, Stasiak P, Kubelka T, Bogaerts I, Wanek A, Stynen B, Holman J, Ketolainen J, Ervasti T, Korhonen O. Continuous direct compression of a commercially batch-manufactured tablet formulation with two different processing lines. Eur J Pharm Biopharm 2024; 199:114278. [PMID: 38583787 DOI: 10.1016/j.ejpb.2024.114278] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/16/2024] [Revised: 03/05/2024] [Accepted: 04/04/2024] [Indexed: 04/09/2024]
Abstract
The transfer from batch-based to continuous tablet manufacturing increases the quality and efficiency of processes. Nonetheless, as in the development of a batch process, the continuous process design requires optimization studies to ensure a robust process. In this study, processing of a commercially batch-manufactured tablet product was tested with two continuous direct compression lines while keeping the original formulation composition and tablet quality requirements. Tableting runs were conducted with different values of process parameters. Changes in parameter settings were found to cause differences in tablet properties. Most of these quality properties could be controlled and maintained within the set limits effortlessly already at this stage of studies. However, the API content and content uniformity seemed to require more investigation. The observed content uniformity challenges were traced to individual tablets with a high amount of API. This was suspected to be caused by API micro-agglomerates since tablet weight variability did not explain the issue. This could be solved by adding a mill between two blenders in the process line. Overall, this case study produced promising results with both tested manufacturing lines since many tablet properties complied with the test result limits without optimization of process parameter settings.
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Affiliation(s)
- Jenna Lyytikäinen
- School of Pharmacy, PromisLab, University of Eastern Finland, Kuopio, Finland.
| | | | | | | | - Adam Wanek
- Zentiva, Prague, Czech Republic; UCT Prague, Prague, Czech Republic.
| | - Bart Stynen
- GEA Process Engineering, Wommelgem, Belgium.
| | | | - Jarkko Ketolainen
- School of Pharmacy, PromisLab, University of Eastern Finland, Kuopio, Finland.
| | - Tuomas Ervasti
- School of Pharmacy, PromisLab, University of Eastern Finland, Kuopio, Finland.
| | - Ossi Korhonen
- School of Pharmacy, PromisLab, University of Eastern Finland, Kuopio, Finland.
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2
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Toson P, Khinast JG. A DEM Model to Evaluate Refill Strategies of a Twin-Screw Feeder. Int J Pharm 2023; 641:122915. [PMID: 37015295 DOI: 10.1016/j.ijpharm.2023.122915] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/10/2023] [Revised: 03/28/2023] [Accepted: 03/29/2023] [Indexed: 04/04/2023]
Abstract
Residence time distribution (RTD) modeling has proven to be a valuable tool for material tracking in continuous pharmaceutical processes. Refilling is thoroughly studied in the literature, but the main focus lies on the feed rate disturbances. The impact of the feeders themselves on intermixing of different material batches is often overlooked. Since the experimental methods to measure the RTD feeder discharging processes feeder are complex and material intensive, there is only limited experimental RTD data available in the literature. A DEM (discrete element method) simulation of a discharge of a twin-screw feeder shows that a large fraction of material that is moved and intermixed by the agitator. In addition to the intermixing, there is a tendency to discharge material located above the agitator early. In order to predict the behavior during multiple refill events, three models in order of increasing complexity are presented: (1) A simple exponential RTD assuming perfect intermixing of material batches; (2) a RTD model based on DEM results; (3) particle-level material tracking by extrapolation of the DEM results. All three of these models are able to predict the survival function of old material for late refills at low fill levels, however, earlier refills at high fill levels require more complex models to accurately represent the dynamics inside the hopper of the feeder.
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3
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Digital twin of a continuous direct compression line for drug product and process design using a hybrid flowsheet modelling approach. Int J Pharm 2022; 628:122336. [DOI: 10.1016/j.ijpharm.2022.122336] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/13/2022] [Revised: 10/19/2022] [Accepted: 10/20/2022] [Indexed: 11/06/2022]
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4
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Shier AP, Kumar A, Mercer A, Majeed N, Doshi P, Blackwood DO, Verrier HM. Development of a predictive model for gravimetric powder feeding from an API-rich materials properties library. Int J Pharm 2022; 625:122071. [PMID: 35931397 DOI: 10.1016/j.ijpharm.2022.122071] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/02/2022] [Revised: 07/20/2022] [Accepted: 07/30/2022] [Indexed: 10/16/2022]
Abstract
A model was developed for predicting the feed factor profile of a powder, processed through a gravimetric feeder, as a function of material properties and process parameters. Predictive models proposed in existing literature have often used excipients and active pharmaceutical ingredients (APIs) with good powder flow characteristics in their development. In this work, a material properties library containing a large proportion of APIs, as well as excipients and co-processed blends, was used to build the model and enhance the prediction of feed factor profile for cohesive powders. Gravimetric feeder trials were performed at varying mass flow rates and screw geometries to determine the feed factor profiles. A semi-empirical exponential model, with parameters fmax, fmin, and β, was then used to fit the experimental feed factor profiles. Bayesian optimisation and Support Vector Regression (SVR) modelling techniques were utilised to optimise and predict the exponential model parameters as a function of material properties. The parameters found to strongly influence the model were particle size, bulk density, FFC and FT4 rheometer parameters. Results showed low prediction errors between the estimated and experimental data. The final model produces good estimations of the feed factor profile and requires minimal powder consumption.
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Affiliation(s)
- Andrew P Shier
- Worldwide Research and Development, Pfizer Inc, Sandwich, Kent, UK.
| | | | - Amy Mercer
- Worldwide Research and Development, Pfizer Inc, Sandwich, Kent, UK
| | - Naimah Majeed
- Worldwide Research and Development, Pfizer Inc, Groton, CT, USA
| | - Pankaj Doshi
- Worldwide Research and Development, Pfizer Ltd, Mumbai, India
| | | | - Hugh M Verrier
- Worldwide Research and Development, Pfizer Inc, Sandwich, Kent, UK
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Kreiser MJ, Wabel C, Wagner KG. Impact of Vertical Blender Unit Parameters on Subsequent Process Parameters and Tablet Properties in a Continuous Direct Compression Line. Pharmaceutics 2022; 14:pharmaceutics14020278. [PMID: 35214014 PMCID: PMC8879867 DOI: 10.3390/pharmaceutics14020278] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/09/2021] [Revised: 01/14/2022] [Accepted: 01/20/2022] [Indexed: 12/10/2022] Open
Abstract
The continuous manufacturing of solid oral-dosage forms represents an emerging technology among the pharmaceutical industry, where several process steps are combined in one production line. As all mixture components, including the lubricant (magnesium stearate), are passing simultaneously through one blender, an impact on the subsequent process steps and critical product properties, such as content uniformity and tablet tensile strength, is to be expected. A design of experiment (DoE) was performed to investigate the impact of the blender variables hold-up mass (HUM), impeller speed (IMP) and throughput (THR) on the mixing step and the subsequent continuous manufacturing process steps. Significant impacts on the mixing parameters (exit valve opening width (EV), exit valve opening width standard deviation (EV SD), torque of lower impeller (TL), torque of lower impeller SD (TL SD), HUM SD and blend potency SD), material attributes of the blend (conditioned bulk density (CBD), flow rate index (FRI) and particle size (d10 values)), tableting parameters (fill depth (FD), bottom main compression height (BCH) and ejection force (EF)) and tablet properties (tablet thickness (TT), tablet weight (TW) and tensile strength (TS)) could be found. Furthermore, relations between these process parameters were evaluated to define which process states were caused by which input variables. For example, the mixing parameters were mainly impacted by impeller speed, and material attributes, FD and TS were mainly influenced by variations in total blade passes (TBP). The current work presents a rational methodology to minimize process variability based on the main blender variables hold-up mass, impeller speed and throughput. Moreover, the results facilitated a knowledge-based optimization of the process parameters for optimum product properties.
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Affiliation(s)
- Marius J. Kreiser
- Product and Process Development, Pfizer Manufacturing Deutschland GmbH, 79108 Freiburg, Germany; (M.J.K.); (C.W.)
- Department of Pharmaceutical Technology and Biopharmaceutics, University of Bonn, 53121 Bonn, Germany
| | - Christoph Wabel
- Product and Process Development, Pfizer Manufacturing Deutschland GmbH, 79108 Freiburg, Germany; (M.J.K.); (C.W.)
| | - Karl G. Wagner
- Department of Pharmaceutical Technology and Biopharmaceutics, University of Bonn, 53121 Bonn, Germany
- Correspondence:
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Bhalode P, Tian H, Gupta S, Razavi SM, Roman-Ospino A, Talebian S, Singh R, Scicolone JV, Muzzio FJ, Ierapetritou M. Using residence time distribution in pharmaceutical solid dose manufacturing - A critical review. Int J Pharm 2021; 610:121248. [PMID: 34748808 DOI: 10.1016/j.ijpharm.2021.121248] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/20/2021] [Revised: 10/04/2021] [Accepted: 10/27/2021] [Indexed: 11/18/2022]
Abstract
While continuous manufacturing (CM) of pharmaceutical solid-based drug products has been shown to be advantageous for improving the product quality and process efficiency in alignment with FDA's support of the quality-by-design paradigm (Lee, 2015; Ierapetritou et al., 2016; Plumb, 2005; Schaber, 2011), it is critical to enable full utilization of CM technology for robust production and commercialization (Schaber, 2011; Byrn, 2015). To do so, an important prerequisite is to obtain a detailed understanding of overall process characteristics to develop cost-effective and accurate predictive models for unit operations and process flowsheets. These models are utilized to predict product quality and maintain desired manufacturing efficiency (Ierapetritou et al., 2016). Residence time distribution (RTD) has been a widely used tool to characterize the extent of mixing in pharmaceutical unit operations (Vanhoorne, 2020; Rogers and Ierapetritou, 2015; Teżyk et al., 2015) and manufacturing lines and develop computationally cheap predictive models. These models developed using RTD have been demonstrated to be crucial for various flowsheet applications (Kruisz, 2017; Martinetz, 2018; Tian, 2021). Though extensively used in the literature (Gao et al., 2012), the implementation, execution, evaluation, and assessment of RTD studies has not been standardized by regulatory agencies and can thus lead to ambiguity regarding their accurate implementation. To address this issue and subsequently prevent unforeseen errors in RTD implementation, the presented article aims to aid in developing standardized guidelines through a detailed review and critical discussion of RTD studies in the pharmaceutical manufacturing literature. The review article is divided into two main sections - 1) determination of RTD including different steps for RTD evaluation including experimental approach, data acquisition and pre-treatment, RTD modeling, and RTD metrics and, 2) applications of RTD for solid dose manufacturing. Critical considerations, pertaining to the limitations of RTDs for solid dose manufacturing, are also examined along with a perspective discussion of future avenues of improvement.
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Affiliation(s)
- Pooja Bhalode
- Department of Chemical and Biochemical Engineering, Rutgers - The State University of New Jersey, Piscataway, NJ 08854, USA
| | - Huayu Tian
- Department of Chemical and Biomolecular Engineering, University of Delaware, Newark, DE 19716, USA
| | - Shashwat Gupta
- Department of Chemical and Biochemical Engineering, Rutgers - The State University of New Jersey, Piscataway, NJ 08854, USA
| | - Sonia M Razavi
- Department of Chemical and Biochemical Engineering, Rutgers - The State University of New Jersey, Piscataway, NJ 08854, USA
| | - Andres Roman-Ospino
- Department of Chemical and Biochemical Engineering, Rutgers - The State University of New Jersey, Piscataway, NJ 08854, USA
| | - Shahrzad Talebian
- Department of Chemical and Biochemical Engineering, Rutgers - The State University of New Jersey, Piscataway, NJ 08854, USA
| | - Ravendra Singh
- Department of Chemical and Biochemical Engineering, Rutgers - The State University of New Jersey, Piscataway, NJ 08854, USA
| | - James V Scicolone
- Department of Chemical and Biochemical Engineering, Rutgers - The State University of New Jersey, Piscataway, NJ 08854, USA
| | - Fernando J Muzzio
- Department of Chemical and Biochemical Engineering, Rutgers - The State University of New Jersey, Piscataway, NJ 08854, USA
| | - Marianthi Ierapetritou
- Department of Chemical and Biomolecular Engineering, University of Delaware, Newark, DE 19716, USA.
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7
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Toson P, Doshi P, Matic M, Siegmann E, Blackwood D, Jain A, Brandon J, Lee K, Wilsdon D, Kimber J, Verrier H, Khinast J, Jajcevic D. Continuous mixing technology: Validation of a DEM model. Int J Pharm 2021; 608:121065. [PMID: 34481005 DOI: 10.1016/j.ijpharm.2021.121065] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/24/2021] [Revised: 08/18/2021] [Accepted: 08/29/2021] [Indexed: 10/20/2022]
Abstract
Continuous powder mixing is an important technology used in the development and manufacturing of solid oral dosage forms. Since critical quality attributes of the final product greatly depend on the performance of the mixing step, an analysis of such a process using the Discrete Element Method (DEM) is of crucial importance. On one hand, the number of expensive experimental runs can be reduced dramatically. On the other hand, numerical simulations can provide information that is very difficult to obtain experimentally. In order to apply such a simulation technology in product development and to replace experimental runs, an intensive model validation step is required. This paper presents a DEM model of the vertical continuous mixing device termed CMT (continuous mixing technology) and an extensive validation workflow. First, a cohesive contact model was calibrated in two small-scale characterization experiments: a compression test with spring-back and a shear cell test. An improved, quicker calibration procedure utilizing the previously calibrated contact models is presented. The calibration procedure is able to differentiate between the blend properties caused by different API particle sizes in the same formulation. Second, DEM simulations of the CMT were carried out to determine the residence time distribution (RTD) of the material inside the mixer. After that, the predicted RTDs were compared with the results of tracer spike experiments conducted with two blend material properties at two mass throughputs of 15 kg/h and 30 kg/h. Additionally, three hold-up masses (500, 730 and 850 g) and three impeller speeds (400, 440 and 650 rpms) were considered. Finally, both RTD datasets from DEM and tracer experiments were used to predict the damping behavior of incoming feeder fluctuations and the funnel of maximum duration and magnitude of incoming deviations that do not require a control action. The results for both tools in terms of enabling a control strategy (the fluctuation damping and the funnel plot) are in excellent agreement, indicating that DEM simulations are well suited to replace process-scale tracer spike experiments to determine the RTD.
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Affiliation(s)
- Peter Toson
- Research Center Pharmaceutical Engineering, Inffeldgasse 13, 8010 Graz, Austria
| | - Pankaj Doshi
- Worldwide Research and Development, Pfizer Inc., Groton, CT, USA.
| | - Marko Matic
- Research Center Pharmaceutical Engineering, Inffeldgasse 13, 8010 Graz, Austria
| | - Eva Siegmann
- Research Center Pharmaceutical Engineering, Inffeldgasse 13, 8010 Graz, Austria
| | - Daniel Blackwood
- Worldwide Research and Development, Pfizer Inc., Groton, CT, USA
| | - Ashwinkumar Jain
- Worldwide Research and Development, Pfizer Inc., Groton, CT, USA
| | - Jenna Brandon
- Worldwide Research and Development, Pfizer Inc., Groton, CT, USA
| | - Kai Lee
- Worldwide Research and Development, Pfizer Inc., Sandwich, Kent, United Kingdom
| | - David Wilsdon
- Worldwide Research and Development, Pfizer Inc., Sandwich, Kent, United Kingdom
| | - James Kimber
- Worldwide Research and Development, Pfizer Inc., Sandwich, Kent, United Kingdom
| | - Hugh Verrier
- Worldwide Research and Development, Pfizer Inc., Sandwich, Kent, United Kingdom
| | - Johannes Khinast
- Research Center Pharmaceutical Engineering, Inffeldgasse 13, 8010 Graz, Austria; Institute of Process and Particle Engineering, Graz University of Technology, Inffeldgasse 13, 8010 Graz, Austria
| | - Dalibor Jajcevic
- Research Center Pharmaceutical Engineering, Inffeldgasse 13, 8010 Graz, Austria.
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