1
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Jolliffe HG, Prostredny M, Mendez Torrecillas C, Bordos E, Tierney C, Ojo E, Elkes R, Reynolds G, Li Song Y, Meir B, Fathollahi S, Robertson J. A modified Kushner-Moore approach to characterising small-scale blender performance impact on tablet compaction. Int J Pharm 2024; 659:124232. [PMID: 38759740 DOI: 10.1016/j.ijpharm.2024.124232] [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: 01/30/2024] [Revised: 05/02/2024] [Accepted: 05/14/2024] [Indexed: 05/19/2024]
Abstract
Continuous Direct Compaction (CDC) has emerged as a promising route towards producing solid dosage forms while reducing material, development time and energy consumption. Understanding the response of powder processing unit operations, especially blenders, is crucial. There is a substantial body of work around how lubrication via batch blender operation affects tablet critical quality attributes such as hardness and tensile strength. But, aside from being batch operations, the design of these blenders is such that they operate with low-shear, low-intensity mixing at Froude number values significantly below 0.4 (Froude number Fr being the dimensionless ratio of inertial to gravitational forces). The present work explores the performance of a mini-blender which has a fundamentally different mode of operation (static vessel with rotating blades around a mixing shaft as opposed to rotating vessel with no mixing shaft). This difference allows a substantially wider operating range in terms of speed and shear (and Fr values). The present work evaluates how its performance compares to other blenders studied in the literature. Tablet compaction data from blends produced at various intensities and regimes of mixing in the mini-blender follow a common trajectory. Model equations from literature are suitably modified by inclusion of the Froude number Fr, but only for situations where the Froude number was sufficiently high (1 < Fr). The results suggest that although a similar lubrication extent plateau is eventually reached it is the intensity of mixing (i.e. captured using the Froude number as a surrogate) which is important for the lubrication dynamics in the mini-blender, next to the number of revolutions. The degree of fill or headspace, on the other hand, is only crucial to the performance of common batch blenders. Testing using alternative formulations shows the same common trend across mixing intensities, suggesting the validity of the approach to capture lubrication dynamics for this system.
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Affiliation(s)
- Hikaru G Jolliffe
- CMAC, Technology and Innovation Centre, 99 George Street, Glasgow G1 1RD, UK
| | - Martin Prostredny
- CMAC, Technology and Innovation Centre, 99 George Street, Glasgow G1 1RD, UK
| | | | - Ecaterina Bordos
- CMAC, Technology and Innovation Centre, 99 George Street, Glasgow G1 1RD, UK
| | - Collette Tierney
- CMAC, Technology and Innovation Centre, 99 George Street, Glasgow G1 1RD, UK
| | - Ebenezer Ojo
- CMAC, Technology and Innovation Centre, 99 George Street, Glasgow G1 1RD, UK
| | - Richard Elkes
- GSK Ware R&D, Harris's Lane, Ware, Hertfordshire SG12 0GX, UK
| | - Gavin Reynolds
- Oral Product Development, PT&D, Operations, AstraZeneca UK Limited, Charter Way, Macclesfield SK10 2NA, UK
| | - Yunfei Li Song
- GSK Ware R&D, Harris's Lane, Ware, Hertfordshire SG12 0GX, UK
| | - Bernhard Meir
- Gericke AG, Althardstrasse 120, CH-8105 Regensdorf, Switzerland
| | - Sara Fathollahi
- DFE Pharma GmbH & Co. KG, Kleverstrasse 187, 47568 Goch, Germany
| | - John Robertson
- CMAC, Technology and Innovation Centre, 99 George Street, Glasgow G1 1RD, UK.
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2
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Bekaert B, Janssen P, Fathollahi S, Vanderroost D, Roelofs T, Dickhoff B, Vervaet C, Vanhoorne V. Batch vs. continuous direct compression - a comparison of material processability and final tablet quality. Int J Pharm X 2024; 7:100226. [PMID: 38235316 PMCID: PMC10792456 DOI: 10.1016/j.ijpx.2023.100226] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/10/2023] [Revised: 12/06/2023] [Accepted: 12/18/2023] [Indexed: 01/19/2024] Open
Abstract
In this study, an in-depth comparison was made between batch and continuous direct compression using similar compression set-ups. The overall material processability and final tablet quality were compared and evaluated. Correlations between material properties, process parameters and final tablet properties were made via multivariate data analyses. In total, 10 low-dosed (1% w/w) and 10 high-dosed (40% w/w) formulations were processed, using a total of 10 different fillers/filler combinations. The trials indicated that the impact of filler type, drug load or process settings was similar for batch and continuous direct compression. The main differentiator between batch and continuous was the flow dynamics in the operating system, where properties related to flow, compressibility and permeability played a crucial role. The less consistent flow throughout a batch process resulted in a significantly higher variability within the tablet press (σCF) and for the tablet quality responses (σMass, σTS). However, the better controlled blending procedure prior to batch processing was reflected in a more consistent API concentration variability. Overall, the comparison showed the benefits of selecting appropriate excipients and process settings to achieve a specific outcome, keeping in mind some key differentiators between both processes.
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Affiliation(s)
- B. Bekaert
- Laboratory of Pharmaceutical Technology, Department of Pharmaceutics, Ghent University, Ottergemsesteenweg 460, B-9000 Ghent, Belgium
| | - P.H.M. Janssen
- Department of Pharmaceutical Technology and Biopharmacy, University of Groningen, Antonius Deusinglaan 1, 9713 AV Groningen, the Netherlands
- DFE Pharma, Klever Strasse 187, 47568 Goch, Germany
| | | | - D. Vanderroost
- GEA Process Engineering, Keerbaan 70, B-2160 Wommelgem, Belgium
| | - T. Roelofs
- DFE Pharma, Klever Strasse 187, 47568 Goch, Germany
| | | | - C. Vervaet
- Laboratory of Pharmaceutical Technology, Department of Pharmaceutics, Ghent University, Ottergemsesteenweg 460, B-9000 Ghent, Belgium
| | - V. Vanhoorne
- Laboratory of Pharmaceutical Technology, Department of Pharmaceutics, Ghent University, Ottergemsesteenweg 460, B-9000 Ghent, Belgium
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3
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Kobayashi Y, Kim S, Nagato T, Oishi T, Kano M. Feed factor profile prediction model for two-component mixed powder in the twin-screw feeder. Int J Pharm X 2024; 7:100242. [PMID: 38601059 PMCID: PMC11004622 DOI: 10.1016/j.ijpx.2024.100242] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/08/2023] [Revised: 03/14/2024] [Accepted: 03/28/2024] [Indexed: 04/12/2024] Open
Abstract
In continuous pharmaceutical manufacturing processes, it is crucial to control the powder flow rate. The feeding process is characterized by the amount of powder delivered per screw rotation, referred to as the feed factor. This study aims to develop models for predicting the feed factor profiles (FFPs) of two-component mixed powders with various formulations, while most previous studies have focused on single-component powders. It further aims to identify the suitable model type and to determine the significance of material properties in enhancing prediction accuracy by using several FFP prediction models with different input variables. Four datasets from the experiment were generated with different ranges of the mass fraction of active pharmaceutical ingredients (API) and the powder weight in the hopper. The candidates for the model inputs are (a) the mass fraction of API, (b) process parameters, and (c) material properties. It is desirable to construct a high-performance prediction model without the material properties because their measurement is laborious. The results show that using (c) as input variables did not improve the prediction accuracy as much, thus there is no need to use them.
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Affiliation(s)
- Yuki Kobayashi
- Department of Systems Science, Kyoto University, Yoshida-honmachi, Sakyo-ku, Kyoto 6068501, Kyoto, Japan
| | - Sanghong Kim
- Department of Applied Physics and Chemical Engineering, Tokyo University of Agriculture and Technology, 2-24-16 Naka-cho, Koganei, 1840012 Tokyo, Japan
| | - Takuya Nagato
- Research and Development Division, Powrex Corporation, 5-5-5 Kitagawara, Itami 6640837, Hyogo, Japan
| | - Takuya Oishi
- Research and Development Division, Powrex Corporation, 5-5-5 Kitagawara, Itami 6640837, Hyogo, Japan
- Department of Applied Chemistry, Tokyo University of Agriculture and Technology, 2-24-16 Naka-cho, Koganei, 1840012 Tokyo, Japan
| | - Manabu Kano
- Department of Systems Science, Kyoto University, Yoshida-honmachi, Sakyo-ku, Kyoto 6068501, Kyoto, Japan
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4
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Hebbink GA, Janssen PHM, Kok JH, Menarini L, Giatti F, Funaro C, Consoli SF, Dickhoff BHJ. Lubricant Sensitivity of Direct Compression Grades of Lactose in Continuous and Batch Tableting Process. Pharmaceutics 2023; 15:2575. [PMID: 38004554 PMCID: PMC10674241 DOI: 10.3390/pharmaceutics15112575] [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: 10/11/2023] [Revised: 10/30/2023] [Accepted: 11/01/2023] [Indexed: 11/26/2023] Open
Abstract
Modern pharmaceutical manufacturing based on Quality by Design and digitalisation is revolutionising the pharmaceutical industry. Continuous processes are promoted as they increase efficiency and improve quality control. Compared to batch blending, continuous blending is easier to scale and provides advantages for achieving blend homogeneity. One potential challenge of continuous blending is the risk of over-lubrication. In this study, blending homogeneity and lubricant sensitivity are investigated for both batch and continuous processes. Given their distinct chemical structures and morphologies, anhydrous lactose and granulated lactose are expected to exhibit varying sensitivities to changes in process settings across both technologies. The findings suggest that both lactose grades provide highly stable blends that can be safely utilised in both batch and continuous modes. Optimisation should focus on process variables, such as the quality of loss-in-weight feeders used for dosing low doses of ingredients. The most significant process parameter for lubricant sensitivity was the type of lactose used. Anhydrous lactose produced harder tablets than the more porous granulated lactose but was more sensitive to lubrication at the same settings. The magnesium stearate content and its interaction with the type of lactose are also critical factors, with magnesium stearate having a counterproductive impact on tabletability.
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Affiliation(s)
| | - Pauline H. M. Janssen
- DFE Pharma GmbH & Co. KG, 47574 Goch, Germany (B.H.J.D.)
- Department of Pharmaceutical Technology and Biopharmacy, University of Groningen, 9713 AV Groningen, The Netherlands
| | - Jurjen H. Kok
- DFE Pharma GmbH & Co. KG, 47574 Goch, Germany (B.H.J.D.)
| | - Lorenzo Menarini
- IMA S.p.A. Active Division, 40064 Ozzano dell’Emilia Bologna, Italy; (L.M.)
| | - Federica Giatti
- IMA S.p.A. Active Division, 40064 Ozzano dell’Emilia Bologna, Italy; (L.M.)
| | - Caterina Funaro
- IMA S.p.A. Active Division, 40064 Ozzano dell’Emilia Bologna, Italy; (L.M.)
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5
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C Dias R, Korhonen O, Ketolainen J, A Lopes J, Ervasti T. Flowsheet modelling of a powder continuous feeder-mixer system. Int J Pharm 2023; 639:122969. [PMID: 37084833 DOI: 10.1016/j.ijpharm.2023.122969] [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: 09/23/2022] [Revised: 03/28/2023] [Accepted: 04/15/2023] [Indexed: 04/23/2023]
Abstract
In this study, an integrated flowsheet model of the continuous feeder-mixer system was calibrated, simulated and compared against experimental data. The feeding process was first investigated using two major components (ibuprofen and microcrystalline cellulose (MCC)), in a formulation comprised of: 30 wt% of ibuprofen, 67.5 wt% MCC, 2 wt% of sodium starch glycolate and 0.5 wt% of magnesium stearate. The impact of a refill on feeder performance was experimentally evaluated for different operating conditions. Results showed that it had no influence on feeder performance. While simulations with the feeder model fairly reproduced the material behaviour observed in the feeder, unintended disturbances were underpredicted due to the model's low complexity. Experimentally, mixer's efficiency was assessed based on ibuprofen residence time distribution. Mean residence time pointed to a higher mixer's efficiency at lower flow rates. Blend homogeneity results showed that for the entire set of experiments, ibuprofen RSD <5%, irrespective of process variables. A feeder-mixer flowsheet model was calibrated, after regressing the axial model coefficients. The regression curves exhibited a R2 above 0.96, whereas the RMSE varied from 1.58x10-4 to 1.06x10-3 s-1 across all fitted curves. Simulations confirmed that flowsheet model captured the powder dynamics inside the mixer and qualitatively predicted the mixer's filtering ability against feeding composition fluctuations, as well as ibuprofen RSD in blend, in line with real experiments.
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Affiliation(s)
- Rute C Dias
- PromisLab, School of Pharmacy, University of Eastern Finland, 70211 Kuopio, Finland; iMed.ULisboa, Faculty of Pharmacy, University of Lisbon, 1649-003 Lisbon, Portugal.
| | - Ossi Korhonen
- PromisLab, School of Pharmacy, University of Eastern Finland, 70211 Kuopio, Finland
| | - Jarkko Ketolainen
- PromisLab, School of Pharmacy, University of Eastern Finland, 70211 Kuopio, Finland
| | - João A Lopes
- iMed.ULisboa, Faculty of Pharmacy, University of Lisbon, 1649-003 Lisbon, Portugal
| | - Tuomas Ervasti
- PromisLab, School of Pharmacy, University of Eastern Finland, 70211 Kuopio, Finland
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6
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Bekaert B, Van Snick B, Pandelaere K, Dhondt J, Di Pretoro G, De Beer T, Vervaet C, Vanhoorne V. Continuous direct compression: Development of an empirical predictive model and challenges regarding PAT implementation. Int J Pharm X 2022; 4:100110. [PMID: 35024605 PMCID: PMC8732775 DOI: 10.1016/j.ijpx.2021.100110] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/30/2021] [Revised: 12/21/2021] [Accepted: 12/23/2021] [Indexed: 11/21/2022] Open
Abstract
In this study, an empirical predictive model was developed based on the quantitative relationships between blend properties, critical quality attributes (CQA) and critical process parameters (CPP) related to blending and tableting. The blend uniformity and API concentration in the tablets were used to elucidate challenges related to the processability as well as the implementation of PAT tools. Thirty divergent ternary blends were evaluated on a continuous direct compression line (ConsiGma™ CDC-50). The trials showed a significant impact of the impeller configuration and impeller speed on the blending performance, whereas a limited impact of blend properties was observed. In contrast, blend properties played a significant role during compression, where changes in blend composition significantly altered the tablet quality. The observed correlations allowed to develop an empirical predictive model for the selection of process configurations based on the blend properties, reducing the number of trial runs needed to optimize a process and thus reducing development time and costs of new drug products. Furthermore, the trials elucidated several challenges related to blend properties that had a significant impact on PAT implementation and performance of the CDC-platform, highlighting the importance of further process development and optimization in order to solve the remaining challenges.
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Key Words
- #BP, Number of blade passes
- #RMB1, Number of radial mixing blades of the main blender
- API, Active pharmaceutical ingredient
- API_sd, Spray dried API
- BRT, Bulk residence time
- BU, Blend uniformity
- CDC, Continuous direct compression
- CDC-50
- CU, Content uniformity
- C_P, Caffeine anhydrous powder
- Continuous direct compression
- Continuous manufacturing
- DCP, Dicalcium phosphate / Emcompress AN
- FD, Fill depth
- HM1/HM2, Hold-up mass main blender/Hold-up mass lubricant blender
- Imp1, Impeller speed main blender
- LC, Percentage label claim
- MCF, Main compression force
- MCH, Main compression height
- MPT_μ, Metoprolol micronized
- MgSt, Magnesium stearate/Ligamed MF-2-V
- Multivariate data-analysis
- NIR, Near infrared
- PAT
- PAT, Process Analytical Technology
- PC, Principle component
- PCA, Principle component analysis
- PCD, Pre-compression displacement
- PCF, Pre-compression force
- PCH, Pre-compression height
- PH101, Microcrystalline cellulose / Avicel PH-101
- PH200, Microcrystalline cellulose / Avicel PH-200
- PLS, Partial least squares
- P_DP, Paracetamol dense powder
- P_P, Paracetamol powder
- P_μ, Paracetamol micronized
- Predictive modeling
- Q2, Goodness of prediction
- R2Y, Goodness of fit
- RMSEcv, Root mean squared error of cross validation
- RSDTW, Relative standard deviation of tablet weight
- SD100, Mannitol / Pearlitol 100 SD
- T80, Lactose / Tablettose 80
- T_P, Theophylline anhydrous powder
- rpm, Revolutions per minute
- σForce, Main compression force variability
- σPCD, Variability in pre-compression displacement
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Affiliation(s)
- B. Bekaert
- Laboratory of Pharmaceutical Technology, Department of Pharmaceutics, Ghent University, Ottergemsesteenweg 460, B-9000 Ghent, Belgium
| | - B. Van Snick
- Oral Solid Dosage, Drug Product Development, Discovery Product Development and Supplies, Pharmaceutical Research and Development, Division of Janssen Pharmaceutica, Johnson & Johnson, Turnhoutseweg 30, B-2340 Beerse, Belgium
| | - K. Pandelaere
- Laboratory of Pharmaceutical Technology, Department of Pharmaceutics, Ghent University, Ottergemsesteenweg 460, B-9000 Ghent, Belgium
| | - J. Dhondt
- Oral Solid Dosage, Drug Product Development, Discovery Product Development and Supplies, Pharmaceutical Research and Development, Division of Janssen Pharmaceutica, Johnson & Johnson, Turnhoutseweg 30, B-2340 Beerse, Belgium
| | - G. Di Pretoro
- Oral Solid Dosage, Drug Product Development, Discovery Product Development and Supplies, Pharmaceutical Research and Development, Division of Janssen Pharmaceutica, Johnson & Johnson, Turnhoutseweg 30, B-2340 Beerse, Belgium
| | - T. De Beer
- Laboratory of Pharmaceutical Process Analytical Technology, Department of Pharmaceutical Analysis, Ghent University, Ottergemsesteenweg 460, B-9000 Ghent, Belgium
| | - C. Vervaet
- Laboratory of Pharmaceutical Technology, Department of Pharmaceutics, Ghent University, Ottergemsesteenweg 460, B-9000 Ghent, Belgium
| | - V. Vanhoorne
- Laboratory of Pharmaceutical Technology, Department of Pharmaceutics, Ghent University, Ottergemsesteenweg 460, B-9000 Ghent, Belgium
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7
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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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8
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Tian H, Bhalode P, Razavi SM, Koolivand A, Muzzio FJ, Ierapetritou MG. Characterization and propagation of RTD uncertainty for continuous powder blending processes. Int J Pharm 2022; 628:122326. [DOI: 10.1016/j.ijpharm.2022.122326] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/15/2022] [Revised: 09/18/2022] [Accepted: 10/16/2022] [Indexed: 10/31/2022]
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9
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Forgber T, Rehrl J, Matic M, Sibanc R, Sivanesapillai R, Khinast J. Experimental and numerical investigations of the RTD in a GEA ConsiGma CTL25 tablet press. POWDER TECHNOL 2022. [DOI: 10.1016/j.powtec.2022.117507] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/18/2022]
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10
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Han Q, Zhang XY, Wu HB, Zhou XT, Ji HB. Different efficiency toward the biomimetic aerobic oxidation of benzyl alcohol in microchannel and bubble column reactors: Hydrodynamic characteristics and gas-liquid mass transfer. Chin J Chem Eng 2022. [DOI: 10.1016/j.cjche.2022.05.020] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
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11
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Fan Y, Li C, Wang S, Wang H, Wei Y, Xu J, Xiao Q. Enhancement of mixing efficiency in mechanical stirring reactors via chaotic stirring techniques: Application to the treatment of zinc-containing solid waste. Chem Eng Sci 2022. [DOI: 10.1016/j.ces.2021.117367] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/26/2022]
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12
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Peterwitz M, Jodwirschat J, Loll R, Schembecker G. Tracking raw material flow through a continuous direct compression line Part I of II: Residence time distribution modeling and sensitivity analysis enabling increased process yield. Int J Pharm 2022; 614:121467. [PMID: 35032576 DOI: 10.1016/j.ijpharm.2022.121467] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/05/2021] [Revised: 12/30/2021] [Accepted: 01/08/2022] [Indexed: 11/30/2022]
Abstract
Continuous manufacturing (CM) offers advantages in quality and space-time yield compared to common batch manufacturing. However, higher yield losses due to the start-up procedure make a broad application uneconomical. This work discusses the possibility of reducing yield losses by adjusting the degree of back-mixing. Back-mixing of nonconforming material from disturbances or start-up will result in the contamination of subsequent material. Therefore, higher degrees of back-mixing cause the discharge of additional material. Choosing an advantageous setting of operational parameters may be a simple way to change the degree of back-mixing. Based on direct compression, this work demonstrates the identification of promising parameters. Therefore, step-change experiments using color-marked material in the feeder, blender, and tablet press quantify the impact of three operational parameters per device. Models for the devices and the entire process result from those measurements. Subsequently, a global variance-based sensitivity analysis identifies the most influential parameters. As a result, adjusting the minimal filling level of the feeder and the rotational feed frame speed of the tablet press reduces back-mixing by more than 30%. At high costs of the raw materials, the resulting savings can significantly improve the economic performance of CM compared to batch manufacturing.
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Affiliation(s)
- Moritz Peterwitz
- Laboratory of Plant and Process Design, Faculty of Biochemical and Chemical Engineering, TU Dortmund University, Emil-Figge-Straße 70, D-44227 Dortmund, Germany; Invite GmbH, Otto-Bayer-Straße 32, D-51061 Cologne, Germany
| | - Janis Jodwirschat
- Laboratory of Plant and Process Design, Faculty of Biochemical and Chemical Engineering, TU Dortmund University, Emil-Figge-Straße 70, D-44227 Dortmund, Germany
| | - Rouven Loll
- Laboratory of Plant and Process Design, Faculty of Biochemical and Chemical Engineering, TU Dortmund University, Emil-Figge-Straße 70, D-44227 Dortmund, Germany
| | - Gerhard Schembecker
- Laboratory of Plant and Process Design, Faculty of Biochemical and Chemical Engineering, TU Dortmund University, Emil-Figge-Straße 70, D-44227 Dortmund, Germany
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13
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Moritz P, Simon B, Meier R, Gerhard S. Tracking raw material flow through a continuous direct compression line. Part II of II: Predicting dynamic changes in quality attributes of tablets due to disturbances in raw material properties using an independent residence time distribution model. Int J Pharm 2022; 615:121528. [DOI: 10.1016/j.ijpharm.2022.121528] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/05/2021] [Revised: 01/23/2022] [Accepted: 01/26/2022] [Indexed: 10/19/2022]
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14
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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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15
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Beke ÁK, Gyürkés M, Nagy ZK, Marosi G, Farkas A. Digital twin of low dosage continuous powder blending - Artificial neural networks and residence time distribution models. Eur J Pharm Biopharm 2021; 169:64-77. [PMID: 34562574 DOI: 10.1016/j.ejpb.2021.09.006] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/10/2021] [Revised: 08/24/2021] [Accepted: 09/17/2021] [Indexed: 10/20/2022]
Abstract
In this paper we present a thorough description of the digital twin development for a continuous pharmaceutical powder blending process in accordance with the Process Analytical Technologies (PAT) and Quality by Design (QbD) guidelines. A low-dosage system of caffeine active pharmaceutical ingredient (API) and dextrose excipient was examined via continuous blending experiments. Near infrared (NIR) spectroscopy-based process analytics were applied; quantitative evaluation of spectra was achieved using multivariate data analysis. The blending system was represented with mechanistic residence time distribution (RTD) models and two types of recurrent artificial neural networks (ANN), experimental datasets were used for model training or fitting and validation. Detailed comparison of the two modelling approaches, the optimization of the model-based digital twin, and efficiency of the soft sensor-based process monitoring is presented through several validating simulations. Both RTD models and nonlinear autoregressive neural networks demonstrated excellent predictive power for the low dosage blending process. RTD models can prove to be more advantageous in industrial development as they are less resource-intensive to develop and prediction accuracy on low concentration levels lacks dependency from the precision of chemometric calibration. Reduced material costs and limited development timeframe render the digital twin an efficient tool in technological development.
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Affiliation(s)
- Áron Kristóf Beke
- Department of Organic Chemistry and Technology, Budapest University of Technology and Economics (BME), Műegyetem rakpart 3, Budapest H-1111, Hungary
| | - Martin Gyürkés
- Department of Organic Chemistry and Technology, Budapest University of Technology and Economics (BME), Műegyetem rakpart 3, Budapest H-1111, Hungary
| | - Zsombor Kristóf Nagy
- Department of Organic Chemistry and Technology, Budapest University of Technology and Economics (BME), Műegyetem rakpart 3, Budapest H-1111, Hungary
| | - György Marosi
- Department of Organic Chemistry and Technology, Budapest University of Technology and Economics (BME), Műegyetem rakpart 3, Budapest H-1111, Hungary
| | - Attila Farkas
- Department of Organic Chemistry and Technology, Budapest University of Technology and Economics (BME), Műegyetem rakpart 3, Budapest H-1111, Hungary.
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Holman J, Tantuccio A, Palmer J, van Doninck T, Meyer R. A very boring 120 h: 15 million tablets under a continuous state of control. POWDER TECHNOL 2021. [DOI: 10.1016/j.powtec.2020.12.073] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/24/2022]
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17
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Peterwitz M, Schembecker G. Evaluating the potential for optimization of axial back-mixing in continuous pharmaceutical manufacturing. Comput Chem Eng 2021. [DOI: 10.1016/j.compchemeng.2021.107251] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/25/2022]
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18
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Tian G, Koolivand A, Gu Z, Orella M, Shaw R, O’Connor TF. Development of an RTD-Based Flowsheet Modeling Framework for the Assessment of In-Process Control Strategies. AAPS PharmSciTech 2021; 22:25. [PMID: 33400033 DOI: 10.1208/s12249-020-01913-8] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/27/2020] [Accepted: 12/21/2020] [Indexed: 11/30/2022] Open
Abstract
Continuous manufacturing (CM) is an emerging technology which can improve pharmaceutical manufacturing and reduce drug product quality issues. One challenge that needs to be addressed when adopting CM technology is material traceability through the entire continuous process, which constitutes one key aspect of control strategy. Residence time distribution (RTD) plays an important role in material traceability as it characterizes the material spreading through the process. The propagation of upstream disturbances could be predictively tracked through the entire process by convolution of the disturbance and the RTD. The present study sets up the RTD-based modeling framework in a commonly used process modeling environment, gPROMS, and integrates it with existing modules and built-in tools (e.g., parameter estimation). Concentration calculations based on the convolution integral requires access to historical stream property information, which is not readily available in flowsheet modeling platforms. Thus, a novel approach is taken whereby a partial differential equation is used to propagate and store historical data as the simulation marches forward in time. Other stream properties not modeled by an RTD are determined in auxiliary modules. To illustrate the application of the framework, an integrated RTD-auxiliary model for a continuous direct compression manufacturing line was developed. An excellent agreement was found between the model predictions and experiments. The validated model was subsequently used to assess in-process control strategies for feeder and material traceability through the process. Our simulation results show that the employed modeling approach facilitates risk-based assessment of the continuous line by promoting our understanding on the process.
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Liu H, Meyer R, Flamm M, Wareham L, Metzger M, Tantuccio A, Yoon S. Optimization of Critical Quality Attributes in Tablet Film Coating and Design Space Determination Using Pilot-Scale Experimental Data. AAPS PharmSciTech 2021; 22:17. [PMID: 33389197 DOI: 10.1208/s12249-020-01884-w] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/10/2020] [Accepted: 11/18/2020] [Indexed: 11/30/2022] Open
Abstract
In this study, the novel high-speed tablet film coating process in the continuous manufacturing was investigated. The influence of key process variables (inlet air flow rate, inlet air temperature, and suspension spray rate) were investigated using a Box-Behnken experimental design method. Statistical regression models were developed to predict the outlet air temperature and relative humidity, the coating efficiency, the tablet moisture content, and coating uniformity. The effects of the three key process variables were comprehensively investigated based on mathematical analysis, contour plots, and interaction plots. The results indicate that all the process responses are affected by changing the inlet air flow rate, temperature, and suspension spray rate. A design space (DS) in terms of failure probability was determined based on specifications for tablet moisture content (< 3.5%) and coating uniformity (tablet weight standard deviation < 4 mg for tablet weight of 200 mg) using Monte Carlo simulations. Independent experiments were carried out and successfully validated the robustness and accuracy of the determined DS for the investigated tablet film coating process. All the data were generated using an industrial pilot-scale novel high-speed tablet coating unit from a continuous manufacturing line. The work facilitates the quality by design implementation of continuous pharmaceutical manufacturing.
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20
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Furukawa R, Singh R, Ierapetritou M. Effect of material properties on the residence time distribution (RTD) of a tablet press feed frame. Int J Pharm 2020; 591:119961. [DOI: 10.1016/j.ijpharm.2020.119961] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/27/2020] [Revised: 09/18/2020] [Accepted: 10/05/2020] [Indexed: 11/24/2022]
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21
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Huang Z, Galbraith SC, Cha B, Liu H, Park S, Flamm MH, Metzger M, Tantuccio A, Yoon S. Effects of process parameters on tablet critical quality attributes in continuous direct compression: a case study of integrating data-driven statistical models and mechanistic compaction models. Pharm Dev Technol 2020; 25:1204-1215. [PMID: 32808839 DOI: 10.1080/10837450.2020.1805760] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/23/2022]
Abstract
Continuous manufacturing of oral-dosage drug products is increasing the need for rigorous process understanding both from a process design and control perspective. The purpose of this study is to develop a methodology that analyzes the effects of upstream process parameters on continuous tablet compaction and then correlates associated upstream variables to the final tablet attributes (e.g. relative density and hardness). The impact of three process parameters (system throughput, blender speed, and compaction force) on tablet attributes is investigated using a full factorial experimental design. As expected, the compaction force was found to be the most significant process parameter. However, importantly, throughput was discovered to have a non-negligible impact which was previously unaccounted for. This impact is proposed to be related to differing levels of powder pre-compression. An empirical model for this relationship is regressed and incorporated into a flowsheet model. The flowsheet model is then used to develop an in silico design space which is compared favorably to that built from experiments. Moreover, in the future, the in silico design space based on the validated flowsheet model can provide better manufacturing flexibility and make control strategy development simpler.
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Affiliation(s)
- Zhuangrong Huang
- Department of Chemical Engineering, University of Massachusetts Lowell, Lowell, MA, USA
| | - Shaun C Galbraith
- Department of Chemical Engineering, University of Massachusetts Lowell, Lowell, MA, USA
| | - Bumjoon Cha
- Department of Chemical Engineering, University of Massachusetts Lowell, Lowell, MA, USA
| | - Huolong Liu
- Department of Chemical Engineering, University of Massachusetts Lowell, Lowell, MA, USA
| | - Seoyoung Park
- Department of Chemical Engineering, University of Massachusetts Lowell, Lowell, MA, USA
| | - Matthew H Flamm
- Applied Mathematics and Modeling, Scientific Modeling Platforms, Merck & Co., Inc, Kenilworth, NJ, USA
| | - Matt Metzger
- Pharmaceutical Commercialization Technology, Merck & Co., Inc, Kenilworth, NJ, USA
| | - Anthony Tantuccio
- Pharmaceutical Commercialization Technology, Merck & Co., Inc, Kenilworth, NJ, USA
| | - Seongkyu Yoon
- Department of Chemical Engineering, University of Massachusetts Lowell, Lowell, MA, USA
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22
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Zhong L, Gao L, Li L, Zang H. Trends-process analytical technology in solid oral dosage manufacturing. Eur J Pharm Biopharm 2020; 153:187-199. [DOI: 10.1016/j.ejpb.2020.06.008] [Citation(s) in RCA: 12] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/26/2020] [Revised: 06/11/2020] [Accepted: 06/14/2020] [Indexed: 10/24/2022]
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