1
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Kiricenko K, Klinken S, Kleinebudde P. Proof of a LOD Prediction Model with Orthogonal PAT Methods in Continuous Wet Granulation and Drying. J Pharm Sci 2024:S0022-3549(24)00254-5. [PMID: 39004417 DOI: 10.1016/j.xphs.2024.07.008] [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: 04/05/2024] [Revised: 07/09/2024] [Accepted: 07/09/2024] [Indexed: 07/16/2024]
Abstract
Real-time monitoring of critical quality attributes, such as residual water in granules after drying which can be determined through loss-on-drying (LOD), during wet granulation and drying is essential in continuous manufacturing. Near-infrared (NIR) spectroscopy has been widely used as process analytical technology (PAT) for in-line LOD monitoring. This study aims to develop and apply a model for predicting the LOD based on process parameters. Additionally, the efficacy of an orthogonal PAT approach using NIR and mass balance (MB) for a vibrating fluidized bed dryer (VFBD) is demonstrated. An in-house-built, cost-effective NIR sensor was utilized for measurements and exhibited good correlation compared to standard method via infrared drying. The combination of NIR and MB, as independent methods, has demonstrated their applicability. A good correlation, with a Pearson r above 0.99, was observed for LOD up to 16 % (w/w). The use of an orthogonal PAT method mitigated the risk of false process adaption. In some experiments where the NIR sensor might have been covered by powder and therefore did not measure accurately, LOD monitoring via MB remained feasible. The developed model effectively predicted LOD or process parameters, resulting in an R2 of 0.882 and a RMSE of 0.475 between predicted and measured LOD using the standard method.
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Affiliation(s)
- Katharina Kiricenko
- Heinrich Heine University Düsseldorf, Faculty of Mathematics and Natural Science, Institute of Pharmaceutics and Biopharmaceutics, Universitätsstraße 1, 40225 Düsseldorf, Germany
| | - Stefan Klinken
- Heinrich Heine University Düsseldorf, Faculty of Mathematics and Natural Science, Institute of Pharmaceutics and Biopharmaceutics, Universitätsstraße 1, 40225 Düsseldorf, Germany
| | - Peter Kleinebudde
- Heinrich Heine University Düsseldorf, Faculty of Mathematics and Natural Science, Institute of Pharmaceutics and Biopharmaceutics, Universitätsstraße 1, 40225 Düsseldorf, Germany.
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2
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Casian T, Nagy B, Lazurca C, Marcu V, Tőkés EO, Kelemen ÉK, Zöldi K, Oprean R, Nagy ZK, Tomuta I, Kovács B. Development of a PAT platform for the prediction of granule tableting properties. Int J Pharm 2023; 648:123610. [PMID: 37977288 DOI: 10.1016/j.ijpharm.2023.123610] [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: 09/04/2023] [Revised: 10/26/2023] [Accepted: 11/14/2023] [Indexed: 11/19/2023]
Abstract
In this work, the feasibility of implementing a process analytical technology (PAT) platform consisting of Near Infrared Spectroscopy (NIR) and particle size distribution (PSD) analysis was evaluated for the prediction of granule downstream processability. A Design of Experiments-based calibration set was prepared using a fluid bed melt granulation process by varying the binder content, granulation time, and granulation temperature. The granule samples were characterized using PAT tools and a compaction simulator in the 100-500 kg load range. Comparing the systematic variability in NIR and PSD data, their complementarity was demonstrated by identifying joint and unique sources of variation. These particularities of the data explained some differences in the performance of individual models. Regarding the fusion of data sources, the input data structure for partial least squares (PLS) based models did not significantly impact the predictive performance, as the root mean squared error of prediction (RMSEP) values were similar. Comparing PLS and artificial neural network (ANN) models, it was observed that the ANNs systematically provided superior model performance. For example, the best tensile strength, ejection stress, and detachment stress prediction with ANN resulted in an RMSEP of 0.119, 0.256, and 0.293 as opposed to the 0.180, 0.395, and 0.430 RMSEPs of the PLS models, respectively. Finally, the robustness of the developed models was assessed.
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Affiliation(s)
- Tibor Casian
- Department of Pharmaceutical Technology and Biopharmacy, "Iuliu Hatieganu" University of Medicine and Pharmacy, 400012 Cluj-Napoca, Romania
| | - Brigitta Nagy
- Department of Organic Chemistry and Technology, Faculty of Chemical Technology and Biotechnology, Budapest University of Technology and Economics, Műegyetem rkp. 3., H-1111 Budapest, Hungary.
| | - Cristiana Lazurca
- Department of Pharmaceutical Technology and Biopharmacy, "Iuliu Hatieganu" University of Medicine and Pharmacy, 400012 Cluj-Napoca, Romania
| | - Victor Marcu
- Department of Pharmaceutical Technology and Biopharmacy, "Iuliu Hatieganu" University of Medicine and Pharmacy, 400012 Cluj-Napoca, Romania
| | | | | | | | - Radu Oprean
- Analytical Chemistry Department, "Iuliu Haţieganu" University of Medicine and Pharmacy, 400349 Cluj-Napoca, Romania
| | - Zsombor Kristóf Nagy
- Department of Organic Chemistry and Technology, Faculty of Chemical Technology and Biotechnology, Budapest University of Technology and Economics, Műegyetem rkp. 3., H-1111 Budapest, Hungary
| | - Ioan Tomuta
- Department of Pharmaceutical Technology and Biopharmacy, "Iuliu Hatieganu" University of Medicine and Pharmacy, 400012 Cluj-Napoca, Romania
| | - Béla Kovács
- Gedeon Richter Romania 540306, Tîrgu Mureș, Romania; Department of Biochemistry and Environmental Chemistry, Faculty of Pharmacy, George Emil Palade University of Medicine, Pharmacy, Science, and Technology of Târgu Mureș, 540139 Târgu Mureș, Romania
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3
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Wikström H, Martin de Juan L, Remmelgas J, Meier R, Altmeyer A, Emanuele D, Jormanainen M, Juppo A, Tajarobi P. Drying capacity of a continuous vibrated fluid bed dryer - Statistical and mechanistic model development. Int J Pharm 2023; 645:123368. [PMID: 37669728 DOI: 10.1016/j.ijpharm.2023.123368] [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: 05/16/2023] [Revised: 08/31/2023] [Accepted: 09/01/2023] [Indexed: 09/07/2023]
Abstract
The drying capacity of a continuous vibrated fluid bed dryer was studied using a DoE by varying microcrystalline cellulose content in the formulation, water amount in the twin-screw granulation, inlet air temperature, air flow rate and the acceleration of the horizontal fluid-bed. Temperature and humidity profiles were measured along the dryer using wireless sensors. For the parameter space explored in this study, acceleration was the most influential process parameter of the dryer regarding the resulting granule moisture content. An empirical model was developed that allowed for fast and accurate moisture content prediction that could be incorporated into an enhanced control strategy. In addition, a mechanistic model was formulated that allow for prediction of temperature and moisture profiles, and most importantly the moisture content of the granules inside the dryer. The mechanistic model can be integrated to other unit operation models to provide overall understanding of an integrated continuous process line. The mechanistic model also makes it possible to define the equipment design requirements (e.g., length of the dryer) to meet the specific needs in terms of drying capacity, temperature and moisture profile.
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Affiliation(s)
- Håkan Wikström
- Early Product Development and Manufacturing, Pharmaceutical Sciences, BioPharmaceuticals R&D, AstraZeneca, Gothenburg, Sweden
| | - Luis Martin de Juan
- Oral Product Development, Pharmaceutical Technology & Development, AstraZeneca, Gothenburg, Sweden
| | | | - Robin Meier
- L.B. Bohle Maschinen und Verfahren GmbH, Ennigerloh, Germany
| | | | - Daniel Emanuele
- L.B. Bohle Maschinen und Verfahren GmbH, Ennigerloh, Germany
| | - Miika Jormanainen
- Division of Pharmaceutical Chemistry and Technology, University of Helsinki, Finland
| | - Anne Juppo
- Division of Pharmaceutical Chemistry and Technology, University of Helsinki, Finland
| | - Pirjo Tajarobi
- Early Product Development and Manufacturing, Pharmaceutical Sciences, BioPharmaceuticals R&D, AstraZeneca, Gothenburg, Sweden.
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4
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Yang TL, Szewc J, Zhong L, Leonova A, Giebułtowicz J, Habashy R, Isreb A, Alhnan MA. The Use of Near-infrared as Process Analytical Technology (PAT) during 3D Printing Tablets at the Point-of-Care. Int J Pharm 2023:123073. [PMID: 37230372 DOI: 10.1016/j.ijpharm.2023.123073] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/30/2023] [Revised: 05/20/2023] [Accepted: 05/22/2023] [Indexed: 05/27/2023]
Abstract
Fused deposition modelling (FDM) is one of the most researched 3D printing technologies that holds great potential for low-cost manufacturing of personalised medicine. To achieve real-time release, timely quality control is a major challenge for applying 3D printing technologies as a point-of-care (PoC) manufacturing approach. This work proposes the use of a low-cost and compact near-infrared (NIR) spectroscopy modality as a process analytical technology (PAT) to monitor a critical quality attribute (drug content) during and after FDM 3D printing process. 3D printed caffeine tablets were used to manifest the feasibility of the NIR model as a quantitative analytical procedure and dose verification method. Caffeine tablets (0-40% w/w) were fabricated using polyvinyl alcohol and FDM 3D printing. The predictive performance of the NIR model was demonstrated in linearity (correlation coefficient, R2) and accuracy (root mean square error of prediction, RMSEP). The actual drug content values were determined using the reference high-performance liquid chromatography (HPLC) method. The model of full-completion caffeine tablets demonstrated linearity (R2 = 0.985) and accuracy (RMSEP =1.4%), indicated to be an alternative dose quantitation method for 3D printed products. The ability of the models to assess caffeine contents during the 3D printing process could not be accurately achieved using the model built with complete tablets. Instead, by building a predictive model for each completion stage of 20%, 40%, 60% and 80%, the model of different completion caffeine tablets displayed linearity (R2 of 0.991, 0.99, 0.987, and 0.983) and accuracy (RMSEP of 2.22%, 1.65%, 1.41%, 0.83%), respectively. Overall, this study demonstrated the feasibility of a low NIR model as a non-destructive, low-cost, compact, and rapid analysis dose verification method enabling the real-time release to facilitate 3D printing medicine production in the clinic.
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Affiliation(s)
- Tzuyi L Yang
- Centre for Pharmaceutical Medicine, Institute of Pharmaceutical Science, King's College London, London, SE1 9NH, UK
| | - Jakub Szewc
- Faculty of Pharmacy with the Laboratory Medicine Division, Medical University of Warsaw, Warsaw, Poland
| | - Lingu Zhong
- Centre for Pharmaceutical Medicine, Institute of Pharmaceutical Science, King's College London, London, SE1 9NH, UK
| | - Anna Leonova
- Centre for Pharmaceutical Medicine, Institute of Pharmaceutical Science, King's College London, London, SE1 9NH, UK
| | - Joanna Giebułtowicz
- Faculty of Pharmacy with the Laboratory Medicine Division, Medical University of Warsaw, Warsaw, Poland
| | | | - Abdullah Isreb
- School of Pharmacy and Biomolecular Sciences, Liverpool John Moores University, Liverpool L3 3AF, UK
| | - Mohamed A Alhnan
- Centre for Pharmaceutical Medicine, Institute of Pharmaceutical Science, King's College London, London, SE1 9NH, UK.
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5
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Nambiar AG, Singh M, Mali AR, Serrano DR, Kumar R, Healy AM, Agrawal AK, Kumar D. Continuous Manufacturing and Molecular Modeling of Pharmaceutical Amorphous Solid Dispersions. AAPS PharmSciTech 2022; 23:249. [PMID: 36056225 DOI: 10.1208/s12249-022-02408-4] [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: 06/25/2022] [Accepted: 08/24/2022] [Indexed: 11/30/2022] Open
Abstract
Amorphous solid dispersions enhance solubility and oral bioavailability of poorly water-soluble drugs. The escalating number of drugs with poor aqueous solubility, poor dissolution, and poor oral bioavailability is an unresolved problem that requires adequate interventions. This review article highlights recent solubility and bioavailability enhancement advances using amorphous solid dispersions (ASDs). The review also highlights the mechanism of enhanced dissolution and the challenges faced by ASD-based products, such as stability and scale-up. The role of process analytical technology (PAT) supporting continuous manufacturing is highlighted. Accurately predicting interactions between the drug and polymeric carrier requires long experimental screening methods, and this is a space where computational tools hold significant potential. Recent advancements in data science, computational tools, and easy access to high-end computation power are set to accelerate ASD-based research. Hence, particular emphasis has been given to molecular modeling techniques that can address some of the unsolved questions related to ASDs. With the advancement in PAT tools and artificial intelligence, there is an increasing interest in the continuous manufacturing of pharmaceuticals. ASDs are a suitable option for continuous manufacturing, as production of a drug product from an ASD by direct compression is a reality, where the addition of multiple excipients is easy to avoid. Significant attention is necessary for ongoing clinical studies based on ASDs, which is paving the way for the approval of many new ASDs and their introduction into the market.
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Affiliation(s)
- Amritha G Nambiar
- Department of Pharmaceutical Engineering and Technology, Indian Institute of Technology (BHU), Varanasi, 221005, India
| | - Maan Singh
- Department of Pharmaceutical Engineering and Technology, Indian Institute of Technology (BHU), Varanasi, 221005, India
| | - Abhishek R Mali
- Department of Pharmaceutical Engineering and Technology, Indian Institute of Technology (BHU), Varanasi, 221005, India
| | | | - Rajnish Kumar
- Department of Pharmaceutical Engineering and Technology, Indian Institute of Technology (BHU), Varanasi, 221005, India
| | - Anne Marie Healy
- School of Pharmacy and Pharmaceutical Sciences, Trinity College Dublin, Dublin 2, Ireland
| | - Ashish Kumar Agrawal
- Department of Pharmaceutical Engineering and Technology, Indian Institute of Technology (BHU), Varanasi, 221005, India
| | - Dinesh Kumar
- Department of Pharmaceutical Engineering and Technology, Indian Institute of Technology (BHU), Varanasi, 221005, India.
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6
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Gyürkés M, Madarász L, Záhonyi P, Köte Á, Nagy B, Pataki H, Nagy ZK, Domokos A, Farkas A. Soft sensor for content prediction in an integrated continuous pharmaceutical formulation line based on the residence time distribution of unit operations. Int J Pharm 2022; 624:121950. [PMID: 35753540 DOI: 10.1016/j.ijpharm.2022.121950] [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: 03/31/2022] [Revised: 06/14/2022] [Accepted: 06/20/2022] [Indexed: 12/01/2022]
Abstract
In this study, a concentration predicting soft sensor was achieved based on the Residence Time Distribution (RTD) of an integrated, three-step pharmaceutical formulation line. The RTD was investigated with color-based tracer experiments using image analysis. Twin-screw wet granulation (TSWG) was directly coupled with a horizontal fluid bed dryer and an oscillating mill. Based on integrated measurement, we proved that it is also possible to couple the unit operations in silico. Three surrogate tracers were produced with a coloring agent to investigate the separated unit operations and the solid and liquid inputs of the TSWG. The soft sensor's prediction was compared to validating experiments of a 0.05 mg/g (15% of the nominal) concentration change with High-Performance Liquid Chromatography (HPLC) reference measurements of the active ingredient proving the adequacy of the soft sensor (RMSE < 4%).
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Affiliation(s)
- Martin Gyürkés
- Department of Organic Chemistry and Technology, Budapest University of Technology and Economics, Műegyetem rkp. 3., H-1111 Budapest, Hungary
| | - Lajos Madarász
- Department of Organic Chemistry and Technology, Budapest University of Technology and Economics, Műegyetem rkp. 3., H-1111 Budapest, Hungary
| | - Petra Záhonyi
- Department of Organic Chemistry and Technology, Budapest University of Technology and Economics, Műegyetem rkp. 3., H-1111 Budapest, Hungary
| | - Ákos Köte
- Department of Organic Chemistry and Technology, Budapest University of Technology and Economics, Műegyetem rkp. 3., H-1111 Budapest, Hungary
| | - Brigitta Nagy
- Department of Organic Chemistry and Technology, Budapest University of Technology and Economics, Műegyetem rkp. 3., H-1111 Budapest, Hungary
| | - Hajnalka Pataki
- Department of Organic Chemistry and Technology, Budapest University of Technology and Economics, Műegyetem rkp. 3., H-1111 Budapest, Hungary
| | - Zsombor Kristóf Nagy
- Department of Organic Chemistry and Technology, Budapest University of Technology and Economics, Műegyetem rkp. 3., H-1111 Budapest, Hungary
| | - András Domokos
- Department of Organic Chemistry and Technology, Budapest University of Technology and Economics, Műegyetem rkp. 3., H-1111 Budapest, Hungary
| | - Attila Farkas
- Department of Organic Chemistry and Technology, Budapest University of Technology and Economics, Műegyetem rkp. 3., H-1111 Budapest, Hungary
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7
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Challenges and Opportunities of Implementing Data Fusion in Process Analytical Technology—A Review. Molecules 2022; 27:molecules27154846. [PMID: 35956791 PMCID: PMC9369811 DOI: 10.3390/molecules27154846] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/30/2022] [Revised: 07/20/2022] [Accepted: 07/22/2022] [Indexed: 12/03/2022] Open
Abstract
The release of the FDA’s guidance on Process Analytical Technology has motivated and supported the pharmaceutical industry to deliver consistent quality medicine by acquiring a deeper understanding of the product performance and process interplay. The technical opportunities to reach this high-level control have considerably evolved since 2004 due to the development of advanced analytical sensors and chemometric tools. However, their transfer to the highly regulated pharmaceutical sector has been limited. To this respect, data fusion strategies have been extensively applied in different sectors, such as food or chemical, to provide a more robust performance of the analytical platforms. This survey evaluates the challenges and opportunities of implementing data fusion within the PAT concept by identifying transfer opportunities from other sectors. Special attention is given to the data types available from pharmaceutical manufacturing and their compatibility with data fusion strategies. Furthermore, the integration into Pharma 4.0 is discussed.
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8
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Process Analytical Technology for the Production of Parenteral Lipid Emulsions According to Good Manufacturing Practices. Processes (Basel) 2022. [DOI: 10.3390/pr10061174] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/17/2022] Open
Abstract
The good manufacturing practices (GMP) and process analytical technology (PAT) initiatives of the US Food and Drug Administration, in conjunction with International Council for Harmonisation (ICH) quality guidelines Q8, Q9, and Q10, ensure that manufacturing processes for parenteral formulations meet the requirements of increasingly strict regulations. This involves the selection of suitable process analytics for process integration and aseptic processing. In this article, we discuss the PAT requirements for the GMP-compliant manufacturing of parenteral lipid emulsions, which can be used for clinical nutrition or for the delivery of lipophilic active ingredients. There are risks associated with the manufacturing processes, including the potential for unstable emulsions and the formation of large droplets that can induce embolisms in the patient. Parenteral emulsions are currently monitored offline using a statistical approach. Inline analytics, supplemented by measurements of zeta potential, could minimize the above risks. Laser scanning technology, ultrasound attenuation spectroscopy, and photo-optical sensors combined with image analysis may prove to be useful PAT methods. In the future, these technologies could lead to better process understanding and control, thus improving production efficiency.
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9
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Stauffer F, Boulanger E, Pilcer G. Sampling and diversion strategy for twin-screw granulation lines using batch statistical process monitoring. Eur J Pharm Sci 2022; 171:106126. [DOI: 10.1016/j.ejps.2022.106126] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/30/2021] [Revised: 12/21/2021] [Accepted: 01/12/2022] [Indexed: 11/03/2022]
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10
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Madarász L, Köte Á, Hambalkó B, Csorba K, Kovács V, Lengyel L, Marosi G, Farkas A, Nagy ZK, Domokos A. In-line particle size measurement based on image analysis in a fully continuous granule manufacturing line for rapid process understanding and development. Int J Pharm 2022; 612:121280. [PMID: 34774695 DOI: 10.1016/j.ijpharm.2021.121280] [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: 08/31/2021] [Revised: 10/27/2021] [Accepted: 11/06/2021] [Indexed: 12/01/2022]
Abstract
The present paper serves as a demonstration how an in-line PAT tool can be used for rapid and efficient process development in a fully continuous powder to granule line consisting of an interconnected twin-screw wet granulator, vibrational fluid bed dryer, and a regranulating mill. A new method was investigated for the periodic in-line particle size measurement of high mass flow materials to obtain real-time particle size data of the regranulated product. The system utilises a vibratory feeder with periodically altered feeding intensity in order to temporarily reduce the mass flow of the material passing in front of the camera. This results in the drastic reduction of particle overlapping in the images, making image analysis a viable tool for the in-line particle size measurement of high mass-flow materials. To evaluate the performance of the imaging system, the effect of several milling settings and the liquid-to-solid ratio was investigated on the product's particle size in the span of a few hours. The particle sizes measured with the in-line system were in accordance with the expected trends as well as with the results of the off-line reference particle size measurements. Based on the results, the in-line imaging system can serve as a PAT tool to obtain valuable real-time information for rapid process development or quality assurance.
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Affiliation(s)
- Lajos Madarász
- Department of Organic Chemistry and Technology, Budapest University of Technology and Economics, H-1111 Budapest, Műegyetem rakpart 3, Hungary
| | - Ákos Köte
- Department of Automation and Applied Informatics, Budapest University of Technology and Economics, H-1117, Budapest Magyar Tudósok körútja 2 QB-207, Hungary
| | - Bence Hambalkó
- Department of Automation and Applied Informatics, Budapest University of Technology and Economics, H-1117, Budapest Magyar Tudósok körútja 2 QB-207, Hungary
| | - Kristóf Csorba
- Department of Automation and Applied Informatics, Budapest University of Technology and Economics, H-1117, Budapest Magyar Tudósok körútja 2 QB-207, Hungary
| | - Viktor Kovács
- Department of Automation and Applied Informatics, Budapest University of Technology and Economics, H-1117, Budapest Magyar Tudósok körútja 2 QB-207, Hungary
| | - László Lengyel
- Department of Automation and Applied Informatics, Budapest University of Technology and Economics, H-1117, Budapest Magyar Tudósok körútja 2 QB-207, Hungary
| | - György Marosi
- Department of Organic Chemistry and Technology, Budapest University of Technology and Economics, H-1111 Budapest, Műegyetem rakpart 3, Hungary
| | - Attila Farkas
- Department of Organic Chemistry and Technology, Budapest University of Technology and Economics, H-1111 Budapest, Műegyetem rakpart 3, Hungary
| | - Zsombor Kristóf Nagy
- Department of Organic Chemistry and Technology, Budapest University of Technology and Economics, H-1111 Budapest, Műegyetem rakpart 3, Hungary.
| | - András Domokos
- Department of Organic Chemistry and Technology, Budapest University of Technology and Economics, H-1111 Budapest, Műegyetem rakpart 3, Hungary
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11
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Gagnon F, Desbiens A, Poulin É, Bouchard J, Lapointe-Garant PP. Grey-box model calibration and validation for a continuous horizontal fluidized bed dryer. Chem Eng Res Des 2021. [DOI: 10.1016/j.cherd.2021.08.001] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/19/2022]
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12
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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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