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Bhalode P, Razavi SM, Tian H, Roman-Ospino A, Scicolone J, Callegari G, Dubey A, Koolivand A, Krull S, O'Connor T, Muzzio FJ, Ierapetritou MG. Statistical data treatment for residence time distribution studies in pharmaceutical manufacturing. Int J Pharm 2024; 657:124133. [PMID: 38642620 DOI: 10.1016/j.ijpharm.2024.124133] [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: 01/18/2024] [Revised: 04/13/2024] [Accepted: 04/15/2024] [Indexed: 04/22/2024]
Abstract
Residence time distribution (RTD) method has been widely used in the pharmaceutical manufacturing for understanding powder dynamics within unit operations and continuous integrated manufacturing lines. The dynamics thus captured is then used to develop predictive models for unit operations and important RTD-based applications ensuring product quality assurance. Despite thorough efforts in tracer selection, data acquisition, and calibration model development to obtain tracer concentration profiles for RTD studies, there can exist significant noise in these profiles. This noise can make it challenging to identify the underlying signal and get a representative RTD of the system under study. Such concerns have previously indicated the importance of noise handling for RTD measurements in literature. However, the literature does not provide sufficient information on noise handling or data treatment strategies for RTD studies. To this end, we investigate the impact of varying levels of noise using different tracers on measurement of RTD profile and its applications. We quantify the impact of different denoising methods (time and frequency averaging methods). Through this investigation, we see that Savitsky Golay filtering turns out to a good method for denoising RTD profiles despite varying noise levels. The investigation is performed such that the key features of the RTD profile (which are important for RTD based applications) are preserved. Subsequently, we also investigate the impact of denoising on RTD-based applications such as out-of-specification (OOS) analysis and RTD modeling. The results show that the degree of noise levels considered in this work do not significantly impact the RTD-based applications.
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Affiliation(s)
- Pooja Bhalode
- Center of Plastics Innovation, University of Delaware, DE, USA
| | - Sonia M Razavi
- Department of Chemical and Biochemical Engineering, Rutgers University, NJ, USA
| | - Huayu Tian
- Department of Chemical and Biomolecular Engineering, University of Delaware, DE, USA
| | - Andres Roman-Ospino
- Department of Chemical and Biochemical Engineering, Rutgers University, NJ, USA
| | - James Scicolone
- Department of Chemical and Biochemical Engineering, Rutgers University, NJ, USA
| | - Gerardo Callegari
- Department of Chemical and Biochemical Engineering, Rutgers University, NJ, USA
| | - Atul Dubey
- Pharmaceutical Continuous Manufacturing (PCM), United States Pharmacopeia, 12601 Twinbrook Parkway, Rockville, MD, USA
| | - Abdollah Koolivand
- Office of Pharmaceutical Quality, Center for Drug Evaluation Research, Food and Drug Administration, 10903 New Hampshire Ave, Silver Springs, MD 20993, USA
| | - Scott Krull
- Office of Pharmaceutical Quality, Center for Drug Evaluation Research, Food and Drug Administration, 10903 New Hampshire Ave, Silver Springs, MD 20993, USA
| | - Thomas O'Connor
- Office of Pharmaceutical Quality, Center for Drug Evaluation Research, Food and Drug Administration, 10903 New Hampshire Ave, Silver Springs, MD 20993, USA
| | - Fernando J Muzzio
- Department of Chemical and Biochemical Engineering, Rutgers University, NJ, USA
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Zhao J, Tian G, Qu H. Pharmaceutical Application of Process Understanding and Optimization Techniques: A Review on the Continuous Twin-Screw Wet Granulation. Biomedicines 2023; 11:1923. [PMID: 37509561 PMCID: PMC10377609 DOI: 10.3390/biomedicines11071923] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/31/2023] [Revised: 06/15/2023] [Accepted: 07/05/2023] [Indexed: 07/30/2023] Open
Abstract
Twin-screw wet granulation (TSWG) is a method of continuous pharmaceutical manufacturing and a potential alternative method to batch granulation processes. It has attracted more and more interest nowadays due to its high efficiency, robustness, and applications. To improve both the product quality and process efficiency, the process understanding is critical. This article reviews the recent work in process understanding and optimization for TSWG. Various aspects of the progress in TSWG like process model construction, process monitoring method development, and the strategy of process control for TSWG have been thoroughly analyzed and discussed. The process modeling technique including the empirical model, the mechanistic model, and the hybrid model in the TSWG process are presented to increase the knowledge of the granulation process, and the influence of process parameters involved in granulation process on granule properties by experimental study are highlighted. The study analyzed several process monitoring tools and the associated technologies used to monitor granule attributes. In addition, control strategies based on process analytical technology (PAT) are presented as a reference to enhance product quality and ensure the applicability and capability of continuous manufacturing (CM) processes. Furthermore, this article aims to review the current research progress in an effort to make recommendations for further research in process understanding and development of TSWG.
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Affiliation(s)
- Jie Zhao
- Pharmaceutical Informatics Institute, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou 310058, China
| | - Geng Tian
- Pharmaceutical Informatics Institute, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou 310058, China
| | - Haibin Qu
- Pharmaceutical Informatics Institute, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou 310058, China
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A critical review on granulation of pharmaceuticals and excipients: Principle, analysis and typical applications. POWDER TECHNOL 2022. [DOI: 10.1016/j.powtec.2022.117329] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/20/2022]
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Sánchez-Paternina A, Martínez-Cartagena P, Li J, Scicolone J, Singh R, Lugo YC, Romañach RJ, Muzzio FJ, Román-Ospino AD. Residence time distribution as a traceability method for lot changes in a pharmaceutical continuous manufacturing system. Int J Pharm 2022; 611:121313. [PMID: 34822965 DOI: 10.1016/j.ijpharm.2021.121313] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/03/2021] [Revised: 11/03/2021] [Accepted: 11/19/2021] [Indexed: 02/03/2023]
Abstract
Residence time distribution (RTD) models were developed to track raw material lots and investigate batch transitions in a continuous manufacturing system. Two raw materials with similar physical properties (granular metformin and lactose) were identified via Principal Component Analysis (PCA) from a library of bulk material properties and used to simulate the switching of lots during production. In-line near-infrared (NIR) spectra were collected with the powder flowing through a chute in a continuous manufacturing system to monitor metformin and lactose concentration in step-change experiments with Partial Least Squares (PLS) models. RTD provided an understanding of raw material propagation through the continuous manufacturing system. Transition times between raw material changes were identified using the results of two multivariate approaches PLS and PCA. The methodology was implemented to discriminate the transition zone in a raw material change, contributing to design control strategies for acceptance and diverting mechanisms.
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Affiliation(s)
- Adriluz Sánchez-Paternina
- Center for Structured Organic Particulate Systems (C-SOPS), Department of Chemistry, University of Puerto Rico Mayaguez Campus, PO Box 9000, Mayaguez, PR 00681, Puerto Rico
| | - Pedro Martínez-Cartagena
- Center for Structured Organic Particulate Systems (C-SOPS), Department of Chemistry, University of Puerto Rico Mayaguez Campus, PO Box 9000, Mayaguez, PR 00681, Puerto Rico
| | - Jingzhe Li
- Center for Structured Organic Particulate Systems (C-SOPS), Department of Chemical and Biochemical Engineering, Rutgers, The State University of New Jersey, Piscataway, NJ 08854, USA
| | - James Scicolone
- Center for Structured Organic Particulate Systems (C-SOPS), Department of Chemical and Biochemical Engineering, Rutgers, The State University of New Jersey, Piscataway, NJ 08854, USA
| | - Ravendra Singh
- Center for Structured Organic Particulate Systems (C-SOPS), Department of Chemical and Biochemical Engineering, Rutgers, The State University of New Jersey, Piscataway, NJ 08854, USA
| | - Yleana C Lugo
- Janssen Supply Chain, Johnson & Johnson, Gurabo, Puerto Rico
| | - Rodolfo J Romañach
- Center for Structured Organic Particulate Systems (C-SOPS), Department of Chemistry, University of Puerto Rico Mayaguez Campus, PO Box 9000, Mayaguez, PR 00681, Puerto Rico
| | - Fernando J Muzzio
- Center for Structured Organic Particulate Systems (C-SOPS), Department of Chemical and Biochemical Engineering, Rutgers, The State University of New Jersey, Piscataway, NJ 08854, USA
| | - Andrés D Román-Ospino
- Center for Structured Organic Particulate Systems (C-SOPS), Department of Chemical and Biochemical Engineering, Rutgers, The State University of New Jersey, Piscataway, NJ 08854, USA.
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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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Shi G, Lin L, Liu Y, Chen G, Luo Y, Wu Y, Li H. Pharmaceutical application of multivariate modelling techniques: a review on the manufacturing of tablets. RSC Adv 2021; 11:8323-8345. [PMID: 35423324 PMCID: PMC8695199 DOI: 10.1039/d0ra08030f] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/20/2020] [Accepted: 01/26/2021] [Indexed: 11/21/2022] Open
Abstract
The tablet manufacturing process is a complex system, especially in continuous manufacturing (CM). It includes multiple unit operations, such as mixing, granulation, and tableting. In tablet manufacturing, critical quality attributes are influenced by multiple factorial relationships between material properties, process variables, and interactions. Moreover, the variation in raw material attributes and manufacturing processes is an inherent characteristic and seriously affects the quality of pharmaceutical products. To deepen our understanding of the tablet manufacturing process, multivariable modeling techniques can replace univariate analysis to investigate tablet manufacturing. In this review, the roles of the most prominent multivariate modeling techniques in the tablet manufacturing process are discussed. The review mainly focuses on applying multivariate modeling techniques to process understanding, optimization, process monitoring, and process control within multiple unit operations. To minimize the errors in the process of modeling, good modeling practice (GMoP) was introduced into the pharmaceutical process. Furthermore, current progress in the continuous manufacturing of tablets and the role of multivariate modeling techniques in continuous manufacturing are introduced. In this review, information is provided to both researchers and manufacturers to improve tablet quality.
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Affiliation(s)
- Guolin Shi
- Institute of Chinese Materia Medica, China Academy of Chinese Medical Sciences Beijing 100700 China
| | - Longfei Lin
- Institute of Chinese Materia Medica, China Academy of Chinese Medical Sciences Beijing 100700 China
| | - Yuling Liu
- Institute of Chinese Materia Medica, China Academy of Chinese Medical Sciences Beijing 100700 China
| | - Gongsen Chen
- Institute of Chinese Materia Medica, China Academy of Chinese Medical Sciences Beijing 100700 China
| | - Yuting Luo
- Institute of Chinese Materia Medica, China Academy of Chinese Medical Sciences Beijing 100700 China
| | - Yanqiu Wu
- Institute of Chinese Materia Medica, China Academy of Chinese Medical Sciences Beijing 100700 China
| | - Hui Li
- Institute of Chinese Materia Medica, China Academy of Chinese Medical Sciences Beijing 100700 China
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Bwambok DK, Siraj N, Macchi S, Larm NE, Baker GA, Pérez RL, Ayala CE, Walgama C, Pollard D, Rodriguez JD, Banerjee S, Elzey B, Warner IM, Fakayode SO. QCM Sensor Arrays, Electroanalytical Techniques and NIR Spectroscopy Coupled to Multivariate Analysis for Quality Assessment of Food Products, Raw Materials, Ingredients and Foodborne Pathogen Detection: Challenges and Breakthroughs. SENSORS (BASEL, SWITZERLAND) 2020; 20:E6982. [PMID: 33297345 PMCID: PMC7730680 DOI: 10.3390/s20236982] [Citation(s) in RCA: 13] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 11/17/2020] [Revised: 12/01/2020] [Accepted: 12/03/2020] [Indexed: 12/23/2022]
Abstract
Quality checks, assessments, and the assurance of food products, raw materials, and food ingredients is critically important to ensure the safeguard of foods of high quality for safety and public health. Nevertheless, quality checks, assessments, and the assurance of food products along distribution and supply chains is impacted by various challenges. For instance, the development of portable, sensitive, low-cost, and robust instrumentation that is capable of real-time, accurate, and sensitive analysis, quality checks, assessments, and the assurance of food products in the field and/or in the production line in a food manufacturing industry is a major technological and analytical challenge. Other significant challenges include analytical method development, method validation strategies, and the non-availability of reference materials and/or standards for emerging food contaminants. The simplicity, portability, non-invasive, non-destructive properties, and low-cost of NIR spectrometers, make them appealing and desirable instruments of choice for rapid quality checks, assessments and assurances of food products, raw materials, and ingredients. This review article surveys literature and examines current challenges and breakthroughs in quality checks and the assessment of a variety of food products, raw materials, and ingredients. Specifically, recent technological innovations and notable advances in quartz crystal microbalances (QCM), electroanalytical techniques, and near infrared (NIR) spectroscopic instrument development in the quality assessment of selected food products, and the analysis of food raw materials and ingredients for foodborne pathogen detection between January 2019 and July 2020 are highlighted. In addition, chemometric approaches and multivariate analyses of spectral data for NIR instrumental calibration and sample analyses for quality assessments and assurances of selected food products and electrochemical methods for foodborne pathogen detection are discussed. Moreover, this review provides insight into the future trajectory of innovative technological developments in QCM, electroanalytical techniques, NIR spectroscopy, and multivariate analyses relating to general applications for the quality assessment of food products.
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Affiliation(s)
- David K. Bwambok
- Chemistry and Biochemistry, California State University San Marcos, 333 S. Twin Oaks Valley Rd, San Marcos, CA 92096, USA;
| | - Noureen Siraj
- Department of Chemistry, University of Arkansas at Little Rock, 2801 S. University Ave, Little Rock, AR 72204, USA; (N.S.); (S.M.)
| | - Samantha Macchi
- Department of Chemistry, University of Arkansas at Little Rock, 2801 S. University Ave, Little Rock, AR 72204, USA; (N.S.); (S.M.)
| | - Nathaniel E. Larm
- Department of Chemistry, University of Missouri, 601 S. College Avenue, Columbia, MO 65211, USA; (N.E.L.); (G.A.B.)
| | - Gary A. Baker
- Department of Chemistry, University of Missouri, 601 S. College Avenue, Columbia, MO 65211, USA; (N.E.L.); (G.A.B.)
| | - Rocío L. Pérez
- Department of Chemistry, Louisiana State University, 232 Choppin Hall, Baton Rouge, LA 70803, USA; (R.L.P.); (C.E.A.); (I.M.W.)
| | - Caitlan E. Ayala
- Department of Chemistry, Louisiana State University, 232 Choppin Hall, Baton Rouge, LA 70803, USA; (R.L.P.); (C.E.A.); (I.M.W.)
| | - Charuksha Walgama
- Department of Physical Sciences, University of Arkansas-Fort Smith, 5210 Grand Ave, Fort Smith, AR 72913, USA; (C.W.); (S.B.)
| | - David Pollard
- Department of Chemistry, Winston-Salem State University, 601 S. Martin Luther King Jr Dr, Winston-Salem, NC 27013, USA;
| | - Jason D. Rodriguez
- Division of Complex Drug Analysis, Center for Drug Evaluation and Research, US Food and Drug Administration, 645 S. Newstead Ave., St. Louis, MO 63110, USA;
| | - Souvik Banerjee
- Department of Physical Sciences, University of Arkansas-Fort Smith, 5210 Grand Ave, Fort Smith, AR 72913, USA; (C.W.); (S.B.)
| | - Brianda Elzey
- Science, Engineering, and Technology Department, Howard Community College, 10901 Little Patuxent Pkwy, Columbia, MD 21044, USA;
| | - Isiah M. Warner
- Department of Chemistry, Louisiana State University, 232 Choppin Hall, Baton Rouge, LA 70803, USA; (R.L.P.); (C.E.A.); (I.M.W.)
| | - Sayo O. Fakayode
- Department of Physical Sciences, University of Arkansas-Fort Smith, 5210 Grand Ave, Fort Smith, AR 72913, USA; (C.W.); (S.B.)
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Martínez-Cartagena PA, Sierra-Vega NO, Alvarado-Hernández BB, Méndez R, Romañach RJ. An innovative sampling interface for monitoring flowing pharmaceutical powder mixtures. J Pharm Biomed Anal 2020; 194:113785. [PMID: 33280992 DOI: 10.1016/j.jpba.2020.113785] [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: 10/11/2020] [Accepted: 11/17/2020] [Indexed: 10/22/2022]
Abstract
A chute was designed following the principles of the Theory of Sampling to minimize the variations in powder flow and provide all particles in the flowing blends with the same opportunity of being selected as a sample. The design also reduces the thickness of the chute to allow the analysis of a higher portion of the flowing blends by a near infrared spectrometer. The blends that flowed through the chute had Carr's index values that fluctuated between 23 and 25 percent, indicating passable flowability. A powder fowling evaluation demonstrated that there was no powder accumulation at the inspection window of the chute. The mass flow rate profiles indicated that the system achieves mass steady-state in approximately 30 s and a throughput of 30 kg/h which makes it suitable for continuous manufacturing operations. An in-line NIR calibration model was developed to quantify caffeine concentrations between 1.51 and 4.52 % w/w. The spectra obtained from each experiment had minimal baseline variation. The developed NIR method was robust to throughput changes up to approximately ±7 %. The test blends in the caffeine concentration range between 2.02 % w/w and 4.02 % w/w met the dose uniformity requirements of the Ph.Eur. 9.0, chapter 2.9.47. Variographic analysis was done to estimate the analytical and sampling errors which yielded values below 0.01 (%w/w)2. The obtained results showed that this chute could also be used in a continuous manufacturing line or other applications with flowing powders.
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Affiliation(s)
- Pedro A Martínez-Cartagena
- Department of Chemistry, University of Puerto Rico at Mayaguez Call Box 9000, Mayaguez, 00680, Puerto Rico
| | - Nobel O Sierra-Vega
- Department of Chemical Engineering, University of Puerto Rico at Mayaguez, Puerto Rico
| | | | - Rafael Méndez
- Department of Chemical Engineering, University of Puerto Rico at Mayaguez, Puerto Rico
| | - Rodolfo J Romañach
- Department of Chemistry, University of Puerto Rico at Mayaguez Call Box 9000, Mayaguez, 00680, Puerto Rico.
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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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Model-Based Scale-Up Methodologies for Pharmaceutical Granulation. Pharmaceutics 2020; 12:pharmaceutics12050453. [PMID: 32423051 PMCID: PMC7284585 DOI: 10.3390/pharmaceutics12050453] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/11/2020] [Revised: 05/09/2020] [Accepted: 05/11/2020] [Indexed: 12/13/2022] Open
Abstract
In the pharmaceutical industry, it is a major challenge to maintain consistent quality of drug products when the batch scale of a process is changed from a laboratory scale to a pilot or commercial scale. Generally, a pharmaceutical manufacturing process involves various unit operations, such as blending, granulation, milling, tableting and coating and the process parameters of a unit operation have significant effects on the quality of the drug product. Depending on the change in batch scale, various process parameters should be strategically controlled to ensure consistent quality attributes of a drug product. In particular, the granulation may be significantly influenced by scale variation as a result of changes in various process parameters and equipment geometry. In this study, model-based scale-up methodologies for pharmaceutical granulation are presented, along with data from various related reports. The first is an engineering-based modeling method that uses dimensionless numbers based on process similarity. The second is a process analytical technology-based modeling method that maintains the desired quality attributes through flexible adjustment of process parameters by monitoring the quality attributes of process products in real time. The third is a physics-based modeling method that involves a process simulation that understands and predicts drug quality through calculation of the behavior of the process using physics related to the process. The applications of these three scale-up methods are summarized according to granulation mechanisms, such as wet granulation and dry granulation. This review shows that these model-based scale-up methodologies provide a systematic process strategy that can ensure the quality of drug products in the pharmaceutical industry.
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