1
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Dias RC, Korhonen O, Ketolainen J, Ervasti T, Lopes JA. The effect of process parameters on a continuous blending process monitored in-line by near-infrared spectroscopy. Eur J Pharm Sci 2024:106890. [PMID: 39214317 DOI: 10.1016/j.ejps.2024.106890] [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: 04/27/2024] [Revised: 08/23/2024] [Accepted: 08/24/2024] [Indexed: 09/04/2024]
Abstract
The continuous feeding-mixing system ensures the composition uniformity down to the tableting continuous manufacturing line so that a quality end-product is consistently delivered. Near-infrared spectroscopy (NIRS) enables in-line assessment of the blend's critical quality attributes in real-time. In this study, the effect of total feed rate and impeller speed on the continuous blending process monitored in-line by NIRS was examined by principal component analysis (PCA), ANOVA simultaneous component analysis (ASCA) and partial least squares (PLS) regression. Process data were generated by a factorial experimental design with process parameters and a constant formulation comprised of: 30% (wt/wt) ibuprofen, 67.5% (wt/wt) microcrystalline cellulose, 2% (wt/wt) of sodium starch glycolate and 0.5% (wt/wt) of magnesium stearate. The PCA hinted at the prevalence of impeller speed effect on ibuprofen concentration due to path length variation of the NIR light caused by the fluidized behaviour in the powder blend as a result of high speed ranges (>300 rpm). The ASCA model indicated that while both impeller speed and total feed rate effects were statistically significant (p-value=0.004), the impeller speed was the factor that contributed the most to the spectral variance (55.5%). The PLS regression model for the ibuprofen content resulted in a RMSECV of 1.3% (wt/wt) and showed that impeller speed was yet again the factor that exerted the major influence on spectral variance, owing to its wavelength-dependent effect that prevents common pre-processing techniques from eliminating it across the entire NIR range. The best sample presentation to the NIR probe was achieved at low impeller speed ranges (<600 rpm) and low total feed rates (<15 kg/h), such that it enhanced the PLS model ability to predict the ibuprofen concentration in the blend.
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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
| | - Tuomas Ervasti
- 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
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2
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Bin J, Wang Z, Du W, Zhong K, Chen Z. Simulated Spectral Strategy to Enhance Numerical Tobacco Blending Based on Near-Infrared (NIR) Diffuse Reflectance Spectroscopy and Calibration Transfer. ANAL LETT 2022. [DOI: 10.1080/00032719.2022.2153133] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/12/2022]
Affiliation(s)
- Jun Bin
- Technology Center of China Tobacco Hunan Industrial Co. Ltd, Changsha, China
| | - Zhiguo Wang
- College of Chemistry and Chemical Engineering, Hunan University, Changsha, China
| | - Wen Du
- Technology Center of China Tobacco Hunan Industrial Co. Ltd, Changsha, China
| | - Kejun Zhong
- Technology Center of China Tobacco Hunan Industrial Co. Ltd, Changsha, China
| | - Zengping Chen
- College of Chemistry and Chemical Engineering, Hunan University, Changsha, China
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3
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Muthudoss P, Tewari I, Chi RLR, Young KJ, Ann EYC, Hui DNS, Khai OY, Allada R, Rao M, Shahane S, Das S, Babla I, Mhetre S, Paudel A. Machine Learning-Enabled NIR Spectroscopy in Assessing Powder Blend Uniformity: Clear-Up Disparities and Biases Induced by Physical Artefacts. AAPS PharmSciTech 2022; 23:277. [PMID: 36229571 DOI: 10.1208/s12249-022-02403-9] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/03/2022] [Accepted: 08/22/2022] [Indexed: 11/30/2022] Open
Abstract
NIR spectroscopy is a non-destructive characterization tool for the blend uniformity (BU) assessment. However, NIR spectra of powder blends often contain overlapping physical and chemical information of the samples. Deconvoluting the information related to chemical properties from that associated with the physical effects is one of the major objectives of this work. We achieve this aim in two ways. Firstly, we identified various sources of variability that might affect the BU results. Secondly, we leverage the machine learning-based sophisticated data analytics processes. To accomplish the aforementioned objectives, calibration samples of amlodipine as an active pharmaceutical ingredient (API) with the concentrations ranging between 67 and 133% w/w (dose ~ 3.6% w/w), in powder blends containing excipients, were prepared using a gravimetric approach and assessed using NIR spectroscopic analysis, followed by HPLC measurements. The bias in NIR results was investigated by employing data quality metrics (DQM) and bias-variance decomposition (BVD). To overcome the bias, the clustered regression (non-parametric and linear) was applied. We assessed the model's performance by employing the hold-out and k-fold internal cross-validation (CV). NIR-based blend homogeneity with low mean absolute error and an interval estimates of 0.674 (mean) ± 0.218 (standard deviation) w/w was established. Additionally, bootstrapping-based CV was leveraged as part of the NIR method lifecycle management that demonstrated the mean absolute error (MAE) of BU ± 3.5% w/w and BU ± 1.5% w/w for model generalizability and model transferability, respectively. A workflow integrating machine learning to NIR spectral analysis was established and implemented. Impact of various data learning approaches on NIR spectral data.
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Affiliation(s)
- Prakash Muthudoss
- Oncogen Pharma (Malaysia), Sdn Bhd, 3, Jalan Jururancang U1/21, Hicom-glenmarie Industrial Park, 40150, Shah Alam, Selangor, Malaysia.,A2Z4.0 Research and Analytics Private Limited, Old No:810, New No:62, CTH Road, Behind Lenskart, Thirumullaivoil, Chennai, Tamilnadu, India
| | - Ishan Tewari
- The Machine Learning Company, Beed, Maharashtra, India.,Institute of Technology, Nirma University, Ahmedabad, Gujarat, India
| | - Rayce Lim Rui Chi
- Oncogen Pharma (Malaysia), Sdn Bhd, 3, Jalan Jururancang U1/21, Hicom-glenmarie Industrial Park, 40150, Shah Alam, Selangor, Malaysia
| | - Kwok Jia Young
- Oncogen Pharma (Malaysia), Sdn Bhd, 3, Jalan Jururancang U1/21, Hicom-glenmarie Industrial Park, 40150, Shah Alam, Selangor, Malaysia
| | - Eddy Yii Chung Ann
- Oncogen Pharma (Malaysia), Sdn Bhd, 3, Jalan Jururancang U1/21, Hicom-glenmarie Industrial Park, 40150, Shah Alam, Selangor, Malaysia
| | - Doreen Ng Sean Hui
- Oncogen Pharma (Malaysia), Sdn Bhd, 3, Jalan Jururancang U1/21, Hicom-glenmarie Industrial Park, 40150, Shah Alam, Selangor, Malaysia
| | - Ooi Yee Khai
- Perkin Elmer Sdn Bhd, L2, 2-01, Wisma Academy, Jalan 19/1, Seksyen 19, 46300, Petaling Jaya, Selangor, Malaysia
| | - Ravikiran Allada
- Novugen Pharma (Malaysia), Sdn Bhd, 3, Jalan Jururancang U1/21, Hicom-glenmarie Industrial Park, 40150, Shah Alam, Selangor, Malaysia
| | - Manohar Rao
- PerkinElmer (India) Private Limited, Vayudooth Chambers, 12th floor, Trinity Circle, Mahatma Gandhi Rd, Bengaluru, Karnataka, 560001, India
| | | | - Samir Das
- Oncogen Pharma (Malaysia), Sdn Bhd, 3, Jalan Jururancang U1/21, Hicom-glenmarie Industrial Park, 40150, Shah Alam, Selangor, Malaysia
| | - Irfan Babla
- Oncogen Pharma (Malaysia), Sdn Bhd, 3, Jalan Jururancang U1/21, Hicom-glenmarie Industrial Park, 40150, Shah Alam, Selangor, Malaysia
| | - Sandeep Mhetre
- Oncogen Pharma (Malaysia), Sdn Bhd, 3, Jalan Jururancang U1/21, Hicom-glenmarie Industrial Park, 40150, Shah Alam, Selangor, Malaysia
| | - Amrit Paudel
- Research Center Pharmaceutical Engineering GmbH (RCPE), Inffeldgasse 13, 8010, Graz, Austria. .,Institute of Process and Particle Engineering, Graz University of Technology, Inffeldgasse 13/3, 8010, Graz, Austria.
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4
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A hybrid model for multipoint real time potency observation in continuous direct compression manufacturing operations. Int J Pharm 2021; 613:121385. [PMID: 34919995 DOI: 10.1016/j.ijpharm.2021.121385] [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: 10/19/2021] [Revised: 11/22/2021] [Accepted: 12/10/2021] [Indexed: 11/23/2022]
Abstract
The ongoing transition from batch to continuous manufacturing offers both challenges and opportunities in the field of oral solid dosage form production. In turn, Process Analytical Technology (PAT) offers a path towards the successful deployment of continuous tablet manufacturing in rotary tablet presses. One promising PAT tool for this endeavour is the NIR-derived potency measurement. However, the high degree of noise in the data may hamper the extraction of useful information. For this reason, this work focused on the implementation of an adaptive Kalman filter algorithm that incorporates and reconciles the potency prediction given by one or more NIR probes with those of a semi-mechanistic compartmental model developed for the application at hand. This approach allowed for more robust concentration estimations. Furthermore, it was observed that potency levels in multiple locations in the studied tablet press (including those in the finished tablets) could be appropriately inferred using a single in-line measurement data stream. This methodology thus opens the door to advanced process control applications.
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5
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Achouri IE, Rhoden A, Hudon S, Gosselin R, Simard JS, Abatzoglou N. Non-invasive detection technologies of solid foreign matter and their applications to lyophilized pharmaceutical products: A review. Talanta 2021; 224:121885. [PMID: 33379094 DOI: 10.1016/j.talanta.2020.121885] [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: 05/29/2020] [Revised: 10/15/2020] [Accepted: 10/19/2020] [Indexed: 01/28/2023]
Abstract
Good Manufacturing Practice Regulations, under the Food and Drug Administration (FDA), stipulate that all pharmaceutical products must be free of any contaminants, including, namely, any foreign solid objects. Lyophilization is a common manufacturing method that consists of several steps where foreign materials may enter the product. The presence of unintended particles in freeze drying, which will herein be referred to under the term 'Lyophilization', is of great concern to the authorities responsible for drug safety and effectiveness. In the pharmaceutical industry, presently, the inspection of lyophilized products for foreign matter particulates relies on visual inspection where only the outer surface of the lyophilized cake is visible. This review is motivated by the need for new control strategies for foreign matter (FM) detection in lyophilized products; more specifically, it assesses the reliability of non-destructive technologies for FM detection in dried samples. Emerging technologies applied in other industries, such as various types of spectroscopies and imaging (e.g. chemical, X-ray, ultrasound, thermal and terahertz), are evaluated based on compatibility with the intended application, with identification of the possible technical challenges.
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Affiliation(s)
- Inès E Achouri
- Département de Génie Chimique et de Génie Biotechnologique, Université de Sherbrooke, Sherbrooke, QC, Canada.
| | - Alan Rhoden
- Pfizer USA, 100 route 206 North, Peapack, NJ, 07977, USA
| | - Sophie Hudon
- Pfizer Canada, 17300 route transcanadienne, Kirkland, QC, H9J 2M5, Canada
| | - Ryan Gosselin
- Département de Génie Chimique et de Génie Biotechnologique, Université de Sherbrooke, Sherbrooke, QC, Canada
| | | | - Nicolas Abatzoglou
- Département de Génie Chimique et de Génie Biotechnologique, Université de Sherbrooke, Sherbrooke, QC, Canada
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6
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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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7
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An investigation into the impact of key process variables on the uniformity of powder blends containing a low-dose drug in a gentle-wing high shear mixer. J Drug Deliv Sci Technol 2020. [DOI: 10.1016/j.jddst.2020.102036] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/21/2022]
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8
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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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9
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Pino-Torres C, Maspoch S, Castillo-Felices R, Pérez-Rivera M, Aranda-Bustos M, Peña-Farfal C. Evaluation of NIR and Raman spectroscopies for the quality analytical control of a solid pharmaceutical formulation with three active ingredients. Microchem J 2020. [DOI: 10.1016/j.microc.2019.104576] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/25/2022]
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10
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Pedersen T, Rantanen J, Naelapää K, Skibsted E. Near infrared analysis of pharmaceutical powders with empirical target distribution optimization (ETDO). J Pharm Biomed Anal 2020; 181:113059. [PMID: 31978645 DOI: 10.1016/j.jpba.2019.113059] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/11/2019] [Revised: 12/13/2019] [Accepted: 12/18/2019] [Indexed: 11/28/2022]
Abstract
Near infrared (NIR) spectroscopy is a well-established method for analysis of pharmaceutical products, and especially useful for process monitoring and control of continuous production due to high sample throughput. In this work, a previously established method called empirical target distribution optimization (ETDO) wherein reference sample values using information from model prediction of the calibration data was used as a tool to improve the performance of NIR partial least squares (PLS) models. Model performance was assessed using root mean square error (R2), bias and accuracy in prediction of test samples. A target value selection threshold was tested to assess the ETDO procedure for NIR analysis of powder samples. The amount of specific variation captured by the model was examined and compared for models calibrated with and without ETDO. The results reported in this work suggests that PLS models optimized with ETDO of reference values can provide more specific PLS models for NIR analysis for complex powder mixtures. In addition, the model optimization method could also be applied as a tool to verify the necessary amount of PLS components to produce robust models. The ETDO method presented in this work is an approach that could be applied in the development of continuous blending or tableting processes where robust in-line quantitative analysis of powder samples is needed.
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Affiliation(s)
- Troels Pedersen
- Novo Nordisk A/S, Oral Analytical Development, Novo Nordisk Park, Måløv, Denmark
| | - Jukka Rantanen
- Department of Pharmacy, Faculty of Health and Medical Sciences, University of Copenhagen, Denmark
| | - Kaisa Naelapää
- Novo Nordisk A/S, Oral Formulation Research, Novo Nordisk Park, Måløv, Denmark
| | - Erik Skibsted
- Novo Nordisk A/S, Oral Analytical Development, Novo Nordisk Park, Måløv, Denmark.
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11
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Alvarado-Hernández BB, Sierra-Vega NO, Martínez-Cartagena P, Hormaza M, Méndez R, Romañach RJ. A sampling system for flowing powders based on the theory of sampling. Int J Pharm 2019; 574:118874. [PMID: 31837408 DOI: 10.1016/j.ijpharm.2019.118874] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/10/2019] [Revised: 11/10/2019] [Accepted: 11/12/2019] [Indexed: 12/16/2022]
Abstract
An innovative chute and stream sampler system for flowing powders has been developed and tested. The system is designed for representative sampling based on the principles of the Theory of Sampling (TOS). The sampling system was used in combination with near infrared (NIR) spectroscopy to determine the drug concentration of flowing powders. The system is comprised of three parts: a chute, a stream sampler and a sample collection port. The NIR spectra were obtained at the chute, before entering the sampler, and as the powder flowed through the stream sampler. Samples were also collected from the sample collection port to be analyzed using an ultraviolet-visible (UV-Vis) reference method to determine drug content. A total of eight pharmaceutical powder blends, ranging in concentration from 10.5(%w/w) to 19.5(%w/w) of caffeine, were used to test the sampling system. Materials were characterized before blends were made to provide information on flow properties. The throughput of the system was between 30 and 35 kg/h based on the flow properties of the blend. Drug concentration was effectively determined at the chute and stream sampler. The NIR calibration models showed low root mean squared errors of prediction, 0.65(%w/w) and 0.51(%w/w), for the chute and stream sampler respectively. The NIR calibration models also showed low bias values -0.36(%w/w) at the chute and 0.057(%w/w) at the stream sampler. Significant agreement was obtained between the results from the nondestructive NIR versus the destructive UV-Vis method. Variographic analysis was performed to estimate the analytical and sampling errors when determining the drug concentration at the chute and stream sampler respectively. The variographic analysis showed low analytical errors, 0.103(%w/w)2 and 0.181(%w/w)2 at the chute and stream sampler respectively. The analysis also showed that the minimum practical error (MPE) was around 0.2(%w/w)2 at both chute and stream sampler.
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Affiliation(s)
| | - Nobel O Sierra-Vega
- Department of Chemical Engineering, University of Puerto Rico at Mayaguez, Puerto Rico
| | - Pedro Martínez-Cartagena
- Department of Chemistry, University of Puerto Rico at Mayaguez, Call Box 9000, Mayaguez 00680, Puerto Rico
| | - Manuel Hormaza
- IBS Caribe INC., P.O. Box 8849, San Juan PR 00910, 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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12
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Sierra-Vega NO, Romañach RJ, Méndez R. Feed frame: The last processing step before the tablet compaction in pharmaceutical manufacturing. Int J Pharm 2019; 572:118728. [PMID: 31682965 DOI: 10.1016/j.ijpharm.2019.118728] [Citation(s) in RCA: 16] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/23/2019] [Revised: 09/19/2019] [Accepted: 09/21/2019] [Indexed: 10/25/2022]
Abstract
The feed frame is a force-feeding device used in the die filling process. The die filling process is crucial within pharmaceutical manufacturing to guarantee the critical quality attributes of the tablets. In recent years, interest in this unit has increased because it can affect the properties of the powder blend and tablets, and because of the success in real time monitoring of powder blend uniformity potential for Process Analytical Technology as described in this review. The review focuses on the recent advances in understanding the powder flow behavior inside the feed frame and how the residence time distribution of the powder within the feed frame is affected by the operating conditions and design parameters. Furthermore, this review also highlights the effect of the paddle wheel design and feed frame process parameters on the tablet weight, the principal variable for measuring die filling performance.
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Affiliation(s)
- Nobel O Sierra-Vega
- Department of Chemical Engineering, University of Puerto Rico at Mayaguez, PR 00681, United States
| | - Rodolfo J Romañach
- Department of Chemistry, University of Puerto Rico at Mayaguez, PR 00681 United States
| | - Rafael Méndez
- Department of Chemical Engineering, University of Puerto Rico at Mayaguez, PR 00681, United States.
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13
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Razuc M, Grafia A, Gallo L, Ramírez-Rigo MV, Romañach RJ. Near-infrared spectroscopic applications in pharmaceutical particle technology. Drug Dev Ind Pharm 2019; 45:1565-1589. [DOI: 10.1080/03639045.2019.1641510] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/24/2022]
Affiliation(s)
- M. Razuc
- Instituto de Química del Sur (INQUISUR), Universidad Nacional del Sur (UNS)-CONICET, Bahía Blanca, Argentina
- Departamento de Biología, Bioquímica y Farmacia, Universidad Nacional del Sur (UNS), Bahía Blanca, Argentina
| | - A. Grafia
- Planta Piloto de Ingeniería Química (PLAPIQUI), Universidad Nacional del Sur (UNS)- CONICET, Bahía Blanca, Argentina
| | - L. Gallo
- Departamento de Biología, Bioquímica y Farmacia, Universidad Nacional del Sur (UNS), Bahía Blanca, Argentina
- Planta Piloto de Ingeniería Química (PLAPIQUI), Universidad Nacional del Sur (UNS)- CONICET, Bahía Blanca, Argentina
| | - M. V. Ramírez-Rigo
- Departamento de Biología, Bioquímica y Farmacia, Universidad Nacional del Sur (UNS), Bahía Blanca, Argentina
- Planta Piloto de Ingeniería Química (PLAPIQUI), Universidad Nacional del Sur (UNS)- CONICET, Bahía Blanca, Argentina
| | - R. J. Romañach
- Department of Chemistry, Center for Structured Organic Particulate Systems, University of Puerto Rico – Mayagüez, Mayagüez, Puerto Rico
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14
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Evaluation of an in-line NIR spectroscopic method for the determination of the residence time in a tablet press. Int J Pharm 2019; 565:358-366. [DOI: 10.1016/j.ijpharm.2019.05.006] [Citation(s) in RCA: 18] [Impact Index Per Article: 3.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/31/2019] [Revised: 04/28/2019] [Accepted: 05/04/2019] [Indexed: 10/26/2022]
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15
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Crouter A, Briens L. Methods to Assess Mixing of Pharmaceutical Powders. AAPS PharmSciTech 2019; 20:84. [PMID: 30673887 DOI: 10.1208/s12249-018-1286-7] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/29/2018] [Accepted: 12/18/2018] [Indexed: 11/30/2022] Open
Abstract
The pharmaceutical manufacturing process consists of several steps, each of which must be monitored and controlled to ensure quality standards are met. The level of blending has an impact on the final product quality; therefore, it is important to be able to monitor blending progress and identify an end-point. Currently, the pharmaceutical industry assesses blend content and uniformity through the extraction of samples using thief probes followed by analytical methods, such as spectroscopy, to determine the sample composition. The development of process analytical technologies (PAT) can improve product monitoring with the aim of increasing efficiency, product quality and consistency, and creating a better understanding of the manufacturing process. Ideally, these are inline methods to remove issues related to extractive sampling and allow direct monitoring of the system using various sensors. Many technologies have been investigated, including spectroscopic techniques such as near-infrared spectroscopy, velocimetric techniques that may use tracers, tomographic techniques, and acoustic emissions monitoring. While some techniques have demonstrated potential, many have significant disadvantages including the need for equipment modification, specific requirements of the material, expensive equipment, extensive analysis, the location of the probes may be critical and/or invasive, and lastly, the technique may only be applicable to the development phase. Both the advantages and disadvantages of the technologies should be considered in application to a specific system.
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17
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18
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De Leersnyder F, Peeters E, Djalabi H, Vanhoorne V, Van Snick B, Hong K, Hammond S, Liu AY, Ziemons E, Vervaet C, De Beer T. Development and validation of an in-line NIR spectroscopic method for continuous blend potency determination in the feed frame of a tablet press. J Pharm Biomed Anal 2018; 151:274-283. [DOI: 10.1016/j.jpba.2018.01.032] [Citation(s) in RCA: 51] [Impact Index Per Article: 8.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/11/2017] [Revised: 01/16/2018] [Accepted: 01/17/2018] [Indexed: 10/18/2022]
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19
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Bär D, Debus H, Brzenczek S, Fischer W, Imming P. Determining particle size and water content by near-infrared spectroscopy in the granulation of naproxen sodium. J Pharm Biomed Anal 2018; 151:209-218. [DOI: 10.1016/j.jpba.2018.01.005] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/19/2017] [Accepted: 01/04/2018] [Indexed: 10/18/2022]
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20
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Kim DW, Park JB, Lee SH, Weon KY. Development of a Process Analytical Technology(PAT) method using near-infrared spectroscopy system for evaluating an active coating process for a low-dose drug. J Drug Deliv Sci Technol 2017. [DOI: 10.1016/j.jddst.2017.02.008] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/24/2022]
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Wang H, Barona D, Oladepo S, Williams L, Hoe S, Lechuga-Ballesteros D, Vehring R. Macro-Raman spectroscopy for bulk composition and homogeneity analysis of multi-component pharmaceutical powders. J Pharm Biomed Anal 2017; 141:180-191. [PMID: 28448887 DOI: 10.1016/j.jpba.2017.04.003] [Citation(s) in RCA: 29] [Impact Index Per Article: 4.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/20/2016] [Revised: 03/30/2017] [Accepted: 04/04/2017] [Indexed: 11/25/2022]
Abstract
A new macro-Raman system equipped with a motorized translational sample stage and low-frequency shift capabilities was developed for bulk composition and homogeneity analysis of multi-component pharmaceutical powders. Different sampling methods including single spot and scanning measurement were compared. It was found that increasing sample volumes significantly improved the precision of quantitative composition analysis, especially for poorly mixed powders. The multi-pass cavity of the macro-Raman system increased effective sample volumes by 20 times from the sample volume defined by the collection optics, i.e., from 0.02μL to about 0.4μL. A stochastic model simulating the random sampling process of polydisperse microparticles was used to predict the sampling errors for a specific sample volume. Comparison of fluticasone propionate mass fractions of the commercial products Flixotide® 250 and Seretide® 500 simulated for different sampling volumes with experimentally measured compositions verified that the effective sample volume of a single point macro-Raman measurement in the multi-pass cavity of this instrument was between 0.3μL and 0.5μL. The macro-Raman system was also successfully used for blend uniformity analysis. It was concluded that demixing occurred in the binary mixture of l-leucine and d-mannitol from the observation that the sampling errors indicated by the standard deviations of measured leucine mass fractions increased during mixing, and the standard deviation values were all larger than the theoretical lower limit determined by the simulation. Since sample volume was shown to have a significant impact on measured homogeneity characteristics, it was concluded that powder homogeneity analysis results, i.e., the mean of individual test results and absolute and relative standard deviations, must be presented together with the effective sample volumes of the applied testing techniques for any measurement of powder homogeneity to be fully meaningful.
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Affiliation(s)
- Hui Wang
- Department of Mechanical Engineering, University of Alberta, Edmonton, AB T6G 2G8, Canada
| | - David Barona
- Department of Mechanical Engineering, University of Alberta, Edmonton, AB T6G 2G8, Canada
| | - Sulayman Oladepo
- Department of Mechanical Engineering, University of Alberta, Edmonton, AB T6G 2G8, Canada; Department of Chemistry, King Fahd University of Petroleum and Minerals, Dhahran 31261, Saudi Arabia
| | | | - Susan Hoe
- Pearl Therapeutics Inc., Redwood City, CA, USA
| | | | - Reinhard Vehring
- Department of Mechanical Engineering, University of Alberta, Edmonton, AB T6G 2G8, Canada.
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Park JB, Lee BJ, Kang CY, Repka MA. Process Analytical Quality Control of Tailored Drug Release Formulation Prepared via Hot-Melt Extrusion Technology. J Drug Deliv Sci Technol 2017; 38:51-58. [PMID: 29312469 DOI: 10.1016/j.jddst.2017.01.007] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/20/2022]
Abstract
The objective of the present study was to compare the influence of Eudragit® RS PO and RL PO blends on the release of water-soluble and insoluble drugs from hot-melt extruded formulations. In addition, we aimed to evaluate drug content uniformity and distribution by Fourier transform-infrared (FT-IR) chemical imaging. Theophylline (TP) and carbamazepine (CBZ) were selected as the water-soluble and insoluble model drugs, respectively. Eudragit® RS PO and RL PO were selected as the polymeric matrices. FT-IR chemical imaging clearly demonstrated the content uniformity and distribution for both drugs in the extrudates, which was confirmed by HPLC. Increasing the ratio of Eudragit® RL PO led to an increase in the in vitro drug release, whereas an increase in the ratio of Eudragit® RS PO sustained the drug release for up to 12 h. The hot-melt extrusion of TP and CBZ with varying ratios of Eudragit® RS PO and RL PO can be employed to tailor the drug release profiles. In this study, we demonstrated, for the first time, the use of FT-IR chemical imaging as a process analytical technique to determine the drug content uniformity and distribution. Our data correlated well with the results of HPLC analysis in the study of tailored drug release from the prepared hot-melt extruded formulation.
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Affiliation(s)
- Jun-Bom Park
- College of Pharmacy, Sahmyook University, Seoul, Republic of Korea
| | - Beom-Jin Lee
- College of Pharmacy, Ajou University, Suwon, Republic of Korea
| | - Chin-Yang Kang
- College of Pharmacy, Sahmyook University, Seoul, Republic of Korea
| | - Michael A Repka
- Department of Pharmaceutics & Drug Delivery, University of Mississippi, University, MS, USA.,Pii Center for Pharmaceutical Technology, School of Pharmacy, University of Mississippi, University, MS, USA
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