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O'Connor TF, Chatterjee S, Lam J, de la Ossa DHP, Martinez-Peyrat L, Hoefnagel MH, Fisher AC. An examination of process models and model risk frameworks for pharmaceutical manufacturing. Int J Pharm X 2024; 8:100274. [PMID: 39206253 PMCID: PMC11350267 DOI: 10.1016/j.ijpx.2024.100274] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/31/2024] [Revised: 08/05/2024] [Accepted: 08/05/2024] [Indexed: 09/04/2024] Open
Abstract
Process models are a growing tool for pharmaceutical manufacturing process design and control. The Industry 4.0 paradigm promises to increase the amount of data available to understand manufacturing processes. Tools such as Artificial Intelligence (AI) might accelerate process development and allow better predictions of process trajectories. Several examples of process improvements realized through the application of process models have been shown in lyophilization, chromatography, fluid bed drying, bioreactor control, continuous direct compression, and wet granulation. An important consideration of implementing a process model is determining the impact of the model on the quality of the product and the risks associated with model maintenance over the product lifecycle. Several regulatory documents address risk-based considerations for process models. This work discusses existing risk-based frameworks for model validation and lifecycle maintenance that could aid the adoption of process models in pharmaceutical manufacturing. Hypothetical case studies illustrate the implications of applying a model risk framework to facilitate model validation and lifecycle maintenance in the manufacture of pharmaceuticals and biological products.
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Affiliation(s)
- Thomas F. O'Connor
- Food and Drug Administration, Center for Drug Evaluation and Research, Silver Spring, MD 20993, United States
| | - Sharmista Chatterjee
- Food and Drug Administration, Center for Drug Evaluation and Research, Silver Spring, MD 20993, United States
| | - Johnny Lam
- Food and Drug Administration, Center for Biologics Evaluation and Research, Silver Spring, MD 20993, United States
| | | | - Leticia Martinez-Peyrat
- French National Agency for Medicines and Health Products Safety, F-93285, Saint-Denis, France
- Quality Innovation Group (QIG), European Medicines Agency (EMA), Amsterdam, the Netherlands
| | - Marcel H.N. Hoefnagel
- Quality Innovation Group (QIG), European Medicines Agency (EMA), Amsterdam, the Netherlands
- CBG-MEB (Medicines Evaluation Board), Utrecht, the Netherlands
| | - Adam C. Fisher
- Food and Drug Administration, Center for Drug Evaluation and Research, Silver Spring, MD 20993, United States
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Neugebauer P, Zettl M, Moser D, Poms J, Kuchler L, Sacher S. Process analytical technology in Downstream-Processing of Drug Substances- A review. Int J Pharm 2024; 661:124412. [PMID: 38960339 DOI: 10.1016/j.ijpharm.2024.124412] [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: 04/30/2024] [Revised: 06/11/2024] [Accepted: 06/29/2024] [Indexed: 07/05/2024]
Abstract
Process Analytical Technology (PAT) has revolutionized pharmaceutical manufacturing by providing real-time monitoring and control capabilities throughout the production process. This review paper comprehensively examines the application of PAT methodologies specifically in the production of solid active pharmaceutical ingredients (APIs). Beginning with an overview of PAT principles and objectives, the paper explores the integration of advanced analytical techniques such as spectroscopy, imaging modalities and others into solid API substance production processes. Novel developments in in-line monitoring at academic level are also discussed. Emphasis is placed on the role of PAT in ensuring product quality, consistency, and compliance with regulatory requirements. Examples from existing literature illustrate the practical implementation of PAT in solid API substance production, including work-up, crystallization, filtration, and drying processes. The review addresses the quality and reliability of the measurement technologies, aspects of process implementation and handling, the integration of data treatment algorithms and current challenges. Overall, this review provides valuable insights into the transformative impact of PAT on enhancing pharmaceutical manufacturing processes for solid API substances.
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Affiliation(s)
- Peter Neugebauer
- Research Center Pharmaceutical Engineering GmbH, 8010 Graz, Austria; Institute of Process and Particle Engineering, Graz University of Technology, 8010 Graz, Austria
| | - Manuel Zettl
- Research Center Pharmaceutical Engineering GmbH, 8010 Graz, Austria
| | - Daniel Moser
- Research Center Pharmaceutical Engineering GmbH, 8010 Graz, Austria
| | - Johannes Poms
- Research Center Pharmaceutical Engineering GmbH, 8010 Graz, Austria
| | - Lisa Kuchler
- Research Center Pharmaceutical Engineering GmbH, 8010 Graz, Austria
| | - Stephan Sacher
- Research Center Pharmaceutical Engineering GmbH, 8010 Graz, Austria.
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3
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Luong VT, Wang Y, Zurrer T, Scott JA, Selomulya C. Magnesium Citrate Powders from Waste Bitterns via Crystallization and Spray Drying. Ind Eng Chem Res 2022. [DOI: 10.1021/acs.iecr.2c00914] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
Affiliation(s)
- Van Tri Luong
- School of Chemical Engineering, University of New South Wales (UNSW), Sydney, NSW 2052, Australia
- EcoMag Ltd, Sydney, NSW 2067, Australia
| | - Yong Wang
- School of Chemical Engineering, University of New South Wales (UNSW), Sydney, NSW 2052, Australia
| | - Timothy Zurrer
- School of Chemical Engineering, University of New South Wales (UNSW), Sydney, NSW 2052, Australia
| | - Jason Anthony Scott
- School of Chemical Engineering, University of New South Wales (UNSW), Sydney, NSW 2052, Australia
| | - Cordelia Selomulya
- School of Chemical Engineering, University of New South Wales (UNSW), Sydney, NSW 2052, Australia
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Destro F, Barolo M. A review on the modernization of pharmaceutical development and manufacturing - Trends, perspectives, and the role of mathematical modeling. Int J Pharm 2022; 620:121715. [PMID: 35367580 DOI: 10.1016/j.ijpharm.2022.121715] [Citation(s) in RCA: 15] [Impact Index Per Article: 7.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/19/2022] [Revised: 03/23/2022] [Accepted: 03/29/2022] [Indexed: 01/20/2023]
Abstract
Recently, the pharmaceutical industry has been facing several challenges associated to the use of outdated development and manufacturing technologies. The return on investment on research and development has been shrinking, and, at the same time, an alarming number of shortages and recalls for quality concerns has been registered. The pharmaceutical industry has been responding to these issues through a technological modernization of development and manufacturing, under the support of initiatives and activities such as quality-by-design (QbD), process analytical technology, and pharmaceutical emerging technology. In this review, we analyze this modernization trend, with emphasis on the role that mathematical modeling plays within it. We begin by outlining the main socio-economic trends of the pharmaceutical industry, and by highlighting the life-cycle stages of a pharmaceutical product in which technological modernization can help both achieve consistently high product quality and increase return on investment. Then, we review the historical evolution of the pharmaceutical regulatory framework, and we discuss the current state of implementation and future trends of QbD. The pharmaceutical emerging technology is reviewed afterwards, and a discussion on the evolution of QbD into the more effective quality-by-control (QbC) paradigm is presented. Further, we illustrate how mathematical modeling can support the implementation of QbD and QbC across all stages of the pharmaceutical life-cycle. In this respect, we review academic and industrial applications demonstrating the impact of mathematical modeling on three key activities within pharmaceutical development and manufacturing, namely design space description, process monitoring, and active process control. Finally, we discuss some future research opportunities on the use of mathematical modeling in industrial pharmaceutical environments.
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Affiliation(s)
- Francesco Destro
- CAPE-Lab - Computer-Aided Process Engineering Laboratory, Department of Industrial Engineering, University of Padova, via Marzolo 9, 35131 Padova PD, Italy
| | - Massimiliano Barolo
- CAPE-Lab - Computer-Aided Process Engineering Laboratory, Department of Industrial Engineering, University of Padova, via Marzolo 9, 35131 Padova PD, Italy.
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Zhang K, Wang H, Zhong L, Liu L, Huang R, Zhang H, Xu D, Yin W, Li L, Zang H. Evaluation and Monitoring of the API Content of a Portable Near Infrared Instrument Combined with Chemometrics Based on Fluidized Bed Mixing Process. J Pharm Innov 2021. [DOI: 10.1007/s12247-021-09581-2] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/20/2022]
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6
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Soft sensor for real-time estimation of tablet potency in continuous direct compression manufacturing operation. Int J Pharm 2021; 602:120624. [PMID: 33892055 DOI: 10.1016/j.ijpharm.2021.120624] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/17/2020] [Revised: 04/13/2021] [Accepted: 04/17/2021] [Indexed: 11/20/2022]
Abstract
One of the critical quality attributes of the solid oral dosage forms produced in continuous direct compression operations is the tablet potency. A novel soft sensor comprising of a combination of first principle-based and empirical models has been developed to enable real-time monitoring of blend and tablet potency, and concentrations of other excipients at various stream levels along the direct compression line. The soft sensor model has only three adjustable parameters, primarily associated with the equipment design and operation, so the model is product agnostic which is key to enable flexible manufacturing. The estimation accuracy of the soft sensor is demonstrated through a series of real time experiments which include steady state and dynamic transitions of potency during the runs, compared with offline analytically tested tablet cores. The results indicate that the proposed soft sensor can be utilized as a robust tool for real-time monitoring of potency, suggesting an extension of its utilization to higher levels of control. Two potential applications of the soft sensor are: 1. An element of a control strategy for product diversion; 2. A predictive model for advanced process control strategy to minimize the variability in tablet composition.
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Yaginuma K, Tanabe S, Miyano T, Nakagawa H, Suzuki S, Ando S, Kano M. Scale-Free Soft Sensor for Monitoring of Water Content in Fluid Bed Granulation Process. Chem Pharm Bull (Tokyo) 2021; 68:855-863. [PMID: 32879226 DOI: 10.1248/cpb.c20-00315] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/22/2022]
Abstract
In-line monitoring of granule water content during fluid bed granulation is important to control drug product qualities. In this study, a practical scale-free soft sensor to predict water content was proposed to cope with the manufacturing scale changes in drug product development. The proposed method exploits two key ideas to construct a scale-free soft sensor. First, to accommodate the changes in the manufacturing scale, the process parameters (PPs) that are critical to water content at different manufacturing scales were selected as input variables. Second, to construct an accurate statistical model, locally weighted partial least squares regression (LW-PLSR), which can cope with collinearity and nonlinearity, was utilized. The soft sensor was developed using both laboratory (approx. 4 kg) data and pilot (approx. 25 kg) scale data, and the prediction accuracy in the commercial (approx. 100 kg) scale was evaluated based on the assumption that the process was scaled-up from the pilot scale to the commercial scale. The developed soft sensor exhibited a high prediction accuracy, which was equivalent to the commonly used near-infrared (NIR) spectra-based method. The proposed method requires only standard instruments; therefore, it is expected to be a cost-effective alternative to the NIR spectra-based method.
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Affiliation(s)
- Keita Yaginuma
- Formulation Technology Research Laboratories, Pharmaceutical Technology Division, Daiichi Sankyo Co., Ltd.,Department of Systems Science, Kyoto University
| | - Shuichi Tanabe
- Formulation Technology Research Laboratories, Pharmaceutical Technology Division, Daiichi Sankyo Co., Ltd
| | - Takuya Miyano
- Formulation Technology Research Laboratories, Pharmaceutical Technology Division, Daiichi Sankyo Co., Ltd
| | - Hiroshi Nakagawa
- Formulation Technology Research Laboratories, Pharmaceutical Technology Division, Daiichi Sankyo Co., Ltd
| | - Satoshi Suzuki
- Formulation Technology Research Laboratories, Pharmaceutical Technology Division, Daiichi Sankyo Co., Ltd
| | - Shuichi Ando
- Formulation Technology Research Laboratories, Pharmaceutical Technology Division, Daiichi Sankyo Co., Ltd
| | - Manabu Kano
- Department of Systems Science, Kyoto University
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Li LH, Dai YS. Adaptive Soft Sensor Modeling Method for Time-varying and Multi-Dimensional Chemical Processes. JOURNAL OF CHEMICAL ENGINEERING OF JAPAN 2021. [DOI: 10.1252/jcej.20we016] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/24/2023]
Affiliation(s)
- Long-hao Li
- School of Electrical and Electrical Engineering, Shandong University of Technology
| | - Yong-shou Dai
- College of Information and Control Engineering, China University of Petroleum (East China)
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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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Wang Z, Kamyar R, Mehdizadeh H, Pendse PY. Moisture soft sensor for agitated pan dryers using a hybrid modeling approach. Int J Pharm 2020; 586:119518. [DOI: 10.1016/j.ijpharm.2020.119518] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/30/2020] [Revised: 06/01/2020] [Accepted: 06/06/2020] [Indexed: 10/24/2022]
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Zhai Y, Ding X, Jin X, Zhao L. Adaptive LSSVM based iterative prediction method for NOx concentration prediction in coal-fired power plant considering system delay. Appl Soft Comput 2020. [DOI: 10.1016/j.asoc.2020.106070] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/25/2022]
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