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Sripada SA, Hosseini M, Ramesh S, Wang J, Ritola K, Menegatti S, Daniele MA. Advances and opportunities in process analytical technologies for viral vector manufacturing. Biotechnol Adv 2024; 74:108391. [PMID: 38848795 DOI: 10.1016/j.biotechadv.2024.108391] [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: 11/14/2023] [Revised: 03/14/2024] [Accepted: 05/29/2024] [Indexed: 06/09/2024]
Abstract
Viral vectors are an emerging, exciting class of biologics whose application in vaccines, oncology, and gene therapy has grown exponentially in recent years. Following first regulatory approval, this class of therapeutics has been vigorously pursued to treat monogenic disorders including orphan diseases, entering hundreds of new products into pipelines. Viral vector manufacturing supporting clinical efforts has spurred the introduction of a broad swath of analytical techniques dedicated to assessing the diverse and evolving panel of Critical Quality Attributes (CQAs) of these products. Herein, we provide an overview of the current state of analytics enabling measurement of CQAs such as capsid and vector identities, product titer, transduction efficiency, impurity clearance etc. We highlight orthogonal methods and discuss the advantages and limitations of these techniques while evaluating their adaptation as process analytical technologies. Finally, we identify gaps and propose opportunities in enabling existing technologies for real-time monitoring from hardware, software, and data analysis viewpoints for technology development within viral vector biomanufacturing.
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Affiliation(s)
- Sobhana A Sripada
- Department of Chemical and Biomolecular Engineering, North Carolina State University, 911 Partners Way, Raleigh, NC, 27695, USA
| | - Mahshid Hosseini
- Joint Department of Biomedical Engineering, North Carolina State University, and University of North Carolina, Chapel Hill, 911 Oval Dr., Raleigh, NC 27695, USA
| | - Srivatsan Ramesh
- Department of Chemical and Biomolecular Engineering, North Carolina State University, 911 Partners Way, Raleigh, NC, 27695, USA
| | - Junhyeong Wang
- Joint Department of Biomedical Engineering, North Carolina State University, and University of North Carolina, Chapel Hill, 911 Oval Dr., Raleigh, NC 27695, USA
| | - Kimberly Ritola
- North Carolina Viral Vector Initiative in Research and Learning (NC-VVIRAL), North Carolina State University, 890 Oval Dr, Raleigh, NC 27695, USA; Neuroscience Center, Brain Initiative Neurotools Vector Core, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, USA
| | - Stefano Menegatti
- Department of Chemical and Biomolecular Engineering, North Carolina State University, 911 Partners Way, Raleigh, NC, 27695, USA; North Carolina Viral Vector Initiative in Research and Learning (NC-VVIRAL), North Carolina State University, 890 Oval Dr, Raleigh, NC 27695, USA; Biomanufacturing Training and Education Center, North Carolina State University, 890 Main Campus Dr, Raleigh, NC 27695, USA.
| | - Michael A Daniele
- Joint Department of Biomedical Engineering, North Carolina State University, and University of North Carolina, Chapel Hill, 911 Oval Dr., Raleigh, NC 27695, USA; North Carolina Viral Vector Initiative in Research and Learning (NC-VVIRAL), North Carolina State University, 890 Oval Dr, Raleigh, NC 27695, USA; Department of Electrical and Computer Engineering, North Carolina State University, 890 Oval Dr, Raleigh, NC 27695, USA.
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Zheng C, Li L, Nitert BJ, Govender N, Chamberlain T, Zhang L, Wu CY. Investigation of granular dynamics in a continuous blender using the GPU-enhanced discrete element method. POWDER TECHNOL 2022. [DOI: 10.1016/j.powtec.2022.117968] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/31/2022]
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3
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Sacher S, Poms J, Rehrl J, Khinast JG. PAT implementation for advanced process control in solid dosage manufacturing - A practical guide. Int J Pharm 2021; 613:121408. [PMID: 34952147 DOI: 10.1016/j.ijpharm.2021.121408] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/04/2021] [Revised: 12/10/2021] [Accepted: 12/16/2021] [Indexed: 01/14/2023]
Abstract
The implementation of continuous pharmaceutical manufacturing requires advanced control strategies rather than traditional end product testing or an operation within a small range of controlled parameters. A high level of automation based on process models and hierarchical control concepts is desired. The relevant tools that have been developed and successfully tested in academic and industrial environments in recent years are now ready for utilization on the commercial scale. To date, the focus in Process Analytical Technology (PAT) has mainly been on achieving process understanding and quality control with the ultimate goal of real-time release testing (RTRT). This work describes the workflow for the development of an in-line monitoring strategy to support PAT-based real-time control actions and its integration into solid dosage manufacturing. All stages are discussed in this paper, from process analysis and definition of the monitoring task to technology assessment and selection, its process integration and the development of data acquisition.
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Affiliation(s)
- Stephan Sacher
- Research Center Pharmaceutical Engineering GmbH, Inffeldgasse 13/2, 8010 Graz, Austria.
| | - Johannes Poms
- Research Center Pharmaceutical Engineering GmbH, Inffeldgasse 13/2, 8010 Graz, Austria
| | - Jakob Rehrl
- Research Center Pharmaceutical Engineering GmbH, Inffeldgasse 13/2, 8010 Graz, Austria
| | - Johannes G Khinast
- Research Center Pharmaceutical Engineering GmbH, Inffeldgasse 13/2, 8010 Graz, Austria; Institute for Process and Particle Engineering, Graz University of Technology, Inffeldgasse 13/3, 8010 Graz, Austria
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4
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Lou Z, Wang Y, Lu S, Sun P. Minimalist module analysis for fault detection and localization. Sci Rep 2021; 11:23571. [PMID: 34876575 PMCID: PMC8651725 DOI: 10.1038/s41598-021-02676-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/12/2021] [Accepted: 11/19/2021] [Indexed: 11/09/2022] Open
Abstract
Traditional multivariate statistical-based process monitoring (MSPM) methods are effective data-driven approaches for monitoring large-scale industrial processes, but have a shortcoming in handling the redundant correlations between process variables. To address this shortcoming, this study proposes a new MSPM method called minimalist module analysis (MMA). MMA divides process data into several different minimalist modules and one more independent module. All variables in the minimalist module are strongly correlated, and no redundant variables exist; therefore, the extracted feature components in one minimalist module will not be disturbed by noise from the other modules. This study also proposes new monitoring indices and a fault localization strategy for MMA, and simulation tests demonstrate that MMA achieves superior performance in fault detection and localization.
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Affiliation(s)
- Zhijiang Lou
- Institute of Intelligence Science and Engineering, Shenzhen Polytechnic, Shenzhen, 518055, China
| | - Youqing Wang
- College of Electrical Engineering and Automation, Shandong University of Science and Technology, Qingdao, 266590, China
| | - Shan Lu
- Institute of Intelligence Science and Engineering, Shenzhen Polytechnic, Shenzhen, 518055, China.
| | - Pei Sun
- Institute of Intelligence Science and Engineering, Shenzhen Polytechnic, Shenzhen, 518055, China
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Vo AQ, Kutz G, He H, Narala S, Bandari S, Repka MA. Continuous Manufacturing of Ketoprofen Delayed Release Pellets Using Melt Extrusion Technology: Application of QbD Design Space, Inline Near Infrared, and Inline Pellet Size Analysis. J Pharm Sci 2020; 109:3598-3607. [PMID: 32916139 PMCID: PMC7680423 DOI: 10.1016/j.xphs.2020.09.007] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/09/2020] [Revised: 08/27/2020] [Accepted: 09/03/2020] [Indexed: 11/27/2022]
Abstract
Delayed-release dosage forms are mainly manufactured as batch processes and include coated tablets, pellets, or particles with gastric resistant polymers. Authors propose a novel approach using the hot-melt extrusion technique to prepare delayed release dosage forms via a continuous manufacturing process, a new trend in the pharmaceutical industry. A full factorial design was employed to correlate input variables, including stearic acid (SA) content, drug content, and pellet size with drug release properties of the pellets. PLS fit method suitably elaborated the relationship between input and output variables with reasonably good fit and goodness of prediction. All three input factors influenced drug release in enzyme-free simulated gastric fluid (SGF) after 120 min; however, SA content did not significantly affect drug dissolution in the enzyme-free simulated intestinal fluid (SIF). An optimized formulation and design space were determined by overlaying multiple contours established from regression equations. The continuous manufacturing process was successfully monitored using inline near-infrared (NIR) and inline particle size analysis, with drug load and pellet size being well-controlled within the design space. The obtained pellets released less than 5% after 120 min in SGF and more than 85% and 95% after 30 min and 45 min, respectively, after switching to SIF.
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Affiliation(s)
- Anh Q Vo
- School of Pharmacy, University of Mississippi, University, MS 38677, USA; Department of Physical Chemistry and Physics, Hanoi University of Pharmacy, Hanoi, Vietnam
| | - Gerd Kutz
- OWL University of Applied Sciences and Arts, Pharmaceutical Engineering, Lemgo, Germany
| | - Herman He
- Thermo Fisher Scientific, Tewksbury, MA 01876, USA
| | - Sagar Narala
- School of Pharmacy, University of Mississippi, University, MS 38677, USA
| | - Suresh Bandari
- School of Pharmacy, University of Mississippi, University, MS 38677, USA
| | - Michael A Repka
- School of Pharmacy, University of Mississippi, University, MS 38677, USA; Pii Center for Pharmaceutical Technology, The University of Mississippi, University, MS 38677, USA.
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Boehling P, Jacevic D, Detobel F, Holman J, Wareham L, Metzger M, Khinast JG. Validating a Numerical Simulation of the ConsiGma(R) Coater. AAPS PharmSciTech 2020; 22:10. [PMID: 33244725 PMCID: PMC7691303 DOI: 10.1208/s12249-020-01841-7] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/01/2020] [Accepted: 10/07/2020] [Indexed: 11/30/2022] Open
Abstract
Continuous manufacturing is increasingly used in the pharmaceutical industry, as it promises to deliver better product quality while simultaneously increasing production flexibility. GEA developed a semi-continuous tablet coater which can be integrated into a continuous tableting line, accelerating the switch from traditional batch production to the continuous mode of operation. The latter offers certain advantages over batch production, e.g., operational flexibility, increased process/product quality, and decreased cost. However, process understanding is the key element for process control. In this regard, computational tools can improve the fundamental understanding and process performance, especially those related to new processes, such as continuous tablet coating where process mechanics remain unclear. The discrete element method (DEM) and computational fluid dynamics (CFD) are two methods that allow transition from empirical process design to a mechanistic understanding of the individual process units. The developed coupling model allows to track the heat, mass, and momentum exchange between the tablet and fluid phase. The goal of this work was to develop and validate a high-fidelity CFD-DEM simulation model of the tablet coating process in the GEA ConsiGma® coater. After the model development, simulation results for the tablet movement, coating quality, and heat and mass transfer during the coating process were validated and compared to the experimental outcomes. The experimental and simulation results agreed well on all accounts measured, indicating that the model can be used in further studies to investigate the operating space of the continuous tablet coating process.
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Fezai R, Abodayeh K, Mansouri M, Nounou H, Nounou M. Fault diagnosis of biological systems using improved machine learning technique. INT J MACH LEARN CYB 2020. [DOI: 10.1007/s13042-020-01184-6] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/14/2023]
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Towards a novel continuous HME-Tableting line: Process development and control concept. Eur J Pharm Sci 2020; 142:105097. [PMID: 31648048 DOI: 10.1016/j.ejps.2019.105097] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/31/2019] [Revised: 09/25/2019] [Accepted: 09/30/2019] [Indexed: 11/23/2022]
Abstract
The objective of this study was to develop a novel closed-loop controlled continuous tablet manufacturing line, which first uses hot melt extrusion (HME) to produce pellets based on API and a polymer matrix. Such systems can be used to make complex pharmaceutical formulations, e.g., amorphous solid dispersions of poorly soluble APIs. The pellets are then fed to a direct compaction (DC) line blended with an external phase and tableted continuously. Fully-automated processing requires advanced control strategies, e.g., for reacting to raw material variations and process events. While many tools have been proposed for in-line process monitoring and real-time data acquisition, establishing real-time automated feedback control based on in-process control strategies remains a challenge. Control loops were implemented to assess the quality attributes of intermediates and product and to coordinate the mass flow rate between the unit operations. Feedback control for the blend concentration, strand temperature and pellet thickness was accomplished via proportional integral derivative (PID) controllers. The tablet press hopper level was controlled using a model predictive controller. To control the mass flow rates in all unit operations, several concepts were developed, with the tablet press, the extruder or none assigned to be the master unit of the line, and compared via the simulation.
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Goldrick S, Sandner V, Cheeks M, Turner R, Farid SS, McCreath G, Glassey J. Multivariate Data Analysis Methodology to Solve Data Challenges Related to Scale‐Up Model Validation and Missing Data on a Micro‐Bioreactor System. Biotechnol J 2019; 15:e1800684. [DOI: 10.1002/biot.201800684] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/13/2018] [Revised: 09/26/2019] [Indexed: 11/11/2022]
Affiliation(s)
- Stephen Goldrick
- The Advanced Centre for Biochemical EngineeringDepartment of Biochemical EngineeringUniversity College London Gower Street London WC1E 6BT UK
- Cell Sciences, Biopharmaceutical DevelopmentMedImmune Cambridge CB1 6GH UK
| | - Viktor Sandner
- FUJIFILM Diosynth BiotechnologiesProcess Design and Data Science Belasis Ave, Stockton‐on‐Tees Billingham TS23 1LH UK
| | - Matthew Cheeks
- Cell Sciences, Biopharmaceutical DevelopmentMedImmune Cambridge CB1 6GH UK
| | - Richard Turner
- Cell Sciences, Biopharmaceutical DevelopmentMedImmune Cambridge CB1 6GH UK
| | - Suzanne S. Farid
- The Advanced Centre for Biochemical EngineeringDepartment of Biochemical EngineeringUniversity College London Gower Street London WC1E 6BT UK
| | - Graham McCreath
- FUJIFILM Diosynth BiotechnologiesProcess Design and Data Science Belasis Ave, Stockton‐on‐Tees Billingham TS23 1LH UK
| | - Jarka Glassey
- School of EngineeringNewcastle University Newcastle upon Tyne NE1 7RU UK
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Dahlgren G, Tajarobi P, Simone E, Ricart B, Melnick J, Puri V, Stanton C, Bajwa G. Continuous Twin Screw Wet Granulation and Drying-Control Strategy for Drug Product Manufacturing. J Pharm Sci 2019; 108:3502-3514. [PMID: 31276686 DOI: 10.1016/j.xphs.2019.06.023] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/03/2018] [Revised: 06/16/2019] [Accepted: 06/26/2019] [Indexed: 01/28/2023]
Abstract
The use of continuous manufacturing has been increasing within the pharmaceutical industry over the last few years. Continuous direct compression has been the focus of publications on the topic to date. The use of wet granulation can improve segregation resistance, uniformity, enhance density, and flow properties for improved tabletability, or improve stability of products that cannot be manufactured by using a direction compression process. This article focuses on development of appropriate control strategies for continuous wet granulation (especially twin screw wet granulation) through equipment design, material properties and manufacturing process along with areas where additional understanding is required. The article also discusses the use of process analytical technologies as part of the control and automation approach to ensure a higher assurance of product quality. Increased understanding of continuous wet granulation should result in increased utilization of the technique, thereby allowing for an increase in diversity of products manufactured by continuous manufacturing and the benefits that comes with a more complex process such as wet granulation compared with direct compression process.
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Affiliation(s)
| | | | - Eric Simone
- Agios Pharmaceuticals Inc., Cambridge, Massachusetts 02139
| | | | | | - Vibha Puri
- Genentech, Inc., San Francisco, California 94080
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Long J, Li T, Yang M, Hu G, Zhong W. Hybrid Strategy Integrating Variable Selection and a Neural Network for Fluid Catalytic Cracking Modeling. Ind Eng Chem Res 2018. [DOI: 10.1021/acs.iecr.8b04821] [Citation(s) in RCA: 15] [Impact Index Per Article: 2.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
Affiliation(s)
- Jian Long
- Key Laboratory of Advanced Control and Optimization for Chemical Processes, Ministry of Education, East China University of Science and Technology, Shanghai 200237, China
| | - Tianyue Li
- Key Laboratory of Advanced Control and Optimization for Chemical Processes, Ministry of Education, East China University of Science and Technology, Shanghai 200237, China
| | - Minglei Yang
- Key Laboratory of Advanced Control and Optimization for Chemical Processes, Ministry of Education, East China University of Science and Technology, Shanghai 200237, China
| | - Guihua Hu
- Key Laboratory of Advanced Control and Optimization for Chemical Processes, Ministry of Education, East China University of Science and Technology, Shanghai 200237, China
| | - Weimin Zhong
- Key Laboratory of Advanced Control and Optimization for Chemical Processes, Ministry of Education, East China University of Science and Technology, Shanghai 200237, China
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