Difference between revisions of "Journal:A systematic framework for data management and integration in a continuous pharmaceutical manufacturing processing line"

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For decades, the pharmaceutical industry has been dominated by a batch-based manufacturing process. This traditional method can lead to increased inefficiency and delay in time-to-market of product, as well as the possibility of errors and defects. Continuous manufacturing in contrast, is a newer technology in pharmaceutical manufacturing that can enable faster, cleaner, and more economical production. The U.S. [[Food and Drug Administration]] (FDA) has recognized the advancement of this manufacturing mode and has been encouraging its development as part of the FDA's "quality by design" (QbD) paradigm.<ref name="LeeModern15">{{cite journal |title=Modernizing Pharmaceutical Manufacturing: from Batch to Continuous Production |journal=Journal of Pharmaceutical Innovation |author=Lee, S.L.; O'Connor, T.F.; Yang, X. et al. |volume=10 |issue=3 |pages=191–99 |year=2015 |doi=10.1007/s12247-015-9215-8}}</ref> The application of process analytical technology (PAT) and control systems is a very useful effort to gain improved science-based process understanding.<ref name="FDAGuidance04">{{cite web |url=https://www.fda.gov/downloads/Drugs/GuidanceComplianceRegulatoryInformation/Guidances/ucm070305.pdf |format=PDF |title=Guidance for Industry PAT—A Framework for Innovative Pharmaceutical Development, Manufacturing, and Quality Assurance |publisher=U.S. Food and Drug Administration |date=September 2004 |accessdate=15 May 2018}}</ref> One of the advantages of the continuous pharmaceutical manufacturing process is that it provides the ability to monitor and rectify data/product in real time. Therefore, it has been considered a data rich manufacturing process. However, in the face of the enormous amount of data generated from a continuous process, a sophisticated data management system is required for the integration of analytical tools to the control systems, as well as the off-line measurement systems.
For decades, the pharmaceutical industry has been dominated by a batch-based manufacturing process. This traditional method can lead to increased inefficiency and delay in time-to-market of product, as well as the possibility of errors and defects. Continuous manufacturing in contrast, is a newer technology in pharmaceutical manufacturing that can enable faster, cleaner, and more economical production. The U.S. [[Food and Drug Administration]] (FDA) has recognized the advancement of this manufacturing mode and has been encouraging its development as part of the FDA's "quality by design" (QbD) paradigm.<ref name="LeeModern15">{{cite journal |title=Modernizing Pharmaceutical Manufacturing: from Batch to Continuous Production |journal=Journal of Pharmaceutical Innovation |author=Lee, S.L.; O'Connor, T.F.; Yang, X. et al. |volume=10 |issue=3 |pages=191–99 |year=2015 |doi=10.1007/s12247-015-9215-8}}</ref> The application of process analytical technology (PAT) and control systems is a very useful effort to gain improved science-based process understanding.<ref name="FDAGuidance04">{{cite web |url=https://www.fda.gov/downloads/Drugs/GuidanceComplianceRegulatoryInformation/Guidances/ucm070305.pdf |format=PDF |title=Guidance for Industry PAT—A Framework for Innovative Pharmaceutical Development, Manufacturing, and Quality Assurance |publisher=U.S. Food and Drug Administration |date=September 2004 |accessdate=15 May 2018}}</ref> One of the advantages of the continuous pharmaceutical manufacturing process is that it provides the ability to monitor and rectify data/product in real time. Therefore, it has been considered a data rich manufacturing process. However, in the face of the enormous amount of data generated from a continuous process, a sophisticated data management system is required for the integration of analytical tools to the control systems, as well as the off-line measurement systems.


In order to represent, manage, and analyze a large amount of complex information, an ontological informatics infrastructure will be necessary for process and product development in the pharmaceutical industry.<ref name="VenkatasubramanianOnto06">{{cite journal |title=Ontological informatics infrastructure for pharmaceutical product development and manufacturing |journal=Computers & Chemical Engineering |author=Venkatasubramanian, V.; Zhao, C.; Joglekar, G. et al. |volume=30 |issue=10–12 |pages=1482–96 |year=2006 |doi=10.1016/j.compchemeng.2006.05.036}}</ref> The ANSI/ISA-88 batch control standard<ref name="ISA88-01">{{cite web |url=https://www.isa.org/store/ansi/isa-880001-2010-batch-control-part-1-models-and-terminology/116649 |title=ANSI/ISA-88.00.01-2010 Batch Control Part 1: Models and Terminology |publisher=International Society of Automation |date=2010}}</ref><ref name="ISA88-02">{{cite web |url=https://www.isa.org/store/isa-880002-2001-batch-control-part-2-data-structures-and-guidelines-for-languages/116687 |title=ISA-88.00.02-2001 Batch Control Part 2: Data Structures and Guidelines for Languages |publisher=International Society of Automation |date=2001}}</ref><ref name="ISA88-03">{{cite web |url=hhttps://www.isa.org/store/ansi/isa-880003-2003-batch-control-part-3-general-and-site-recipe-models-and-representation-downloadable/118279 |title=ANSI/ISA-88.00.03-2003 Batch Control Part 3: General and Site Recipe Models and Representation |publisher=International Society of Automation |date=2003}}</ref> is an international standard addressing batch process control, which has already been implemented in other industries for years. Therefore, adapting this industrial standard into pharmaceutical manufacturing could provide a design philosophy for describing equipment, material, personnel, and reference models.<ref name="DorresteijnCurrent97">{{cite journal |title=Current good manufacturing practice in plant automation of biological production processes |journal=Cytotechnology |author=Dorresteijn, R.C.; Wieten, G.; van Santen, P.T. et al. |volume=23 |issue=1–3 |pages=19–28 |year=1997 |doi=10.1023/A:1007923820231 |pmid=22358517 |pmc=PMC3449867}}</ref><ref name="Verwater-LukszoAPract98">{{cite journal |title=A practical approach to recipe improvement and optimization in the batch processing industry |journal=Computers in Industry |author=Verwater-Lukszo, Z. |volume=36 |issue=3 |pages=279–300 |year=1998 |doi=10.1016/S0166-3615(98)00078-5}}</ref> This recipe-based execution could work as a hierarchical data structure for the assembly of data from the control system, process analytical technology (PAT) tools, and off-line measurement devices. The combination of the ANSI/ISA-88 recipe model and the data warehouse [[informatics]] strategy<ref name="KimballTheData13">{{cite book |title=The Data Warehouse Toolkit: The Definitive Guide to Dimensional Modeling |author=Kimball, R.; Ross, M. |publisher=John Wiley & Sons, Inc |pages=600 |year=2013 |isbn=9781118530801}}</ref> leads to the “recipe data warehouse” strategy.<ref name="FermierBringing12">{{cite journal |title=Bringing New Products to Market Faster |journal=Pharmaceutical Engineering |author=Fermier, A.; McKenzie, P.; Murphy, T. et al. |volume=32 |issue=5 |pages=1–8 |year=2012 |url=https://www.ispe.org/pharmaceutical-engineering-magazine/2012-sept-oct}}</ref> This strategy could provide the possibility of data management across multiple execution systems, as well as the ability for data analysis and visualization.
In order to represent, manage, and analyze a large amount of complex information, an ontological informatics infrastructure will be necessary for process and product development in the pharmaceutical industry.<ref name="VenkatasubramanianOnto06">{{cite journal |title=Ontological informatics infrastructure for pharmaceutical product development and manufacturing |journal=Computers & Chemical Engineering |author=Venkatasubramanian, V.; Zhao, C.; Joglekar, G. et al. |volume=30 |issue=10–12 |pages=1482–96 |year=2006 |doi=10.1016/j.compchemeng.2006.05.036}}</ref> The ANSI/ISA-88 batch control standard<ref name="ISA88-01">{{cite web |url=https://www.isa.org/store/ansi/isa-880001-2010-batch-control-part-1-models-and-terminology/116649 |title=ANSI/ISA-88.00.01-2010 Batch Control Part 1: Models and Terminology |publisher=International Society of Automation |date=2010}}</ref><ref name="ISA88-02">{{cite web |url=https://www.isa.org/store/isa-880002-2001-batch-control-part-2-data-structures-and-guidelines-for-languages/116687 |title=ISA-88.00.02-2001 Batch Control Part 2: Data Structures and Guidelines for Languages |publisher=International Society of Automation |date=2001}}</ref><ref name="ISA88-03">{{cite web |url=https://www.isa.org/store/ansi/isa-880003-2003-batch-control-part-3-general-and-site-recipe-models-and-representation-downloadable/118279 |title=ANSI/ISA-88.00.03-2003 Batch Control Part 3: General and Site Recipe Models and Representation |publisher=International Society of Automation |date=2003}}</ref> is an international standard addressing batch process control, which has already been implemented in other industries for years. Therefore, adapting this industrial standard into pharmaceutical manufacturing could provide a design philosophy for describing equipment, material, personnel, and reference models.<ref name="DorresteijnCurrent97">{{cite journal |title=Current good manufacturing practice in plant automation of biological production processes |journal=Cytotechnology |author=Dorresteijn, R.C.; Wieten, G.; van Santen, P.T. et al. |volume=23 |issue=1–3 |pages=19–28 |year=1997 |doi=10.1023/A:1007923820231 |pmid=22358517 |pmc=PMC3449867}}</ref><ref name="Verwater-LukszoAPract98">{{cite journal |title=A practical approach to recipe improvement and optimization in the batch processing industry |journal=Computers in Industry |author=Verwater-Lukszo, Z. |volume=36 |issue=3 |pages=279–300 |year=1998 |doi=10.1016/S0166-3615(98)00078-5}}</ref> This recipe-based execution could work as a hierarchical data structure for the assembly of data from the control system, process analytical technology (PAT) tools, and off-line measurement devices. The combination of the ANSI/ISA-88 recipe model and the data warehouse [[informatics]] strategy<ref name="KimballTheData13">{{cite book |title=The Data Warehouse Toolkit: The Definitive Guide to Dimensional Modeling |author=Kimball, R.; Ross, M. |publisher=John Wiley & Sons, Inc |pages=600 |year=2013 |isbn=9781118530801}}</ref> leads to the “recipe data warehouse” strategy.<ref name="FermierBringing12">{{cite journal |title=Bringing New Products to Market Faster |journal=Pharmaceutical Engineering |author=Fermier, A.; McKenzie, P.; Murphy, T. et al. |volume=32 |issue=5 |pages=1–8 |year=2012 |url=https://www.ispe.org/pharmaceutical-engineering-magazine/2012-sept-oct}}</ref> This strategy could provide the possibility of data management across multiple execution systems, as well as the ability for data analysis and visualization.
 
Applying the “recipe data warehouse” strategy to continuous pharmaceutical manufacturing provides a possible approach to handle the data produced via analytical experimentation and process recipe execution. Not only the data itself but also the context of the data can be well-captured and saved for documentation and reporting. However, unlike batch operations, continuous manufacturing is a complicated process containing a series of interconnected unit operations with multiple execution layers. Therefore, it is quite challenging to integrate data across the whole system while maintaining an accurate representation of the complex manufacturing processes.
 
In addition to data collection and integration of the continuous manufacturing plant, the highly variable and unpredictable properties of raw materials are necessary to capture and store in a database because they could have an impact on the quality of the product.<ref name="IerapetritouPerspec16">{{cite journal |title=Perspectives on the continuous manufacturing of powder‐based pharmaceutical processes |journal=AIChE Journal |author=Ierapetritou, M.; Muzzio, F.; Reklaitis, G. |volume=62 |issue=6 |pages=1846-1862 |year=2016 |doi=10.1002/aic.15210}}</ref> These properties of relevance to continuous manufacturing are measured via many different analytical methods, including FT4 powder characterization<ref name="VasilenkoShear11">{{cite journal |title=Shear and flow behavior of pharmaceutical blends — Method comparison study |journal=Powder Technology |author=Vasilenko, A.; Glasser, B.J.; Muzzio, F.J. |volume=208 |issue=3 |pages=628-636 |year=2011 |doi=10.1016/j.powtec.2010.12.031}}</ref>, particle size analysis<ref name="RamachandranAQuant12">{{cite journal |title=A quantitative assessment of the influence of primary particle size polydispersity on granule inhomogeneity |journal=Chemical Engineering Science |author=Ramachandran, R.; Ansari, M.A.; Chaudhury, A. et al. |volume=71 |pages=104–110 |year=2012 |doi=10.1016/j.ces.2011.11.045}}</ref>, and the Washburn technique.<ref name="LlusaMeasur10">{{cite journal |title=Measuring the hydrophobicity of lubricated blends of pharmaceutical excipients |journal=Powder Technology |author=Llusa, M.; Levin, M.; Snee, R.D. et al. |volume=198 |issue=1 |pages=101–107 |year=2010 |doi=10.1016/j.powtec.2009.10.021}}</ref> The establishment of a raw material property database could be achieved by the “recipe data warehouse” strategy. Nevertheless, compared to the computer-aided manufacturing used in the production process, the degree of automation would vary significantly in different analytical platforms. Therefore, an easily accessible recipe management system would be highly desirable in characterization laboratories.


==References==
==References==

Revision as of 19:56, 15 May 2018

Full article title A systematic framework for data management and integration in a continuous pharmaceutical manufacturing processing line
Journal Process
Author(s) Cao, Huiyi; Mushnoori, Srinivas; Higgins, Barry; Kollipara, Chandrasekhar; Fernier, Adam;
Hausner, Douglas; Jha, Shantenu; Singh, Ravendra; Ierapetritou, Marianthi; Ramachandran, Rohit
Author affiliation(s) Rutgers University, Johnson & Johnson, Janssen Research & Development
Primary contact Email: rohit dot r at rutgers dot edu; Tel.: +1-848-445-6278
Year published 2018
Volume and issue 6(5)
Page(s) 53
DOI 10.3390/pr6050053
ISSN 2227-9717
Distribution license Creative Commons Attribution 4.0 International
Website http://www.mdpi.com/2227-9717/6/5/53/htm
Download http://www.mdpi.com/2227-9717/6/5/53/pdf (PDF)

Abstract

As the pharmaceutical industry seeks more efficient methods for the production of higher value therapeutics, the associated data analysis, data visualization, and predictive modeling require dependable data origination, management, transfer, and integration. As a result, the management and integration of data in a consistent, organized, and reliable manner is a big challenge for the pharmaceutical industry. In this work, an ontological information infrastructure is developed to integrate data within manufacturing plants and analytical laboratories. The ANSI/ISA-88 batch control standard has been adapted in this study to deliver a well-defined data structure that will improve the data communication inside the system architecture for continuous processing. All the detailed information of the lab-based experiment and process manufacturing—including equipment, samples, and parameters—are documented in the recipe. This recipe model is implemented into a process control system (PCS), data historian, and electronic laboratory notebook (ELN). Data existing in the recipe can be eventually exported from this system to cloud storage, which could provide a reliable and consistent data source for data visualization, data analysis, or process modeling.

Keywords: data management, continuous pharmaceutical manufacturing, ISA-88, recipe, OSI Process Information (PI)

Introduction

For decades, the pharmaceutical industry has been dominated by a batch-based manufacturing process. This traditional method can lead to increased inefficiency and delay in time-to-market of product, as well as the possibility of errors and defects. Continuous manufacturing in contrast, is a newer technology in pharmaceutical manufacturing that can enable faster, cleaner, and more economical production. The U.S. Food and Drug Administration (FDA) has recognized the advancement of this manufacturing mode and has been encouraging its development as part of the FDA's "quality by design" (QbD) paradigm.[1] The application of process analytical technology (PAT) and control systems is a very useful effort to gain improved science-based process understanding.[2] One of the advantages of the continuous pharmaceutical manufacturing process is that it provides the ability to monitor and rectify data/product in real time. Therefore, it has been considered a data rich manufacturing process. However, in the face of the enormous amount of data generated from a continuous process, a sophisticated data management system is required for the integration of analytical tools to the control systems, as well as the off-line measurement systems.

In order to represent, manage, and analyze a large amount of complex information, an ontological informatics infrastructure will be necessary for process and product development in the pharmaceutical industry.[3] The ANSI/ISA-88 batch control standard[4][5][6] is an international standard addressing batch process control, which has already been implemented in other industries for years. Therefore, adapting this industrial standard into pharmaceutical manufacturing could provide a design philosophy for describing equipment, material, personnel, and reference models.[7][8] This recipe-based execution could work as a hierarchical data structure for the assembly of data from the control system, process analytical technology (PAT) tools, and off-line measurement devices. The combination of the ANSI/ISA-88 recipe model and the data warehouse informatics strategy[9] leads to the “recipe data warehouse” strategy.[10] This strategy could provide the possibility of data management across multiple execution systems, as well as the ability for data analysis and visualization.

Applying the “recipe data warehouse” strategy to continuous pharmaceutical manufacturing provides a possible approach to handle the data produced via analytical experimentation and process recipe execution. Not only the data itself but also the context of the data can be well-captured and saved for documentation and reporting. However, unlike batch operations, continuous manufacturing is a complicated process containing a series of interconnected unit operations with multiple execution layers. Therefore, it is quite challenging to integrate data across the whole system while maintaining an accurate representation of the complex manufacturing processes.

In addition to data collection and integration of the continuous manufacturing plant, the highly variable and unpredictable properties of raw materials are necessary to capture and store in a database because they could have an impact on the quality of the product.[11] These properties of relevance to continuous manufacturing are measured via many different analytical methods, including FT4 powder characterization[12], particle size analysis[13], and the Washburn technique.[14] The establishment of a raw material property database could be achieved by the “recipe data warehouse” strategy. Nevertheless, compared to the computer-aided manufacturing used in the production process, the degree of automation would vary significantly in different analytical platforms. Therefore, an easily accessible recipe management system would be highly desirable in characterization laboratories.

References

  1. Lee, S.L.; O'Connor, T.F.; Yang, X. et al. (2015). "Modernizing Pharmaceutical Manufacturing: from Batch to Continuous Production". Journal of Pharmaceutical Innovation 10 (3): 191–99. doi:10.1007/s12247-015-9215-8. 
  2. "Guidance for Industry PAT—A Framework for Innovative Pharmaceutical Development, Manufacturing, and Quality Assurance" (PDF). U.S. Food and Drug Administration. September 2004. https://www.fda.gov/downloads/Drugs/GuidanceComplianceRegulatoryInformation/Guidances/ucm070305.pdf. Retrieved 15 May 2018. 
  3. Venkatasubramanian, V.; Zhao, C.; Joglekar, G. et al. (2006). "Ontological informatics infrastructure for pharmaceutical product development and manufacturing". Computers & Chemical Engineering 30 (10–12): 1482–96. doi:10.1016/j.compchemeng.2006.05.036. 
  4. "ANSI/ISA-88.00.01-2010 Batch Control Part 1: Models and Terminology". International Society of Automation. 2010. https://www.isa.org/store/ansi/isa-880001-2010-batch-control-part-1-models-and-terminology/116649. 
  5. "ISA-88.00.02-2001 Batch Control Part 2: Data Structures and Guidelines for Languages". International Society of Automation. 2001. https://www.isa.org/store/isa-880002-2001-batch-control-part-2-data-structures-and-guidelines-for-languages/116687. 
  6. "ANSI/ISA-88.00.03-2003 Batch Control Part 3: General and Site Recipe Models and Representation". International Society of Automation. 2003. https://www.isa.org/store/ansi/isa-880003-2003-batch-control-part-3-general-and-site-recipe-models-and-representation-downloadable/118279. 
  7. Dorresteijn, R.C.; Wieten, G.; van Santen, P.T. et al. (1997). "Current good manufacturing practice in plant automation of biological production processes". Cytotechnology 23 (1–3): 19–28. doi:10.1023/A:1007923820231. PMC PMC3449867. PMID 22358517. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3449867. 
  8. Verwater-Lukszo, Z. (1998). "A practical approach to recipe improvement and optimization in the batch processing industry". Computers in Industry 36 (3): 279–300. doi:10.1016/S0166-3615(98)00078-5. 
  9. Kimball, R.; Ross, M. (2013). The Data Warehouse Toolkit: The Definitive Guide to Dimensional Modeling. John Wiley & Sons, Inc. pp. 600. ISBN 9781118530801. 
  10. Fermier, A.; McKenzie, P.; Murphy, T. et al. (2012). "Bringing New Products to Market Faster". Pharmaceutical Engineering 32 (5): 1–8. https://www.ispe.org/pharmaceutical-engineering-magazine/2012-sept-oct. 
  11. Ierapetritou, M.; Muzzio, F.; Reklaitis, G. (2016). "Perspectives on the continuous manufacturing of powder‐based pharmaceutical processes". AIChE Journal 62 (6): 1846-1862. doi:10.1002/aic.15210. 
  12. Vasilenko, A.; Glasser, B.J.; Muzzio, F.J. (2011). "Shear and flow behavior of pharmaceutical blends — Method comparison study". Powder Technology 208 (3): 628-636. doi:10.1016/j.powtec.2010.12.031. 
  13. Ramachandran, R.; Ansari, M.A.; Chaudhury, A. et al. (2012). "A quantitative assessment of the influence of primary particle size polydispersity on granule inhomogeneity". Chemical Engineering Science 71: 104–110. doi:10.1016/j.ces.2011.11.045. 
  14. Llusa, M.; Levin, M.; Snee, R.D. et al. (2010). "Measuring the hydrophobicity of lubricated blends of pharmaceutical excipients". Powder Technology 198 (1): 101–107. doi:10.1016/j.powtec.2009.10.021. 

Notes

This presentation is faithful to the original, with only a few minor changes to presentation, including with grammar and punctuation. In some cases important information was missing from the references, and that information was added.