Journal:A systematic framework for data management and integration in a continuous pharmaceutical manufacturing processing line

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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)
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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.

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. ↑ [hhttps://www.isa.org/store/ansi/isa-880003-2003-batch-control-part-3-general-and-site-recipe-models-and-representation-downloadable/118279 "ANSI/ISA-88.00.03-2003 Batch Control Part 3: General and Site Recipe Models and Representation"]. International Society of Automation. 2003. hhttps://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. 

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.

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