Data integrity within the biopharmaceutical sector in the era of Industry 4.0.
Industry 4.0
PAT
biochemical engineering
bioprocess manufacturing
data analytics
data integrity
Journal
Biotechnology journal
ISSN: 1860-7314
Titre abrégé: Biotechnol J
Pays: Germany
ID NLM: 101265833
Informations de publication
Date de publication:
Jun 2022
Jun 2022
Historique:
revised:
12
03
2022
received:
11
11
2021
accepted:
19
03
2022
pubmed:
24
3
2022
medline:
7
6
2022
entrez:
23
3
2022
Statut:
ppublish
Résumé
Data Integrity (DI) in the highly regulated biopharmaceutical sector is of paramount importance to ensure decisions on meeting product specifications are accurate and hence assure patient safety and product quality. The challenge of ensuring DI within this sector is becoming more complex with the growing amount of data generated given increasing adoption of process analytical technology (PAT), advanced automation, high throughput microscale studies, and managing data models created by machine learning (ML) tools. This paper aims to identify DI risks and mitigation strategies in biopharmaceutical manufacturing facilities as the sector moves towards Industry 4.0. To achieve this, the paper examines common DI violations and links them to the ALCOA+ principles used across the FDA, EMA, and MHRA. The relevant DI guidelines from the ISPE's GAMP5 and ISA-95 standards are also discussed with a focus on the role of validated computerised and automated manufacturing systems to avoid DI risks and generate compliant data. The paper also highlights the importance of DI whilst using data analytics to ensure the developed models meet the required regulatory standards for process monitoring and control. This includes a discussion on possible mitigation strategies and methodologies to ensure data integrity is maintained for smart manufacturing operations such as the use of cloud platforms to facilitate the storage and transfer of manufacturing data, and migrate away from paper-based records.
Identifiants
pubmed: 35318814
doi: 10.1002/biot.202100609
doi:
Substances chimiques
Biological Products
0
Types de publication
Journal Article
Review
Langues
eng
Sous-ensembles de citation
IM
Pagination
e2100609Informations de copyright
© 2022 The Authors. Biotechnology Journal published by Wiley-VCH GmbH.
Références
FDA. (2018, August 10). Warning letter Kyowa Hakko Bio Co., Ltd. U.S. FOOD & DRUG ADMINISTRATION. https://www.fda.gov/inspections-compliance-enforcement-and-criminal-investigations/warning-letters/kyowa-hakko-bio-co-ltd-543924-08102018
International Society for Pharmaceutical Engineering. (2008). GAMP 5: A Risk-based Approach to Compliant Gxp Computerized Systems. International Society for Pharmaceutical Engineering,North Bethesda, MD, USA. https://ispe.org/publications/guidance-documents/gamp-5
Branke, J., Farid, S. S., & Shah, N. (2016). Industry 4.0: A vision for personalized medicine supply chains? Cell and Gene Therapy Insights, 2(2), 263-270. https://doi.org/10.18609/cgti.2016.027
Steinwandter, V., Borchert, D., & Herwig, C. (2019). Data science tools and applications on the way to Pharma 4.0. Drug Discovery Today, 24(9), 1795-1805. https://doi.org/10.1016/j.drudis.2019.06.005
FDA. (2018). Data Integrity and Compliance With Drug CGMP Questions and Answers Guidance for Industry (Issue December). https://www.fda.gov/media/119267/download
Buytaert-Hoefen, K. (2019). A Harmonized approach to Data integrity. BioProcess International. https://bioprocessintl.com/manufacturing/information-technology/a-harmonized-approach-to-data-integrity/
Hodgson, D., Maini, F., Greenrose, W., Christiani, S., Chan, S., & Hargitai, B. (2017). Under the spotlight: Data integrity in life sciences. Deloitte,London, UK. https://www2.deloitte.com/content/dam/Deloitte/global/Documents/Life-Sciences-Health-Care/gx-lshc-data-integrity.pdf
European commission. (2014). The Rules Governing Medicinal Products in the European Union Volume 4 EU Guidelines for Good Manufacturing Practice for Medicinal Products for Human and Veterinary Use Part 1 Chapter 6: Quality Control. https://ec.europa.eu/health/sites/health/files/files/eudralex/vol-4/2014-11_vol4_chapter_6.pdf
European Medicines Agency. (2016). Guidance on good manufacturing practice and good distribution practice. Questions and answers: Data integrity. https://www.ema.europa.eu/en/human-regulatory/research-development/compliance/good-manufacturing-practice/guidance-good-manufacturing-practice-good-distribution-practice-questions-answers#data-integrity-(new-august-2016)-section
MHRA, G. (2018). Data integrity guidance and definitions. Medicines and Healthcare products (Regulatory Agency, London,United Kingdom, 2018). https://assets.publishing.service.gov.uk/government/uploads/system/uploads/attachment_data/file/687246/MHRA_GxP_data_integrity_guide_March_edited_Final.pdf
Pérez, J. R. (2017). Maintaining data integrity. In Quality Progress (Vol. 50, Issue 3, pp. 14-15). http://bec-global.com/wp-content/uploads/2017/10/Article-Data-Integrity.pdf
Harjunkoski, I. (2014). Integration of scheduling and ISA-95. Computer Aided Chemical Engineering, 33, 427-432. https://doi.org/10.1016/B978-0-444-63456-6.50072-7
WHO. (2019). Guideline on data integrity. WHO Drug Information, 33(4), 773-793. http://www.who.int/medicines/areas/quality_safety/quality_assurance/guidelines/en/
European commission. (2011). Chapter 4: Documentation. In EudraLex The Rules Governing Medicinal Products in the European Union (Vol. 4, pp. 1-9). https://ec.europa.eu/health/sites/health/files/files/eudralex/vol-4/chapter4_01-2011_en.pdf
ISPE. (2017). GAMP Guide: Records & Data Integrity. International Society for Pharmaceutical Engineering. https://ispe.org/publications/guidance-documents/gamp-records-pharmaceutical-data-integrity
International Society for Pharmaceutical Engineering. (2020). GAMP RDI Good Practice Guide: Data Integrity by Design. International Society for Pharmaceutical Engineering. https://ispe.org/publications/guidance-documents/gamp-rdi-good-practice-guide-data-integrity-design
Allotrope Foundation. (2017). Creation of Allotrope Ontology and Usage as ELN Metadata (Issue September). Allotrope Foundation. http://www.allotrope.orghttp//www.allotrope.org
Smiatek, J., Jung, A., & Bluhmki, E. (2020). Towards a digital bioprocess replica: Computational approaches in biopharmaceutical development and manufacturing. In Trends in Biotechnology (Vol. 38, Issue 10, pp. 1141-1153). Elsevier Current Trends. https://doi.org/10.1016/j.tibtech.2020.05.008
Römhild, A. (2016). Good manufacturing practice compliance in the manufacture of cell-based medicines. In Guide to Cell Therapy GxP (pp. 107-175). Elsevier Inc. https://doi.org/10.1016/b978-0-12-803115-5.00004-8
Tao, F., Qi, Q., Liu, A., & Kusiak, A. (2018). Data-driven smart manufacturing. Journal of Manufacturing Systems, 48, 157-169. https://doi.org/10.1016/j.jmsy.2018.01.006
Harris, J. R. (2008). Section 1 good manufacturing practices (GMP) and other FDA guidelines. In S. C. Gad (Ed.), Pharmaceutical Manufacturing Handbook: Regulations and Quality (pp. 1-43). John Wiley & Sons, Inc. https://onlinelibrary.wiley.com/doi/pdf/10.1002/9780470259832.ch1?saml_referrer
von Stosch, M., Oliveira, R., Peres, J., & Feyo de Azevedo, S. (2014). Hybrid semi-parametric modeling in process systems engineering: Past, present and future. Computers and Chemical Engineering, 60, 86-101. https://doi.org/10.1016/j.compchemeng.2013.08.008
Goldrick, S., Holmes, W., Bond, N. J., Lewis, G., Kuiper, M., Turner, R., & Farid, S. S. (2017). Advanced multivariate data analysis to determine the root cause of trisulfide bond formation in a novel antibody-peptide fusion. Biotechnology and Bioengineerng, 114(10), 2222-2234. https://doi.org/10.1002/bit.26339
Lu, Y., & Xu, X. (2019). Cloud-based manufacturing equipment and big data analytics to enable on-demand manufacturing services. Robotics and Computer-Integrated Manufacturing, 57, 92-102. https://doi.org/10.1016/j.rcim.2018.11.006
Riley, M., Hattaway, E. Z., & Arthur Felse, P. (2017). Implementation and use of cloud-based electronic lab notebook in a bioprocess engineering teaching laboratory. Journal of Biological Engineering, 11(1), 1-9. https://doi.org/10.1186/s13036-017-0083-2
Paldy, E., & Densow, H. (2021). Data integrity and digital workflow guidance in a contemporary pharma lab. European Pharmaceutical Review. https://www.europeanpharmaceuticalreview.com/webinar/139335/data-integrity-and-digital-workflow-guidance-in-a-contemporary-pharma-lab/2/
Gargalo, C. L., Udugama, I., Pontius, K., Lopez, P. C., Nielsen, R. F., Hasanzadeh, A., Mansouri, S. S., Bayer, C., Junicke, H., & Gernaey, K. V. (2020). Towards smart biomanufacturing: A perspective on recent developments in industrial measurement and monitoring technologies for bio-based production processes. Journal of Industrial Microbiology and Biotechnology, 47(11), 947-964. https://doi.org/10.1007/s10295-020-02308-1
Leal, F., Chis, A. E., Caton, S., González-Vélez, H., García-Gómez, J. M., Durá, M., Sánchez-García, A., Sáez, C., Karageorgos, A., Gerogiannis, V. C., Xenakis, A., Lallas, E., Ntounas, T., Vasileiou, E., Mountzouris, G., Otti, B., Pucci, P., Papini, R., Cerrai, D., & Mier, M. (2021). Smart pharmaceutical manufacturing: Ensuring end-to-end traceability and data integrity in medicine production. Big Data Research, 24, 100172. https://doi.org/10.1016/j.bdr.2020.100172
Hemphill, T. A. (2020). Biologics regulation, second-to-market competition, and the use of blockchain technology: An opportunity for the FDA to support responsible biotechnology innovation. Journal of Responsible Innovation, 7(3), 689-696. https://doi.org/10.1080/23299460.2020.1807669