Open Access Peer-reviewed Review

Blockchain technology for advanced therapy medicinal products: Applications in tracking, data sharing, and supply chain automation

Article Sidebar

Blockchain-driven data integration
Download PDF
Submitted   Apr 1, 2024
Published   Jun 18, 2024
Issue    Vol 5 No 2 (2023)
DOI   10.25082/JPBR.2023.02.004

Views

629

Downloads

292

Citations

PlumX Metrics

Main Article Content

Cristobal Aguilar-Gallardo corresponding author
Unidad de Terapias Avanzadas, Instituto Investigación Sanitaria La Fe, Av. Fernando Abril Martorell, 106, 46026 Valencia, Spain
Ana Bonora-Centelles
Unidad de Terapias Avanzadas, Instituto Investigación Sanitaria La Fe, Av. Fernando Abril Martorell, 106, 46026 Valencia, Spain

Abstract

Advanced therapy medicinal products (ATMPs) like cell and gene therapies offer transformative treatment options for many diseases. However, coordinating the decentralized, patient-specific manufacturing of autologous ATMPs across multiple hospitals poses major supply chain challenges. This paper provides a comprehensive analysis of how blockchain technology can enhance decentralized ATMP manufacturing networks. First, background on ATMPs and complexities of decentralized production is reviewed. An overview of blockchain architecture, key attributes, and existing use cases then follows. The major opportunities for blockchain integration in ATMP manufacturing are discussed in depth, including tracking autologous products across locations, enabling data sharing between hospitals to power AI-based optimization, automating supply chain processes, and maintaining provenance records. Critical limitations around scalability, privacy, regulation, and adoption barriers are examined. Design considerations for developing blockchain ecosystems tailored to the unique ATMP environment are also explored. Blockchain shows immense promise for transforming visibility, coordination, automation, and data unification in decentralized ATMP manufacturing networks. Despite current challenges, blockchain is prepared to profoundly impact the advancement of personalized cell and gene therapies through enhanced supply chain instrumentation. This paper provides a comprehensive analysis of this emerging technological innovation and its applications to address critical needs in ATMP translation and manufacturing.

Keywords
blockchain, advanced therapy medicinal products (ATMPs), artificial intelligence (AI), cell and gene therapies, GMP facilities

Article Details

How to Cite
Aguilar-Gallardo, C., & Bonora-Centelles, A. (2024). Blockchain technology for advanced therapy medicinal products: Applications in tracking, data sharing, and supply chain automation. Journal of Pharmaceutical and Biopharmaceutical Research, 5(2), 430-443. https://doi.org/10.25082/JPBR.2023.02.004
Creative Commons License

This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.

References

  1. European Parliament C of the EU. EC Regulation 1394/2007. 2007, 64(13): 140–156.
  2. Therapies A. Reflection paper on classification of advanced therapy medicinal products Reflection paper on classification of Advanced Therapy Medicinal Products Table of contents. Therapy. 2012, 44: 1–19.
  3. European Commission. Guidelines on Good Manufacturing Practice specific to Advanced Therapy Medicinal Products. European Commission Journal. 2017, 4: 1–32.
  4. Nakamoto S, System APEC. Bitcoin: A Peer-to-Peer Electronic Cash System. 2008: 1–9.
  5. Buterin V. Ethereum: A Next-Generation Smart Contract and Decentralized Application Platform. Whitepaper. 2014, 3(37): 1–36.
  6. Hang L, Choi E, Kim DH. A novel EMR integrity management based on a medical blockchain platform in hospital. Electronics. 2019, 8(4): 467. https://doi.org/10.3390/electronics8040467
  7. Dössegger S, Peltenburg T. Next Generation Supply Chain Automation and Intelligence. Modum. 2018. (version 1.1).
  8. Bocek T, Rodrigues BB, Strasser T, et al. Blockchains everywhere -- a use-case of blockchains in the pharma supply-chain. 2017 IFIP/IEEE Symposium on Integrated Network and Service Management (IM). Published online May 2017. https://doi.org/10.23919/inm.2017.7987376
  9. Gomasta SS, Dhali A, Tahlil T, et al. PharmaChain: Blockchain-based drug supply chain provenance verification system. Heliyon. 2023, 9(7): e17957. https://doi.org/10.1016/j.heliyon.2023.e17957
  10. Androulaki E, Barger A, Bortnikov V, et al. Hyperledger fabric. Proceedings of the Thirteenth EuroSys Conference. Published online April 23, 2018. https://doi.org/10.1145/3190508.3190538
  11. Agbo CC, Mahmoud QH, Eklund JM. Blockchain Technology in Healthcare: A Systematic Review. Healthcare. 2019, 7(2): 56. https://doi.org/10.3390/healthcare7020056
  12. Moosavi J, Naeni LM, Fathollahi-Fard AM, et al. Blockchain in supply chain management: a review, bibliometric, and network analysis. Environmental Science and Pollution Research. Published online February 27, 2021. https://doi.org/10.1007/s11356-021-13094-3
  13. Swan M. Blockchain: Blueprint for a new economy. O’Reilly Media, Inc.; 2015.
  14. Taherdoost H. Smart Contracts in Blockchain Technology: A Critical Review. Information. 2023, 14(2): 117. https://doi.org/10.3390/info14020117
  15. Aguilar-Gallardo C, Bonora-Centelles A. Integrating Artificial Intelligence for Academic Advanced Therapy Medicinal Products: Challenges and Opportunities. Applied Sciences. 2024, 14(3): 1303. https://doi.org/10.3390/app14031303
  16. Sun Y, Gu L. Attention-based Machine Learning Model for Smart Contract Vulnerability Detection. Journal of Physics: Conference Series. 2021, 1820(1): 012004. https://doi.org/10.1088/1742-6596/1820/1/012004
  17. Daniel J, Sargolzaei A, Abdelghani M, et al. Blockchain Technology, Cognitive Computing, and Healthcare Innovations. Journal of Advances in Information Technology. Published online 2017: 194-198. https://doi.org/10.12720/jait.8.3.194-198
  18. Nguyen DC, Ding M, Pham QV, et al. Federated Learning Meets Blockchain in Edge Computing: Opportunities and Challenges. IEEE Internet of Things Journal. 2021, 8(16): 12806-12825. https://doi.org/10.1109/jiot.2021.3072611
  19. Lu Y, Huang X, Dai Y, et al. Blockchain and Federated Learning for Privacy-Preserved Data Sharing in Industrial IoT. IEEE Transactions on Industrial Informatics. 2020, 16(6): 4177-4186. https://doi.org/10.1109/tii.2019.2942190
  20. Bao X, Su C, Xiong Y, et al. FLChain: A Blockchain for Auditable Federated Learning with Trust and Incentive. 2019 5th International Conference on Big Data Computing and Communications (BIGCOM). Published online August 2019. https://doi.org/10.1109/bigcom.2019.00030
  21. Mishra KN, Bhattacharjee V, Saket S, et al. Security provisions in smart edge computing devices using blockchain and machine learning algorithms: a novel approach. Cluster Computing. 2022, 27(1): 27-52. https://doi.org/10.1007/s10586-022-03813-x
  22. Miao Z, Zhao G. Impacts of Digital Information Management Systems on Green Transformation of Manufacturing Enterprises. International Journal of Environmental Research and Public Health. 2023, 20(3): 1840. https://doi.org/10.3390/ijerph20031840
  23. Mantravadi S, Møller C. An Overview of Next-generation Manufacturing Execution Systems: How important is MES for Industry 4.0? Procedia Manufacturing. 2019, 30: 588-595. https://doi.org/10.1016/j.promfg.2019.02.083
  24. Oluwole OG, Oosterwyk C, Anderson D, et al. The Implementation of Laboratory Information Management System in Multi-Site Genetics Study in Africa: The Challenges and Up-Scaling Opportunities. Journal of Molecular Pathology. 2022, 3(4): 262-272. https://doi.org/10.3390/jmp3040022
  25. Dhulavvagol PM, Totad SG. Performance Enhancement of Distributed System Using HDFS Federation and Sharding. Procedia Computer Science. 2023, 218: 2830-2841. https://doi.org/10.1016/j.procs.2023.01.254
  26. Jnr. BA, Sylva W, Watat JK, Misra S. A Framework for Standardization of Distributed Ledger Technologies for Interoperable Data Integration and Alignment in Sustainable Smart Cities. Journal of the Knowledge Economy. Published online October 31, 2023. https://doi.org/10.1007/s13132-023-01554-9
  27. Consumer I, Society T, Committee S, Projects S, Requests PA. Standards Projects. 2022: 1–50.
  28. Francis S. Collins. Testimony on the Implementation of the 21st Century Cures Act: Progress and the Path Forward for Medical Innovation | National Institutes of Health (NIH). Nih [Internet]. 2017: 1–14. https://www.nih.gov
  29. Moniz H. The Istanbul BFT Consensus Algorithm. 2020: 1–24. https://arxiv.org/abs/2002.03613