Deep Transformers for Computing and Predicting ALCOA+Data Integrity Compliance in the Pharmaceutical Industry
Source abstract
Abstract
Strict adherence to data integrity and quality standards is crucial for the pharmaceutical industry to minimize undesired effects and ensure that medicines are of the required quality and safe for patients. A common data quality standard in the pharmaceutical industry is ALCOA+, which is a set of guiding principles for ensuring data integrity. Failure to comply with ALCOA+ guidelines, usually detected after audit inspections, may result in serious consequences for pharmaceutical manufacturers, such as the incurrence of fines, increase in costs, and production delays. It is, therefore, imperative to devise methods able to monitor ALCOA+ compliance and detect decreasing trends in data quality automatically. In this paper we present ALCOAi, a deep learning model based on the transformer architecture, which is able to process large quantities of non-homogeneous data and compute current and future ALCOA+ compliance. The proposed model can estimate trends concerning most ALCOA+ principles. The model was tested on a real dataset comprising raw sensor data, machine-provided values, and human-entered free-text data from two pharmaceutical manufacturing lines. The performed tests led to promising results in forecasting ALCOA+ compliance.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Data Quality and Management · Big Data and Business Intelligence · Cloud Data Security Solutions
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
School of Technology, University of Thessaly, 412 22 Larissa, Greece
Department of Industrial Design and Production Engineering, University of Western Attica, 122 43 Athens, Greece
Funding
Smart Pharmaceutical Manufacturing (SPuMoNI)
References (32)
Show source-provided references
1. Digitalization in pharmaceutical industry: What to focus on under the digital implementation process?
2. Data integrity within the biopharmaceutical sector in the era of Industry 4.010.1002/biot.202100609 ↗
3. An audit of pharmaceutical continuous manufacturing regulatory submissions and outcomes in the US10.1016/j.ijpharm.2022.121778 ↗
4. McDermott, O., Antony, J., Sony, M., and Daly, S. (2022). Barriers and enablers for continuous improvement methodologies within the Irish pharmaceutical industry. Processes, 10.10.3390/pr10010073 ↗
5. Towards a computational approach for the assessment of compliance of ALCOA+ Principles in pharma industry
6. Current status, challenges and preventive strategies to overcome data integrity issues in the pharmaceutical industry
7. Data integrity: History, issues, and remediation of issues10.5731/pdajpst.2017.007765 ↗
8. Cyber-physical-based PAT (CPbPAT) framework for Pharma 4.010.1016/j.ijpharm.2019.06.036 ↗
9. Wolf, T., Debut, L., Sanh, V., Chaumond, J., Delangue, C., Moi, A., Cistac, P., Rault, T., Louf, R., and Funtowicz, M. (2020, January 16–20). Transformers: State-of-the-art natural language processing. Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing: System Demonstrations, Online.10.18653/v1/2020.emnlp-demos.6 ↗
10. Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., and Zhang, W. (2021, January 2–9). Informer: Beyond efficient transformer for long sequence time-series forecasting. Proceedings of the AAAI Conference on Artificial Intelligence, Virtually.10.1609/aaai.v35i12.17325 ↗
11. A survey on machine learning for data fusion10.1016/j.inffus.2019.12.001 ↗
12. A survey of machine learning for big data processing
13. A survey on deep learning for big data10.1016/j.inffus.2017.10.006 ↗
14. From data acquisition to data fusion: A comprehensive review and a roadmap for the identification of activities of daily living using mobile devices10.3390/s16020184 ↗
15. A survey on data fusion in internet of things: Towards secure and privacy-preserving fusion10.1016/j.inffus.2018.12.001 ↗
16. Data fusion and IoT for smart ubiquitous environments: A survey10.1109/ACCESS.2017.2697839 ↗
17. A survey of big data and machine learning
18. Deep learning architectures in emerging cloud computing architectures: Recent development, challenges and next research trend10.1016/j.asoc.2020.106582 ↗
19. A review of deep learning with special emphasis on architectures, applications and recent trends10.1016/j.knosys.2020.105596 ↗
20. Advanced machine-learning techniques in drug discovery10.1016/j.drudis.2020.12.003 ↗
21. Machine learning methods in drug discovery10.3390/molecules25225277 ↗
22. Machine-learning approaches in drug discovery: Methods and applications10.1016/j.drudis.2014.10.012 ↗
23. Machine learning in drug discovery: A review10.1007/s10462-021-10058-4 ↗
24. Machine learning applications in drug development10.1016/j.csbj.2019.12.006 ↗
25. Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, Ł., and Polosukhin, I. (2017). Attention is all you need. Adv. Neural Inf. Process. Syst., 30.
26. Dey, R., and Salem, F.M. (2017, January 6–9). Gate-variants of gated recurrent unit (GRU) neural networks. Proceedings of the 2017 IEEE 60th international midwest symposium on circuits and systems (MWSCAS), Boston, MA, USA.10.1109/MWSCAS.2017.8053243 ↗
27. Li, S., Jin, X., Xuan, Y., Zhou, X., Chen, W., Wang, Y.X., and Yan, X. (2019). Enhancing the locality and breaking the memory bottleneck of transformer on time series forecasting. Adv. Neural Inf. Process. Syst., 32.
28. Wu, N., Green, B., Ben, X., and O’Banion, S. (2020). Deep transformer models for time series forecasting: The influenza prevalence case. arXiv.
29. Kingma, D.P., and Ba, J. (2014). Adam: A method for stochastic optimization. arXiv.
30. Cho, K., Van Merriënboer, B., Gulcehre, C., Bahdanau, D., Bougares, F., Schwenk, H., and Bengio, Y. (2014). Learning phrase representations using RNN encoder-decoder for statistical machine translation. arXiv.10.3115/v1/D14-1179 ↗
31. Long short-term memory10.1162/neco.1997.9.8.1735 ↗
32. Rojat, T., Puget, R., Filliat, D., Del Ser, J., Gelin, R., and Díaz-Rodríguez, N. (2021). Explainable artificial intelligence (xai) on timeseries data: A survey. arXiv.