Affiliation:
1. School of Technology, University of Thessaly, 412 22 Larissa, Greece
2. Department of Industrial Design and Production Engineering, University of Western Attica, 122 43 Athens, Greece
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.
Funder
Smart Pharmaceutical Manufacturing
Subject
Fluid Flow and Transfer Processes,Computer Science Applications,Process Chemistry and Technology,General Engineering,Instrumentation,General Materials Science
Reference32 articles.
1. Digitalization in pharmaceutical industry: What to focus on under the digital implementation process?;Hole;Int. J. Pharm. X,2021
2. Data integrity within the biopharmaceutical sector in the era of Industry 4.0;Alosert;Biotechnol. J.,2022
3. An audit of pharmaceutical continuous manufacturing regulatory submissions and outcomes in the US;Fisher;Int. J. Pharm.,2022
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.
5. Towards a computational approach for the assessment of compliance of ALCOA+ Principles in pharma industry;Leal;Stud. Health Technol. Inform.,2022