LAUSR

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. This article is protected by copyright. All rights reserved.