Deep Transformers for Computing and Predicting ALCOA+Data Integrity Compliance in the Pharmaceutical Industry

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.

Keywords

• ALCOA+ principles
• data integrity
• transformer-based models
• pharmaceuticals manufacturing