A Network Modeling and Analysis Approach for Pharma Industry Regulatory Assessment

Abstract

Regulatory compliance in the pharmaceutical industry is challenging, requiring dedicated resources and meticulous control over production processes to ensure adherence to established regulatory guidelines, specifically ALCOA+ (Attributable, Legible, Contemporaneous, Original, Accurate, Complete, Consistent, Enduring, and Available) principles. This paper introduces an innovative approach to assess pharma regulatory compliance, utilizing a network model of the production process. The model dynamically configures production line characteristics based on manufacturing process data, overcoming complexity and scalability challenges. Purpose: The main purpose is to address the challenges of regulatory compliance in the pharmaceutical industry by introducing a novel approach using a network model. The research question involves assessing the effectiveness of this model in ensuring compliance with ALCOA+ principles. Methods: The approach involves dynamic configuration of the network model parameters based on manufacturing process data. Network analysis methods are then applied to evaluate the conformity of manufacturing process data to ALCOA+ principles. Results: Testing the proposed approach on a real dataset from a representative pharma production line demonstrates its effectiveness in assessing pharma regulatory compliance. The results highlight the potential of network modelling in managing data quality and integrity within the regulatory framework. Conclusions: The study concludes that the network model offers a strategic solution for evaluating and ensuring regulatory compliance in pharmaceutical manufacturing. The approach shows promise in addressing the complexities of data management within the stringent regulatory framework of the industry.