Intelligent Metadata-Driven Automation for eCTD 4.0 Lifecycle Management Using Explainable AI (XAI)

Abstract

Regulatory submissions require both transparency and automation in the era of digital transformation to meet evolving compliance standards. This paper discusses the application of Explainable AI (XAI) on automated intelligent metadata-driven pharmaceutical regulatory submission lifecycle management of eCTD 4.0. The research is based on an explanatory research design and an analysis of secondary data in the form of industry reports, regulatory guides on XAI, as well as case studies to understand how XAI can increase transparency, traceability, and compliance. It is observed through literature and data visualisations that XAI enhances efficient learning of models, the reliability of white-box models, and serves high accountability challenges, as is the case. The examples of case studies of GlaxoSmithKline and AstraZeneca prove impressive improvements in speed of submissions, lowering the error rates, and boosting audits. The challenges involve bias, data privacy and interpretability of the variance among the users. This paper suggests bias-detecting, human-based and role-specific explanations of XAI models in an attempt to establish trust and regulatory conformity. All in all, various links on how to combine XAI and metadata automation demonstrate the necessity of future-ready, transparent and efficient eCTD 4.0 life cycle management.