A Regulatory NLP Framework for Evaluating Data Sufficiency in FDA Meeting Request Submissions

Abstract

During drug development, effective engagement with the U.S. Food and Drug Administration (FDA) is essential for achieving regulatory success. The process begins with submitting well-prepared FDA meeting requests, supported by clear, concise, and complete documentation that justifies the need for the interaction and facilitates a productive exchange of information. Nevertheless, the current process for evaluating the adequacy of information submitted to justify a meeting remains largely manual, time-intensive, and subject to interpretation. Amid the recent progress in the field of Natural Language Processing (NLP), a body of opportunity is emerging to create regulatory-oriented AI systems that automate the process of data adequacy in such submissions. The current paper suggests a regulatory NLP framework that should examine the completeness, clarity, and suitability of data in documents submitted for the FDA meeting requests. The article delves into the elements, approaches, and practical issues of implementation of such frameworks and how the same can help simplify regulatory interactions and maintain compliance with FDA expectations.