Development of a novel terminological framework for quality assurance signal management in pharmaceutical regulatory systems

Abstract

Background: The evolving complexity of pharmaceutical regulatory systems has diversified the compilation of data, yet to date the systematic management of quality assurance (QA) signals remains conceptually and methodologically underdeveloped. In contrast to pharmacovigilance, where signal management is underpinned by well-established terminologies, taxonomies, and analytical methodologies, QA signal management remains unexplored within existing regulatory, quality management, and scientific lexicons. The absence of terminological constructs contributes to interpretative variability, limits methodological harmonisation, and impedes the advancement of quality assurance signal assessment within pharmaceutical regulatory sciences. Addressing this gap, the study introduces a novel glossary framework intended to provide definitional clarity, conceptual consistency, and terminological traceability in the management of QA signals. The framework aims to characterise identified QA signals and support a structured conceptual lexicon to sustain the systematic identification, interpretation and application within a pharmaceutical regulatory context. Method: An inductive-interpretative analytical approach informed the development of a terminological glossary framework. The terminological constructs were derived from the iterative application of the Quality Assurance Signal Analytical Framework to dataset sources, namely quality improvement forms submitted at the Quality, Continuous Improvement and Internal Audits Unit at the Malta Medicines Authority. The approach involved iterative rounds of QA signal data analysis, signal-to-principle mapping, and thematic clustering. The framework was validated through a structured expert focus group (N=7) consultation. The criteria of the glossary framework were evaluated using the Content Validity Method to assess clarity, relevance, and structure and layout. Suggestions and recommendations made during the validation process informed the development of the framework to achieve conceptual clarity and linguistic precision. Results: The glossary framework incorporates a foundational knowledge-building element required to support the application of quality assurance signal intellect. The glossary is presented in a structured tabular format comprising three core criteria: (i) terminological -omics constructs, (ii) conceptual definitions, and (iii) scientific rationales. Analysis indicated that QA signals arose not solely from discrete quality assurance practices but from multidimensional dynamics spanning across governance-associated influences, integrative system interactions, organisational capability and resources-related dynamics, and stakeholder-driven outcomes. To capture these multidimensional QA signal origins, four novel terminological constructs were developed. ‘Governomics’ characterises governance-related signals reflected through the evaluation of the quality management system documentation framework and procedural consistency. ‘Integromics’ reflects interdependencies across quality management systems, operational interfaces, and process integration performances. ‘Capacitomics’ represents organisational capacity-related QA signals, including resource allocation, workforce competencies, infrastructure adequacy, and system maturity. ‘Stakeholderomics’ encompasses behavioural, cultural, communication, and inter-stakeholder interaction. Collectively, the four terminological constructs provide a structured taxonomy enabling consistent classification and interpretation of QA signals. Conclusion: The glossary framework is envisioned as a dynamic conceptual tool intended to focus on a shared scientific understanding amongst quality management and pharmaceutical regulatory professionals. The introduction of a validated QA terminological construct grouping supports the invention of QA intelligence and provides a foundation for future methodological development, professional competency development, and evidence-based decisionmaking. The evolvement and integration of the framework represent a pioneering advancement towards the development of a structured lexicon for QA signal management within medicinal products regulatory sciences.