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Excipients are conventionally described through intrinsic material properties, pharmacopeial specifications, and functional labels such as binder, disintegrant, solubiliser, stabiliser, or release modifier. This vocabulary has supported pharmaceutical development for decades because it simplifies excipient selection and links material identity to expected product performance. Yet the same vocabulary becomes unstable when dosage forms are compositionally dense, structurally heterogeneous, and highly dependent on manufacturing history. The central problem is that excipient performance in complex dosage forms often deviates from what would be predicted by isolated material tests. A polymer that stabilises supersaturation in one amorphous solid dispersion may fail in another, while a lipid excipient that improves solubilisation under one digestion condition may promote precipitation under another. Such behaviour suggests that excipient functionality is not merely carried by the excipient molecule, but is produced within the dosage form system. This article proposes a theoretical reframing of excipient functionality as a system property. In this view, functionality emerges from the combined effects of formulation composition, spatial architecture, and processing history. The purpose is not to replace molecular or compendial characterisation, but to relocate those measurements within a broader systems framework. The proposed theory defines excipient functionality as an emergent outcome of interactions among drugs, excipients, process energy, phase behaviour, and microstructural organisation. It explains why apparently similar formulations can display different dissolution, supersaturation, release, or stability behaviours when their processing route or internal architecture differs. Three tables are used to contrast the reductionist and system-property paradigms, map overlooked interactions, and identify design implications. Adopting a system-property view would shift pharmaceutical development from selecting excipients as isolated ingredients toward designing excipient functions as relational outcomes. It would encourage formulation scientists to evaluate not only what an excipient is, but what it becomes within a particular dosage form. This perspective offers a conceptual basis for more predictive, adaptive, and robust pharmaceutical product design.

Artificial intelligence is rapidly being adopted to optimise pharmaceutical formulation and manufacturing processes, yet its inherent opacity poses a fundamental challenge to regulatory frameworks built on transparency, scientific justification, and mechanistic understanding. In pharmaceutical development, AI systems may influence formulation selection, process parameter optimisation, release modelling, stability prediction, and scale-up strategy. These decisions can directly or indirectly affect product quality and patient safety. The regulatory issue is therefore not whether AI can improve development efficiency, but whether its outputs can be explained, justified, verified, and governed. There is currently no clear regulatory consensus on what constitutes sufficient explainability for AI models used in pharmaceutical formulation and process development. Existing expectations for pharmaceutical development presume that sponsors can describe the relationship between material attributes, process parameters, critical quality attributes, and clinical performance. Many AI models, particularly neural networks, ensemble methods, and adaptive systems, challenge this assumption because their internal decision logic may not be readily interpretable. This uncertainty creates difficulty for industry, regulators, and quality units seeking to evaluate whether AI-supported decisions are scientifically sound. This article identifies the regulatory gaps and risk domains associated with non-explainable AI in pharmaceutical development. It examines how AI is being used across formulation design, process development, manufacturing optimisation, and data-rich pharmaceutical quality systems. It then defines explainability and evidence requirements appropriate for different levels of regulatory risk. The central argument is that explainability should be treated as a regulatory quality attribute of AI systems, not merely as a technical preference. The proposed framework categorises AI applications according to their intended use, model complexity, degree of influence on quality decisions, and potential impact on patient safety. It links these categories to evidence requirements, including training data documentation, performance validation, uncertainty assessment, interpretability justification, human oversight, and lifecycle change controls. Four tables present the AI application landscape, regulatory gap analysis, explainability requirements, and the proposed framework. Together, these elements provide a structured basis for regulatory dialogue and future guidance development. A structured, risk-based approach to regulatory explainability can enable responsible adoption of AI while protecting the integrity of the pharmaceutical regulatory system. Low-risk AI tools may require documented performance and traceability, whereas high-risk tools that influence critical quality decisions require stronger interpretability, validation, and governance. The proposed approach does not require all models to be fully transparent, but it does require that the explanation provided be proportionate to the decision being supported. Regulatory explainability is therefore presented as a necessary bridge between innovation, quality assurance, and patient protection.

Artificial intelligence is increasingly being positioned as a transformative tool in pharmaceutical formulation and process development because it can model complex relationships among molecular properties, excipient behaviour, formulation variables, processing conditions, and product performance. Machine learning, deep learning, hybrid modelling, and optimisation algorithms are now used to support decisions that were previously dominated by empirical screening and expert judgement. Despite this promise, the practical translation of artificial intelligence into pharmaceutical development remains uneven. Many models demonstrate high retrospective accuracy but provide limited mechanistic insight, weak interpretability, and uncertain relevance when moved beyond the specific datasets, formulations, equipment, or scales on which they were trained. This critical review examines artificial intelligence in pharmaceutical formulation and process development through three linked lenses: explainability, transferability, and regulatory trust. It argues that these issues are not secondary implementation details but core determinants of whether artificial intelligence can become credible within quality-driven pharmaceutical development. The analysis shows that artificial intelligence can support formulation and process understanding only when predictive performance is accompanied by transparent reasoning, domain-aware validation, lifecycle governance, and evidence of transferability across development contexts. A coordinated pathway involving explainable-by-design models, standardised transferability testing, and regulatory learning environments is required to move pharmaceutical artificial intelligence from technical promise toward justified regulatory trust.