From Regulatory Text to Executable Constraints: Operationalising Compliance for Medicine Labelling
Regulatory requirements governing safety-critical health domains, such as medicine labelling, are predominantly expressed in legal prose with strong deontic modality (e.g., obligations and prohibitions). While suitable for human interpretation, such requirements are not directly machine-interpretable, limiting their use as deterministic, executable constraints in label design workflows. We introduce a machine-verifiable methodology that operationalises medicine labelling regulations into structured, executable compliance contracts. Legal provisions are systematically extracted, filtered, and translated into normative representations that specify content, structural, and format constraints. These representations are compiled into deterministic checks, enabling regulations to serve as executable constraints on design artefacts and supporting systematic analysis of compliance. We instantiate the methodology using U.S. Over-the-counter and Prescription labelling requirements (21 CFR § 201.66 and 21 CFR § 201.100). Under this process, we operationalise 16 from 81 design-related requirements in OTC regulations, and 7 from 12 in prescription regulations. We further introduce a tiered notion of evaluability over the operationalised subset, distinguishing requirements that are directly artefact-evaluable under a structured SVG representation from those that depend on unresolved product context or representation constraints. Our analysis reveals that only 7.4% of OTC requirements (6/81) and 16.7% of operationalised prescription requirements (2/12) are fully automatable, with the majority requiring additional product-context modelling or information outside of the regulation document. This exposes a fundamental limitation of artefact-level evaluation: many regulatory requirements are not intrinsically evaluable without resolving dependencies on product context, representation, and external regulatory sources. These findings highlight the need for joint modelling of artefact structure and product context, providing a foundation for scalable, auditable, and trustworthy validation of generative healthcare artefacts.