Jovana Stankic*
Dechra, Genera Pharma d.o.o
jovanastankic93.jj [at] gmail.com
Abstract
Regulatory compliance in the importation of veterinary medicinal products remains one of the most labor‑intensive and error‑prone activities within Regulatory Affairs, particularly in non‑EU markets where harmonized standards, centralized databases, and consistent specification formats are often absent. A significant bottleneck in these workflows is the manual comparison of Certificates of Analysis (CoA) with approved release specifications, complicated further by jurisdiction‑specific rules regarding parameter acceptance, shelf‑life calculations, and expiry‑date tolerances. These inconsistencies introduce operational inefficiencies and increase the risk of non‑compliance during product release and importation.
This paper proposes an AI‑Driven Concordance Engine as an innovative decision‑support layer designed to automate detailed compliance checks within global veterinary import processes. The concept integrates natural language processing to extract analytical parameters from CoAs, rule‑based logic to apply regulatory constraints, and machine‑learning‑supported similarity scoring to identify missing parameters, discrepancies, or values that fall outside jurisdiction‑specific requirements. The system flags deviations such as stricter‑than‑approved limits or country‑specific acceptance criteria, directly supporting import eligibility decisions.
Drawing on anonymized real‑world regulatory workflows, the study demonstrates that Concordance Engines significantly reduce manual effort, enhance consistency across regulatory assessments, and create a fully traceable and auditable compliance record. Importantly, the framework incorporates a risk‑based human‑in‑the‑loop model, ensuring that AI augments rather than replaces regulatory expertise. By functioning as an intelligent control mechanism, the Concordance Engine strengthens regulatory decision‑making, shortens operational timelines, and supports scalable compliance across diverse markets.
While the framework is illustrated through its application to non‑EU import compliance, its relevance extends to EU regulatory processes as well. Specifically, the model offers value in post‑approval variation (PAV) management, where changes to quality attributes may influence approved specifications and subsequently CoA evaluation. By providing systematic, structured assessments of such changes, the Concordance Engine enhances cross‑functional alignment between Regulatory Affairs and Quality functions and promotes more efficient regulatory oversight.
Overall, AI‑Driven Concordance Engines represent a first‑in‑class RegTech solution for veterinary Regulatory Affairs, enabling a shift from manual verification to intelligent, technology‑supported regulatory control. This approach offers a scalable pathway for modernizing data‑intensive regulatory processes while ensuring that human expertise remains central to final decision‑making.
Keywords: Veterinary Regulatory Affairs, Regulatory Technology