%0 Journal Article
%T Retrieval-Augmented AI for Interpreting ICH Quality Guidelines and Product Control Strategies
%A Elena Petrova
%A Ivan Georgiev
%A Nikolay Stoyanov
%A Petar Kolev
%J Pharmacophore
%@ 2229-5402
%D 2026
%V 17
%N 1
%R 10.51847/FGAYclS0YD
%P 72-80
%X Pharmaceutical companies face the complex challenge of interpreting and applying ICH quality guidelines while translating general principles into product-specific control strategies, a task made cognitively demanding by variations in modality, dosage form, manufacturing process, and lifecycle stage. Current practices rely heavily on manual document searches, individual expertise, and static templates, which are difficult to scale across diverse product portfolios and evolving regulatory expectations. This article presents a retrieval-augmented AI assistant designed to index ICH quality guidelines, regional regulatory guidance, pharmacopoeial materials, and internal control-strategy documents, enabling natural-language queries about pharmaceutical quality expectations with grounded, citation-backed responses. The system integrates a multi-source ingestion pipeline, a vector database with metadata filtering, a domain-adapted language model, a citation-grounding layer, and a human-review dashboard, emphasizing support for expert interpretation rather than automation of regulatory judgment. By facilitating easier retrieval and comparison of relevant regulatory language, the tool can accelerate the development and review of product-specific control strategies, promote consistency across quality, regulatory affairs, analytical development, and manufacturing teams, and enhance submission readiness while preserving expert oversight, ultimately transforming how pharmaceutical professionals interact with quality guidelines.
%U https://pharmacophorejournal.com/article/retrieval-augmented-ai-for-interpreting-ich-quality-guidelines-and-product-control-strategies-ljn9hnz3bcwwndk