The extraction of pharmacological knowledge from regulatory documents has become a key focus in biomedical natural language processing, with applications ranging from adverse event monitoring to AI-assisted clinical decision support. However, research in this field has predominantly relied on English-language corpora such as DrugBank, leaving a significant gap in resources tailored to other healthcare systems. To address this limitation, we introduce DART (Drug Annotation from Regulatory Texts), the first structured corpus of Italian Summaries of Product Characteristics derived from the official repository of the Italian Medicines Agency (AIFA). The dataset was built through a reproducible pipeline encompassing web-scale document retrieval, semantic segmentation of regulatory sections, and clinical summarization using a few-shot-tuned large language model with low-temperature decoding. DART provides structured information on key pharmacological domains such as indications, adverse drug reactions, and drug-drug interactions. To validate its utility, we implemented an LLM-based drug interaction checker that leverages the dataset to infer clinically meaningful interactions. Experimental results show that instruction-tuned LLMs can accurately infer potential interactions and their clinical implications when grounded in the structured textual fields of DART. We publicly release our code on GitHub: https://github.com/PRAISELab-PicusLab/DART.

翻译：从监管文件中提取药理学知识已成为生物医学自然语言处理的关键焦点，其应用范围涵盖从不良事件监测到人工智能辅助临床决策支持等多个领域。然而，该领域的研究主要依赖于英语语料库（如DrugBank），导致针对其他医疗体系的定制化资源存在显著空白。为弥补这一不足，我们推出了DART（基于监管文本的药物标注），这是首个源自意大利药品管理局官方知识库的意大利语药品特性概要结构化语料库。该数据集通过可复现的流程构建，涵盖网络级文档检索、监管章节的语义分割，以及采用低温解码的少样本微调大语言模型进行临床摘要生成。DART提供了关键药理学领域的结构化信息，包括适应症、药物不良反应及药物相互作用。为验证其实用性，我们开发了基于大语言模型的药物相互作用检查器，该工具利用数据集推断具有临床意义的相互作用。实验结果表明，当基于DART的结构化文本字段时，经过指令微调的大语言模型能够准确推断潜在相互作用及其临床影响。我们已在GitHub公开代码：https://github.com/PRAISELab-PicusLab/DART。