GC Green Cross is leveraging artificial intelligence (AI) to enhance the efficiency of its drug regulation processes. The company recently announced the launch of its AI chatbot, RegulAItor, designed to assist with key tasks such as approval strategy formulation and document review, amidst a growing trend of AI adoption across the pharmaceutical industry.
The RegulAItor chatbot utilizes data from U.S. Food and Drug Administration (FDA) guidelines and internal approval documents. It classifies types of approval changes and analyzes similar cases and submission trends to help formulate optimal regulatory strategies. Previously, staff had to manually review extensive regulatory documents and internal materials, but the use of AI significantly reduces the time spent on research and review.
A notable feature of this system is its operation within a secure internal data environment. By applying Retrieval-Augmented Generation (RAG) technology, it generates responses only within a pre-established data scope, preventing external information from entering the system and minimizing the 'hallucination' issues often associated with generative AI.
According to the company, this is the first instance of a domestically developed AI chatbot specifically tailored for approval change management being applied in actual operations within the South Korean pharmaceutical industry.
Lee Jae-woo, head of the development division at GC Green Cross, stated, "The significance lies in systematizing the experience and data accumulated during the FDA approval process as organizational assets. In the future, we expect to enhance both the speed and accuracy of regulatory responses."
In the pharmaceutical and biopharmaceutical sectors, the use of AI is rapidly expanding across the entire value chain, including research and development (R&D), clinical trials, production, and regulatory approval.
Celltrion is enhancing development efficiency by implementing AI for antibody design and process optimization, while SK Biopharm is applying AI to drug candidate discovery and clinical data analysis to boost research productivity.
An industry insider predicted, "The expansion of AI applications will accelerate further into regulatory responses and quality management areas."
* This article has been translated by AI.
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