CRI

 [ABSTRACT]

This study examines global and domestic trends in the AI-based pharmaceutical and biopharmaceutical industry and proposes policy directions for Chungbuk Province. The AI-based pharmaceutical and biopharmaceutical industry is a highly integrated field in which data, experimental validation, manufacturing processes, quality management, and regulatory response must be closely connected to generate practical outcomes. While AI-based drug discovery can improve the efficiency of target identification and drug design, its results require long-term validation through non-clinical testing, clinical trials, regulatory approval, and manufacturing process development. In contrast, AI applications in manufacturing and quality management, such as process optimization, anomaly detection, smart GMP, and automated manufacturing records, are relatively suitable for field-based implementation.

Major countries regard AI as a strategic technology for strengthening national bio-industrial competitiveness. The United States is leading AI-based drug discovery and smart biomanufacturing through TechBio-driven open innovation and regulatory science, while the European Union is building a trustworthy AI ecosystem based on data governance and regulatory frameworks such as the AI Act and the European Health Data Space. China and Japan are also expanding AI R&D and biomanufacturing capabilities through government-led investment and the linkage of clinical and manufacturing innovation. These cases suggest that competitiveness in the AI-based pharmaceutical and biopharmaceutical industry depends not only on algorithms, but also on high-quality data, validation infrastructure, manufacturing application capabilities, and regulatory response systems.

In Korea, AI applications are expanding into AI drug discovery platforms, federated learning-based data collaboration, non-clinical and clinical prediction models, biomanufacturing innovation, and smart GMP. The Korean government is promoting both AI-based drug discovery and biomanufacturing innovation through national strategies, including the AI Bio Strategy, K-MELLODDY, national bio-data infrastructure, and the Biomanufacturing Innovation Strategy. However, data linkage, validation systems, convergence talent, and regulatory response frameworks remain areas that require further improvement.

Chungbuk is not a region where AI platform companies are highly concentrated. Rather, it has strengths as a manufacturing and validation base where AI-based pharmaceutical and biopharmaceutical technologies can be applied and verified in real industrial settings. With its pharmaceutical production base, bio-service functions, and research, non-clinical, clinical, GMP, and quality analysis infrastructure centered on Osong, Chungbuk has strong potential to link AI-based drug discovery with biomanufacturing and follow-up validation.

Based on these findings, Chungbuk should focus on becoming a hub for the demonstration, validation, and manufacturing application of AI-based pharmaceutical and biopharmaceutical technologies, rather than competing in AI technology development itself. By connecting K-Bio Square, AI R&D-based research hospitals, AI bio-data centers, AI digital biopharmaceutical drug development platforms, and next-generation AI toxicity prediction platforms, Chungbuk can build a regional AI pharmaceutical and biopharmaceutical demonstration ecosystem and differentiate itself as a practical hub linking R&D outcomes to validation, manufacturing, quality management, and regulatory approval.