Department of Regulatory Science for AI Drug Development
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address
(14662) NP206, Jeong Jin-seok Chugigyeong Yakhakgwan, 43, Jibong-ro, Wonmi-gu, Bucheon-si, Gyeonggi-do, Republic of Korea -
hours of operation
Mon~Fri 08:00 ~ 17:00 -
tel
+82 2 2164 4052 -
e-mail
bhw00719@catholic.ac.kr
About Us
The Catholic University of Korea's AI New Drug Development Regulatory Science Convergence Human Resources Development Project is an education and research project group established to cultivate convergent professionals who can comprehensively understand AI-based new drug development and regulatory science and apply it to practice based on cooperation with pharmaceutical colleges, medical schools, and the departments of medical artificial intelligence. The importance of AI and data-based decision-making is rapidly expanding in recent years in the development of new drugs, such as finding candidate substances, drug design, toxicity and pharmacokinetics prediction, non-clinical and clinical trial design, manufacturing and quality management, and preparation of approved data. As a result, it is essential to cultivate professionals who can systematically respond to new regulatory issues such as reliability, data quality and representativeness, explainability, reproducibility, change management, and regulatory documentation of AI models.
This project group aims to cultivate master's and doctoral-level professionals with new drug development strategies, CMC, non-clinical and clinical trials, and ability to respond to permission and examination. To this end, we work closely with medical schools, medical artificial intelligence departments, industries, hospitals and regulatory experts centered on the College of Pharmacy to provide practical training courses such as PBL, field training, capstone projects, and corporate mentoring. Through this, it fosters regulatory scientists who connect R&D sites with regulatory agencies, and further contributes to strengthening the competitiveness of the national biohealth industry.
Educational Goals
- Strategic Track: Develop field experts who can use AI to implement/review fast-fail/quick-win paradigms
- CMC Design Optimization Track: Cultivating up-and-coming researchers and field experts with advanced GMP-based manufacturing and quality management capabilities utilizing AI
- Non-Clinical Trial Track: Cultivating up-and-coming researchers and field experts with GLP-based non-clinical data evaluation capabilities utilizing AI
- Clinical Trials Track: Emerging Researchers Integrating AI to Design Efficient/Ethical Clinical Trials/Training field experts
Requirements
| Degree Programs | Required Subjects | Total credits |
|---|---|---|
| Master degree | 추가 예정 | 24 credits |
| Doctoral degree | 추가 예정 | 36 credits |
| Master-doctoral joint degree | 추가예정 | 60 credits |
Career Paths
1. Pharmaceutical and Bio Companies (Network of Domestic and Foreign Industries)
- New Drug Development Strategy Planner: Evaluate AI-derived target materials or candidates for success from a regulatory or scientific perspective, develop initial pipeline and establish partnership strategies.
- Digital/AI Licensing (RA) Expert: Plan licensing strategies for AI-based new drugs, innovative formulations, or digital therapeutics (e.g., MFDS, FDA, EMA) and prepare review documents tailored to regulatory guidelines.
- CMC and Preclinical/Clinical Regulatory Specialist: Review the regulatory validity and scientific basis of AI-based drug delivery system (DDS) design, toxicity prediction, and clinical trial data.
2. Regulatory Agencies and Public Research Institutes
- Regulatory Agency Examiner/Researcher: Review the licensing of AI-based drugs and medical devices and establish regulatory guidelines and evaluation standards for emerging AI convergence technologies.
- National R&D Planning/Evaluation Specialist: Participate in planning and evaluating national AI drug development projects and conducting related policy research.
- Healthcare Data Compliance Specialist: Manage the safe use and de-identification of medical big data while ensuring compliance with personal information protection regulations.
3. AI Healthcare and IT Solution Companies
- Healthcare Data Quality Manager: Build high-quality clinical and medical datasets and validate the reliability and bias of AI models used in drug development.
- AI Solution Product Manager (PM): Evaluate the performance of AI software used throughout the drug development process and reflect regulatory risks from the early stages of product development.
4. Laboratories and Professional Consulting Firms
- Regulatory Science Policy Researcher: Conduct research on regulatory advancement and global harmonization strategies for AI-driven drug development at universities or national research institutes.
- Healthcare Professional Consultant: Provide consulting services to bio ventures on AI technology licensing risks, global regulatory strategies, and target product profile (TPP) development.