Research

1. Clinical informatics

Clinical data contain rich information about patients, treatments, and outcomes, but much of this information remains difficult to use effectively because it is large, heterogeneous, and often unstructured. We develop computational methods to transform electronic health records, clinical trial data, medical literature, and drug safety data into actionable evidence. Our research applies AI/ML models to problems such as patient phenotyping, clinical outcome prediction, clinical trial design and eligibility optimization, and pharmacovigilance. We aim to bridge clinical data and decision-making by developing methods that make biomedical evidence more accessible, computable, and useful.

2. Pharmacometrics

Drug response varies widely between individuals due to differences in pharmacokinetics, disease characteristics, concomitant medications, and other biological factors. We use quantitative models to understand how these factors influence drug exposure and response and to translate this knowledge into better decisions in drug development and clinical pharmacotherapy. Our research includes pharmacokinetic and pharmacodynamic modeling, model-based meta-analysis, and quantitative systems pharmacology, and model-informed clinical trial design. Ultimately, we aim to identify the right dose and treatment strategy for the right patient using data-driven quantitative approaches.

1. (식품의약품안전평가원) 신기술·신개념 의약품 신속개발 지원을 위한 임상 가이드라인 개발 (2026.03–2026.12)

[참여연구원] 역할: AI 기반 의약품 개발 관련 정보자료집, 가이드라인 개발 담당

2. (식품의약품안전평가원) 의약품 허가 심사 지원을 위한 동물대체시험법 연계 QSP 기술 도입 및 평가체계 마련 (2026.02–2030.11)

[참여연구원] 역할: 정량 시스템 약리 모델 개발, 전북대학교병원 전향적/후향적 임상시험 진행

3. (보건산업진흥원) 연합학습 기반 ADMET-PK/PD 모델 개발 (2024.07–2026.12)

[참여연구원] 역할: 과제 총괄 및 연합학습 모델 개발 지원

4. (보건산업진흥원) 위식도역류질환 치료 합성의약품-한약제제 병용투여 약물상호작용 연구 및 지침 개발 (2025.04–2029.12)

[참여연구원] 역할: 상호작용 임상시험 프로토콜 개발 및 최적화

5. (중앙부처 RISE 사업) 바이오의생명 융합인재 양성사업 (2025.04–2029)

[연구책임자] 역할: 약학과를 중심으로 융합 인재 양성 프로그램 운영