Sooyoung Lee, PhD, is an Assistant Professor in the Center for Translational Medicine (CTM) at the University of Maryland School of Pharmacy.
Dr. Lee is a pharmacometrician specializing in model-informed drug development (MIDD), Bayesian pharmacokinetic/pharmacodynamic (PK/PD) modeling, and AI-enabled quantitative methods. His research focuses on analyzing and simulating clinical trial and real-world data to inform drug dosing, optimize clinical trial design, and support regulatory decision-making. His current work advances model-integrated evidence and model-informed bioequivalence (MIBE) approaches to accelerate the development of innovative and generic therapies.
Dr. Lee’s expertise spans pharmacometrics, therapeutic drug monitoring, machine learning applications in drug development and safety, and quantitative clinical pharmacology. He has contributed to regulatory and clinical development programs through modeling and simulation approaches supporting NDA and 505(b)(2) pathways, Bayesian population PK/PD analyses, and pharmacovigilance research using large-scale safety datasets.
Research Interests
- Model-informed drug development (MIDD)
- Bayesian pharmacokinetic/pharmacodynamic modeling
- Model-informed bioequivalence (MIBE)
- Clinical trial simulation and quantitative clinical pharmacology
- Machine learning and AI applications in pharmacometrics
- Therapeutic drug monitoring
- Pharmacovigilance and drug safety analytics
Clinical and Technical Expertise
- Population PK/PD modeling and simulation
- Bayesian therapeutic drug monitoring
- Regulatory pharmacometrics
- Clinical trial design optimization
- Real-world data analysis
- Machine learning applications in drug development
- Quantitative methods for generic drug development
Education and Training
- PhD, Nanopharmaceutical Science, Kyung Hee University, South Korea
- MPH, Health Care Management and Policy, Seoul National University, South Korea
- PharmD, Kyung Hee University, South Korea
Professional Experience
- Assistant Professor, Center for Translational Medicine, University of Maryland School of Pharmacy
- Postdoctoral Research Fellow, Center for Translational Medicine, University of Maryland School of Pharmacy
- Visiting Scientist, Center for Translational Medicine, University of Maryland School of Pharmacy
- Postdoctoral Research Fellow, East-West Medical Research Institute, Kyung Hee University
- Graduate Research Assistant, Department of Clinical Pharmacology and Therapeutics, Kyung Hee University
- Hospital Pharmacist, Cheil Hospital, South Korea
Research and Scholarly Contributions
Dr. Lee has led and contributed to research focused on Bayesian population pharmacokinetic modeling, machine learning-enabled therapeutic drug monitoring, and clinical trial simulation. His doctoral work included the development of novel methods for individual pharmacokinetic parameter estimation and the creation of HMCtdm, an open-source R package for Bayesian therapeutic drug monitoring applications.
His recent research has explored pharmacokinetic and pharmacodynamic interactions, optimization of antimicrobial dosing strategies, pharmacovigilance analyses, and applications of machine learning to improve therapeutic drug monitoring performance.
Select Publications
Lee S, Chae S, Kwon M, et al. (2025). Pharmacokinetic and Pharmacodynamic Interaction of Metformin and Ojeok-san in Healthy Volunteers. Drug Design, Development and Therapy.
Kim J, Kwon M, Lee S, et al. (2025). Comparisons of pharmacokinetics of glimepiride in combination with Ojeok-san versus glimepiride alone.Scientific Reports.
Lee S, Song M, Han J, et al. (2022). Application of Machine Learning Classification to Improve the Performance of Vancomycin Therapeutic Drug Monitoring. Pharmaceutics.
Lee S, Song M, Lim W, et al. (2022). Development and Validation of Open-Source R Package HMCtdm for Therapeutic Drug Monitoring. Pharmaceuticals.
Jung SY, Lee SH, Lee SY, et al. (2017). Antimicrobials for the treatment of drug-resistant Acinetobacter baumannii pneumonia in critically ill patients: a systematic review and Bayesian network meta-analysis. Critical Care.
Awards and Recognition
- ACoP2024 Abstract Trainee Award, American Conference on Pharmacometrics
- Clinical Pharmacometrics SIG Trainee Award, ACoP14
Research Funding
- Supporting the Growth of a Methodology Expert in Clinical Study for Efficient Clinical Trials in the Biopharmaceutical Industry
- Korea Health Industry Development Institute (HI22C2060)
- Principal Investigator | 2022–2024
Computational and Analytical Tools
- R
- Pumas
- NONMEM
- Python
- STATA
Professional Licensure
- Licensed Pharmacist, Republic of Korea Ministry of Health and Welfare