Sarah Kim, Ph.D.

Sarah Kim, Ph.D., is a practicing pharmacometrician with a background in Applied Mathematics and a tenure-track Assistant Professor in the Department of Pharmaceutics at the University of Florida (UF) College of Pharmacy. She graduated summa cum laude from Hanyang University with a B.S. and M.S. in Applied Mathematics. She received her Ph.D. degree in Mathematics from Florida State University. During her postdoctoral training at UF Center for Pharmacometrics and Systems Pharmacology, she received recognition from the international pharmacometrics scientific community, including the David Goldstein Award and multiple Presidential Trainee Awards from the American Society for Clinical Pharmacology and Therapeutics (ASCPT).

Dr. Kim is currently leading several computational modeling projects to create and innovate quantitative solutions in healthcare, with the theme of Model-informed and AI-powered Drug Development. Since 2019, she has secured research funding as the PI, including the NIH National Center for Advancing Translational Sciences R21 award, two JDRF-funded projects, Sail Bio, Critical Path Institute, and four pharmaceutical industry research agreement funds. In addition, she is a co-investigator of the NIH National Institute of Allergy and Infectious Diseases R01 award. Through these research projects, her trainees have received multiple awards.

Dr. Kim is a course coordinator and one of the core instructors of the Population Pharmacokinetics and Pharmacodynamics course. She has been serving as an AI initiative committee member at the UF College of Pharmacy since 2020, as a Pharmacometrics & Pharmacokinetics Community Steering Committee member of ASCPT since 2022, and as an Editorial Advisory Board member for the Journal of Pharmacokinetics and Pharmacodynamics since 2023.

Dr. Sarah Kim has developed two pillars of the research focus with a model-informed and AI-powered drug development theme: 1. disease-drug-trial modeling and simulation and 2. AI applications to maximize the use of imaging data in drug development.

1. Disease-drug-trial modeling and simulation: We are developing disease progression model-based clinical trial simulation tools to optimize clinical trial designs to investigate potential therapies’ efficacy for Duchenne Muscular Dystrophy, Type 1 Diabetes, and Autosomal Dominant Tubulointerstitial Kidney Disease. In addition, we are developing population pharmacokinetic/ pharmacodynamic (PK/PD) models to inform experimental designs of anti-Mycobacterium Tuberculosis agents to identify optimal regimens which markedly shorten the length of Tuberculosis treatments and prevent the emergence of resistance.

2. AI applications to maximize the use of imaging data in drug development: We are developing AI-assisted imaging analysis and informatics tools that will accelerate feature extraction from imaging data and provide insights into a better understanding of disease progression for Type 1 Diabetes and Duchenne Muscular Dystrophy.