Sarah Kim, Ph.D.

Sarah Kim, Ph.D. is a leading 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. Dr. Kim has the distinction of being the inaugural faculty hire for the UF Health Sciences Artificial Intelligence (AI) initiative. She graduated summa cum laude from Hanyang University with a B.S. and M.S. in Applied Mathematics. She received her Ph.D. in Mathematics from Florida State University, where she was also honored with the Dean’s Scholarship award for her outstanding academic performance. During her postdoctoral training at the UF Center for Pharmacometrics and Systems Pharmacology, she was honored with multiple trainee awards, including the David Goldstein and Presidential Trainee Awards from the American Society for Clinical Pharmacology and Therapeutics.

Dr. Kim has established a successful program in model-informed, AI-powered drug development, leveraging her expertise in Applied Mathematics and Pharmacometrics. Her research focuses on developing novel methods and innovative tools for understanding disease mechanisms, predicting disease progression, drug response, and understanding the complex relationships between drug concentration, efficacy, and toxicity. Since 2019, she has secured research funding as the Principal Investigator, including the NIH National Center for Advancing Translational Sciences R21 award, two Breakthrough T1D (formerly JDRF)-supported projects, Sail Bio, Critical Path Institute, and four pharmaceutical industry research agreement funds. Her research is highly collaborative, and she has worked closely with numerous researchers and institutions to advance the field of pharmacometrics.

Dr. Kim is a dedicated educator and mentor, committed to teaching and mentoring the next generation of scientists. Her trainees have received multiple awards for their research projects, reflecting the high quality of her mentorship and the caliber of her students. She teaches multiple courses in UF’s Pharm.D. and Ph.D. programs, including PHA 6122 (formerly PHA 6935: Population Pharmacokinetics and Pharmacodynamics), and serves as the course coordinator. Her service roles include serving on the AI Initiative Committee at UF College of Pharmacy, the Pharmacometrics & Pharmacokinetics Community Steering Committee of the American Society for Clinical Pharmacology and Therapeutics, and the Editorial Advisory Board of the Journal of Pharmacokinetics and Pharmacodynamics.

Dr. Kim’s achievements demonstrate her commitment to advancing the field of pharmacometrics and her dedication to mentoring and teaching the next generation of scientists. Specifically, she received the prestigious 2025 UF Excellence Award for Assistant Professors, one of the university’s most esteemed early-career honors bestowed upon only 10 faculty members each year. She was also recognized with the 2023-24 Teaching & Service Award, reflecting consistently high teaching evaluations.

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.

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