Yuezhe Li

Yuezhe Li, Ph.D.

Research Scientist II

Yuezhe earned her Ph.D. in Biomedical Sciences from the University of Connecticut. Her thesis work aimed to understand molecular mechanisms that link diabetes with ciliopathy, a group of rare diseases. Other fields of experience include systems biology, machine learning, and mathematical modeling. More recently, she focused on developing physiologically based pharmacokinetic (PBPK) models to get a better mechanistic understanding of drugs’ pharmacokinetics.

Recent publications by this scientist

Deep QSP Modeling: Leveraging Machine Learning for QSP Model Development and Evaluation

November 14, 2023

Presented at ACoP14. DQSP framework successfully implemented a UDE that characterized the PK of remoxipride and its effect on PRL release from lactotrophs to plasma. The model also characterized the positive feedback effect of plasma PRL on lactotroph PRL stimulation using an ANN.

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A multi-organ integrated QSP model for hematopoietic stem cell differentiation to predict the immune cell reconstitution in ex-vivo gene therapy

November 14, 2023

Presented at ACoP14. The differentiation of mammalian hematopoietic stem cells (HSCs) is complex and multiscale, providing an opportunity for mathematical modeling and simulation to aid in mechanistic understanding, and ultimately, to inform drug development efforts. Historically, HSC mathematical models were focused on the development of a subset of cells, but mathematical models encompassing the overall cellular system’s complexity are rarely available. Here, an integrated quantitative systems pharmacology (QSP) model that characterizes multi-organ HSC differentiation was developed by integrating literature models and adding novel features.

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Hands-On Tutorial: Introduction to Immuno-Oncology (IO) Quantitative Systems Pharmacology (QSP) Modeling Using the Open Source Julia Programming Language

November 14, 2023

ACoP14 pre-meeting workshop. Highlights include exploring Julia as an open source alternative for QSP support of model-informed drug development, gaining insights into IO QSP modeling for antibody drug conjugate (ADCs) and bispecific mAb (bsAb) therapies to drive your own implementations of Project Optimus, and accessing open source Julia versions of two pivotal IO models and parameterizations.

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