Kierstey Utsey

Kiersten Utsey, Ph.D.

Research Scientist II

Kiersten joined Metrum in 2020 after completing her Ph.D. in Mathematics at the University of Utah. Her dissertation was focused on building and analyzing mathematical models to understand bistability in biological systems. This work included modeling the maintenance of DNA methylation patterns during cell division and modeling flagellar gene regulation and biosynthesis in Salmonella enterica.

Recent publications by this scientist

In relapsed or refractory diffuse large B-cell lymphoma, CD19 expression by immunohistochemistry alone is not a predictor of response to loncastuximab tesirine

December 12, 2023

This study explores Lonca’s efficacy across varying CD19 expression levels, revealing its effectiveness in patients, regardless of low or undetectable CD19 levels by conventional methods. The study integrates quantitative systems pharmacology (QSP) modeling to predict treatment responses, indicating that CD19 expression alone may not predict Lonca’s effectiveness, however, response predictions are improved by considering CD19 surface density.

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Exploring the Influence of Bispecific Antibody Mechanisms on In Vitro Dose Response: Insights from an Open-Science Quantitative Systems Pharmacology Model in Julia

November 14, 2023

Presented at ACoP14. Analysis of the QSP model found that the internalization rate of the target receptors may be an important, yet often underestimated, factor for understanding bsAb efficacy in vitro. Simulations of the model indicated that high rates of receptor turnover can increase model predictions of efficacy, particularly at higher antibody concentrations.

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Hierarchical Deep Compartment Modeling: A Workflow to Leverage Machine Learning for Hierarchical Pharmacometric Modeling

November 14, 2023

ACoP14 Abstract Quality Award winner. The Hierarchical Deep Compartment Modeling (HDCM) framework introduced in this abstract was developed using open-source tools in the Julia Programming Language and integrated Bayesian inference to quantify the uncertainty around the model parameters. It provides a convenient, readily accessible HDCM framework to the pharmacometrics community interested in applying deep learning to hierarchical compartmental models.

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