Kierstey Utsey

Kiersten Utsey, Ph.D.

Senior Scientist I

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

Pharmacometric Machine Learning: Integrating Neural Networks for Flexible, Advanced Covariate Analysis

June 13, 2025

Presented at ASCPT 2025 Annual Meeting. Neural networks can be integrated with traditional pharmacometric models using several free open-source programming languages. Both Julia and R environments are suitable platforms, but there are tradeoffs regarding development speed, built-in capabilities, and documentation. DCM simplifies the covariate modeling process and uncovers complex, non-linear relationships in computationally efficient workflows.

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Quantitative Systems Pharmacology Modeling of X-linked Hypophosphatemia Disease Pathway Normalization to Predict the Impact of Burosumab Treatment on Serum Biomarkers in Adult and Pediatric Patients

December 6, 2024

Presented at ACoP 2024. The Bone Health QSP model was extended by incorporating XLH disease mechanisms and burosumab impact on serum phosphate and other biomarkers using clinical data from adult and pediatric patients with XLH. The model reproduced clinically observed changes in pharmacodynamic markers in both adult and pediatric patients with XLH; normalization of serum phosphate with burosumab treatment was successfully replicated, facilitating a better understanding of burosumab dosing in patients with XLH going forward. The model could potentially be used to optimize treatment in the clinical setting.

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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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