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.
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.
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.
January 30, 2024
Oral presentation for 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.