Kyle Baron

Kyle T. Baron, Pharm.D., Ph.D.

Group Leader PKPD, Principal Scientist II

Kyle joined MetrumRG in 2010 after receiving his Pharm.D. and Ph.D. in  Experimental and Clinical Pharmacology from the University of Minnesota. As a member of our translational and systems pharmacology group, Kyle has worked to support sponsor development programs in a variety of therapeutic areas, including chronic hepatitis C infection, HIV, calcium homeostasis and bone health, type-2 diabetes, and cystic fibrosis.  

Kyle is the developer and maintainer of mrgsolve ( and also leads workshops at all levels to guide other M&S scientists in the effective use of this simulation tool in PKPD, PBPK, and QSP modeling. Kyle also serves as adjunct faculty in Experimental and Clinical Pharmacology at the University of Minnesota.  

Recent publications by this scientist

bbr.bayes: An Open-Source Tool to Facilitate an Efficient, Reproducible Bayesian Workflow Using NONMEM

July 8, 2024

Presented at PAGE 2024. The bbr.bayes package reduces much of the friction associated with a Bayesian pharmacometrics analysis in NONMEM® and promotes good practice applications. 

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gPKPDviz: A flexible R shiny tool for pharmacokinetic/pharmacodynamic simulations using mrgsolve

January 12, 2024

GPKPDviz is a Shiny application designed for real-time simulation, visualization, and assessment of PK/PD models. The app allows the generation of virtual populations, simulation of diverse dosing scenarios, and assessment of covariate and dosing regimen impacts on PK/PD endpoints. It supports loading actual population data from clinical trials and offers a streamlined workflow for efficient modeling.

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simpar: an R Package for Parameter Uncertainty Simulations in Pharmacometric Modeling

November 15, 2023

Presented at ACoP14. This project is dedicated to integrating parameter uncertainty into pharmacometric simulations, which plays a crucial role in making informed decisions in drug development. Initially, the metrumrg package in R was instrumental for simulating both fixed and random effect parameters. However, this package has since been deprecated. Consequently, the primary objective was to create a new R package named simpar. This new package aimed to retain the essential functionalities of metrumrg while expanding its capabilities. The overarching goal was to significantly enhance the support for incorporating parameter uncertainty into pharmacometric simulations, thereby aiding more comprehensive and accurate decision-making processes in this field.

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