Kyle Barrett

Kyle Barrett

Senior Data Science Engineer I

Kyle joined Metrum in January 2021 as a Data Science Engineer with research and lab experience in PKPD modeling, biological colloids, and image analysis, including a recent focus on software development, creating tools and automating certain processes using R/R Shiny, C++, and HTML/CSS. His programming experience has involved PBPK and PKPD modeling and simulation and R shiny app development, and he has also modeled anaerobic digestion and created numerous other web apps. Kyle intends to expand his knowledge base in software development and further develop skills in data analysis and visualization.

Recent publications by this scientist

Cost Effectiveness of Individualized Dosing for Hypothetical New Drug in Atopic Dermatitis: A Pharmacometric-Pharmacoeconomic Simulation Study

May 27, 2025

A companion poster to the ASCPT print copy, this visual presentation emphasizes the simulation approach used to evaluate individualized dosing strategies and their economic implications. Results underscore the importance of reducing discontinuation rates to enhance value.

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Individualized dosing may improve the cost effectiveness of new therapeutics in atopic dermatitis

May 27, 2025

This study uses pharmacometric and pharmacoeconomic modeling to explore the cost effectiveness of individualized dosing for a hypothetical new atopic dermatitis drug compared to dupilumab. Simulation scenarios tested the impact of treatment discontinuation rates, efficacy variability, and pricing on cost utility outcomes.

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Pharmacometric-Pharmacoeconomic Modeling and Simulation in Atopic Dermatitis: Informing Early Drug Development Decisions for a Hypothetical New Therapeutic.

December 6, 2024

Presented at ACoP 2024. Early drug development decision making, such as the definition and assessment of target product profile characteristics, rarely includes pharmacoeconomic (PE) considerations. The disciplines of phamacometrics (PM) and PE are closely aligned and intersect at the goal of a quantitative understanding of the system. Connection of these two disciplines is a logical extension of typical PM objectives and should lead to a more complete and accurate understanding of the probability of success for new therapeutics.

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