Samuel Callisto, Ph.D.

Senior Scientist I

Samuel joined MetrumRG in 2019 after earning his PhD in Experimental and Clinical Pharmacology with an emphasis in Pharmacometrics from the University of Minnesota College of Pharmacy. His thesis work focused on modeling cognitive side effects of the anti-epileptic drug topiramate using a combination of pharmacokinetic-pharmacodynamic models and unsupervised machine learning algorithms. While in graduate school he also researched the impact of pharmacogenomics on the pharmacokinetics and pharmacodynamics of multiple drug classes.

Recent publications by this scientist

Population pharmacokinetic-pharmacodynamic (popPKPD) model of the impact of iclepertin on hemoglobin levels.

July 8, 2024

Presented at PAGE 2024This model, developed by Boehringer Ingelheim in collaboration with Metrum Research Group, provides insights into potential anemia risks and informs monitoring strategies for patients with cognitive impairment associated with schizophrenia.

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An Introduction to R Programming Language

April 3, 2024

Presented by Dr. Sam Castillo and Michael Heathman at the CTSI Disease and Therapeutic Response Modeling Symposium, February 2024.
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Illustrating Integration and Interpretation of the Deep Compartment Model Approach using Keras and R in a Population PK Modeling Analysis

November 14, 2023

Presented at ACoP14. Deep compartment models (DCMs) are a proposed alternative to traditional nonlinear mixed effect (NLME) pharmacometrics approaches [1]. DCM uses neural networks to represent estimated pharmacokinetic parameters which can then be used in either closed-form or ordinary differential equation (ODE)-based representations of pharmacokinetic models

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