Samuel Callisto, Ph.D.

Senior Scientist II

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

Reinforcement Learning for Pharmacometrics: A Proof of Concept and Future Directions.

December 6, 2024

Presented at ACoP 2024. A proof-of-concept was conducted using deep reinforcement learning to optimize vancomycin dosing based on predicted PK profiles. The reinforcement learner was able to produce dose recommendations that (on average) exceeded the standard-of-care recommended dose, and future opportunities for using reinforcement learning within pharmacometrics are vast with a large potential for impact through personalized dosing.

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

July 8, 2024

Presented at PAGE 2024. This 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.

An Introduction to R Programming Language

April 3, 2024

Presented by Dr. Sam Callisto and Michael Heathman at the CTSI Disease and Therapeutic Response Modeling Symposium, February 2024.
View presentation recording.

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