Nicolas Grégoire

This paper demonstrates that in vitro time-kill experiments with pharmacometric modeling can identify PK/PD targets equivalent to animal models for meropenem against P. aeruginosa, while quantifying inter-strain variability.

The paper evaluates TAPs transfer kinetics and resensitization efficiency in E. coli using a mechanistic model of subpopulation dynamics, finding plasmid delivery is the main bottleneck.

This paper develops and evaluates model ensembling and machine learning approaches to predict the first dose of amoxicillin in intensive care, finding that ensembling and ML outperform standard dosing.

Splitting each dose into equal-amount continuous infusions of varying duration, computed by a new algorithm, lets a standard hollow-fiber model reproduce linezolid CSF pharmacokinetics within 20% of target parameters.