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X. Barbaut

Researcher at University of Southern California

Publications -  11
Citations -  1088

X. Barbaut is an academic researcher from University of Southern California. The author has contributed to research in topics: Population & Endocarditis. The author has an hindex of 7, co-authored 11 publications receiving 965 citations.

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The Hill equation: a review of its capabilities in pharmacological modelling

TL;DR: The Hill equation has many different properties which can be of great interest for those interested in mathematical modelling in pharmacology and biosciences, and is introduced as a probabilistic view of the Hill equation.
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Individualizing drug dosage regimens : roles of population pharmacokinetic and dynamic models, bayesian fitting, and adaptive control

TL;DR: The role of population pharmacokinetic modeling is to store experience with drug behavior to select a specific serum level therapeutic goal based on each individual patient's need for the drug and on the risk of adverse reactions.
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Model-based, goal-oriented, individualised drug therapy. Linkage of population modelling, new 'multiple model' dosage design, bayesian feedback and individualised target goals.

TL;DR: The use of population pharmacokinetic models to store experiences about drugs in patients and to apply that experience to the care of new patients is examined, especially for bacterial and viral infections, cardiovascular therapy, and cancer and transplant situations.
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Comparisons between Antimicrobial Pharmacodynamic Indices and Bacterial Killing as Described by Using the Zhi Model

TL;DR: A new composite index, weighted AUC (WAUC), is proposed, which appears to be useful for both concentration-dependent and concentration-independent drugs.
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Preliminary results of three methods for population pharmacokinetic analysis (NONMEM, NPML, NPEM) of amikacin in geriatric and general medicine patients

TL;DR: This work analyzed data of patients receiving amikacin, using three methods of population analysis: the First Order (FO) method implemented in NONMEM, and the non-parametric methods NPML and NPEM.