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Bayesian model-guided antimicrobial therapy in pediatrics

Antimicrobials have transformed the practice of medicine, making life-threatening infections treatable, but determining optimal dosing, particularly in pediatric patients, remains a challenge. The lack of pediatric data can largely be traced back to pharmaceutical companies, which, until recently, w...

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Autores principales: Bunn, Haden T., Gobburu, Jogarao V. S., Floryance, Lindsey M.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10323137/
https://www.ncbi.nlm.nih.gov/pubmed/37426816
http://dx.doi.org/10.3389/fphar.2023.1118771
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author Bunn, Haden T.
Gobburu, Jogarao V. S.
Floryance, Lindsey M.
author_facet Bunn, Haden T.
Gobburu, Jogarao V. S.
Floryance, Lindsey M.
author_sort Bunn, Haden T.
collection PubMed
description Antimicrobials have transformed the practice of medicine, making life-threatening infections treatable, but determining optimal dosing, particularly in pediatric patients, remains a challenge. The lack of pediatric data can largely be traced back to pharmaceutical companies, which, until recently, were not required to perform clinical testing in pediatrics. As a result, most antimicrobial use in pediatrics is off-label. In recent years, a concerted effort (e.g., Pediatric Research Equality Act) has been made to fill these knowledge gaps, but progress is slow and better strategies are needed. Model-based techniques have been used by pharmaceutical companies and regulatory agencies for decades to derive rational individualized dosing guidelines. Historically, these techniques have been unavailable in a clinical setting, but the advent of Bayesian-model-driven, integrated clinical decision support platforms has made model-informed precision dosing more accessible. Unfortunately, the rollout of these systems remains slow despite their increasingly well documented contributions to patient-centered care. The primary goals of this work are to 1) provide a succinct, easy-to-follow description of the challenges associated with designing and implementing dose-optimization strategies; and 2) provide supporting evidence that Bayesian-model informed precision dosing can meet those challenges. There are numerous stakeholders in a hospital setting, and our intention is for this work to serve as a starting point for clinicians who recognize that these techniques are the future of modern pharmacotherapy and wish to become champions of that movement.
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spelling pubmed-103231372023-07-07 Bayesian model-guided antimicrobial therapy in pediatrics Bunn, Haden T. Gobburu, Jogarao V. S. Floryance, Lindsey M. Front Pharmacol Pharmacology Antimicrobials have transformed the practice of medicine, making life-threatening infections treatable, but determining optimal dosing, particularly in pediatric patients, remains a challenge. The lack of pediatric data can largely be traced back to pharmaceutical companies, which, until recently, were not required to perform clinical testing in pediatrics. As a result, most antimicrobial use in pediatrics is off-label. In recent years, a concerted effort (e.g., Pediatric Research Equality Act) has been made to fill these knowledge gaps, but progress is slow and better strategies are needed. Model-based techniques have been used by pharmaceutical companies and regulatory agencies for decades to derive rational individualized dosing guidelines. Historically, these techniques have been unavailable in a clinical setting, but the advent of Bayesian-model-driven, integrated clinical decision support platforms has made model-informed precision dosing more accessible. Unfortunately, the rollout of these systems remains slow despite their increasingly well documented contributions to patient-centered care. The primary goals of this work are to 1) provide a succinct, easy-to-follow description of the challenges associated with designing and implementing dose-optimization strategies; and 2) provide supporting evidence that Bayesian-model informed precision dosing can meet those challenges. There are numerous stakeholders in a hospital setting, and our intention is for this work to serve as a starting point for clinicians who recognize that these techniques are the future of modern pharmacotherapy and wish to become champions of that movement. Frontiers Media S.A. 2023-06-22 /pmc/articles/PMC10323137/ /pubmed/37426816 http://dx.doi.org/10.3389/fphar.2023.1118771 Text en Copyright © 2023 Bunn, Gobburu and Floryance. https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
spellingShingle Pharmacology
Bunn, Haden T.
Gobburu, Jogarao V. S.
Floryance, Lindsey M.
Bayesian model-guided antimicrobial therapy in pediatrics
title Bayesian model-guided antimicrobial therapy in pediatrics
title_full Bayesian model-guided antimicrobial therapy in pediatrics
title_fullStr Bayesian model-guided antimicrobial therapy in pediatrics
title_full_unstemmed Bayesian model-guided antimicrobial therapy in pediatrics
title_short Bayesian model-guided antimicrobial therapy in pediatrics
title_sort bayesian model-guided antimicrobial therapy in pediatrics
topic Pharmacology
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10323137/
https://www.ncbi.nlm.nih.gov/pubmed/37426816
http://dx.doi.org/10.3389/fphar.2023.1118771
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