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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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Formato: | Online Artículo Texto |
Lenguaje: | English |
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Frontiers Media S.A.
2023
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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. |
format | Online Article Text |
id | pubmed-10323137 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | Frontiers Media S.A. |
record_format | MEDLINE/PubMed |
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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