Evidence map›Paper›PMID 29760144›Full record

SynthesisAntimicrobial agents and chemotherapy2018

Personalizing Polymyxin B Dosing Using an Adaptive Feedback Control Algorithm.

Elizabeth A Lakota, Cornelia B Landersdorfer, Roger L Nation, Jian Li, Keith S Kaye, Gauri G Rao, Alan Forrest

Open access · hybridAbstract readMeta-Analysis
In one paragraph

Synthesis in Antimicrobial agents and chemotherapy, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 39 papers, 2 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
39citing papers in PubMed, 2 pooled it
7.0field-weighted citation impact, top 2% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

39 citing papers in PubMed, 2 syntheses or guidelines pooled it, 103 citations in OpenAlex.

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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

7 authors at 5 institutions in 2 countries.

Elizabeth A LakotaUniversity at Buffalo, Buffalo, New York, USA.
Cornelia B LandersdorferDrug Delivery, Disposition and Dynamics, Monash Institute of Pharmaceutical Sciences, Monash University, Parkville Victoria, Australia.
Roger L NationDrug Delivery, Disposition and Dynamics, Monash Institute of Pharmaceutical Sciences, Monash University, Parkville Victoria, Australia.
Jian LiMonash Biomedicine Discovery Institute, Department of Microbiology, Monash University, Victoria, Australia.
Keith S KayeDepartment of Internal Medicine, Division of Infectious Diseases, University of Michigan Medical School, Ann Arbor, Michigan.
Gauri G RaoUniversity at Buffalo, Buffalo, New York, USA gaurirao@live.unc.edu systat10@email.unc.edu.
Alan ForrestUniversity at Buffalo, Buffalo, New York, USA gaurirao@live.unc.edu systat10@email.unc.edu.
Monash University · AUUniversity at Buffalo, State University of New York · USAustralian Regenerative Medicine Institute · AUUniversity of Michigan–Ann Arbor · USUniversity of North Carolina at Chapel Hill · US

Funding

Optimizing Clinical Use of Polymyxin B: Teaching an Old Drug to Treat SuperbugsR01AI119446 · NIAID · WAYNE STATE UNIVERSITY · PI LI, JIAN, POGUE, JASON MICHAEL · 2015 to 2019
$5.0M
NIAID NIH HHS R01 AI119446
6 · The paper itself

Abstract

Polymyxin B is used as an antibiotic of last resort for patients with multidrug-resistant Gram-negative bacterial infections; however, it carries a significant risk of nephrotoxicity. Herein we present a polymyxin B therapeutic window based on target area under the concentration-time curve (AUC) values and an adaptive feedback control algorithm (algorithm) which allows for the personalization of polymyxin B dosing. The upper bound of this therapeutic window was determined through a pharmacometric meta-analysis of polymyxin B nephrotoxicity data, and the lower bound was derived from murine thigh infection pharmacokinetic (PK)/pharmacodynamic (PD) studies. A previously developed polymyxin B population pharmacokinetic model was used as the backbone for the algorithm. Monte Carlo simulations (MCS) were performed to evaluate the performance of the algorithm using different sparse PK sampling strategies. The results of the nephrotoxicity meta-analysis showed that nephrotoxicity rate was significantly correlated with polymyxin B exposure. Based on this analysis and previously reported murine PK/PD studies, the target AUC

Indexed as

AlgorithmsAnti-Bacterial AgentsArea Under CurveDrug Resistance, Multiple, BacterialGram-Negative Bacterial InfectionsHumansKidneyMicrobial Sensitivity TestsPolymyxin BAnti-Bacterial AgentsPolymyxin Badaptive feedback controlmultidrug-resistant organismsnephrotoxicitypolymyxins

Identifiers

PMID29760144
PMCPMC6021635
OpenAlexW2804492723

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.