Evidence map›Paper›PMID 36335752›Full record

ArticleComputer methods and programs in biomedicine2022

Deciphering longitudinal optical-density measurements to guide clinical dosing regimen design: A model-based approach.

Iordanis Kesisoglou, Brianna M Eales, Paul R Merlau, Vincent H Tam, Michael Nikolaou

Open access · greenAbstract read
In one paragraph

Article in Computer methods and programs in biomedicine, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing papers in PubMed
0.5field-weighted citation impact, top 33% 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

3 citing papers in PubMed, 4 citations in OpenAlex.

  1. Article
  2. Rapid Design of Combination Antimicrobial Therapy againstComputers & chemical engineering · 2025
    Article
  3. Article
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

5 authors at 1 institution in 1 country.

Iordanis KesisoglouDepartment of Chemical & Biomolecular Engineering, University of Houston, 4226 Martin Luther King Boulevard, Houston TX 77204, United States of America.
Brianna M EalesDepartment of Pharmacy Practice and Translational Research, University of Houston, 4349 Martin Luther King Boulevard, Houston TX 77204, United States of America.
Paul R MerlauDepartment of Pharmacy Practice and Translational Research, University of Houston, 4349 Martin Luther King Boulevard, Houston TX 77204, United States of America.
Vincent H TamDepartment of Chemical & Biomolecular Engineering, University of Houston, 4226 Martin Luther King Boulevard, Houston TX 77204, United States of America; Department of Pharmacy Practice and Translational Research, University of Houston, 4349 Martin Luther King Boulevard, Houston TX 77204, United States of America.
Michael NikolaouDepartment of Chemical & Biomolecular Engineering, University of Houston, 4226 Martin Luther King Boulevard, Houston TX 77204, United States of America. Electronic address: Nikolaou@uh.edu.
University of Houston · US

Funding

Personalized Antimicrobial Combinations to Combat ResistanceR01AI140287 · NIAID · UNIVERSITY OF HOUSTON · PI VINCENT H TAM · 2018 to 2026
$5.9M
NIAID NIH HHS R01 AI140287
6 · The paper itself

Abstract

backgroundModel-based analysis of longitudinal optical density measurements from a bacterial suspension exposed to antibiotics has been proposed as a potentially efficient and effective method for extracting useful information to improve the individualized design of treatments for bacterial infections. To that end, the authors developed in previous work a mathematical modeling framework that can use such measurements for design of effective dosing regimens.

objectivesHere we further explore ways to extract information from longitudinal optical density measurements to predict bactericidal efficacy of clinically relevant antibiotic exposures.

methodsLongitudinal optical density measurements were collected in an automated instrument where Acinetobacter baumannii, ATCC BAA747, was exposed to ceftazidime concentrations of 1, 4, 16, 64, and 256 mg/L and to ceftazidime/amikacin concentrations of 1/0.5, 4/2, 16/8, 64/32, and 256/128 (mg/L)/(mg/L) over 20 h. Calibrated conversion of measurements produced total (both live and dead) bacterial cell concentration data (CFU/mL equivalent) over time. Model-based data analysis predicted the bactericidal efficacy of ceftazidime and of ceftazidime/amikacin (at ratio 2:1) for periodic injection every 8 h and subsequent exponential decline with half-life of 2.5 h. Predictions were experimentally tested in an in vitro hollow-fiber infection model, using peak concentrations of 60 and 150 mg/L for injected ceftazidime and of 40/20 (mg/L)/(mg/L) for injected ceftazidime/amikacin.

resultsModel-based analysis predicted low (<62%) confidence in clinically relevant suppression of the bacterial population by periodic injections of ceftazidime alone, even at high peak concentrations. Conversely, analysis predicted high (>95%) confidence in bacterial suppression by periodic injections of ceftazidime/amikacin combinations at a wide range of peak concentrations ratioed at 2:1. Both predictions were experimentally confirmed in an in vitro hollow fiber infection model, where ceftazidime was periodically injected at peak concentrations 60 and 150 mg/L (with predicted suppression confidence 38% and 59%, respectively) and a combination of ceftazidime/amikacin was periodically injected at peak concentrations 40/20 (mg/L)/(mg/L) (with predicted suppression confidence 98%).

conclusionsThe paper highlights the potential of clinicians using the proposed mathematical framework to determine the utility of different antibiotics to suppress a patient-specific isolate. Additional studies will be needed to consolidate and expand the utility of the proposed method.

Indexed as

AmikacinCeftazidimeAnti-Bacterial AgentsHumansMicrobial Sensitivity TestsAmikacinAnti-Bacterial AgentsCeftazidimeMathematical modelingModelingMultidrug resistant bacteriaPharmacodynamicsPharmacokineticsPharmacometrics

Identifiers

PMID36335752
PMCPMC10225978
OpenAlexW4307906173

What OpenQuestion holds

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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.