Evidence map›Paper›PMID 25712313›Full record

ArticleThe Journal of antimicrobial chemotherapy2015

Colistin and doripenem combinations against Pseudomonas aeruginosa: profiling the time course of synergistic killing and prevention of resistance.

Neang S Ly, Jürgen B Bulitta, Gauri G Rao, Cornelia B Landersdorfer, Patricia N Holden, Alan Forrest, Phillip J Bergen, Roger L Nation, Jian Li, Brian T Tsuji

Open access · bronzeAbstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in The Journal of antimicrobial chemotherapy, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 38 papers, 3 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
38citing papers in PubMed, 3 pooled it
3.2field-weighted citation impact, top 7% 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

38 citing papers in PubMed, 3 syntheses or guidelines pooled it, 59 citations in OpenAlex.

  1. Pooled it
  2. Pooled it
  3. Guideline
  4. Review
  5. DynamicAntibiotics (Basel, Switzerland) · 2024
    Review
  6. Article
  7. Review
  8. Article
  9. Review
  10. Review
  11. Review
  12. Using machine learning to optimize antibiotic combinations: dosing strategies for meropenem and polymyxin B against carbapenem-resistant Acinetobacter baumannii.Clinical microbiology and infection : the official publication of the European Society of Clinical Microbiology and Infectious Diseases · 2020
    Article
  13. Article
  14. Review
  15. Article
  16. Generating Robust and Informative NonclinicalAntimicrobial agents and chemotherapy · 2019
    Review
  17. Review
  18. Article
  19. Article
  20. Antibiotic selection in the treatment of acute invasive infections by Pseudomonas aeruginosa: Guidelines by the Spanish Society of Chemotherapy.Revista espanola de quimioterapia : publicacion oficial de la Sociedad Espanola de Quimioterapia · 2018
    Review
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

10 authors at 2 institutions in 2 countries.

Neang S LyLaboratory for Antimicrobial and Bacterial Dynamics, School of Pharmacy and Pharmaceutical Sciences, University at Buffalo, State University of New York, Buffalo, NY, USA The New York State Center of Excellence in Bioinformatics & Life Sciences, University at Buffalo, State University of New York, Buffalo, NY, USA.
Jürgen B BulittaLaboratory for Antimicrobial and Bacterial Dynamics, School of Pharmacy and Pharmaceutical Sciences, University at Buffalo, State University of New York, Buffalo, NY, USA Drug Delivery, Disposition and Dynamics, Monash Institute of Pharmaceutical Sciences, Monash University, Parkville, Victoria, Australia.
Gauri G RaoLaboratory for Antimicrobial and Bacterial Dynamics, School of Pharmacy and Pharmaceutical Sciences, University at Buffalo, State University of New York, Buffalo, NY, USA The New York State Center of Excellence in Bioinformatics & Life Sciences, University at Buffalo, State University of New York, Buffalo, NY, USA.
Cornelia B LandersdorferLaboratory for Antimicrobial and Bacterial Dynamics, School of Pharmacy and Pharmaceutical Sciences, University at Buffalo, State University of New York, Buffalo, NY, USA Drug Delivery, Disposition and Dynamics, Monash Institute of Pharmaceutical Sciences, Monash University, Parkville, Victoria, Australia.
Patricia N HoldenLaboratory for Antimicrobial and Bacterial Dynamics, School of Pharmacy and Pharmaceutical Sciences, University at Buffalo, State University of New York, Buffalo, NY, USA The New York State Center of Excellence in Bioinformatics & Life Sciences, University at Buffalo, State University of New York, Buffalo, NY, USA.
Alan ForrestLaboratory for Antimicrobial and Bacterial Dynamics, School of Pharmacy and Pharmaceutical Sciences, University at Buffalo, State University of New York, Buffalo, NY, USA.
Phillip J BergenCentre for Medicine Use and Safety, Monash University, Parkville, Victoria, Australia.
Roger L NationDrug Delivery, Disposition and Dynamics, Monash Institute of Pharmaceutical Sciences, Monash University, Parkville, Victoria, Australia.
Jian LiDrug Delivery, Disposition and Dynamics, Monash Institute of Pharmaceutical Sciences, Monash University, Parkville, Victoria, Australia.
Brian T TsujiLaboratory for Antimicrobial and Bacterial Dynamics, School of Pharmacy and Pharmaceutical Sciences, University at Buffalo, State University of New York, Buffalo, NY, USA The New York State Center of Excellence in Bioinformatics & Life Sciences, University at Buffalo, State University of New York, Buffalo, NY, USA btsuji@buffalo.edu.
Monash University · AUUniversity at Buffalo, State University of New York · US

Funding

Novel PK/PD Strategies for Polymyxin Combinations against Gram-negative SuperbugsR01AI111990 · NIAID · STATE UNIVERSITY OF NEW YORK AT BUFFALO · PI LI, JIAN, TSUJI, BRIAN · 2014 to 2018
$4.4M
Targeting MDR hetero-resistant Gram-negatives: PK/PD for rational combinationsR01AI079330 · NIAID · MONASH UNIVERSITY · PI NATION, ROGER L. · 2008 to 2011
$2.2M
NIAID NIH HHS R01AI079330NIAID NIH HHS R01 AI111990NIAID NIH HHS R01AI111990
6 · The paper itself

Abstract

objectivesColistin is an 'old' drug, which is being increasingly utilized due to limited therapeutic options. However, resistance emergence during monotherapy is concerning. Here, our objective was to optimize colistin combinations against Pseudomonas aeruginosa by profiling the time course of synergistic killing and prevention of resistance.

methodsHollow-fibre infection models over 10 days simulated clinically relevant dosage regimens of colistin and doripenem against two heteroresistant P. aeruginosa strains (MIC 1 mg/L) and one resistant (MIC 128 mg/L) strain (inoculum 10(9.3) cfu/mL). New mathematical mechanism-based models (MBMs) were developed using S-ADAPT.

resultsAgainst heteroresistant P. aeruginosa strains, colistin monotherapy resulted in initial killing (up to 2.64 log10 cfu/mL) within 24 h followed by regrowth. High-intensity combinations involving free steady-state colistin concentrations of 5 mg/L achieved complete eradication (>9.3 log10 killing) within 48 h. These combinations achieved synergy with up to 9.38 log10 greater killing compared with the most active monotherapy. Against the colistin-resistant strain, the combination yielded marked initial synergy with up to 6.11 log10 cfu/mL bacterial reductions within 72 h followed by regrowth. The MBMs quantified total and resistant subpopulations and the proposed synergy between colistin and doripenem.

conclusionsOur findings provide insight into optimal antibiotic treatment and may serve as a framework for new drug combinations and combination modelling.

Indexed as

Drug Resistance, BacterialDrug SynergismAnti-Bacterial AgentsCarbapenemsColistinDoripenemHumansMicrobial Sensitivity TestsMicrobial ViabilityModels, TheoreticalPseudomonas aeruginosaTime FactorsAnti-Bacterial AgentsCarbapenemsColistinDoripenemcombination therapyP. aeruginosapharmacodynamicspharmacokineticspolymyxin

Identifiers

PMID25712313
PMCPMC4398476
OpenAlexW2126058908

What OpenQuestion holds

Textmetadata
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.