Evidence map›Paper›PMID 32941420›Full record

ArticlePLoS biology2020

CombiANT: Antibiotic interaction testing made easy.

Nikos Fatsis-Kavalopoulos, Roderich Roemhild, Po-Cheng Tang, Johan Kreuger, Dan I Andersson

Open access · goldAbstract readValidation Study
In one paragraph

Article in PLoS biology, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 26 papers.

0numbers the graph read from it
0cells of the map it votes in
26citing papers in PubMed
3.0field-weighted citation impact, top 8% 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

26 citing papers in PubMed, 44 citations in OpenAlex.

  1. Article
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  8. Article
  9. Antibiotic killing of drug-induced bacteriostatic cells.Antimicrobial agents and chemotherapy · 2025
    Article
  10. Microbial community interactions on a chip.Proceedings of the National Academy of Sciences of the United States of America · 2024
    Article
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  14. Review
  15. Article
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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

5 authors at 1 institution in 1 country.

Nikos Fatsis-KavalopoulosDepartment of Medical Biochemistry and Microbiology, Uppsala University, Uppsala, Sweden.ORCID 0000-0002-5081-0138
Roderich RoemhildDepartment of Medical Biochemistry and Microbiology, Uppsala University, Uppsala, Sweden.ORCID 0000-0001-9480-5261
Po-Cheng TangDepartment of Medical Biochemistry and Microbiology, Uppsala University, Uppsala, Sweden.ORCID 0000-0001-5642-0360
Johan KreugerDepartment of Medical Cell Biology, Uppsala University, Uppsala, Sweden.ORCID 0000-0002-2055-7776
Dan I AnderssonDepartment of Medical Biochemistry and Microbiology, Uppsala University, Uppsala, Sweden.ORCID 0000-0001-6640-2174
Uppsala University · SE

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Antibiotic combination therapies are important for the efficient treatment of many types of infections, including those caused by antibiotic-resistant pathogens. Combination treatment strategies are typically used under the assumption that synergies are conserved across species and strains, even though recent results show that the combined treatment effect is determined by specific drug-strain interactions that can vary extensively and unpredictably, both between and within bacterial species. To address this problem, we present a new method in which antibiotic synergy is rapidly quantified on a case-by-case basis, allowing for improved combination therapy. The novel CombiANT methodology consists of a 3D-printed agar plate insert that produces defined diffusion landscapes of 3 antibiotics, permitting synergy quantification between all 3 antibiotic pairs with a single test. Automated image analysis yields fractional inhibitory concentration indices (FICis) with high accuracy and precision. A technical validation with 3 major pathogens, Escherichia coli, Pseudomonas aeruginosa, and Staphylococcus aureus, showed equivalent performance to checkerboard methodology, with the advantage of strongly reduced assay complexity and costs for CombiANT. A synergy screening of 10 antibiotic combinations for 12 E. coli urinary tract infection (UTI) clinical isolates illustrates the need for refined combination treatment strategies. For example, combinations of trimethoprim (TMP) + nitrofurantoin (NIT) and TMP + mecillinam (MEC) showed synergy, but only for certain individual isolates, whereas MEC + NIT combinations showed antagonistic interactions across all tested strains. These data suggest that the CombiANT methodology could allow personalized clinical synergy testing and large-scale screening. We anticipate that CombiANT will greatly facilitate clinical and basic research of antibiotic synergy.

Indexed as

Drug SynergismAlgorithmsAmdinocillinAnti-Bacterial AgentsDrug Resistance, Multiple, BacterialDrug Therapy, CombinationEscherichia coliEscherichia coli InfectionsHumansMicrobial Sensitivity TestsNitrofurantoinPseudomonas aeruginosaPseudomonas InfectionsReproducibility of ResultsStaphylococcal InfectionsStaphylococcus aureusAmdinocillinAnti-Bacterial AgentsNitrofurantoinTrimethoprim

Identifiers

PMID32941420
PMCPMC7524002
OpenAlexW3087383192

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.