Evidence map›Paper›PMID 35606559›Full record

ReviewNature chemical biology2022

Expanding the search for small-molecule antibacterials by multidimensional profiling.

Karin Ortmayr, Roberto de la Cruz Moreno, Mattia Zampieri

Abstract readReview
PubMed Publisher
In one paragraph

Review in Nature chemical biology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
15citing papers in PubMed, 1 pooled it
–field-weighted citation impact
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

15 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
  2. Article
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  5. Article
  6. Article
  7. Review
  8. Article
  9. Article
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  12. Review
  13. Sensing of Antibiotic-Bacteria Interactions.Antibiotics (Basel, Switzerland) · 2023
    Review
  14. Article
  15. 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

3 authors.

Karin OrtmayrInstitute of Molecular Systems Biology, ETH Zürich, Zürich, Switzerland.ORCID http://orcid.org/0000-0002-0603-1073
Roberto de la Cruz MorenoInstitute of Molecular Systems Biology, ETH Zürich, Zürich, Switzerland.
Mattia ZampieriInstitute of Molecular Systems Biology, ETH Zürich, Zürich, Switzerland. zampieri@imsb.biol.ethz.ch.ORCID http://orcid.org/0000-0002-9339-068X

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

New techniques for systematic profiling of small-molecule effects can enhance traditional growth inhibition screens for antibiotic discovery and change how we search for new antibacterial agents. Computational models that integrate physicochemical compound properties with their phenotypic and molecular downstream effects can not only predict efficacy of molecules yet to be tested, but also reveal unprecedented insights on compound modes of action (MoAs). The unbiased characterization of compounds that themselves are not growth inhibitory but exhibit diverse MoAs, can expand antibacterial strategies beyond direct inhibition of core essential functions. Early and systematic functional annotation of compound libraries thus paves the way to new models in the selection of lead antimicrobial compounds. In this Review, we discuss how multidimensional small-molecule profiling and the ever-increasing computing power are accelerating the discovery of unconventional antibacterials capable of bypassing resistance and exploiting synergies with established antibacterial treatments and with protective host mechanisms.

Indexed as

Anti-Bacterial AgentsAnti-Infective AgentsAnti-Bacterial AgentsAnti-Infective Agents

Identifiers

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.