Evidence map›Paper›PMID 27764095›Full record

ArticlePLoS computational biology2016

Population Density Modulates Drug Inhibition and Gives Rise to Potential Bistability of Treatment Outcomes for Bacterial Infections.

Jason Karslake, Jeff Maltas, Peter Brumm, Kevin B Wood

Abstract read
In one paragraph

Article in PLoS computational biology, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 45 papers.

0numbers the graph read from it
0cells of the map it votes in
45citing papers in PubMed
–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

45 citing papers in PubMed.

  1. Article
  2. Pyocyanin produced byAntimicrobial agents and chemotherapy · 2026
    Article
  3. Article
  4. Article
  5. Article
  6. Article
  7. Article
  8. Spatial population dynamics of bacterial colonies with social antibiotic resistance.Proceedings of the National Academy of Sciences of the United States of America · 2025
    Article
  9. Review
  10. Article
  11. Review
  12. Article
  13. Article
  14. Article
  15. Antibiotic resistance in bacterial communities.Current opinion in microbiology · 2023
    Review
  16. Article
  17. Article
  18. Article
  19. Article
  20. Inoculum effect of antimicrobial peptides.Proceedings of the National Academy of Sciences of the United States of America · 2021
    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

4 authors.

Jason KarslakeDepartment of Biophysics, University of Michigan, Ann Arbor, MI.ORCID http://orcid.org/0000-0002-1596-6866
Jeff MaltasDepartment of Biophysics, University of Michigan, Ann Arbor, MI.
Peter BrummDepartment of Biophysics, University of Michigan, Ann Arbor, MI.
Kevin B WoodDepartment of Biophysics, University of Michigan, Ann Arbor, MI.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The inoculum effect (IE) is an increase in the minimum inhibitory concentration (MIC) of an antibiotic as a function of the initial size of a microbial population. The IE has been observed in a wide range of bacteria, implying that antibiotic efficacy may depend on population density. Such density dependence could have dramatic effects on bacterial population dynamics and potential treatment strategies, but explicit measures of per capita growth as a function of density are generally not available. Instead, the IE measures MIC as a function of initial population size, and population density changes by many orders of magnitude on the timescale of the experiment. Therefore, the functional relationship between population density and antibiotic inhibition is generally not known, leaving many questions about the impact of the IE on different treatment strategies unanswered. To address these questions, here we directly measured real-time per capita growth of Enterococcus faecalis populations exposed to antibiotic at fixed population densities using multiplexed computer-automated culture devices. We show that density-dependent growth inhibition is pervasive for commonly used antibiotics, with some drugs showing increased inhibition and others decreased inhibition at high densities. For several drugs, the density dependence is mediated by changes in extracellular pH, a community-level phenomenon not previously linked with the IE. Using a simple mathematical model, we demonstrate how this density dependence can modulate population dynamics in constant drug environments. Then, we illustrate how time-dependent dosing strategies can mitigate the negative effects of density-dependence. Finally, we show that these density effects lead to bistable treatment outcomes for a wide range of antibiotic concentrations in a pharmacological model of antibiotic treatment. As a result, infections exceeding a critical density often survive otherwise effective treatments.

Indexed as

Models, BiologicalAnti-Bacterial AgentsBacterial LoadComputer SimulationDose-Response Relationship, DrugDrug Resistance, BacterialEnterococcus faecalisGram-Positive Bacterial InfectionsHumansMicrobial Sensitivity TestsAnti-Bacterial Agents

Identifiers

PMID27764095
PMCPMC5072716

What OpenQuestion holds

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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.