Evidence map›Paper›PMID 19056402›Full record

ArticleJournal of theoretical biology2009

Exploring the role of the immune response in preventing antibiotic resistance.

Andreas Handel, Elisa Margolis, Bruce R Levin

Abstract read
In one paragraph

Article in Journal of theoretical biology, 2009. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 47 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
47citing papers in PubMed, 1 pooled it
0.9field-weighted citation impact, top 29% 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

47 citing papers in PubMed, 1 synthesis or guideline pooled it, 99 citations in OpenAlex.

  1. Pooled it
  2. Article
  3. Review
  4. Article
  5. Article
  6. The role of innate immunity, antibiotics, and bacteriophages in the course of bacterial infections and their treatment.Proceedings of the National Academy of Sciences of the United States of America · 2025
    Article
  7. Article
  8. Article
  9. Article
  10. Review
  11. Within-Host Mathematical Models of Antibiotic Resistance.Methods in molecular biology (Clifton, N.J.) · 2024
    Article
  12. Article
  13. Article
  14. Review
  15. Immuno-pharmacokinetics of Meglumine Antimoniate in Patients With Cutaneous Leishmaniasis Caused by Leishmania (Viannia).Clinical infectious diseases : an official publication of the Infectious Diseases Society of America · 2021
    Article
  16. Article
  17. Fighting microbial pathogens by integrating host ecosystem interactions and evolution.BioEssays : news and reviews in molecular, cellular and developmental biology · 2021
    Article
  18. Article
  19. Article
  20. 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

3 authors at 1 institution in 1 country.

Andreas HandelDepartment of Biology, Emory University, Atlanta, GA 30322, USA. ahandel@uga.edu
Elisa Margolis
Bruce R Levin
Emory University · US

Funding

MEDICAL SCIENTIST TRAINING PROGRAMT32GM008169 · NIGMS · EMORY UNIVERSITY · PI GROSS, ROBERT E · 1987 to 2021
$20.3M
Theoretical and experimental strudy of the population and evolutionary dynamics oR01GM091875 · NIGMS · EMORY UNIVERSITY · PI LEVIN, BRUCE RICHARD · 2009 to 2017
$2.5M
POPULATION GENETICS &EVOLUTION OF ANTIBIOTIC RESISTANCER01AI040662 · NIAID · EMORY UNIVERSITY · PI LEVIN, BRUCE RICHARD · 1997 to 2008
$1.1M
NIAID NIH HHS AI40662NIAID NIH HHS R01 AI040662NIGMS NIH HHS R01 GM091875NIGMS NIH HHS T32 GM008169
6 · The paper itself

Abstract

For many bacterial infections, drug resistant mutants are likely present by the time antibiotic treatment starts. Nevertheless, such infections are often successfully cleared. It is commonly assumed that this is due to the combined action of drug and immune response, the latter facilitating clearance of the resistant population. However, most studies of drug resistance emergence during antibiotic treatment focus almost exclusively on the dynamics of bacteria and the drug and neglect the contribution of immune defenses. Here, we develop and analyze several mathematical models that explicitly include an immune response. We consider different types of immune responses and investigate how each impacts the emergence of resistance. We show that an immune response that retains its strength despite a strong drug-induced decline of bacteria numbers considerably reduces the emergence of resistance, narrows the mutant selection window, and mitigates the effects of non-adherence to treatment. Additionally, we show that compared to an immune response that kills bacteria at a constant rate, one that trades reduced killing at high bacterial load for increased killing at low bacterial load is sometimes preferable. We discuss the predictions and hypotheses derived from this study and how they can be tested experimentally.

Indexed as

Models, ImmunologicalAnti-Bacterial AgentsBacteriaBacterial InfectionsDrug Administration ScheduleDrug Resistance, BacterialDrug Resistance, MicrobialHumansImmunity, CellularMutationPatient ComplianceSelection, GeneticAnti-Bacterial Agents

Identifiers

PMID19056402
PMCPMC5814249
OpenAlexW2065936095

What OpenQuestion holds

Textmetadata
LicenceTDM
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.