Evidence map›Paper›PMID 26820986›Full record

ArticlePLoS computational biology2016

Does High-Dose Antimicrobial Chemotherapy Prevent the Evolution of Resistance?

Troy Day, Andrew F Read

Open access · goldAbstract 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 69 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
69citing papers in PubMed, 1 pooled it
22.5field-weighted citation impact, top 1% 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

69 citing papers in PubMed, 1 synthesis or guideline pooled it, 139 citations in OpenAlex.

  1. Pooled it
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  7. Developmental synchrony and extraordinary multiplication rates in pathogenic organisms.Philosophical transactions of the Royal Society of London. Series B, Biological sciences · 2025
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  8. Impacts of limits to adaptation on population and community persistence in a changing environment.Philosophical transactions of the Royal Society of London. Series B, Biological sciences · 2025
    Review
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  14. Review
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  18. Article
  19. Article
  20. Article

9 more citing papers are in PubMed but not listed here.

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

2 authors at 1 institution in 2 countries.

Troy DayDepartment of Mathematics and Statistics, Jeffery Hall, Queen's University, Kingston, Ontario, Canada.
Andrew F ReadThe Fogarty International Center, National Institutes of Health, Bethesda, Maryland, United States of America.
Fogarty International Center · US

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

High-dose chemotherapy has long been advocated as a means of controlling drug resistance in infectious diseases but recent empirical studies have begun to challenge this view. We develop a very general framework for modeling and understanding resistance emergence based on principles from evolutionary biology. We use this framework to show how high-dose chemotherapy engenders opposing evolutionary processes involving the mutational input of resistant strains and their release from ecological competition. Whether such therapy provides the best approach for controlling resistance therefore depends on the relative strengths of these processes. These opposing processes typically lead to a unimodal relationship between drug pressure and resistance emergence. As a result, the optimal drug dose lies at either end of the therapeutic window of clinically acceptable concentrations. We illustrate our findings with a simple model that shows how a seemingly minor change in parameter values can alter the outcome from one where high-dose chemotherapy is optimal to one where using the smallest clinically effective dose is best. A review of the available empirical evidence provides broad support for these general conclusions. Our analysis opens up treatment options not currently considered as resistance management strategies, and it also simplifies the experiments required to determine the drug doses which best retard resistance emergence in patients.

Indexed as

Biological EvolutionAnimalsAnti-Infective AgentsCommunicable DiseasesComputational BiologyDrug ResistanceDrug TherapyHumansMiceModels, BiologicalAnti-Infective Agents

Identifiers

PMID26820986
PMCPMC4731197
OpenAlexW2262017093

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.