Evidence map›Paper›PMID 41754583›Full record

ArticleViruses2026

Modeling the Phage Properties Best for Therapy.

James J Bull, Gurneet Kaur, Stephen M Krone

Abstract read
In one paragraph

Article in Viruses, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing 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

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

3 authors.

James J BullDepartment of Biological Sciences, University of Idaho, Moscow, ID 83844, USA.
Gurneet KaurUniversity of Idaho, Moscow, ID 83844, USA.
Stephen M KroneDepartment of Mathematics and Statistical Science, University of Idaho, Moscow, ID 83844, USA.

Funding

NIH Common Fund P20-GM104420
6 · The paper itself

Abstract

The phages used to treat bacterial infections in phage therapy are commonly chosen based on their abilities to form plaques on the infecting bacterium-on host range. In practice, phage therapy is not always successful, leaving room for improvement. Here, we use computational models to investigate whether some standard phage properties (burst size, lysis rate, adsorption rate constant, intrinsic decay rate, and growth rate) might serve as predictors of treatment success. As our measure of treatment success, we deviate from many other approaches by calculating the number of phages needed to suppress bacterial densities 100-fold in the short term, given that the patient's immune system is expected to regain control once bacterial numbers are reduced. Numerical analysis of single-phage trials across 2400 combinations of phage phenotypes reveals that, on average, adsorption rate constant and growth rate provide the most useful predictive values, decay rate provides some value, whereas burst size and lysis time offer essentially little or no value. Bacterial density is especially informative of the number of phages required for treatment. There is nonetheless often considerable variation around average behavior for a single phenotype. These results raise the possibility that the adsorption rate constant and growth rate may be especially important in phage therapy performance for both high and low bacterial densities. Given that therapeutic phages are often evolved in vitro for broad host ranges rather than for individual hosts, it should be considered that selection for broad host range may have a downside of compromising adsorption to and growth rate on individual bacterial hosts.

Indexed as

Bacterial InfectionsBacteriophagesPhage TherapyBacteriaComputer SimulationHost SpecificityHumansModels, Biologicaladsorption ratebacteriacomputational modeldecay ratephage choicephage therapy

Identifiers

PMID41754583
PMCPMC12945266

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Registered trials

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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.