Evidence map›Paper›PMID 31443146›Full record

ArticleInternational journal of molecular sciences2019

Reaction Kinetic Models of Antibiotic Heteroresistance.

Antal Martinecz, Fabrizio Clarelli, Sören Abel, Pia Abel Zur Wiesch

Abstract read
In one paragraph

Article in International journal of molecular sciences, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing 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

3 citing papers in PubMed.

  1. Review
  2. Article
  3. The Physical Chemistry and Chemical Physics (PCCP) Section of theInternational journal of molecular sciences · 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.

Antal MartineczDepartment of Pharmacy, Faculty of Health Sciences, UiT-The Arctic University of Norway, 9037 Tromsø, Norway.ORCID 0000-0001-6539-6358
Fabrizio ClarelliDepartment of Pharmacy, Faculty of Health Sciences, UiT-The Arctic University of Norway, 9037 Tromsø, Norway.ORCID 0000-0002-3738-3469
Sören AbelDepartment of Pharmacy, Faculty of Health Sciences, UiT-The Arctic University of Norway, 9037 Tromsø, Norway.ORCID 0000-0002-4041-6989
Pia Abel Zur WieschDepartment of Pharmacy, Faculty of Health Sciences, UiT-The Arctic University of Norway, 9037 Tromsø, Norway. pia.z.wiesch@uit.no.ORCID 0000-0001-9420-9005

Funding

Bill and Melinda Gates Foundation OPP1111658Helse Nord RHF 14796Norges Forskningsråd 249979Norges Forskningsråd 262686
6 · The paper itself

Abstract

Bacterial heteroresistance (i.e., the co-existence of several subpopulations with different antibiotic susceptibilities) can delay the clearance of bacteria even with long antibiotic exposure. Some proposed mechanisms have been successfully described with mathematical models of drug-target binding where the mechanism's downstream of drug-target binding are not explicitly modeled and subsumed in an empirical function, connecting target occupancy to antibiotic action. However, with current approaches it is difficult to model mechanisms that involve multi-step reactions that lead to bacterial killing. Here, we have a dual aim: first, to establish pharmacodynamic models that include multi-step reaction pathways, and second, to model heteroresistance and investigate which molecular heterogeneities can lead to delayed bacterial killing. We show that simulations based on Gillespie algorithms, which have been employed to model reaction kinetics for decades, can be useful tools to model antibiotic action via multi-step reactions. We highlight the strengths and weaknesses of current models and Gillespie simulations. Finally, we show that in our models, slight normally distributed variances in the rates of any event leading to bacterial death can (depending on parameter choices) lead to delayed bacterial killing (i.e., heteroresistance). This means that a slowly declining residual bacterial population due to heteroresistance is most likely the default scenario and should be taken into account when planning treatment length.

Indexed as

AlgorithmsAnti-Bacterial AgentsDrug Resistance, BacterialKineticsMicrobial Sensitivity TestsAnti-Bacterial Agentsantibiotic resistanceantibioticsbacterial persistenceGillespie algorithmpharmacodynamicsreaction kineticsstochastic simulation

Identifiers

PMID31443146
PMCPMC6719119

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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.