A birth–death model to understand bacterial antimicrobial heteroresistance from time-kill curves

Antimicrobial heteroresistance refers to the presence of different subpopulations with heterogeneous antimicrobial responses within the same bacterial isolate, so they show reduced susceptibility compared with the main population. Though it is widely accepted that heteroresistance can play a crucial...

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Bibliographic Details
Published in:	Mathematical biosciences Vol. 376; p. 109278
Main Authors:	Martínez-López, Nerea, Vilas, Carlos, García, Míriam R.
Format:	Journal Article
Language:	English
Published:	United States Elsevier Inc 01.10.2024
Subjects:	AMR fitness cost Antimicrobial Resistance (AMR) Maximum Likelihood Estimation (MLE) Model identifiability Stochastic modelling Stochastic Simulation Algorithm (SSA) Stochastic Simulation Algorithm (SSA) Maximum Likelihood Estimation (MLE) Stochastic modelling AMR fitness cost Antimicrobial Resistance (AMR) Model identifiability
ISSN:	0025-5564, 1879-3134, 1879-3134
Online Access:	Get full text
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Summary:	Antimicrobial heteroresistance refers to the presence of different subpopulations with heterogeneous antimicrobial responses within the same bacterial isolate, so they show reduced susceptibility compared with the main population. Though it is widely accepted that heteroresistance can play a crucial role in the outcome of antimicrobial treatments, predictive Antimicrobial Resistance (AMR) models accounting for bacterial heteroresistance are still scarce and need to be refined as the techniques to measure heteroresistance become standardised and consistent conclusions are drawn from data. In this work, we propose a multivariate Birth-Death (BD) model of bacterial heteroresistance and analyse its properties in detail. Stochasticity in the population dynamics is considered since heteroresistance is often characterised by low initial frequencies of the less susceptible subpopulations, those mediating AMR transmission and potentially leading to treatment failure. We also discuss the utility of the heteroresistance model for practical applications and calibration under realistic conditions, demonstrating that it is possible to infer the model parameters and heteroresistance distribution from time-kill data, i.e., by measuring total cell counts alone and without performing any heteroresistance test. •New stochastic model to understand heteroresistance using birth–death processes.•Model calibration is possible from standard experiments measuring time-kill curves.•Parameters are estimated uniquely (structurally & practically identifiable).•Model shows complex known behaviours, as faster regrowth when raising drug pressure.
Bibliography:	ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 23
ISSN:	0025-5564 1879-3134 1879-3134
DOI:	10.1016/j.mbs.2024.109278