A dose response model for quantifying the infection risk of antibiotic-resistant bacteria

Abstract

Quantifying the human health risk of microbial infection helps inform regulatory policies concerning pathogens, and the associated public health measures. Estimating the infection risk requires knowledge of the probability of a person being infected by a given quantity of pathogens, and this relationship is modeled using pathogen specific dose response models (DRMs). However, risk quantification for antibiotic-resistant bacteria (ARB) has been hindered by the absence of suitable DRMs for ARB. A new approach to DRMs is introduced to capture ARB and antibiotic-susceptible bacteria (ASB) dynamics as a stochastic simple death (SD) process. By bridging SD with data from bench experiments, we demonstrate methods to (1) account for the effect of antibiotic concentrations and horizontal gene transfer on risk; (2) compute total risk for samples containing multiple bacterial types (e.g., ASB, ARB); and (3) predict if illness is treatable with antibiotics. We present a case study of exposure to a mixed population of Gentamicin-susceptible and resistant Escherichia coli and predict the health outcomes for varying Gentamicin concentrations. Thus, this research establishes a new framework to quantify the risk posed by ARB and antibiotics.

Metagenomics as a Tool To Monitor Reclaimed-Water Quality

“…This value can then be multiplied by the normalized marker gene x count per prokaryote cell obtained via metagenomics to derive the marker gene x count per liter. However, even with these estimated values, dose-response models and transmission probabilities associated with emerging contaminants such as antibiotic-resistant bacteria (ARB) or ARGs are still unavailable to facilitate QMRA, although recent efforts have been made to introduce dose-response models that incorporate stochastic death dynamics between ARB and antibiotic-susceptible bacteria ( 56 ), hence allowing the consideration of ARB in existing dose-response models.…”

“…For example, the total cell counts can be first estimated by enumerating them with nucleic acid stains and flow cytometry. This would generate a value associated with the number of cells per L. This value can then be multiplied by the normalized marker gene x count per prokaryote cell obtained via metagenomics to derive the marker gene x count per L. However, even with these estimated values, the dose-response models and transmission probability associated with emerging contaminants such as ARB or ARG are still unavailable to facilitate QMRA although recent efforts have been made to introduce dose-response models that incorporate stochastic death dynamics between ARB and antibiotic-susceptible bacteria(57), hence allowing the consideration of ARB in existing dose-response models.Applications of metagenomics to monitor reclaimed water qualityMetagenomics is commonly used to conduct a baseline characterization of the diversity and relative abundance of contaminants that are present in reclaimed water. For example, Chopyk et alcollected water samples from tidal brackish rivers, freshwater ponds and creeks and water reclamation facilities and proceeded to process these samples for shotgun metagenomics(58).…”

A dose response model for Staphylococcus aureus

“…Simulating this model as a stochastic process gives rise to dose-response probabilities, and we fit this model to the SA data from Singh et al In comparison with the RH model, the 2C model has a fully mechanistic basis in the kinetics of SA growth. This allows it to be applied in cases with (1) non-instantaneous exposures 16 , (2) environment-host transmission dynamics with multiple exposures, without assuming independence between exposures and (3) antibiotic treatment and presence of an antibiotic resistant strain 17 . Simulating non-instantaneous or multiple exposures with classical DRMs may require assuming that each of the two doses are independent of each other, without accounting for the duration between the two doses.…”

“…Simulating non-instantaneous or multiple exposures with classical DRMs may require assuming that each of the two doses are independent of each other, without accounting for the duration between the two doses. Antibiotic concentrations and an antibiotic-resistant subpopulation may affect disease outcomes (e.g, if the patient can be treated with a certain antibiotic or not) 17 . A fully mechanistic model fitted to sensitive strains can be applied to resistant strains with transparent assumptions e.g., any difference in fitness between strains can be attributed to the change in one or more parameters.…”

Metabolic Current Production by an Oral Biofilm Pathogen Corynebacterium matruchotii

“…In recent times, bacterial resistance to antibiotics has become a formidable problem for the treatment of many infections and resulted in many clinical deaths and huge economic burdens [ 12 ]. Therefore, the discovery of new antimicrobial molecules and the development of easy and effective techniques for their assessment against bacterial colonization is in great demand [ 13 ].…”