Monte Carlo Simulations Suggest Current Chlortetracycline Drug-Residue Based Withdrawal Periods Would Not Control Antimicrobial Resistance Dissemination from Feedlot to Slaughterhouse

Modelling the impact of wastewater flows and management practices on antimicrobial resistance in dairy farms

Dairy slurry is a major source of environmental contamination with antimicrobial resistant genes and bacteria. We developed mathematical models and conducted on-farm research to explore the impact of wastewater flows and management practices on antimicrobial resistance (AMR) in slurry. Temporal fluctuations in cephalosporin-resistant Escherichia coli were observed and attributed to farm activities, specifically the disposal of spent copper and zinc footbath into the slurry system. Our model revealed that resistance should be more frequently observed with relevant determinants encoded chromosomally rather than on plasmids, which was supported by reanalysis of sequenced genomes from the farm. Additionally, lower resistance levels were predicted in conditions with lower growth and higher death rates. The use of muck heap effluent for washing dirty channels did not explain the fluctuations in cephalosporin resistance. These results highlight farm-specific opportunities to reduce AMR pollution, beyond antibiotic use reduction, including careful disposal or recycling of waste antimicrobial metals.

Modelling Plasmid-Mediated Horizontal Gene Transfer in Biofilms

In this study, we present a mathematical model for plasmid spread in a growing biofilm, formulated as a nonlocal system of partial differential equations in a 1-D free boundary domain. Plasmids are mobile genetic elements able to transfer to different phylotypes, posing a global health problem when they carry antibiotic resistance factors. We model gene transfer regulation influenced by nearby potential receptors to account for recipient-sensing. We also introduce a promotion function to account for trace metal effects on conjugation, based on literature data. The model qualitatively matches experimental results, showing that contaminants like toxic metals and antibiotics promote plasmid persistence by favoring plasmid carriers and stimulating conjugation. Even at higher contaminant concentrations inhibiting conjugation, plasmid spread persists by strongly inhibiting plasmid-free cells. The model also replicates higher plasmid density in biofilm's most active regions.

Mathematical Models of Plasmid Population Dynamics

With plasmid-mediated antibiotic resistance thriving and threatening to become a serious public health problem, it is paramount to increase our understanding of the forces that enable the spread and maintenance of drug resistance genes encoded in mobile genetic elements. The relevance of plasmids as vehicles for the dissemination of antibiotic resistance genes, in addition to the extensive use of plasmid-derived vectors for biotechnological and industrial purposes, has promoted the in-depth study of the molecular mechanisms controlling multiple aspects of a plasmids' life cycle. This body of experimental work has been paralleled by the development of a wealth of mathematical models aimed at understanding the interplay between transmission, replication, and segregation, as well as their consequences in the ecological and evolutionary dynamics of plasmid-bearing bacterial populations. In this review, we discuss theoretical models of plasmid dynamics that span from the molecular mechanisms of plasmid partition and copy-number control occurring at a cellular level, to their consequences in the population dynamics of complex microbial communities. We conclude by discussing future directions for this exciting research topic.

Phenotypic and molecular characterizations of multidrug-resistant diarrheagenic E. coli of calf origin

Escherichia coli has become one of the most important causes of calf diarrhea. The aim of this study is to determine the patterns of antimicrobial resistance of E. coli isolates from six cattle farms and to identify prominent resistance genes and virulence genes among the strains isolated from the diarrhea of calves. Antimicrobial susceptibility tests were performed using the disk diffusion method, and PCR was used to detect resistance and virulence genes. The prevalence of multidrug resistant (MDR) E. coli was 77.8% in dairy cattle and 63.6% in beef cattle. There were high resistance rates to penicillin (100%, 100%) and ampicillin (96.3%, 86.4%) in E. coli from dairy cattle and beef cattle. Interestingly, resistance rate to antimicrobials and distribution of resistance genes in E. coli isolated from dairy cattle were higher than those in beef cattle. Further analysis showed that the most prevalent resistance genes were blaTEM and aadA1 in dairy cattle and beef cattle, respectively. Moreover, seven diarrheagenic virulence genes (irp2, fyuA, Stx1, eaeA, F41, K99 and STa) were present in the isolates from dairy cattle, with a prevalence ranging from 3.7% to 22.22%. Six diarrheagenic virulence genes (irp2, fyuA, Stx1, eaeA, hylA and F41) were identified in the isolates from beef cattle, with a prevalence ranging from 2.27% to 63.64%. Our results provide important evidence for better exploring their interaction mechanism. Further studies are also needed to understand the origin and transmission route of E. coli in cattle to reduce its prevalence.

The Role of PK/PD Analysis in the Development and Evaluation of Antimicrobials

Pharmacokinetic/pharmacodynamic (PK/PD) analysis has proved to be very useful to establish rational dosage regimens of antimicrobial agents in human and veterinary medicine. Actually, PK/PD studies are included in the European Medicines Agency (EMA) guidelines for the evaluation of medicinal products. The PK/PD approach implies the use of in vitro, ex vivo, and in vivo models, as well as mathematical models to describe the relationship between the kinetics and the dynamic to determine the optimal dosing regimens of antimicrobials, but also to establish susceptibility breakpoints, and prevention of resistance. The final goal is to optimize therapy in order to maximize efficacy and minimize side effects and emergence of resistance. In this review, we revise the PK/PD principles and the models to investigate the relationship between the PK and the PD of antibiotics. Additionally, we highlight the outstanding role of the PK/PD analysis at different levels, from the development and evaluation of new antibiotics to the optimization of the dosage regimens of currently available drugs, both for human and animal use.

Modeling the Effect of Tylosin Phosphate on Macrolide-Resistant Enterococci in Feedlots and Reducing Resistance Transmission

Tylosin phosphate (TYL) is administered to more than 50% of U.S. beef cattle to reduce the incidence of liver abscesses but may increase the risk of macrolide-lincosamide-streptogramin-resistant bacteria disseminating from the feedlot. Limited evidence has been collected to understand how TYL affects the proportion of resistant bacteria in cattle or the feedlot environment. We created a mathematical model to investigate the effects of TYL administration on Enterococcus dynamics and examined preharvest strategies to mitigate the impact of TYL administration on resistance. The model simulated the physiological pharmacokinetics of orally administered TYL and estimated the pharmacodynamic effects of TYL on populations of resistant and susceptible Enterococcus within the cattle large intestine, feedlot pen, water trough, and feed bunk. The model parameters' population distributions were based on the available literature; 1000 Monte Carlo simulations were performed to estimate the likely distribution of outcomes. At the end of the simulated treatment period, the median estimated proportion of macrolide-resistant enterococci was only 1 percentage point higher within treated cattle compared with cattle not fed TYL, in part because the TYL concentrations in the large intestine were substantially lower than the enterococci minimum inhibitory concentrations. However, 25% of the simulated cattle had a >10 percentage point increase in the proportion of resistant enterococci associated with TYL administration, termed the TYL effect. The model predicts withdrawing TYL treatment and moving cattle to an antimicrobial-free terminal pen with a low prevalence of resistant environmental enterococci for as few as 6 days could reduce the TYL effect by up to 14 percentage points. Additional investigation of the importance of this subset of cattle to the overall risk of resistance transmission from feedlots will aid in the interpretation and implementation of resistance mitigation strategies.

The effect of enrofloxacin on enteric Escherichia coli: Fitting a mathematical model to in vivo data

Antimicrobial drugs administered systemically may cause the emergence and dissemination of antimicrobial resistance among enteric bacteria. To develop logical, research-based recommendations for food animal veterinarians, we must understand how to maximize antimicrobial drug efficacy while minimizing risk of antimicrobial resistance. Our objective is to evaluate the effect of two approved dosing regimens of enrofloxacin (a single high dose or three low doses) on Escherichia coli in cattle. We look specifically at bacteria above and below the epidemiological cutoff (ECOFF), above which the bacteria are likely to have an acquired or mutational resistance to enrofloxacin. We developed a differential equation model for the antimicrobial drug concentrations in plasma and colon, and bacteria populations in the feces. The model was fit to animal data of drug concentrations in the plasma and colon obtained using ultrafiltration probes. Fecal E. coli counts and minimum inhibitory concentrations were measured for the week after receiving the antimicrobial drug. We predict that the antimicrobial susceptibility of the bacteria above the ECOFF pre-treatment strongly affects the composition of the bacteria following treatment. Faster removal of the antimicrobial drugs from the colon throughout the study leads to improved clearance of bacteria above the ECOFF in the low dose regimen. If we assume a fitness cost is associated with bacteria above the ECOFF, the increased fitness costs leads to reduction of bacteria above the ECOFF in the low dose study. These results suggest the initial E. coli susceptibility is a strong indicator of how steers respond to antimicrobial drug treatment.

Mathematical modelling to study the horizontal transfer of antimicrobial resistance genes in bacteria: current state of the field and recommendations

Antimicrobial resistance (AMR) is one of the greatest public health challenges we are currently facing. To develop effective interventions against this, it is essential to understand the processes behind the spread of AMR. These are partly dependent on the dynamics of horizontal transfer of resistance genes between bacteria, which can occur by conjugation (direct contact), transformation (uptake from the environment) or transduction (mediated by bacteriophages). Mathematical modelling is a powerful tool to investigate the dynamics of AMR; however, the extent of its use to study the horizontal transfer of AMR genes is currently unclear. In this systematic review, we searched for mathematical modelling studies that focused on horizontal transfer of AMR genes. We compared their aims and methods using a list of predetermined criteria and used our results to assess the current state of this research field. Of the 43 studies we identified, most focused on the transfer of single genes by conjugation in Escherichia coli in culture and its impact on the bacterial evolutionary dynamics. Our findings highlight the existence of an important research gap in the dynamics of transformation and transduction and the overall public health implications of horizontal transfer of AMR genes. To further develop this field and improve our ability to control AMR, it is essential that we clarify the structural complexity required to study the dynamics of horizontal gene transfer, which will require cooperation between microbiologists and modellers.

Expanding behavior pattern sensitivity analysis with model selection and survival analysis

Background

Sensitivity analysis is an essential step in mathematical modeling because it identifies parameters with a strong influence on model output, due to natural variation or uncertainty in the parameter values. Recently behavior pattern sensitivity analysis has been suggested as a method for sensitivity analyses on models with more than one mode of output behavior. The model output is classified by behavior mode and several behavior pattern measures, defined by the researcher, are calculated for each behavior mode. Significant associations between model inputs and outputs are identified by building linear regression models with the model parameters as independent variables and the behavior pattern measures as the dependent variables. We applied the behavior pattern sensitivity analysis to a mathematical model of tetracycline-resistant enteric bacteria in beef cattle administered chlortetracycline orally. The model included 29 parameters related to bacterial population dynamics, chlortetracycline pharmacokinetics and pharmacodynamics. The prevalence of enteric resistance during and after chlortetracycline administration was the model output. Cox proportional hazard models were used when linear regression assumptions were not met.

Results

We have expanded the behavior pattern sensitivity analysis procedure by incorporating model selection techniques to produce parsimonious linear regression models that efficiently prioritize input parameters. We also demonstrate how to address common violations of linear regression model assumptions. Finally, we explore the semi-parametric Cox proportional hazards model as an alternative to linear regression for situations with censored data. In the example mathematical model, the resistant bacteria exhibited three behaviors during the simulation period: (1) increasing, (2) decreasing, and (3) increasing during antimicrobial therapy and decreasing after therapy ceases. The behavior pattern sensitivity analysis identified bacterial population parameters as high importance in determining the trajectory of the resistant bacteria population.

Conclusions

Interventions aimed at the enteric bacterial population ecology, such as diet changes, may be effective at reducing the prevalence of tetracycline-resistant enteric bacteria in beef cattle. Behavior pattern sensitivity analysis is a useful and flexible tool for conducting a sensitivity analysis on models with varied output behavior, enabling prioritization of input parameters via regression model selection techniques. Cox proportional hazard models are an alternative to linear regression when behavior pattern measures are censored or linear regression assumptions cannot be met.

The farm cost of decreasing antimicrobial use in dairy production

Antimicrobials are used in animal agriculture to cure bacterial infectious diseases. However, antimicrobial use (AMU) inevitably leads to the selection of resistant bacteria, potentially infecting humans. As a global public threat, antimicrobial resistance has led policy makers to implement regulations supervising AMU. The objective of our research was to investigate the farm impact of several potential policies aimed at decreasing AMU. We modeled a dairy herd of 1000 cows with an average level of disease prevalence for the nine most frequent bacterial dairy diseases found in western countries. We calculated the farm net costs of AMU prohibition, as well as cost increases in antimicrobial treatments prices, and an increase in the milk withdrawal period after AMU. Sensitivity analyses were conducted to assess the impact of output and input prices, and disease prevalence. At a mean disease prevalence, the average net costs of not using antimicrobials were $61 per cow per year greater compared to a scenario modeling current farm AMU. The model predicted that the minimum and maximum increased costs associated with AMU prohibition were $46 and $73 per cow per year compared to current AMU. In each scenario, the cost difference increased with disease prevalence. Sensitivity analysis showed that the three stochastic variables which most significantly influenced the cost difference were respectively, cow replacement prices, cow slaughter price, and the milk price. Antimicrobial price increases of a factor of five, or extending the milk withdrawal period by 15 days, resulted in increasing the costs of diseases to a level where the farmer was better off not using antimicrobials. Our results suggest that the farm level costs of AMU prohibition in many cases might be minor, although the consequences of any policy instrument should be carefully evaluated to reach the ultimate goal of decreasing AMU without threatening the sustainability of milk production.