The Risk of Some Veterinary Antimicrobial Agents on Public Health Associated with Antimicrobial Resistance and their Molecular Basis

Abstract

The risk of antimicrobial agents used in food-producing animals on public health associated with antimicrobial resistance continues to be a current topic of discussion as related to animal and human public health. In the present review, resistance monitoring data, and risk assessment results of some important antimicrobial agents were cited to elucidate the possible association of antimicrobial use in food animals and antimicrobial resistance in humans. From the selected examples, it was apparent from reviewing the published scientific literature that the ban on use of some antimicrobial agents (e.g., avoparcin, fluoroquinolone, tetracyclines) did not change drug resistance patterns and did not mitigate the intended goal of minimizing antimicrobial resistance. The use of some antimicrobial agents (e.g., virginiamycin, macrolides, and cephalosporins) in food animals may have an impact on the antimicrobial resistance in humans, but it was largely depended on the pattern of drug usage in different geographical regions. The epidemiological characteristics of resistant bacteria were closely related to molecular mechanisms involved in the development, fitness, and transmission of antimicrobial resistance.

A glimpse of antimicrobial resistance gene diversity in kefir and yoghurt

“…22 The study highlighted that Staphylococci, Enterococci and interestingly, Pediococcus acidilactici harboring the gene are all of animal origin, and can be spread horizontally via mobile genetic elements. In line with other papers 23 the study suggests that phenicols and other antiribosomal agents used in veterinary medicine might have played a role in the selection of poxtA. This was also confirmed by Elghaieb et al 24 who identified the gene in cow milk and animal wastewater.…”

Factors potentially linked with the occurrence of antimicrobial resistance in selected bacteria from cattle, chickens and pigs: A scoping review of publications for use in modelling of antimicrobial resistance (IAM.AMR Project)

“…the relationships between antimicrobial use and antimicrobial‐resistant infections in livestock and humans. However, these often investigate a single exposure (e.g., use of fluoroquinolones) and its association with resistance to a single antimicrobial agent in a particular bacteria (i.e., hazard) in a single population (e.g., fluoroquinolone‐resistant Campylobacter in broiler chicken) (Hao et al, ; Horigan, Kosmider, Horton, Randall, & Simons, ; Hurd, Vaughn, Holtkamp, Dickson, & Warnick, ; Lewis et al, ; McEwen, ), and not the over‐all contribution of antimicrobial use, or the complexity of the epidemiology on the occurrence of resistance (e.g., multiple antimicrobials, bacteria, genes, host population, levels of aggregation, metrics of measurement and pathways of exposure).…”

Addressing the Unknowns of Antimicrobial Resistance: Quantifying and Mapping the Drivers of Burden

“…For the identification and quantification of transmission pathways, a Similarly comparison of isolates between settings can use using genetic distance to infer can help identify overlapping sources. [19,30,31] The map also requires systems level information on the places where antimicrobial are prescribed and transmission pathways; for example, the amount of intensive farming (such as has been mapped globally in [32]) and how much antimicrobials are used where (for some drugs, this has been done globally at the national level [17]). A comparison of the existing resistance environment, using for example composite measures such as the Drug Resistance Index [33], can then be complemented by a comparison of underlying AMR drivers and transmission routes.…”

“…a lack of an established surveillance system hampers efforts to systematically gather such data. [29] Collating and analysing existing datasets, such as those on intensive farming areas [30] and existing national level clinical datasets, [18,31] could help to identify areas of AMR emergence and areas where effective control has been achieved[GK2]. Similarly comparison of isolates between settings can use genetic distance to infer overlapping sources.…”