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. 2020 Apr 22;98(4):skaa109. doi: 10.1093/jas/skaa109

Association between antimicrobial drug class selection for treatment and retreatment of bovine respiratory disease and health, performance, and carcass quality outcomes in feedlot cattle

Johann F Coetzee 1,✉, Natalia Cernicchiaro 2, Pritam K Sidhu 1, Michael D Kleinhenz 3
PMCID: PMC7179807  PMID: 32255182

Abstract

Treatment and control of bovine respiratory disease (BRD) is predicated on the use of two categories of antimicrobials, namely bacteriostatic drugs that inhibit bacterial growth and replication (STATIC), and bactericidal drugs that kill bacteria in in vitro culture systems (CIDAL). Recently, we reported that initial BRD treatment with a STATIC antimicrobial followed by retreatment with a CIDAL antimicrobial was associated with a higher frequency of multidrug-resistant bacteria isolated from field cases of BRD submitted to a veterinary diagnostic laboratory. The present study was conducted to test the hypothesis that calves administered the same class of antimicrobial for first and second BRD treatment (i.e., CIDAL-CIDAL or STATIC-STATIC) would have improved health and performance outcomes at the feedlot compared to calves that received a different antimicrobial class for retreatment (i.e., STATIC-CIDAL or CIDAL-STATIC). The association between antimicrobial treatments and health, performance, and carcass quality outcomes were determined by a retrospective analysis of 4,252 BRD treatment records from a commercial feedlot operation collected from 2001 to 2005. Data were compared using generalized linear mixed statistical models that included gender, season, and arrival weight as covariates. The mean (±SE) probability of BRD cases identified as requiring four or more treatments compared to three treatments was greater in calves that received STATIC-CIDAL (73.58 ± 2.38%) or STATIC-STATIC (71.32 ± 2.52%) first and second antimicrobial treatments compared to calves receiving CIDAL-CIDAL (50.35 ± 3.46%) first and second treatments (P < 0.001). Calves receiving CIDAL-CIDAL first and second treatments also had an increased average daily gain (1.11 ± 0.03 kg/d) compared to calves receiving STATIC-CIDAL (0.95 ± 0.03 kg/d) and STATIC-STATIC (0.84 ± 0.02 kg/d) treatments (P < 0.001). Furthermore, CIDAL-CIDAL-treated calves had a higher probability of a choice quality grade at slaughter (36.44 ± 4.80%) compared to STATIC-CIDAL calves (28.09 ± 3.88%) (P = 0.037). There was no effect of antimicrobial treatment combination on BRD mortality (P = 0.855) or yield grade (P = 0.240) outcomes. These observations suggest that consideration should be given to antimicrobial pharmacodynamics when selecting drugs for retreatment of BRD. These findings have implications for developing BRD treatment protocols that address both post-treatment production and antimicrobial stewardship concerns.

Keywords: antimicrobial, bactericidal, bacteriostatic, bovine respiratory disease, antimicrobial stewardship

Introduction

Bovine respiratory disease (BRD) is the most economically significant disease for cattle producers in the United States (Panciera and Confer, 2010;USDA-APHIS, 2011). Treatment and control of BRD are currently predicated on administration of antimicrobial therapy directed toward the primary bacterial pathogens Mannheimia haemolytica, Pasteurella multocida, and Histophilus somni. The National Animal Health Monitoring System Feedlot 2011 study reported that approximately 15% of BRD cases in feedlot cattle require a second antimicrobial treatment to control the infection and 90% of these cases receive retreatment with a different mechanistic class of antimicrobial (USDA-APHIS, 2011). However, studies examining the impact of antimicrobial drug class on post-treatment health and performance outcomes are deficient in the published literature. Knowledge of the impact of antimicrobial drug choice on production outcomes and the emergence of antimicrobial resistance (AMR) is needed to develop judicious use guidelines that preserve antimicrobial efficacy and advance antimicrobial stewardship.

Antimicrobials used to treat BRD are broadly classified as bacteriostatic (STATIC) or bactericidal (CIDAL) based on their effect on bacterial growth, replication, and survival in in vitro culture systems. At the minimum inhibitory concentration (MIC) for an antimicrobial, no bacterial growth is observed in specific culture media under standardized temperature and carbon dioxide conditions (Wald-Dickler et al., 2018). In contrast, at the minimum bactericidal concentration (MBC), a 1,000-fold reduction in bacterial density is observed at 24 h of growth in a standardized cell culture system. Based on these endpoints, the formal definition of a CIDAL antibiotic is one for which the ratio of MBC-to-MIC is ≤4, whereas a STATIC agent has an MBC-to-MIC ratio of >4 (Pankey and Sabath, 2004; French, 2006; Wald-Dickler et al., 2018). Despite being a useful way to broadly characterize antimicrobial pharmacodynamics, the terms STATIC and CIDAL are based on the in vitro characteristics of a drug and are not considered to have any predictive ability of the clinical outcome of infections in vivo. (Wald-Dickler et al., 2018).

Recently, we reported that administration of a STATIC antimicrobial for first treatment, followed by retreatment with a CIDAL antimicrobial in cases of BRD relapse, was associated with a higher frequency of isolation of multidrug resistant bacteria from field cases of BRD submitted to the Iowa State University Veterinary Diagnostic Laboratory (Coetzee et al., 2019). Based on these results, we hypothesized that calves that administered a CIDAL-CIDAL or STATIC-STATIC antimicrobial for first and second BRD treatment would have improved health and performance outcomes at the feedlot compared to calves that received STATIC-CIDAL or CIDAL-STATIC first and second treatments. The objective of the study was to estimate associations between the combination of antimicrobial drug class administered for treatment and retreatment of BRD cases with health (subsequent BRD treatments), performance (ADG), and carcass [quality grade (QG) and calculated yield grade (CYG)] outcomes in finishing cattle from a large commercial feedlot operation over a 5-yr (2001 to 2005) period.

Materials and Methods

Institutional Animal Care and Use Committee approval was not obtained because this study did not involve the use of animals; data were obtained from an existing database.

Study population

This dataset represents a subset from a larger database evaluating health and production data from multiple commercial feedlots in the Midwest United States in various states over 10 yr (Cernicchiaro et al., 2012, 2013). This specific subset includes operational, retrospective data on individual animal performance, health (including treatments for BRD), and carcass (closeout data) traits from multiple groups of cattle (cohorts) collected from one commercial feedlot over 5 yr (2001 to 2005). Available data include information collected routinely on lot (cohort) management characteristics, BRD morbidity and mortality, animal health (e.g., number of treatments, drugs used), performance (e.g., arrival weight, ADG), and carcass data (e.g., QG, CYG). The ADG was calculated based on arrival weight and close-out body weight. Carcass data (QG and CYD) were obtained from plant reports using USDA carcass grading standards.

Statistical analyses

Associations between independent variables with outcomes of interest were estimated using generalized linear mixed models. Residual pseudo-likelihood estimation, Newton–Raphson with ridging optimization, and Kenward–Roger adjustment for denominator degrees of freedom were specified in Proc Glimmix in SAS (SAS 9.4, SAS Institute Inc., Cary, NC). Independent variables consisted of 1) the combination of antimicrobial products applied in the first and second treatments (categorical; bacteriostatic–bacteriostatic, bacteriostatic–bactericidal, bactericidal– bacteriostatic, and bactericidal–bactericidal characterized in Table 1), 2) sex (dichotomous: steer, heifer), 3) arrival weight categories (in kg, categorical; 136 (136 to 180), 181 (181 to 226), 227 (227 to 271), 272 (272 to 317), ≥318 (>318 to 466), and 4) season (categorical; summer (July to September), fall (October to December), winter (January to March), and spring (April to June)).

Table 1.

Descriptive statistics stratified by the combination of antimicrobials administered in first and second BRD treatments

Category n % Drug classes used for first and second treatment Examples of antimicrobials used for first and second treatment n %
CIDAL‒CIDAL 566 13.3 Beta lactam ‒ aminoglycoside Ampicillin or ceftiofur‒Gentamicin 43 1.0
Beta lactam ‒ beta lactam Ampicillin or ceftiofur‒Ampicillin or ceftiofur 344 8.1
Beta lactam ‒ fluoroquinolone Ampicillin or ceftiofur‒Enrofloxacin or danofloxacin 179 4.2
CIDAL-STATIC 26 0.6 Beta lactam ‒ phenicol Ampicillin or ceftiofur‒Florfenicol 26 0.6
STATIC‒CIDAL 2,033 47.8 Macrolide ‒ beta lactam Tilmicosin or tulathromycin‒ Ampicillin or ceftiofur 646 15.2
Macrolide ‒ fluoroquinolone Tilmicosin or tulathromycin‒ Enrofloxacin or danofloxacin 481 11.3
Phenicol ‒ beta lactam Florfenicol‒Ampicillin or ceftiofur 101 2.4
Phenicol ‒ fluoroquinolone Florfenicol‒Enrofloxacin or danofloxacin 778 18.3
Tetracycline ‒ beta lactam Oxytetracycline‒Ampicillin or ceftiofur 27 0.6
STATIC‒STATIC 1,607 37.8 Macrolide ‒ macrolide Tilmicosin or tulathromycin‒ Tilmicosin or tulathromycin 64 1.5
Macrolide ‒ phenicol Tilmicosin or tulathromycin‒ florfenicol 886 20.8
Macrolide ‒ tetracycline Tilmicosin or tulathromycin‒Oxytetracycline 95 2.2
Phenicol ‒ macrolide Florfenicol‒Tilmicosin or tulathromycin 115 2.7
Phenicol ‒ phenicol Florfenicol‒florfenicol 10 0.2
Phenicol ‒ tetracycline Florfenicol‒oxytetracycline 22 0.5
Tetracycline ‒ macrolide Oxytetracycline‒Tilmicosin or tulathromycin 313 7.4
Tetracycline ‒ phenicol Oxytetracycline ‒florfenicol 89 2.1
Tetracycline ‒ tetracycline Oxytetracycline ‒oxytetracycline 13 0.3
Other1 20 0.5 20 0.5
Total 4,252
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1Other includes the following combinations (and frequency): aminoglycoside-beta lactam (n = 3), βbeta lactam-macrolide (9), beta lactam-tetracycline (2), macrolide-aminoglycoside (2), phenicol-aminoglycoside (1), quinolone-beta lactam (1), quinolone-phenicol (1), and quinolone-quinolone.

Outcomes consisted of the following: receiving three or more BRD treatments (dichotomous; 1 = 3 pulls, 2 = 4 or more pulls (4 to 14)); mortality (dichotomous; 1 = mortality of respiratory cause; 2 = mortality due to causes other than respiratory); QG (dichotomous; 1 = choice or better, 2 = less than choice); ADG (in kg, continuous), HCW (in kg, continuous); and calculated yield grade (1 to 5, recorded on a continuous (decimal) scale). Models were fit using a binary distribution and logit link for dichotomous outcomes (probability of BRD pulling, mortality, and QG) and a Gaussian distribution and identity link for continuous outcomes (ADG, HCW, and CYG). Random intercepts for cohort and year were included in all models to account for the hierarchical structure of cohorts nested within years.

A univariable screen (P < 0.05) was conducted initially to determine associations between predictors with each of the outcomes. The variable pertaining to combination of antimicrobials applied in the first and second pulls was considered our main independent variable of interest. Variables related to sex, arrival weight, and season were considered a priori confounders of the association between our main predictor of interest and the different study outcomes. Their confounding effect was assessed by incorporating each of these predictors, one at a time, and evaluating changes in the magnitude (>30%) and/or direction of the coefficient or change in the P-value of our main variable of interest (i.e., receiving one or two treatments for BRD). Two-way interaction terms between the main variable of interest and a priori confounders were also evaluated. Confounders and variables significant at the 5% significance level were kept in multivariable models. Model assumptions and residual diagnostics were evaluated to assess model fit and to explore the presence of outliers or influential observations.

The Bonferroni multiplicity correction was used to prevent inflation of Type I error due to multiple comparisons between predictor categories. Means corresponding to model-adjusted means obtained from regression models accounting for the hierarchical structure of the data. Mean values of probabilities from the SAS reports and their 95% CIs were computed. P-values ≤ 0.05 were considered statistically significant.

Results

Descriptive statistics for the number of treatment records based on the combination of antimicrobial used for first and second BRD treatment is presented in Table 1. Table 2 represents the model-adjusted mean probabilities or mean values, based on unconditional (i.e., univariable associations) analysis, for the different outcome variables stratified by the combination of antimicrobial drugs administered for first and second BRD treatment. Neither univariable nor multivariable models converged for HCW.

Table 2.

Univariable association between combinations of antimicrobial drug classes administered as the first and second BRD treatment with performance, and carcass outcomes

Variable n Outcome CIDAL-CIDAL CIDAL-STATIC STATIC-CIDAL STATIC-STATIC P-value1
>4 BRD treatments2 2,981 Mean, % 50.4a 59.3a 73.6b 71.3b <0.0001
95% CI 42.5 to 58.1 41.6 to 74.9 67.1 to 79-2 64.6 to 77.2
BRD Mortality3 576 Mean, % 83.8 85.7 81.4 79.7 0.855
95% CI 73.5 to 90.6 41.7 to 98.1 77.2 to 85.0 74.0 to 84.3
Quality grade4 3,132 Mean, % 36.4a 13.2ab 28.1b 30.0ab 0.014
95% CI 25.8 to 48.6 4.0 to 35.5 19.1 to 39.3 20.6 to 41.5
ADG, kg5 4,252 Mean, kg 1.11a 0.97abc 0.95b 0.84c <0.0001
95% CI 1.05 to 1.18 0.77 to 1.16 0.88 to 1.01 0.78 to 0.90
Yield grade6 2,344 Mean, % 2.39 2.20 2.37 2.43 0.240
95% CI 2.21 to 2.58 1.89 to 2.50 2.19 to 2.55 2.25 to 2.61
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1 P-value from unconditional associations between predictor and each of the outcomes, modeled independently, using generalized linear mixed models.

2Probability of an animal of being treated four or more pulls compared to being treated three times due to BRD.

3Probability of mortality due to respiratory cause versus mortality due to causes other than respiratory.

4Probability that quality grade is choice or better versus less than choice.

5Average daily weight gain in kilograms.

6Yield grade, recorded on a continuous scale ranging from 1 to 5.

a,b,csignificantly different (P < 0.001).

Number of BRD pulls

The choice of antimicrobial drug class for first and second BRD treatment was associated with the probability of an animal being pulled four or more times compared to three times for BRD relapse (P < 0.001). Specifically, the mean (±SE) probability of BRD cases identified as requiring four or more treatments compared to three treatments was greater in calves that received STATIC-CIDAL (73.58 ± 2.38%) or STATIC-STATIC (71.32 ± 2.52%) first and second antimicrobial treatments compared to calves receiving CIDAL-CIDAL (50.35 ± 3.46%) first and second treatments (P < 0.001) (Table 2).

Mortality

The combination of antimicrobials administered in the first and second BRD treatment was not associated (P = 0.855) with a higher probability of mortality due to BRD compared to mortality due to any other cause. Results from the univariable model are presented for reference purposes only (Table 2) and no multivariable models were attempted.

Average daily weight gain

The combination of antimicrobial drug class administered for first and second BRD treatment was associated with average daily gain (ADG) (P < 0.0001). Specifically, the mean (±SE) ADG in calves that received CIDAL-CIDAL (1.11 ± 0.03 kg/d) first and second treatments was greater than the ADG in calves receiving STATIC-CIDAL (0.95 ± 0.03 kg/d) and STATIC-STATIC (0.84 ± 0.02 kg/d) treatments (P < 0.001). Furthermore, the ADG was greater in calves receiving the STATIC-CIDAL first and second treatments for BRD compared to the STATIC-STATIC combination (P = 0.0003).

Results of the multivariable model indicated that although sex did not confound the association between antimicrobial drug combination and ADG, it was a predictor of the outcome (P < 0.0001) and was, thus, retained in the model. Furthermore, the association between the antimicrobial drug class and ADG was dependent on arrival weight, as indicated by the two-way interaction term (P = 0.001). Specifically, calves with an arrival weight of ≥318 kg that received a CIDAL-CIDAL antimicrobial combination for first and second treatment had a greater mean ADG (1.15 kg/d; 95% CI 1.05 to 1.25 kg/d) than calves of the same weight category that received a STATIC-CIDAL combination (0.96 kg/d; 95% CI 0.90 to 1.03 kg/d; P = 0.018) or a STATIC-STATIC combination (0.77 kg/d; 95% CI 0.67 to 0.86 kg/d; P < 0.0001).

Quality grade

The combination of antimicrobials administered for first and second BRD treatment was associated with the probability of the carcass being assigned a QG “choice” or better, based on the outcome of the univariable analysis (P < 0.014; Table 2). Specifically, CIDAL-CIDAL-treated calves had an increased probability of a “choice” quality grade at slaughter (36.44 ± 4.80%) compared to STATIC-CIDAL calves (28.09 ± 3.88%; P = 0.037; Table 2).

The multivariable model includes variables pertaining to the combination of antimicrobials administered in the first and second BRD pulls (which was no longer significant (P = 0.213) after season was included in the model but was forced in the model), season (which acted as an analytical confounder of the association between combination of antimicrobials with quality grade) (P < 0.001), and sex (P = 0.009). The probability of choice or better was significantly higher (P < 0.001) in heifers than steers, and in winter versus fall (P = 0.025) and summer (P = 0.027).

Calculated yield grade

The combination of antimicrobials administered in the first and second BRD treatments was not associated with the CYG of the carcass (P = 0.240). Results from the univariable model are presented for reference purposes only (Table 2), and no multivariable models were attempted.

Discussion

In North America, BRD in feedlot cattle results in substantial economic losses due to treatment costs and negative effects on animal health and production (Griffin, 1997; Snowder et al., 2007; Cernicchiaro et al., 2013). Although BRD has a complex, multifactorial etiology, antimicrobials are essential for the control and treatment of bacterial pathogens that are commonly associated with the disease (Griffin, 1997). Commonly used antimicrobial drugs approved for treatment of BRD in the United States include ceftiofur, tilmicosin, tulathromycin, florfenicol, enrofloxacin, and danofloxacin (O’Connor et al., 2013; Apley, 2015; O’Connor et al., 2016). Schneider et al. (2009) examined BRD treatment records from 5,976 feedlot cattle and reported that 53% were treated once, 34% were treated twice, and 13% were treated three or more times (Schneider et al., 2009). Although the majority of BRD relapses are reported to receive a second treatment with a different class of antimicrobial (USDA, 2011), studies examining the impact of drug selection on health and production outcomes after retreatment are lacking. Therefore, the goal of this study was to estimate associations between drug selection for the initial treatment and second treatment of BRD with health and performance outcomes in feedlot cattle.

The findings of the present study suggest that calves treated with STATIC-CIDAL or STATIC-STATIC for the first and second antimicrobial treatments demonstrated an increase in BRD relapses, a reduced ADG and lower carcass quality grade compared to calves that received CIDAL-CIDAL first and second treatments. These observations could be attributed to an antagonistic interaction between STATIC and CIDAL antimicrobials resulting in less favorable health and performance outcomes (Gunnison et al., 1953; Brown and Alford, 1984; Johansen et al., 2000; Ocampo et al., 2014). Alternatively, these findings may suggest that STATIC antimicrobials are less effective than CIDAL drugs in feedlot cattle on arrival because these are immune compromised due to stress, management factors, or concurrent disease (Taylor et al., 2010). A third hypothesis is that initial therapy with a STATIC antimicrobial followed by retreatment with a CIDAL antimicrobial may favor the selection of antimicrobial resistant bacterial mutants to a greater extent than CIDAL-CIDAL first and second treatments. As a consequence, calves treated with a STATIC followed by a CIDAL antimicrobial are more likely to suffer episodes of BRD relapse with associated reduced performance and carcass quality grade.

Gunnison et al. (1953) described an overall reduction in antimicrobial efficacy when antimicrobials that cause target organism death (i.e., CIDAL agents) are used in combination with antimicrobials that inhibit bacterial replication (i.e., STATIC agents). It has been proposed that when STATIC-CIDAL first and second treatments are used, the inhibition of bacterial growth and replication induced by a bacteriostatic agent may result in an overall reduction of efficacy of the bactericidal agent (Ocampo et al., 2014; Bollenbach, 2015). Specifically, Ocampo et al. (2014) reported antagonistic interactions between pairwise STATIC-CIDAL combinations involving 21 antimicrobials. Furthermore, Sweeney et al. (2008) described an antagonistic interaction between the CIDAL antimicrobial, ceftiofur, and the STATIC antimicrobial florfenicol against one isolate of Pasteurella multocida. These types of drug antagonisms have been associated with poorer clinical outcomes in human patients (Chait et al., 2007; Yeh et al., 2009).

In vitro studies have suggested that CIDAL-CIDAL combinations may possess greater antibacterial activity than other combinations of antimicrobials (Watanakunakorn, 1983; Campanile et al., 2019). However, a study examining the in vitro activities of the STATIC antimicrobial, tulathromycin, and the CIDAL antimicrobial, ceftiofur in combination with seven other antimicrobials against M. haemolytica and P. multocida reported mostly indifferent responses with rare occurrences of antagonism (Sweeney et al., 2008). CIDAL antimicrobials typically kill bacteria by inhibiting cell wall synthesis (e.g., beta-lactams) or inhibiting DNA replication (e.g. fluoroquinolones). Bacteriostatic antimicrobials typically inhibit protein synthesis by reversibly binding to ribosomes thus slowing cell growth and preventing bacterial replication. Therefore, when microbes are pre-exposed to a STATIC antimicrobial, killing of bacteria by a CIDAL drug may be impaired because these compounds require bacteria to be actively dividing. At a molecular level, it has been proposed that suppression of cellular respiration by STATIC antimicrobials may block the CIDAL effect of other compounds because these require accelerated respiration to kill bacteria (Lobritz et al., 2015). These findings suggest that the choice of antimicrobial drug class (i.e., bactericidal or bacteriostatic) in cases of BRD relapse and retreatment may be a critical control point for optimizing antimicrobial efficacy and disease outcomes in beef production systems.

Stress from weaning, transportation, dietary changes, management practices, and comingling of feedlot cattle combined with concurrent viral infections cause immunosuppression resulting in an increased susceptibility to BRD (Galyean et al., 1999; Taylor et al., 2010). It is assumed that STATIC antibiotics require phagocytic cells to eliminate bacteria and are therefore less effective without a fully functional immune response (Nemeth et al., 2015). As a consequence, several authors have suggested that antimicrobial therapy of diseases such as endocarditis and meningitis infections should target bacterial eradication from the site of infection (Pankey et al., 2004; French, 2006). A similar recommendation for bacterial eradication in cases of respiratory tract infection has been made (Dagan et al., 2001). However, recent reviews of the published literature did not find any evidence that CIDAL antimicrobials were clinically superior to STATIC antimicrobials for abdominal infections, skin, and soft tissue infections and pneumonia (Nemeth et al., 2015; Wald-Dickler et al., 2018). It is noteworthy that studies included in these reviews focused on disease outcomes following a single course of treatment with either a STATIC or CIDAL antimicrobial. To our knowledge, studies examining clinical outcomes following the use of STATIC or CIDAL antimicrobials in cases of disease relapse (second treatment) are deficient in the literature

A significant increase in the proportion of M. haemolytica isolates that are resistant to five or more antimicrobials from samples submitted to veterinary diagnostic laboratories has been reported recently (Lubbers and Hanzlicek., 2013; Magstadt et al., 2018). Dagan et al. (2001) suggested that increased prevalence of macrolide-resistant strains of S. pneumonia in human populations is due to the failure to eradicate bacteria resulting in the selection of resistant clones that will recolonize mucous membranes after therapy is discontinued. Recently, we reported that initial treatment of BRD with a STATIC antimicrobial followed by retreatment with a CIDAL antimicrobial was associated with a higher frequency of multidrug resistant bacteria isolated from field cases of BRD submitted to a veterinary diagnostic laboratory (Coetzee et al., 2019). French (2006) suggested that if pathogens are killed rather than inhibited, the risk of resistant bacterial mutants emerging as the result of antibiotic pressure is reduced. Therefore, the use of CIDAL-CIDAL antimicrobials for first and second treatment may reduce the potential for antimicrobial resistance to emerge.

The mutant selection window is the antimicrobial concentration range between the MIC and the concentration needed to inhibit the growth of the least susceptible bacterial mutant, known as the mutant prevention concentration (MPC) (Blondeau et al., 2001; Drlica, 2003). Blondeau et al. (2012) compared the MIC and MPC of enrofloxacin, ceftiofur, florfenicol, tilmicosin, and tulathromycin against bovine clinical isolates of M. haemolytica. In this study, the CIDAL antimicrobials, enrofloxacin, and ceftiofur were found to be more potent based on MIC90 and MPC90 concentrations than the STATIC antimicrobials, tulathromycin, florfenicol, and tilmicosin. Studies examining the impact of antimicrobial combinations or sequential exposure to antimicrobials of a different mechanistic class on the mutant selection window are deficient in the published literature. However, it is reasonable to conclude that the sequential use of antimicrobials that possess different pharmacodynamic endpoints may alter the selection pressure applied to a bacterial population that could favor the emergence of a multidrug-resistant phenotype.

It is noteworthy that the classification of antimicrobials as CIDAL or STATIC is based on in vitro observations, and as such, this may not be absolute for every combination of antimicrobial and bacterial isolate (Wald-Dickler et al., 2018). For example, it has been reported that oxytetracycline and florfenicol may have bactericidal as opposed to bacteriostatic activity against pneumonia pathogens of pigs and cattle under certain in vitro and ex vivo conditions (Sidhu et al., 2014; Dorey et al., 2017). However, despite a few exceptions, broad mechanisms of action, such as protein synthesis inhibition and impacts on the growth of the bacterial cell wall, tend to be conserved within these mechanistic classes. Therefore, this classification serves as a useful guideline for clinicians to determine when two drugs may potentially show synergistic or antagonistic interactions if used sequentially or in combination (Bollenbach, 2015).

Retrospective analysis of feedlot treatment records and associated health outcomes have several potential limitations that should be considered before broader inferences can be made. For example, we assume that treatment history and treatment sequence provided by the feedlot are accurate. As such, concurrent use of oral antimicrobials in feed, for example, may not have been disclosed and could potentially have increased the risk of selecting resistant organisms (Kanwar et al., 2013). A further limitation is that data examining certain antimicrobial combinations, such as CIDAL-STATIC treatments, were sparse while others, such as STATIC-CIDAL combinations, were more abundant. These data were also assembled before many of the recently approved long-acting antimicrobials for BRD, such as tildipirosin and gamithromycin, were in common use. Although the data acquisition was restricted to one feedlot to control for variability in animal management, we do not have information about the specific case definitions or treatment criteria that were used to select the different antimicrobial combinations. Furthermore, neither the causative agent nor antimicrobial susceptibility test outcomes were available for this data set. Despite these limitations, this report supports further examination of the relationship between treatment protocols and production outcomes that could assist practitioners in making decisions regarding antimicrobial retreatments when cattle exhibit BRD relapses.

The exploratory data presented in this report suggest that treatment protocols involving first-line treatment with a STATIC antimicrobial followed by retreatment with a CIDAL antimicrobial may be associated with an increased risk of BRD relapses and an associated reduction in performance and carcass quality. These findings have implications for developing BRD treatment protocols that address both post-treatment production and antimicrobial resistance concerns. As concern about antimicrobial resistance increases from both an animal welfare and public health perspective, knowledge of the impact of antimicrobial selection on disease outcomes is urgently needed to assist producers to preserve antimicrobial efficacy and advance antimicrobial stewardship.

Conflict of interest statement

Dr. Coetzee has served as a consultant for Merck Animal Health, Bayer Animal Health, Boehringer-Ingelheim Vetmedica, Zoetis, and Norbrook Laboratories Ltd. Other authors have no conflicts of interest to declare.

Acknowledgments

Dr. Coetzee and Dr. Kleinhenz are supported by the Agriculture and Food Research Initiative Competitive Grant no. 2017-67015-27124 from the USDA National Institute of Food and Agriculture.

Glossary

Abbreviations

ADG

average daily gain

AMR

antimicrobial resistance

BRD

bovine respiratory disease

CYG

calculated yield grade

CIDAL

bactericidal

MBC

minimum bactericidal concentration

MIC

minimum inhibitory concentration

MPC

mutant prevention concentration

QG

quality grade

STATIC

bacteriostatic

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