NIAA: 2017 Antibiotic Symposium Oct 31 Nov 2, 2017 Joel Nerem, DVM Pipestone Veterinary Services
5 Locations Pipestone, MN Independence, IA Ottumwa, IA DeKalb, IL Rensselaer, IN Mixed Animal Practice. 35 Veterinarians Primary focus is providing veterinary service to pig farmers in the USA and internationally Industry leader in biosecurity, applied research, and pig farm filtration.
DOING OUR PART FOR THE REPONSIBLE USE OF ANTIBIOTICS: Interactive, web-based tool to help farmers monitor and track total antibiotic use Helps demonstrate responsible use of antibiotics Works to track resistance over time Gives better information to: Select the best treatment options for sick animals Focus on growers/locations/flows with higher health challenges Benchmark internally and externally Do our PART to safeguard antimicrobial resistance RECORD REVIEW RESPOND
Launched Jan 1, 2017 To date subscribers: 175 producer Representing 6.5M pigs Subscriber access to PART Website Quarterly Reports sent to subscribers Regular report/data review with farm veterinarian Antimicrobial Resistance Monitoring work begun at PVS in 2017
Antibiotic Resistance First Look TH 10.16
Data Resistance data starting in 2001 from 3 VDLS UMN ISU SDSU Looking at all samples submitted not just Pipestone Veterinary Services
Intro 5 bacteria A suis E coli hemolytic and non hemolytic HPS Salmonella all subtypes S suis 17 antibiotics Ampicillin Ceftiofur Chlortetracycline Clindamycin Enrofloxacin Florfenicol Gentamicin Neomycin Oxytetracycline Penicillin Spectinomycin Sulfadimethoxine Tiamulin Tilmicosin Trimethoprim/Sulfa Tulathromycin Tylosin Tartrate
Description Breakdown of bacteria submissions over time E coli all 17207 Streptococcus suis 13065 Pasteurella multocida 10323 Salmonella all 7297 Haemophilus parasuis 6426 Bordetella bronchiseptica 5244 Actinobacillus suis 3846
Method For every antibiotic per case, assigned a value of 0 or 1 0 = Resistant 1 = Susceptible or Intermediate Each antibiotic is binary (1 v 0) Each case is the average of all drugs for that case (continuous) This is the case Index Used this Index across bacteria, antibiotics and time to describe resistance patterns
Index = average = 0.15
Total Index Over time, all five bacteria seem to increase then decrease in susceptibility. True for all bacteria and top 5.
Actinobacillus suis This increase in susceptibility includes all antibiotics. Visual over time. S R
E. coli Shows a visual increase in susceptibility, then decrease starting in 2016. S R
Hemophilus parasuis Visually: flat, stable, highly susceptible. S R
Summary 100 point moving average for individual top 5 bacteria. S R
AMR Pilot Update Dr. Scott Dee Pipestone Applied Research
AMR Project Review New approach: Food safety basis Coordinate with NARMS (CDC, FDA, USDA) No one testing at farm level! Farm-based pilot study RB3: low health/high use RB 4: high health/low use Environmental and rectal swabs collected Outcome: Dunging area best sample (comprehensive & repeatable) NARMS metrics Designated food-safety bacteria Antimicrobial sensitivity panel Phenotyping (SDSU) Prediction of resistance (MIC results) Genotyping (USDA) Confirmation of resistance (Actual presence of gene)
AMR Pilot Project RB 3 vs RB 4 Comparison: Bacterial recovery and percent resistance by antibiotic tested Conclusions: In general, results were similar across the 2 population tested. Percent recovery by population sampled Bacteria RB 3 RB 4 E coli 83% 80% Sal 0% 7% Camp 0% 11% Entero 61% 52%
AMR Pilot Project Percent resistance by population sampled Antibiotic RB 3 RB 4 ampicillin 69% 25% amoxicillin 7% 2% cefitoxin 7% 2% ceftiofur 9% 4% ceftriaxone 9% 4% chloramphenicol 10-17% 8-25% ciprofloxacillin 2% 2% daptomycin 0% 0% erythromycin 83% 75% gentamycin 9-14% 3-4% kanamycin 72% 3% lincomycin 100% 100% linezolid 0% 0% naladixic acid 2% 2% nitrofurantoin 10% 8% penicillin 38% 19% streptomycin 50-83% 31-53%
How does Phenotype Compare to Genotype? % Corroboration: phenotype was in agreement with genotype.. Enterococcus: 96% E coli: 83% Salmonella: 100% Campylobacter: 82% In cases where a match did not occur, phenotype predicted resistance in the absence of a detectable gene 83% of the time (sensitivity vs specificity). Possible explanations: 1. Gene was not expressed at the time of testing. 2. Gene was on a MGE and moved between the time of phenotyping and genotyping.. 3. MIC data was overly sensitive. 4. A more in-depth computational analysis may have identified the gene.
Genotyping: Summary by Population Sampled # isolates phenotyped = 138, # isolates genotyped = 30 Bacteria evaluated Total isolates genotyped # AMR genes identified # AMR genes high health/low use # AMR genes low health/high use Enterococcus sp. 11 79 42 37 E coli 11 45 10 35 Salmonella sp. 3 12 12 0 (no isolates recovered) Campylobacter sp. 5 19 19 0 (no isolates recovered)
Summary of data regarding % corroboration between genotype and phenotype data across 187 opportunities. Phenotype (+)/Genotype (+) Phenotype (+)/Genotype (-) Phenotype (-)/Genotype (+) 164/187 = 87.8% agreement 16/187 = 8.6% disagreement 7/187 = 3.7% disagreement Observation: Most disagreement seen within E coli isolates (FWIW). Conclusion: Data clearly show a small percentage of disagreement between the two tests; however, the overall % corroboration between the two tests is very high (88%). In my opinion, this warrants using phenotype data as a predictor of AMR in the PART protocol.
Observations More AMR genes observed in E coli isolates from low health/high use population (3.5X) versus high health/low use population. Need to validate this observation. Next experiment? Mechanisms of AMR reported. Drug efflux pumps Random mutations Mobile genetic elements Phenotypic observations confirmed by genotyping. Class-wide resistance across isolates. Multi-drug resistance within isolates. Resistance observed in the absence of use.
PART Antibiotic Usage Data
127 Producers Excludes Outliers