National Residue Program: Lab Methodology and Results

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2 US Department of Agriculture Food Safety and Inspection Service National Residue Program: Lab Methodology and Results Louis Bluhm, Ph.D. Lead Chemist, Laboratory Quality Assurance Staff Office of Public Health Science October

3 US Department of Agriculture Food Safety and Inspection Service I. Background II. FSIS Chemistry Methods (primary screens) Pesticides Veterinary Drugs III. Preliminary Test Results Domestic Scheduled Samples IV. Preliminary Test Results Inspector Generated Samples (In-Plant Screening and Lab Analysis) V. Preliminary Test Results Import Samples VI. NARMS and FSIS s Role VII. Where can I find more information? 3

4 I. Background National Residue Program (NRP): Goals/Purpose Provide a structured process for identifying and evaluating chemical compounds of concern in food animals. Test for the presence of chemical compounds: veterinary drugs, pesticides, hormones, and environmental contaminants in meat, poultry, and egg products. Identify need for regulatory follow-up when violative levels of chemicals residues are found. 4

5 I. Background National Residue Program (NRP): Process FSIS inspectors collect samples of meat, poultry, and Siluriformes (catfish) muscle tissue as well as egg products at federally inspected establishments. Samples are analyzed at FSIS laboratories for chemical residues of veterinary drugs, pesticides, and environmental contaminants. Laboratory confirmed results are compared with tolerances established by the FDA and the EPA, to prevent adulterated products from entering into commerce. 5

6 I. Background Laboratory System Western Laboratory (Albany, CA) Midwestern Laboratory (St. Louis, MO) Eastern Laboratory and LQAS (Athens, GA) 6

7 I. Background Analytical Chemistry Capabilities of the Laboratories Midwestern Laboratory Inspector-generated residue analysis, primarily veterinary drugs Western Laboratory and Eastern Laboratory Scheduled chemistry analysis as described in the National Residue Program (NRP) EL Metals; food chemistry WL Pesticides All three labs are ISO and ALACC accredited 7

8 II. Chemistry Methods Multi-Residue Screens to Support the NRP Pesticides Based on QuEChERS extraction developed at USDA ARS Muscle, not fat, of various animal species Uses UPLC-MS-MS Multi-Class Residue Method (MRM) Based on QuEChERS extraction Muscle, kidney of various animal species Uses UPLC-MS-MS Central framework to add analytes, matrices waters.com (endorsement not implied) 8

9 Analytes Food Safety and Inspection Service: II. Chemistry Methods Evolution of Pesticides Screen Capability April 2015: 108 July 2014: April 2011: Beef Pork Poultry Goat Sheep Horse Catfish 9 Matrices (Muscle)

10 Analytes Food Safety and Inspection Service: II. Chemistry Methods Evolution of Multi-Class Residue Screen Capability 89 Veterinary drug classes included: Tetracycline, Macrolide/Lincosamide, β-lactams/ Cephalosporin, β-agonist, Fluoroquinolones, Hormones, Sulfonamides, Analgesics/Anti-inflammatory February 2012: 51 February 2013: 53 February 2014: 53 Beef Pork Poultry Goat Sheep Catfish (muscle) 10 Species (Kidney)

11 III. Preliminary Test Results Domestic Scheduled Samples Year Number of Samples # of Lab Confirmed Violative Carcasses # of Lab Confirmed Non-Violative Positive Analyses # of Distinct Violative Chemical Residues FY , FY , FY , FY 2017 (Preliminary Results) 7, Source: FSIS Data Warehouse (DW) & Public Health Information System (PHIS) 11

12 III. Preliminary Test Results Domestic Scheduled Samples Animal Class Number of Non- Detect Samples Number of Non- Violative Positive Samples Number of Violative Samples Total Samples Beef Cows Bob Veal Bulls/Stags Dairy Cows Formula Fed veal Goats Heavy Calves Heifers Market Swine Mature Sheep Non-Formula Fed Veal Old Breeder Turkeys Roaster Swine Siluriformes (catfish) Sows Steers Young Chickens Young Turkeys Total Source: FSIS Data Warehouse (DW) & Public Health Information System (PHIS) 12

13 IV. Preliminary Test Results Inspector Generated Samples In-Plant Screening From FSIS Directive 10,800.1: II. INSPECTOR-GENERATED SAMPLING A. In-Plant Screening Test (KIS Test) Under the direction of the Public Health Veterinarian (PHV), In-Plant Personnel (IPP) are to conduct a KIS test on any carcass that, based on herd history or ante-mortem or post-mortem inspection findings, may contain a violative drug residue. 13

14 IV. Preliminary Test Results Inspector Generated Samples In-Plant Screening KIS = Kidney Inhibition Swab Score kidney, collect juice using swab Incubate in bacteria/agar solution If bacteria produce acid, no antimicrobial drugs are present, and the test is yellow (negative) If bacteria are inhibited due to antimicrobial drugs, the color remains purple (positive) 14

15 IV. Preliminary Test Results Inspector Generated Samples Summary of KIS test data of submitted samples, Oct Mar The following tables and graphs show data from: FY 2014, FY 2015, FY 2016 (complete) FY 2017 (year-to-date [YTD], through March 2017) FY (to show what YTD totals are on pace to reach) FIGURE 1. Number of KIS Test Samples Submitted for Lab Testing 5,000 4,500 4,000 3,500 3,000 2,500 2,000 1,500 1, FY 2014 FY 2015 FY 2016 FY 2017 (YTD) FY 2017 (x2) 15

16 IV. Preliminary Test Results Inspector Generated Samples Summary of KIS test data of submitted samples, Oct Mar ,400 FIGURE 2. Number of Violative Samples and Violative Results, FY 2014 to FY 2017 (YTD and 2) 1,200 1, FY 2014 FY 2015 FY 2016 FY 2017 YTD FY 2017 x # of violative samples # of violative results # of samples containing multiple violative compounds 16

17 IV. Preliminary Test Results Inspector Generated Samples Summary of KIS test data of submitted samples, Oct Mar FIGURE 3. Number of Samples with a Given Number of Distinct Compounds, FY 2014 to FY 2017 (YTD and 2) 1,200 1, FY2014 FY2015 FY2016 FY2017x compound 2+ compounds 17

18 IV. Preliminary Test Results Inspector Generated Samples Summary of KIS test data of submitted samples, Oct Mar In the PHIS database, 30+ reasons can be selected for performing a KIS test. Most reasons cite a medical condition For FY 2015, FY 2016, and FY 2017 YTD: The top 10 reasons comprised 85% of violations each year. The top 2 reasons (pneumonia and pyemia/septicemia) accounted for 35% to 37% of violations. 18

19 IV. Preliminary Test Results Inspector Generated Samples Summary of KIS test data of submitted samples, Oct Mar FIGURE 4. Reasons for Performing Test, Violative Samples, FY 2014 to FY 2017 YTD Top 10 Reasons in FY 2014 Pneumonia Pyemia/Septicemia Bob Veal Calf ( c & d) Peritonitis Pericarditis, Endocarditis Injection Site Metritis General Misc. FY 2014 FY 2015 FY 2016 FY 2017 (x2) Statistically Selected Nephritis/Cystitis

20 IV. Preliminary Test Results Inspector Generated Samples Summary of KIS test data of submitted samples, Oct Mar In FY 2016: The top 6 compounds accounted for 82% of all violative results: Desfuroylceftiofur Penicillin Neomycin Flunixin Sulfamethazine Sulfadimethoxine There were 30 compounds with at least one violative finding. FY 2017 YTD: 24 compounds have at least one violative finding. 20

21 IV. Preliminary Test Results Inspector Generated Samples Summary of KIS test data of submitted samples, Oct Mar FIGURE 5. Violative Compounds Found in KIS Test Samples, FY 2014 to FY 2017 Top 6 Compounds in FY 2014 Desfuroylceftiofur Penicillin Neomycin Flunixin Sulfamethazine FY 2014 FY 2015 FY 2016 FY 2017 (x2) Sulfadimethoxine

22 IV. Preliminary Test Results Inspector Generated Samples Summary of KIS test data of submitted samples, Oct Mar FY 2016: Dairy cows and bob veal accounted for 81% of lab-confirmed KIS violations. 13 livestock production classes were represented (Figures 6 and 7). FY 2017 YTD: Dairy cows and bob veal account for 82% of lab-confirmed KIS violations. 10 livestock production classes are represented. 22

23 IV. Preliminary Test Results Inspector Generated Samples Summary of KIS test data of submitted samples, Oct Mar FIGURE 6. KIS Test Results: # of Violations Per Animal Class, Sorted by Top 10 in FY 2014, FY 2014 to FY 2017 YTD Dairy Cow Bob Veal Beef Cow Sow Steer Heifer Heavy Calf Bull/Stag Non Formula-fed Veal FY 2014 FY 2015 FY 2016 FY 2017 (x2) Market Swine

24 IV. Preliminary Test Results Inspector Generated Samples Summary of KIS test data of submitted samples, Oct Mar FIGURE 7. KIS Test Results: # of Violations per Animal Class, FY 2014 to FY 2017 YTD Sorted by Top 6 in FY 2014, after Dairy Cow and Bob Veal Beef Cow Sow Steer Heifer Heavy Calf Bull/Stag Non Formula-fed Veal FY 2014 FY 2015 FY 2016 FY 2017 (x2) Market Swine

25 V. Preliminary Test Results Import Samples Year # of Samples Total Violations FY ,967 FY ,922 FY ,676 FY ,763 8 Ivermectin (7) Zilpaterol (1) 7 Abamectin (1), Ethion (5), Piperonyl Butoxide (1) 22 Ethion (21), Diazinon (1) 18 (all fish) Dyes (14), Chlorpyrifos (1), Nitrofurans (2), Enrofloxacin (1) Source: FSIS Data Warehouse (DW) & Public Health Information System (PHIS) 25

26 VI. NARMS (National Antimicrobial Resistance Monitoring System) and FSIS s Role FSIS began cecal sampling in 2013 pre-intervention yet post slaughter Cecal sampling has further strengthened NARMS surveillance by adding another sampling point and additional ~ 5000 samples FSIS laboratory processes successfully sequenced and uploaded into NCBI the NARMS Salmonella and Campylobacter isolates in near-real-time FSIS initiated a process to inform FSIS-regulated entities about unusual AMR findings 26

27 VI. NARMS and FSIS s Role FSIS continues to work with NARMS partners to: Develop a new strategic plan based on the recommendations from Science Board Host a NARMS Public Meeting (Oct 24 th and 25 th ) Adopt the new Tableau system for data handling, processing, and visualization of FSIS NARMS data 27

28 VII. Where can I find more information? For NARMS: See the FSIS website 28

29 VII. Where can I find more information? For Chemistry Methods: FSIS Chemistry Laboratory Guidebook (CLG) website To get updates by 29

30 VII. Where can I find more information? Residue Sampling Plans (a.k.a. the FSIS Blue Book) Residue Sample Results (a.k.a. the FSIS Red Book) 30

31 Questions? Acknowledgments: Naser Abdelmajid Jennifer Highland Rebecca Fields Sam Zipperer Glenn Tillman, Ph.D. Jovita Haro 31