Estimating provisional acceptable residues for extralabel drug use in livestock

Development of machine learning algorithms to estimate maximum residue limits for veterinary medicines

Establishing maximum-residue limits (MRLs) for veterinary medicine helps to protect the human food supply. Guidelines for establishing MRLs are outlined by regulatory authorities that drug sponsors follow in each country. During the drug approval process, residue limits are targeted for specific animal species and matrices. Therefore, MRLs are commonly not established for other species. This study demonstrates unestablished MRLs can be reliably predicted for under-represented food commodity groups using machine learning (ML). Classification methods with imbalanced data were used to analyze MRL data from multiple countries by implementing resampling techniques in different ML classifiers. Afterward, we developed and evaluated a data-mining method for predicting unestablished MRLs. Seven different ML classifiers such as support vector classifier, multi-layer perceptron (MLP), random forest, decision tree, k-neighbors, Gaussian NB, and AdaBoost have been selected in this baseline study. Among these, the neural network MLP classifier reliably scored the highest average-weighted F1 score (accuracy >99% with markers and ≈88% without markets) in predicting unestablished MRLs. This provides the first study to apply ML algorithms in regulatory food animal medicine. By predicting and estimating MRLs, we can potentially decrease the use and cost of live animals and the overall research burden of determining new MRLs.

Health concerns and management of select veterinary drug residues

The aim of this manuscript is to review the potential adverse health effects in humans if exposed to residues of selected veterinary drugs used in food-producing animals. Our other objectives are to briefly inform the reader of why many of these drugs are or were approved for use in livestock production and how drug residues can be mitigated for these drugs. The selected drugs include several antimicrobials, beta agonists, and phenylbutazone. The antimicrobials continue to be of regulatory concern not only because of their acute adverse effects but also because their use as growth promoters have been linked to antimicrobial resistance. Furthermore, nitroimidazoles and arsenicals are no longer approved for use in food animals in most jurisdictions. In recent years, the risk assessment and risk management of beta agonists, have been the focus of national and international agencies and this manuscript attempts to review the pharmacology of these drugs and regulatory challenges. Several of the drugs selected for this review can cause noncancer effects (e.g., penicillins) and others are potential carcinogens (e.g., nitroimidazoles). This review also focuses on how regulatory and independent organizations manage the risk of these veterinary drugs based on data from human health risk assessments.

Antibiotic residues in milk from small dairy farms in rural Peru

The use of antibiotics in livestock can pose a public health threat, especially if antibiotic residues remain in the food product. Understanding how often and why farmers sell products with antibiotic residues is critical to improving the quality of these products. To understand how often milk with antibiotic residues is sold on small farms in a major dairy-producing region of Peru and identify factors associated with selling milk with antibiotic residues, we tested milk samples for antibiotic residues from every provider on three routes of commercial milk companies and from bulk tanks of farmers currently treating cows with antibiotics. We also asked farmers if they sold milk from treated cows and examined factors associated with the tendency to do so. The prevalence of milk contamination with antibiotic residues on commercial routes was low (0-4.2%); however, 33/36 farmers treating their animals with antibiotics sold milk that tested positive for antibiotic residues. The self-reported sale of milk from treated cows had a sensitivity, specificity, and positive and negative predictive values of 75.8%, 100%, 100% and 27.2%, respectively (with testing of milk for residues as the gold standard). Finally, 69/156 randomly selected farmers reported selling milk from treated cows, and farmers' knowledge of antibiotics and the milk purchaser were significantly associated with a farmer's tendency to report doing so. Educating farmers on the risks associated with antibiotics and enforcement of penalties for selling contaminated milk by milk companies are needed to improve milk quality.

Positive and negative electrospray LC-MS-MS methods for quantitation of the antiparasitic endectocide drugs, abamectin, doramectin, emamectin, eprinomectin, ivermectin, moxidectin and selamectin in milk

Avermectin endectocides are used for the treatment of cattle against a variety of nematode and arthropod parasites, and consequently may appear in milk after normal or off-label use. The compounds abamectin, doramectin, and ivermectin, contain only C, H and O and may be expected to be detected by LC-MS in negative ion mode. The others contain nitrogen in addition and would be expected to be preferentially ionized in positive mode. The use of positive ion and negative ion methods with electrospray LC-MS-MS were compared. Using negative ion the compounds abamectin, doramectin, ivermectin, emamectin, eprinomectin, and moxidectin gave a curvilinear response and were quantified in raw milk by LC-MS-MS with a triethylamine-acetonitrile buffer over the concentration range 1-60 ppb (microg/kg) using selamectin as the internal standard. The limits of detection (LOD) were between 0.19 ppb (doramectin) and 0.38 ppb (emamectin). The compounds gave maximum sensitivity with positive ionisation from a formic acid-ammonium formate-acetonitrile buffer and were detected in milk (LC-MS-MS) also with a curvilinear response over the range 0.5-60 ppb. Although the positive ion signals were larger, with somewhat lower limits of detection (LOD between 0.06 ppb (doramectin) and 0.32 ppb (moxidectin) the negative ion procedure gave a more linear response and more consistent results. Comparison of spiked samples in the range 2-50 ppb showed a high degree of correlation between the two methods.

Application of risk assessment and management principles to the extralabel use of drugs in food-producing animals

A risk assessment of the food safety implications of drugs used in food-producing animals is an essential component of the regulatory approval process for products containing these drugs. This ensures that there is negligible risk to human health if these drugs are used according to the instructions that appear on the approved label. A relative paucity of approved products for veterinary species; however, forces veterinarians worldwide to use drugs in an extralabel manner to treat disease and alleviate suffering in animals. In food-producing animals, this may result in residues that are potentially harmful to the human consumer. This review describes how risk assessment principles can be extended to evaluate the risks posed by different classes of extralabel drug use. Risk management practices in the United States and Europe are summarized and contrasted to illustrate the application of these principles.

Interspecies considerations in the evaluation of human food safety for veterinary drugs

Residues are composed of the parent drug and metabolites, and therefore interspecies comparisons must involve a consideration of comparative xenobiotic metabolism. The focus of this article will be the residue studies that are required to establish human food safety, and the interspecies pharmacokinetic differences and similarities that impact drug residues in animal- derived foods. To illustrate the factors that can complicate and assist these comparisons, 2 drugs will be examined in detail: ivermectin and fenbendazole. In addition, the activities of 2 US programs, the Food Animal Residue Avoidance Databank (FARAD) and the NRSP-7 (National Research Support Project Number 7) Minor Use Animal Drug Program will be presented, along with strategies that may be employed in the study of species differences.

Extrapolated withdrawal-interval estimator (EWE) algorithm: a quantitative approach to establishing extralabel withdrawal times

The extralabel use of drugs can be defined as the use of drugs in a manner inconsistent with their FDA-approved labeling. The passage of the Animal Medicinal Drug Use Clarification Act (AMDUCA) in 1994 and its implementation by the FDA-Center for Veterinary Medicine in 1996 has allowed food animal veterinarians to use drugs legally in an extralabel manner, as long as an appropriate withdrawal period is established. The present study introduces and validates with simulated and experimental data the Extrapolated Withdrawal-Period Estimator (EWE) Algorithm, a procedure aimed at predicting extralabel withdrawal intervals (WDIs) based on the label and pharmacokinetic literature data contained in the Food Animal Residue Avoidance Databank (FARAD). This is the initial and first attempt at consistently obtaining WDI estimates that encompass a reasonable degree of statistical soundness. Data on the determination of withdrawal times after the extralabel use of the antibiotic oxytetracycline were obtained both with simulated disposition data and from the literature. A withdrawal interval was computed using the EWE Algorithm for an extralabel dose of 25 mg/kg (simulation study) and for a dose of 40 mg/kg (literature data). These estimates were compared with the withdrawal times computed with the simulated data and with the literature data, respectively. The EWE estimates of WDP for a simulated extralabel dose of 25 mg/kg was 39 days. The withdrawal time (WDT) obtained for this dose on a tissue depletion study was 39 days. The EWE estimate of WDP for an extralabel intramuscular dose of 40 mg/kg in cattle, based on the kinetic data contained in the FARAD database, was 48 days. The withdrawal time experimentally obtained for similar use of this drug was 49 days. The EWE Algorithm can obtain WDI estimates that encompass the same degree of statistical soundness as the WDT estimates, provided that the assumptions of the approved dosage regimen hold for the extralabel dosage regimen. Population models could be fitted to fragmentary data to predict residue concentrations in tissues, validate the EWE estimates, and obtain WDI estimates.