The contamination risks of plant-derived foods due to the co-existence of pesticides and veterinary drugs (P&VDs) have not been fully understood. With an increasing number of unexpected P&VDs illegally added to foods, it is essential to develop a non-targeted screening method for P&VDs for their comprehensive risk assessment. In this study, a modified support vector machine (SVM)-assisted metabolomics approach by screening eligible variables to represent marker compounds of 124 multi-class P&VDs in maize was developed based on the results of high-performance liquid chromatography-tandem mass spectrometry. Principal component analysis and orthogonal partial least squares discriminant analysis indicate the existence of variables with obvious inter-group differences, which were further investigated by S-plot plots, permutation tests, and variable importance in projection to obtain eligible variables. Meanwhile, SVM recursive feature elimination under the radial basis function was employed to obtain the weight-squared values of all the variables ranging from large to small for the screening of eligible variables as well. Pairwise t-tests and fold changes of concentration were further employed to confirm these eligible variables to represent marker compounds. The results indicate that 120 out of 124 P&VDs can be identified by the SVM-assisted metabolomics method, while only 109 P&VDs can be found by the metabolomics method alone, implying that SVM can promote the screening accuracy of the metabolomics method. In addition, the method\'s practicability was validated by the real contaminated maize samples, which provide a bright application prospect in non-targeted screening of contaminants. The limits of detection for 120 P&VDs in maize samples were calculated to be 0.3~1.5 µg/kg.

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Anna Judson reports on BVA\'s recent activities around the Competition and Markets Authority\'s market investigation.

A rapid and online microvolume flow-through dialysis probe designed for sample preparation in the analysis of veterinary drug residues is introduced. This study addresses the need for efficient and green sample preparation methods that reduce chemical waste and reagent use. The dialysis probe integrates with liquid chromatography and mass spectrometry (LC-MS) systems, facilitating automated, high-throughput analysis. The dialysis method utilizes minimal reagent volumes per sample, significantly reducing the generation of solvent waste compared to traditional sample preparation techniques. Several veterinary drugs were spiked into tissue homogenates and analyzed to validate the probe\'s efficacy. A diagnostic sensitivity of >97% and specificity of >95% were obtained for this performance evaluation. The results demonstrated the effective removal of cellular debris and particulates, ensuring sample integrity and preventing instrument clogging. The automated dialysis probe yielded recovery rates between 27 and 77% for multiple analytes, confirming its potential to streamline veterinary drug residue analysis, while adhering to green chemistry principles. The approach highlights substantial improvements in both environmental impact and operational efficiency, presenting a viable alternative to conventional sample preparation methods in regulatory and research applications.

This work compares the electroanalytical performance of two electroanalytical systems based on (1) the glassy carbon electrode (GCE), and (2) the electrified liquid-liquid interface (eLLI), for the detection of fluoroquinolone antibiotic-danofloxacin (DANO). Our aim was to define the optimal conditions to detect the chosen analyte with two employed systems, extract a number of electroanalytical parameters, study the mechanism of the charge transfer reactions (oxidation at GCE and ion transfer across the eLLI), and to provide physicochemical constants for DANO. Detection of the chosen analyte was also performed in the spiked milk samples. To the best of our knowledge, this is the first work that directly compares the electroanalytical parameters obtained with solid electrode (in this case GCE) and eLLI. We have found that for DANO the latter provides better electroanalytical parameters (lower LOD and LOQ) as well as good selectivity when the milk was analyzed.

The escalating introduction of pesticides/veterinary drugs into the environment has necessitated a rapid evaluation of their potential risks to ecosystems and human health. The developmental toxicity of pesticides/veterinary drugs was less explored, and much less the large-scale predictions for untested pesticides, veterinary drugs and bio-pesticides. Alternative methods like quantitative structure-activity relationship (QSAR) are promising because their potential to ensure the sustainable and safe use of these chemicals. We collected 133 pesticides and veterinary drugs with half-maximal active concentration (AC50) as the zebrafish embryo developmental toxicity endpoint. The QSAR model development adhered to rigorous OECD principles, ensuring that the model possessed good internal robustness (R2 > 0.6 and QLOO2 > 0.6) and external predictivity (Rtest2 > 0.7, QFn2 >0.7, and CCCtest > 0.85). To further enhance the predictive performance of the model, a quantitative read-across structure-activity relationship (q-RASAR) model was established using the combined set of RASAR and 2D descriptors. Mechanistic interpretation revealed that dipole moment, the presence of C-O fragment at 10 topological distance, molecular size, lipophilicity, and Euclidean distance (ED)-based RA function were main factors influencing toxicity. For the first time, the established QSAR and q-RASAR models were combined to prioritize the developmental toxicity of a vast array of true external compounds (pesticides/veterinary drugs/bio-pesticides) lacking experimental values. The prediction reliability of each query molecule was evaluated by leverage approach and prediction reliability indicator. Overall, the dual computational toxicology models can inform decision-making and guide the design of new pesticides/veterinary drugs with improved safety profiles.

Veterinary medications are necessary for both contemporary animal husbandry and food production, but their residues can linger in foods obtained from animals and pose a dangerous human risk. In this review, we aim to highlight the sources, occurrence, human exposure pathways, and human health effects of drug residues in food-animal products. Following the usage of veterinary medications, pharmacologically active compounds known as drug residues can be found in food, the environment, or animals. They can cause major health concerns to people, including antibiotic resistance development, the development of cancer, teratogenic effects, hypersensitivity, and disruption of normal intestinal flora. Drug residues in animal products can originate from variety of sources, including water or food contamination, extra-label drug use, and ignoring drug withdrawal periods. This review also examines how humans can be exposed to drug residues through drinking water, food, air, and dust, and discusses various analytical techniques for identifying these residues in food. Furthermore, we suggest some potential solutions to prevent or reduce drug residues in animal products and human exposure pathways, such as implementing withdrawal periods, monitoring programs, education campaigns, and new technologies that are crucial for safeguarding public health. This review underscores the urgency of addressing veterinary drug residues as a significant and emerging public health threat, calling for collaborative efforts from researchers, policymakers, and industry stakeholders to develop sustainable solutions that ensure the safety of the global food supply chain.

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BACKGROUND: Antimicrobial resistance (AMR) is a One Health issue and a major threat to animal and human health. Antibiotic use (ABU) drives AMR development, and several hotspots for ABU, and AMR, in livestock have been identified in Southeast Asia, including Vietnam. There are often multiple drivers of ABU at farms, and to identify all of them there is a need to look beyond farm level.
OBJECTIVE: The overall aim of this study was to identify routines and/or competencies, related to antibiotic sales, among veterinary drug shop workers that may be improved in order to decrease the medically non-rational use of antibiotics in livestock production.
METHODS: A questionnaire-based survey was conducted at 50 veterinary drug shops in northern Vietnam.
RESULTS: Results showed high education and knowledge levels. According to the respondents, antibiotic treatment advice was almost always provided to the farmers, and the recommended treatment was most commonly based on recommendations for the specific disease. However, farmers had almost never had their animals properly diagnosed. Antibiotics were the most sold drug category, penicillins being the most common. Several broad-spectrum antibiotics were also quite frequently sold. Further, >50% of respondents recommended antibiotics for disease prevention.
CONCLUSIONS: Even though education and knowledge levels might be high, several challenges can prevent drug shop workers from contributing to more prudent ABU at farms, for example, lack of proper diagnosis, commercial interests and individual farmer motives, often in combination with poor compliance to regulations.

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