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Antibiotics
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Veterinary Antibiotics in Food-Producing Animals: Residue Detection, Risk Assessment and...
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Veterinary Antibiotics in Food-Producing Animals: Residue Detection, Risk Assessment and Regulatory Advances

A special issue of Antibiotics (ISSN 2079-6382). This special issue belongs to the section "Antibiotics in Animal Health".

Deadline for manuscript submissions: 30 September 2026 | Viewed by 8349

Special Issue Editors

Dr. Liliana J.G. Silva

E-Mail Website
Guest Editor
Faculty of Pharmacy of the University of Coimbra, Polo III, Azinhaga de Stª Comba, 3000-548 Coimbra, Portugal
Interests: analytical chemistry; food safety; LC-MS; mycotoxins
Special Issues, Collections and Topics in MDPI journals
Dr. Marta Leite

E-Mail Website
Guest Editor Assistant
1. Associated Laboratory for Green Chemistry (LAQV) of the Network of Chemistry and Technology (REQUIMTE), Faculty of Pharmacy, University of Coimbra, R. D. Manuel II, 4051-401 Porto, Portugal
2. National Institute for Agricultural and Veterinary Research (INIAV), Rua dos Lágidos, Lugar da Madalena, 4485-655 Vila do Conde, Portugal
Interests: analytical chemistry; food safety; mycotoxins; veterinary drugs; mass spectrometry
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The widespread and often misuse of veterinary antibiotics to control infectious diseases in food-producing animals now stands at the connection between animal health, food safety, and antimicrobial resistance (AMR) management. This Special Issue will gather cutting-edge research and reviews that highlight three important areas of research.

Residue detection includes advances in sampling strategies, chromatographic–mass-spectrometric methods, rapid biosensors, and multi-omics fingerprints for quantification of veterinary drugs and their metabolites in different animal-based products (e.g., meat, milk, eggs, fish, honey, pollen, royal jelly, and propolis).

Risk assessment involves toxicological, ecological, and One-Health studies that trace how antibiotic residues influence animal and environmental microbiomes, select for resistant pathogens, migrate through the food chain, and influence consumer exposure.

Regulatory advances include contributions that compare national surveillance data, model maximum-residue-limit compliance, evaluate policies, and explore alternative therapeutics that reduce antibiotic dependence.

By integrating analytical chemistry, microbiology, toxicology, and policy science, this collection aims to chart evidence-based pathways toward sustainable animal husbandry, safe animal-based products, and standardized global guidelines.

Dr. Liliana J.G. Silva
Guest Editor

Dr. Marta Leite
Guest Editor Assistant

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 250 words) can be sent to the Editorial Office for assessment.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Antibiotics is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2900 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • veterinary drugs
  • analytical chemistry
  • food safety
  • risk assessment
  • regulatory frameworks

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Published Papers (5 papers)

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Research

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18 pages, 1599 KB  
Article
Antibiotic Residues in Raw Cow Milk Collected from Smallholder Dairy Farms in Kasama and Mbala, Zambia
by Goliath Eneya Zulu, Bernard Mudenda Hang’ombe, Geoffrey Mainda, Edgar Kayesa, Chitwambi Makungu, Abel Compbel Chipembo, Gilbert Nchima, Alberto Pondja, Niura Madalena Bila and Belisário Moiane
Antibiotics 2025, 14(12), 1197; https://doi.org/10.3390/antibiotics14121197 - 26 Nov 2025
Cited by 1 | Viewed by 1254
Abstract
Background/Objectives: The deposition of antibiotic residues in animal source foods has become a global public health threat. This study aimed to assess antibiotic class residues in raw cow milk from smallholder dairy farms in Mbala and Kasama, Zambia. Methods: A cross-sectional study was [...] Read more.
Background/Objectives: The deposition of antibiotic residues in animal source foods has become a global public health threat. This study aimed to assess antibiotic class residues in raw cow milk from smallholder dairy farms in Mbala and Kasama, Zambia. Methods: A cross-sectional study was conducted, in which 93 milk samples (54 from Mbala and 39 from Kasama) were randomly collected from lactating cows on 56 farms between May and June, 2025. The samples were analyzed using the Charm II assay for beta-lactams, tetracyclines, macrolides, sulfonamides, and aminoglycosides. A total of 100 mL of milk was collected in sterile plain tubes, placed in a cooler box with ice packs, and transported to the district laboratory’s freezer and then delivered to the Central Veterinary Research Institute at (−18 to −20 °C), where they were stored at −20 °C. Statistical significance between districts was determined using Pearson’s chi-square, and associations between a district and the occurrence of antibiotic residues in milk were evaluated using logistic regression. Data were analyzed using Stata 14.2 at a 95% confidence level (p = 0.05). Results: A total of 91.4% (n = 85) of samples had antibiotic residues above EU/MRLs, with mean positive samples being 0.91 ± 0.28 and a significant association between a district and residue occurrence (OR = 0.086; p = 0.025). Approximately 44.1% of the samples had multiple antibiotic residues. Approximately 82.1% of samples from Kasama and 98.1% from Mbala had antibiotic residues (p = 0.006). Approximately 68.8% of samples had sulphonamides, and 58.1% macrolides, indicating their widespread use. Tetracyclines were 12.9%, beta-lactams 9.7%, and aminoglycosides 2.2%. Conclusions: A majority of milk samples had antibiotic residues above EU/MRLs, raising public health threats and necessitating the development and implementation of policies. Full article
(This article belongs to the Special Issue Veterinary Antibiotics in Food-Producing Animals: Residue Detection, Risk Assessment and Regulatory Advances)
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37 pages, 2717 KB  
Article
The Potential for Sample Testing at the Pen Level to Inform Prudent Antimicrobial Selection for Bovine Respiratory Disease Treatment: Investigations Using a Feedlot Simulation Tool
by Dana E. Ramsay, Wade McDonald, Sheryl P. Gow, Lianne McLeod, Simon J. G. Otto, Nathaniel D. Osgood and Cheryl L. Waldner
Antibiotics 2025, 14(10), 1009; https://doi.org/10.3390/antibiotics14101009 - 11 Oct 2025
Cited by 2 | Viewed by 1137
Abstract
Background: Antimicrobial drugs are used to treat bacterial diseases in livestock production systems, including bovine respiratory disease (BRD) in feedlot cattle. It is recommended that therapeutic antimicrobial use (AMU) in food animals be informed by diagnostic tests to limit the emergence of antimicrobial [...] Read more.
Background: Antimicrobial drugs are used to treat bacterial diseases in livestock production systems, including bovine respiratory disease (BRD) in feedlot cattle. It is recommended that therapeutic antimicrobial use (AMU) in food animals be informed by diagnostic tests to limit the emergence of antimicrobial resistance (AMR) and preserve the effectiveness of available drugs. Recent evidence demonstrates preliminary support for the pen as a prospective target for AMR testing-based interventions in higher-risk cattle. Methods: A previously reported agent-based model (ABM) was modified and then used in this study to investigate the potential for different pen-level sampling and laboratory testing-informed BRD treatment strategies to favorably impact selected antimicrobial stewardship and management outcomes in the western Canadian context. The incorporation of sample testing to guide treatment choice was hypothesized to reduce BRD relapses, subsequent AMU treatments and resultant AMR in sentinel pathogen Mannheimia haemolytica. The ABM was extended to include a discrete event simulation (DES) workflow that models the testing process, including the time at sample collection (0 or 13 days on feed) and the type of AMR diagnostic test (antimicrobial susceptibility testing or long-read metagenomic sequencing). Candidate testing scenarios were simulated for both a test-only control and testing-informed treatment (TI) setting (n = 52 total experiments). Key model outputs were generated for both the pen and feedlot levels and extracted to data repositories. Results: There was no effect of the TI strategy on the stewardship or economic outcomes of interest under baseline ecological and treatment conditions. Changes in the type and number of uses by antimicrobial class were observed when baseline AMR in M. haemolytica was assumed to be higher at feedlot arrival, but there was no corresponding impact on subsequent resistance or morbidity measures. The impacts of sample timing and diagnostic test accuracy on AMR test positivity and other outputs were subsequently explored with a theoretical “extreme” BRD treatment protocol that maximized selection pressure for AMR. Conclusions: The successful implementation of a pen-level sampling and diagnostic strategy would be critically dependent on many interrelated factors, including the BRD treatment protocol, the prevalences of resistance to the treatment classes, the accuracy of available AMR diagnostic tests, and the selected “treatment change” thresholds. This study demonstrates how the hybrid ABM-DES model can be used for future experimentation with interventions proposed to limit AMR risk in the context of BRD management. Full article
(This article belongs to the Special Issue Veterinary Antibiotics in Food-Producing Animals: Residue Detection, Risk Assessment and Regulatory Advances)
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12 pages, 267 KB  
Article
Multi-Analyte Method for Antibiotic Residue Determination in Honey Under EU Regulation 2021/808
by Helena Rodrigues, Marta Leite, Maria Beatriz P. P. Oliveira and Andreia Freitas
Antibiotics 2025, 14(10), 987; https://doi.org/10.3390/antibiotics14100987 - 2 Oct 2025
Viewed by 2127
Abstract
Background/Objectives: Antibiotic detection in honey is challenging due to the complexity of this product, the typically low levels of residues, and the absence of Maximum Residue Levels (MRLs) for beehive products. The use of antibiotics in apiculture poses potential risks to human health, [...] Read more.
Background/Objectives: Antibiotic detection in honey is challenging due to the complexity of this product, the typically low levels of residues, and the absence of Maximum Residue Levels (MRLs) for beehive products. The use of antibiotics in apiculture poses potential risks to human health, including antimicrobial resistance and toxic effects. Reliable, sensitive, and selective analytical methods are essential to ensure food safety and enable accurate monitoring of antibiotic contamination in honey. This study aimed to validate a multi-analyte procedure in accordance with the parameters established in Commission Implementing Regulation (EU) 2021/808 for the identification and quantification of antibiotics, including tetracyclines, lincosamides, quinolones, macrolides, β-lactams, sulfonamides, and diaminopyrimidines. Methods: An extraction protocol was developed using 0.1% formic acid in ACN:H2O (80:20, v/v), followed by a modified QuEChERS with the addition of 1 g NaCl and 2 g MgSO4. The extracts were analyzed by UHPLC-TOF-MS. Results: The method, validated under CIR (EU) 2021/808, demonstrated robust performance, with recoveries ranging from 80.1% to 117.6%, repeatability between 0.5% and 32.2%, reproducibility between 2.3% and 31.6%, and determination coefficients (R2) ranging from 0.9429 to 0.9982. Validation was achieved for 15 antibiotic residues, with CCβ from 3 to 15 μg·kg−1, LODs between 0.09 and 6.19 μg·kg−1, and LOQs between 0.29 and 18.77 μg·kg−1. Application to 10 commercial Portuguese honey revealed no detectable levels of the target antibiotics. Conclusions: The combination of a simplified extraction with UHPLC-TOF-MS provides a reliable approach for the determination of antibiotics in honey. This validated method represents a valuable tool for food safety monitoring and risk assessment of apiculture practices. Full article
(This article belongs to the Special Issue Veterinary Antibiotics in Food-Producing Animals: Residue Detection, Risk Assessment and Regulatory Advances)
26 pages, 57341 KB  
Article
AI-Powered Embedded System for Rapid Detection of Veterinary Antibiotic Residues in Food-Producing Animals
by Ximing Li, Lanqi Chen, Qianchao Wang, Mengting Zhou, Jingheng Long, Xi Chen, Jiangsan Zhao, Junjun Yu and Yubin Guo
Antibiotics 2025, 14(9), 917; https://doi.org/10.3390/antibiotics14090917 - 11 Sep 2025
Cited by 1 | Viewed by 1701
Abstract
Background: Veterinary antibiotics are widely used in food-producing animals, raising public health concerns due to drug residues and the risk of antimicrobial resistance. Rapid and reliable detection systems are critical to ensure food safety and regulatory compliance. Colloidal gold immunoassay (CGIA)-based antigen–antibody test [...] Read more.
Background: Veterinary antibiotics are widely used in food-producing animals, raising public health concerns due to drug residues and the risk of antimicrobial resistance. Rapid and reliable detection systems are critical to ensure food safety and regulatory compliance. Colloidal gold immunoassay (CGIA)-based antigen–antibody test cards are widely used in food safety for the rapid screening of veterinary antibiotic residues. However, manual interpretation of test cards remains inefficient and inconsistent. Methods: To address this, we propose a complete AI-based detection system for veterinary antibiotic residues. The system is built on the Rockchip RK3568 platform and integrates a five-megapixel OV5640 autofocus USB camera (60° field of view) with a COB LED strip (6000 K, rated 5 W/m). It enables high-throughput, automated interpretation of colloidal gold test cards and can generate structured detection reports for regulatory documentation and quality control. The core challenge lies in achieving accurate and fast inference on resource-constrained embedded devices, where traditional detection networks often struggle to balance model size and performance. To this end, we propose VetStar, a lightweight detection algorithm specifically optimized for this task. VetStar integrates StarBlock, a shallow feature extractor, and Depthwise Separable-Reparameterization Detection Head (DR-head), a compact, partially decoupled detection head that accelerates inference while preserving accuracy. Results: Despite its compact size, with only 0.04 M parameters and 0.3 GFLOPs, VetStar maintains strong performance after distillation with the Bridging Cross-task Protocol Inconsistency Knowledge Distillation (BCKD) method. For our custom Veterinary Drug Residue Rapid Test Card (VDR-RTC) dataset, it achieves an mAP50 of 97.4 and anmAP50-95of 89.5. When deployed on the RK3568 device, it delivers results in just 5.4 s—substantially faster than comparable models. Conclusions: These results highlight the system’s strong potential for high-throughput, cost-effective, and rapid veterinary antibiotic residue screening, supporting food safety surveillance efforts. Full article
(This article belongs to the Special Issue Veterinary Antibiotics in Food-Producing Animals: Residue Detection, Risk Assessment and Regulatory Advances)
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Review

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21 pages, 826 KB  
Review
Multi-Detection of Veterinary Medicines in Animal Feed for Production: A Review
by Ana Lúcia Lopes, Marta Leite, Maria Beatriz P. P. Oliveira and Andreia Freitas
Antibiotics 2025, 14(12), 1233; https://doi.org/10.3390/antibiotics14121233 - 7 Dec 2025
Cited by 2 | Viewed by 1531
Abstract
Background/Objectives: The inappropriate use of veterinary medicines in feed for food-producing animals can compromise food safety. Intensive animal production is associated with the inappropriate use of antibiotics in feed, at subtherapeutic concentrations, to promote animal growth. It is therefore crucial to develop [...] Read more.
Background/Objectives: The inappropriate use of veterinary medicines in feed for food-producing animals can compromise food safety. Intensive animal production is associated with the inappropriate use of antibiotics in feed, at subtherapeutic concentrations, to promote animal growth. It is therefore crucial to develop an effective multi-detection method to ensure that this feed complies with the requirements of European Commission Regulations. This control is essential to ensure consumer protection, as adequate supervision contributes to reducing antimicrobial resistance, a growing concern worldwide. Methods: A literature search was conducted using scientific databases, namely PubMed, ScienceDirect, Scopus and Google Scholar, as well as European Union Regulations. Results: It was observed that the most used standard solution solvents are methanol, acetonitrile, ultrapure water, or mixtures of these solvents. For extraction, the most frequently used solvents include trichloroacetic acid combined with McIlvaine buffer or with acetonitrile, and acetonitrile or methanol combined with formic acid or with ethylenediaminetetraacetic acid disodium (Na2EDTA). For extraction and purification of the analyte, several steps were verified, such as solid-phase extraction (SPE), dispersive solid-phase extraction (d-SPE), liquid–liquid extraction (LLE), Quick, Easy, Cheap, Effective, Rugged, and Safe (QuEChERS), protein precipitation through freezing and dilution prior to analysis. Liquid chromatography coupled with mass spectrometry is the preferred choice, especially for multiple detection methods. Conclusions: Based on this data, the foundation is established for the development of an appropriate method for the simultaneous extraction of multiple classes of antibiotics, which is applicable to feed different food-production animals. Full article
(This article belongs to the Special Issue Veterinary Antibiotics in Food-Producing Animals: Residue Detection, Risk Assessment and Regulatory Advances)
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