The effect of breed and sex on sulfamethazine, enrofloxacin, fenbendazole and flunixin meglumine pharmacokinetic parameters in swine.

Drug use in livestock has received increased attention due to welfare concerns and food safety. Characterizing heterogeneity in the way swine populations respond to drugs could allow for group-specific dose or drug recommendations. Our objective was to determine whether drug clearance differs across genetic backgrounds and sex for sulfamethazine, enrofloxacin, fenbendazole and flunixin meglumine. Two sires from each of four breeds were mated to a common sow population. The nursery pigs generated (n = 114) were utilized in a random crossover design. Drugs were administered intravenously and blood collected a minimum of 10 times over 48 h. A non-compartmental analysis of drug and metabolite plasma concentration vs. time profiles was performed. Within-drug and metabolite analysis of pharmacokinetic parameters included fixed effects of drug administration date, sex and breed of sire. Breed differences existed for flunixin meglumine (P-value<0.05; Cl, Vdss ) and oxfendazole (P-value<0.05, AUC0→∞ ). Sex differences existed for oxfendazole (P-value < 0.05; Tmax ) and sulfamethazine (P-value < 0.05, Cl). Differences in drug clearance were seen, and future work will determine the degree of additive genetic variation utilizing a larger population.

Selection of appropriate training and validation set chemicals for modelling dermal permeability by U-optimal design.

Quantitative structure-activity relationship (QSAR) models are being used increasingly in skin permeation studies. The main idea of QSAR modelling is to quantify the relationship between biological activities and chemical properties, and thus to predict the activity of chemical solutes. As a key step, the selection of a representative and structurally diverse training set is critical to the prediction power of a QSAR model. Early QSAR models selected training sets in a subjective way and solutes in the training set were relatively homogenous. More recently, statistical methods such as D-optimal design or space-filling design have been applied but such methods are not always ideal. This paper describes a comprehensive procedure to select training sets from a large candidate set of 4534 solutes. A newly proposed 'Baynes' rule', which is a modification of Lipinski's 'rule of five', was used to screen out solutes that were not qualified for the study. U-optimality was used as the selection criterion. A principal component analysis showed that the selected training set was representative of the chemical space. Gas chromatograph amenability was verified. A model built using the training set was shown to have greater predictive power than a model built using a previous dataset [1].

Milk and plasma disposition of thymol following intramammary administration of a phytoceutical mastitis treatment.

Despite the recent growth of the organic dairy industry, organic producers and veterinarians have limited information when choosing mastitis treatments for animals in organic dairy production. Organic producers commonly administer homeopathic or other plant-based products without having research evaluating the efficacy of these products and using estimated or no withholding times to treat mastitis and other health problems in their herds. In this pilot study, we attempted to identify several active ingredients of Phyto-Mast (Penn Dutch Cow Care, Narvon, PA), a plant-based mastitis treatment used on organic dairy farms, and to quantify the product residue in milk and plasma after intramammary administration. We developed an assay to quantify thymol (one of the active ingredients in Phyto-Mast) in milk and plasma using gas chromatography and mass spectrometry (GC-MS). Thymol is a volatile aromatic compound with antiinflammatory properties. As a model for dairy cows, 5 healthy, lactating alpine dairy goats were given 5 mL of Phyto-Mast per udder half. For 10 d following treatment, we analyzed blood and milk samples for thymol residues using GC-MS. The GC-MS assay was very sensitive for thymol detection, to a concentration of 0.01 µg/mL in plasma. Using thymol as a marker, Phyto-Mast was detectable and quantifiable in plasma beginning with the 15-min posttreatment sample, but was no longer detectable in the 4-h posttreatment sample. Thymol residues were only detected in the 12-h posttreatment milk sample. An inflammatory response was not evident in the udder following phytoceutical administration. Although this study provides information about the elimination of thymol, the product contains several other active chemicals, which may have different pharmacokinetic behaviors. Further analysis and additional study animals will help to determine a milk withholding time for Phyto-Mast. Given the recent growth of the organic dairy industry, understanding the pharmacokinetics of therapeutics used in organic production and developing accurate withholding recommendations will help to ensure milk safety.