Assessment of metabolic stability using the rainbow trout (Oncorhynchus mykiss) liver S9 fraction

Standard protocols are given for assessing metabolic stability in rainbow trout using the liver S9 fraction. These protocols describe the isolation of S9 fractions from trout livers, evaluation of metabolic stability using a substrate depletion approach, and expression of the result as in vivo intrinsic clearance. Additional guidance is provided on the care and handling of test animals, design and interpretation of preliminary studies, and development of analytical methods. Although initially developed to predict metabolism impacts on chemical accumulation by fish, these procedures can be used to support a broad range of scientific and risk assessment activities including evaluation of emerging chemical contaminants and improved interpretation of toxicity testing results. These protocols have been designed for rainbow trout and can be adapted to other species as long as species-specific considerations are modified accordingly (e.g., fish maintenance and incubation mixture temperature). Rainbow trout is a cold-water species. Protocols for other species (e.g., carp, a warm-water species) can be developed based on these procedures as long as the specific considerations are taken into account.

Efficiency of risk-based vs . random sampling for the monitoring of tetracycline residues in slaughtered calves in Switzerland

In all European Union countries, chemical residues are required to be routinely monitored in meat. Good farming and veterinary practice can prevent the contamination of meat with pharmaceutical substances, resulting in a low detection of drug residues through random sampling. An alternative approach is to target-monitor farms suspected of treating their animals with antimicrobials. The objective of this project was to assess, using a stochastic model, the efficiency of these two sampling strategies. The model integrated data on Swiss livestock as well as expert opinion and results from studies conducted in Switzerland. Risk-based sampling showed an increase in detection efficiency of up to 100% depending on the prevalence of contaminated herds. Sensitivity analysis of this model showed the importance of the accuracy of prior assumptions for conducting risk-based sampling. The resources gained by changing from random to risk-based sampling should be transferred to improving the quality of prior information.