Biomedical subjects

Dwayne R J Moore

Publications and source records attributed to Dwayne R J Moore.

4 recordsLinked to original sources

PubMed2006

Dietary exposure of mink (Mustela vison) to fish from the housatonic river, Berkshire County, Massachusetts, USA: effects on reproduction, kit growth, and survival.

We evaluated the effects of feeding farm-raised mink (Mustela vison) diets containing polychlorinated biphenyl (PCB)-contaminated fish from the Housatonic River (HR; Berkshire County, MA, USA) on adult reproductive performance and kit growth and survival. Diets contained 0.22-3.54% HR fish, providing 0.34-3.7 microg total PCBs (TPCB)/g feed wet wt (3.5-68.5 pg toxic equivalence [TEQ]/g). Female mink were fed diets before breeding through weaning of kits. Twelve kits from each treatment were maintained on their respective diets for an additional 180 d. Dietary PCBs had no effect on the number of offspring produced, gestation period, or other measures of adult reproductive performance. Mink kits exposed to 3.7 microg TPCB/g feed (68.5 pg TEQ/ g) in utero and during lactation had reduced survivability between three and six weeks of age. The lethal concentrations to 10 and 20% of the population (LC10 and LC20, respectively) were estimated to be 0.231 and 0.984 microg TPCB/g feed, respectively. Because inclusion of PCB-contaminated fish that composed approximately 1% of the diet would reduce mink kit survival by 20% or more, it is likely that consumption of up to 30-fold that quantity of HR fish, as could be expected for wild mink, would have an adverse effect on wild mink populations.

Animals↗

PubMed2003

Evaluation of probabilistic risk assessment of pesticides in the UK: chlorpyrifos use on top fruit.

The use of probabilistic risk assessment (PRA) for examining chemical impacts has become an important area of debate within the European Union. This paper describes a case study on probabilistic techniques to assess pesticide risks in the UK aquatic environment. The main aim of this paper is to demonstrate both the potential strengths and weaknesses of PRA for assessing pesticides when compared with the conventional deterministic approach, and to examine whether PRA is useful within the European regulatory context. The organophosphate insecticide, chlorpyrifos, was selected as a model compound and toxicity exposure ratios calculated using Monte Carlo analysis and different distributions of spray drift and toxicity values following application to top fruit. Chlorpyrifos is highly toxic to arthropods but less toxic to fishes. Species sensitivity followed a log-normal distribution when fitted to all toxicity data. Toxicity data quantity had little influence on species sensitivity distribution model parameters when n was greater than 10 species. Below this, estimates were less accurate and precise, possibly because of the inclusion of data from many different sources. Estimates of chlorpyrifos exposure derived from the standard spray drift model differed substantially from measurements of chlorpyrifos in European surface waters. When a distribution based on measured concentrations was used in a PRA, the risk of acute fish mortality was low, and the risk of acute arthropod mortality was lower than in other scenarios, although not negligible. If PRA is used to assess pesticides, risk managers need further guidance on how to conduct a PRA and what constitutes 'unacceptable risk' under EC Directive 91/414/EEC, as judgement is required when simple trigger values are no longer used.

Animals↗

PubMed2003

A comparison of model performance for six quantitative structure-activity relationship packages that predict acute toxicity to fish.

Some regulatory programs rely on quantitative structure-activity relationship (QSAR) models to predict toxic effects to biota. Many currently existing QSAR models can predict the effects of a wide range of substances to biota, particularly aquatic biota. The difficulty for regulatory programs is in choosing the appropriate QSAR model or models for application in their new and existing substances programs. We evaluated model performance of six QSAR modeling packages: Ecological Structure Activity Relationship (ECOSAR), TOPKAT, a Probabilistic Neural Network (PNN), a Computational Neural Network (CNN), the QSAR components of the Assessment Tools for the Evaluation of Risk (ASTER) system, and the Optimized Approach Based on Structural Indices Set (OASIS) system. Using a testing data set of 130 substances that had not been included in the training data sets of the QSAR models under consideration, we compared model predictions for 96-h median lethal concentrations (LC50s) to fathead minnows to the corresponding measured toxicity values available in the AQUIRE database. The testing data set was heavily weighted with neutral organics of low molecular weight and functionality. Many of the testing data set substances also had a nonpolar narcosis mode of action and/or were chlorinated. A variety of statistical measures (correlation coefficient, slope and intercept from a linear regression analysis, mean absolute and squared difference between log prediction and log measured toxicity, and the percentage of predictions within factors of 2, 5, 10, 100, and 1,000 of measured toxicity values) indicated that the PNN model had the best model performance for the full testing data set of 130 substances. The rank order of the remainder of the models depended on the statistical measure employed. TOPKAT also had excellent model performance for substances within its optimum prediction space. Only 37% of the substances in the testing data set, however, fell within this optimum prediction space.

Animals↗

PubMed2003

Derivation of an ambient water quality criterion for mercury: taking account of site-specific conditions.

Mercury is considered to be a serious risk to wildlife. As a result, the Great Lakes Water Quality Initiative and others have developed ambient water quality criteria (AWQC) for the protection of wildlife. These AWQC have been controversial, however, because the AWQC were single values that did not account for site-specific conditions, derivation of the AWQC relied on a single no-observed-adverse-effect level, and the AWQC had an unknown level of conservatism because of reliance on both average and conservative assumptions and uncertainty factors. Rather than develop a single-value AWQC for total mercury, we derived an AWQC model that explicitly incorporates factors controlling bioavailability, methylation rates, and bioaccumulation in the aquatic environment (e.g., pH, dissolved organic carbon). To derive our AWQC model, field data were collected from 31 lakes in Ontario and an additional 10 lakes in Nova Scotia (North America). In the field study, levels of total and methylmercury in water and fish as well as levels of key water quality variables were determined. We conducted multiple-regression analysis to derive a model that estimates mercury levels in prey of mink. Mink are very sensitive to mercury exposure. An independent dataset consisting of 51 water bodies in the United States was then used to confirm the validity and robustness of the AWQC model. Next, we combined the results of chronic-feeding studies with similar protocols and endpoints in a meta-analysis to derive a dose-response curve for mink exposed to mercury in the diet. In the final step, we used a probabilistic risk model to estimate the concentrations of methylmercury in water that would lead to levels in fish sufficient for a 10% probability of exceeding the lethal dose affecting 5% of the mink population. The result is an AWQC equation for mercury for the protection of wildlife that can be used with a variety of site-specific conditions.

Animals↗