Toxicity endpoints also

TEST allows for estimates of the value for several toxicity endpoints [29] 96 h Fathead minnow LC50, 48 h Daphnia magna LC50, 48 h Tetrahymena pyriformis IGC50, Oral rat LD50, bioaccumulation factor, developmental toxicity, and Ames mutagenicity. TEST also estimates several physical properties... 

Quantitative Structure-Activity Relationship studies search for a relationship between the activity/toxicity of chemicals and the numerical representation of their structure and/or features. The overall task is not easy. For instance, several environmental properties are relatively easy to model, but some toxicity endpoints are quite difficult, because the toxicity is the result of many processes, involving different mechanisms. Toxicity data are also affected by experimental errors and their availability is limited because experiments are expensive. A 3D-QSAR model reflects the characteristics of... 

GSH may also be coupled to electrophilic reaction intermediates nonenzymatically or by GSH transferase (GST)-catalyzed reactions. Many different types of substrates will undergo GSH conjugation, including epoxides, halogenated compounds, aromatic nitro compounds, and many others. In these reactions, GSH can interact with an electrophilic carbon or heteroatom (O, N, and S) [35]. One such substrate is a reactive metabolite of acetaminophen (APAP), N-acetyl-p-benzoquinonimine (NAPQI), which will readily form a GSH conjugate (Scheme 3.2). Other examples of Phase II bioactivation reactions that lead to toxic endpoints are shown in Table 3.1. 

Several papers have also reported toxicity data for a variety of metals and organic substances simultaneously. Reasons for conducting such investigations include 1) establishing the concentrations at which chemicals exert their adverse effects (e.g., at the ng/L, pg/L or mg/L levels), 2) estimating environmental risk based on measured toxicity endpoints and predicted environmental concentrations for specific chemicals and 3) defining toxicant concentrations harmful for specific biotic levels and/or assemblages of species within each level. 

Also important to the validation process of QSARs is vertical validation. In this instance, quantitatively similar QSARs are developed with similar descriptors but using data for a different toxic endpoint. For example, the investigation of Karabunarliev et al. (1996b) modeled acute aquatic toxicity data for the fathead minnow Pimephales promelas. The compounds considered in the analysis were confined to substituted benzenes, and descriptors limited to log Kow and Amjx. The fish toxicity QSAR (log [LQ,]-1 = 0.62 log K, + 9.17 A - 3.21 n = 122 R2 = 0.83 i = 0.16 F = 292) of Karabunarliev et al. (1996b) was very similar in terms of slope, intercept, and statistical fit to the QSAR presented in Equation 12.2. The fact that different endpoints provide very similar QSARs indicates that the QSAR is valid across protocols. This shows the universality of the model. 

Reproductive or developmental toxicity endpoints must be interpreted in the context of general toxicity that could also occur in the same animals. Toxic effects reported from other studies can be particularly valuable because excessive toxicity could significantly confound the interpretation of a reproductive or developmental toxicity study. Observations from studies of other toxicity endpoints might either strengthen or weaken the conclusions to be drawn from a reproductive or developmental study and provide information about target organs that should be evaluated further in developing animals. 

Risk characterization is a combination of two components (1) a toxicological characterization of each pesticide having a common mechanism for the given toxicity endpoint and exposure duration, and (2) the exposure characterization for a specified population. Exposure characterization is illustrated in the case study and is also discussed later in this chapter. The methodology for combining the exposure characterization with the toxicological information to form the dose and risk characterizations is discussed in the remainder of this section. 

In contrast to the aforementioned toxicity tests, in situ toxicity tests involve exposing organisms to contaminants on-site. This provides for more environmental realism, but there is also less control over confounding variables that may affect toxicity (spatial or temporal variation in temperature, sunlight, nutrients, pH, etc.), or other factors that may disturb or disrupt the test (animals, winds, floods, vandalism, etc.). For these tests, animals may be placed in mesh cages or corralled by impermeable barriers, such as wood, metal, or plastic sheets, at various locations throughout the contaminated zone. Plants may be planted in plots of contaminated soils. Toxicity endpoints may include survival, sublethal effects, or accumulation of contaminants in body tissues. For these tests, organisms are also placed in less contaminated sites for comparison. 

QSARs for the prediction of toxicity to aquatic organisms, including fish, invertebrates and algae, are relatively well developed for a broad range of chemical classes. More than 100 SARs for 55 chemical classes are available in a free, downloadable model called ECOSAR from the EPA website, based on test data and assumptions from test data. Aquatic toxicity endpoints include reproduction, growth and mortality, such as acute toxicity to fish, invertebrates, and algae. The PBT Profiler also estimates chronic toxicity to fish by means of the ECOSAR model it compares the fish chronic value to maximum water solubility, in order to estimate potential for aquatic risk. 

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