Exposure assessments predictive modeling parameters

Risk Assessment. This model successfully described the disposition of chloroform in rats, mice and humans following various exposure scenarios and developed dose surrogates more closely related to toxicity response. With regard to target tissue dosimetry, the Corley model predicts the relative order of susceptibility to chloroform toxicity consequent to binding to macromolecules (MMB) to be mouse > rat > human. Linking the pharmacokinetic parameters of this model to the pharmacodynamic cancer model of Reitz et al. (1990) provides a biologically based risk assessment model for chloroform. 

Predictive methods of exposure assessment often rely on single values for input parameters to the exposure model that represent one point on the distribution curve of all possible values for this parameter. This point value can range from a 50th percentile, mean, median, or typical value to a worst-case estimate. In the predictive exposure assessment, a number of parameters are integrated through an algorithm to produce an output such as the predicted environmental concentration (PEC). If many worst-case values are involved, this integration can result in a PEC that has a... 

Finally, the MOS should also take into account the uncertainties in the estimated exposure. For predicted exposure estimates, this requires an uncertainty analysis (Section 8.2.3) involving the determination of the uncertainty in the model output value, based on the collective uncertainty of the model input parameters. General sources of variability and uncertainty in exposure assessments are measurement errors, sampling errors, variability in natural systems and human behavior, limitations in model description, limitations in generic or indirect data, and professional judgment. 

Because the objective of an exposure assessment is to characterize both the magnitude and the reliability of exposure scenarios, planning for an uncertainty analysis is a key element of an exposure assessment. The aims of the uncertainty analysis in this context are to individually and jointly characterize and quantify the exposure prediction uncertainties resulting from each step of the analysis. In performing an uncertainty analysis, typically the main sources of uncertainties are first characterized qualitatively and then quantified using a tiered approach (see chapter 4). In general, exposure uncertainty analyses attempt to differentiate between key sources of uncertainties scenario uncertainties, model uncertainties and parameter uncertainties (for definitions, see section 3.2). 

The compound-specific data required for exposure assessments comprise the 1-octanol/water partition coefficient (log water solubility (S ), vapour pressure (p ), Henry s law constant (H, H ), soil sorption coefficient hydrolysis half-life time, photolysis half-life time and information on biodegradability (OECD, 1993c). These parameters generally relate to steady-state conditions - conditions that are rarely met in the real environment. The experimental data underlying the QSAR models are preferably determined by standardized protocols, but, even then, the absolute values are of variable reliability and precision, which clearly affects the accuracy of the predictions based on the acquired QSARs. The endpoints discussed in the following sections were selected because of their consideration in regulatory evaluation schemes in, for example, the EU (EEC, 1990). 

As probabilistic exposure and risk assessment methods are developed and become more frequently used for environmental fate and effects assessment, OPP increasingly needs distributions of environmental fate values rather than single point estimates, and quantitation of error and uncertainty in measurements. Probabilistic models currently being developed by the OPP require distributions of environmental fate and effects parameters either by measurement, extrapolation or a combination of the two. The models predictions will allow regulators to base decisions on the likelihood and magnitude of exposure and effects for a range of conditions which vary both spatially and temporally, rather than in a specific environment under static conditions. This increased need for basic data on environmental fate may increase data collection and drive development of less costly and more precise analytical methods. 

There is no experimental evidence available to assess whether the toxicokinetics of -hexane differ between children and adults. Experiments in the rat model comparing kinetic parameters in weanling and mature animals after exposure to -hexane would be useful. These experiments should be designed to determine the concentration-time dependence (area under the curve) for blood levels of the neurotoxic /7-hcxane metabolite 2,5-hexanedione. w-Hcxanc and its metabolites cross the placenta in the rat (Bus et al. 1979) however, no preferential distribution to the fetus was observed. -Hexane has been detected, but not quantified, in human breast milk (Pellizzari et al. 1982), and a milk/blood partition coefficient of 2.10 has been determined experimentally in humans (Fisher et al. 1997). However, no pharmacokinetic experiments are available to confirm that -hexane or its metabolites are actually transferred to breast milk. Based on studies in humans, it appears unlikely that significant amounts of -hexane would be stored in human tissues at likely levels of exposure, so it is unlikely that maternal stores would be released upon pregnancy or lactation. A PBPK model is available for the transfer of M-hcxanc from milk to a nursing infant (Fisher et al. 1997) the model predicted that -hcxane intake by a nursing infant whose mother was exposed to 50 ppm at work would be well below the EPA advisory level for a 10-kg infant. However, this model cannot be validated without data on -hexane content in milk under known exposure conditions. 

Recently, some models have been derived to analyze the occurrence of interactive joint action in binary single-species toxicity experiments (Jonker 2003). Such detailed analysis models are well equipped to serve as null models for a precision analysis of experimental data, next to the generalized use of concentration addition and response addition as alternative null models. However, in our opinion these models are not applicable to quantitatively predict the combined toxicity of mixtures with a complexity that is prevalent in a contaminated environment, because the parameters of such models are typically not known. Recently a hazard index (Hertzberg and Teus-chler 2002) was developed for human risk assessment for exposure to multiple chemicals. Based on a weight-of-evidence approach, this index can be equipped with an option to adjust the index value for possible interactions between toxicants. It seems plausible that a comparable kind of technique could be applied in ecotoxicological risk assessments of mixtures for single species. However, at present, the widespread application of this approach is prevented by lack of available information. 

It is clear that an accurate exposure prediction at the landscape level requires models calibrated and validated for the landscape unit of interest and that the input parameters used have a high precision and accuracy for the area of interest (see Section 1.7 in Chapter 1). However, in a prospective risk assessment for new chemicals not yet placed on the market, chemical monitoring data are not yet available, and exposure predictions at the landscape level may be characterized by a relatively high uncertainty because the scale and intensity of the use of these chemicals are not... 

These data clearly demonstrate that IPPSF flux profiles can be charactoized by five physical chemical parameters (H-acidity, H-basicity, S-Polarizability, and HjO solubility). The correlation of the AUC of the IPPSF flux profile to these parameters was high = 0.978), an important finding because AUC from skin would be the prime measure of systemic exposure in a toxicological risk assessment and could serve as the input function for a physiologically based pharmacokinetic model for a compound. The IPPSF efflux profile from skin reflected by its AUC has been previously used as the input profile for predicting in vivo human plasma-concentration time profiles after transdermal drug delivery (Riviere, Williams, etal., 1992). 

Models are also useful because they can help us with experimental design of laboratory studies by predicting appropriate doses, exposure times, or sampling intervals. They can help us develop and test hypotheses about a disease or normal process or about actions of a specific biological component. A valid model is useful for interpolation within experimental parameters or extrapolation to simations that are difficult to observe experimentally. Ultimately, they can be used to improve the risk assessment process and assist in the design of prophylactic or therapeutic interventions. 

There is a considerable latent period between radiation exposure and the appearance of cancer. For most cancers in adults, the latent period is at least 10 years, or even longer. The shortest latent period is for leukemia and thyroid cancer (3 to 5 years The appearance of radiation-induced cancers follows additive or multiplicative models of prediction with absolute or relative risks as main parameters. Assessment of the risk coefficients is based on the follow-up of exposed persons through epidemiological studies. 

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