Activity-specific exposure data

AUC and Cmax are commonly measured to identify safety ratios for new chemical entities. Since the analytical methods used for biotechnologically derived pharmaceuticals may lack specificity, a clinical marker of biological activity or efficacy may sometimes be more appropriate than exposure data. 

The US-EPA Child Specific Exposure Factors Handbook (US-EPA 2006), first published in 2002, consolidates all children s exposure factors data into one document. The document provides a summary of the available and up-to-date statistical data on various factors assessing children s exposures. These factors include drinking water consumption soil ingestion inhalation rates dermal factors including skin area and soil adherence factors consumption of fruits, vegetables, fish, meats, dairy products, homegrown foods, and breast milk activity patterns body weight consumer products and life expectancy. 

An individual s absorbed dose is assumed to be proportional to the amount of a.i. applied. In this paper that proportion (mg a.i. absorbed/lb a.i. applied) is derived from the exposure information in USEPA s Pesticide Handlers Database (PHED, 1992) and herbicide-specific absorption data. PHED provides exposure information on 12 parts of the body (as opposed to the body as a whole). For each body part, PHED provides data on the amount of active ingredient that comes into contact with that body part per pound of active ingredient applied (amount inhaled or amount of dermal contact per pound applied). The PHED data used here assume that the individual is wearing normal clothing and gloves but not additional protective devices such as aprons or respirators. Based on atrazine- and simazine-specific studies conducted by Syngenta, the fraction of atrazine and simazine absorbed as a result of dermal contact is 0.056 when the exposure is less than or equal to 8. ig/cm2, 0.012 for exposures greater than or equal to 80 i,g/cm2, and a linear interpolated value for intermediate exposures. The fraction of the inhaled atrazine or simazine that is assumed to be absorbed is 1.0. 

A more recent macro-event -based approach to post-application dermal exposure modehng involves the use of body-part-specific transfer factor (TF) point-estimates and/or underlying distributions. The unitless TFs represent an activity-specific basis for estimating dermal loading (g/cm ) for various anatomical regions from compound-specific transferable residue data (g/cm ). 

Approaches for aggregating exposure for simple scenarios have been proposed in the literature (Shurdut et al., 1998 Zartarian et al., 2000). The USEPA s National Exposure Research Laboratory has developed the Stochastic Human Exposure and Dose Simulation (SHEDS) model for pesticides, which can be characterized as a first-generation aggregation model and the developers conclude that to refine and evaluate the model for use as a regulatory decision-making tool for residential scenarios, more robust data sets are needed for human activity patterns, surface residues for the most relevant snrface types, and cohort-specific exposure factors (Zartarian et al, 2000). The SHEDS framework was used by the USEPA to conduct a probabilistic exposure assessment for the specific exposure scenario of children contacting chromated copper arsenate (CCA)-treated playsets and decks (Zartarian et al, 2003). 

An action spectrum shows the relationship between the effectiveness of a particular photoprocess and the wavelength of irradiation. They can be conveniently generated from the measurement of photodamage resulting from exposure of samples to near-monochromatic radiation of known irradiance. Action spectra can also be calculated firom source-specific activation spectra, provided the irradiance distribution used in generating the data is accurately known. Accordingly, action spectra have been derived from polychromatic exposure data obtained using a series of cut-on filters [20]. (While no activation... 

Several plants are using methods for planning and recording the radiation dose to personnel that are linked to specific work activities and system operations. This provides a history of exposure data for maintenance work and allows for a continuous process of improvement at the level of work planning, preparation and execution. The data can be used for cost-benefit decisions in terms of dose reduction. 

QRA is fundamentally different from many other chemical engineering activities (e.g., chemistry, heat transfer, reaction kinetics) whose basic property data are theoretically deterministic. For example, the physical properties of a substance for a specific application can often be established experimentally. But some of the basic property data used to calculate risk estimates are probabilistic variables with no fixed values. Some of the key elements of risk, such as the statistically expected frequency of an accident and the statistically expected consequences of exposure to a toxic gas, must be determined using these probabilistic variables. QRA is an approach for estimating the risk of chemical operations using the probabilistic information. And it is a fundamentally different approach from those used in many other engineering activities because interpreting the results of a QRA requires an increased sensitivity to uncertainties that arise primarily from the probabilistic character of the data. 

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