Quantitation specific response

The UEL for reproductive and developmental toxicity is derived by applying uncertainty factors to the NOAEL, LOAEL, or BMDL. To calculate the UEL, the selected UF is divided into the NOAEL, LOAEL, or BMDL for the critical effect in the most appropriate or sensitive mammalian species. This approach is similar to the one used to derive the acute and chronic reference doses (RfD) or Acceptable Daily Intake (ADI) except that it is specific for reproductive and developmental effects and is derived specifically for the exposure duration of concern in the human. The evaluative process uses the UEL both to avoid the connotation that it is the RfD or reference concentration (RfC) value derived by EPA or the ADI derived for food additives by the Food and Drug Administration, both of which consider all types of noncancer toxicity data. Other approaches for more quantitative dose-response evaluations can be used when sufficient data are available. When more extensive data are available (for example, on pharmacokinetics, mechanisms, or biological markers of exposure and effect), one might use more sophisticated quantitative modeling approaches (e.g., a physiologically based pharmacokinetic or pharmacodynamic model) to estimate low levels of risk. Unfortunately, the data sets required for such modeling are rare. 

Figure 23.4. Schematic of induction of HTLV-I specific CD8+ T cells responses associated with immunopathogenesis of HAM/TSP. Quantitative PCR (DNA Taqman) demonstrates high HTLV-I proviral loads in HAM/TSP patients that are directly proportional to increased HTLV-I mRNA expression. In quantitative RT-PCR (RNA Taqman) analyses, elevated HTLV-I mRNA expression leads to increases of HTLV-I protein expression. This protein can be detected by flow cytometry. HTLV-I proteins (e.g.. Tax) can be processed into immunodominant peptides such as Taxi 1-19 peptide and presented on the cell surface of infected cells in context with MHC class I molecules. HTLV-I Taxi 1-19 peptide strongly binds to HLA-A 0201 molecules and stimulates a vims-specific CD8-I- T cells response. This Taxi 1-19/HLA-A 0201 complex can be detected with a TCR-like antibody, while the frequency of virus-specific CD8-I- T cells can be determined by HLA-A 0201/Taxl 1-19 tetramers. These antigen-specific responses are expanded in the CSF of HAM/TSP patients and may contribute to disease progression by recognition of HTLV-I-processed antigens in the CNS associated with lysis of HTLV-I-infected inflammatory cells, HTLV-I-infected glial cells, and/or through induction of proinflammatory cytokines and chemokines.

It is conceivable that quantitative structure-activity (QSAR) approaches (e.g., TOPKAT see Chapter 7) could be applied to predict response levels for uncharacterized contaminants for use in the HI approach. Further, specific submodels existing (e.g., that for developmental toxicity) could be applied to estimate system-specific response levels for application in the IT D approach. To our knowledge, there are no computer-assisted programs available that can automate the prediction of toxicity for mixtures. Much of the reason may reside in the relative lack of empirical observations and characterizations of chemical interactions. Many QSAR approaches rely on training set approaches to the development of automated programs. Another impediment may be the many examples of the levels, types and biochemical bases for chemical interactions, the intricacies of which would benefit from an automated approach. This area is a useful area for exploration. 

Another feature of NCI measurements is the fact that, like ECD, the response depends not only on the number of halogen atoms, but also on their position in the molecule. Precise quantitative determinations are therefore only possible with defined reference systems via the determination of specific response factors. 

The final part of a gas chromatograph is the detector. The ideal detector has several desirable features, including low detection limits, a linear response over a wide range of solute concentrations (which makes quantitative work easier), responsiveness to all solutes or selectivity for a specific class of solutes, and an insensitivity to changes in flow rate or temperature. 

Dose-Response Cune A graphical representation of the quantitative relationship between the administered, applied, or internal dose of a chemical or agent, and a specific biological response to that chemical or agent. 

Altliough the technical conununity has come a long way in understanding how to do a better job in luizard identification, dose-response assessment, and exposure assessment portions of risk assessment, it lias only begun to understand how to best cluiractcrize hcaltli risks and how to present tliese risks most appropriately to both the public and decision makers. Tlie next tliree sections specifically address tlicse issues. Tliis section deals witli qualitative risk assessment while tlie next two sections deal witli quantitative risk assessment. 

The refractive index detector, in general, is a choice of last resort and is used for those applications where, for one reason or another, all other detectors are inappropriate or impractical. However, the detector has one particular area of application for which it is unique and that is in the separation and analysis of polymers. In general, for those polymers that contain more than six monomer units, the refractive index is directly proportional to the concentration of the polymer and is practically independent of the molecular weight. Thus, a quantitative analysis of a polymer mixture can be obtained by the simple normalization of the peak areas in the chromatogram, there being no need for the use of individual response factors. Some typical specifications for the refractive index detector are as follows ... 

The normalization method is the easiest and most straightforward to use but, unfortunately, it is also the least likely to be appropriate for most LC analyses. To be applicable, the detector must have the same response to all the components of the sample. An exceptional example, where the normalization procedure is frequently used, is in the analysis of polymers by exclusion chromatography using the refractive index detector. The refractive index of a specific polymer is a constant for all polymers of that type having more than 6 monomer units. Under these conditions normalization is the obvious quantitative method to use. 

Tables (3-1, 3-2, and 3-3) and figures (3-1 and 3-2) are used to summarize health effects and illustrate graphically levels of exposure associated with those effects. These levels cover health effects observed at increasing dose concentrations and durations, differences in response by species, minimal risk levels (MRLs) to humans for noncancer end points, and EPA s estimated range associated with an upper- bound individual lifetime cancer risk of 1 in 10,000 to 1 in 10,000,000. Use the LSE tables and figures for a quick review of the health effects and to locate data for a specific exposure scenario. The LSE tables and figures should always be used in conjunction with the text. All entries in these tables and figures represent studies that provide reliable, quantitative estimates of No-Observed-Adverse-Effect Levels (NOAELs), Lowest-Observed-Adverse-Effect Levels (LOAELs), or Cancer Effect Levels (CELs).

In many cases, where one is concerned with the effects of specific environmental factors it is appropriate to replace the general term stress by the appropriate quantitative measure (e.g. soil water content or water potential) together with an appropriate measure of the plant response (e.g. growth rate). 

One of the metabolic responses of plants exposed to environmental stress is the production of proteins which may be qualitatively and/or quantitatively different from those produced in the absence of the stress (see Chapter 9 for general discussion). In some cases these responses have been found to depend on genotype for example, when a salt-tolerant cultivar and a salt-sensitive cultivar of barley were exposed to salt stress the shoot tissue responded by synthesising proteins which were cultivar specific. Five new proteins not found in the salt-sensitive barley were identified in the salt-tolerant cultivar (Ramagopal, 1987). No differences in proteins were found in the roots of either cultivar. 

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