Exposure response

Mechanical properties of plastics can be determined by short, single-point quaUty control tests and longer, generally multipoint or multiple condition procedures that relate to fundamental polymer properties. Single-point tests iaclude tensile, compressive, flexural, shear, and impact properties of plastics creep, heat aging, creep mpture, and environmental stress-crackiag tests usually result ia multipoint curves or tables for comparison of the original response to post-exposure response. 

Risks to human health and the environment will vary considerably depending upon the type and extent of exposure. Responsible authorities are strongly encouraged to characterize risk on the basis of locally measured or predicted exposure scenarios. To assist the reader, examples of exposure estimation and risk characterization are provided in CICADs, whenever possible. These examples cannot be considered as representing all... 

Figure 21.2 The exposure-response road map passes through pharmacokinetics and pharmacodynamics. This sequence of events is essentially the same as that which informs compnter simulation of clinical trials, with the addition of complicating, bnt important, factors snch as protocol adherence and dropouts.

Analysis of most (perhaps 65%) pharmacokinetic data from clinical trials starts and stops with noncompartmental analysis (NCA). NCA usually includes calculating the area under the curve (AUC) of concentration versus time, or under the first-moment curve (AUMC, from a graph of concentration multiplied by time versus time). Calculation of AUC and AUMC facilitates simple calculations for some standard pharmacokinetic parameters and collapses measurements made at several sampling times into a single number representing exposure. The approach makes few assumptions, has few parameters, and allows fairly rigorous statistical description of exposure and how it is affected by dose. An exposure response model may be created. With respect to descriptive dimensions these dose-exposure and exposure-response models... 

Model equations can be augmented with expressions accounting for covariates such as subject age, sex, weight, disease state, therapy history, and lifestyle (smoker or nonsmoker, IV drug user or not, therapy compliance, and others). If sufficient data exist, the parameters of these augmented models (or a distribution of the parameters consistent with the data) may be determined. Multiple simulations for prospective experiments or trials, with different parameter values generated from the distributions, can then be used to predict a range of outcomes and the related likelihood of each outcome. Such dose-exposure, exposure-response, or dose-response models can be classified as steady state, stochastic, of low to moderate complexity, predictive, and quantitative. A case study is described in Section 22.6. 

Microbial risks are mostly due to single exposures (except for microbial toxins) chemical risks are affected by chronic duration of exposure. Responses to infective pathogens are probably more variable as compared to chemical agents due to different subpopulations and depending on immune status. 

Impact the exposure response relations are based on epidemiological studies. 

Defining cross-species exposure-response continuums... 

Accepting both Cb/U as a valid Cisf surrogate and the ability to project Cb/U accurately in large animal species, Cb/U values may be utilized to develop a mechanism-based cross-species exposure-response continuum for CNS... 

Figure 5 The C y-normalized cross-species exposure-response continuum for 7 across multiple pharmacology models [42]. A listed value (Cx) is the C iU (nM) affecting a response in assay X. RAM(r), rat radial arm maze DSR(nhp), nonhuman primate delayed spatial response task e-phys(r), rat electrophysiology model NOR(r), rat novel object recognition cGMP(m,r), mouse/rat cerebellar cGMP trem(nhp), nonhuman primate tremor rot(m), mouse rotarod.

Assessment of Exposure-Response Functions for Rocket-Emission Toxicants (1998) Review of a Screening Level Risk Assessment for the Naval Air Facility at Atsugi, Japan (Letter Report) (1998)... 

Exposure-response data from animal studies were used to derive acute exposure guideline level (AEGL) values for arsine. AEGL values derived with animal data which had complete exposure data were more scientifically valid than AEGLs estimated from limited anecdotal human data. The greater conser... 

Consistent with the human responses to arsine exposure, observations in several animal species (rats, mice, and hamsters) indicated hematologic involvement. Cumulative exposures of 540-1,800 ppm-min produced decreases in hematocrit levels, RBC counts, packed cell volumes, and increases in absolute and relative spleen weights (consistent with erythrocyte damage). For acute exposures, the exposure-response curve is steep generally less than a 10-fold difference between no-effect and lethality exposures. 

The most definitive data set for deriving AEGL-3 values is that of Peterson and Bhattacharyya (1985), which provides exposure response data for mice exposed to arsine for 1 h at concentrations of 0, 5, 9, 11, 15, or 26 ppm. A1... 

Reference Peterson, D.P., and M.H.Bhattacharyya. 1985. Hematological responses to arsine exposure quantitation of exposure response in mice. Fundam. Appl. Toxicol. 5 499-505... 

Uncertainty Factors/Rationale Total uncertainty factor 30 Interspecies 10—The 10-min LC50 value for the monkey was about 60% of the rat value and one-third the rabbit value. The mouse data were used to calculate the AEGL levels, because the data exhibited a good exposure-response relationship and the endpoint of decreased hematocrit levels can be considered a sensitive indicator of arsine toxicity. In addition, arsine has an extremely steep dose-response relationship, allowing little margin in exposure between no effects and lethality. 

Data Adequacy The study was considered adequate for AEGL-3 derivation. It was carefully designed and performed, used adequate numbers of animals, used an appropriate exposure regimen, and identified an endpoint consistent with AEGL-3 definition and with the known effects of arsine. The available data indicate that the exposure-response relationship for arsine is very steep, thereby justifying the approach taken to derive the AEGL-3 values. ... 

The AEGL-2 values were derived by a three-fold reduction of the AEGL-3 values. This approach for estimating a threshold for irreversible effects was used in the absence of exposure-response data related to irreversible or other serious long-lasting effects. It is believed that a 3-fold reduction in the estimated threshold for lethality is adequate to reach the AEGL-2 threshold level because of the steep dose-response relationship. 

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