Exposure variability

Jacobson et al. (1955) assessed the lethality of 1,2-dimethylhydrazine and 1,1-dimethylhydrazine in rats following a 4-h exposure. Lethality was assessed over a 14-d post-exposure variability in the response. For 1,1-dimethylhydrazine, an LC50 of 252 ppm was calculated, and an LC2o of 210 ppm (515 mg/m3) was estimated from the exposure-response graphs in the report. The exposure-response curve was steep (slope = 8.65 SE = 2.8), suggesting very little variability among the test groups. 

Derelanko, M.J., Gad, S.C., Gavigan, F.A., Babich, P.C. and Rinehart, W.E. (1987). Toxicity of hydroxylamine sulfate following dermal exposure Variability with exposure method and... 

As somewhat of a side note on the exposure of these materials, it was also reported that there was a distinct difference in the coloration and impact properties of the UV-stabilized PECT, depending strongly on an exposure variable not often reported in the literature, that of the effect of sunlight reaching the back side of... 

Intuitively, GSD can be considered to be inversely related to the efficacy of installed engineering controls. A GSD of 1.0 means that all exposures are identical, a condition nearly realized in some laminar flow clean rooms. When GSD > 2.5, as may be the case for some kinds of maintenance work, it is likely that there are no functioning engineering controls. The exposure variability for most American workers is characterized by a GSD lying between 1.2 and 2.5 (3). 

The outcome of the exposure equation is a dose. This dose varies because of the variability of the components in the equation. The probability distribution of the dose is generally quite difficult to calculate analytically, but can be fairly readily approximated using a Monte Carlo simulation. The simulation consists of numerous iterations. In an iteration, a single value for each component in the exposure equation is randomly sampled from its corresponding distribution. These component values are then substituted into the exposure equation, and the outcome (exposure) is explicitly calculated. The frequency distribution of the calculated values from numerous iterations is the simulated exposure distribution. The exposure equations and the probability distributions of the components are treated as known in the distributional results presented in this chapter. Thus, the simulated exposure distributions reflect exposure variability - but not uncertainty about these equations, the distributions of the components, and related assumptions. This uncertainty and its quantitative impact on the simulated exposure distribution are presented in Sielken et al. (1996). 

Study Design Factors Affecting Exposure Variability 33 Specific Requirements for Guideline Studies 34 Label Compliance 34 Sample Size 34 Observational Bias 35 Motivational Bias 35... 

With the base model fit in hand, any of a number of different strategies may be employed to evaluate the influence of the exposure variables and covariates on the response. As with other population PK (and PK/PD or PD) analyses, many different techniques and processes have been advocated for efficiently and effectively screening and selecting covariates for inclusion in a model (17-19). For the purposes of this chapter, the model including the effect of exposure (AUC) on the response is illustrated, as is the final model, including other covariate effects (presumably derived following the application of some technique to screen all potential covariates). 

There are several methods that offer the opportunity to shorten trial length, without increasing risk of toxicity for the patients. All the above schemes based on the dose-response curve can deal with exposure variables (peak concentrations, AUC, time above a threshold) or any other meaningful PK parameter in place of dose. Thus, the ideal scheme should be a combination of the existing schemes ... 

Step 3. Combination of individual PK (exposure) variables from virtual subjects with subject-specific covariate data together with the real data set (including the pharmacodynamics-biomarker response data) to create a PK/PD knowledge creation data set. 

A case-control study assembles a group of cases (people who have the disease of interest) and controls (people who do not). The exposure histories of the cases and the controls are determined to establish the extent of association between exposure(s) of interest and disease. Case-control studies compare patients with a specific disease with a control group composed of similar people but without the disease. Case-control studies attempt to identify risk factors for a disease by examining differences in antecedent exposure variables between cases and controls. For example, one can select cases of women of childbearing age with ovarian cysts and compare them with controls, looking for differences in prior use of oral contraceptives. Such a smdy was performed to determine if the then newly introduced triphasic oral contraceptives were associated with functional ovarian cysts. ... 

The exposure variables used in this study were maternal serum and milk samples as well as cord blood specimens. Maternal serum was collected during the last month (weeks 36 0) of pregnancy while milk samples were collected at 2 and 6 weeks post delivery. Data on the duration of breast feeding in weeks... 

Linear regression analyses using the NOS as the dependent variable and either maternal PCB-cord sum, maternal PCB-serum sum, or the child s PCB level at 42 months as the exposure found no associations between this outcome and any of these exposure variables. (Each exposure variable was modeled separately). Potential confounders in each model included the study center, the type of feeding during early life, the duration of breast feeding, and several items from the obstetrical optimality score (i.e.,... 

SES, obstetrical and perinatal conditions) (Ranting et al. 1998c). A similar model with PCB breast milk levels (TEQ method) as a measure of postnatal PCB exposure with NOS as the outcome, also found no association between the dependent and independent variables. In the last set of four models, fluency score, the dependent variable, was not found to be significantly associated with any of the four exposure variables. 

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