Endocrine Disruption: Biological Bases for Health Effects in by David O. Norris

By David O. Norris

This e-book addresses the organic results of the quite huge variety of periods of compounds which have been famous as endocrine disrupters. those compounds were chanced on to persist as toxins within the setting, and feature been blamed for inflicting developmental problems and/or fertility difficulties in fish, amphibians, reptiles, birds, and doubtless people. This booklet provides the correct basics of the endocrine structures of animals and people, the toxicology, developmental toxicology, ecology, and danger evaluation tools, and lays out the present nation of realizing for the full box, prepared through the periods of compounds which were pointed out as endocrine disrupters.

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Steroid hormone receptors, for example, may display preference for specific stereoisomers, and this stereoselectivity should be consistent with physiological responses induced by specific isomers. The distribution of binding sites should be consistent with physiology. Receptors should be found in tissues or in cells that exhibit direct physiological responses to the ligand. Control experiments should demonstrate that conditions that denature proteins, such as boiling and protease treatment, also inhibit binding and physiological responses.

For example, an EDC, unlike the endogenous ligand, may bind irreversibly to a hormone receptor. Dose Responses An important criterion in confirming a suspected EDC can is the demonstration of a biological dose response to the compound within the range of concentrations found in the environment. One might expect the dose–response curve for a given compound to parallel the receptor occupancy curve for that compound in terms of shape, potency, and efficacy. However, pharmacodynamics and endocrinology indicate Hormone Action on Receptors 31 that biological dose responses are often quite different from receptor occupancy curves.

This situation is rarely encountered. Analysis of equilibrium saturation binding data using a Scatchard-Rosenthal transformation violates most of the assumptions of linear regression analysis. Linear regression analysis assumes that (1) error is associated only with the dependent variable, (2) the distributions of x and y are independent, (3) errors are normally distributed, and (4) error variance is constant (Lutz and Kenakin, 1999). In a Scatchard analysis, the dependent variable [RL], the amount bound, appears in both the x-axis and y-axis, so any error in the measurement of the amount bound, x-axis, is passed on to the y-axis.

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