Toxicity data, extrapolation

For most chemicals, actual human toxicity data are not available or critical information on exposure is lacking, so toxicity data from studies conducted in laboratory animals are extrapolated to estimate the potential toxicity in humans. Such extrapolation requires experienced scientific judgment. The toxicity data from animal species most representative of humans in terms of pharmacodynamic and pharmacokinetic properties are used for determining AEGLs. If data are not available on the species that best represents humans, the data from the most sensitive animal species are used to set AEGLs. Uncertainty factors are commonly used when animal data are used to estimate minimal risk levels for humans. The magnitude of uncertainty factors depends on the quality of the animal data used to determine the no-observed-adverse-effect level (NOAEL) and the mode of action of the substance in question. When available, pharmocokinetic data on tissue doses are considered for interspecies extrapolation. 

The fixed safety factors are apphed in this EPA method. The safety factors used are based on arbitrary extrapolation values of 10 going from (i) the laboratory (single species) to the held (whole ecosystem) situation and (ii) acute to chronic toxicity data. A similar approach is used to derive critical limits for surface water. 

This rule holds reasonably well when C or t varies within a narrow range for acute exposure to a gaseous compound (Rinehart and Hatch, 1964) and for chronic exposure to an inert particle (Henderson et al., 1991). Excursion of C or / beyond these limits will cause the assumption Ct = K to be incorrect (Adams et al., 1950, 1952 Sidorenko and Pinigin, 1976 Andersen et al., 1979 Uemitsu et al., 1985). For example, an animal may be exposed to 1000 ppm of diethyl ether for 420 min or 1400 ppm for 300 min without incurring any anesthesia. However, exposure to 420,000 ppm for lmin will surely cause anesthesia or even death of the animal. Furthermore, toxicokinetic study of fiver enzymes affected by inhalation of carbon tetrachloride (Uemitsu et al., 1985), which has a saturable metabolism in rats, showed that Ct = K does not correctly reflect the toxicity value of this compound. Therefore, the limitations of Haber s rule must be recognized when it is used in interpolation or extrapolation of inhalation toxicity data. 

Intermediate-duration oral studies in humans for mirex are lacking. A review of the animal oral intermediate toxicity data for mirex indicates that the available studies are not adequate to derive intermediate oral MRL for mirex. The most suitable study provides a LOAEL of 0.25 mg/kg/day for endocrine effects-dilation of rough endoplasmic reticulum cisternae of the thyroid of weanling Sprague-Dawley rats (Singh et al. 1985). Adjusting the LOAEL of 0.25 mg/kg/day determined from this study with a total uncertainty factor of 1,000 (10 for use of a LOAEL, 10 for animal to human extrapolation, and 10 for interspecies variability) yields an intermediate oral MRL of 0.0003 mg/kg/day, which is lower than the chronic-duration oral MRL of 0.0008 mg/kg/day derived from an NTP (1990) study in rats (see chronic-duration MRL). Therefore, no oral intermediateduration MRL was developed for mirex. 

The reader is advised to exercise caution in the extrapolation of toxicity data from animals to humans. Species-related differences in sensitivity must be accounted for. Some studies utilized to derive MRLs or otherwise extrapolate data, is dated however, they do represent the body of knowledge regarding chloroform toxicity. In addition, many of the human studies quoted involved clinical case reports in which chloroform was utilized either as an anesthetic or as an agent of suicide. Such doses are clearly excessive and would not be encountered by the general population. These and other issues are addressed in Section 2.10. 

PNECs themselves are preferentially extrapolated from chronic toxicity data or, if no long-term data are available, acute toxicity data [51] they generally refer to algae, daphnia or fish toxicity. Sanderson et al., Stuer-Lauridsen et al., Boillot and Ferrari et al. [108-111] have reported PNECs for a large list of PhCs, and [49,112] recently proposed a model for the predicting PNEC of a substance, taking into consideration its acidic or basic behaviour in the environment. This procedure... 

Data from studies in experimental animals are the typical starting points for hazard and risk assessments of chemical substances and thus differences in sensitivity between experimental animals and humans need to be addressed, with the default assumption that humans are more sensitive than experimental animals. The rationale for extrapolation of toxicity data across species is founded in the commonality of anatomic characteristics and the universality of physiological functions and biochemical reactions, despite the great diversity of sizes, shapes, and forms of mammalian species. 

Extrapolation of data from studies in experimental animals to the human situation involves two steps a first step is to adjust the dose levels applied in the experimental animal studies to human equivalent dose levels, i.e., a correction for differences in body size between laboratory animals and humans. A second step involves the application of an assessment factor to compensate for uncertainties inherent in toxicity data as well as the mterspecies variation in biological susceptibility. These two steps are addressed in the following sections. 

The rationale for extrapolation of toxicity data across species is founded in the commonality of anatomic characteristics and the universality of physiological functions and biochemical reactions, despite the great diversity of sizes, shapes, and forms of mammalian species. 

The interindividual variability reflects differences in toxicokinetics as well as in toxicodynamics. With respect to toxicokinetic factors, interindividual differences in the metabolism of chemicals are generally considered as the most significant explanatory factor. Hardly any knowledge is available with respect to the factors that influence toxicodynamics. Thus, it is necessary to take such variation into account when extrapolating animal toxicity data to the human simation. 

A Dutch smdy (Wilschut et al. 1998, as reviewed in Vermeire et al. 1999) has evaluated route-to-route extrapolation on the basis of absorption or acute toxicity data. Data were collected primarily on dermal and inhalation repeated dose toxicity. An extrapolation factor, defined as the factor that is applied in route-to-route extrapolation to account for differences in the expression of systemic toxicity between exposure routes, was determined for each substance by using data on absorption and acute toxicity data. As experimental data on absorption often were not available, default values for absorption were also used to determine an extrapolation factor. Despite a rather large overall database, relatively few data could be used for the evaluation and the selection criteria were modified in order to include data that initially were considered less suitable for data analysis interspecies extrapolation based on caloric demands was introduced, and a factor of 3 was applied in case a LOAEL instead of a NOAEL was available. The choice of NOAELs for different exposure routes known for a substance suitable for analysis was based primarily on the same effect, but this criterion could not be maintained. 

In a more recent publication, ECETOC (2003) noted that route-to-route extrapolation is only feasible for substances with a systemic mode of action, and should take dose rate and toxicokinetic data into account. It was noted that the following points need to be taken into consideration when conduction a route-to-route extrapolation with systemic toxicity data ... 

Adequate systemic toxicity data are available for the route used as a basis for extrapolation. 

The TGD has noted that in practice, relevant data on kinetics and metabolism, especially after dermal and inhalation exposure, are frequently missing. As a consequence, corrections can only be made for differences in bioavailability. There are some pragmatic approaches in order to calculate a NAEL (or LAEL) by extrapolation, when specific data are not available. The methods described are for extrapolating from oral toxicity data since this is the route most often used for repeated dose toxicity studies in animals. The TGD emphasized that it should be noted that insight into the reliability of the current methodologies for route-to-route extrapolation has not been obtained yet, with a reference to the smdy performed by WUschut et al. (1998), see above. 

In case that relevant data are lacking on the exposure route of interest for the derivation of a tolerable intake, a route-to-route extrapolation might be considered. There is no simple and generally applicable way in which toxicity data derived from one route of exposure can be used to evaluate the effects of another exposure route. It should be noted that, in general, route-to-route extrapolation... 

For extrapolation of typical subchronic toxicity data (e.g., NOAELs), a value of 2 is most plausible, and a value of 3 or less should be employed. 

Pieters, M.N., H.J. Kramer, and W. Slob. 1998. Evaluation of the uncertainty factor for subchronic-to-chronic extrapolation Statistical analysis of toxicity data. Regul. Toxicol. Pharmacol. 27 108-111. 

Vocci, F. and T. Farber. 1988. Extrapolation of animal toxicity data to man. Regul. Toxicol. Pharmacol. 8 389-398. 

Aldenberg T, Luttik R. 2002. Extrapolation factors for tiny toxicity data sets from species sensitivity distribntions with known standard deviation. In Posthuma L, Snter II GW, Traas TP, editors. Species sensitivity distributions in ecotoxicology. Boca Raton (FL) Lewis Publishers, CRC Press. 

Luttik R, Aldenberg T. 1997. Extrapolation factors for small samples of pesticide toxicity data Special focus on LD50 values for birds and mammals. Environ Toxicol Chem 16 1785-1788. 

Extrapolations of therapeutic index and toxicity data from animals to humans are reasonably predictive for many but not for all toxicities. Seeking an improved process, a Predictive Safety Testing Consortium of five of America s largest pharmaceutical companies with an advisory role by the Food and Drug Administration (FDA) has been formed to share internally developed laboratory methods to predict the safety of new treatments before they are tested in humans. In 2007, this group presented to the FDA a set of biomarkers for early kidney damage. 

A weight of evidence approach to assessing reproductive toxicity requires rigorous evaluation of all available data. However, often only limited information is available, and default assumptions must be made because of uncertainties in understanding mechanisms, dose-response relationships at low dose levels and human exposure patterns. Several of these assumptions are basic to the extrapolation of toxicity data from animals to humans, while others are specific to reproductive toxicity. The general default assumptions for reproductive toxicity stated in the IPCS (1995) report are summarized as follows ... 

Human toxicity data, especially the median lethal dose, is extrapolated from animals or from accidental poisoning, homicides and suicides. Extrapolations from animal data are educated estimates which consider the differences in species and building in a safety factor. If a lethal dose is 10 mg/kg in a rat and we consider a human to be 10 times more sensitive 1 mg/kg will have another 10-fold safety margin. Animal testing also involves using what may seem as ridiculous doses in order to cover the safety factor. To find a statistically valid effect which occurs once in one million subjects, several million animals would have to be used, which is exhorbitantly... 

In summary, in studies of chemical toxicity, pathways and rates of metabolism as well as effects resulting from toxicokinetic factors and receptor affinities are critical in the choice of the animal species and experimental design. Therefore it is important that the animal species chosen as a model for humans in safety evaluations metabolize the test chemical by the same routes as humans and, furthermore, that quantitative differences are considered in the interpretation of animal toxicity data. Risk assessment methods involving the extrapolation of toxic or carcinogenic potential of a chemical from one species to another must consider the metabolic and toxicokinetic characteristics of both species. 

Refinements of the RfC have utilized mechanistic data to modify the interspecies uncertainty factor of 10 (Jarabek, 1995). The reader should appreciate that with the inhalation route of exposure, dosimetric adjustments are necessary and can affect the extrapolations of toxicity data of inhaled agents for human health risk assessment. The EPA has included dosimetry modeling in RfC calculations, and the resulting dosimetric adjustment factor (DAF) used in determining the RfC is dependent on physiochemical properties of the inhaled toxicant as well as type of dosimetry model ranging from rudimentary to optimal model structures. In essence, the use of the DAF can reduce the default uncertainty factor for interspecies extrapolation from 10 to 3.16. 

Extrapolation of toxicity data from animals to humans is not completely reliable. For any given compound, the total toxicity data from all species have a very high predictive value for its toxicity in humans. However, there are limitations on the amount of information it is practical to obtain. 

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