Journal of Toxicology and Environmental Health
ISSN: 0098-4108 (Print) (Online) Journal homepage: http://www.tandfonline.com/loi/uteh19
Estimation of risks of irreversible, delayed toxicity David G. Hoel , David W. Gaylor , Ruth L. Kirschstein , Umberto Saffiotti & Marvin A. Schneiderman To cite this article: David G. Hoel , David W. Gaylor , Ruth L. Kirschstein , Umberto Saffiotti & Marvin A. Schneiderman (1975) Estimation of risks of irreversible, delayed toxicity, Journal of Toxicology and Environmental Health, 1:1, 133-151, DOI: 10.1080/15287397509529314 To link to this article: http://dx.doi.org/10.1080/15287397509529314
Published online: 20 Oct 2009.
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Date: 06 November 2015, At: 05:50
ESTIMATION OF RISKS OF IRREVERSIBLE, DELAYED TOXICITY David G. Hoel National Institute of Environmental Health Sciences, National Institutes of Health, Research Triangle Park, North Carolina David W. Gaylor
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National Center for Toxicological Research, Food and Drug Administration, Jefferson, Arkansas Ruth L. Kirschstein National Institute of General Medical Sciences, National Institutes of Health, Bethesda, Maryland Umberto Saffiotti, Marvin A. Schneiderman National Cancer Institute, National Institutes of Health, Bethesda, Maryland This report reviews the current statistical methods used (1) to establish doseresponse relationships for irreversible self-replicating toxic effects (i.e., carcinogenesis) in laboratory animals and (2) to extrapolate these data to man. The method or procedure for estimating human risk based on animal studies is basically carried out in four sequential steps: 7. Design and/or assessment of laboratory experiments as to quality and biological appropriateness. 2. Statistical extrapolation of the experimental results to low dose levels. 3. Extrapolation of the estimated results in animals at the low level to man. 4. Assessment of the risk to man based upon experimental data. Each of these steps is difficult, and the purpose of this report is to review what is known about them and to determine what research is required in order to improve the quality of the total procedure.
BIOLOGICAL CONSIDERATIONS The first step of the sequence requires that experimental results considered for use in the estimation of risks be scrutinized through expert This work is the report of the Subcommittee on Estimation of Risks of Irreversible, Delayed Toxicity to the Department of Health, Education, and Welfare Committee to Coordinate Toxicology and Related Programs. Requests for reprints should be sent to David G. Hoel, National Institute of Environmental Health Sciences, National Institutes of Health, P.O. Box 12233, Research Triangle Park, North Carolina 27709. 133 Journal of Toxicology and Environmental Health, 1:133-151, 1975 Copyright ©1975 by Hemisphere Publishing Corporation
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scientific review of experimental design of criteria of interpretation of results and of data reporting. Such review, taking into consideration all details of the biological experiments, should provide evidence of adequacy and good technical quality of the bioassay data to be used. Adequacy should be determined for each bioassay, considering the following points: choice of animal model (one which is susceptible to the induction of the effects under consideration); the number of animals in test and control groups; choice of route of administration [which should include the route(s) of human exposure]; the physical state of the administered agent and its metabolic requirements (if known); toxicity of the agent and side effects (with particular attention to the weight patterns and food intake of the animals and to the induction of competing risks); organotropism of the agent; and other pertinent biological information. Adequacy and appropriateness should be determined also for the evaluation of bioassay results, considering the following points: adequate survival of test animals and lack of important intercurrent diseases unrelated to the experiments; quality and extent of gross and microscopic study; quality and extent of data collection during the test period and at the death of the animals; accessibility of detailed data on all the above-mentioned points, including survival and pathologic diagnoses on individual animals. Quality standards for carcinogenesis bioassays have been recommended in several published reports (Advisory Panel on Carcinogenesis . . . , 1969; Berenblum, 1969; Fifth R e p o r t . . . , 1961; Food and D r u g . . . , 1971; Food Protection Committee, 1960; Hueper and Conway, 1964; The testing of chemicals . . . , 1973; Weisburger and Weisburger, 1967). The role of combined factors in the induction of carcinogenic effects has been studied in a limited number of cases. Marked synergism has been found in several instances between different carcinogens. Certain factors, which are not carcinogenic per se, may provide the conditions for revealing carcinogenic effects of test chemicals. Present test systems are mostly used to detect the effects of individual chemicals rather than those of multiple exposures. Human exposures, however, are all in the context of multiple factors. Once a given bioassay system has been defined and accepted as adequate for a specific purpose, it should be recognized that it has a given level of "sensitivity" for detecting certain biological effects. This sensitivity limit gives an indication of the extent of biological effects that may be induced by the test treatment without being detected. Such limitations may be both qualitative and quantitative. Tumors of certain organs (e.g., nasal cavities, brain) will go undetected unless special tissue processing techniques are used to look for them; microscopic tumors will be detected with a frequency proportional to the number of tissues examined histologically as well as to the degree of sampling of each organ; the number of tumors classified as malignant may depend on the extent of
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efforts devoted to a search for metastases or for transplantability. The number of animals at risk and the extent of controls also determine the lower limit of detectability of a risk. These factors contribute to what could be called a detectability threshold of an effect within a given test system. Another step based on biological factors is considered in the sequence outlined at the beginning of this report, that is, step 3, which requires the extrapolation from low-level effects in the selected animal model to those in man. The extrapolation from animal to man is made by using a "species conversion factor" which is arbitrarily selected but which must be based on informed judgment. This species conversion factor (previously called "safety factor") should be selected so as to avoid underestimating the risk for man. There may even be cases in which there is almost a direct one-to-one conversion from the most sensitive species to man. In this case, the conversion factor would be taken to be equal to one. The routine use of a dose factor of 100 from the highest "no observed' effect" level, often employed in acute toxicity studies without further extrapolation, is deemed inadequate to cover the complex assessment of carcinogenic risks in man. The total species conversion factor should be arrived at by giving separate careful consideration to each of the following aspects: genetic species susceptibility, tissue storage, retention and distribution of material, metabolic pathways for activation and/or detoxification, interaction and synergism with other exposures, solvent effects, physiologic states (e.g., age, pregnancy, hormonal state), nutritional conditions, pathologic states (e.g., chronic inflammatory diseases, endocrine diseases), and conditions of exposure in man. In calculating the conversion factor for dose exposure, it is recommended that the dose unit should be a "surface" unit, which is approximately the 2/3 power of the weight of the two species. There is no evidence as to what an appropriate total species conversion factor should be. A single dose factor of 5,000 from the lowest observed effect level was recently proposed by Weil (1972) to cover the product of a species conversion factor and a low dose extrapolation factor (corresponding to step 2), but this may prove grossly inadequate in some cases and possibly excessive in others. A factor should be determined for each substance considered for the extrapolation from a given animal model to man, by informed judgment on as many aspects as possible.
MATHEMATICAL MODELS OF DOSE-RESPONSE RELATIONSHIPS In order to predict a response outside the experimental range, a functional relationship between dose and response is needed. This relationship is usually expressed by a parametric model which describes the distribution of response for a given set of experimental conditions, including dose level. These models are completely specified except for a few unknown parameters which are either estimated from specific
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experimental data which the model attempts to describe or by appealing empirically to the results of large classes of similar experiments. It is possible to have mathematical models which have had a history of adequately fitting certain types of experimental data, but which have little biological basis or justification. The mathematical models used for relating toxic response and dose levels in animal survival studies fall basically into two categories. Most common are models which deal with a dichotomous response. That is, the response is either that a particular condition is present or that it is not. Examples are the one-hit model and the Mantel-Bryan use of the probit model for evaluating levels of toxicity (Mantel and Bryan, 1961). The second category consists of models which deal with the distribution of the "time-to-occurrence" and its relationship to dose level. The occurrence may, for a given situation, be the appearance or detection of a tumor. In another situation, the occurrence may be death due to a particular disease. The parametric models most often considered for representing this time distribution are the lognormal and the Weibull (Peto et al., 1972). The low dose extrapolation problem is concerned with determining the dose that will produce a response which is, at most, a given preassigned level. Given a model and a set of data, this dose can be directly estimated giving an "estimated average effect." Further, a dose can be estimated which will be below the desired unknown dose with a given level of confidence and will be referred to as an "estimated maximum average effect." The difficulty with this approach, however, is that it is dependent on the assumption of a particular model. Closely related to this approach is the direct estimation of the mean risk and its associated confidence limits. Again model assumptions are necessary, and there is the resulting dependency of the estimates on model. However, by considering classes of models which appear biologically reasonable the model dependency could possibly be incorporated into the estimated confidence limits of the risk estimates. There clearly is the need for obtaining some type of best estimate of risk along with confidence limits on this best estimate. No established methodology is currently available for this purpose. A second approach to the extrapolation problem is to select a mathematical model which, with high confidence, will provide an upper bound to the true unknown response curve. The remaining parameters for this model are then estimated from data and the estimated dose produced. It must be emphasized that this dose is not the best estimate of the unknown dose associated with the desired response. It is, instead, a dose which is most likely to be below the dose required to give the desired response. The justification for this more conservative approach lies in the fact that, since very little is known about the lower ends of a dose-response curve, the choice of the model becomes critical in that very region of the curve. Basically, the problem is that, in the experimental
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region of the curve, several competing models will appear to f i t the data well, while the tails .of these response distribution curves often differ by many orders of magnitude. Therefore, until there is a better understanding of the biological mechanisms involved, it will be difficult to choose properly a model based solely on experimental data.
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Dichotomous Response Models The dichotomous response models relate dose to the probability of a particular condition being present (or observed). This condition is generally defined as the presence of some irreversible toxicological effect such as a particular form of cancer. Experimental data are used to estimate the parameters of dose-response models in order to predict the response at a given low dose or to predict that dose which gives a particular response level. In performing this estimation procedure, great care must be given to the interpretation of the experimental data. In an animal survival study, a number of factors, for example, survival patterns, numbers of animals at risk, and weight losses, must be carefully taken into consideration before an observed incidence is applied to the model. Linear or one-hit model. The dose-response model which has probably received the most attention in analyzing dichotomous data is the one-hit model. A basis for this model is the concept that the response can be induced after a single susceptible target has been hit by a single biologically effective unit of dose. This action implies that the probability of response pd at dose d is given by
pd = 1 -
exp(-yd)
where y is an unknown parameter. For small values of yd (i.e., low dose) it follows that approximately Pd
=yd
For low dose levels, this linear model is essentially numerically identical to the one-hit model. The parameter y represents the slope of the linear dose-response relationship. For carcinogenesis, no adequate experimental data exist on the shape of dose-response curves at low levels, for example, below a response of 1 in 100. It is, however, currently assumed that most dose-response curves are concave upward in the low dose regions when dose is plotted against response. Intuitively then the linear model provides an upper bound to such dose-response curves and hence a conservative estimate of that dose which yields a probability of response below a specified level. For further
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discussion and specific details of the application of the linear model, see Gross et al. (1970). Probit, logistic, and extreme-value models. For any cumulative distribution function F a whole class of models is given by
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pd = F(a + p log d) where a, 0 are parameters whose values must be determined. If F is the cumulative normal distribution function, then the model states that the probit of the probability of response is linear in log dose. The logistic and normal distributions have a long tradition in the analysis of quantal response data. This in itself, however, does not justify their use in extrapolation. The probit model is related to the use of a lognormal distribution in time-to-occurrence models. When the Weibull distribution is used in place of the lognormal, then the extreme value distribution is obtained for quantal response (see Chand and Hoel, 1974). The three models can be written as Probit: pd = $(0:1 + /3X \ogd) Logistic: pd - [exp(a2 + P2 log cO + 1 ]
-1
Extreme value: pd = 1 — exp[—exp(