Click for next page ( 238


The National Academies | 500 Fifth St. N.W. | Washington, D.C. 20001
Copyright © National Academy of Sciences. All rights reserved.
Terms of Use and Privacy Statement



Below are the first 10 and last 10 pages of uncorrected machine-read text (when available) of this chapter, followed by the top 30 algorithmically extracted key phrases from the chapter as a whole.
Intended to provide our own search engines and external engines with highly rich, chapter-representative searchable text on the opening pages of each chapter. Because it is UNCORRECTED material, please consider the following text as a useful but insufficient proxy for the authoritative book pages.

Do not use for reproduction, copying, pasting, or reading; exclusively for search engines.

OCR for page 237
4 DETAI LED DESCRIPTION OF THE OPERATION OF AN ILLUSTRATIVE SYSTEM INTRODUCTION An illustrative priority-setting system has been developed to demonstrate the application of principles described in Chapter 2 and to demonstrate the feasibility of the approach to designing priority-setting systems. The system responds to the approach taken in Part 1, in that it uses the same classes of intended use and incorporates two of the toxicity tests used by the Committee on Sampling Strategies and the Committee on Toxicity Data Elements. The approaches differ in that the illustrative system is structured to be specific for a human health effect, whereas those two committees take a more general approach. ELEMENTS OF DESIGN Authors of priority-setting systems have several choices to make: goal of the system, universe of chemicals considered, structure of the system, and assessment. GOAL The goal chosen for this illustrative priority-setting and testing system is to assess accurately the public-health concern about all chemicals to which there is human exposure. This goal was chosen to reflect the wide range of chemicals of interest to NTP. A goal might be chosen to be oriented more toward regulation, such as minimizing the impact on human health caused by chemicals to which there is exposure. The Committee on Priority Mechanisms did not choose this goal, because NTP does not have regulatory authority and because NTP does testing for purposes other than regulation. UNIVERSE The universe of chemicals to be considered is defined to include all chemicals to which there is potential human exposure. The universe defined by the Committee on Sampling Strategies has five categories of intended use: food chemicals, drug ingredients, pesticide chemicals, cosmetic ingredients, and general industrial chemicals (TSCA Inventory) There are two additional categories: "other," such as pollutants; and "unknown." 237

OCR for page 237
ASSESSMENT The degree of public-health concern is a combination of degree of human exposure and degree of toxicity to humans. For purposes of designing this illustrative system, toxicity is limited to carcinogenesis. This health effect was chosen because of its severity and the high degree of public interest in it. Also, more effort has been devoted to discovering carcinogens; therefore, more data are available for designing the system. Both toxicity and exposure are expressed as high, medium, or low, because available information seems inadequate to define more than three levels. Toxicity, in this case carcinogenic potency, is defined in terms of TD50 (Ames et al., 1982~: high is a TD50 less than 102 ~g/kg-d, medium between 10~-and 106 ~g/kg-d, and low greater than 106 ~g/kg-d. His definition assumes a linear dose-response curve. The category of "low" toxicity also includes noncarcinogens. For exposure, high is greater than 107 person-grams per year (p-g/yr), medium is between 107 and 105 p-g/yr, and low is less than 105 p-g/yr. Note that, in this population-exposure formulation, large exposure of a small number of people could be equivalent to small exposure of a large number of people. Which situation is more important is a matter of social judgment. STRUCTURE A multistage structure was chosen, so that the system could handle many thousands of chemicals and fit the current institutional structure of the NTP selection system. A multistage structure provides for using small amounts of data to assess large numbers of chemicals in the early stages and examining fewer chemicals in depth in later stages. RULES FOR SELECTION The result of each stage is an assessment of the toxicity and exposure of each chemical considered in that stage and an estimate of confidence in each assessment. Having these assessments from each stage, it is still necessary to decide whether a chemical should receive further consideration or be removed from consideration. A mathematical model chooses rules for selection such that the errors in assessment are minimized, for a chosen amount of resources for selection and testing (Appendix B). That optimization model not only guided the Committee in designing the illustrative priority-setting scheme, but also allowed it to examine the values of and interactions among various indicators of exposure and 238

OCR for page 237
toxicity. Even the simplified model used for choosing the rules of the illustrative system validated some intuitive judgments about testing priorities and provided valuable additional insights; further development of the model may be justified to sharpen the rules for chemical selection in an operational priority-setting system and to modify them as additional information is gained by operating the system and conducting the tests it selects. DESIGN PARAMETERS Several characteristics of the universe were estimated and serve as data for the model used to develop the rules for the illustrative system. These estimates were based on fragmentary data and are intended to be replaced with better estimates based on data obtained from operating the system. The universe was estimated to consist of 70,000 discrete chemicals. The Committee on Priority Mechanisms intends that the definition of the universe be considered flexible and subject to change as needed. The estimate of 70,000 is not intended to become either a maximal or a minimal number of chemicals to be considered. On the basis of the above definitions of degree of exposure, the overall distribution of low, medium, and high exposure is 0.7, 0.2 and 0.1. On the basis of the definition of carcinogenic potencies, the estimated distribution of potency is 0.95 low, 0.04 medium, and 0.01 high. These estimates are only for purposes of designing the illustrative system and are highly speculative. Estimates of the proportion of carcinogens in a group of chemicals may vary widely (U.S. Congress, 1981), because of the definition of "carcinogen" and the group of chemicals considered. STAGE 1 me purpose of Stage 1 is to provide a mechanism for scanning the entire population of chemicals to be considered by the system. It is intended that this scanning could be performed in many ways: by category or combination of categories of intended use or by chemical structure. The scanning need not be performed in one year, but might be performed over a period of a few years. Stage 1 is limited to data in machine-readable data bases, so that human intervention is minimized. The Committee on Priority Mechanisms chose to use the data bases and searching capability of the Chemical Information System (CIS) developed by the National Library of Medicine (NLM) and EPA. This system was chosen because many chemicals of interest are already included in the data bases. In addition, the Structure and Nomenclature Search System of CIS provides a searching capability for chemical structures and thus makes possible a crude analysis of structure-activity relationships. 239

OCR for page 237
EXPOSURE DATA ELEMENTS The purpose of exposure data elements in all stages is to contribute to an attempt at estimating exposure on the basis of surrogate data--data that are related to exposure only incompletely or inaccurately. "Exposure" is a concept that embraces the quantities of material that reach people, the number of people exposed, the rates and patterns of exposure, the characteristics of the exposed population, and other factors. A fully developed system for priority-setting not only would take into account as many of these dimensions as feasible, but also would ensure that they interacted properly with the dimensions of toxicity. However, complexity may not be justified for screening decisions at early stages of the system, and it also obscures the logic of the illustrative system. Therefore, the committee conducted its analysis with a single exposure variable to which degree of hazard would be proportional for a constant degree of toxicity. If a linear dose-response relationship is assumed for toxicity as a first approximation, then exposure can be measured by the total mass of a substance that is ingested, inhaled, or otherwise taken in by all members of the population, in units of person-grams per year. This quantity can be estimated by summing the products of the per capita annual intakes and the number of persons for all exposed groups with different intakes. Two surrogates of exposure--class of intended use and production volume--are used to define the possible classifications of a Stage 1 exposure data element (Table 3~; each classification consists of a pair of subelement classes--use and production volume. The first is derived from the definitions of intended use devised for the select universe, and the second from the TSCA Inventory or other automated sources. We assume that, on the average, a higher fraction of food-chemical and drug production than of cosmetic, general TSCA chemical, or other (as yet undefined) chemical production eventually results in human exposure. New classes would need to be examined to test this assumption. Prime candidates for new classes are general environmental chemicals in air and water (including degradation products and "natural" pollutants) and food constituents and their products. If one accepts the exposure assumption, then the production volume required for a high probability of exposure should be lower for food chemicals and drugs than for chemicals in the other use classes. Table 3 lists an illustrative exposure classification, in which there is a decreasing probability that exposure is high; where the probabilities are identical, the list is in decreasing order of confidence in the estimates. The ability of the classifications to identify high-exposure chemicals cannot be determined without estimating the numbers of chemicals in the universe of concern that fit these classifications. Table 4 is a hypothetical classification and would need to be revised as data and experience accumulate. 240

OCR for page 237
TABLE 3 Illustrative Stage 1 Estimates of Probability of Exposure in Relation to Use and Production Probability That Exposure Is: UseaProduction, lb/yr Low Medium High F,D>104 0.40 0.40 0.20 P,C>105 0.40 0.40 0.20 G,O> 108 0.40 0.40 0.20 U> 106 0.40 0.40 0~20 G,O1o6_1o8 0.50 0.35 0.15 P,C104-105 0.50 0.35 0.15 U1o4_1o6 0.68 0.20 0.12 F,DU 0.68 0.20 0.12 F,D,U<104 0.73 0.18 0.09 P,C,G,O,UU 0.73 0.18 0.09 P,C< 104 0.75 0.17 0.08 G,O104-106 0.75 0.17 0.08 G,O<104 0.76 0.17 0.07 a F = food chemical. D = drug. P = pesticide. C = cosmetic. = general commerce (TSCA). Other (known, not previously classified). unclassified. 241

OCR for page 237
1 TABLE 4 Illustrative Estimates of Distr ibution of Production Volumes in Relation to Use Categories Frac tion Fr action by Production Volume, lb/yr of All Chem Use Class icals > 108 1o6 _ 1o8 105 _ 1o6 104 - 105 <104 ua b -- 0.001 0.003 Food 0. 12 Drug 0.10 Pesticide 0.05 Cosmetic 0.05 0.003 0.003 0.11 c 0.001 0.009 0.020 0.03 0.06 0.001 0.002 c 0.001 0.004 0.015 0.025 0.055 0.004 0.093 0.001 0.002 0.002 0.045 c 0.0002 0.006 0.005 0.013 0.026 b -- 0.001 0.001 c 0.001 0.002 0.003 __ 0.002 0.002 0~044 0.014 0.03 __ General 0.066 0.02 0.06 0.06 0.09 0.28 0.3 Other 0.01d 0 o o Unknown 0.01d 0 o o o o a In inventory, but without stated production volume. b Estimated distribution of production volumes listed in CIS. c Estimated distribution of production volumes if all were known. d At present--these categories will grow. 242 0 0.01 0 0.01

OCR for page 237
TOXICITY DATA ELEMENTS Two surrogates of toxicity, RTECS status and chemical class, are used in Stage 1. Each is used to estimate the likelihood that a substance may have toxic effects. These surrogates indicate that the likelihood that a given chemical or class of chemicals will have health effects is different from the likelihood that any substance in the universe of chemicals will have those health effects. Given the present state of knowledge, it is only slightly possible to assess the potential for an adverse response on the basis of chemical structure. But it would be desirable to use knowledge of structure-activity relationships (SARs) to identify potentially toxic substances by analyzing substructures that have been associated with adverse effects in humans or animals. Such an assessment is ordinarily based on expert knowledge, experience, and intuition and is used in considering the type of testing that may be required. A number of systems have been created to place SAR analysis on a more formal footing. They range from simple classifications of key types of substances to sophisticated statistical treatments that involve weighting of subgroups and from detailed treatments of specific health effects to general considerations of toxicity. These approaches have merit as research efforts, but none has evolved enough to provide a practical or accurate method for identifying potentially toxic chemicals. A more detailed review of the possible contribution of SAR analysis is given in Appendix D. To be usable in Stage 1, any data base used for SAR analysis must contain a large proportion of the universe of chemicals being considered. The data must also be susceptible to a search for chemical subgroups that can be associated with specific types of toxicity. The SANSS data base may provide a potentially useful data base. It can be searched for any of 271 functional groups containing at least two nonhydrogen atoms, any of 137 specific cyclic nuclei, and a number of hydrocarbon radicals. Each structural group or feature is described by a code that can be modified to allow for other structural features, such as the attachment of phenyl nuclei and aromaticity in ring structures. At the simplest level, the system provides a machine-readable way of identifying all the compounds in the system's collections that contain a specified structural group. The system may also be programed to identify structures with specified subgroups; thus, the code for phenols can be modified to produce separately monocyclic, dicyclic, and tricyclic phenols. And the system allows identification of compounds that have more than one specified functional group; this permits the identification, in lists of compounds that contain a given structure, of substances in which that structure is accompanied by another structure that might modify its biologic activity. 243

OCR for page 237
About 80 of the SANSS specific functional-group codes are associated with one or more human health effects, as shown in Table 5. The groups are assumed to produce the ascribed effect either by direct action or after metabolic activation. The table is illustrative and should not be considered comprehensive or definitive. The estimated degree of association between a given chemical structure and a specific form of toxicity is indicated in the table as low (L), medium (M), or high tH). An association between a chemical structure and an effect and the degree of that association imply that chemicals that contain the structure are more likely to cause the particular health effect in question~than are randomly selected chemicals in the universe being considered. RTECS, published by the National Institute for Occupational Safety and Health, is the most comprehensive (but not the only) summary of information on the toxicity of chemicals that is available in easily accessible, computerized form. In the 1980 edition, positive results of toxicity testing of some 45,000 chemicals are reported. In general, negative results of toxicity testing are not reported, so RTECS is biased toward toxicity. But RTECS does report negative results of lifetime bioassays for carcinogenicity by NTP (and earlier by the National Cancer Institute). Consequently, mere listing in RTECS slightly raises the probability of high or moderate toxicity at the expense of the probability of low toxicity. RTECS also reports specific positive results, such as carcinogenicity, mutagenicity, teratogenicity, reproductive effects, and a variety of acute and other chronic toxicities. Nevertheless, in practice, even listing as a carcinogen carries with it a false-positive rate, because of the imperfect correlation in cancer hazards among different species, experimental uncertainties, faulty experimental procedures, or mere error. The absence of a CAR code implies that the substance may have proved negative in a bioassay, but the false-negative rate for such an assumption is high. Similar problems beset other toxicity codes. In a fully developed priority-setting system, each toxic effect of concern would be related to at least one RTECS code. Matrices of probability for a given effect would be constructed to show how the underlying prevalence rates are modified for appearance or nonappearance of particular codes, which are retrievable by computer search. In this report, we discuss only carcinogenicity, both because time was too limited to develop other "performance characteristics" and because the theory and data base for carcinogenicity are better developed than those for other kinds of toxicity. Six codes are thought to be related to carcinogenicity: NTP POS,* NTP NEG, CAR, NEO, ETA, and MUT (MTDS in the latest RTECS). NTP POS *Many RTECS codes refer to NCI-positive or -negative results, rather than to quantitative results of tests conducted according to NTP-approved protocols. 244

OCR for page 237
:' l ye ~ o ~ ~ - ~e cooux:~a 0 1 V 41 ~ 1 ~ V 44 cq ~ ~ w ~ 0 ~ 14 1 0 C Hi-- ~ x;a 0 ~ U 1 U 15 1 D'- - C ~ ~ ~ ~ ~ ~ L' ~ sea 0 C ~ ~ 1 1 0 ~ ~ ~ a 0 OF .-, v .. ~1 a: 1 1 TIC - u . - 4, :~: w Ld o u ~4 . - C) TIC 4J . - :, 1 1 ~ 1 00 ~ 0 a- - ~ :> ~ C: 0 1 ~ 1 -4 C ~ o aa 0 1 1 ..C ~ 0 v Z ~ ~ 1 ~ V o. 0 ., 1 -~ C a 1 C 1 0 ~ C C U. . - 0 C U a' ~4 U) in eC Z V ~ ~ c :~: TIC 4 V . - U 0 0 ~ ~ o v u Us ~ . - a, Cal ~ ~ J ~ O C7 Ed :~: ~: ~r ~o ~:! ~ . ,0 1 oo _I ~ o' o o, In 0 0 u ~0 0 ~n GD ~GD cr ~ v~ P4 ~10 O '0 ~J\zo o o C~ ~CJ ~U I Q1 ~ ~Y ro 0 v 0 V U . - V V ~ ~C _ -I U U _ . V ~0 a' c ~ J: O ~04 .c ~ ~o aC ~ -4 0 ~ U ~ 0 0 :> 0 ~a ~: 245

OCR for page 237
C ~ 0 1 C 1 ~- ~ ~ ~ 0 ~ ~ ~ O ~ 4-~ 4 u, 0 ox: Ma 0 1 00 ~ 41 1 t.) Y U] ~ ~ Ad 4~ TIC c 3 o 4 4 1 0= 4 0 ~ _ C ~ ~ 4 ~ ~ x: a 0 1 ~ ~ 1 =~- c m ~ ~ ~ ~ 4 Hi; 4~ a: a 0 C 14 :, 1 1 do ~ ~ do- - 4 ~ u, ~ a 0 :' 4 1 ~ ~ 1 0 4 0 U' 4 U' ~ as- - ~ be- - 4 c' ~ :> ~ a 0 `4 4 1 ~ 1 C ~ ~ O - 4 o' ea 0 I ~ 0 Z 4 1 ~1 0 4 1 _1 C ~1 4' ~ .,. 00 1 0 C C ~ ~ - ~ . - C) ~ ~ ~ EN U) Z 4 :, -4 ~0 ~ - C - Vc o _ US ~ - o. O ED ~ ~ A: A: A: J S X J -1 X ~: J J J 3: red 0 or ~: 0= 1 1 1 1 1 O= z Z zz 1 1 1 1 `2: 1 r~ r~ _1 1 ~o C~ r~ ~D ~4 ~: z c] z '1 z ~=z c> ~ u 3 ~J - 4 4 111 .C ~U ~C O go ~C ~o ~o ~e ~c ~ ~C ~0 . - ~U _ - 0C) U) 4 1U _ ~_1 4 ~4 C ~>~ e ~4 X - ~V 0 - O OO ~C ~N ~N 246

OCR for page 237
a: ~ o ~ " - 0 of Ha 0 1 0 1 U dew to ~ ~ w c ~ ~ 0 ~ O ~ 1 0 ~ ~ ~ ~ _I ~ of- - 4 a: ~ O ~ ~4 1 U ~ 1 - C (U dJ o. - ~ ~ ~ a 0 C Ll :, 1 1 ~ us ~ ~ 0 :, 1 1 U 1 ~ 0 ot ~ ~ ~ - c, ~ ~ ~ a 0 u ~4 w tic ~x ~x: ~Ill 1 ~ 1 a, a" 0 I ~ 3 ~ 0 z 1 ~1 ~ u 0 ~ ~ 1 _1 ~ W w a >~= 1 C 4) ~ . - ~ cn 0 1 0 ~ _ u I: ~ E. in In :c ~ 8 In to ret up ~ rid I ~ ,, - 1 \0 Z ~$ ez ~ ~ ~ ~ ~0=1 O-1 Z: 0 0 =0 C e. ~ U L. ~ ~0~ u o _I ~ ~N D4 N ~ ~D o" ~O~ ~U 247 11 ~, 11 0 = 1 ~0 u 0 .~4 D o

OCR for page 237
TABLE 16 Hypothetical Estimates of Probability Distributions of Carcinogenic Potency as Generated in Stage 3, in Relation to Assigned Degree of Concern about Carcinogenicity and Degree of Confidence in Assignment Degree of Toxicity Low Medium High Degree of Confidence in Estimate (Estimated Probability Distribution of True Toxicity), % Low Medium High 96-3-1 97-2-1 98-1 1 75-21-4 57-40-3 40-57-3 40-40-20 30-40-30 25-35-40 m e corresponding distribution of judgments by the expert committee might be as follows: Degree of Degree of Concern,% Confidence, ~ Low Med. High Low Med. High Stage 3 toxicity dossier 60 30 10 60 25 15 These distributions are consistent with an assumpton that 81% of the chemicals moving from Stage 2 to Stage 3 are noncarcinogens, 15% are weak carcinogens, and 4% are strong carcinogens. Note that the expert committee is not asked to make any judgment about the value of proposed tests. We propose that systematic and largely predetermined rules be used to determine the value of a mutagenicity assay or a lifetime bioassay in clarifying the concern about carcinogenicity for a specific chemical that has already been assessed by an expert committee. The recommendations for testing would then be reviewed by the board of scientific counselors, or a similar body, to ensure that they are reasonable, given the status of existing information and the detailed properties of the substance in question. Unfortunately, this whole approach to testing decisions is not as well suited to evaluating reconnaissance testing (e.g., 90-d feeding studies), in which an effect of concern may be specified only generally, if at all. The performance characteristics for such tests would have to be either created from the aggregate of several effects that tests could detect or defined for a generalized toxicity measure, which would be sought only because exposure is fairly high and fairly certain. 274

OCR for page 237
SELECTION RULES As in Stage 2, the Stage 3 rules (Appendix B) operate on the finding of high, medium, or low toxicity and high, medium, or low exposure; later, they should be revised to consider degree of confidence. The choices for Stage 4 are a short-term test, a long-term cancer bioassay, and no action. STAGE 4 Because the objective of priority-setting must be the minimization of misclassification of the potential public-health concern about chemicals, the system cannot be complete without an analysis of the predictive power of the tests to which it leads. We discuss here some problems related to the selection of toxicity tests and the relations between operating characteristics of tests and selection of chemicals for toxicity testing. At this stage of the priority-setting process, the goal is to select, for the chemicals to be evaluated, tests that provide the most useful toxicity information for the resources invested. Because testing resources are limited, selection of an optimal test or combination of tests involves a tradeoff between the total number of chemicals evaluated and the completeness with which any one chemical can be characterized. To illustrate how this tradeoff can be made, we constructed a mathematical model for the allocation of resources for one kind of toxicity, carcinogenicity (Appendix B). This choice was made both because methods for carcinogenicity testing are better than methods for most other toxicity testing and because carcinogenicity testing consumes a larger fraction of NTP resources than does testing for any other toxic effect. Tests of other effects, like tests to assess carcinogenicity, entail consideration of both cost and accuracy of testing. These tests range in cost from a few hundred dollars for some short-term tests with bacteria to more than a half-million dollars for a standard long-term bioassay in rats and mice (Table 17~. The cost of a given test may vary among laboratories, depending on the substance being tested, the conditions of testing, institutional overhead expenses, and other factors (Lave et al., 1982). Tests vary in predictive accuracy. None is considered accurate enough to predict with complete reliability the carcinogenicity of a substance for humans; that can be established only by epidemiologic evidence of carcinogenic effects in humans. Nevertheless, the demonstration that a substance is carcinogenic in appropriately conducted animal tests is generally considered an adequate basis for classifying it a presumptive human carcinogen (IRLG, 1979; U.S. Congress, 1981; IARC, 1982~. Interpretation of animal tests is uncertain, however, and requires expert judgment. Extrapolation from animal test results to quantitative estimation of human risks entails large uncertainties in 275

OCR for page 237
TABLE 17 Estimates of Costs of Some Carcinogenicity Testsa . Testsb TO e of Test - P Bacterial cell: Estimated Average Cost, No. $ Responses _ 1-4,6-13 Salmonella his-1,200 10 19 Escherichia cold WP2400 2 21 Bacillus subtilis rec-800 1 22-24 Escherichia cold rec-1,500 1 29 Degranulation 2,500 1 37 Yeast cell: Saccharomyces D7 ~ cereals 1ae Mammalian cell: 1,400 1 40-42 Unscheduled DNA synthesis-- 5,200 3 human fibroblasts, HeLa ceils 43,45 Sister-chromatid exchange-- 3,000 1 CHO cells 44 Chromosomal aberrations-- 7,500 3 CHO cells, rat liver cells c Transformation--CHO cells 1,400 1 c Transformation--C3H-lOT 1/2 5,400 4 48 TK +/- L5178Y mouse 4,900 1 lymphoblasts 50,51 HGPRT-CHO cells, 6,500 4 V79 cells Whole Animal: 56-58 Sex-linked recessive lethal-- 10,000 1 Drosophila melanoqaster (injection) 59 Sister-chromatid exchange--mouse 3,000 1 60-62 Micronucleus--mouse 3,400 2 63 Sperm morphology--mouse 11,400 1 Whole-animal two-species rodent bioassay a Modified from Lave et al., 1982. b See Table 19 for list of tests. c Test was not conducted in International Collaborative Program (de Serres and Ashby, 1981) in either CHO or C3H cells, but in BHK-21 cells. d From Weinstein, 1983. 500,000d 1 276

OCR for page 237
connection with mechanisms of carcinogenesis and species differences in carcinogen metabolism (IRLG, 1979; Calkins et al., 1980; IARC, 1982~. The difficulties in interpreting tests for carcinogenicity have led the International Agency for Research on Cancer (1982) to publish guidelines for interpretation. Long-term bioassays in rats and mice are so expensive (Table 17) and time-consuming (requiring up to 5 yr for completion) that such tests are not feasible for more than a small percentage of the many thousands of chemicals to be tested. Faster and less expensive assays are needed. To this end, more than 100 short-term tests have been introduced (Hollstein _ al., 1979~. Emus far, however, such tests have been used only for screening purposes, and not for definitive predictions of carcinogenicity, pending further standardization and validation of their accuracy. The need for standardization of such tests is indicated by the variability in results of the most widely used short-term screening test--the Salmonella typhimurium/microsome plate mutagenicity assay. Table 18 shows the degree of agreement found by the International Collaborative Program, which had 12 laboratories apply the test to a series of 19 carcinogens and noncarcinogens (de Serres and Ashby, 1981~. The discrepancies are attributable at least in part to the use of differing procedures. Despite the variations, the Salmonella mutation assay yielded preponderantly positive results with chemicals known to be carcinogenic in rodents. Nevertheless, the observed correlation between mutagenicity and carcinogenicity has varied from one class of chemicals to another. For some carcinogens (e.g., asbestos and halogenated hydrocarbons, such as DDT) and for most tumor-promoting agents (such as phorbol esters and some naturally occurring hormones), the test has given negative results (Rinkus and Legator, 1979; Ames and McCann, 1981; Purchase, 1982~. For chemicals of all classes tested to date, its overall accuracy is 60-80% (see Table 19) (Ames, 1979; Purchase, 1982; Upton et al., in press). Other short-term tests have received less systematic evaluation than has the Salmonella test; only limited data are available on their comparative results (see Table 19~. One naturally successful combination of the two tests is the Salmonella test with the in vitro cell-transformation assay. Combinations of various short-term tests, in batteries or in tiers, have been observed to yield greater accuracy than any one of the tests alone (Bridges, 1976; Weisburger and Williams, 1981; Lave et al., 1982~. Six combinations of tests have been calculated to have predictive accuracies of 81.6-89.7% (Table 20) for a limited number of animal carcinogens and noncarcinogens (Table 21~. None of the combinations appears capable of avoiding a substantial percentage of false-positives and false-negatives. However, where a false-positive is observed, it may be suspected that the carcinogenicity of the chemical under consideration escaped detection because of deficiencies in the animal test used as a criterion. 277

OCR for page 237
TABLE 18 Correlation among Results of Salmonella/microsome Tests Performed by 12 Investigatorsa Investi- 1 2 3 4 6 7 8 9 10 11 12 13 1 1.00 0.81 0.53 0.47 0.47 0.34 1.00 0.62 0.81 0.47 0.34 0.22 1.00 0.65 0.62 0.62 0.53 0.81 0.81 1.00 0.65 0.53 0.15 1.00 0.65 0.65 0.34 0.53 0.53 0.65 0.42 0.34 0.19 4 1.00 0.33 0.53 0.47 0.47 0.62 0.65 0.26 0.15 6 1.00 0.53 0.47 0.47 0.62 0.35 0.26 0.03 7 1.00 0.34 0.34 0.53 0.46 0.30 0.05 8 1.00 0.62 0.81 0.47 0.34 0.22 9 1.00 0.81 0.47 0.34 0.07 10 11 12 13 a From Lave et al., 1982. 1.00 0.65 0.53 0.15 1.00 0.46 0.02 1.00 0.21 1.00 Entries are squared correlation coefficients computed from test results on 19 chemicals reported to International Collaborative Program by 12 investigators. 1.00 indicates complete agreement and O indicates no agreement between investigators. 278

OCR for page 237
TABLE 19 Predictive Accuracy of Various Short-Term Tests for Carcinogenicitya Test Accuracy of Test Resultsb Code Number Type of Test N Accuracy Bacterial Mutation Assays 1 S. typhimurium/plate 37 0.70 2 S. typhimurium/plate 37 0.68 3 S. tYphimurium/plate 36 0.59 4 S. typhimurium/plate 38 0.71 5 S. typhimurium/fluctuation 31 0.71 6 S. typhimurium/plate 38 0.63 7 S. typhimurium/plate 33 0.64 8 S. typhimurium/plate 38 0.68 9 S. typhimurium/plate 37 0.57 10 S. typhimurium/plate 37 0.65 11 S. typhimurium/plate 37 0.73 12 S. typhimurium/plate 38 0.68 13 S. typhimurium/plate 38 0.71 14 S. typhimurium/plate 28 0.57 norharman 15 S. typhimurium/ 33 0.55 f luctuation 16 S. tYphimurium/ 38 0. 66 azaguanine res 17 S. typhimurium/E. cold 36 0.67 W2/f luctuation hepatocytes 18 S. typhimurium/E. cold 36 0.78 WP2/plate 19 E. cold WP/2/plate 34 0.65 20 E. cold 343 18 0.72 Bacterial Repair, Phage Tnduction, Degranulation, and Nuclear Enlargement Assays 21 B. subtilis, M45 rec- 38 0.79 22 Ee coli, RecA/PolA 34 0.59 23 Ee coli, RecA- 37 Oe62 24 E e coli, RecA-/PolA 36 0.58 25 E. coli, Pol 36 0.58 26 X-induction (gal+) 20 0. 65 27 X-induction (lysis) 36 0.56 279

OCR for page 237
TABLE 19 (continued) Test Accuracy of Test Resultsb Code Number Type of Test _ Accuracy 28 Degranulation of RER 33 0.39 29 Degranulation of RER 30 0.67 ribonuclease post treatment 30 Nuclear enlargement, 22 0.32 He La cells 31 Nuclear enlargement, 22 0.64 human fibroblasts Yeast Assays 32 S. cerevisiae XV185-14C 30 0.70 l 33 S. pombe PI 29 0.66 34 S. cerevisiae PG 148 35 0.43 PG-154 PG-155 PG-166 35 S. cerevisiae D4 31 0.42 _ , 36 _. cerevisiae D6 37 0.65 37 S. cerevisiae D7 35 0.66 38 S. cerevisiae JD1 32 0.72 39 S. cerevisiae red 32 0.72 In Vitro Mammalian Test Systems 40 Unscheduled DNA synthesis, 18 0.39 human fibroblasts 41 Unscheduled DNA synthesis, 21 0.62 human fibroblasts 42 Unscheduled DNA synthesis, 38 0.68 human fibroblasts 43 Sister chromatic exchange, 19 0.53 CHO cells 44 Sister chromatic exchange, 18 0.67 CHO cells 45 Sister chromatic exchange, 33 O.S2 CHO cells 46 Cytogenetic analysis, 21 0.57 micronucleus test 47 Cytogenet~c analysis, 18 0.72 micronucleus test 48 Forward-mutation assay, 19 0.53 mouse lymphoma cells L518Y 280

OCR for page 237
TABLE 19 (continued) Test Code Number Accuracy of Test Resultsb Type of Test N - AccuracY 49 Gene-mutation assay, CHO 9 0.44 cells, HGPRT gene 50 Gene-mutation assay, CHO 3 0.67 cells, HGPRT gene 51 Gene-mutation assay, 5 0.60 V79 hamster cells 53 Cell transformation 34 0.59 54 Cell transformation, 38 0.82 BKH-21 cells In Vivo Assays 56 Sex-linked recessive lethal 9 0.44 Droposphila 57 Sex-linked recessive lethal 15 0.53 58 Sex-linked recessive lethal 9 0.44 59 Sister chromatic exchange, 16 0.63 mouse 60 Micronucleus assay, mouse 29 0.66 61 Micronucleus assay, mouse 17 0.47 62 Micronucleus assay, mouse 33 0.45 63 Sperm morphology, mouse 15 0.47 aFrom Lave _ al., 1982. bFigures tabulated indicate accuracy (number of chemicals correctly identified divided by number of chemicals tested) for tests 1 through 63. Data subset includes all 42 chemicals listed in Table 21 except presumptive noncarcinogens 2, 16, 20, 22, and 27, which had high frequencies of positive results in international study (de Serres and Ashby, 19817. Diphenylnitrosamine (22) was found to be carcinogenic. 281

OCR for page 237
TABLE 20 Predictive Accuracy of Several Short-Term Tier Testing Regimensa Tier Number Tests Included in Tierb No. of Chemicals on Which Results Predic tive are Based Accuracy, - typhimurium/plate (4) Differential killing, B. subtilis M45 Rec~ (21) Cell transformation, BHK-21 cells (54) S. typhimurium/plate (4) Cell transformation, BHK-21 cells (54) Forward mutation, S. Bombs (33) S . typhimur ium/plate (4) - Forward mutation, S. pombe (33) Cell transformation, BHK-21 cells (54) Unscheduled DNA synthesis, HeLa cells (42) 4 typhimurium/plate (4) Unscheduled DNA synthesis, HeLa cells (42) Cell transformation, BHK-21 cells (54) S. typhimurium/plate (4) Cell transformation, BHK-21 cells (54) Rabin's test (degranulation) rat liver cells (29) _. tyPhimurium/plate (4) Unscheduled DNA synthesis, HeLa cells {42) Cell transformation, BHK-21 cells (54) 38 29 29 38 30 38 84.2 86.2 89.7 81.6 83.3 81.6 . a From Lave et al., 1982. ~ _ _ Numbers in parentheses are code numbers of tests (Table 19~. 282

OCR for page 237
TABLE 21 Chemicals Analyzed by Short-Term Tests (International Collaborative Study~a Chemical Carcinogenicity Class if icationb 2 3 4 5 6 7 8 9 B-Propiolactone 10 Y-Butyrolactone 11 9,10-Dimethylanthracene 12 Anthracene 13 Chloroform 14 1, 1,1-Trichloroethane 15 2-Acetylaminofluorene 16 4-Acetylaminofluorene 17 Dimethylcarbamoyl chloride 18 Dimethylformamide 19 2-Naphthylamine 20 1-Naphthylamine 21 N-Nitrosomorpholine 22 Diphenyloitrosamine 23 Dinitrosopentamethylene tetramine 24 Urethane 25 Isooroovl N-(3-chloronhenYl)carbamate 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 4-Nitroquinoline-N-oxide 3-Methyl-4-nitroquinoline-N-oxide Benzidine 3,3',5,5'-Tetramethylbenzidine 4-Dimethylaminoazobenzenetbutter yellow) 4-Dimethylaminoazobenzene-4-sulfonic acid, Na salt Benzo~alpyrene Pyrene _ , _ ,, ~ , ~, _, Methylazoxymethanol acetate Azoxybenzene DL-Ethionine Methionine Hydrazine sulfate Hexamethylphosphoramide (HMPA) Ethylenethiourea Diethylstilbestrol Safrole Cyclophosphamide Epichlorhydrin 3-Aminotriazole 4,4'-Methylenebis(2-chloroaniline) (MOCA) o-Toluidine hydrochloride Auramine (technical grade) Sugar (sucrose) Ascorbic acid - + + - - + + + + + + + + + + + + + - a From de Serres and Ashby, 1981. b Based on effects in human populations or experimental animals. 283

OCR for page 237
Other approaches being emphasized in current research efforts include those for identifying nongenotoxic carcinogens, including cocarcinogens and tumor-promoting agents (Sivak, 1982; Upton, in press), and the exploitation of in viva short-term bioassays (Ashby, in press). It may be envisioned that the test systems of the future will include various assays in addition to those represented in Table 19. It may also be expected that the particular combinations of tests used and the sequences in which they are used will depend on the nature of the chemicals being tested and their patterns of use. Whether a tier regimen culminates in a long-term bioassay in rats and mice will depend on the results of the antecedent short-term tests and their apparent predictive accuracy. The principles involved in setting priorities for carcinogenicity testing apply also to the setting of priorities for testing chemicals suspected of other forms of toxicity. It must be recognized that the process of predicting the toxic potential of a chemical is extremely complex. There are no firm rules, or even guidelines, for obtaining reliable answers. Attention is generally focused on devising animal models or tests for human health effects with attributable chemical causes. Thus, in the best circumstances, it may be possible to predict the biologic activity of a given chemical, identify a useful end point with an established animal model or test, and draw some tentative conclusion regarding the chemical's likely toxicity in humans. Results of toxicity tests on animals have been recorded in RTECS, a machine-readable file of approximately 15,000 chemicals, although the universe of chemicals defined by the Committee on Sampling Strategies contains over 70,000 chemicals. Even information on well-characterized chemicals is often limited to data on molecular structure and physical characteristics. A chemical may have several effects on human health that are individually dose-dependent. To set testing priorities among different types of toxic effects, two tasks must be accomplished: a catalog of human health effects must be established, and the various effects must be ranked according to relative severity. The relative importance of different types of health effects depends on their consequences to the affected persons and to society. Ranking of effects according to severity is implicit in most priority-setting schemes. It involves not only technical judgments of toxicity, but also individual perceptions of harm. An approach to determining the relative severity of toxic effects is described in Chapter 2. When a chemical is suspected of causing more than one toxic effect, which is often the case, the combined impacts of all its potential effects must be taken into account in setting priority for its testing. Because of the multiplicity, diversity, and complexity of the health effects of different chemicals, no attempt is made here to develop a detailed or comprehensive system for this purpose. 284