Fanny K. Ennever

The ability of plant genotoxicity assays to predict carcinogenicity.

A number of assays have been developed which use higher plants for measuring mutagenic or cytogenetic effects of chemicals, as an indication of carcinogenicity. Plant assays require less extensive equipment, materials and personnel than most other genotoxicity tests, which is a potential advantage, particularly in less developed parts of the world. We have analyzed data on 9 plant genotoxicity assays evaluated by the Gene-Tox program of the U.S. Environmental Protection Agency, using methodologies we have recently developed to assess the capability of assays to predict carcinogenicity and carcinogenic potency. All 9 of the plant assays appear to have high sensitivity (few false negatives). Specificity (rate of true negatives) was more difficult to evaluate because of limited testing on non-carcinogens; however, available data indicate that only the Arabidopsis mutagenicity (ArM) test appears to have high specificity. Based upon their high sensitivity, plant genotoxicity tests are most appropriate for a risk-averse testing program, because although many false positives will be generated, the relatively few negative results will be quite reliable.

Evaluating the potential for genotoxic carcinogenicity of methyl isocyanate

The carcinogenicity prediction and battery selection method was used to predict the probability of carcinogenicity of methyl isocyanate (MIC) based upon the results of short-term tests. The analysis predicts that MIC has a significant potential for inducing cancer in rodents. However, the pattern of response suggests that the carcinogenic potency would be low. Obviously, the realization of the identified risk would be dependent upon level, duration, and mode of exposure.

Invited contribution: An objective approach to the development of short-term tests predictive of carcinogenicity

The Carcinogenicity Prediction and Battery Selection procedure was developed to address two problems: (1) the identification of highly predictive, yet cost-effective, batteries of short-term tests and (2) the objective prediction of the potential carcinogenicity of chemicals based upon the results of short-term tests even when a mixture of positive and negative results is obtained. In the present report the usefulness of the Carcinogenicity Prediction and Battery Selection procedure is demonstrated using benzo[a]pyrene, benzoin and diethylstilbestrol as examples. In addition, its applicability in the analysis of all the possible outcomes of a battery is illustrated together with an analysis of the worth of additional testing.

An association between mutagenicity and carcinogenic potency

A comparison between mutagenic and non-mutagenic rodent carcinogens studied by the U.S. National Toxicology Program revealed that as a group, rat carcinogens mutagenic in Salmonella typhimurium are more potent than their non-mutagenic counterparts.

Quantifying genotoxicity and non-genotoxicity

Since the ability to induce genotoxicity is often equated with the potential for initiating the carcinogenic process, a method for quantitating genotoxicity would provide a useful measure for this potential. It is demonstrated herein that CPBS, the Carcinogenicity Prediction and Battery Selection method, provides a useful quantitative measure of genotoxicity as well as allowing for the detailed evaluation of the performance of batteries of short-term tests in order to select those predictive of carcinogenic potential.

Methodologies for Interpretation of Short-Term Test Results which may Allow Reduction in the use of Animals in Carcinogenicity Testing

Assessment of the risk to humans posed by chemical substances currently relies primarily on experimental exposure of animals in lifetime feeding studies. Short-term tests for genotoxicity are much less costly and use fewer or no animals, but have not replaced the long-term animal bioassay because their results do not coincide completely. We have developed methodologies for interpretation of short-term tests which improve the usefulness of their results, and may allow them to replace the long-term animal bioassay in some circumstances.

Fundamental Basics of the CPBS Approach

In this chapter we will examine four basic methodologies and decision tools that are utilized in the CPBS approach to decision making. The first is Bayesian decision analysis, which forms the heart of the CPBS approach. Tests and measurements that are used to identify or detect a property of interest are generally not perfect. When tests are biased or inaccurate, it is often advantageous to use more than one test. The interpretation of a combination of test results can be problematic because there often exists a variable amount of information overlap (positive dependence) and differences (negative dependence) among the tests. It is a difficult problem to account for both the imperfection of the individual tests as well as their interdependencies in their joint interpretation.

Applications of CPBS to Cancer Hazard Identification

The field of genetic toxicology finds itself at a crossroads. On the one hand, the premise of the somatic mutation theory of cancer, which provides a scientific basis for the development of short-term tests for predicting cancers, has been amply vindicated by the discovery of oncogene activation. On the other hand, however, recent NTP-sponsored studies have cast doubt upon the performance of short-term tests as predictors of carcinogenicity (Tennant et al., 1987). Analysis of the NTP results by the CPBS shows that this is an incorrect conclusion resulting from an oversimplification (Rosenkranz and Ennever, 1988a). Also, it appears that we have no choice but to continue using short-term tests since the other alternatives are (a) not to test but to wait for untoward effects in our exposed human population and (b) to continue relying solely on animal bioassays.

The Carcinogenicity Prediction and Battery Selection Approach

Decisions are most often based upon the results of experiments coupled with the knowledge of experts. When sampling or experimental results are available, they often constitute the major factualinformation input into the decision-making process. For example, clinicians and physicians use diagnostic tests and clinical findings along with their expert knowledge to diagnose their patients’ problems. When the physician estimates that the “risk” of a disease is high enough (here we define “risk” as both the probability and the severity), expert knowledge is again used to develop appropriate treatment plans. Toxicologists (in industry, government, and academia) use test results on live animals as well as short-term in vitro tests to study the carcinogenic potential of chemicals. If the risk of carcinogenicity for a particular chemical is high, then a pharmaceutical or chemical company may decide to stop or delay the development of the chemical, a regulatory agency may decide to ban the development or restrict the use of such a chemical, or a researcher in academia may decide to study this chemical further to examine its modes of action. In industry, quality control managers interpret results from multiple “inspectors” (humans or machines) to identify defective parts and to decide whether a defective part should be destroyed or sent to a rework station. Water resources and environmental engineers utilize results from well sampling to decide what type of action is warranted on an aquifer found to be contaminated.

Risk Assessment Using Test Results

Consider the situation in which we would like to determine whether some object has a certain property. For example, the object might be a chemical and the unknown property might be the potential carcinogenicity of the chemical. Suppose, further, that we have a set of tests that we can use to help us determine whether the property is present in the object. In Chapter 3 we discussed several analyses for computing the performances of the tests and the interdependencies between the pairs of tests, and in Chapter 4 we discussed how one can select the “best” battery of tests to use for a particular application. In this chapter, it is assumed that we have chosen a battery to use, we have applied this battery on the object, and now we must interpret the results of this battery.