Julia Pet-Edwards

Application of the carcinogenicity prediction and battery selection (CPBS) method to the Gene-Tox data base.

The carcinogenicity prediction and battery selection (CPBS) method (Chankong et al., 1985) utilizes the results of short-term tests to predict the carcinogenicity of chemicals and select batteries of tests that are capable of giving accurate predictions at reasonable costs. The CPBS method has been applied to the data compiled under the aegis of the Gene-Tox Program of the U.S. Environmental Protection Agency as a demonstration of the method on a typical data base. A number of batteries were selected by the methodology as having superior performance characteristics. The Bayesian predictions resulting from most of the selected 3-assay batteries were very good (greater than 90% of the carcinogens were correctly identified). It was also found that the 3-assay batteries of specified composition gave generally more accurate predictions than batteries of 4 or more assays of unspecified composition. A number of problems which may have affected our results have been identified: (1) the reliability of the sensitivities and specificities of the individual assays, (2) the prior probability that a chemical is a carcinogen was assumed to be 0.5, and (3) we have not (as yet) taken into account that some of the carcinogens are non-genotoxic and will produce false negative assays results. We are currently investigating approaches to take these factors into consideration. Our analysis also indicates that more testing of chemicals for carcinogenicity (especially probable non-carcinogens) is needed to further enhance the predictive capability of the CPBS method.

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.

Conditional expectations in the evaluation of fault-tolerant systems

The concept of conditional expectations for comparing fault-tolerant designs is introduced. Specifically, the traditional mean time to failure (MTTF) is shown to be one case of a more general conditional expectation function, E(T/sub f/ mod t/sub l/<or=T/sub f/<or=t/sub 2/), defined as the expected time of failure T/sub f/, given that t/sub l/<or=T/sub f/<or=t/sub 2/ for time t. Two examples are considered in detail to provide an illustration of the use and importance of conditional expectations.<<ETX>>

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.