Health-effects related structure–toxicity relationships: a paradigm for the first decade of the new millennium

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Abstract

Prediction of the effects of industrial chemicals to humans will be an area of increasing concern in the next century. The role of quantitative structure–activity relationships (QSARs) is to aid in the prediction of effects by determination of the limits of variation in structure that are consistent with the production of a specific toxic effect and define the ways in which alterations in structure influences toxicity. The paradigm followed in the development of QSARs for ecotoxic-endpoints has been successful as a direct result of the availability of in vivo toxicity data in which to build initial QSARs and validate surrogate test systems. However, the lack of quantitative in vivo toxicity data means this paradigm cannot be used in the prediction of human health-effect endpoints. Therefore, a new paradigm, which provides guidance in the use of predictive QSARs for health-effects that serves to circumvent the problems associated with the lack of whole-mammal toxicity data must be established. A scenario is given that provides for the development, standardization, and validation of health-effects related QSARs in the first decade of the 21st century. Due to the structural diversity and sheer number of industrial organic chemicals, assays used to garner health-effects data must be based on quantifiable, rapid, reliable, and inexpensive surrogate endpoints. New ‘biotools’ developed using modern molecular techniques will aid in the circumvention of in vivo health-effects data and provide measurable endpoints, which can be used as surrogates for health-effects endpoints. This has particular application in receptor-mediated toxicity and gene expression. The lack of whole-mammal health-effects data means that the standardization and validation, of these endpoints will be accomplished in novel ways. Databases garnered using modern biotools will allow the derivation of QSARs developed for validated surrogate health-effect endpoints. If QSARs are to be useful in bridging gaps in health-effects data, they must be based on accurate, reproducible data for a robust series of non-congeneric chemicals. The latter applies to both toxicity and descriptor values. Because health-effects are often the result of metabolic activation, if these new QSARs are to be meaningful, validated software for predicting metabolite formation must be incorporated. Lastly, once QSARs and software are developed and validated, they will need to be linked into some type of expert system.

Introduction

As we progress into the next millennium, we have seen concern in the area of chemical toxicants in the environment moving from one of remediation, to one of prevention. The dangerous ‘solution by dilution’ in response to chemical spills of the 20th century led to the passage in the US of the Clean Air Act of 1970, Clear Water Act of 1972, and Toxic Substances Control Act of 1976. These acts of the United States Congress, especially the latter, were the pivotal force behind modern day structure–toxicity relationships. Later the Resource Conservation and Recovery Act of 1976 and Comprehensive Environmental Response, Compensation and Liability Act (i.e. Superfund) of 1980 served to center dogma concerning hazard waste management, hazardous substances in the environment and hazardous waste clean-up. Due to the considerable cost involved in remediation of contaminated sites and the realization that there is a continued need and demand for the usage of chemicals, both industry and government have turned from dealing with potential problems at the ‘end of the pipe’ toward approaches that are more economical. These include prevention and a better understanding of potential ecotoxicological and human health-effects prior to release of chemicals into the environment. Methods were sought which would predict toxic effects before the fact, rather than concerted efforts to assess effects after chemicals had been released or, indeed, manufactured.

Since it is not possible to eliminate the use of chemicals, techniques such as risk assessment and life cycle analysis are being developed to aid making scientifically-based decisions about chemicals and their uses. It is envisioned that as we advance into the 21st century, issues such as ‘clean technology’ and ‘environmental friendly’ products will become more critical and a priori assessment techniques will move rapidly from the conceptual to application phase.

The applications of decision-assisting techniques will require the assessment of human health-effects. Such data are central to scoring or ranking systems currently being examined (Swanson et al., 1997). While it is immediately evident that there is the need for toxicological data that would provide information regarding the health-effects of chemicals, an examination of existing data reveal it to be scattered, fragmentary and more often qualitative. Due to the high number of chemicals produced worldwide, gaps in toxicological assessment of various human health-effects can be expected to be a continuing dilemma. Moreover, these data gaps can be a critical problem and major hindrance to a priori predictions of the effects a substance will elicit.

Of course, the data gaps can be addressed partly by more toxicity testing. However, it is clear that the lack of knowledge of direct and indirect toxicological effects will force the development of approaches that provide a rational, rapid, and inexpensive means of accurately predicting such effects. Among these approaches are techniques that predict toxicological hazards from chemical structure (i.e. structure–toxicity relationships).

Great strides have been made in the modeling of acute toxicity, especially in aquatic systems. However, our current ability to model chronic and other health-effects is woefully inadequate. Unfortunately, the relative success of ecotoxicological toxicity QSARs does not provide an example to follow in assessment of human health-effects. Specifically, whole-animal toxicity assays with endpoints that are quantifiable and easily ascertained are not available. Moreover, standardized and validated surrogate systems along with the quality databases need for modeling are just now being developed. However, if quantitative structure–toxicity relationships are going to be truly meaningful instruments with which to address gaps in effects data, we must devise high quality QSARs for health-effect endpoints.

Toxicity endpoint selection will be perhaps the most critical aspect of health-related QSAR development. Due to problems with chronic in vivo testing, assessment endpoints will be different from measurement endpoints. Assessment endpoints need to be appropriate and relevant and should accurately characterize health-effects. They include; carcinogenesis, mutagenesis, neural and behavioral toxicity, as well as developmental and reproductive toxicities.

Measurement endpoints are measurable factors that respond to the stressor and describe or measure characteristics that are classified by the assessment endpoint (Suter, 1990). Measured endpoints will be the end products of new ‘biotools’. These biotools will be based on receptor-mediated responses and include induction of gene expression.

The design and selection of measurement endpoints will be based on relevance to assessment-endpoint measurement, sensitivity and response time, diagnostic ability, practicality, signal-to-noise ratio, and consistency with assessment-endpoint exposure scenarios. Of these, relevance and consistency are the most important. The relevance of the measurement endpoint is the degree to which it can be associated to the assessment endpoint under consideration. Consistency simply means that the measurement endpoint is exposed to the stressor in a manner similar to that of the assessment endpoint.

Section snippets

Quantitative structure–activity relationships (QSARs)

Toxicological-based quantitative structure–activity relationships (QSARs) are mathematical models that correlate molecular structure to biological activity (e.g. toxic potency). The QSAR approach is based on the assumption that the structure of a molecule (i.e. its geometric and electronic properties) contains the features responsible for its physical, chemical, and biological properties. It is assumed that toxicity of substances is governed by it’s properties. In turn, properties are

The health-effects QSAR paradigm

While there is a lack of quality dose–response in vivo data, it does not mean that progress in the implementation of QSARs for chronic and other health-effect endpoints is not possible. It only means that the pattern developed for acute toxicity (Table 1) cannot be followed. We envision a scenario similar to that outlined in Table 2 will be the template for the development, standardization, and validation of health-effects related QSARs in the first decade of the new millennium. The usefulness

A glimpse of health-effects related to structure–toxicity relationships in the first decade of the new millennium

Several examples of the usefulness of gene expression assays in the development of structure–toxicity relationships exist, especially as they relate to endocrine disruption. While mimicry is the most often stated, there are five mechanisms of action by which an endocrine disrupter may elicit its effect. These mechanisms include binding to the cellular receptor designed for endogenous hormones, blocking of binding sites so as endogenous hormones are unable to bind, creation of extra receptors,

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