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Biomedical subjects

R C Hertzberg

Publications and source records attributed to R C Hertzberg.

8 recordsLinked to original sources
PubMed1995

Current and future risk assessment guidelines, policy, and methods development for chemical mixtures.

Humans are typically exposed to low doses of combinations of chemicals rather than to one or two chemicals at a time, yet most of the available toxicity data provide information on single chemicals or binary pairs, rather than on whole mixtures. The use of existing interactions study data for the quantitative risk assessment of chemical mixtures is problematic. These studies generally lack the necessary statistical characterizations to be useful in quantitative risk assessment procedures. The U.S. EPA developed guidelines for risk assessment for chemical mixtures in 1986 and is currently in the process of making revisions. Significant advances have been made in both the theoretical development and application of procedures such as dose addition, response addition, toxicity equivalence factors, comparative potency and interactions data characterizations. Details on the current revisions to the guidelines are given, along with information on the research efforts that have influenced these revisions or that represent future directions in chemical mixtures risk assessment.

Drug Interactions↗
PubMed1995

Upper bound risk estimates for mixtures of carcinogens.

The excess cancer risk that might result from exposure to a mixture of chemical carcinogens usually is estimated with data from experiments conducted on individual chemicals. An upper bound on the total excess risk is estimated commonly by summing individual upper bound risk estimates. The degree to which this approach might overstate the true risk associated with the mixture has not been evaluated previously. This paper reports the results of a Monte Carlo simulation study on the degree of reduction in conservation that might be achieved using alternative methods for calculating mixture upper bounds. An unexpected finding is that for chemicals that exhibit strongly linear dose-response relationships, the summing of multistage-model-based upper bounds on excess risk can be anti-conservative, that is, it can provide less than the nominal 100(1-alpha)% coverage.

Animals↗
PubMed1995

Chemical mixtures from a public health perspective: the importance of research for informed decision making.

When considered from a public health perspective, the central question regarding chemical mixtures is deceptively simple: Are current approaches to risk assessment for chemical mixtures affording effective (adequate) and efficient (cost-effective) protection for members of our society? Answering this question realistically depends on an understanding of the hierarchical goals of public health (i.e. prevention, intervention, treatment) and an accurate evaluation of the extent to which these goals are being achieved. To allow decision makers to make informed judgments about the health risks of chemical mixtures, adequate scientific knowledge and understanding must be available to support risk assessment activities, which are an integral part of the regulatory decision making process. Designing and implementing relevant research depends on the existence of a feedback loop between researchers and regulators, where the information needs of regulators influence the nature and direction of research and the information and understanding generated by researchers improves the scientific basis for public health decisions. A clear, consistent, commonly accepted taxonomy for describing important mixture-related phenomena is a key factor in creating and maintaining the necessary feedback loop. Ultimately, both researchers and regulators share a common goal with regard to chemical mixtures; improving the state-of-the-science so that we can make informed decisions about protecting public health. A survey of research issues and needs that are crucial to attaining this goal is presented.

Decision Making↗
PubMed1993

A statistical test of compatibility of data sets to a common dose-response model.

Quantitative estimates of cancer risk generally involve low-dose extrapolation based on an exponential dose-response model for dichotomous response data. Frequently more than one data set is available. If a careful analysis of the biological issues indicates that more than one of the available data sets could be used in the quantitative estimate of cancer risk, it is reasonable to think of combining the data. Before combining data, however, it would be prudent to test whether the data sets are compatible with a common dose-response model. If they are not, it could be concluded that an underlying biological factor is responsible. If they are statistically compatible, the decision to combine data sets based on biological issues would be reinforced. A statistical test based on the generalized likelihood ratio method is proposed for evaluating the compatibility of different data sets with a common dose-response model. This method of constructing a statistical test and the associated asymptotic theory is consistent with the approach used by GLOBAL86 (R. B. Howe, K. S. Crump, and C. Van Landingham, GLOBAL86: A Computer Program to Extrapolate Quantal Animal Toxicity Data to Low Doses, K. S. Crump & Co., Ruston, LA, 1986) for estimating the confidence limits that are used as a basis for quantitative estimates.

Animals↗
PubMed1989

Fitting a model to categorical response data with application to species extrapolation of toxicity.

The evaluation of toxicity data for noncarcinogens is complicated by the multiplicity of possible end points, and variations in both severity of effect and response rate. Often the response rates are not reported, so that "dose-response" analysis involves the relation between dose and severity of effect. Severity is usually reported as a description of the nature of the effects; measured values are rare. One approach is to then assign severity descriptions to ordered categories and to model the dose-category relationship. The application presented here is to interspecies scaling, i.e., the estimation of parameters in the model used to convert animal doses into equally toxic human doses.

Animals↗
PubMed1985

Novel methods for the estimation of acceptable daily intake.

This paper describes two general methods for estimating ADIs that circumvent some of the limitations inherent in current approaches. The first method is based on a graphic presentation of toxicity data and is also shown to be useful for estimating acceptable intakes for durations of toxicant exposure other than the entire lifetime. The second method uses dose-response or dose-effect data to calculate lower CLs on the dose rate associated with specified response or effect levels. These approaches should lead to firmer, better established ADIs through increased use of the entire spectrum of toxicity data.

Animals↗
PubMed1985

A statistical model for species extrapolation using categorical response data.

Predictions of human health risk for single chemicals are often based on animal studies and hence require some sort of adjustment for species differences in toxic susceptibility. In the past, either the animal dose has been divided by an uncertainty factor or the dose has been transformed by a mathematical model into a human equivalent dose. A generalization of the allometric model previously used for carcinogens, the so-called "surface area model," is investigated here for use with graded severity response data for noncarcinogenic systemic toxicity. Statistical methods for estimating one of the model's parameters, the power of body weight, are proposed and tested on simulated and actual toxicity data. Early results indicate reasonable accuracy if data are available for a large number of dose groups.

Animals↗