The Benchmark Dose Method—Review of Available Models, and Recommendations for Application in Health Risk Assessment

Abstract

The benchmark dose method has been proposed as an alternative to the no-observed-adverse-effect level (NOAEL) approach for assessing noncancer risks associated with hazardous compounds. The benchmark dose method is a more powerful statistical tool than the traditional NOAEL approach and represents a step in the right direction for a more accurate risk assessment. The benchmark dose method involves fitting a mathematical model to all the dose-response data within a study, and thus more biological information is incorporated in the resulting estimates of guidance values (e.g., acceptable daily intakes, ADIs). Although there is an increasing interest in the benchmark dose approach, it has not yet found its way into the regulatory toxicology in Europe, while in the United States the U.S. Environmental Protection Agency (EPA) already uses the benchmark dose in health risk assessment. Several software packages are today available for benchmark dose calculations. The availability of software to facilitate the analysis can make modeling appear simple, but often the interpretation of the results is not trivial, and it is recommended that benchmark dose modeling be performed in collaboration with a toxicologist and someone familiar with this type of statistical analysis. The procedure does not replace expert judgments of toxicologists and others addressing the hazard characterization issues in risk assessment. The aim of this article is to make risk assessors familiar with the concept, to show how the method can be used, and to describe some possibilities, limitations, and extensions of the benchmark dose approach. In this article the benchmark dose approach is presented in detail and compared to the traditional NOAEL approach. Statistical methods essential for the benchmark dose method are presented in Appendix A, and different mathematical models used in the U.S. EPA's BMD software, the Crump software, and the Kalliomaa software are described in the text and in Appendix B. For replacement of NOAEL in health risk assessment it is considered important that consensus is reached on the crucial parts of the benchmark dose method, that is, selection of risk types and the determination of a response level corresponding to the BMD, especially for continuous data. It is suggested that the BMD method is used as a first choice and that in cases where it is not possible to fit a model to the data the traditional NOAEL approach should be used instead. The possibilities to make benchmark dose calculations on continuous data need to be further investigated. In addition, it is of importance to study whether it would be appropriate to increase the number of dose levels by decreasing the number of animals in each dose group.

A validated algorithm for selecting non-toxic chemical concentrations_suppl1

“…While the NOEC and ECx are still the most often used measures for ecotoxicological purposes, the benchmark dose (BMD) is frequently used in human toxicology and health risk assessment (Allen et al, 1994;Filipsson et al, 2003). However, this…”

Some New Aspects of Statistical Inference for Multistage Dose-Response Models with Applications

“…A variety of models are in use (Krewski and The benchmark dose method (Crump 1984, Filipsson et al 2003, Parham and Portier 2005 consists of estimating a lower confidence limit for the dose associated with a specified increase  in adverse response (i.e., increased risk) above the background level. In practice, the specified increase is typically 1% to 10% for cancer quantal response models (Filipsson et al 2003, Parham andPortier 2005).…”

“…The increase in risk above background for a quantal response is quantified as "extra risk" or "additional risk" (Filipsson et al 2003). These quantities are defined below.…”

“…This is explicitly presented in Kopylev and Sinha (2011). space, note that all we need to compute are the restricted MLEs of 1  , 2  and 3  , subject to their being non-negative. This is derived as follows.…”

“…We should note that for statistical inference about ( *) ER d and BMD which involve the parameters 1  and 2  , all we would need is the asymptotic joint distribution of the relevant MLEs. Using Prop.…”