Guidance for the classification of carcinogens under the Globally Harmonised System of Classification and Labelling of Chemicals (GHS)

The United Nations Conference on Environment and Development (UNCED) has developed criteria for a globally harmonised system of classification and labelling of chemicals (GHS). With regard to carcinogenicity, GHS distinguishes between Category 1 ('known or presumed human carcinogens') and Category 2 ('suspected human carcinogens'). Category 1 carcinogens are divided into Category 1A ('known to have carcinogenic potential for humans'), based largely on human evidence, and 1B ('presumed to have carcinogenic potential for humans'), based largely on experimental animal data. Concerns have been raised that the criteria for applying these carcinogenicity classifications are not sufficiently well defined and potentially allow different conclusions to be drawn. The current document describes an attempt to reduce the potential for diverse conclusions resulting from the GHS classification system through the application of a series of questions during the evaluation of data from experiments with rodents; epidemiological data, which could lead to Category 1A, have not been considered. Answers to each question can lead either to a classification decision or to the next question, but this process should only be implemented in an environment of informed scientific opinion. The scheme is illustrated with five case studies. These questions are: (1) Has a relevant form of the substance been tested? (2) Is the study design relevant to human exposure? (3) Is there a substance-related response? (4) Is the target tissue exposure relevant to humans? (5) Can a mode of action be established? (6) Is the mode of action relevant to humans? (7) What is the potency?

Bayesian analysis of mutational spectra

Studies that examine both the frequency of gene mutation and the pattern or spectrum of mutational changes can be used to identify chemical mutagens and to explore the molecular mechanisms of mutagenesis. In this article, we propose a Bayesian hierarchical modeling approach for the analysis of mutational spectra. We assume that the total number of independent mutations and the numbers of mutations falling into different response categories, defined by location within a gene and/or type of alteration, follow binomial and multinomial sampling distributions, respectively. We use prior distributions to summarize past information about the overall mutation frequency and the probabilities corresponding to the different mutational categories. These priors can be chosen on the basis of data from previous studies using an approach that accounts for heterogeneity among studies. Inferences about the overall mutation frequency, the proportions of mutations in each response category, and the category-specific mutation frequencies can be based on posterior distributions, which incorporate past and current data on the mutant frequency and on DNA sequence alterations. Methods are described for comparing groups and for assessing dose-related trends. We illustrate our approach using data from the literature.

From quantal counts to mechanisms and systems: the past, present, and future of biometrics in environmental toxicology

As appreciation for human impact on the environment has developed, so have the experimental systems and associated statistical tools that quantify this impact. Toxicological study in particular has grown in its complexity and its need for advanced statistical support. Within this perspective, we describe statistical practice in environmental toxicology and risk assessment. We present two case studies, one from mammalian toxicology and one from aquatic toxicology, that highlight the evolution of statistical practice in environmental toxicology.