“Chemophobia” Today: Consumers’ Knowledge and Perceptions of Chemicals

This mixed‐methods study investigated consumers’ knowledge of chemicals in terms of basic principles of toxicology and then related this knowledge, in addition to other factors, to their fear of chemical substances (i.e., chemophobia). Both qualitative interviews and a large‐scale online survey were conducted in the German‐speaking part of Switzerland. A Mokken scale was developed to measure laypeople's toxicological knowledge. The results indicate that most laypeople are unaware of the similarities between natural and synthetic chemicals in terms of certain toxicological principles. Furthermore, their associations with the term “chemical substances” and the self‐reported affect prompted by these associations are mostly negative. The results also suggest that knowledge of basic principles of toxicology, self‐reported affect evoked by the term “chemical substances,” risk‐benefit perceptions concerning synthetic chemicals, and trust in regulation processes are all negatively associated with chemophobia, while general health concerns are positively related to chemophobia. Thus, to enhance informed consumer decisionmaking, it might be necessary to tackle the stigmatization of the term “chemical substances” as well as address and clarify prevalent misconceptions.     

Quantal Risk Assessment Database: A Database for Exploring Patterns in Quantal Dose‐Response Data in Risk Assessment and its Application to Develop Priors for Bayesian Dose‐Response Analysis

Quantitative risk assessments for physical, chemical, biological, occupational, or environmental agents rely on scientific studies to support their conclusions. These studies often include relatively few observations, and, as a result, models used to characterize the risk may include large amounts of uncertainty. The motivation, development, and assessment of new methods for risk assessment is facilitated by the availability of a set of experimental studies that span a range of dose‐response patterns that are observed in practice. We describe construction of such a historical database focusing on quantal data in chemical risk assessment, and we employ this database to develop priors in Bayesian analyses. The database is assembled from a variety of existing toxicological data sources and contains 733 separate quantal dose‐response data sets. As an illustration of the database's use, prior distributions for individual model parameters in Bayesian dose‐response analysis are constructed. Results indicate that including prior information based on curated historical data in quantitative risk assessments may help stabilize eventual point estimates, producing dose‐response functions that are more stable and precisely estimated. These in turn produce potency estimates that share the same benefit. We are confident that quantitative risk analysts will find many other applications and issues to explore using this database.     

Functional analysis of high-content high-throughput imaging data

High-content automated imaging platforms allow the multiplexing of several targets simultaneously to generate multi-parametric single-cell data sets over extended periods of time. Typically, standard simple measures such as mean value of all cells at every time point are calculated to summarize the temporal process, resulting in loss of time dynamics of the single cells. Multiple experiments are performed but observation time points are not necessarily identical, leading to difficulties when integrating summary measures from different experiments. We used functional data analysis to analyze continuous curve data, where the temporal process of a response variable for each single cell can be described using a smooth curve. This allows analyses to be performed on continuous functions, rather than on original discrete data points. Functional regression models were applied to determine common temporal characteristics of a set of single cell curves and random effects were employed in the models to explain variation between experiments. The aim of the multiplexing approach is to simultaneously analyze the effect of a large number of compounds in comparison to control to discriminate between their mode of action. Functional principal component analysis based on T-statistic curves for pairwise comparison to control was used to study time-dependent compound effects.     

“How Exposed Is Exposed Enough?” Lay Inferences About Chemical Exposure

The concept of exposure is central to chemical risk assessment and plays an important role in communicating to the public about the potential health risks of chemicals. Research on chemical risk perception has found some indication that the model lay people use to judge chemical exposure differs from that of toxicologists, thereby leading to different conclusions about chemical safety. This paper presents the results of a series of studies directed toward developing a model for understanding how lay people interpret the concept of chemical exposure. The results indicate that people's beliefs about chemical exposure (and its risks) are based on two broad categories of inferences. One category of inferences relates to the nature in which contact with a chemical has taken place, including the amount of a chemical involved and its potential health consequences. A second category of inferences about chemical exposure relates to the pragmatics of language interpretation, leading to beliefs about the motives and purposes behind chemical risk communication. Risk communicators are encouraged to consider how alternative models of exposure and language interpretation can lead to conflicting conclusions on the part of the public about chemical safety.     

Locally optimal designs for some dose–response models with continuous endpoints

We consider the problem of constructing static (or non sequential), approximate optimal designs for a class of dose–response models with continuous outcomes. We obtain conditions for a design being D-optimal or c-optimal. The designs are locally optimal in that they depend on the model parameters. The efficiency studies show that these designs have high efficiency when the mis-specification of the initial values of model parameters is not severe. A case study indicates that using an optimal design may result in a significant saving of resources.     

Summary of the Workshop on Issues in Risk Assessment: Quantitative Methods for Developmental Toxicology

This report summarizes the proceedings of a conference on quantitative methods for assessing the risks of developmental toxicants. The conference was planned by a subcommittee of the National Research Council's Committee on Risk Assessment Methodology ⁴ in conjunction with staff from several federal agencies, including the U.S. Environmental Protection Agency, U.S. Food and Drug Administration, U.S. Consumer Products Safety Commission, and Health and Welfare Canada. Issues discussed at the workshop included computerized techniques for hazard identification, use of human and animal data for defining risks in a clinical setting, relationships between end points in developmental toxicity testing, reference dose calculations for developmental toxicology, analysis of quantitative dose-response data, mechanisms of developmental toxicity, physiologically based pharmacokinetic models, and structure-activity relationships. Although a formal consensus was not sought, many participants favored the evolution of quantitative techniques for developmental toxicology risk assessment, including the replacement of lowest observed adverse effect levels (LOAELs) and no observed adverse effect levels (NOAELs) with the benchmark dose methodology.     

Issues in Qualitative and Quantitative Risk Analysis for Developmental Toxicology1

The qualitative and quantitative evaluation of risk in developmental toxicology has been discussed in several recent publications.(^1–3) A number of issues still are to be resolved in this area. The qualitative evaluation and interpretation of end points in developmental toxicology depends on an understanding of the biological events leading to the end points observed, the relationships among end points, and their relationship to dose and to maternal toxicity. The interpretation of these end points is also affected by the statistical power of the experiments used for detecting the various end points observed. The quantitative risk assessment attempts to estimate human risk for developmental toxicity as a function of dose. The current approach is to apply safety (uncertainty) factors to die no observed effect level (NOEL). An alternative presented and discussed here is to model the experimental data and apply a safety factor to an estimated risk level to achieve an “acceptable” level of risk. In cases where the dose-response curves upward, this approach provides a conservative estimate of risk. This procedure does not preclude the existence of a threshold dose. More research is needed to develop appropriate dose-response models that can provide better estimates for low-dose extrapolation of developmental effects.     

Intuitive Toxicology: Expert and Lay Judgments of Chemical Risks

Human beings have always been intuitive toxicologists, relying on their senses of sight, taste, and smell to detect harmful or unsafe food, water, and air. As we have come to recognize that our senses are not adequate to assess the dangers inherent in exposure to a chemical substance, we have created the sciences of toxicology and risk assessment to perform this function. Yet despite this great effort to overcome the limitations of intuitive toxicology, it has become evident that even our best scientific methods still depend heavily on extrapolations and judgments in order to infer human health risks from animal data. Many observers have acknowledged the inherent subjectivity in the assessment of chemical risks and have indicated a need to examine the subjective or intuitive elements of expert and lay risk judgments. We have begun such an examination by surveying members of the Society of Toxicology and the lay public about basic toxicological concepts, assumptions, and interpretations. Our results demonstrate large differences between toxicologists and laypeople, as well as differences between toxicologists working in industry, academia, and government. In addition, we find that toxicologists are sharply divided in their opinions about the ability to predict a chemical's effect on human health on the basis of animal studies. We argue that these results place the problems of risk communication in a new light. Although the survey identifies misconceptions that experts should clarify for the public, it also suggests that controversies over chemical risks may be fueled as much by limitations of the science of risk assessment and disagreements among experts as by public misconceptions.