Framework for use of toxicity screening tools in context-based decision-making

Aggregate Exposure Pathways in Support of Risk Assessment

Over time, risk assessment has shifted from establishing relationships between exposure to a single chemical and a resulting adverse health outcome, to evaluation of multiple chemicals and disease outcomes simultaneously. As a result, there is an increasing need to better understand the complex mechanisms that influence risk of chemical and non-chemical stressors, beginning at their source and ending at a biological endpoint relevant to human or ecosystem health risk assessment. Just as the Adverse Outcome Pathway (AOP) framework has emerged as a means of providing insight into mechanism-based toxicity, the exposure science community has seen the recent introduction of the Aggregate Exposure Pathway (AEP) framework. AEPs aid in making exposure data applicable to the FAIR (i.e., findable, accessible, interoperable, and reusable) principle, especially by (1) organizing continuous flow of disjointed exposure information;(2) identifying data gaps, to focus resources on acquiring the most relevant data; (3) optimizing use and repurposing of existing exposure data; and (4) facilitating interoperability among predictive models. Herein, we discuss integration of the AOP and AEP frameworks and how such integration can improve confidence in both traditional and cumulative risk assessment approaches.

Heuristics structure and pervade formal risk assessment

Lay perceptions of risk appear rooted more in heuristics than in reason. A major concern of the risk regulation literature is that such "error-strewn" perceptions may be replicated in policy, as governments respond to the (mis)fears of the citizenry. This has led many to advocate a relatively technocratic approach to regulating risk, characterized by high reliance on formal risk and cost-benefit analysis. However, through two studies of chemicals regulation, we show that the formal assessment of risk is pervaded by its own set of heuristics. These include rules to categorize potential threats, define what constitutes valid data, guide causal inference, and to select and apply formal models. Some of these heuristics lay claim to theoretical or empirical justifications, others are more back-of-the-envelope calculations, while still more purport not to reflect some truth but simply to constrain discretion or perform a desk-clearing function. These heuristics can be understood as a way of authenticating or formalizing risk assessment as a scientific practice, representing a series of rules for bounding problems, collecting data, and interpreting evidence (a methodology). Heuristics are indispensable elements of induction. And so they are not problematic per se, but they can become so when treated as laws rather than as contingent and provisional rules. Pitfalls include the potential for systematic error, masking uncertainties, strategic manipulation, and entrenchment. Our central claim is that by studying the rules of risk assessment qua rules, we develop a novel representation of the methods, conventions, and biases of the prior art.

Use and validation of HT/HC assays to support 21st century toxicity evaluations

Advances in high throughput and high content (HT/HC) methods such as those used in the fields of toxicogenomics, bioinformatics, and computational toxicology have the potential to improve both the efficiency and effectiveness of toxicity evaluations and risk assessments. However, prior to use, scientific confidence in these methods should be formally established. Traditional validation approaches that define relevance, reliability, sensitivity and specificity may not be readily applicable. HT/HC methods are not exact replacements for in vivo testing, and although run individually, these assays are likely to be used as a group or battery for decision making and use robotics, which may be unique in each laboratory setting. Building on the frameworks developed in the 2010 Institute of Medicine Report on Biomarkers and the OECD 2007 Report on (Q)SAR Validation, we present constructs that can be adapted to address the validation challenges of HT/HC methods. These are flexible, transparent, and require explicit specification of context and purpose of use such that scientific confidence (validation) can be defined to meet different regulatory applications. Using these constructs, we discuss how anchoring the assays and their prediction models to Adverse Outcome Pathways (AOPs) could facilitate the interpretation of results and support scientifically defensible fit-for-purpose applications.

Perspectives on validation of high-throughput assays supporting 21st century toxicity testing

In vitro high-throughput screening (HTS) assays are seeing increasing use in toxicity testing. HTS assays can simultaneously test many chemicals but have seen limited use in the regulatory arena, in part because of the need to undergo rigorous, time-consuming formal validation. Here we discuss streamlining the validation process, specifically for prioritization applications. By prioritization, we mean a process in which less complex, less expensive, and faster assays are used to prioritize which chemicals are subjected first to more complex, expensive, and slower guideline assays. Data from the HTS prioritization assays is intended to provide a priori evidence that certain chemicals have the potential to lead to the types of adverse effects that the guideline tests are assessing. The need for such prioritization approaches is driven by the fact that there are tens of thousands of chemicals to which people are exposed, but the yearly throughput of most guideline assays is small in comparison. The streamlined validation process would continue to ensure the reliability and relevance of assays for this application. We discuss the following practical guidelines: (1) follow current validation practice to the extent possible and practical; (2) make increased use of reference compounds to better demonstrate assay reliability and relevance; (3) de-emphasize the need for cross-laboratory testing; and (4) implement a web-based, transparent, and expedited peer review process.

Developmental toxicity prediction

Developmental toxicity may be estimated using commercial and noncommercial software that is already available in the market and/or literature, or models may be built from scratch using both commercial and noncommercial software packages. In this chapter, commonly available software programs that can predict the developmental toxicity of chemicals are described. In addition, a method for developing qualitative structure-activity relationship (SAR) models to predict the developmental toxicity of chemicals qualitatively (yes/no prediction) and quantitative structure-activity relationship (QSAR) models to predict quantitative estimates (e.g., LOAEL) of developmental toxicants is also described in this chapter. Additional information described in this chapter include methods to predict physicochemical properties of chemicals that can be used as descriptor variables in the model building process, statistical methods that be used to build QSAR models as well as methods to validate the models that are developed. Most of the methods described in this chapter can be used to develop models for health endpoints other than developmental toxicity as well.

A review of contemporary methods for the presentation of scientific uncertainty

Graphic methods for displaying uncertainty are often the most concise and informative way to communicate abstract concepts. Presentation methods currently in use for the display and interpretation of scientific uncertainty are reviewed. Numerous subjective and objective uncertainty display methods are presented, including qualitative assessments, node and arrow diagrams, standard statistical methods, box-and-whisker plots,robustness and opportunity functions, contribution indexes, probability density functions, cumulative distribution functions, and graphical likelihood functions.

Meeting the common needs of a more effective and efficient testing and assessment paradigm for chemical risk management

Significant advances have been made in human health and ecological risk assessment over the last decade. Substantial challenges, however, remain in providing credible scientific information in a timely and efficient manner to support chemical risk assessment and management decisions. A major challenge confronting risk managers is the need for critical information to address risk uncertainties in large chemical inventories such as high- and medium-production-volume industrial chemicals or pesticide inert ingredients. From a strategic and tactical viewpoint, an integrated approach that relies on all existing knowledge and uses a range of methods, including those from emerging and novel technologies, is needed to advance progressive and focused testing strategies, as well as to advance the utility and predictability of the risk assessment by providing more relevant information. A hypothesis-based approach that draws on all relevant information is consistent with the vision articulated in the 2007 report by the National Research Council, Toxicity Testing in the 21st Century: A Vision and a Strategy. This article describes the current practices in evaluating chemical risks and ongoing efforts to enhance the quality and efficiency of risk assessment and risk management decisions within the Office of Prevention, Pesticides, and Toxic Substances at the U.S. Environmental Protection Agency.

Risk assessment of pesticides and heavy metals contaminants in vegetables: a novel bioassay method using Daphnia magna Straus

Cucumber and potato samples of known levels of pesticides and heavy metal residues, as respectively measured by gas chromatography and atomic absorption, were subjected to a bioassay method using Daphnia magna in order to assess the potential of the toxic hazard of their contaminants. Based on the estimated lethal time for 50% mortality (LT50) in daphnids, we suggested a classification to categorize toxic hazards in six definite ratings. Either samples of cucumbers (from conventional, greenhouse and organic farming) or potatoes (from conventional and organic farming) were evaluated for toxic hazard of the mixtures of pesticide residues and heavy metals, as well as mixtures of both. Accordingly, a 53.7% of cucumber samples were ranked as "Highly Toxic: HT"; a 18.5% "Moderately Toxic: MT); a 9.3% "Slightly Toxic: ST"; and a 18.5% "Practically Non-Toxic: NT". For potato samples, the ranking pattern to different classes was: Extremely Toxic: ET (LT50=<1h) for 11.1%; Very Toxic: VT (LT50=1-<3h) for 50.0%; HT (LT50=3-<12h) for 13.9%; MT (LT50=12-<24h) for 11.1%; ST (LT50=24-48 h) for 0.0%; and NT (LT50= >48 h) for 13.9% of the samples bioassayed.