Bette Meek

Mode of action framework analysis for receptor-mediated toxicity: The peroxisome proliferator-activated receptor alpha (PPARα) as a case study

Abstract Several therapeutic agents and industrial chemicals induce liver tumors in rodents through the activation of the peroxisome proliferator-activated receptor alpha (PPARα). The cellular and molecular events by which PPARα activators induce rodent hepatocarcinogenesis has been extensively studied and elucidated. This review summarizes the weight of evidence relevant to the hypothesized mode of action (MOA) for PPARα activator-induced rodent hepatocarcinogenesis and identifies gaps in our knowledge of this MOA. Chemical-specific and mechanistic data support concordance of temporal and dose–response relationships for the key events associated with many PPARα activators including a phthalate ester plasticizer di(2-ethylhexyl) phthalate (DEHP) and the drug gemfibrozil. While biologically plausible in humans, the hypothesized key events in the rodent MOA, for PPARα activators, are unlikely to induce liver tumors in humans because of toxicodynamic and biological differences in responses. This conclusion is based on minimal or no effects observed on growth pathways, hepatocellular proliferation and liver tumors in humans and/or species (including hamsters, guinea pigs and cynomolgous monkeys) that are more appropriate human surrogates than mice and rats at overlapping dose levels. Overall, the panel concluded that significant quantitative differences in PPARα activator-induced effects related to liver cancer formation exist between rodents and humans. On the basis of these quantitative differences, most of the workgroup felt that the rodent MOA is “not relevant to humans” with the remaining members concluding that the MOA is “unlikely to be relevant to humans”. The two groups differed in their level of confidence based on perceived limitations of the quantitative and mechanistic knowledge of the species differences, which for some panel members strongly supports but cannot preclude the absence of effects under unlikely exposure scenarios.

Linear low-dose extrapolation for noncancer health effects is the exception, not the rule

The nature of the exposure-response relationship has a profound influence on risk analyses. Several arguments have been proffered as to why all exposure-response relationships for both cancer and noncarcinogenic endpoints should be assumed to be linear at low doses. We focused on three arguments that have been put forth for noncarcinogens. First, the general “additivity-to-background” argument proposes that if an agent enhances an already existing disease-causing process, then even small exposures increase disease incidence in a linear manner. This only holds if it is related to a specific mode of action that has nonuniversal properties—properties that would not be expected for most noncancer effects. Second, the “heterogeneity in the population” argument states that variations in sensitivity among members of the target population tend to “flatten out and linearize” the exposure-response curve, but this actually only tends to broaden, not linearize, the dose-response relationship. Third, it has been argued that a review of epidemiological evidence shows linear or no-threshold effects at low exposures in humans, despite nonlinear exposure-response in the experimental dose range in animal testing for similar endpoints. It is more likely that this is attributable to exposure measurement error rather than a true nonthreshold association. Assuming that every chemical is toxic at high exposures and linear at low exposures does not comport to modern-day scientific knowledge of biology. There is no compelling evidence-based justification for a general low-exposure linearity; rather, case-specific mechanistic arguments are needed.

Increasing Scientific Confidence in Adverse Outcome Pathways: Application of Tailored Bradford-Hill Considerations for Evaluating Weight of Evidence

Systematic consideration of scientific support is a critical element in developing and, ultimately, using adverse outcome pathways (AOPs) for various regulatory applications. Though weight of evidence (WoE) analysis has been proposed as a basis for assessment of the maturity and level of confidence in an AOP, methodologies and tools are still being formalized. The Organization for Economic Co-operation and Development (OECD) Users Handbook Supplement to the Guidance Document for Developing and Assessing AOPs (OECD 2014a; hereafter referred to as the OECD AOP Handbook) provides tailored Bradford-Hill (BH) considerations for systematic assessment of confidence in a given AOP. These considerations include (1) biological plausibility and (2) empirical support (dose-response, temporality, and incidence) for Key Event Relationships (KERs), and (3) essentiality of key events (KEs). Here, we test the application of these tailored BH considerations and the guidance outlined in the OECD AOP Handbook using a number of case examples to increase experience in more transparently documenting rationales for assigned levels of confidence to KEs and KERs, and to promote consistency in evaluation within and across AOPs. The major lessons learned from experience are documented, and taken together with the case examples, should contribute to better common understanding of the nature and form of documentation required to increase confidence in the application of AOPs for specific uses. Based on the tailored BH considerations and defining questions, a prototype quantitative model for assessing the WoE of an AOP using tools of multi-criteria decision analysis (MCDA) is described. The applicability of the approach is also demonstrated using the case example aromatase inhibition leading to reproductive dysfunction in fish. Following the acquisition of additional experience in the development and assessment of AOPs, further refinement of parameterization of the model through expert elicitation is recommended. Overall, the application of quantitative WoE approaches hold promise to enhance the rigor, transparency and reproducibility for AOP WoE determinations and may play an important role in delineating areas where research would have the greatest impact on improving the overall confidence in the AOP.

Development of good modelling practice for physiologically based pharmacokinetic models for use in risk assessment: The first steps

The increasing use of tissue dosimetry estimated using pharmacokinetic models in chemical risk assessments in various jurisdictions necessitates the development of internationally recognized good modelling practice (GMP). These practices would facilitate sharing of models and model evaluations and consistent applications in risk assessments. Clear descriptions of good practices for (1) model development i.e., research and analysis activities, (2) model characterization i.e., methods to describe how consistent the model is with biology and the strengths and limitations of available models and data, such as sensitivity analyses, (3) model documentation, and (4) model evaluation i.e., independent review that will assist risk assessors in their decisions of whether and how to use the models, and also model developers to understand expectations for various purposes e.g., research versus application in risk assessment. Next steps in the development of guidance for GMP and research to improve the scientific basis of the models are described based on a review of the current status of the application of physiologically based pharmacokinetic (PBPK) models in risk assessments in Europe, Canada, and the United States at the International Workshop on the Development of GMP for PBPK Models in Greece on April 27-29, 2007.

Pragmatic challenges for the vision of toxicity testing in the 21st century in a regulatory context: another Ames test? ...or a new edition of "the Red Book"?

Most toxicologists were pleased when the National Research Council appointed a committee in 2004 to review established methodologies and develop a long-range vision and strategy for toxicity testing in the future. This committee reviewed reports from the U.S. Environmental Protection Agency (EPA) and other sources and issued an interim report in 2006 entitled ‘‘Toxicity Testing for Assessment of Environmental Agents’’ (NCR, 2006). This report distinguished general toxicity tests from those designed to evaluate specific health effects and classified such tests as battery, tiered or tailored depending on the approach. It also reviewed the use of human data, alternative approaches and emerging technologies. Three chapters in this report included cogent committee observations and these sections plus the summarized information in boxes, tables and the appendix make this soft-cover report a valuable reference companion for the subsequent hard cover report. One of the many observations in this report is that toxicity testing protocols never die but unlike old soldiers who fade away, ‘‘grow like Topsy’’ (e.g., derived from the character, Topsy, in Harriet Beecher Stowe’s Uncle Tom’s Cabin; to ‘‘grow like Topsy’’ is to grow ‘wild, with neither plan, structure or direction) in response to new perceived or real safety concerns. This approach results in a cookbook/checklist of protocols which is increasingly difficult to apply efficiently in the testing of new agents and is totally inadequate to deal with the substantial backlog of untested existing substances prioritized for consideration in evolving regulatory mandates.

An exposure-response curve for copper excess and deficiency.

There is a need to define exposure-response curves for both Cu excess and deficiency to assist in determining the acceptable range of oral intake. A comprehensive database has been developed where different health outcomes from elevated and deficient Cu intakes were assigned ordinal severity scores to create common measures of response. A generalized linear model for ordinal data was used to estimate the probability of response associated with dose, duration and severity. The model can account for differences in animal species, the exposure medium (drinking water and feed), age, sex, and solubility. Using this model, an optimal intake level of 2.6 mg Cu/d was determined. This value is higher than the current U.S. recommended dietary intake (RDI; 0.9 mg/d) that protects against toxicity from Cu deficiency. It is also lower than the current tolerable upper intake level (UL; 10 mg/d) that protects against toxicity from Cu excess. Compared to traditional risk assessment approaches, categorical regression can provide risk managers with more information, including a range of intake levels associated with different levels of severity and probability of response. To weigh the relative harms of deficiency and excess, it is important that the results be interpreted along with the available information on the nature of the responses that were assigned to each severity score.

Quantitative weight of evidence to assess confidence in potential modes of action

&NA; The evolved World Health Organization/International Programme on Chemical Safety mode of action (MOA) framework provides a structure for evaluating evidence in pathways of causally linked key events (KE) leading to adverse health effects. Although employed globally, variability in use of the MOA framework has led to different interpretations of the sufficiency of evidence in support of hypothesized MOAs. A proof of concept extension of the MOA framework is proposed for scoring confidence in the supporting data to improve scientific justification for MOA use in characterizing hazards and selecting dose‐response extrapolation methods for specific chemicals. This involves selecting hypothesized MOAs, and then, for each MOA, scoring the weight of evidence (WOE) in support of causality for each KE using evolved Bradford Hill causal considerations (biological plausibility, essentiality, dose‐response concordance, consistency, and analogy). This early proof of concept method is demonstrated by comparing two potential MOAs (mutagenicity and peroxisome proliferator activated receptor‐alpha) for clofibrate, a rodent liver carcinogen. Quantitative confidence scoring of hypothesized MOAs is shown to be useful in characterizing the likely operative MOA. To guide method refinement and future confidence scoring for a spectrum of MOAs, areas warranting further focus and lessons learned, including the need to incorporate a narrative discussion of the weights used in the evaluation and an overall evaluation of the plausibility of the outcome, are presented. HighlightsA proof of concept for quantitative confidence scoring to compare hypothesized MOAs.Confidence scoring affords numerical dimension to identify the likely operative MOA.The case example demonstrates utility and identifies areas meriting further focus.

Use of Adverse Outcome Pathways in Human Risk Assessment and Toxicology

Mechanistic information has been used for many years to inform chemical hazard and risk assessments. NRC reports and several agency strategic plans in recent years promote the large-scale use of mechanistic information, organized in the form of pathways at different levels of biological organization as a basis to underpin a dramatic change in the way chemical assessment is performed. As a result, there now exist international collaborations to develop the data and knowledge bases, guidance and principles for development and use of “Adverse Outcome Pathways” (AOPs). Many of the principles for developing and using pathways are based on experience with Mode of Action frameworks for human health risk assessment. Expert groups within the Organization for Economic Cooperation and Development (OECD) are publishing guidance and partnering with the US EPA and European Commissions Joint Research Centre (JRC) to develop a public knowledge base for building AOPs on a large scale. Although this direction is fairly new, there are many pathways already in development. In addition, pathway-based approaches are increasingly being applied to a variety of assessments of hazard in a number of sectors. This chapter describes the genesis of the AOP concept, the development of the necessary tools based on international collaborations, and provides some examples of the use of AOPs in human health risk assessment.