Advances in Causal Understanding for Human Health Risk-Based Decision-Making Proceedings of a Workshop—in Brief (2018) / Chapter Skim
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Advances in Causal Understanding for Human Health Risk-Based Decision-Making: Proceedings of a Workshop - in Brief
Advances in Causal Understanding for Human Health Risk-Based Decision-Making: Proceedings of a Workshop - in Brief
Pages 1-12
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From page 1... ...
For this reason, on March 6–7, 2017, the National Academies' Standing Committee on Emerging Science for Environmental Health Decisions, held a 2-day workshop to explore advances in causal understanding for human health risk-based decision-making. The workshop, sponsored by the National Institute of Environmental Health Sciences (NIEHS)
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From page 2... ...
Expert judgment is currently the best way to integrate in vivo data with in vitro data, such as is generated by -omics tools, according to the 2017 National Academies report Using 21st Century Science to Improve Risk-Related Evaluations. Samet, who chaired the committee that produced that report, stressed a key report finding that insufficient attention has been given to analysis, interpretation, and integration of various data streams from exposure science, toxicology, and epidemiology.
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From page 3... ...
Similarly, when EPA scientists realized that the agency's Endocrine Disruptor Screening Program, called for by Congress, could not achieve its goals with conventional animal testing, the program pivoted to consider a pathway-based approach, pointed out Stanley Barone of EPA. The EPA began using evidence from various cell lines and systems, as well as animal toxicology and other mechanistic studies, and later began to incorporate that information into a causal model framework, he said.
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From page 4... ...
Indeed, the recent Using 21st Century Science to Improve Risk-Related Evaluations report proposed that scientists develop key characteristics for other adverse outcomes and health hazards similar to those developed for cancer. These could then be used as a basis for understanding assay results for a particular compound and for evaluating the risk associated with that compound.
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The work brought to light at least five distinct ways that smoking can damage DNA, results consistent with the hypothesis that increased somatic mutations caused by smoking can increase cancer risk. Molecular epidemiology approaches also have promise for shedding light on obesity, a complex disease with a number of different potential causes, said Jessie Buckley of the Johns Hopkins Bloomberg School of Public Health.
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This gave the appearance of a vaccine protective effect against hospitalizations, emphasizing a need to consider protective effects limited to outcomes plausibly linked to influenza. STATISTICAL MODELING AND COMPUTATIONAL TOOLS The use of statistical modeling to determine causality has evolved to become useful in situations where there are a lot of variables, such as genes, but not much other knowledge, to compute plausible hypotheses that can be explored experimentally or otherwise, said Richard Scheines of Carnegie Mellon University.
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From page 7... ...
Ginsberg also pointed to the analogous decision advising pregnant women to limit their fish consumption due to the effects of methylmercury on neurodevelopment, despite a similar preponderance of mixed results from epidemiology studies; asking the question, if the evidence is sufficient to protect infants why is it not sufficient to protect against MI? Ginsberg ended his argument by discussing the plausibility of an adverse outcome pathway from animal and in vitro studies, including evidence that mercury exposure can impact pathways involved in oxidative stress and protein denaturation, to demonstrate a biologically plausible mechanism.
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The recommendations for quantitative read-across in the recently released Using 21st Century Science to Improve Risk-Related Evaluations call for researchers to identify appropriate analogs and investigate their toxicity data; select a point of departure and adjust it on the basis of pharmacokinetics or structural activity data; and then to apply appropriate uncertainty factors. She explained that this case study explores the use of in vitro data in lieu of bracketing in vivo data for a quantitative risk assessment.
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He concluded his remarks by stating that while there is value in incorporating in vitro data into any weight of evidence framework, but it is important to be realistic about the assumptions made and the magnitude of the uncertainty surrounding them. Debate 3: Animal models provide a sufficient basis for presumption for toxicity in safety testing.
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Kaminski went on to discuss a large study demonstrating that genomic responses in mouse models poorly mimic human inflammatory responses. This study analyzed differences in the immune responses among humans following blunt trauma, burn injuries, or exposures to low-dose bacterial endotoxins, and also compared the human immune responses to the mouse immune responses to the same three stressors.
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From page 11... ...
Rasoulpour suggested that if companies shared more about their internal processes, which are based mainly on predicting human outcomes, not animal models, it could help move the paradigm forward. For example, he said that the approach used at Dow AgroSciences to screen for impacts on the aromatase pathway, which is involved in a key step in the biosynthesis of estrogens, could easily be used for regulatory prioritization of untested chemicals or perhaps even in decision-making.
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From page 12... ...
PLANNING COMMITTEE FOR ADVANCES IN CAUSAL UNDERSTANDING FOR HUMAN HEALTH RISK-BASED DECISIONMAKING: Kim Boekelheide, Brown University; Weihsueh Chiu, Texas A&M University; Kristi Pullen Fedinick, Natural Resource Defense Council; Gary Ginsberg, Connecticut Department of Public Health; Reza Rasoulpour, Dow AgroSciences. SPONSOR: This workshop was supported by the National Institute of Environmental Health Sciences.
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