Cumulative Risk: Toxicity and Interactions of Physical and Chemical Stressors

Recent efforts to update cumulative risk assessment procedures to incorporate nonchemical stressors ranging from physical to psychosocial reflect increased interest in consideration of the totality of variables affecting human health and the growing desire to develop community-based risk assessment methods. A key roadblock is the uncertainty as to how nonchemical stressors behave in relationship to chemical stressors. Physical stressors offer a reasonable starting place for measuring the effects of nonchemical stressors and their modulation of chemical effects (and vice versa), as they clearly differ from chemical stressors; and “doses” of many physical stressors are more easily quantifiable than those of psychosocial stressors. There is a commonly held belief that virtually nothing is known about the impact of nonchemical stressors on chemically mediated toxicity or the joint impact of coexposure to chemical and nonchemical stressors. Although this is generally true, there are several instances where a substantial body of evidence exists. A workshop titled “Cumulative Risk: Toxicity and Interactions of Physical and Chemical Stressors” held at the 2013 Society of Toxicology Annual Meeting provided a forum for discussion of research addressing the toxicity of physical stressors and what is known about their interactions with chemical stressors, both in terms of exposure and effects. Physical stressors including sunlight, heat, radiation, infectious disease, and noise were discussed in reference to identifying pathways of interaction with chemical stressors, data gaps, and suggestions for future incorporation into cumulative risk assessments

Development of the InTelligence And Machine LEarning (TAME) Toolkit for Introductory Data Science, Chemical-Biological Analyses, Predictive Modeling, and Database Mining for Environmental Health Research

Research in environmental health is becoming increasingly reliant upon data science and computational methods that can more efficiently extract information from complex datasets. Data science and computational methods can be leveraged to better identify relationships between exposures to stressors in the environment and human disease outcomes, representing critical information needed to protect and improve global public health. Still, there remains a critical gap surrounding the training of researchers on these in silico methods. We aimed to address this gap by developing the inTelligence And Machine lEarning (TAME) Toolkit, promoting trainee-driven data generation, management, and analysis methods to “TAME” data in environmental health studies. Training modules were developed to provide applications-driven examples of data organization and analysis methods that can be used to address environmental health questions. Target audiences for these modules include students, post-baccalaureate and post-doctorate trainees, and professionals that are interested in expanding their skillset to include recent advances in data analysis methods relevant to environmental health, toxicology, exposure science, epidemiology, and bioinformatics/cheminformatics. Modules were developed by study coauthors using annotated script and were organized into three chapters within a GitHub Bookdown site. The first chapter of modules focuses on introductory data science, which includes the following topics: setting up R/RStudio and coding in the R environment; data organization basics; finding and visualizing data trends; high-dimensional data visualizations; and Findability, Accessibility, Interoperability, and Reusability (FAIR) data management practices. The second chapter of modules incorporates chemical-biological analyses and predictive modeling, spanning the following methods: dose-response modeling; machine learning and predictive modeling; mixtures analyses; -omics analyses; toxicokinetic modeling; and read-across toxicity predictions. The last chapter of modules was organized to provide examples on environmental health database mining and integration, including chemical exposure, health outcome, and environmental justice indicators. Training modules and associated data are publicly available online (https://uncsrp.github.io/Data-Analysis-Training-Modules/). Together, this resource provides unique opportunities to obtain introductory-level training on current data analysis methods applicable to 21st century science and environmental health

Methods for specifying the target difference in a randomised controlled trial : the Difference ELicitation in TriAls (DELTA) systematic review

Peer reviewedPublisher PD

Using the Key Characteristics of Carcinogens to Develop Research on Chemical Mixtures and Cancer.

BackgroundPeople are exposed to numerous chemicals throughout their lifetimes. Many of these chemicals display one or more of the key characteristics of carcinogens or interact with processes described in the hallmarks of cancer. Therefore, evaluating the effects of chemical mixtures on cancer development is an important pursuit. Challenges involved in designing research studies to evaluate the joint action of chemicals on cancer risk include the time taken to perform the experiments because of the long latency and choosing an appropriate experimental design.ObjectivesThe objectives of this work are to present the case for developing a research program on mixtures of environmental chemicals and cancer risk and describe recommended approaches.MethodsA working group comprising the coauthors focused attention on the design of mixtures studies to inform cancer risk assessment as part of a larger effort to refine the key characteristics of carcinogens and explore their application. Working group members reviewed the key characteristics of carcinogens, hallmarks of cancer, and mixtures research for other disease end points. The group discussed options for developing tractable projects to evaluate the joint effects of environmental chemicals on cancer development.Results and discussionThree approaches for developing a research program to evaluate the effects of mixtures on cancer development were proposed: a chemical screening approach, a transgenic model-based approach, and a disease-centered approach. Advantages and disadvantages of each are discussed. https://doi.org/10.1289/EHP8525

Statistical Approaches for Assessing Health Effects of Environmental Chemical Mixtures in Epidemiology: Lessons from an Innovative Workshop

Summary: Quantifying the impact of exposure to environmental chemical mixtures is important for identifying risk factors for diseases and developing more targeted public health interventions. The National Institute of Environmental Health Sciences (NIEHS) held a workshop in July 2015 to address the need to develop novel statistical approaches for multi-pollutant epidemiology studies. The primary objective of the workshop was to identify and compare different statistical approaches and methods for analyzing complex chemical mixtures data in both simulated and real-world data sets. At the workshop, participants compared approaches and results and speculated as to why they may have differed. Several themes emerged: a) no one statistical approach appeared to outperform the others, b) many methods included some form of variable reduction or summation of the data before statistical analysis, c) the statistical approach should be selected based upon a specific hypothesis or scientific question, and d) related mixtures data should be shared among researchers to more comprehensively and accurately address methodological questions and statistical approaches. Future efforts should continue to design and optimize statistical approaches to address questions about chemical mixtures in epidemiological studies