@article{3035350,
    title = "A Novel Approach to Chemical Mixture Risk Assessment—Linking Data from Population-Based Epidemiology and Experimental Animal Tests",
    author = "Bornehag, C.-G. and Kitraki, E. and Stamatakis, A. and Panagiotidou, E. and Rudén, C. and Shu, H. and Lindh, C. and Ruegg, J. and Gennings, C.",
    journal = "Microbial Risk Analysis",
    year = "2019",
    volume = "39",
    number = "10",
    pages = "2259-2271",
    publisher = "Blackwell Publishing Inc.",
    issn = "2352-3522",
    doi = "10.1111/risa.13323",
    keywords = "Animals;  Chemicals;  Endocrine disrupters;  Health risks;  Mixtures;  Population statistics;  Risk management;  Risk perception, Adverse health effects;  Assessment strategies;  Chemical exposure;  Endocrine disrupting chemicals;  Experimental evidence;  Sexual development;  Statistical measures;  Systematic integration, Risk assessment, chemical pollutant;  dose-response relationship;  endocrine disruptor;  epidemiology;  health risk;  pollution exposure;  risk assessment;  testing method;  womens health, Animalia, endocrine disruptor, animal;  drug mixture;  environmental exposure;  female;  human;  infant;  pollutant;  pregnancy;  risk assessment;  toxicity, Animals;  Complex Mixtures;  Endocrine Disruptors;  Environmental Exposure;  Environmental Pollutants;  Female;  Humans;  Infant;  Pregnancy;  Risk Assessment",
    abstract = "Humans are continuously exposed to chemicals with suspected or proven endocrine disrupting chemicals (EDCs). Risk management of EDCs presents a major unmet challenge because the available data for adverse health effects are generated by examining one compound at a time, whereas real-life exposures are to mixtures of chemicals. In this work, we integrate epidemiological and experimental evidence toward a whole mixture strategy for risk assessment. To illustrate, we conduct the following four steps in a case study: (1) identification of single EDCs (“bad actors”)—measured in prenatal blood/urine in the SELMA study—that are associated with a shorter anogenital distance (AGD) in baby boys; (2) definition and construction of a “typical” mixture consisting of the “bad actors” identified in Step 1; (3) experimentally testing this mixture in an in vivo animal model to estimate a dose–response relationship and determine a point of departure (i.e., reference dose [RfD]) associated with an adverse health outcome; and (4) use a statistical measure of “sufficient similarity” to compare the experimental RfD (from Step 3) to the exposure measured in the human population and generate a “similar mixture risk indicator” (SMRI). The objective of this exercise is to generate a proof of concept for the systematic integration of epidemiological and experimental evidence with mixture risk assessment strategies. Using a whole mixture approach, we could find a higher rate of pregnant women under risk (13%) when comparing with the data from more traditional models of additivity (3%), or a compound-by-compound strategy (1.6%). © 2019 Society for Risk Analysis"
}