TY - JOUR
TI - A Novel Approach to Chemical Mixture Risk Assessment—Linking Data from Population-Based Epidemiology and Experimental Animal Tests
AU - Bornehag, C.-G.
AU - Kitraki, E.
AU - Stamatakis, A.
AU - Panagiotidou, E.
AU - Rudén, C.
AU - Shu, H.
AU - Lindh, C.
AU - Ruegg, J.
AU - Gennings, C.
JO - Microbial Risk Analysis
PY - 2019
VL - 39
TODO - 10
SP - 2259-2271
PB - Blackwell Publishing Inc.
SN - 2352-3522
TODO - 10.1111/risa.13323
TODO - 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
TODO - 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
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