Invited Perspective: Examining Chemicals in Food as a Priority for Toxicity Testing.

Vol. 131, No. 3 Invited PerspectiveOpen AccessInvited Perspective: Examining Chemicals in Food as a Priority for Toxicity Testingis accompanied byHExpPredict: In Vivo Exposure Prediction of Human Blood Exposome Using a Random Forest Model and Its Application in Chemical Risk Prioritization Courtney C. Carignan Courtney C. Carignan Address correspondence to Courtney C. Carignan, 469 Wilson Rd., Room 208, East Lansing, MI 48864 USA. Telephone: (517) 884.2039. Email: E-mail Address: [email protected] https://orcid.org/0000-0002-5500-0222 Department of Food Science and Human Nutrition, Department of Pharmacology and Toxicology, Michigan State University, East Lansing, Michigan, USA Search for more papers by this author Published:13 March 2023CID: 031304https://doi.org/10.1289/EHP12630AboutSectionsPDF ToolsDownload CitationsTrack Citations ShareShare onFacebookTwitterLinked InReddit In their new paper,1 Zhao et al. present an elegant computational approach to estimate concentrations of exogenous chemicals in human blood and calculated corresponding toxic equivalencies to prioritize toxicity testing. Application of their new model identified a surprisingly high proportion of food additives, indirect additives, and food-contact substances predicted to have the highest total toxic equivalencies [referred to by Zhao et al. as bioanalytical equivalencies (BEQ%)] for the general population. Other predominant categories included industrial chemicals, pesticides, and household, fragrance, and personal care products.Zhao et al. used biomonitoring2 and ExpoCast3 data for 216 compounds to train and test a machine learning algorithm. They employed the model to predict blood concentrations for 7,858 chemicals from ToxCast,4 which they used to calculate %BEQ for 12 endocrine-disruption assays. The authors listed the top 25 chemicals with the highest BEQ% for each of the assays, a total of 145 unique chemicals, in their Excel Table S8. I visualized these results in Figure 1 by summing BEQ% by application category and within each category by assay end point.Figure 1. Proportional summed toxic equivalencies by application category and within each category by ToxCast end point (lowercase italic n equals 145n=145). Application categories derived by summing bioanalytic equivalencies (bioanalytical equivalencies percent 1 plus bioanalytical equivalencies percent 2BEQ%1 + BEQ%2) for chemicals within each category in Zhao et al., Excel Table S8.1The unexpected predominance of the 50 food additives, indirect additives, and food-contact substances identified by this research highlights the need for comprehensive and agnostic approaches to toxicity testing prioritization. Resulting BEQ% were especially notable for two flavoring agents (2,3-butanedione and methyl formate), a colorant (FD&C Yellow 5), and three plasticizers used in food-contact substances (dimethyl isophthalate, diisobutyl phthalate, and diethyl phthalate), indicating the need to prioritize these chemicals for closer evaluation. Rigorous evaluation is also a priority among consumers, who are increasingly choosing organic foods and those with fewer ingredients and less packaging in an effort to avoid potentially harmful chemical exposures.5Chemical production is on the rise,6 and only a small fraction of the estimated 350,000-plus chemicals in commerce have undergone careful screening or testing.7 Identification of harmful chemicals in commerce with widespread exposure has been ongoing for decades.8 Notorious examples include dichlorodiphenyltrichloroethane (DDT), lead, radium, dioxins, polybrominated diphenyl ethers, phthalates, bisphenols, and per- and polyfluoroalkyl substances.9,10 These discoveries have occurred alongside soaring rates of chronic diseases and conditions—including infertility, metabolic syndrome, thyroid disease, cancer, and neurodevelopmental and neurodegenerative conditions—that are not fully accounted for by genetic, lifestyle, or nutritional factors11 and that have been linked with exposure to many of these contaminants.12The new model can also be used to screen alternatives that may have been introduced without adequate toxicity testing, potentially avoiding the continued use of regrettable substitutions. For example, one of the plasticizers identified by Zhao et al. for prioritization, diisobutyl phthalate, has been used as a replacement for di(2-ethylhexyl) phthalate.13 The authors also identified tri-isobutyl phosphate for prioritization; this compound is in the class of organophosphate flame retardants that have been widely used as replacements for polybrominated diphenyl ethers.14,15Zhao et al. trained the model using biomonitoring data from the nationally representative National Health and Nutrition Examination Survey (NHANES),2 so it should be reasonably inclusive of communities with disparate burdens of chemical exposures. Improved chemical prioritization for toxicity testing is vital to better protect low-income communities and communities of color. A future evaluation of model performance could test the accuracy of predictions among vulnerable and sensitive populations. The authors noted several other ways to improve the model, such as use of more robust toxicity data and periodic future updates to incorporate new estimates of exposure and measured blood concentrations of chemicals.1The task of predicting population blood concentrations for thousands of chemicals is seemingly impossible, yet it is a necessary step in overhauling current methods for prioritization of chemicals for toxicity testing. I applaud Zhao et al. for their innovative approach to tackling it.AcknowledgmentsC.C.C. is supported in part by the National Institute of Environmental Health Sciences, National Institutes of Health (R01ES028311), the U.S. Environmental Protection Agency National Priorities Program (No. 84025201 and Assistance Agreement No. R839482), and the U.S. Department of Agriculture National Institute of Food and Agriculture (Hatch Project MICL02565). This perspective has not been formally reviewed by the funding agencies. The views expressed in this document are solely from the author and do not necessarily reflect those of the funding agencies. The agencies do not endorse any products or commercial services mentioned in this paper.References1. Zhao F, Li L, Lin P, Chen Y, Xing S, Du H, et al.2023. HExpPredict: in vivo exposure prediction of human blood exposome using a random forest model and its application in chemical risk prioritization. Environ Health Perspect 131(3):037009, 10.1289/EHP11305. Link, Google Scholar2. Calafat AM. 2012. The U.S. National Health and Nutrition Examination Survey and human exposure to environmental chemicals. Int J Hyg Environ Health 215(2):99–101, PMID: 21937270, 10.1016/j.ijheh.2011.08.014. Crossref, Medline, Google Scholar3. Wambaugh JF, Rager JE. 2022. Exposure forecasting–ExpoCast–for data-poor chemicals in commerce and the environment. J Expo Sci Environ Epidemiol 32(6):783–793, PMID: 36347934, 10.1038/s41370-022-00492-z. Crossref, Medline, Google Scholar4. 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Crossref, Medline, Google ScholarC.C.C. has served as a plaintiff’s expert witness for a case involving exposure to per- and polyfluoroalkyl substances.FiguresReferencesRelatedDetailsRelated articlesHExpPredict: In Vivo Exposure Prediction of Human Blood Exposome Using a Random Forest Model and Its Application in Chemical Risk Prioritization13 March 2023Environmental Health Perspectives Vol. 131, No. 3 March 2023Metrics About Article Metrics Publication History Manuscript received19 December 2022Manuscript revised10 February 2023Manuscript accepted14 February 2023Originally published13 March 2023 Financial disclosuresPDF download License information EHP is an open-access journal published with support from the National Institute of Environmental Health Sciences, National Institutes of Health. All content is public domain unless otherwise noted. Note to readers with disabilities EHP strives to ensure that all journal content is accessible to all readers. 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