Risk-Based Chemical Ranking and Generating a Prioritized Human Exposome Database

Abstract

Background: Due to the ubiquitous use of chemicals in modern society, humans are increasingly exposed to thousands of chemicals that contribute to a major portion of the human exposome. Should a comprehensive and risk-based human exposome database be created, it would be conducive to the rapid progress of human exposomics research. In addition, once a xenobiotic is biotransformed with distinct half-lives upon exposure, monitoring the parent compounds alone may not reflect the actual human exposure. To address these questions, a comprehensive and risk-prioritized human exposome database is needed.

Objectives: Our objective was to set up a comprehensive risk-prioritized human exposome database including physicochemical properties as well as risk prediction and develop a graphical user interface (GUI) that has the ability to conduct searches for content associated with chemicals in our database.

Methods: We built a comprehensive risk-prioritized human exposome database by text mining and database fusion. Subsequently, chemicals were prioritized by integrating exposure level obtained from the Systematic Empirical Evaluation of Models with toxicity data predicted by the Toxicity Estimation Software Tool and the Toxicological Priority Index calculated from the ToxCast database. The biotransformation half-lives (${\text{HL}}_{B}s$) of all the chemicals were assessed using the Iterative Fragment Selection approach and biotransformation products were predicted using the previously developed BioTransformer machine-learning method.

Results: We compiled a human exposome database of $>\mathrm{20,000}$ chemicals, prioritized 13,441 chemicals based on probabilistic hazard quotient and 7,770 chemicals based on risk index, and provided a predicted biotransformation metabolite database of $>\mathrm{95,000}$ metabolites. In addition, a user-interactive Java software (Oracle)-based search GUI was generated to enable open access to this new resource.

Discussion: Our database can be used to guide chemical management and enhance scientific understanding to rapidly and effectively prioritize chemicals for comprehensive biomonitoring in epidemiological investigations. https://doi.org/10.1289/EHP7722.

Figure 1.

Schematic workflow for Human Exposome and Metabolite Database (HExpMetDB) establishment. Note: CDC, Centers for Disease Control and Prevention; FDA, U.S. Food and Drug Administration; GUI, graphical user interface; ISF, Iterative Fragment Selection; NIH, National Institutes of Health; PrHQ, probabilistic hazard quotient; RI, risk index; SEEM3, Systematic Empirical Evaluation of Models; TEST, Toxicity Estimation Software Tool; ToxPi, Toxicological Priority Index.

Figure 2.

Overlap analysis of five major database mapping in HExpMetDB. High production volume (HPV) chemicals, European Inventory of Existing Commercial Chemical Substances (EINECS), U.S. EPA Chemical Inventory for ToxCast (CHEMINV), NIH toxicology in the 21st Century (Tox21) chemicals and U.S. EPA pesticides contain a total of 18,909 chemicals, covering 91% of the whole database (20,756). Note: EPA, Environmental Protection Agency; HExpMetDB, Human Exposome and Metabolite Database; NIH, National Institutes of Health.

Figure 3.

The cumulative distribution of (A) predicted rat oral ${\text{LD}}_{50}$ ($n=\mathrm{14,827}$); (B) Toxicological Priority Index (ToxPi) score ($n=\mathrm{8,845}$); (C) Systematic Empirical Evaluation of Model (SEEM) predicted exposure values ($n=\mathrm{15,408}$). Some typical environmental pollutants are labeled. The summary data are listed in Table S2 and Excel Table S2. Note: BCF, bioconcentration factor; BW, body weight; CI, confidence interval; Emax, efficacy; ${\text{LD}}_{50}$, median lethal dose; NR, nuclear receptor; PPAR, peroxisome proliferator-activated receptor; ToxPi, Toxicological Priority Index.

Figure 4.

(A) The cumulative distribution of chemical probabilistic hazard quotients (PrHQs) ($n=\mathrm{13,441}$). The inset shows the PrHQs for typical environmental pollutants represented as exposure (blue) and toxicity (green) component slices. For each slice, the distance from the origin is proportional to the normalized value. (B) The cumulative distribution of chemical risk indexes (RIs) ($n=\mathrm{7,770}$). The summary data are listed in Excel Table S2.

Figure 5.

The graphical user interface (GUI) of our developed HExpMetDB. The compound search module can perform searches based on CASRN, formula, mass-charge-ratio (m/z), adduct search with mass accuracy (in ppm), and retrieve the corresponding metadata including chemical identifiers, structures, and predicted data of ${\text{HL}}_{\mathrm{B}}\mathrm{s}$, exposure and rat oral ${\text{LD}}_{50}$. The biotransformation metabolite prediction module can further search the candidate metabolites of the searched compound. Di(2-ethylhexyl) phthalate (CASRN 117-81-7) biotransformation metabolite prediction was used as an example. Note: ALogP, predicted values of the logarithm transformed 1-octanol/water partition coefficient; CASRN, Chemical Abstracts Service Registry Number; DTXSID, Distributed Structure-Searchable Toxicity substance identifier; EC-based, enzyme commission based; HExpMetDB, Human Exposome and Metabolite Database; ${\text{HL}}_{\mathrm{B}}$, biotransformation half-life; ID, identifier; InChI, International Chemical Identifier; InChIKey, condensed version of the InChI; IUPAC, International Union of Pure and Applied Chemistry; ${\text{LD}}_{50}$, median lethal dose; PrRD, probabilistic reference dose; PrHQ, probabilistic hazard quotient; RI, risk index; SEEM3, Systematic Empirical Evaluation of Models.