An annotation database for chemicals of emerging concern in exposome research. (July 2021)

• Record Type: Journal Article
• Authors: Meijer, Jeroen; Lamoree, Marja; Hamers, Timo; Antignac, Jean-Philippe; Hutinet, Sébastien; Debrauwer, Laurent; Covaci, Adrian; Huber, Carolin; Krauss, Martin; Walker, Douglas I.; Schymanski, Emma L.; Vermeulen, Roel; Vlaanderen, Jelle
• Is Part Of: Environment international. Volume 152(2021)
• Publication Date: 2021-07
• Subjects: Annotation database -- High-resolution mass spectrometry -- Chemicals of emerging concern -- Exposome research; Environmental protection -- Periodicals; Environmental health -- Periodicals; Environmental monitoring -- Periodicals; Environmental Monitoring -- Periodicals; Environnement -- Protection -- Périodiques; Hygiène du milieu -- Périodiques; Environnement -- Surveillance -- Périodiques; Environmental health; Environmental monitoring; Environmental protection; Periodicals; 333.705
• DOI: 10.1016/j.envint.2021.106511
• Languages: English
• ISSNs: 0160-4120

Abstract

Highlights: CECscreen is an annotation database for CECs in human biological samples. CECscreen includes 70, 397 structures, 306, 071 simulated metabolites, and metadata. CECscreen is openly accessible and is incorporated into Metfrag. CECscreen facilitates large-scale detection of chemicals in exposome research. Abstract: Background: Chemicals of Emerging Concern (CECs) include a very wide group of chemicals that are suspected to be responsible for adverse effects on health, but for which very limited information is available. Chromatographic techniques coupled with high-resolution mass spectrometry (HRMS) can be used for non-targeted screening and detection of CECs, by using comprehensive annotation databases. Establishing a database focused on the annotation of CECs in human samples will provide new insight into the distribution and extent of exposures to a wide range of CECs in humans. Objectives: This study describes an approach for the aggregation and curation of an annotation database (CECscreen) for the identification of CECs in human biological samples. Methods: The approach consists of three main parts. First, CECs compound lists from various sources were aggregated and duplications and inorganic compounds were removed. Subsequently, the list was curated by standardization of structures to create "MS-ready" and "QSAR-ready" SMILES, as well as calculation of exact masses (monoisotopic and adducts) and molecular formulas. The second step included the simulation of Phase I metabolites. The third and final step included the calculation of QSAR predictions related to physicochemical properties, environmental fate, toxicity and Absorption, Distribution, Metabolism, Excretion (ADME) processes and the retrieval of information from the US EPA CompTox Chemicals Dashboard. Results: All CECscreen database and property files are publicly available (DOI: https://doi.org/10.5281/zenodo.3956586 ). In total, 145, 284 entries were aggregated from various CECs data sources. After elimination of duplicates and curation, the pipeline produced 70, 397 unique "MS-ready" structures and 66, 071 unique QSAR-ready structures, corresponding with 69, 526 CAS numbers. Simulation of Phase I metabolites resulted in 306, 279 unique metabolites. QSAR predictions could be performed for 64, 684 of the QSAR-ready structures, whereas information was retrieved from the CompTox Chemicals Dashboard for 59, 739 CAS numbers out of 69, 526 inquiries. CECscreen is incorporated in the in silico fragmentation approach MetFrag. Discussion: The CECscreen database can be used to prioritize annotation of CECs measured in non-targeted HRMS, facilitating the large-scale detection of CECs in human samples for exposome research. Large-scale detection of CECs can be further improved by integrating the present database with resources that contain CECs (metabolites) and meta-data measurements, further expansion towards in silico and experimental (e.g., MassBank) generation of MS/MS spectra, and development of bioinformatics approaches capable of using correlation patterns in the measured chemical features.