FRCD: A comprehensive food risk component database with molecular scaffold, chemical diversity, toxicity, and biodegradability analysis.

The presence of natural toxins, pesticide residues, and illegal additives in food products has been associated with a range of potential health hazards. However, no systematic database exists that comprehensively includes and integrates all research information on these compounds, and valuable information remains scattered across numerous databases and extensive literature reports. Thus, using natural language processing technology, we curated 12,018 food risk components from 152,737 literature reports, 12 authoritative databases, and numerous related regulatory documents. Data on molecular structures, physicochemical properties, chemical taxonomy, absorption, distribution, metabolism, excretion, toxicity properties, and physiological targets within the human body were integrated to afford the comprehensive food risk component database (FRCD, http://www.rxnfinder.org/frcd/). We also analyzed the molecular scaffold and chemical diversity, in addition to evaluating the toxicity and biodegradability of the food risk components. The FRCD could be considered a highly promising tool for future food safety studies.

[1]  Károly Héberger,et al.  Why is Tanimoto index an appropriate choice for fingerprint-based similarity calculations? , 2015, Journal of Cheminformatics.

[2]  Giorgia Purcaro,et al.  Determination of polycyclic aromatic hydrocarbons (PAHs) in commonly consumed Nigerian smoked/grilled fish and meat , 2009, Food additives & contaminants. Part A, Chemistry, analysis, control, exposure & risk assessment.

[3]  Jie Shen,et al.  admetSAR: A Comprehensive Source and Free Tool for Assessment of Chemical ADMET Properties , 2012, J. Chem. Inf. Model..

[4]  Evan Bolton,et al.  ClassyFire: automated chemical classification with a comprehensive, computable taxonomy , 2016, Journal of Cheminformatics.

[5]  G. Bemis,et al.  The properties of known drugs. 1. Molecular frameworks. , 1996, Journal of medicinal chemistry.

[6]  Dachuan Zhang,et al.  AdditiveChem: A comprehensive bioinformatics knowledge-base for food additive chemicals. , 2020, Food chemistry.

[7]  S. Ou,et al.  The Formation of Acrylamide from and Its Reduction by 3-Aminopropanamide Occur Simultaneously During Thermal Treatment. , 2018, Journal of food science.

[8]  Omry Koren,et al.  Dietary emulsifiers impact the mouse gut microbiota promoting colitis and metabolic syndrome , 2015, Nature.

[9]  Alfonso Lampen,et al.  Use of in silico models for prioritization of heat-induced food contaminants in mutagenicity and carcinogenicity testing , 2017, Archives of Toxicology.

[10]  Tao Jiang,et al.  A maximum common substructure-based algorithm for searching and predicting drug-like compounds , 2008, ISMB.

[11]  Jim Stevenson,et al.  Food additives and hyperactive behaviour in 3-year-old and 8/9-year-old children in the community: a randomised, double-blinded, placebo-controlled trial , 2007, The Lancet.

[12]  David Weininger,et al.  SMILES, a chemical language and information system. 1. Introduction to methodology and encoding rules , 1988, J. Chem. Inf. Comput. Sci..

[13]  Peter Ertl,et al.  JSME: a free molecule editor in JavaScript , 2013, Journal of Cheminformatics.

[14]  Agnes L Karmaus,et al.  Curation of food-relevant chemicals in ToxCast. , 2017, Food and chemical toxicology : an international journal published for the British Industrial Biological Research Association.

[15]  David S. Wishart,et al.  T3DB: the toxic exposome database , 2014, Nucleic Acids Res..

[16]  Xuelong Li,et al.  Learning k for kNN Classification , 2017, ACM Trans. Intell. Syst. Technol..

[17]  Yailé Caballero Mota,et al.  Application of KNN algorithm in determining the total antioxidant capacity of flavonoid-containing foods , 2015 .

[18]  Antony J. Williams,et al.  ToxCast Chemical Landscape: Paving the Road to 21st Century Toxicology. , 2016, Chemical research in toxicology.

[19]  David Hoksza,et al.  Scaffold analysis of PubChem database as background for hierarchical scaffold-based visualization , 2016, Journal of Cheminformatics.

[20]  Gang Fu,et al.  PubChem Substance and Compound databases , 2015, Nucleic Acids Res..

[21]  Roberto Kawakami Harrop Galvão,et al.  Binary classification of chalcone derivatives with LDA or KNN based on their antileishmanial activity and molecular descriptors selected using the Successive Projections Algorithm feature-selection technique. , 2014, European journal of pharmaceutical sciences : official journal of the European Federation for Pharmaceutical Sciences.

[22]  David Weininger,et al.  SMILES, 3. DEPICT. Graphical depiction of chemical structures , 1990, J. Chem. Inf. Comput. Sci..

[23]  Jacqueline M. Cole,et al.  ChemDataExtractor: A Toolkit for Automated Extraction of Chemical Information from the Scientific Literature , 2016, J. Chem. Inf. Model..

[24]  David S. Wishart,et al.  T3DB: a comprehensively annotated database of common toxins and their targets , 2009, Nucleic Acids Res..

[25]  C. Lino,et al.  A review on ochratoxin A occurrence and effects of processing of cereal and cereal derived food products. , 2010, Food microbiology.

[26]  Fatemeh Barzegar,et al.  Heterocyclic aromatic amines in cooked food: A review on formation, health risk-toxicology and their analytical techniques. , 2019, Food chemistry.

[27]  Matthias Lehr,et al.  Tetrazolylpropan-2-ones as inhibitors of fatty acid amide hydrolase: Studies on structure-activity relationships and metabolic stability. , 2018, European journal of medicinal chemistry.

[28]  Arthur Dalby,et al.  Description of several chemical structure file formats used by computer programs developed at Molecular Design Limited , 1992, J. Chem. Inf. Comput. Sci..

[29]  John M. Barnard,et al.  Substructure searching methods: Old and new , 1993, J. Chem. Inf. Comput. Sci..

[30]  P. Zancan,et al.  Effects of Food Additives on Immune Cells As Contributors to Body Weight Gain and Immune-Mediated Metabolic Dysregulation , 2017, Front. Immunol..

[31]  Agnes L Karmaus,et al.  Evaluation of food-relevant chemicals in the ToxCast high-throughput screening program. , 2016, Food and chemical toxicology : an international journal published for the British Industrial Biological Research Association.

[32]  Qiang Wang,et al.  Multiple toxicity endpoint-structure relationships for substituted phenols and anilines. , 2019, The Science of the total environment.

[33]  Yu Tian,et al.  PrecursorFinder: a customized biosynthetic precursor explorer , 2019, Bioinform..

[34]  Ann M Richard,et al.  Distributed structure-searchable toxicity (DSSTox) public database network: a proposal. , 2002, Mutation research.

[35]  Pietro Cozzini,et al.  FADB: a food additive molecular database for in silico screening in food toxicology , 2014, Food additives & contaminants. Part A, Chemistry, analysis, control, exposure & risk assessment.

[36]  Carlos M. Franco,et al.  Food additives, contaminants and other minor components: effects on human gut microbiota—a review , 2018, Journal of Physiology and Biochemistry.

[37]  Iltizam Nasrullah,et al.  Prediction of Hazard Identification and Characterization of Several Compounds used as Food Additives Applying In Silico Methods , 2015 .

[38]  Ruibo Wu,et al.  Exploring Chemical and Biological Space of Terpenoids , 2019, J. Chem. Inf. Model..