Advertisement

Application of Computational Methods for the Safety Assessment of Food Ingredients

  • Patra VolarathEmail author
  • Yu (Janet) Zang
  • Shruti V. Kabadi
Chapter
  • 439 Downloads
Part of the Challenges and Advances in Computational Chemistry and Physics book series (COCH, volume 30)

Abstract

At the Office of Food Additive Safety (OFAS) in the Center for Food Safety and Applied Nutrition at the United States Food and Drug Administration, scientists review toxicological data submitted by industry or published in scientific journals as a part of premarket safety assessments of food ingredients. OFAS also reviews relevant safety data during postmarket assessments of food ingredients as new toxicological data or exposure information become available. OFAS is committed to maintaining a high standard of science-based safety reviews and to staying abreast of novel computational approaches used by industry that could add value to improve safety assessments of food ingredients. In this chapter, we discuss some computational approaches, including quantitative structure–activity relationships, toxicokinetic modeling and simulation, and bioinformatics, as well as OFAS’s in-house food ingredient knowledgebase. We describe the scientific utility of these computational approaches for improving the efficiency of the review process and reducing uncertainties in decisions about the safe use of food ingredients and highlight some challenges with their use for food ingredient safety assessments.

Keywords

QSAR SAR TK/PBTK Database Bioinformatics Food ingredients Safety Allergenicity IVIVE 

Abbreviations

3D

Three dimensional

5:3 acid

5:3 fluorotelomer carboxylic acid

AA

Amino acid

AOL

AllergenOnline database

AUC

Area Under the Curve

BBDR

Biologically based dose response

CASRN

Chemical Abstract Service Registration Numbers

CERES

Chemical Evaluation and Risk Estimation System

CosIng

Cosmetic Ingredient

CR

Compound Registration

CRS-IDs

CERES compound identifiers

CXFSAN

Center for Food Safety and Applied Nutrition

DSSTox

Distributed Structure-Searchable Toxicity

E-memos

Electronic memoranda

FAO

Food and Agriculture Organization

FARM

Food Application Regulatory Management System

FARRP

Food Allergy Research and Resource Program

FCN

Food contact notification

FCS

Food contact substance

FOIA

Freedom of Information Act

FTOH

Fluorotelomer alcohol

GSIDs

General Substance Identifiers

GRAS

Generally recognized as safe

HPT

Hypothalamic–pituitary–thyroid

IgE

Immunoglobulin E

IUIS

International Union of Immunological Societies

IVIVE

In vitro to in vivo extrapolation

Km

Michaelis–Menten constant

NCBI

National Center for Biotechnology Information

OFAS

Office of Food Additive Safety

PAFA

Priority-Based Assessment of Food Additive

PBTK

Physiologically based toxicokinetic

PDF

Portable Document Format

PLETHEM

Population Lifecourse Exposure-to-Health-Effects Models

PNC

Pre-notification consultation

QSAR

Quantitative Structure Activity Relationship

RCA

Research Collaboration Agreements

SAR

Structure–Activity Relationship

SD

Structure data

SMILES

Simplified molecular-input line-entry system

STARI

Scientific Terminology and Regulatory Information

Notes

Acknowledgements

The authors would like to acknowledge the following peer reviewers from the FDA for their intellectual contribution to the chapter: Dr. Michael Adams (OFAS, CFSAN), Dr. Kirk Arvidson (OFAS, CFSAN), Dr. Jason Aungst (OFAS, CFSAN), Dr. Omari Bandele (OFAS, CFSAN), Dr. Supratim Choudhuri (OFAS, CFSAN), Dr. Mary Ditto (OFAS, CFSAN), Dr. Jeffrey Fisher (OR, NCTR), Dr. Suzanne Fitzpatrick (OCD, CFSAN), Celeste Johnston (OFAS, CFSAN), Dr. Antonia Mattia (OFAS, CFSAN), Dr. Geoffrey Patton (OFAS, CFSAN), Dr. Catherine Whiteside (OFAS, CFSAN), and Andrew Zajac (OFAS, CFSAN).

Disclaimer The views and data interpretations expressed in this chapter represent that of the authors and not necessarily of the US FDA. The use of trade names, company names, product names, etc., is for example/illustrative purposes only and should not be construed as endorsements by the FDA or the authors.

References

  1. 1.
    CFSAN-FDA. Redbook: guidance for industry and other stakeholders. Toxicological principles for the safety assessment of food ingredients, College Park, MD: CFSAN, US FDA, DHHS, 1993 Revised July 2007. Report No. https://www.fda.gov/downloads/food/guidanceregulation/ucm222779.pdf
  2. 2.
    FDA (2007) Guidance for industry: preparation of premarket submissions for food contact substances (chemistry recommendations), College Park, MD. https://www.fda.gov/Food/GuidanceRegulation/GuidanceDocumentsRegulatoryInformation/IngredientsAdditivesGRASPackaging/ucm081818.htm
  3. 3.
    FDA (2009) Guidance for industry: recommendations for submission of chemical and technological data for direct food additive petitions, College Park, MD. https://www.fda.gov/Food/GuidanceRegulation/ucm124917.htm
  4. 4.
    FDA (2002) Guidance for industry: preparation of food contact notifications for food contact substances (toxicology recommendations). DFCN/OFAS/CFSAN, College Park, MD. https://www.fda.gov/food/guidanceregulation/guidancedocumentsregulatoryinformation/ingredientsadditivesgraspackaging/ucm081825.htm
  5. 5.
  6. 6.
    Workgroup EM, Marshall SF, Burghaus R, Cosson V, Cheung SY, Chenel M, DellaPasqua O, Frey N, Hamren B, Harnisch L, Ivanow F, Kerbusch T, Lippert J, Milligan PA, Rohou S, Staab A, Steimer JL, Tornoe C, Visser SA (2016) Good practices in model-informed drug discovery and development: practice, application, and documentation. CPT Pharmacometrics Syst Pharmacol 5(3):93–122CrossRefGoogle Scholar
  7. 7.
    Roy K, Kar S, Das RN (2015) Chemical information and descriptors. Understanding the basics of QSAR for applications in pharmaceutical sciences and risk assessment. Academic Press, TokyoCrossRefGoogle Scholar
  8. 8.
    Danishuddin, Khan AU (2016) Descriptors and their selection methods in QSAR analysis: paradigm for drug design. Drug Discov Today 21(8):1291–1302CrossRefGoogle Scholar
  9. 9.
    Parthasarathi R, Dhawan A (2018) In silico approaches for predictive toxicology. In: Dhawan AK, Kwon S (eds) In vitro toxicology. Academic Press, pp 91–109Google Scholar
  10. 10.
    Arvidson KB, Chanderbhan R, Muldoon-Jacobs K, Mayer J, Ogungbesan A (2010) Regulatory use of computational toxicology tools and databases at the United States Food and Drug Administration’s Office of Food Additive Safety. Expert Opin Drug Metab Toxicol 6(7):793–796CrossRefGoogle Scholar
  11. 11.
    FDA (2018) FDA Cooperative Research and Development Agreements (CRADAs). https://www.fda.gov/ScienceResearch/CollaborativeOpportunities/CooperativeResearchandDevelopmentAgreementsCRADAs/ucm122820.htm (cited 2018 06/06)
  12. 12.
    Leadscope (2018) Leadscope: enterprise and model applier. http://www.leadscope.com/ (cited 2018 06/06)
  13. 13.
    Lhasa Limited (2018) Lhasa Limited: Derek Nexus. https://www.lhasalimited.org/products/derek-nexus.htm (cited 2018 06/06)
  14. 14.
    Lhasa Limited (2018) Lhasa Limited: Vitic Nexus. https://www.lhasalimited.org/products/vitic-nexus.htm (cited 2018 06/06)
  15. 15.
    MultiCASE (2017) MultiCASE. http://www.multicase.com/ (04/07/2018)
  16. 16.
    Valencia A, Prous J, Mora O, Sadrieh N, Valerio LG Jr (2013) A novel QSAR model of Salmonella mutagenicity and its application in the safety assessment of drug impurities. Toxicol Appl Pharmacol 273(3):427–434CrossRefGoogle Scholar
  17. 17.
    ACD/Labs (2018) ACD/labs. https://www.acdlabs.com/support/version/ (07/08/2018)
  18. 18.
    MNAM (2018) ChemTunes: https://www.mn-am.com/products/chemtunes (cited 04/07/2018)
  19. 19.
    NLM (2018) ChemIDplus. https://chem.nlm.nih.gov/chemidplus/ (cited 2018 06/06)
  20. 20.
    NLM (2018) TOXNET. https://toxnet.nlm.nih.gov/ (cited 2018 06/06)
  21. 21.
    Ideaconsult Ltd (2018) Toxtree. http://toxtree.sourceforge.net/ (cited 2018 06/06)
  22. 22.
    Welling PG (1995) Differences between pharmacokinetics and toxicokinetics. Toxicol Pathol 23(2):143–147CrossRefGoogle Scholar
  23. 23.
    Shen DD (2013) Toxicokinetics. In: Klaassen CD (ed) Cassarett and Doull’s toxicology: the basic science of poisons (8th edn). McGraw-Hill Professional PublishingGoogle Scholar
  24. 24.
    Dhillon SG, Gill K (2006) Basic pharmacokinetics. In: Dhillon S (ed) Clinical pharmacokinetics (1st edn). Pharmaceutical PressGoogle Scholar
  25. 25.
    Withey JR (1978) The toxicology of styrene monomer and its pharmacokinetics and distribution in the rat. Scand J Work Environ Health 4(Suppl 2):31–40CrossRefGoogle Scholar
  26. 26.
    Withey JR, Collins PG (1977) Pharmacokinetics and distribution of styrene monomer in rats after intravenous administration. J Toxicol Environ Health 3(5–6):1011–1020CrossRefGoogle Scholar
  27. 27.
    Withey JR, Collins PG (1978) Styrene monomer in foods a limited Canadian Survey. Bull Environ Contam Toxicol 19(1):86–94CrossRefGoogle Scholar
  28. 28.
    Withey JR, Collins PG (1979) The distribution and pharmacokinetics of styrene monomer in rats by the pulmonary route. J Environ Pathol Toxicol 2(6):1329–1342PubMedPubMedCentralGoogle Scholar
  29. 29.
    Withey JR, Karpinski K (1985) Fetal distribution of styrene in rats after vapor phase exposures. Biol Res Pregnancy Perinatol 6(2):59–64PubMedPubMedCentralGoogle Scholar
  30. 30.
    Cruzan G, Cushman JR, Andrews LS, Granville GC, Johnson KA, Bevan C, Hardy CJ, Coombs DW, Mullins PA, Brown WR (2001) Chronic toxicity/oncogenicity study of styrene in CD-1 mice by inhalation exposure for 104 weeks. J Appl Toxicol 21(3):185–198CrossRefGoogle Scholar
  31. 31.
    NCI. Bioassay of styrene for possible carcinogenicity. NCI, 1979 NCI-CG-TR-185Google Scholar
  32. 32.
    Cruzan G, Bus JS, Andersen ME, Carlson GP, Banton MI, Sarang SS, Waites R (2018) Based on an analysis of mode of action, styrene-induced mouse lung tumors are not a human cancer concern. Regul Toxicol Pharmacol 95:17–28CrossRefGoogle Scholar
  33. 33.
    Rosenbaum SE (2016) Introduction to noncompartmental analysis. In: Rosenbaum SE (ed) Basic pharmacokinetics and pharmacodynamics: an integrated textbook and computer simulations (2nd edn). Wiley, HobokenGoogle Scholar
  34. 34.
    Russell MH, Himmelstein MW, Buck RC (2015) Inhalation and oral toxicokinetics of 6:2 FTOH and its metabolites in mammals. Chemosphere 120:328–335CrossRefGoogle Scholar
  35. 35.
    Kabadi SV, Fisher J, Aungst J, Rice P (2018) Internal exposure-based pharmacokinetic evaluation of potential for biopersistence of 6:2 fluorotelomer alcohol (FTOH) and its metabolites. Food Chem Toxicol 112:375–382CrossRefGoogle Scholar
  36. 36.
    Yang RS, Dennison JE, Andersen ME, Ou YC, Liao KH, Reisfeld B (2004) Physiologically based pharmacokinetic and pharmacodynamic modeling. In: Holland EC (ed) Mouse models of human cancer. Wiley, pp 391–405Google Scholar
  37. 37.
    Andersen ME, Krishnan K (1994) Physiologically based pharmacokinetics and cancer risk assessment. Environ Health Perspect 102(Suppl 1):103–108CrossRefGoogle Scholar
  38. 38.
    Clewell HJ 3rd, Andersen ME (1994) Physiologically-based pharmacokinetic modeling and bioactivation of xenobiotics. Toxicol Ind Health 10(1–2):1–24CrossRefGoogle Scholar
  39. 39.
    Krishnan K, Clewell HJ 3rd, Andersen ME (1994) Physiologically based pharmacokinetic analyses of simple mixtures. Environ Health Perspect 102(Suppl 9):151–155CrossRefGoogle Scholar
  40. 40.
    Ramsey JC, Andersen ME (1984) A physiologically based description of the inhalation pharmacokinetics of styrene in rats and humans. Toxicol Appl Pharmacol 73(1):159–175CrossRefGoogle Scholar
  41. 41.
    Csanady GA, Mendrala AL, Nolan RJ, Filser JG (1994) A physiologic pharmacokinetic model for styrene and styrene-7,8-oxide in mouse, rat and man. Arch Toxicol 68(3):143–157CrossRefGoogle Scholar
  42. 42.
    Sarangapani R, Teeguarden JG, Cruzan G, Clewell HJ, Andersen ME (2002) Physiologically based pharmacokinetic modeling of styrene and styrene oxide respiratory-tract dosimetry in rodents and humans. Inhal Toxicol 14(8):789–834CrossRefGoogle Scholar
  43. 43.
    McLanahan ED, Andersen ME, Fisher JW (2008) A biologically based dose-response model for dietary iodide and the hypothalamic-pituitary-thyroid axis in the adult rat: evaluation of iodide deficiency. Toxicol Sci 102(2):241–253CrossRefGoogle Scholar
  44. 44.
    McLanahan ED, White P, Flowers L, Schlosser PM (2014) The use of PBPK models to inform human health risk assessment: case study on perchlorate and radioiodide human lifestage models. Risk Anal 34(2):356–366CrossRefGoogle Scholar
  45. 45.
    Schlosser PM (2016) Revision of the affinity constant for perchlorate binding to the sodium-iodide symporter based on in vitro and human in vivo data. J Appl Toxicol 36(12):1531–1535CrossRefGoogle Scholar
  46. 46.
    Aalberse RC, Stadler BM (2006) In silico predictability of allergenicity: from amino acid sequence via 3-D structure to allergenicity. Mol Nutr Food Res 50(7):625–627CrossRefGoogle Scholar
  47. 47.
    Commission CA. Guideline for the conduct of food safety assessment of foods derived from recombinant-DNA plants. Annex on the Assessment of Possible Allergenicity. 2003 CAC/GL 45-2003Google Scholar
  48. 48.
    Commission CA (2009) Foods derived from modern biotechnology. Italy, RomeGoogle Scholar
  49. 49.
    Gendel SM (2009) Allergen databases and allergen semantics. Regul Toxicol Pharmacol 54(3 Suppl):S7–S10CrossRefGoogle Scholar
  50. 50.
    Goodman RE, Ebisawa M, Ferreira F, Sampson HA, van Ree R, Vieths S, Baumert JL, Bohle B, Lalithambika S, Wise J, Taylor SL (2016) AllergenOnline: a peer-reviewed, curated allergen database to assess novel food proteins for potential cross-reactivity. Mol Nutr Food Res 60(5):1183–1198CrossRefGoogle Scholar
  51. 51.
    Choudhuri S (2004) Bioinformatics for beginners: genes, genomes, molecular evolution, databases and analytical tools, LondonGoogle Scholar
  52. 52.
    Remington B, Broekman HCH, Blom WM, Capt A, Crevel RWR, Dimitrov I, Faeste CK, Fernandez-Canton R, Giavi S, Houben GF, Glenn KC, Madsen CB, Kruizinga AK, Constable A (2018) Approaches to assess IgE mediated allergy risks (sensitization and cross-reactivity) from new or modified dietary proteins. Food Chem Toxicol 112:97–107CrossRefGoogle Scholar
  53. 53.
    Herman RA, Song P, Thirumalaiswamysekhar A (2009) Value of eight-amino-acid matches in predicting the allergenicity status of proteins: an empirical bioinformatic investigation. Clin Mol Allergy 7:9CrossRefGoogle Scholar
  54. 54.
    FAO/WHO (2001) Evaluation of allergenicity of genetically modified foodsGoogle Scholar
  55. 55.
    Aalberse RC (2000) Structural biology of allergens. J Allergy Clin Immunol 106(2):228–238CrossRefGoogle Scholar
  56. 56.
    Herman RA, Song P, Kumpatla S (2015) Percent amino-acid identity thresholds are not necessarily conservative for predicting allergenic cross-reactivity. Food Chem Toxicol 81:141–142CrossRefGoogle Scholar
  57. 57.
    Cressman RF, Ladics G (2009) Further evaluation of the utility of “sliding window” FASTA in predicting cross-reactivity with allergenic proteins. Regul Toxicol Pharmacol 54(3 Suppl):S20–S25CrossRefGoogle Scholar
  58. 58.
    Ladics GS (2008) Current codex guidelines for assessment of potential protein allergenicity. Food Chem Toxicol 46(Suppl 10):S20–S23CrossRefGoogle Scholar
  59. 59.
    Benz RD, Irausquin H (1991) Priority-based assessment of food additives database of the U.S. Food and Drug Administration Center for Food Safety and Applied Nutrition. Environ Health Perspect 96:85–89CrossRefGoogle Scholar
  60. 60.
    Cherkasov A, Muratov EN, Fourches D, Varnek A, Baskin II, Cronin M, Dearden J, Gramatica P, Martin YC, Todeschini R, Consonni V, Kuz’min VE, Cramer R, Benigni R, Yang C, Rathman J, Terfloth L, Gasteiger J, Richard A, Tropsha A (2014) QSAR modeling: where have you been? Where are you going to? J Med Chem 57(12):4977–5010CrossRefGoogle Scholar
  61. 61.
    Arvidson K, Rathman J, Volarath P, Mostrag A, Tarkhov A, Bienfait B, Vitcheva V, Yang C (eds) Evaluation of the chemical inventories in the US FDA’s Office of Food Additive Safety for human health endpoints using a toxicity prediction system. In: 55th Society of toxicology annual meeting 2016, New Orleans, LAGoogle Scholar
  62. 62.
    FDA (2017) U.S. FDA Substances Added to Food Inventory (formerly EAFUS). https://www.accessdata.fda.gov/scripts/fdcc/?set=FoodSubstances
  63. 63.
    FDA (2017) U.S. FDA inventory of effective food contact substance (FCS) notifications. https://www.fda.gov/food/ingredientspackaginglabeling/packagingfcs/notifications/default.htm
  64. 64.
    FDA (2018) U.S. FDA inventory of GRAS notices. https://www.accessdata.fda.gov/scripts/fdcc/?set=GRASNotices
  65. 65.
    FDA (2018) U.S. FDA List of Indirect Additives used in Food Contact substances. https://www.fda.gov/food/ingredientspackaginglabeling/packagingfcs/indirectadditives/default.htm
  66. 66.
    FDA (2018) U.S. FDA GRAS Substances (SCOGS) Database. https://www.fda.gov/food/ingredientspackaginglabeling/gras/scogs/default.htm
  67. 67.
    Price K, Krishnan K (2011) An integrated QSAR-PBPK modelling approach for predicting the inhalation toxicokinetics of mixtures of volatile organic chemicals in the rat. SAR QSAR Environ Res 22(1–2):107–128CrossRefGoogle Scholar
  68. 68.
    Peyret T, Krishnan K (2011) QSARs for PBPK modelling of environmental contaminants. SAR QSAR Environ Res 22(1–2):129–169CrossRefGoogle Scholar
  69. 69.
    Peyret T, Poulin P, Krishnan K (2010) A unified algorithm for predicting partition coefficients for PBPK modeling of drugs and environmental chemicals. Toxicol Appl Pharmacol 249(3):197–207CrossRefGoogle Scholar
  70. 70.
    Tan YM, Worley RR, Leonard JA, Fisher JW (2018) Challenges associated with applying physiologically based pharmacokinetic modeling for public health decision-making. Toxicol Sci 162(2):341–348CrossRefGoogle Scholar
  71. 71.
    Pearce R, Woodrow Setzer R, Strope C, Sipes N, Wambaugh J (2017) HTTK: R package for high-throughput toxicokinetics. J Stat Softw 79(4):1–26CrossRefGoogle Scholar
  72. 72.
    ScitoVation. PLETHEM 2017 (cited 2017). Available from http://www.scitovation.com/plethem.html
  73. 73.
    Bale AS, Kenyon E, Flynn TJ, Lipscomb JC, Mendrick DL, Hartung T, Patton GW (2014) Correlating in vitro data to in vivo findings for risk assessment. ALTEX 31(1):79–90CrossRefGoogle Scholar
  74. 74.
    Barter ZE, Bayliss MK, Beaune PH, Boobis AR, Carlile DJ, Edwards RJ, Houston JB, Lake BG, Lipscomb JC, Pelkonen OR, Tucker GT, Rostami-Hodjegan A (2007) Scaling factors for the extrapolation of in vivo metabolic drug clearance from in vitro data: reaching a consensus on values of human microsomal protein and hepatocellularity per gram of liver. Curr Drug Metab 8(1):33–45CrossRefGoogle Scholar
  75. 75.
    Lipscomb JC, Poet TS (2008) In vitro measurements of metabolism for application in pharmacokinetic modeling. Pharmacol Ther 118(1):82–103CrossRefGoogle Scholar
  76. 76.
    Sweeney LM, Himmelstein MW, Gargas ML (2001) Development of a preliminary physiologically based toxicokinetic (PBTK) model for 1,3-butadiene risk assessment. Chem Biol Interact 135–136:303–322CrossRefGoogle Scholar
  77. 77.
    Johanson G, Filser JG (1996) PBPK model for butadiene metabolism to epoxides: quantitative species differences in metabolism. Toxicology 113(1–3):40–47CrossRefGoogle Scholar
  78. 78.
    Kohn MC, Melnick RL (2000) The privileged access model of 1,3-butadiene disposition. Environ Health Perspect 108(Suppl 5):911–917CrossRefGoogle Scholar
  79. 79.
    Martin SA, McLanahan ED, Bushnell PJ, Hunter ES 3rd, El-Masri H (2015) Species extrapolation of life-stage physiologically-based pharmacokinetic (PBPK) models to investigate the developmental toxicology of ethanol using in vitro to in vivo (IVIVE) methods. Toxicol Sci 143(2):512–535CrossRefGoogle Scholar
  80. 80.
    Campbell J, Van Landingham C, Crowell S, Gentry R, Kaden D, Fiebelkorn S, Loccisano A, Clewell H (2015) A preliminary regional PBPK model of lung metabolism for improving species dependent descriptions of 1,3-butadiene and its metabolites. Chem Biol Interact 238:102–110CrossRefGoogle Scholar
  81. 81.
    Mirsky HP, Cressman RF Jr, Ladics GS (2013) Comparative assessment of multiple criteria for the in silico prediction of cross-reactivity of proteins to known allergens. Regul Toxicol Pharmacol 67(2):232–239CrossRefGoogle Scholar
  82. 82.
    Song P, Herman RA, Kumpatla S (2014) Evaluation of global sequence comparison and one-to-one FASTA local alignment in regulatory allergenicity assessment of transgenic proteins in food crops. Food Chem Toxicol 71:142–148CrossRefGoogle Scholar
  83. 83.
    Pearson WR (2016) Finding protein and nucleotide similarities with FASTA. Curr Protoc Bioinform 53(1):3–9Google Scholar
  84. 84.
    Silvanovich A, Bannon G, McClain S (2009) The use of E-scores to determine the quality of protein alignments. Regul Toxicol Pharmacol 54(3 Suppl):S26–S31CrossRefGoogle Scholar
  85. 85.
    Willett PB, Barnard JM, Downs GM (1998) Chemical similarity searching. J Chem Inf Comput Sci 38(6):983–996CrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Patra Volarath
    • 1
    Email author
  • Yu (Janet) Zang
    • 1
  • Shruti V. Kabadi
    • 1
  1. 1.FDA/CFSAN/OFASCollege ParkUSA

Personalised recommendations