Predictive Toxicology and Toxicogenomics of Potassium Sorbate-Gene-Diseases Association

Document Type : Original Article

Authors

Centre for Environmental and Medical Sciences, PG & Research, Department of Advanced Zoology & Biotechnology, Loyola Institute of Frontier Energy (LIFE), Loyola College, University of Madras, Chennai, India-600034

Abstract

In this century, exposure to numerous chemical from different sources became common in human life. Conversely, the toxicological data for a large portion of chemicals for its risk assessment are unknown. Potassium sorbate (PS) is preservative used in wide variety of food, cosmetic and pharmaceutical products and there many authors reported about the effect of PS. This investigation is to integrate computational TGx and predictive toxicology and first report of potassium sorbate on this aspect. It was aimed in order to understand the potential adverse health effects of PS by ADMET prediction and their curated interactions between PS-gene–disease relationships. PreADMET and Comparative Toxicogenomics Database were used for the computational study. PreADMET revealed prediction data for ADME via physic-chemical parameter along with Caco-2 cell, MDCK cell and BBB (blood-brain barrier), HIA (human intestinal absorption), skin permeability and plasma protein binding and toxicological prediction using chemical structures, such as mutagenicity and carcinogenicity. CTD results established curated and inferred interactions between PS-gene–disease relationships. The CTD outcomes exposed that PS may possess endocrine disruption potency and have impact on endocrine system diseases etiology. It is concluded, that computational prediction approach offers both a better understanding of the potential risks of chemical exposure to humans and a direction for future toxicological investigation.

Keywords


  1. Pawar G., Madden J.C., Ebbrell D., Firman, J.W., Cronin M.T.D., 2019. In Silico Toxicology Data Resources to Support Read-Across and (Q)SAR. Front. Pharmacol. 10, 561.
  2. Kongsbak K., Hadrup N., Audouze K., Vinggaard A.M., 2014. Applicability of computational systems biology in toxicology. Basic Clin Pharmacol Toxicol 115, 45–49.
  3. Raies A.B., Bajic V.B., 2016. In silico toxicology: computational methods for the prediction of chemical toxicity: Computational methods for the prediction of chemical toxicity. WIREs Comput Mol Sci. 6, 147–172.
  4. Davis A.P., Wiegers J., Wiegers,T.C., Mattingly C.J., 2019. Public data sources to support systems toxicology applications. Curr Opin Toxicol. 16, 17–24.
  5. Taghavi F., Moosavi-Movahedi A.A., Bohlooli M., Alijanvand H.H., Salami M., Maghami P., Saboury A.A., Farhadi M., Yousefi R., Habibi-Rezaei M., Sheibani N., 2013. Potassium sorbate as an AGE activator for human serum albumin in the presence and absence of glucose. International Journal of Biological Macromolecules. 62, 146–154.
  6. Cheng F., Li W., Zhou Y., Li J., Shen J., Lee P.W., Tang Y., 2013. Prediction of human genes and diseases targeted by xenobiotics using predictive toxicogenomic-derived models (PTDMs). Mol. Bio Syst. 9, 1316.
  7. Lee S.K., Chang G.S., Lee I.H., Chung J.E., Sung K.Y., No K.T., 2004. The PreADME: PC-Based Program for Batch Prediction of ADMET Properties. Presented at the EuroQSAR 2004, Istanbul, Turkey. 9.5-10.
  8. Davis A.P., Murphy C.G., Saraceni-Richards C.A., Rosenstein M.C., Wiegers T.C., Mattingly C.J., 2009. Comparative Toxicogenomics Database: a knowledgebase and discovery tool for chemical-gene-disease networks. Nucleic Acids Res. 37, D786-792.
  9. Tsaioun K., 2016. Evidence-based absorption, distribution, metabolism, excretion (ADME) and its interplay with alternative toxicity methods. ALTEX. 343–358.
  10. Davis A.P., Grondin C.J., Johnson R.J., Sciaky D., McMorran R., Wiegers J., Wiegers T.C., Mattingly C.J., 2019. The Comparative Toxicogenomics Database: update 2019. Nucleic Acids Res. 47, D948–D954.
  11. Boyle E.I., Weng S., Gollub J., Jin H., Botstein D., Cherry J.M., Sherlock G., 2004. GO: TermFinder—open source software for accessing Gene Ontology information and finding significantly enriched Gene Ontology terms associated with a list of genes. Bioinformatics. 20, 3710–3715.
  12. Ursula G.R., Ralf S., 2010. The blood-brain barrier in toxicology. Front Pharmacol. 1.
  13. Gupta R.C., Pitt J., Zaja-Milatovic S., 2020. Blood–brain barrier damage and dysfunction by chemical toxicity, in: Handbook of Toxicology of Chemical Warfare Agents. Elsevier. pp. 811–827.
  14. Angelis I.D., Turco L., 2011. Caco‐2 Cells as a Model for Intestinal Absorption. Current Protocols in Toxicology. 47. https:// doi.org/ 10.1002/ 0471140856. tx2006s47.
  15. Hakkola J., Hukkanen J., Turpeinen M., Pelkonen O., 2020. Inhibition and induction of CYP enzymes in humans: an update. Arch Toxicol. 94, 3671–3722.
  16. Efferth T., Volm M., 2017. Multiple resistances to carcinogens and xenobiotics: P-glycoproteins as universal detoxifiers. Arch Toxicol. 91, 2515–2538.
  17. Svennebring A., 2016. The impact of plasma protein binding on toxic plasma drug concentration. IJCBDD. 9, 345.
  18. Karadzovska D., Brooks J.D., Monteiro-Riviere N.A., Riviere J.E., 2013. Predicting skin permeability from complex vehicles. Adv Drug Deliv Rev. 65, 265–277.
  19. Mitragotri S., Anissimov Y.G., Bunge A.L., Frasch H.F., Guy R.H., Hadgraft J., Kasting G.B., Lane M.E., Roberts, M.S., 2011. Mathematical models of skin permeability: An overview. International Journal of Pharmaceutics. 418, 115–129.
  20. Chen H.H., Xu, X.L., Shang Y., Jiang, J.G., 2017. Comparative toxic effects of butylparaben sodium, sodium diacetate and potassium sorbate to Dunaliella tertiolecta and HL7702 cells. Food Funct. 8, 4478–4486.
  21. Wypych G., Wypych A., 2015. Potassium (E,E)-hexa-2,4-dienoate, in: Databook of Preservatives. Elsevier. pp. 185–186.
  22. Jung R., Cojocel C., Müller W., Böttger D., Lück E., 1992. Evaluation of the genotoxic potential of sorbic acid and potassium sorbate. Food Chem Toxicol. 30, 1–7.
  23. Davis A.P., Murphy C.G., Rosenstein M.C., Wiegers T.C., Mattingly C.J., 2008. The Comparative Toxicogenomics Database facilitates identification and understanding of chemical-gene-disease associations: arsenic as a case study. BMC Med Genomics. 1, 48.
  24. Grondin C.J., Davis A.P., Wiegers T.C., King B.L., Wiegers J.A., Reif D.M., Hoppin J.A., Mattingly C.J., 2016. Advancing Exposure Science through Chemical Data Curation and Integration in the Comparative Toxicogenomics Database. Environmental Health Perspectives. 124, 1592–1599.
  25. Davis A.P., Wiegers T.C., King B.L., Wiegers J., Grondin C.J., Sciaky D., Johnson R.J., Mattingly C.J., 2016. Generating Gene Ontology-Disease Inferences to Explore Mechanisms of Human Disease at the Comparative Toxicogenomics Database. PLoS One. 11, e0155530.
  26. Pivonello C., Muscogiuri G., Nardone A., Garifalos F., Provvisiero D.P., Verde N., de Angelis C., Conforti A., Piscopo M., Auriemma R.S., Colao A., Pivonello R., 2020. Bisphenol A: an emerging threat to female fertility. Reprod Biol Endocrinol. 18, 22.
  27. Björnström L., Sjöberg M., 2005. Mechanisms of Estrogen Receptor Signaling: Convergence of Genomic and Nongenomic Actions on Target Genes. Molecular Endocrinology. 19, 833–842.
  28. Calaf G., Ponce Cusi R., Aguayo F., Munoz J., Bleak T., 2020. Endocrine disruptors from the environment affecting breast cancer (Review). Oncol Lett. 20(1), 19–32.

29.Ma Y., Liu H., Wu J., Yuan L., Wang Y., Du X., Wang R., Marwa P.W., Petlulu P., Chen X., Zhang H.,

  1. The adverse health effects of bisphenol A and related toxicity mechanisms. Environmental Research. 176, 108575.

30.Vidal M., 2009. A unifying view of 21st century systems biology. FEBS Letters. 583, 3891–3894.

31.Vidal M., Cusick M.E., Barabási A.L., 2011. Interactome Networks and Human Disease. Cell. 144, 986–998.