Improved Model for Biodegradability of Organic Compounds: The Correlation Contributions of Rings

Protocol
Part of the Methods in Pharmacology and Toxicology book series (MIPT)

Abstract

The CORAL software was utilized to build up predictive model for biodegradability of organic compounds. The model was calculated with correlation weights of attributes of simplified molecular input-line entry system (SMILES). The previous model of the endpoint calculated with the CORAL software has been based on the attributes extracted from SMILES, which reflect the presence of various atoms and covalent bonds. In this work, the attributes of different rings (size, presence of heteroatoms) are involved in the modeling process. The comparison of these models with models where rings were not taken into account has shown significant improvement of the statistical quality of the biodegradation prediction.

Key words

QSAR Biodegradability Monte Carlo method CORAL software 

Notes

Acknowledgment

Authors thank the LIFE-COMBASE contract (LIFE15 ENV/ES/000416) for financial support.

Glossary

QSAR

quantitative structure – activity relationships

CWs

correlation weights

SMILES

simplified molecular input-line entry system

CORAL

correlation and logic

RMSE

root-mean square error

R

correlation coefficient

q

leave-one-out cross-validated correlation coefficient

HSG

hydrogen suppressed graph

References

  1. 1.
    Trautwein C, Kümmerer K (2012) Ready biodegradability of trifluoro methylated phenothiazine drugs, structural elucidation of their aquatic transformation products, and identification of environmental risks studied by LC-MSn and QSAR. Environ Sci Pollut Res 19:3162–3177CrossRefGoogle Scholar
  2. 2.
    Gutowski L, Baginska E, Olsson O, Leder C, Kümmerer K (2015) Assessing the environmental fate of S-metolachlor, its commercial product Mercantor Gold and their photoproducts using a water–sediment test and in silico methods. Chemosphere 138:847–855CrossRefPubMedGoogle Scholar
  3. 3.
    Toropov AA, Toropova AP, Lombardo A, Roncaglioni A, De Brita N, Stella G, Benfenati E (2012) CORAL: the prediction of biodegradation of organic compounds with optimal SMILES-based descriptors. Cent Eur J Chem 10:1042–1048Google Scholar
  4. 4.
    Khaleel NDH, Mahmoud WMM, Olsson O, Kümmerer K (2017) Initial fate assessment of teratogenic drug trimipramine and its phototransformation products – role of pH, concentration and temperature. Water Res 108:197–211CrossRefPubMedGoogle Scholar
  5. 5.
    Lapertot ME, Pulgarin C (2006) Biodegradability assessment of several priority hazardous substances: choice, application and relevance regarding toxicity and bacterial activity. Chemosphere 65:682–690CrossRefPubMedGoogle Scholar
  6. 6.
    Khaleel NDH, Mahmoud WMM, Olsson O, Kümmerer K (2016) UV-photodegradation of desipramine: impact of concentration, pH and temperature on formation of products including their biodegradability and toxicity. Sci Total Environ 566–567:826–840CrossRefPubMedGoogle Scholar
  7. 7.
    Schaafers C, Boshof U, Jurling H, Belanger SE, Sanderson H, Dyer SD, Nielsen AM, Willing A, Gamone K, Kasai Y, Eadsforth CV, Fisk PR, Girling AE (2009) Environmental properties of long-chain alcohols, part 2: structure–activity relationship for chronic aquatic toxicity of long-chain alcohols. Ecotoxicol Environ Saf 72:996–1005CrossRefGoogle Scholar
  8. 8.
    Rastogi T, Leder C, Kümmerer K (2014) Qualitative environmental risk assessment of photolytic transformation products of iodinated X-ray contrast agent diatrizoic acid. Sci Total Environ 482–483:378–388CrossRefPubMedGoogle Scholar
  9. 9.
    Toolaram AP, Kummerer K, Schneider M (2014) Environmental risk assessment of anti-cancer drugs and their transformation products: a focus on their genotoxicity characterization-state of knowledge and short comings. Mutat Res Rev Mutat Res 760:18–35CrossRefGoogle Scholar
  10. 10.
    Yin LI, Dan-li XI (2007) Quantitative structure-activity relationship study on the biodegradation of acid dyestuffs. J Environ Sci 19:800–804CrossRefGoogle Scholar
  11. 11.
    Bertelkamp C, Reungoat J, Cornelissen ER, Singhal N, Reynisson J, Cabo AJ, van der Hoek JP, Verliefde ARD (2014) Sorption and biodegradation of organic micropollutants during river bank filtration: a laboratory column study. Water Res 52:231–241CrossRefPubMedGoogle Scholar
  12. 12.
    Xu X, Li XG, Sun SW (2012) A QSAR study on the biodegradation activity of PAHs in aged contaminated sediments. Chemometr Intell Lab Syst 114:50–55CrossRefGoogle Scholar
  13. 13.
    Rastogi T, Leder C, Kümmerer K (2014) Designing green derivatives of b-blocker metoprolol: a tiered approach for green and sustainable pharmacy and chemistry. Chemosphere 111:493–499CrossRefPubMedGoogle Scholar
  14. 14.
    Yu JT, Bouwer EJ, Coelhan M (2006) Occurrence and biodegradability studies of selected pharmaceuticals and personal care products in sewage effluent. Agric Water Manag 86:72–80CrossRefGoogle Scholar
  15. 15.
    Wilde ML, Menz J, Trautwein C, Leder C, Kümmerer K (2016) Environmental fate and effect assessment of thioridazine and its transformation products formed by photodegradation. Environ Pollut 213:658–670CrossRefPubMedGoogle Scholar
  16. 16.
    Ceriani L, Papa E, Kovarich S, Boethling R, Gramatica P (2015) Modeling ready biodegradability of fragrance materials. Environ Toxicol Chem 34:1224–1231CrossRefPubMedGoogle Scholar
  17. 17.
    Damborskyl J, Schultz TW (1997) Comparison of the qsar models for toxicity and biodegradability of anilines and phenols. Chemosphere 34:429–446CrossRefGoogle Scholar
  18. 18.
    Toropova AP, Toropov AA, Benfenati E, Gini G, Leszczynska D, Leszczynski J (2011) CORAL: quantitative structure-activity relationship models for estimating toxicity of organic compounds in rats. J Comput Chem 32:2727–2733CrossRefPubMedGoogle Scholar
  19. 19.
    Toropov AA, Toropova AP, Benfenati E, Gini G, Puzyn T, Leszczynska D, Leszczynski J (2012) Novel application of the CORAL software to model cytotoxicity of metal oxide nanoparticles to bacteria Escherichia coli. Chemosphere 89:1098–1102CrossRefPubMedGoogle Scholar
  20. 20.
    Toropov AA, Toropova AP, Puzyn T, Benfenati E, Gini G, Leszczynska D, Leszczynski J (2013) QSAR as a random event: modeling of nanoparticles uptake in PaCa2 cancer cells. Chemosphere 92:31–37CrossRefPubMedGoogle Scholar
  21. 21.
    Toropov AA, Toropova AP (2001) Prediction of heteroaromatic amine mutagenicity by means of correlation weighting of atomic orbital graphs of local invariants. THEOCHEM J Mol Struct 538:287–293CrossRefGoogle Scholar
  22. 22.
    Toropov AA, Toropova AP, Gutman I (2005) Comparison of QSPR models based on hydrogen-filled graphs and on graphs of atomic orbitals. Croat Chem Acta 78:503–509Google Scholar
  23. 23.
    Toropova MA, Toropov AA, Raška I, Rašková M (2015) Searching therapeutic agents for treatment of Alzheimer disease using the Monte Carlo method. Comput Biol Med 64:148–154CrossRefPubMedGoogle Scholar
  24. 24.
    Toropov AA, Toropova AP, Martyanov SE, Benfenati E, Gini G, Leszczynska D, Leszczynski J (2012) CORAL: Predictions of rate constants of hydroxyl radical reaction using representation of the molecular structure obtained by combination of SMILES and Graph approaches. Chemometr Intell Lab Syst 2012(112):65–70CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media LLC 2018

Authors and Affiliations

  1. 1.Laboratory of Environmental Chemistry and Toxicology, Department of Environmental Health ScienceIRCCS-Istituto di Ricerche Farmacologiche Mario NegriMilanItaly

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