Quantum chemistry based QSAR prediction and priority setting for toxicity of nitrobenzenes on EINECS list

E. Zvinavashe, A.J. Murk, J.J.M. Vervoort, A.E.M.F. Soffers, A. Freidig, I.M.C.M. Rietjens

Research output: Contribution to journalArticleAcademicpeer-review

19 Citations (Scopus)


Fifteen experimental literature data sets on the acute toxicity of substituted nitrobenzenes to algae (Scenedesmus obliquus, Chlorella pyrenoidosa, C. vulgaris), daphnids (Daphnia magna, D. carinata), fish (Cyprinus carpio, Poecilia reticulata), protozoa (Tetrahymena pyriformis), bacteria (Phosphobacterium phosphoreum), and yeast (Saccharomyces cerevisiae) were used to establish quantum chemistry based quantitative structure¿activity relationships (QSARs). The logarithm of the octanol/water partition coefficient, log Kow, and the energy of the lowest unoccupied molecular orbital, Elumo, were used as descriptors. Suitable QSAR models (0.65 <r2 <0.98) to predict acute toxicity of substituted mononitrobenzenes to protozoa, fish, daphnids, yeast, and algae have been derived. The log Kow was a sufficient descriptor for all cases, with the additional Elumo descriptor being required only for algae. The QSARs were found to be valid for neutral substituted mononitrobenzenes with no -OH, -COOH, or -CN substituents attached directly to the ring. From the 100,196 European Inventory of Existing Commercial Substances (EINECS), 497 chemicals were identified that fit the selection criteria for the established QSARs. Based on these results, an advisory tool has been developed that directs users to the appropriate QSAR model to apply for various types of organisms within specified log Kow ranges. Using this tool, it is possible to obtain a good indication of the toxicity of a large set of EINECS chemicals and newly developed substituted mononitrobenzenes to five different organisms without the need for additional experimental testing
Original languageEnglish
Pages (from-to)2313-2321
Number of pages9
JournalEnvironmental Toxicology and Chemistry
Issue number9
Publication statusPublished - 2006


  • nitrobenzene derivatives
  • partition-coefficients
  • qsar
  • prediction
  • information
  • validation
  • pollutants
  • indexes


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