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Categorical QSAR models for skin sensitization based on local lymph node assay measures and both ground and excited state 4D-fingerprint descriptors

  • Jianzhong Liu
  • Petra S. Kern
  • G. Frank Gerberick
  • Osvaldo A. Santos-Filho
  • Emilio X. Esposito
  • Anton J. Hopfinger
  • Yufeng J. Tseng
Article

Abstract

In previous studies we have developed categorical QSAR models for predicting skin-sensitization potency based on 4D-fingerprint (4D-FP) descriptors and in vivo murine local lymph node assay (LLNA) measures. Only 4D-FP derived from the ground state (GMAX) structures of the molecules were used to build the QSAR models. In this study we have generated 4D-FP descriptors from the first excited state (EMAX) structures of the molecules. The GMAX, EMAX and the combined ground and excited state 4D-FP descriptors (GEMAX) were employed in building categorical QSAR models. Logistic regression (LR) and partial least square coupled logistic regression (PLS-CLR), found to be effective model building for the LLNA skin-sensitization measures in our previous studies, were used again in this study. This also permitted comparison of the prior ground state models to those involving first excited state 4D-FP descriptors. Three types of categorical QSAR models were constructed for each of the GMAX, EMAX and GEMAX datasets: a binary model (2-state), an ordinal model (3-state) and a binary-binary model (two-2-state). No significant differences exist among the LR 2-state model constructed for each of the three datasets. However, the PLS-CLR 3-state and 2-state models based on the EMAX and GEMAX datasets have higher predictivity than those constructed using only the GMAX dataset. These EMAX and GMAX categorical models are also more significant and predictive than corresponding models built in our previous QSAR studies of LLNA skin-sensitization measures.

Keywords

Skin sensitization Categorical QSAR models Excited state structures 

Notes

Acknowledgements

This work was funded by the National Institutes of Health through the NIH Roadmap for Medical Research, Grant 1 R21 GM075775-01. Information on Novel Preclinical Tools for Predictive ADME-Toxicology can be found at http://grants.nih.gov/grants/guide/rfa-files/RFA-RM-04-023.html. Links to nine initiatives are found at http://nihroadmap.nih.gov/initiatives.asp. This work was also supported in part by The Procter & Gamble Company. Resources of the Laboratory of Molecular Modeling and Design at UIC and The Chem21 Group, Inc. were used in performing these studies.

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Copyright information

© Springer Science+Business Media B.V. 2008

Authors and Affiliations

  • Jianzhong Liu
    • 1
    • 2
  • Petra S. Kern
    • 3
  • G. Frank Gerberick
    • 4
  • Osvaldo A. Santos-Filho
    • 2
  • Emilio X. Esposito
    • 2
  • Anton J. Hopfinger
    • 1
    • 2
  • Yufeng J. Tseng
    • 2
    • 5
  1. 1.College of Pharmacy1 University of New MexicoAlbuquerqueUSA
  2. 2.The Chem21 Group, Inc.Lake ForestILUSA
  3. 3.Procter & Gamble EurocorStrombeek-BeverBelgium
  4. 4.The Procter & Gamble CompanyMiami Valley Innovation CenterCincinnatiUSA
  5. 5.Department of Computer Science and Information Engineering, Graduate Institute of Biomedical Electronics and BioinformaticsNational Taiwan UniversityTaipeiTaiwan

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