Development of QSAR models for predicting hepatocarcinogenic toxicity of chemicals.

A dataset comprising 55 chemicals with hepatocarcinogenic potency indices was collected from the Carcinogenic Potency Database with the aim of developing QSAR models enabling prediction of the above unwanted property for New Chemical Entities. The dataset was rationally split into training and test sets by means of a sphere-exclusion type algorithm. Among the many algorithms explored to search regression models, only a Support Vector Machine (SVM) method led to a QSAR model, which was proved to pass rigorous validation criteria, in accordance with the OECD guidelines. The proposed model is capable to explain the hepatocarcinogenic toxicity and could be exploited for predicting this property for chemicals at the early stage of their development, so optimizing resources and reducing animal testing.

[1]  Maykel Pérez González,et al.  QSAR modeling of the rodent carcinogenicity of nitrocompounds. , 2008, Bioorganic & medicinal chemistry.

[2]  Romualdo Benigni,et al.  Structure-activity models of chemical carcinogens: state of the art, and new directions. , 2006, Annali dell'Istituto superiore di sanita.

[3]  D. Bristol,et al.  The NIEHS Predictive-Toxicology Evaluation Project. , 1996, Environmental health perspectives.

[4]  P Willett,et al.  Comparison of algorithms for dissimilarity-based compound selection. , 1997, Journal of molecular graphics & modelling.

[5]  E. Guinó,et al.  Organochlorine Exposure and Colorectal Cancer Risk , 2004, Environmental Health Perspectives.

[6]  A. Tropsha,et al.  Beware of q2! , 2002, Journal of molecular graphics & modelling.

[7]  Brian D. Hudson,et al.  Parameter Based Methods for Compound Selection from Chemical Databases , 1996 .

[8]  Eamonn F. Healy,et al.  Development and use of quantum mechanical molecular models. 76. AM1: a new general purpose quantum mechanical molecular model , 1985 .

[9]  Luis G Valerio,et al.  Prediction of rodent carcinogenic potential of naturally occurring chemicals in the human diet using high-throughput QSAR predictive modeling. , 2007, Toxicology and applied pharmacology.

[10]  A. Niazi,et al.  Prediction of toxicity of nitrobenzenes using ab initio and least squares support vector machines. , 2008, Journal of hazardous materials.

[11]  Bernhard Schölkopf,et al.  A tutorial on support vector regression , 2004, Stat. Comput..

[12]  Ian H. Witten,et al.  Data mining: practical machine learning tools and techniques, 3rd Edition , 1999 .

[13]  O. Deeb,et al.  Effect of the electronic and physicochemical parameters on the carcinogenesis activity of some sulfa drugs using QSAR analysis based on genetic-MLR and genetic-PLS. , 2007, Chemosphere.

[14]  Douglas M. Hawkins,et al.  The Problem of Overfitting , 2004, J. Chem. Inf. Model..

[15]  R. Tennant,et al.  Prediction of the outcome of rodent carcinogenicity bioassays currently being conducted on 44 chemicals by the National Toxicology Program. , 1990, Mutagenesis.

[16]  Zhide Hu,et al.  Structure-activity relationship study of oxindole-based inhibitors of cyclin-dependent kinases based on least-squares support vector machines. , 2007, Analytica chimica acta.

[17]  A. Bianucci,et al.  QSAR study on a novel series of 8-azaadenine analogues proposed as A1 adenosine receptor antagonists. , 2008, European journal of medicinal chemistry.

[18]  Maykel Pérez González,et al.  Quantitative structure-carcinogenicity relationship for detecting structural alerts in nitroso compounds: species, rat; sex, female; route of administration, gavage. , 2008 .

[19]  P. Bhatnagar,et al.  Blood levels of organochlorine pesticide residues and risk of reproductive tract cancer among women from Jaipur, India. , 2008, Advances in experimental medicine and biology.

[20]  J. Topliss,et al.  Chance factors in studies of quantitative structure-activity relationships. , 1979, Journal of medicinal chemistry.

[21]  W. Russell,et al.  Ethical and Scientific Considerations Regarding Animal Testing and Research , 2011, PloS one.

[22]  Bing-Zhong Wang,et al.  Artificial Neural Network Model for the Gap Discontinuity in Shielded Coplanar Waveguide , 2001 .

[23]  Robert D. Clark,et al.  OptiSim: An Extended Dissimilarity Selection Method for Finding Diverse Representative Subsets , 1997, J. Chem. Inf. Comput. Sci..

[24]  A. Bianucci,et al.  QSAR studies on BK channel activators. , 2009, Bioorganic & medicinal chemistry.

[25]  Chih-Jen Lin,et al.  LIBSVM: A library for support vector machines , 2011, TIST.

[26]  Ramaswamy Nilakantan,et al.  Database diversity assessment: New ideas, concepts, and tools , 1997, J. Comput. Aided Mol. Des..

[27]  Xinggao Liu,et al.  Melt index prediction by weighted least squares support vector machines , 2006 .