First report on development of quantitative interspecies structure-carcinogenicity relationship models and exploring discriminatory features for rodent carcinogenicity of diverse organic chemicals using OECD guidelines.

Different regulatory agencies in food and drug administration and environmental protection worldwide are employing quantitative structure-activity relationship (QSAR) models to fill the data gaps related with properties of chemicals affecting the environment and human health. Carcinogenicity is a toxicity endpoint of major concern in recent times. Interspecies toxicity correlations may provide a tool for estimating sensitivity towards toxic chemical exposure with known levels of uncertainty for a diversity of wildlife species. In this background, we have developed quantitative interspecies structure-carcinogenicity correlation models for rat and mouse [rodent species according to the Organization for Economic Cooperation and Development (OECD) guidelines] based on the carcinogenic potential of 166 organic chemicals with wide diversity of molecular structures, spanning a large number of chemical classes and biological mechanisms. All the developed models have been assessed according to the OECD principles for the validation of QSAR models. Consensus predictions for carcinogenicity of the individual compounds are presented here for any one species when the data for the other species are available. Informative illustrations of the contributing structural fragments of chemicals which are responsible for specific carcinogenicity endpoints are identified by the developed models. The models have also been used to predict mouse carcinogenicities of 247 organic chemicals (for which rat carcinogenicities are present) and rat carcinogenicities of 150 chemicals (for which mouse carcinogenicities are present). Discriminatory features for rat and mouse carcinogenicity values have also been explored.

[1]  R. Darlington,et al.  Regression and Linear Models , 1990 .

[2]  Anna Artese,et al.  Rational Approaches to Anticancer Drug Design/in silico Drug Development , 2008 .

[3]  Kunal Roy,et al.  On Selection of Training and Test Sets for the Development of Predictive QSAR models , 2006 .

[4]  E. Benfenati,et al.  Regulatory Assessment of Chemicals within OECD Member Countries, EU and in Russia , 2008, Journal of environmental science and health. Part C, Environmental carcinogenesis & ecotoxicology reviews.

[5]  Ralph Kühne,et al.  External Validation and Prediction Employing the Predictive Squared Correlation Coefficient Test Set Activity Mean vs Training Set Activity Mean , 2008, J. Chem. Inf. Model..

[6]  I. Andriot,et al.  Retention-release equilibrium of aroma compounds in polysaccharide gels: study by quantitative structure-activity/property relationships approach. , 2010 .

[7]  P. Roy,et al.  Exploring the impact of size of training sets for the development of predictive QSAR models , 2008 .

[8]  Ann M Richard,et al.  A novel approach: chemical relational databases, and the role of the ISSCAN database on assessing chemical carcinogenicity. , 2008, Annali dell'Istituto superiore di sanita.

[9]  Maykel Pérez González,et al.  Quantitative structure-activity relationship to predict differential inhibition of aldose reductase by flavonoid compounds. , 2005, Bioorganic & medicinal chemistry.

[10]  Michael A. Stephens,et al.  Asymptotic Results for Goodness-of-Fit Statistics with Unknown Parameters , 1976 .

[11]  H. Lilliefors On the Kolmogorov-Smirnov Test for Normality with Mean and Variance Unknown , 1967 .

[12]  Roberto Todeschini,et al.  Handbook of Molecular Descriptors , 2002 .

[13]  Seymour Geisser,et al.  The Predictive Sample Reuse Method with Applications , 1975 .

[14]  S. Weisberg Plots, transformations, and regression , 1985 .

[15]  K. Roy,et al.  Further exploring rm2 metrics for validation of QSPR models , 2011 .

[16]  Alexander Golbraikh,et al.  QSAR Modeling of the Blood–Brain Barrier Permeability for Diverse Organic Compounds , 2008, Pharmaceutical Research.

[17]  M Natália D S Cordeiro,et al.  Probing the anticancer activity of nucleoside analogues: a QSAR model approach using an internally consistent training set. , 2007, Journal of medicinal chemistry.

[18]  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.

[19]  D. Delistraty Acute toxicity to rats and trout with a focus on inhalation and aquatic exposures. , 2000, Ecotoxicology and environmental safety.

[20]  Alexander Golbraikh,et al.  Rational selection of training and test sets for the development of validated QSAR models , 2003, J. Comput. Aided Mol. Des..

[21]  Gilles Klopman,et al.  ADME evaluation. 2. A computer model for the prediction of intestinal absorption in humans. , 2002, European journal of pharmaceutical sciences : official journal of the European Federation for Pharmaceutical Sciences.

[22]  Marcello Imbriani,et al.  Development of QSAR models for predicting hepatocarcinogenic toxicity of chemicals. , 2009, European journal of medicinal chemistry.

[23]  P. Fernández-Salguero,et al.  Polycyclic aromatic hydrocarbon‐inducible DNA adducts: Evidence by 32P‐postlabeling and use of knockout mice for Ah receptor‐independent mechanisms of metabolic activation in vivo , 2003, International journal of cancer.

[24]  J. B. Pearson,et al.  Methodology in Social Research. , 1968 .

[25]  P. Mineau,et al.  Estimation of chemical toxicity to wildlife species using interspecies correlation models. , 2007, Environmental science & technology.

[26]  J N Weinstein,et al.  Quantitative structure-antitumor activity relationships of camptothecin analogues: cluster analysis and genetic algorithm-based studies. , 2001, Journal of medicinal chemistry.

[27]  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 .

[28]  Scott D. Kahn,et al.  Current Status of Methods for Defining the Applicability Domain of (Quantitative) Structure-Activity Relationships , 2005, Alternatives to laboratory animals : ATLA.

[29]  Fuart Gatnik Mojca,et al.  Review of QSAR Models and Software Tools for Predicting of Genotoxicity and Carcinogenicity , 2010 .

[30]  Worth Andrew,et al.  Review of QSAR Models and Software Tools for predicting Biokinetic Properties , 2010 .

[31]  Malcolm C. Pike,et al.  The TD50: a proposed general convention for the numerical description of the carcinogenic potency of chemicals in chronic-exposure animal experiments. , 1984 .

[32]  E Benfenati,et al.  A new bioconcentration factor model based on SMILES and indices of presence of atoms. , 2010, European journal of medicinal chemistry.

[33]  Igor V. Tetko,et al.  Combinatorial QSAR Modeling of Chemical Toxicants Tested against Tetrahymena pyriformis , 2008, J. Chem. Inf. Model..

[34]  T. Gichner IARC Monographs on the Evaluation of the Carcinogenic Risk of Chemicals to Humans. Volume 32. Polynuclear Aromatic Compounds, Part 1, Chemical, Environmental and Experimental Data , 2008, Biologia Plantarum.

[35]  Maykel Pérez González,et al.  Quantitative structure activity relationship for the computational prediction of nitrocompounds carcinogenicity. , 2006, Toxicology.

[36]  Alexander Tropsha,et al.  Best Practices for QSAR Model Development, Validation, and Exploitation , 2010, Molecular informatics.

[37]  P. Popelier,et al.  QSAR with quantum topological molecular similarity indices: toxicity of aromatic aldehydes to Tetrahymena pyriformis , 2010, SAR and QSAR in environmental research.

[38]  Kunal Roy,et al.  Predictive toxicology using QSAR: A perspective , 2010 .

[39]  Romualdo Benigni,et al.  The second National Toxicology Program comparative exercise on the prediction of rodent carcinogenicity: definitive results. , 2004, Mutation research.

[40]  Kunal Roy,et al.  On further application of r  m2 as a metric for validation of QSAR models , 2009, Journal of Chemometrics.

[41]  M. Pike,et al.  Calculation of carcinogenic potency from long-term animal carcinogenesis experiments. , 1984, Biometrics.

[42]  M. Hewitt,et al.  Assessing Applicability Domains of Toxicological QSARs: Definition, Confidence in Predicted Values, and the Role of Mechanisms of Action , 2007 .

[43]  Alessandro Giuliani,et al.  Putting the Predictive Toxicology Challenge Into Perspective: Reflections on the Results , 2003, Bioinform..

[44]  K. Roy,et al.  On Two Novel Parameters for Validation of Predictive QSAR Models , 2009, Molecules.

[45]  Aliuska Morales Helguera,et al.  Quantitative structure carcinogenicity relationship for detecting structural alerts in nitroso-compounds: species: rat; sex: male; route of administration: water. , 2008, Toxicology and applied pharmacology.

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

[47]  D. Freedman,et al.  Carcinogenicity Tests and Interspecies Concordance , 1995 .

[48]  J. Ward Evolution of the uses of rats and mice for assessing carcinogenic risk from chemicals in humans. , 2010, Asian Pacific journal of cancer prevention : APJCP.

[49]  Kunal Roy,et al.  On some aspects of validation of predictive quantitative structure–activity relationship models , 2007, Expert opinion on drug discovery.

[50]  Kunal Roy,et al.  First report on interspecies quantitative correlation of ecotoxicity of pharmaceuticals. , 2010, Chemosphere.

[51]  F. Massey The Kolmogorov-Smirnov Test for Goodness of Fit , 1951 .

[52]  Paola Gramatica,et al.  Principles of QSAR models validation: internal and external , 2007 .

[53]  C Helma,et al.  Predictive Models for Carcinogenicity and Mutagenicity: Frameworks, State-of-the-Art, and Perspectives , 2009, Journal of environmental science and health. Part C, Environmental carcinogenesis & ecotoxicology reviews.

[54]  F. Beland,et al.  DNA Adducts and Their Consequences , 2005 .

[55]  A.M. Richard,et al.  AI and SAR approaches for predicting chemical carcinogenicity: Survey and status report , 2002, SAR and QSAR in environmental research.

[56]  Kunal Roy,et al.  Molecular docking and QSAR studies of aromatase inhibitor androstenedione derivatives , 2010, The Journal of pharmacy and pharmacology.

[57]  Kunal Roy,et al.  Development and validation of a robust QSAR model for prediction of carcinogenicity of drugs. , 2011, Indian journal of biochemistry & biophysics.

[58]  P. Roy,et al.  On Some Aspects of Variable Selection for Partial Least Squares Regression Models , 2008 .

[59]  Kunal Roy,et al.  QSAR modeling of toxicity of diverse organic chemicals to Daphnia magna using 2D and 3D descriptors. , 2010, Journal of hazardous materials.

[60]  D. Gaylor,et al.  On interspecies correlations of carcinogenic potencies. , 1996, Journal of toxicology and environmental health.

[61]  H. Bolt,et al.  Mechanisms of carcinogenicity of methyl halides. , 1993, Critical reviews in toxicology.

[62]  Jack A. Taylor,et al.  Avoided and avoidable risks of cancer. , 1997, Carcinogenesis.

[63]  Anton J. Hopfinger,et al.  Application of Genetic Function Approximation to Quantitative Structure-Activity Relationships and Quantitative Structure-Property Relationships , 1994, J. Chem. Inf. Comput. Sci..

[64]  J. Devillers,et al.  Prediction of acute mammalian toxicity from QSARs and interspecies correlations , 2009, SAR and QSAR in environmental research.

[65]  D. Paustenbach,et al.  The European Union’s REACH regulation: a review of its history and requirements , 2009, Critical reviews in toxicology.

[66]  Afshin Fassihi,et al.  QSAR study of anthranilic acid sulfonamides as inhibitors of methionine aminopeptidase-2 using LS-SVM and GRNN based on principal components. , 2010, European journal of medicinal chemistry.

[67]  K. Roy,et al.  Exploring quantitative structure–activity relationship studies of antioxidant phenolic compounds obtained from traditional Chinese medicinal plants , 2010 .

[68]  Davide Ballabio,et al.  Evaluation of model predictive ability by external validation techniques , 2010 .

[69]  B. Butterworth,et al.  A classification framework and practical guidance for establishing a mode of action for chemical carcinogens. , 2006, Regulatory toxicology and pharmacology : RTP.

[70]  A. Dharma Chinese medicinal plants , 1990, The Lancet.

[71]  K. Héberger Sum of ranking differences compares methods or models fairly , 2010 .

[72]  Ashwin Srinivasan,et al.  Statistical Evaluation of the Predictive Toxicology Challenge 2000-2001 , 2003, Bioinform..

[73]  S. Wold,et al.  PLS-regression: a basic tool of chemometrics , 2001 .

[74]  A. Delcher,et al.  Human, mouse, and rat genome large-scale rearrangements: stability versus speciation. , 2004, Genome research.

[75]  Paola Gramatica,et al.  The Importance of Being Earnest: Validation is the Absolute Essential for Successful Application and Interpretation of QSPR Models , 2003 .