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QSAR Modelling of Carcinogenicity for Regulatory Use in Europe

QSAR Modelling of Carcinogenicity for Regulatory Use in Europe. Natalja Fjodorova, Marjana Novič , Marjan Vračk o, Marjan Tušar, National institute of Chemistry, Ljubljana, Slovenia. CAESAR MEETING, 17.11.2008, BERLIN, GERMANY. Overview. Carcinogenic potency prediction-

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QSAR Modelling of Carcinogenicity for Regulatory Use in Europe

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  1. QSAR Modelling of Carcinogenicity for Regulatory Use in Europe NataljaFjodorova, Marjana Novič, Marjan Vračko, Marjan Tušar, National institute of Chemistry, Ljubljana, Slovenia

  2. CAESAR MEETING, 17.11.2008,BERLIN, GERMANY

  3. Overview • Carcinogenic potency prediction- state of art • Data and methods used for modeling by NIC_LJU • Statistical performance of obtained models and their evaluation • Some findings about structural alerts • Conclusion

  4. Carcinogenic potency prediction- state of art The QSAR models can be divided into two families: • congeneric (for certain classes of chemicals); external prediction performance for rodent carcinogenicity is 58 to 71% accurate • noncongeneric(for different classes of chemicals); accuracyisaround 65%. Further studies are required to improve the predictive reliability of noncongenericchemicals. Ref.Romualdo Benigni, Cecilia Bossa, Tatiana Netzeva, Andrew Worth. Collection and Evaluation of (Q)SAR Models for Mutagenicity and Carcinogenicity.EUR 22772EN, 2007

  5. The chemicals involved in the study belong to different chemical classes, (noncongeneric substances) • The work is addressed to industrial chemicals, referring to REACH initiative. The aim is to cover chemical space as much as possible

  6. Carcinogenicity prediction in scope ofCAESAR project Present state: - compilation of dataset for carcinogenicity  • cross-checking of structures  • calculation of descriptors  • selection of descriptors  • development of models – carcingenicity • investigation of structural alerts (SA)- ongoing

  7. Dataset: 805 chemicals were extracted from rodent carcinogenicity study findings for1481chemicals taken from Distributed Structure-Searchable Toxicity (DSSTox) Public Database Network http://www.epa.gov/ncct/dsstox/sdf_cpdbas.html derived from the Lois Gold Carcinogenic Database (CPDBAS)

  8. Response: for quantitative models TD50_Rat- Carcinogenic potency in rat (expressed in mmol/kg body wt/day) for qualitative models yes/no principle P-positive-active NP-not positive-inactive

  9. Training and test sets 805 chemicals were splitted into training set (644 chemicals) and test set (161 chamicals) (done at the Helmholtz Centre forEnvironmental Research – UFZ (Germany)

  10. Distribution of active (P) and inactive (NP) chemicals in the total, training and test sets

  11. Descriptors: 254 MDL descriptorscalculated by MDL QSAR software, 254MDLdes_806carcinogenicity.rar file 835 Dragon descriptorscalculated by DRAGON software, Dragon_Carc.xlsfile 88 CODESSA descriptors calculated using CODESSA software 88_CODESSA_descr_Cancer.xls  file

  12. Descriptors used for modeling Model CARC_NIC_CPANN_01 27 MDL descriptors provided by NIC_LJU (method for variable selection: Kohonen network and PCA). Model CARC_NIC_CPANN_02 18 DRAGON and MDL descriptors were taken from one of the best models(CARC_CSL_KNN_05) developed by CSL. The goal was to compare results obtained for carcinogenicity prediction using different methods. Model CARC_NIC_CPANN_03 34CODESSA descriptors were taken from one of the best models(CARC_CSL_KNN_02) developed by CSL. (method for variable selection for models 2 and 3- cross correlation matrix, multicolinearity technique, fisher ratio and genetic algorithm)

  13. Counter Propagation Artificial Neural Network Step1: mapping of molecule Xs (vector representing structure) into the Kohonen layer Step2: correction of weights in both, the Kohonen and the Output layer Step3: prediction of the four-dementional target (toxicity) Ts=carcinogenicity

  14. Model input parameters • Minimal correction factor- 0.01 • Maximum correction factor- 0.5 • Number of neurons in x direction- (35) • Number of neurons in y direction- (35) • Number of learning epochs- 100, 200, 400, 600, 800, 1000, 1200, 1400, 1600, 1800

  15. Statistical evaluation of models Confusion matrix for two class True positive (TP)True negative (TN) False positive (FP)False negative (FN) Accuracy (AC) =(TN+TP)/(TN+TP+FN+FP) Sensitivity(SE)=TP/(TP+FN) Specificity(SP)=TN/(TN+FP)

  16. Statistical performance of models

  17. Changing the threshold from 0 to 1 leads to decrease the number of false positive and increases and number of false negative increases. This tendency is common for all our models 1, 2 and 3.

  18. In the figure we have marked the maximum accuracy and corresponding thresholds. For model 1 the optimal threshold is equal to 0.45. In this case accuracy has a maximal value of 0.68, sensitivity is 0.71 and specificity is 0.65.

  19. For model 2 optimal threshold for test set is 0.6 and accuracy has maximal value of 0.70. Sensitivity in this point is 0.69 and specificity is 0.72.

  20. For model 3 optimal threshold is equal to 0.5, maximum accuracy is 0.68, sensitivity is 0.70 and specificity is 0.62. Changing the threshold leads to revision of sensitivity and specificity. It may be used to increase the number of correctly predicted carcinogens or non carcinogens.

  21. The closer the curve tends towards (0,1) the more accurate are the prediction made A model with no predicted ability yields the diagonal line

  22. Accuracy of prediction and area under the curve (AUC) (models 1,2,3)

  23. Study structural alerts for our dataset collected from Benigni Toxtree program • We have extracted the following alerts for out dataset of 805 compounds • GA-genotoxic alerts • nGA-non-genotoxic alerts • NA-no carcinogenic alerts • When we have calculated how many chemicals with pointed alerts fall into NP-not positive and P-positive area.

  24. P-positive and NP-not positive relates only for results for rats For substances with GA about 2/3 belong to Positive and about 1/3 to NP-not positive For substances with nGA about half substances belong to Positive and half to NP For substances with NA-no carcinogenic alerts about 2/3 belongs to NP and 1/3 belong to Positive Needs for future investigations

  25. Conclusion • Quantitative models with dependent variable-tumorgenic dose TD50 for rats, have shown low prediction power with correlation coefficient for the test set less than 0.5. • Conversely, qualitative models demonstrated an excellent accuracy of internal performance (accuracy of the training set is 91-93%) and good external performance (accuracy of the test set is 68-70%, sensitivity is 69-73% and specificity 63-72%). • Changing the threshold leads to revision of sensitivity and specificity. It may be used to increase the number of correctly predicted carcinogens or non carcinogens.

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