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

CAESAR MEETING, , BERLIN, GERMANY

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

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); accuracy is around 65%. Further studies are required to improve the predictive reliability of noncongeneric chemicals. Ref.Romualdo Benigni, Cecilia Bossa, Tatiana Netzeva, Andrew Worth. Collection and Evaluation of (Q)SAR Models for Mutagenicity and Carcinogenicity. EUR 22772EN, 2007

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

Carcinogenicity prediction in scope of CAESAR 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

Dataset: 805 chemicals were extracted from rodent carcinogenicity study findings for 1481chemicals taken from Distributed Structure-Searchable Toxicity (DSSTox) Public Database Network derived from the Lois Gold Carcinogenic Database (CPDBAS)

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

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

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

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

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 34 CODESSA 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)

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

Model input parameters Minimal correction factor 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

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)

Statistical performance of models

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.

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

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

For model 3 optimal threshold is equal to 0.5, maximum accuracy is 0.68, sensitivity is 0.70 and specificity is 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.

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

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

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.

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 P-positive and NP-not positive relates only for results for rats Needs for future investigations

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.