QSAR Modelling of Carcinogenicity for Regulatory Use in Europe

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• QSAR Modelling of Carcinogenicity for Regulatory
  Use in Europe

Natalja Fjodorova, Marjana Novic, Marjan Vracko,
Marjan Tušar, National institute of Chemistry,
Ljubljana, Slovenia
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CAESAR MEETING, 17.11.2008,BERLIN, GERMANY
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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

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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

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• 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

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• 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

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Dataset

• 805 chemicals were extracted from rodent
• carcinogenicity study findings for 1481chemicals
• 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)

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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

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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)

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

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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)

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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) Tscarcinogenicity
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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

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Statistical evaluation of models

• Confusion matrix for two class

True positive (TP) True negative (TN) False
positive (FP) False negative (FN) Accuracy (AC)
(TNTP)/(TNTPFNFP) Sensitivity(SE)TP/(TPFN)
Specificity(SP)TN/(TNFP)
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Statistical performance of models
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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.
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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.
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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.
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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.
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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
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Accuracy of prediction and area under the curve
(AUC) (models 1,2,3)
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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.

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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
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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.