1. Carcinogenicity prediction for Regulatory Use Natalja Fjodorova Marjana Novič, Marjan Vračko, Marjan Tušar National institute of Chemistry, Ljubljana, Slovenia

2. Kemijske Dnevi 25-27 September 2008 UNIVERZA MARIBOR

3. Overview 1. EU project CAESAR aimed for development of QSAR models for prediction of toxicological properties of substances, used for regulatory purposes. 2. The principles of validations of QSARs which will be used for chemical regulation. 3. Carcinogenicity models using Counter Propagation Artificial Network

4. It is estimated that over 30000 industrial chemicals used in Europe require additional safety testing to meet requirements of new chemical regulation REACH. If conducted on animals this testing would require the use of an extra 10- 20 million animal experiments. Quantitative Structure Activity Relationships (QSAR) is one major prospect between alternative testing methods to be used in a regulatory context.

5. aimed to develop (Q)SARs as non-animal alternative tools for the assessment of chemical toxicity under the REACH. FR6- CAESAR European Project Computer Assisted Evaluation of Industrial chemical Substances According to Regulations Coordinator- Emilio Benfenati- Istituto di Ricerche Farmacologiche “Mario Negri”

6. The general aim of CAESAR is 1. To produce QSAR models for toxicity prediction of chemical substances, to be used for regulatory purposes under REACH in a transparent manner by applying new and unique modelling and validation methods.

7. 2. Reduce animal testing and its associated costs, in accordance with Council Directive 86/609/EEC and Cosmetics Directive (Council Directive 2003/15/EC)

8. CAESAR is solving several problems: Ethical- save animal lifes; Economical- cost reduction on testing; Political- REACH implementation- new chemical legislation

9. CAESAR aimed to develop new (Q)SAR models for 5 end-points: Bioaccumulation (BCF), Skin sensitisation Mutagenicity Carcinogenicity Teratogenicity

10. The characterization of the QSAR models follows the general scheme of 5 OECD principles: 1.A defined endpoint 2.An unambiguous algorithm 3.A defined domain of applicability 4.Appropriate measures of goodness- of-fit, robustness and predictivity 5.A mechanistic interpretation, if possible.

11. Principle1- A defined endpoint Endpoint is the property or biological activity determined in experimental protocol, (OECDTest Guideline). Carcinogenicity is a defined endpoint addressed by an officially recognized test method (Method B.32 Carcinogenicity test – Annex V to Directive 67/548/EEC).

12. Principle2- An unambiguous algorithm Algorithm is the form of relationship between chemical structure and property or biological activity being modelled. Examples: 1. Statistically (regression) based QSARs 2. Neural network model, which includes both learning process and prediction process.

13. Transparency in the (Q)SAR algorithm can be provided by means of the following information: a) Definition of the mathematical form of a QSAR model, or of the decision rule (e.g. in the case of a SAR) b) Definitions of all descriptors in the algorithm, and a description of their derivation c) Details of the training set used to develop the algorithm.

14. Principle3- A Defined Domain of Applicability The definition of the Applicability Domain (AD) is based on the assumption that a model is capable of making reliable predictions only within the structural, physicochemical and response space that is known from its training set. List of basic structures (for example, aniline, fluorene..) The range of chemical descriptors values.

15. The assessment of model performance is sometimes called statistical validation. Principle4- Appropriate measures goodness-of-fit, robustness (internal performance) and predictivity (external performance)

16. Principle5- A mechanistic interpretation, if possible Mechanistic interpretation of (Q)SAR provides a ground for interaction and dialogue between model developer, and toxicologists and regulators, and permits the integration of the (Q)SAR results into wider regulatory framework, where different types of evidence and data concur or compliment each other as a basis for making decisions and taking actions. Example: enhancing/inhibition the metabolic activation of substances may be discussed.

17. National Institute of Chemistry in Ljubljana (NIC-LJU) is responsible for development of models for predicton of carcinogenicity

18. DATA ON CARCINOGENICITY 1.Studies of carcinogenicity in humans 2.Carcinogenicity studies in animals 3.Other relevant data additional evidence related to the possible carcinogenicity Genetic Toxicology Structure-Activity Comparisons Pharmacokinetics and Metabolism Pathology

19. Cancer Risk Assessment IARC International Agency for Research of Cancer IARC For animals Group Classification Explanation Classification Group AHuman Carcinogen sufficient human evidence for causal association between exposure and cancer Group B1Probable Humanlimited evidence in human Group B2Probable Human inadequate evidence in humans and sufficient evidence in animalsclear evidence Group C Possible Human Carcinogenlimited evidence in animalssome evidence Group D Not Classifiable as Human Carcinogenicityinadequate evidence in animalsequivocal Group E No Evidence of Carcinogenicity in Human at least two adequate animal tests or both negative epidemiology and animal studiesno evidence

20. Predictive Toxicology Approaches 1. Quantitative models (QSARs) Continuous data prediction on the basis of experimental evidence of rodent carcinogenic potential (TD50 tumorgenic dose) 2. Categorical models based on YES/NO data. (P-positive; NP-not positive)

21. Dataset: 805 chemicals were filtered from 1481compounds taken from Distributed Structure-Searchable Toxicity (DSSTox) Public Database Network http://www.epa.gov/ncct/dsstox/sdf_cpdb as.html http://www.epa.gov/ncct/dsstox/sdf_cpdb as.html which was derived from the Lois Gold Carcinogenic Database (CPDBAS) The chemicals involved in the study belong to different chemical classes, (noncongeneric substances)

22. Descriptors: 1.252 MDL descriptors were calculated in program MDL QSAR. 2. Descriptors dataset was reduced to 27 MDL descriptors, using Kohonen map and Principle Component Analisis.

23. 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

24. Investigation of quantitative models shows us low results RESPONCE- TD50mmol 1. Correlation coefficient in the external validation is lower then 0.5

25. Continuouse data models (Quantitative models) Models Reduction of descriptors method, model TRAININGTEST R_trainRMSER_testRMSE CP ANN_model 250MDLdescriptors 0.741.510.471.78 CP ANN_model 86MDLdescriptors Kohonen map 0.721.540.421.90 CP ANN_model 27MDLdescriptors PCA 0.741.520.451.80 SVM_model (Thomas Ferrary) 86MDLdescriptors 0.821.230.471.81

26. Investigation of categorical models shows us satisfactory results YES/NO principe RESPONCE: P-positive-active NP-not positive-inactive

27. Characteristics used for validation of categorical model true positive(TP), true negative (TN) Accuracy(AC), AC=(TN+TP)/(TN+TP+FN+FP) TPrate=Sensitivity(SE)=TP/(TP+FN) TNrate=Specificity(SP)=TN/(TN+FP)

28. Categorical model for dataset 805 chemicals (Training=644 and Test=161), using 27 MDL descriptors TrainingTest ACC, % SE, % SP, % ACC, % SE, % SP, % Model _1 889086686967 Model _2 929985687363

29. Confusion matrix TR(644)/TE(161) classes (Positive- Negative) Class Positive (predict.) Negative (predict.) Number TR(TE) 644(161) Positive (experim.) 329(65) 3(24)332(89) Negative (experim.) 47(27)265(45)312(72) FP FNTP TN

30. How we find optimal model, using threshold Threshold=0.45 Accuracy=0.68 SE=0.73 SP=0.63

31. Changing of threshold allows us to get models with different statistical performances. TrSESPACC 0.050.910.150.57 0.10.830.360.62 0.150.80.470.65 0.20.790.470.65 0.250.790.470.65 0.30.790.530.67 0.350.780.570.68 0.40.730.60.67 0.450.730.630.68 0.50.650.630.64 0.550.620.720.66 0.60.620.740.67 0.650.60.760.67 0.70.580.760.66 0.750.540.780.65 0.80.520.790.64 0.850.450.830.62 0.90.310.890.57 0.950.240.930.55 1010.45

32. ROC(Receiver operating characteristic) curve Training set Test set The area under the curve is 0.988 and 0.699 in the training and test sets, respectively.

33. How requrements of REACH reflect development of models To focus model to high sensitivity in prediction of carcinogenicity From regulatory perspective, the higher sensitivity in predicting carcinogens is more desirable than high specificity Sensitivity- percentage of correct predictions of carcinogens Specificity- percentage of correct predictions of non-carcinogens

34. Conclusion 1.We have bult the carcinogenicity models in accordance with 5 OECD principles principle of validation 2. We have got satisfactory results for categorical models with accuracy 68% which is good for carcinogenicity as it meet the level of uncertanty of test data. 3. The goal of our future investigation will be dedicated to research of relationship between results of carcinogenicity tests and presence of Genotoxic, non Genotoxic alerts using TOX TREE program.

35. Acknowledgements The financial support of the European Union through CAESAR project (SSPI- 022674) as well as of the Slovenian Ministry of Higher Education, Science and Technology (grant P1-017) is gratefully acknowledged.

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