1. McKim Workshop on Strategic Approaches for Reducing Data Redundancy in Cancer Assessment Jay R. Niemelä Technical University of Denmark National Food Institute Division of Toxicology and Risk Assessment e-mail: jarn@food.dtu.dk In silico methods for predicting chromosomal endpoints for carcinogens

2. 2DTU Food, Technical University of Denmark Eva Bay Wedebye Gunde Egeskov Jensen Marianne Dybdahl Nikolai Nikolov Svava Jonsdottir Tine Ringsted

3. 3DTU Food, Technical University of Denmark Data set: EINECS 49,292 discrete organics European Inventory of Existing Chemical Substances Very similar to U.S TSCA inventory and expected to contain most REACH chemicals.

4. 4DTU Food, Technical University of Denmark Objective 1. To define a large set of carcinogens and non-carcinogens 2. Analyse these chemicals for genotoxic potential in a set of in vitro models 3. Further assess performance in in vivo models.

5. 5DTU Food, Technical University of Denmark Pure In Silico Any relation to test data is incidental

6. 6DTU Food, Technical University of Denmark Method Global (Q)SARs in between Local (Q)SARs Closely related structures Accurate predictions for a small number of chemicals Fragment rule-based Fast High throughput Diverse

7. 7DTU Food, Technical University of Denmark Model Platform: MULTICASE Cancer models MULTICASE FDA proprietary, male and female mouse and rat MULTICASE Ashby fragments

8. 8DTU Food, Technical University of Denmark Gentotoxicity models. Developed in-house. QMRF’s and training sets available In Vitro HGPRT forward mutation in CHO cell Mutations in mouse lymphoma Chromosomal aberration CHL Reverse mutation test, Ames SHE cell transformation In Vivo Drosophila melanogaster Sex-Linked Recessive Lethal Mutations in mouse micronucleus Dominant lethal mutations in rodent Sister chromatid exchange in mouse bone marrow COMET assay in mouse

9. 9DTU Food, Technical University of Denmark Domaine Only predicitons with no fragment- or statistical warnings were used. For positive cancer predictions, ICSAS criteria, meaning that at least two were positive (trans-gender or trans-species) To be considerd a non-carcinogen, chemicals had to be predicted negative in all four models (MM, FM, MR, FR)

10. 10DTU Food, Technical University of Denmark Activity distribution

11. 11DTU Food, Technical University of Denmark Clustering actives

12. 12DTU Food, Technical University of Denmark Structures

13. 13DTU Food, Technical University of Denmark Activity distribution with Ashby positives removed

14. 14DTU Food, Technical University of Denmark In vitro results for Ashby negative carcinogens AmesCAMLHGPRTUDSSHE Ames93415950429391345 CA51618910145103 ML1167395116472 HGPRT55980288 UDS25987 SHE768

15. 15DTU Food, Technical University of Denmark General estimates and in vitro predictions (4037) Ames test934(21.1%) Chromosomal aberrations516(12.8%) Mouse lymphoma1167(28.9%) HGPRT559(13.8%) Unscheduled DNA synthesis259(6.4%) Cell transformation (SHE)768(19.0%)

16. 16DTU Food, Technical University of Denmark In vitro mutagens Predicted positive in Ames test, Mouse lymphoma, or Chromosomal aberrations CHL

17. 17DTU Food, Technical University of Denmark Distribution of in vivo positives (1853) 1853 Genotoxic carcinogens 15753 Non- carcinogens Mouse micronucleus2311640 Sister chromatid exchange8002671 Comet assay2882330 Drosophila sex-linked recessive lethal77550 Rodent dominant lethal102741

18. 18DTU Food, Technical University of Denmark Distribution of in vivo positives by percent Genotoxic carcinogens, % Non- carcinogens, % Mouse micronucleus12.510.4 Sister chromatid exchange43.217.0 Comet assay15.514.8 Drosophila sex-linked recessive lethal4.23.5 Rodent dominant lethal5.54.7

19. 19DTU Food, Technical University of Denmark In vivo models as predictors of genotoxic carcinogenicity AM CA ML (1853) Model utility (TP - FP) shown by red bars

20. 20DTU Food, Technical University of Denmark In vivo models as predictors of carcinogenicity - Cell transformation SHE (768) Model utility (TP - FP) shown by red bars

21. 21DTU Food, Technical University of Denmark Cluster of SHE/SCE positives

22. 22DTU Food, Technical University of Denmark Activity distribution with Ashby negatives removed

23. 23DTU Food, Technical University of Denmark In vitro results for Ashby positive carcinogens AmesCAMLHGPRTUDSSHE Ames918472498336160349 CA944434319110343 ML982412128383 HGPRT49686253 UDS23080 SHE560

24. 24DTU Food, Technical University of Denmark General estimates and in vitro predictions (2140) Ames test918(42.9%) Chromosomal aberrations944(44.1%) Mouse lymphoma982(45.9%) HGPRT496(23.2%) Unscheduled DNA synthesis230(10.7%) Cell transformation (SHE)560(26.2%)

25. 25DTU Food, Technical University of Denmark In vitro mutagens from Ashby positives Predicted positive in Ames test, Mouse lymphoma, or Chromosomal aberrations CHL

26. 26DTU Food, Technical University of Denmark Distribution of in vivo positives (1703) 1703 Genotoxic carcinogens 15753 Non- carcinogens Mouse micronucleus2721640 Sister chromatid exchange6492671 Comet assay4582330 Drosophila sex-linked recessive lethal194550 Rodent dominant lethal159741

27. 27DTU Food, Technical University of Denmark Distribution of in vivo positives by percent Genotoxic carcinogens, % Non- carcinogens, % Mouse micronucleus1610.4 Sister chromatid exchange38.117.0 Comet assay26.914.8 Drosophila sex-linked recessive lethal11.43.5 Rodent dominant lethal9.34.7

28. 28DTU Food, Technical University of Denmark In vivo models as predictors of genotoxic carcinogenicity AM CA ML (1703) Model utility (TP - FP) shown by red bars

29. 29DTU Food, Technical University of Denmark Conclusions: ”Fragment” or ”Rule-Based ” systems provide extremely valuable information, particularly for genotoxic carcinogens In Silico methods could help scientists looking for new fragments or rules Current regulatory use of in vivo tests may need to be modified if they are going to replace carcinogenicity bioassays