McKim Workshop on Strategic Approaches for Reducing Data Redundancy in Cancer Assessment

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

2. Eva Bay Wedebye Gunde Egeskov Jensen Marianne Dybdahl Nikolai Nikolov Svava Jonsdottir Tine Ringsted

3. 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. 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. Pure In Silico Any relation to test data is incidental

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

7. Model Platform: MULTICASE • Cancer models • MULTICASE FDA proprietary, male and female mouse and rat • MULTICASE Ashby fragments

8. 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. 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. Activity distribution

11. Clustering actives

12. Structures

13. Activity distribution with Ashby positives removed

14. In vitro results for Ashby negative carcinogens

15. General estimates and in vitro predictions (4037)

16. In vitro mutagensPredicted positive in Ames test, Mouse lymphoma, or Chromosomal aberrations CHL

17. Distribution of in vivo positives (1853)

18. Distribution of in vivo positives by percent

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

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

21. Cluster of SHE/SCE positives

22. Activity distribution with Ashby negatives removed

23. In vitro results for Ashby positive carcinogens

24. General estimates and in vitro predictions (2140)

25. In vitro mutagens from Ashby positivesPredicted positive in Ames test, Mouse lymphoma, or Chromosomal aberrations CHL

26. Distribution of in vivo positives (1703)

27. Distribution of in vivo positives by percent

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

29. 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