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 In silico methods for predicting chromosomal endpoints for carcinogens

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

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.

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.

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

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

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

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

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)

10DTU Food, Technical University of Denmark Activity distribution

11DTU Food, Technical University of Denmark Clustering actives

12DTU Food, Technical University of Denmark Structures

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

14DTU Food, Technical University of Denmark In vitro results for Ashby negative carcinogens AmesCAMLHGPRTUDSSHE Ames CA ML HGPRT UDS25987 SHE768

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%)

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

17DTU Food, Technical University of Denmark Distribution of in vivo positives (1853) 1853 Genotoxic carcinogens Non- carcinogens Mouse micronucleus Sister chromatid exchange Comet assay Drosophila sex-linked recessive lethal77550 Rodent dominant lethal102741

18DTU Food, Technical University of Denmark Distribution of in vivo positives by percent Genotoxic carcinogens, % Non- carcinogens, % Mouse micronucleus Sister chromatid exchange Comet assay Drosophila sex-linked recessive lethal Rodent dominant lethal5.54.7

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

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

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

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

23DTU Food, Technical University of Denmark In vitro results for Ashby positive carcinogens AmesCAMLHGPRTUDSSHE Ames CA ML HGPRT UDS23080 SHE560

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%)

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

26DTU Food, Technical University of Denmark Distribution of in vivo positives (1703) 1703 Genotoxic carcinogens Non- carcinogens Mouse micronucleus Sister chromatid exchange Comet assay Drosophila sex-linked recessive lethal Rodent dominant lethal159741

27DTU Food, Technical University of Denmark Distribution of in vivo positives by percent Genotoxic carcinogens, % Non- carcinogens, % Mouse micronucleus Sister chromatid exchange Comet assay Drosophila sex-linked recessive lethal Rodent dominant lethal9.34.7

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

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