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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: [email protected] Eva Bay Wedebye Gunde Egeskov Jensen Marianne Dybdahl Nikolai Nikolov Svava Jonsdottir Tine Ringsted 2 DTU 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. 3 DTU 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. 4 DTU Food, Technical University of Denmark Pure In Silico Any relation to test data is incidental 5 DTU Food, Technical University of Denmark 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 6 DTU Food, Technical University of Denmark Model Platform: MULTICASE • Cancer models • MULTICASE FDA proprietary, male and female mouse and rat • MULTICASE Ashby fragments 7 DTU 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 8 DTU 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) 9 DTU Food, Technical University of Denmark Activity distribution 30000 27362 25000 20000 15753 15000 10000 6177 5000 0 Positive 10 DTU Food, Technical University of Denmark Unpredicted Negative Clustering actives 11 DTU Food, Technical University of Denmark Structures 12 DTU Food, Technical University of Denmark Activity distribution with Ashby positives removed 30000 25000 20000 15000 27362 10000 15753 5000 2140 4037 0 Positive 13 DTU Food, Technical University of Denmark Unpredicted Negative In vitro results for Ashby negative carcinogens Ames CA Ames CA ML HGPRT 934 159 504 516 ML HGPRT UDS SHE 14 DTU Food, Technical University of Denmark UDS SHE 293 91 345 189 101 45 103 1167 395 116 472 559 80 288 259 87 768 General estimates and in vitro predictions (4037) Ames test 934 (21.1%) Chromosomal aberrations 516 (12.8%) 1167 (28.9%) HGPRT 559 (13.8%) Unscheduled DNA synthesis 259 (6.4%) Cell transformation (SHE) 768 (19.0%) Mouse lymphoma 15 DTU Food, Technical University of Denmark In vitro mutagens Predicted positive in Ames test, Mouse lymphoma, or Chromosomal aberrations CHL Mutagens 1853 Non-mutagens 16 DTU Food, Technical University of Denmark Non-mutagens 2184 Mutagens Distribution of in vivo positives (1853) 1853 Genotoxic carcinogens 15753 Noncarcinogens Mouse micronucleus 231 1640 Sister chromatid exchange 800 2671 Comet assay 288 2330 Drosophila sex-linked recessive lethal 77 550 Rodent dominant lethal 102 741 17 DTU Food, Technical University of Denmark Distribution of in vivo positives by percent Genotoxic Noncarcinogens, % carcinogens, % Mouse micronucleus 12.5 10.4 Sister chromatid exchange 43.2 17.0 Comet assay 15.5 14.8 Drosophila sex-linked recessive lethal 4.2 3.5 Rodent dominant lethal 5.5 4.7 18 DTU Food, Technical University of Denmark In vivo models as predictors of genotoxic carcinogenicity AM CA ML (1853) SLRL COMET DL MM SCE 0 10 20 FP 30 TP 40 TP - FP Model utility (TP - FP) shown by red bars 19 DTU Food, Technical University of Denmark 50 In vivo models as predictors of carcinogenicity - Cell transformation SHE (768) SLRL DL MM COMET SCE -10 0 10 20 FP 30 TP 40 50 TP - FP Model utility (TP - FP) shown by red bars 20 DTU Food, Technical University of Denmark 60 Cluster of SHE/SCE positives 21 DTU Food, Technical University of Denmark Activity distribution with Ashby negatives removed 30000 25000 20000 15000 27362 10000 15753 5000 2140 4037 0 Positive 22 DTU Food, Technical University of Denmark Unpredicted Negative In vitro results for Ashby positive carcinogens Ames CA Ames CA ML HGPRT 918 472 498 944 ML HGPRT UDS SHE 23 DTU Food, Technical University of Denmark UDS SHE 336 160 349 434 319 110 343 982 412 128 383 496 86 253 230 80 560 General estimates and in vitro predictions (2140) Ames test 918 (42.9%) Chromosomal aberrations 944 (44.1%) Mouse lymphoma 982 (45.9%) HGPRT 496 (23.2%) Unscheduled DNA synthesis 230 (10.7%) Cell transformation (SHE) 560 (26.2%) 24 DTU Food, Technical University of Denmark In vitro mutagens from Ashby positives Predicted positive in Ames test, Mouse lymphoma, or Chromosomal aberrations CHL Non-mutagens 437 Mutagens 1703 Non-mutagens 25 DTU Food, Technical University of Denmark Mutagens Distribution of in vivo positives (1703) 1703 Genotoxic carcinogens 15753 Noncarcinogens Mouse micronucleus 272 1640 Sister chromatid exchange 649 2671 Comet assay 458 2330 Drosophila sex-linked recessive lethal 194 550 Rodent dominant lethal 159 741 26 DTU Food, Technical University of Denmark Distribution of in vivo positives by percent Genotoxic Noncarcinogens, % carcinogens, % Mouse micronucleus 16 10.4 Sister chromatid exchange 38.1 17.0 Comet assay 26.9 14.8 Drosophila sex-linked recessive lethal 11.4 3.5 Rodent dominant lethal 9.3 4.7 27 DTU Food, Technical University of Denmark In vivo models as predictors of genotoxic carcinogenicity AM CA ML (1703) DL MM SLRL COMET SCE 0 10 20 FP 30 TP 40 TP - FP Model utility (TP - FP) shown by red bars 28 DTU Food, Technical University of Denmark 50 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 29 DTU Food, Technical University of Denmark