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.

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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 25000 20000 15000 10000 5000 0 6177 Positive 27362 Unpredicted 10

DTU Food, Technical University of Denmark

15753 Negative

Clustering actives

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DTU Food, Technical University of Denmark

Structures

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DTU Food, Technical University of Denmark

Activity distribution with Ashby positives removed

30000 25000 20000 15000 10000 5000 0 27362 15753 2140 4037 Positive Unpredicted Negative 13

DTU Food, Technical University of Denmark

In vitro results for Ashby negative carcinogens

Ames CA ML HGPRT Ames 934 UDS SHE 14

DTU Food, Technical University of Denmark

CA 159 516 ML HGPRT 504 293 189 1167 101 395 559 UDS 91 45 116 80 259 SHE 345 103 472 288 87 768

General estimates and in vitro predictions (4037)

Ames test Chromosomal aberrations Mouse lymphoma HGPRT Unscheduled DNA synthesis Cell transformation (SHE) 15

DTU Food, Technical University of Denmark

934 516 1167 559 259 768 (21.1%) (12.8%) (28.9%) (13.8%) (6.4%) (19.0%)

In vitro mutagens

Predicted positive in Ames test, Mouse lymphoma, or Chromosomal aberrations CHL Mutagens 1853 Non-mutagens 2184 Non-mutagens Mutagens 16

DTU Food, Technical University of Denmark

Distribution of in vivo positives (1853)

Mouse micronucleus Sister chromatid exchange Comet assay Drosophila sex-linked recessive lethal Rodent dominant lethal 17

DTU Food, Technical University of Denmark

1853 Genotoxic carcinogens 15753 Non carcinogens 231 1640 800 288 77 102 2671 2330 550 741

Distribution of in vivo positives by percent

Mouse micronucleus Sister chromatid exchange Comet assay Drosophila sex-linked recessive lethal Rodent dominant lethal 18

DTU Food, Technical University of Denmark

Genotoxic carcinogens, % Non carcinogens, % 12.5

43.2

10.4

17.0

15.5

4.2

5.5

14.8

3.5

4.7

In vivo models as predictors of genotoxic

carcinogenicity AM CA ML (1853)

SLRL COMET DL MM SCE 0 10 20 FP TP 30 TP - FP 40 19 Model utility (TP - FP) shown by red bars

DTU Food, Technical University of Denmark

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In vivo models as predictors of carcinogenicity - Cell transformation SHE (768)

SLRL DL MM COMET -10 SCE 0 10 20 30 FP TP TP - FP 40 50 20 Model utility (TP - FP) shown by red bars

DTU Food, Technical University of Denmark

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Cluster of SHE/SCE positives

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DTU Food, Technical University of Denmark

Activity distribution with Ashby negatives removed

30000 25000 20000 15000 10000 5000 0 2140 4037 27362 15753 Positive Unpredicted Negative 22

DTU Food, Technical University of Denmark

In vitro results for Ashby positive carcinogens

Ames CA ML HGPRT Ames 918 UDS SHE 23

DTU Food, Technical University of Denmark

CA 472 944 ML HGPRT 498 434 982 336 319 412 496 UDS 160 110 128 86 230 SHE 349 343 383 253 80 560

General estimates and in vitro predictions (2140)

Ames test Chromosomal aberrations Mouse lymphoma HGPRT Unscheduled DNA synthesis Cell transformation (SHE) 24

DTU Food, Technical University of Denmark

918 944 982 496 230 560 (42.9%) (44.1%) (45.9%) (23.2%) (10.7%) (26.2%)

In vitro mutagens from Ashby positives

Predicted positive in Ames test, Mouse lymphoma, or Chromosomal aberrations CHL Non-mutagens 437 Mutagens 1703 25 Non-mutagens

DTU Food, Technical University of Denmark

Mutagens

Distribution of in vivo positives (1703)

Mouse micronucleus Sister chromatid exchange Comet assay Drosophila sex-linked recessive lethal Rodent dominant lethal 26

DTU Food, Technical University of Denmark

1703 Genotoxic carcinogens 15753 Non carcinogens 272 1640 649 458 194 159 2671 2330 550 741

Distribution of in vivo positives by percent

Mouse micronucleus Sister chromatid exchange Comet assay Drosophila sex-linked recessive lethal Rodent dominant lethal 27

DTU Food, Technical University of Denmark

Genotoxic carcinogens, % Non carcinogens, % 16 10.4

38.1

26.9

11.4

9.3

17.0

14.8

3.5

4.7

In vivo models as predictors of genotoxic

carcinogenicity AM CA ML (1703)

DL MM SLRL COMET SCE 0 10 20 FP TP 30 TP - FP 40 28 Model utility (TP - FP) shown by red bars

DTU Food, Technical University of Denmark

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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