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
Eva Bay Wedebye
Gunde Egeskov Jensen
Marianne Dybdahl
Nikolai Nikolov
Svava Jonsdottir
Tine Ringsted
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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.
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DTU Food, Technical University of Denmark
Pure In Silico
Any relation to test data is incidental
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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
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DTU Food, Technical University of Denmark
Model Platform: MULTICASE
• Cancer models
• MULTICASE FDA proprietary, male and female mouse and rat
• MULTICASE Ashby fragments
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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
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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)
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DTU Food, Technical University of Denmark
Activity distribution
30000
27362
25000
20000
15753
15000
10000
6177
5000
0
Positive
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DTU Food, Technical University of Denmark
Unpredicted
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
27362
10000
15753
5000
2140
4037
0
Positive
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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
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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
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DTU Food, Technical University of Denmark
In vitro mutagens
Predicted positive in Ames test, Mouse lymphoma, or
Chromosomal aberrations CHL
Mutagens 1853
Non-mutagens
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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
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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
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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
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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
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DTU Food, Technical University of Denmark
60
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
27362
10000
15753
5000
2140
4037
0
Positive
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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
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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%)
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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
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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
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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
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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
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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
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DTU Food, Technical University of Denmark