mckim workshop on strategic approaches for reducing data redundancy in cancer assessment

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

Jay R. Niemelä Technical University of Denmark

National Food InstituteDivision of Toxicology and Risk Assessment

e-mail: [email protected]

In silico methods for predicting chromosomal endpoints for carcinogens

2 DTU Food, Technical University of Denmark

Eva Bay WedebyeGunde Egeskov Jensen

Marianne DybdahlNikolai NikolovSvava Jonsdottir

Tine Ringsted


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.


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.


Pure In Silico

Any relation to test data is incidental


Method

Global (Q)SARs

in between

Local (Q)SARsClosely related structuresAccurate predictions for a small number of chemicals

Fragment rule-basedFastHigh throughputDiverse


Model Platform: MULTICASE

• Cancer models

• MULTICASE FDA proprietary, male and female mouse and rat• MULTICASE Ashby fragments


Gentotoxicity models. Developed in-house. QMRF’s and training sets availableIn 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


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)


Activity distribution

6177

27362

15753

0

5000

10000

15000

20000

25000

30000

Positive Unpredicted Negative


Clustering actives


Structures


Activity distribution with Ashby positives removed

4037

27362

15753

2140

0

5000

10000

15000

20000

25000

30000



In vitro results for Ashby negative carcinogens

Ames CA ML HGPRT UDS SHE

Ames 934 159 504 293 91 345

CA 516 189 101 45 103

ML 1167 395 116 472

HGPRT 559 80 288

UDS 259 87

SHE 768


General estimates and in vitro predictions (4037)

Ames test 934 (21.1%)

Chromosomal aberrations 516 (12.8%)

Mouse lymphoma 1167 (28.9%)

HGPRT 559 (13.8%)

Unscheduled DNA synthesis 259 (6.4%)

Cell transformation (SHE) 768 (19.0%)


In vitro mutagens

Predicted positive in Ames test, Mouse lymphoma, or Chromosomal aberrations CHL

Non-mutagens 2184

Mutagens 1853

Non-mutagens Mutagens


Distribution of in vivo positives (1853)

1853 Genotoxic carcinogens

15753 Non-carcinogens

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


Distribution of in vivo positives by percent

Genotoxic carcinogens, %

Non-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


In vivo models as predictors of genotoxic carcinogenicity AM CA ML (1853)

Model utility (TP - FP) shown by red bars

0 10 20 30 40 50

SCE

MM

DL

COMET

SLRL

FP TP TP - FP


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


-10 0 10 20 30 40 50 60

SCE

COMET

MM

DL

SLRL

FP TP TP - FP


Cluster of SHE/SCE positives


Activity distribution with Ashby negatives removed

4037

27362

15753

2140

0

5000

10000

15000

20000

25000

30000



In vitro results for Ashby positive carcinogens

Ames CA ML HGPRT UDS SHE

Ames 918 472 498 336 160 349

CA 944 434 319 110 343

ML 982 412 128 383

HGPRT 496 86 253

UDS 230 80

SHE 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%)


In vitro mutagens from Ashby positives

Predicted positive in Ames test, Mouse lymphoma, or Chromosomal aberrations CHL

Non-mutagens 437

Mutagens 1703

Non-mutagens Mutagens


Distribution of in vivo positives (1703)

1703 Genotoxic carcinogens

15753 Non-carcinogens

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


Distribution of in vivo positives by percent

Genotoxic carcinogens, %

Non-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


In vivo models as predictors of genotoxic carcinogenicity AM CA ML (1703)


0 10 20 30 40 50

SCE

COMET

SLRL

MM

DL

FP TP TP - FP


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

mckim workshop on strategic approaches for reducing data redundancy in cancer assessment

Documents

dtu food

female mouse

mouse micronucleus14

large set of carcinogens

rodent dominant lethal7

mouse bone marrowcomet

sister chromatid exchange40

positive cancer predictions