INNO-TOX Projet ANR-07-CP2D-09

Validation of in silico and in vitro methodologies for

the evaluation of Toxicity and Ecotoxicity of

substances and preparations

Pr R. Bureau (CERMN)

In silico evaluation of toxicity and ecotoxicity against traditional in

vivo evaluation

REACH

European chemical policy since june 2007

Reduce animal testing (in vivo) to the absolute minimum (REACH).

Alternative (in silico / in vitro) methods.

In silico approaches.

(Q)SARs : (Quantitative) Structure-Activity Relationships

Read-across or Grouping of chemicals : Based on molecular similarity

Increase the capacity of detection of carcinogens.

Cell Transformation Assays (CTA) : in vitro approach.

Epigenetic (non genotoxic) and genotoxic carcinogens.

Objective of the study

2

Information requirements for REACH

3

Informations on physico-chemical

properties

Melting point

Boliing point

Density

Vapour pressure

Surface tension

Water solubility

n-octanol/eau partition coefficient

…

Informations on toxicological

properties

Irritation (skin / eye)

Sensibilisation (skin)

Acute toxicity

Repeated dose toxicity (chronic)

Reproductive toxicity

Toxicokinetics

Genotoxicity

Carcinogenicity

Informations on ecotoxicological

properties

Aquatic toxicity (acute / chronic)

Biodegradation

Bioaccumulation

Effects on terrestrial organisms

Effets on sediment organisms

Effets on birds

INNO-TOX : Partners

4

CERMN

EA 4258, FR CNRS 3038 INC3M

GREYC UMR CNRS 6072

LIEBE

UMR CNRS 7146

PCAS

Selection of a large range of structures.

Analysis of physico-chemical and

(eco)toxicological data for these

structures.

Selection of specific derivatives for

carcinogenetic studies

In silico approaches on the selected

structures.

New in silico approaches (development).

In vitro (CTA) approach

M. Bouquet

R. Bureau

P. Vasseur

B. Cuissart

Evaluation of in silico

methods (results)

Ranges of substances and formulations

6

Representative sampling of the fine chemical activity associated to

PCAS

Clustering of chemicals by categories :

Active Pharmaceutical Ingredients (APIs)

Organic synthesis intermediates

Substances for graphic arts or photo industry

Substances for the electronic industry

Aroma for food industry

Raw material for cosmetic industry

Raw material for perfumes

Preparations for lubricants

Preparation for anti corrosion applications.

At least three derivatives / category.

More than 50 derivatives to analyse.

7

8

Case of one aldehyde

Trivertal

Exemple

Ecotoxicological Endpoints MSDS

Fish LC50 (96h)

R52 Daphnid EC50 (48h)

Green Alguae EC50 (96h)

Biodegradability R53

Toxicological Endpoints MSDS

Carcinogenicity −

Ames Mutagenicity −

Mammalian Mutagenicity −

Developmental Toxicity Potential −

Skin Irritation R38

Ocular Irritancy R36

Skin Sensitization R43

Rat Oral LD50 > 2000 mg/kg

O

H

9

Trivertal (CAS# 68039-49-6)

Perfumery

Ecotoxicological Endpoints MSDS Predicted Data Source

Fish LC50 (96h)

R52

13.5 mg/l

3 mg/l

TOPKAT

EPI Suite

Daphnid EC50 (48h) 1.5 mg/l

3.2 mg/l

TOPKAT

EPI Suite

Green Alguae EC50 (96h) 6.6 mg/l EPI Suite

Biodegradability R53 No

Yes

TOPKAT

EPI Suite

Case of one aldehyde

Trivertal

Exemple

Toxicological Endpoints MSDS Predicted Data Source

Carcinogenicity − Plausible TOPKAT

Ames Mutagenicity − No TOPKAT

Mammalian Mutagenicity − − −

Developmental Toxicity Potential − No TOPKAT

Skin Irritation R38 − −

Ocular Irritancy R36 No TOPKAT

Skin Sensitization R43 Plausible

No

TOPKAT

MultiCASE

Rat Oral LD50 > 2000 mg/kg 8600 mg/kg

4085 mg/kg

TOPKAT

MultiCASE

O

H Alternative methods

QSARs

10

Trivertal (CAS# 68039-49-6)

Perfumery

Case of one aldehyde

Trivertal

Exemple

Alternative methods

QSARs

R1 H

O

R1 = C (not multiply bonded or attached

to any heteroatoms)

“… Alkyl aldehydes have been shown to give a positive response in gene

mutation assays in V79 Chinese hamster lung cells in the absence of

metabolic activation (Mutagenesis 1989, 4, 277) and for unscheduled DNA

synthesis in primary cultures of rat hepatocytes (Mutat. Res. 1994, 323, 121)

…”

Alert structure

Derek Nexus

Alert 306: Alkyl aldehyde or precursor

Chromosome damage, genotoxicity, mutagenicity

Colonie de cellules SHE normales Colonie de cellules SHE transformées

Colonie de cellules SHE normales Colonie de cellules SHE transformées

Exposition 7j

in vitro CTA

in vitro CTA (Cell Transformation Assay)

O

HTrivertal

(CAS# 68039-49-6)

Perfumery

Toxicological Endpoints MSDS Predicted Data Source

Carcinogenicity − Plausible TOPKAT

11

Case of one aldehyde

Trivertal

Exemple

Alternative methods

QSARs

O

LD50 = 4500 mg/kg

O

LD50 = 5000 mg/kg

Read-across

Extrapolation from similar structures

Databases : Leadscope / Toxfind / Internal (CERMN)

Toxicological Endpoints MSDS Predicted Data Source

Rat Oral LD50 > 2000 mg/kg 8600 mg/kg

4085 mg/kg

TOPKAT

MultiCASE

O

HTrivertal

(CAS# 68039-49-6)

Perfumery

12

Ecotoxicological Endpoints MSDS Predicted Data Source

Fish LC50 (96h)

R52 R51 EPI Suite, Read-across Daphnid EC50 (48h)

Green Alguae EC50 (96h)

Biodegradability R53 R53 TOPKAT, Read-across

Case of one aldehyde

Trivertal

Exemple

Toxicological Endpoints MSDS Predicted Data Source

Carcinogenicity − Plausible TOPKAT, Derek, CTA (in vitro)

Ames Mutagenicity − − −

Mammalian Mutagenicity − − −

Developmental Toxicity Potential − − −

Skin Irritation R38 R38 Read-across

Ocular Irritancy R36 R36 Read-across

Skin Sensitization R43 R43 TOPKAT, Derek

Rat Oral LD50 > 2000 mg/kg 4000 mg/kg MultiCASE, Read-across

O

HTrivertal

(CAS# 68039-49-6)

Perfumery

Decisions

13

90% of endpoints have an in silico estimation.

Only 30% have an estimation estimated to be reliable (missing data).

Toxicology

Topkat and MultiCase (QSARs)

Performances from 20% (sensitization) to 50% (DL50)

Derek (fragment – based expert systems)

Performing for skin and ocular toxicity, sensitization, mutagenicity and

cancerogenicity

Ecotoxicology

Topkat and ECOSAR

Toxicity for aquatic organims : Performances 75%

Importance to get experimental LogP values (PCAS / Innotox).

Biodegradability: Performances 45%

Important to be able to combine several in silico informations.

QSAR + Read-across or QSAR + fragment associated to a risk

In silico approaches: general assessment

14

New in silico approach in

(eco)toxicology

Expert systems

Predictions based on the detection of alert structures.

Definition of new methods to extract toxicophores

Derek Nexus

HazardExpert

OncoLogic Decision rules based on human knowledge

16

Valerio et al. Toxicol. Appl. Pharmacol. 2009, 241, 356-370

Automatic extraction of fragments ?

Decision rules based on the association of fragments ?

A negative response does not demonstrate a lack of toxicity

Limitations !!

MultiCASE Statistical analysis for the discovery of alert fragments and modulators

D Database

General method

D2 Non-toxic

Molecules

Emerging Patterns as Toxicophores.

Subset Molecules Fragments

frag1 frag2 frag3 frag4 frag5

D1

Toxic

molecules

mol1 x x x mol2 x x x x mol3 x x x mol4 x x x mol5 x x x

D2

Non toxic

molecules

mol6 x x mol7 x x x mol8 x mol9 x x mol10 x x x x

D1 Toxic

Molecules

Search of frequent fragment (simple

pattern) in toxic dataset (D1)

Cutoff for minimum frequency

Gaston (Nijssen et al., ACM SIGKDD, 2004)

5

2fmin = = 40% inD1

Minimum Growth rate

rmin from D2 to D1 17

Search for emerging patterns in D1

Definition of all fragments in toxic dataset

(D1)

Growth rate

r = f (D1)/f(D2)

=2

Emerging Patterns : a simple example

Subset Molecules Fragments

D1

Molécules

toxiques

x x

x x x x

x

x x x

x x x

D2

Molécules

non-toxiques

x x x

x

x

x x x

x x

O

O

OH

O

Cl

S SP

O

SO

Cl Cl

ONH2

O

OH

Cl

Cl

Cl

OH

NH

O

OOH

S SP

O

SO

Cl

ClCar O

HC

O

Cl

OHCl

Cl

Cl Cl

OH Cl

5

2fmin = = 40% dans D1

SP

O

SO

18

Emerging Patterns : a simple example.

Subset Molecules Fragments

D1

Toxic

molecules

x x

x x x x

x

x x x

x x x

D2

Non toxic

molecules

x x x

x

x

x x x

x x

O

O

OH

O

Cl

S SP

O

SO

Cl

OHCl

Cl

Cl Cl

OH Cl

Cl Cl

ONH2

O

OH

Cl

Cl

Cl

OH

NH

O

OOH

S SP

O

SO

Cl

ClCar O

H

5

2fmin = = 40% dans D1

CO

SP

O

SO

Molecular pattern

ClCar, m1 =

19

Emerging Patterns. A simple example.

Subset Molecules Fragments

D1

Toxic

molecules

x x

x x x x

x

x x x

x x x

D2

Non toxic

molecules

x x x

x

x

x x x

x x

O

O

OH

O

Cl

S SP

O

SO

Cl Cl

ONH2

O

OH

Cl

Cl

Cl

OH

NH

O

OOH

S SP

O

SO

Cl

ClCar O

H

Frequency ( f )

fm1 = = 60% in D1

Cl

OHCl

Cl

Cl Cl

OH Cl

5

2fmin = = 40% in D1

CO

SP

O

SO

Molecular pattern

ClCar, m1 =

20

5

3

Emerging Patterns. A simple example.

Subset Molecules Fragments

D1

Toxic

Molecules

x x

x x x x

x

x x x

x x x

D2

Non-toxic

Molecules

x x x

x

x

x x x

x x

O

O

OH

O

Cl

S SP

O

SO

Cl Cl

ONH2

OH

Cl

Cl

Cl

OH

NH

O

OOH

S SP

O

SO

Cl

ClCar O

H

O

Cl

OHCl

Cl

Cl Cl

OH Cl

5

2fmin = = 40% dans D1

CO

SP

O

SO

Molecular pattern

ClCar, m1 =

Frequency ( f )

5

3fm1

= = 60% in D1

fm1 = = 20% in D2 5

1

Growth rate ( r )

rm1 from D2 to D1 = 3 = 20

60

Emerging pattern

rmin If ≤ 3 then m1 is an emerging pattern from D2 to D1

21

Prediction of Risk Phrases in ecotoxicology

Classification function of acute data

Three species :

Fish

Daphnids

Algae

QSARs and Toxicophore

LC50

EC50

R50 (very toxic) R51 (toxic) R52 (harmful) (non-toxic)

1 mg/L 10 mg/L 100 mg/L

22

LC50

EC50

R50 (very toxic) R51 (toxique) R52 (Harmful) (non-toxique)

1 mg/L 10 mg/L 100 mg/L

372 substances 64 substances

Prediction of Risk Phrases in ecotoxicology

Results from QSAR methods

QSARs and Toxicophores (EP)

Real Classification Estimated Classification Failure rate

R50 (R51) R52 Not classified

R50 176 (122) 70 1 19%

R52 3 (18) 43 0 5%

In reviewing the 71 substances whose toxicity was underestimated

68 of the 71 R50 substances badly predicted have been reclassified (cutoff for the minimum frequency = 1% corresponding to 4 toxic substances)

Rule: if a molecule contains at least one toxicophore (i.e. a JEP) then it must

be reclassified R50

23

Problems on EP

cOP Clc(c(cc1)Cl)cc1 cccOP(OC)(O)=S c(ccc1OP=S)cc1 c(ccc(NO)cc)O cccOC(N)=O cccc(OC(N)=O)c cOPO Clcc(cccc)Cl cccOP(OC)OC cccc(OP(=S)O)c c(ccc1NO)(O)cc1 C(C(CC1)C)CC1 cccc(OC(N)=O)cc

cOPOC ClccccccCl cccOP(OC)(OC)=S cccc(OP(=S)O)cc ccc(cccN=O)O cOP(OCC)=S c(ccc1OC(N)=O)cc1 ccOP ccc(c(cc)Cl)Cl ccc(OC=O)cc c(ccc1OP(=S)O)cc1 ccc(cccN(=O)O)O ccOPOCC cccccOCN

ccOPO ccOP(O)O ccccOC=O ccccc(OP=S)c ccc(cccNO)O n(cNC)c ccccc(OCN)c cccOP c(ccO)C cccc(OC=O)c ccccc(OP(=S)O)c cc(OP(OC)=S)c c(ccc1ccc)cc1 cccccOC(N)=O

cccOPO cOC=O cccc(OC=O)cc ccccccOP=S ccc(OP(OC)=S)c ccccOP(=S)(O)O ccccc(OC(N)=O)c cOP=S ccOC=O c(ccc1OC=O)cc1 ccccccOP(=S)O ccc(OP(OC)=S)cc ccccOP(OC)O ccccccOCN

cOP(=S)O cccOC=O cccccOC=O c(ccO)(C)c cccc(OP(OC)=S)c ccccOP(OC)(O)=S ccccccOC(N)=O ccOPOC cc(OP)c ccccc(OC=O)c c(cc(O)c)C cccc(OP(OC)=S)cc ccccOP(OC)OC cccOPOCC

OP(=S)(O)O ccOP(=S)(O)O ccccccOC=O c(cc(O)c)(C)c c(ccc1OP(OC)=S)cc1 ccccOP(OC)(OC)=S n(c(NC)n)c O(P(OC)(O)=S)C cc(OPO)c cc(OPOC)c ccc(ccO)C ccccc(OP(OC)=S)c cccccOP(=S)(O)O n(c(nc)NC)c O(C)P(=S)(O)O ccOP(OC)O ccc(OPOC)c ccc(cc(O)c)C ccccccOP(OC)=S cccccOP(OC)O nc(NC)n

cOP(OC)=S ccOP(OC)(O)=S ccc(OPOC)cc c(cc(c1)C)cc1O cCOC cccccOP(OC)(O)=S cc(OP(O)O)c cccOPOC ccOP(OC)OC ccccOP=S ccc(ccC)O ccCOC cccccOP(OC)OC ccc(OP(O)O)c ccOP=S ccOP(OC)(OC)=S ccccOP(=S)O ccc(cc(C)c)O cc(COC)c cccccOP(OC)(OC)=S ccc(OP(O)O)cc

ccOP(=S)O ccc(OP)c cccc(OPOC)c cccc(ccO)C O(P(OCC)(O)=S)CC cCOCC cccc(OP(O)O)c ccOP(OC)=S ccc(OP)cc cccc(OPOC)cc cccc(ccC)O O(P(OCC)(O)=S)C ccCOCC cccc(OP(O)O)cc

cccOP=S ccc(OPO)c c(ccc1OPOC)cc1 Clc(cc(cCl)Cl)c O(CC)P(=S)(O)O cc(COCC)c c(ccc1OP(O)O)cc1 cccOP(=S)O ccc(OPO)cc cccccOP=S Clcc(ccCl)Cl n(cO)c ccc(COC)cc ccccc(OP(O)O)c

ClccCl cccOP(O)O cccccOP(=S)O cOPOCC ccccOP(O)O ccccCOC ccccccOP(O)O Clc(cCl)c cccc(OP)c ccccc(OPOC)c ncNC cccccOP(O)O cccc(COC)c cccCOCC Clcc(cc)Cl cccc(OP)cc ccccccOPOC c(cccN=O)O Clc(c(ccCl)Cl)c cccc(COC)cc ccc(COCC)c Clcc(ccc)Cl c(ccc1OP)cc1 ncO c(cccN=O)(O)c Clc(c(cc(Cl)c)Cl)c c(ccc1COC)cc1 c(cC=O)C cOP(O)O cccc(OPO)c ccccOP(OC)=S c(ccc(N=O)c)O Clc(c(cc1Cl)Cl)cc1 cccccCOC c(c(C=O)c)C

cccOP(OC)=S cccc(OPO)cc cccccOP(OC)=S c(ccc(N=O)c)(O)c Clc(ccc(cCl)Cl)c ccccc(COC)c cOCNC cOP(=S)(O)O c(ccc1OPO)cc1 c(ccccO)C c(ccc(N=O)cc)O Clc(c(cccCl)Cl)c ccccccCOC cOC(NC)=O

cOP(OC)O ccccOPOC c(ccccO)(C)c c(ccc1N=O)(O)cc1 Clcc(cccCl)Cl cc(OCN)c ccOCNC cOP(OC)(O)=S ccccc(OP)c c(cccc(O)c)C c(cccN(=O)O)O Clcc(cc(cc)Cl)Cl cc(OC(N)=O)c ccOC(NC)=O

cOP(OC)OC ccccc(OPO)c cS c(cccN(=O)O)(O)c Clccc(c(cc)Cl)Cl ccc(OCN)c cccOCNC cOP(OC)(OC)=S cccccOPOC cc(OP=S)c c(ccc(N(=O)O)c)O Clccc(cccCl)Cl ccc(OCN)cc cccOC(NC)=O

ccccOP ccccccOP cc(OP(=S)O)c c(ccc(N(=O)O)c)(O)c cccCOC ccc(OC(N)=O)c ccOP(OCC)=S ccccOPO ccccccOPO ccc(OP=S)c c(ccc(N(=O)O)cc)O ccc(COC)c ccc(OC(N)=O)cc cccccOP cc(OC=O)c ccc(OP=S)cc c(ccc1N(=O)O)(O)cc1 cOCN ccccOCN

cccccOPO ccc(OC=O)c ccc(OP(=S)O)c c(cccNO)O cOC(N)=O cccc(OCN)c Clc(c(Cl)c)c c(ccc1OCC)cc1 ccc(OP(=S)O)cc c(cccNO)(O)c ccOCN cccc(OCN)cc Clc(c(cc)Cl)c cccOP(=S)(O)O cccc(OP=S)c c(ccc(NO)c)O ccOC(N)=O c(ccc1OCN)cc1 Clc(c(ccc)Cl)c cccOP(OC)O cccc(OP=S)cc c(ccc(NO)c)(O)c cccOCN ccccOC(N)=O

ClccCl

Incomplet fragment

Clc(c(cc1Cl)Cl)cc1

Clc(c(cc1)Cl)cc1

Sub-structure ?

24

A simple example

RPMP (Representative Pruned Molecular Patterns) as

toxicophores

Frequency and Emergence

fmin if = 50% then m1 and m2 are frequent pattern in D1

rmin If = 2 then m1 and m2 are Emerging Pattern from D2 to D1

m1 and m2 have the same extents in D, i.e {mol1, mol2, mol3} m2 is included in m1 and can be pruned.

O

C

CO

H2

H

O

C

CO

Br

H3

CO

OCH3

H

NH

CCH

3

OOH

mol1

mol2

mol3 mol4

D1

D2

Molecular patterns

OC

,…, m1 = CO

OC

m2 = frag1 frag2 frag1

,…

OC

,…, m1 = CO

frag1 frag2

Closed pattern

The fragments corresponding to the sub-fragments of frag1 are pruned

OC

, m1 = CO

frag1 frag2

RPMP

25

Exemple

EPAFHM for the dataset (EPA Fathead minnow Acute Toxicity Database)

223 substances toxiques

172 substances non-toxiques

= 2.8%

Emerging Patterns towards RPMP

fmin

Growth rate

Number of patterns Length of patterns

EP Closed EP Closed EP RPMP

2 2 414 271 394 735 15.2 2.29

5 1 629 688 309 415 15.7 2.36

10 1 629 688 309 264 16.4 2.40

25 1 632 132 495 238 16.9 2.43

∞ 1 632 131 769 236 16.9 2.43

26

EPAFHM dataset

74 very toxic substances

172 non toxic substances

= 8% (5 molecules at least)

RPMP analysis / Validation

Acute toxicity on fish (Fathead minnow)

Growth rate Coverage

rate True Positif True negatif

Global

success on

a test set

2 71.1 94.5 38.9 55.7

5 44.7 83.7 72 75.6

10 34.1 77 84.3 82.1

25 23.1 56.7 91.2 80.9

∞ 20.3 48.6 91.8 78.9

fmin

Decision rule

If a molecule contains at least one RPMP Then

it must be classified as very toxic

27

In vitro Cancerogenesis

28

In vitro Cancerogenesis

29

Normal SHE Colony transformed colony

Subcutaneous injection to hamster

Nothing Tumors after several

weeks

Examination of colony

morphology under

Stereomicroscope

(after 7 days of exposure)

criterion is validated by the

fact that:

SHE cells : Syrian hamster embryo cells

30

(Non) genotoxic

substances

With in vitro

cancerogen

properties

(SHE model)

Substance Structure Cytotoxicity

For SHE cells

Potential Carcinogen in vitro/SHE cells

Triazine X

Textile 200 t/year

Public

CI50 0.5 µg/mL

Positive at 10-7µg/mL

Trivertal 68039-49-6

Perfumery, cosmetic Public

> 1 µg/mL Positive at 1 µg/mL

Phenylethyl salicylate 87-22-9

cosmetic 5t/an

Public

> 25 µg/mL Positive at 25 µg/mL

Irisone 98-53-3

4-tert-Butylcyclohexanone

cosmetic 5t/an

Public

> 100 µg/mL Positive at 100 µg/mL

Allyl Caproate 123-68-2

cosmetic 5t/an

Public

> 25 µg/ml Positive at 10-5 µg/ml

Losma 347 Engrais Métallurgie

230 t/an

Positive at 0.05 µg/ml.

Losma 1214 Métallurgie

130 t/an

Positive at 10 µg/mL.

In vitro Cancerogenesis

Conclusion and perspectives.

31

Most chemical molecules have mainly acute or irritation data.

Fundamental physico-chemical data like logP (lipophily of derivatives) are often missing.

Accuracy on logP values strengthens in silico estimation in ecotoxicology.

Reluctance of companies to determine sensitive properties like CMR properties.

Basic tests such as the Ames test are absent .

Chronic data are missing.

Data correspond to MSDS.

Alternative in silico methods are effective on the basis of a rational : (Q)SAR and

emerging fragments.

All endpoints may be predicted.

The lack of cross data (read-across, emerging fragments) makes difficult the validation.

Success rate between 20 and 50% in function of the endpoints.

In vitro carcinogen are required for non-genotoxic substances.

The results demonstrate the weakness of current data, critical point for an estimate of the

major risks like carcinogenicity.

Publications (11).

Lozano, S. et al. J. Enz. Inh. Med. Chem., 2010, 25, 195-203.

Lozano, S. et al. Molecular informatics, 2010, 29, 803-813.

Maire M-A. et al. Toxicon. 55, 1317-1322

Lozano, S. et al. J Chem Inf Model, 2010, 50(8), 1330-1339.

Jacquet N. et al. Arch. Toxicology, 2012, 86, 305–314.

Jacquet N. et al. Environ Sci Pollut Res 2012, 19, 2537–2549.

Vasseur P. et al. Mutation Research 2012, 744, 8-11 .

Maire M-A. et al. Mutation Research 2012a, 744 (2012), 64-75.

Maire M-A. et al. Mutation Research 2012b, 744, 76-81

Maire M-A. et al. Mutation Research 2012c, 744, 97-110

Cuissart, B. et al. Contrast Data Mining. Dong, G. & Bailey, J., Eds.;

2012, pp. 259-270.

Listing of publications related ANR Inno-Tox

32

Communications par affiches (10).

Lozano, S. et al. Cambridge, 9-10 décembre 2008.

Lozano, S. et al. Paris, 5 février 2009.

Lozano, S. et al. Rouen, 20 mars 2009.

Lozano, S. et al. Deauville, 11-12 Mai 2010.

Lozano, S. et al. Obernai, 20-24 juin 2010.

Maire, M-A . et al. Metz 29 August-3 september 2009.

Bazin , E. et al. Metz 29 August-3 september 2009.

Jacquet, N. et al. Metz 29 August-3 september 2009.

Lozano, S. et al. Montpellier, 24-25 juin 2009.

Lozano, S. et al. Metz 29 August-3 september 2009.

Listing of communications and conferences

33

Communications / conférences orales (6)

Lozano, S. et al. 24th International Collaborative Group Meetings (ICGMs), Cambridge, 9-10

décembre 2008.

Maire, M-A. et al. Société d’Ecotoxicologie Fondamentale et Appliquée, 31 mars – 1 avril

2010, Versailles, France.

Vasseur P. et al Workshop on strategic Approaches for Reducing Data Redundancy in

Cancer Assessment, International QSAR foundation, Duluth, Minessota, USA, 19– 21 mai

2010.

Maire, M-A. et al. 15th International Symposium on Toxicity Assessment. Hong Kong. 3-8

July 2011. Communication orale.

Jacquet, N. et al. 15th International Symposium on Toxicity Assessment. Hong Kong. 3-8

July 2011.

Lepailleur, A. et al. CMTPI 2011, Maribor, 3-7 septembre 2011.

Conférences invités (2).

Vasseur, P. 2010. Workshop on Strategic Approaches for Reducing Data Redundancy in

Rodent Cancer Bioassays and Cancer Assessment. International QSAR Foundation to

Reduce Animal Testing. Duluth, US-EPA, Minnesota, 19-21 May.

Vasseur, P. 2011. Emerging pollutants. Challenges and Research progress on carcinogenic

potential. 15th International Symposium on Toxicity Assessment. Hong Kong. 3-8 July 2011

Listing of communications and conferences

34

PhD (2).

JACQUET Nelly, Etude in vitro du potentiel cancérogène de polluants émergents de

l’environnement, et analogues industriels. Thèse de doctorat : Sciences de la Vie:

Université Paul Verlaine - Metz, LIEBE, CNRS UMR 7146. Direction : Pr P. Vasseur.

LOZANO Sylvain Estimation des propriétés écotoxicologiques de substances chimiques

par méthodes in silico : définition de modèles globaux ou spécifiques. Thèse de doctorat

de l’Université de Caen Basse-Normandie, 15 décembre 2010, Direction : Pr R. Bureau

Start-up : (2)

Predireach

Estimation des propriétés (éco)toxicologiques par des méthodes in silico.

Développement de nouveaux modèles in silico.

Iseetech

Institut Supérieur Européen de l’Entreprise et de ses Techniques

Start-up fondée sur la mise en place d’une plateforme de transfert

technologique pour la cancérogénèse in-vitro.

PhD/ Valorisation

35

Publications

Poezevara, G.; Lepailleur, A.; Bureau, R.; Crémilleux, B. Using conjunctions of molecular fragments as toxicophores to predict LD50

endpoint. En préparation.

Poezevara, G.; Cuissart, B.; Crémilleux, B. Extracting and summarizing the frequent emerging graph patterns from a dataset of

graphs. J. Intell. Inf. Syst. 2011, 37, 333-353.

Lozano, S.; Poezevara, G.; Halm-Lemeille, M.P.; Lescot-Fontaine, E.; Lepailleur, A.; Bissell-Siders, R.; Crémilleux, B.; Rault, S.;

Cuissart, B.; Bureau, R. Introduction of jumping fragments in combination with QSARs for the assessment of classification in

ecotoxicology. J. Chem. Inf. Model. 2010, 50, 1330-1339.

Chapitre de livre

Cuissart, B.; Poezevara, G.; Crémilleux, B.; Lepailleur, A.; Bureau, R. Emerging Patterns as Structural Alerts for Computational

Toxicology. In Contrast Data Mining: Concepts, Algorithms and Applications, Dong, G. & Bailey, J., Eds.; Taylor & Francis Group,

2012, pp. 259-270.

Proceedings

Bissell-Siders, R.; Cuissart, B.; Crémilleux, B. On the stimulation of patterns: definitions, calculation method and first usages. Proc. of

the 18th International Conference on Conceptual Structures (ICCS 2010), Lecture Notes in Artificial Intelligence, 2010, pp 56-69.

Poezevara, G.; Cuissart, B.; Crémilleux, B. Discovering emerging graph patterns from chemicals. Proc. of the 18th International

Symposium on Methodologies for Intelligent Systems (ISMIS 2009), Lecture Notes in Computer Science, 2009, pp 45-55.

Communications orales

Discovering patterns (sets) in chemoinformatics.

Crémilleux, B.; Cuissart, B.; Bureau, R.; Lepailleur, A.; Plantevit, M.; Poezevara, G.; Raïssi, C.; Soulet, A.

3rd Spring Workshop on Mining and Learnin, Bad Neuenahr (Allemagne), 18-20 Avril 2012.

New measures of interest associated to chemical patterns : definition, implementation and experimental assessment.

Schietgat, L.; Poezevara, G.; Bissell-Siders, R.; Bureau, R.; Crémilleux, B.; Lepailleur, A.; Halm-Lemeille, M.P.; Ramon, J.; Cuissart, B.

5èmes Journées de la Société Française de Chémoinformatique (SFCi), Cabourg (France), 13-14 Octobre 2011.

Mining patterns and subgraphs as potential toxicophores to predict contextual ecotoxicity.

Bissel-Siders, R.; Poezevara, G.; Cuissart, B.; Crémilleux, B.

5th Workshop on Computers in Scientific Discovery, Sheffield, Juillet 2010.

Valorisation scientifique

36

Suivi des personnels recrutés en CDD

37

Nom et prénom Poste dans le projet Durée missions

(mois)

Date de fin de

mission sur le projet Devenir professionnel Type d’employeur Type d’emploi

MAIRE Marie Aline Post Doct 36 01/02/2011 CDD Autre privé Chercheur

JACQUET Nelly Doctorant 36 31/05/2011 Fin thèse

LOZANO Sylvain CDD Thèse 36 01/01/2011 CDI Les Laboratoires Servier Chercheur

FONTAINE Elodie Post Doc 8 31/07/2008 CDI Arkema Analyse risque

LEPAILLEUR Alban Post Doc 13 31/08/2009 CDI Enseignement et recherche

publique Enseignant chercheur

DUVAL Sarah Technicien 2 30/10/2010 CDD Laboratoires d’analyses

médicales/EF Technicien