Transcript Diapositiva 1

QSAR prediction of physico-chemical properties
and biological activities of emerging pollutants:
brominated flame retardants and
perfluorinated-chemicals
Paola Gramatica
Barun Bhhatarai, Simona Kovarich and Ester Papa
QSAR Research Unit in Environmental Chemistry and Ecotoxicology
DBSF -University of Insubria, Varese - Italy
E-mail: [email protected]
http://www.qsar.it
Sixth Indo-US Workshop on Mathematical Chemistry
Kolkata, 8-10 January 2010
THE CHEMICAL UNIVERSE
NEW
11.000.000 / year
More than 50.000.000 (sept.2009)
34,849,353 on the market
Q
S
A
R
Regulated 247,952
EINECS TSCA
100.204
Predictive methods
Environmental fate?
Human effects?
5%
Known
data
experiments
EU-REACH
Prof. Paola Gramatica - QSAR Research Unit - DBSF - University of Insubria - Varese (Italy)
INTRODUCTION – REACH and QSAR
Limited availability of experimental
data
Lack of knowledge of the properties
and activities of existing substances
Complexity of “old” regulations
New EU-regulation:
Registration
Evaluation
Authorisation
of Chemicals
Interest on development and validation of
alternative methods, such as QSARs.
The use of predictive QSAR models is suggested :
 To highlight dangerous chemicals
 To prioritize chemicals and to focus the experimental
tests
 To fill the data gaps
in Environmental Chemistry
and Ecotoxicology
http://www.qsar.it
DBSF - University of Insubria
Varese - Italy
Staff
Prof. Paola Gramatica
Dr. Ester Papa, Ph.D
Dr. Simona Kovarich
Dr. Jr. Mara Luini
Dr. Barun Bhhatarai, Ph.D
(Dr. Jiazhong Li, Ph.D)
INTRODUCTION – Brominated Flame Retardants
• Class of emerging pollutants used in a variety of consumer
products (plastics, polyurethane foams, textiles, electronic
equipments..) to increase fire resistancy
• Three most marked HPV products:
Br
Br
CH3
Br
HO
CH3
Br
TBBPA
TetraBromoBisphenol-A
O
Br
OH
Br
209 possible
CONGENERS
Br
Br
Br
Br
Br
Br
HBCD
Hexabromocyclododecane
PBDE
Polybrominated Diphenyl Ethers
• Levels in the environment and humans increased since they
came into use
• Ban of penta- and octa-BDE formulations (DecaBDE under
evaluation); HBCD in candidate list?
Prof. Paola Gramatica - QSAR Research Unit - DBSF - University of Insubria - Varese (Italy)
INTRODUCTION – Brominated Flame Retardants
Background knowledge about BFRs:
•
•
•
•
•
Low water solubility
High LogKow > 5
Persistence in the environment
Liver toxicity, thyroid toxicity, developmental toxicity
Endocrine disruptors
The available amount of experimental data is very small and
mainly related to already banned BFRs.
There is the need to extend knowledge about
properties and ecotoxicological data for a
better understanding of BFRs behaviour and
related risks
Prof. Paola Gramatica - QSAR Research Unit - DBSF - University of Insubria - Varese (Italy)
INTRODUCTION – Perfluorinated Compounds
• Perfluorinated compounds (PFCs) are
chemicals containing a long fluorinated
carbon tail attached to different functional
groups
• PFCs as perfluoro-octanesulfonate (PFOS),
perfluoro-octanoate (PFOA) and perfluorooctane sulfonylamide (PFOSA) are stable
chemicals with a wide range of industrial
and consumer applications
• Degradable products of commercial PFCs
are found in environment and biota and
diPAPs (a group of PFCs used on food
wrappers) was recently reported in human
blood
• PFCs are considered emerging pollutants
and are believed to have potential toxic
effects in humans and wildlife
• PFCs along with Polyfluoro compounds are
studied for LC50 inhalation toxicity of
Mouse and Rat
Predictive QSAR
approaches is used to fill
the data gap and to predict
toxicity of 250 PFCs on two
different species viz.
Mouse and Rat
Prof. Paola Gramatica - QSAR Research Unit - DBSF - University of Insubria - Varese (Italy)
7
Aims of the Modelling Studies
EU-FP7 Project - CADASTER
 Development of QSAR models for available end-points paying
attention to external validation and applicability domain
analysis.
 Evaluation of environmental behaviour and physico-chemical
properties of emerging pollutants: BFRs and PFCs.
 Identification of more toxic and dangerous chemicals based
on the studied end-points.
 Prioritization of chemicals for experimental tests under
CADASTER project
 Mechanistic
interpretation
of
selected
descriptors,
highlighting the fate, distribution and properties of chemicals.
Prof. Paola Gramatica - QSAR Research Unit - DBSF - University of Insubria - Varese (Italy)
OECD Principles for QSAR models in REACH
To facilitate the consideration of a QSAR model
for regulatory purposes, it should be associated with the
following information:
 a defined endpoint
 an unambiguous algorithm
 a defined domain of applicability
-
 appropriate measures of goodness of fit,
robustness and predictivity
 a mechanistic interpretation, if possible
Prof. Paola Gramatica - QSAR Research Unit - DBSF - University of Insubria - Varese (Italy)
METHODS
Application of the OECD principles for QSAR models
1. Defined end-points of Phys-chem and Toxicity
2. Unambiguous algorithm:
•
Chemical representation by theoretical molecular descriptors
(DRAGON) selected by Genetic Algorithms
•
Statistical method  MLR regression (OLS)
3. Validation for model stability and predictivity (internal and
external validation)
4. Applicability Domain Analysis:
 leverage approach by Hat matrix (MLR)
5. Interpretation of the selected molecular descriptors, if possible.
Prof. Paola Gramatica - QSAR Research Unit - DBSF - University of Insubria - Varese (Italy)
RESULTS
QSAR/QSPR models
developed for
Brominated Flame Retardants
Simona Kovarich
Prof. Paola Gramatica - QSAR Research Unit - DBSF - University of Insubria - Varese (Italy)
RESULTS – QSPR models
Physico-chemical and degradation Properties
Train
obj.
Test
obj.
Full
30
-
k-ANN Split
24
6
Full
20
-
k-ANN Split
14
6
Full
25
-
k-ANN Split
20
5
Full
34
-
k-ANN Split
28
6
LogS
Full
12
-
LogH
Full
7
LogKp*
Full
LogHLp*
Full
Endpoint
LogKOA
LogKOW
MP
LogPL
Model
R2%
Q2LOO
%
Q2EXT
%
on 243 BFR
97.4
96.8
-
81.9
96.1
95.0
95.2
-
96.4
95.6
-
86.0
97.1
95.9
94.7
-
84.4
81.9
-
95.9
82.2
78.5
93.7
-
98.7
98.5
-
83.1
98.8
98.5
98.6
-
Mor23m
91.8
88.5
-
95.1
-
BEHe7
96.9
93.3
-
55.6
15
-
MW
94.9
93.8
-
91.4
15
-
T(O..Br)
94.3
92.6
-
81.9
Desc.
T(O..Br)
T(O..Br)
X2A
T(O..Br)
AD%
* Photodegradation
E. Papa, S. Kovarich, P. Gramatica, 2009. Development, validation and inspection of
the applicability domain of QSPR models for physico-chemical properties of
polybrominated diphenyl ethers. QSAR & Comb. Sci., 28, 790-796.
RESULTS - Model for Log Koa
LogKoa= 6.654 +0.222 T(O..Br)
13
183
156
Training set
Prediction set
n° Obj
Descriptor
153
30
T(O..Br)
126
12
154
119
99
82
Q2boot%
Q2 EXT(rand20%) %
97.36
96.77
99.56
85
100
77
66
75
47
69
10
37
3528
distance from the structural
Domain (hat)
LogKoa Pred.
11
R2%
21
30
32 17
15
9
13
12
7 8
Are the predictions
in the structural
domain ?
90.4 % into AD
0.2
10
8
nona-deca
0.4
2
1
7
0
1
7
8
9
10
11
21
41
12
61
81
13
101
121
141
161
181
BDE
LogKoa Exp.
Experimental range of LogKoa: 7.34 (mono-BDE) – 11.96 (hepta-BDE)
201
RESULTS – Interpretation of descriptors
The same descriptor, i.e. T(O...Br), was selected
as the best modeling variable for three different
properties which are related to each other
(LogPL, LogKoa, LogKow, LogHLp).
This descriptor gives a double structural information:
its values increases according to both the number and the distance
of bromine substituents from the oxygen ether,
on each phenyl ring.
Thus, T(O...Br) takes also into account the information related to
the position of the bromine atoms on the phenyl rings.
Prof. Paola Gramatica - QSAR Research Unit - DBSF - University of Insubria - Varese (Italy)
Comparison with some existing models
Exp. vs. Pred. data
Predicted and Experimental data for 30 PBDEs
15
14
tetra-hepta
13
LogKoa
12
11
mono-tri
10
9
8
7
6
1
2
7
8
10
12
13
15
17
21
28
30
32
35
37
47
66
69
75
77
82
85
99
100 119 126 153 154 156 183
PBDE
Exp. LogKoa
Author
Method
Chen (2003)
N° obj.
Papa (2008)
N° vars
R2%
Xu (2007)
KoaWIN
Q2LOO%
Q2 EXT %
RMSE
(30 obj)
Papa et al. (2009)
MLR
30
1
97.4
96.8
99.6
0.23
Xu et al. (2007)
MLR
22
2
97.6
97.2
-
0.31
Chen et al. (2003)
PLS
13
10
97.9
97.5
-
-
KoaWIN (Episuite)
KOW/KAW
0.81
Comparison with some existing models
Predictions for 209 PBDEs
3.5
3
n° bromine increase = D increase
D log units
2.5
2
1.5
1
0.5
0
monoBDE
diBDE
triBDE
tetraBDE
pentaBDE
average Δ (|YPapa- YKoaWIN|)
hexaBDE
heptaBDE
octaBDE
nonaBDE
decaBDE
average Δ (|YPapa-YXu|)
YPapa = Predictions by our model (range Log Koa: 7.32 – 15.09)
YEpisuite = Predictions by KoaWIN (Dmax = 3.33 log units; range Log Koa: 6.81-18.23)
YXu = Predictions by Xu et al. (2007) (Dmax =1.06 log units; range Log Koa: 7.4-15.73)
High difference with EPISUITE for highly brominated PBDEs
Prof. Paola Gramatica - QSAR Research Unit - DBSF - University of Insubria - Varese (Italy)
RESULTS – Environmental fate of BFRs
5 <LogKow<7
Risk for
tri-penta BDE!!
Resistance to Photodegradation / Mobility
RESULTS – QSAR models
Endocrine Disrupting Activity
Train
obj.
Test
obj.
Full
18
-
Random Split
10
8
Log1/IC50
PRANT
Full
19
-
Random Split
10
9
Log T4REP
Full
17
-
9
8
21
-
11
10
Endpoint
Log1/RBA
Model
Random Split
LogE2SULT Full
-REP
Random Split
R2
%
Q2LOO
%
Q2EXT
%
on 243 BFR
RDF080v
RDF035v
86.1
79.3
-
88.5
87.2
74.0
76.8
R7e+
GATS8e
85.9
81.7
-
91.3
85.9
71.2
qpmax
MATS6v
95.2
92.9
-
96.7
91.9
90.5
B08[C-O]
GGI7
87.6
83.6
-
87.2
73.2
87.6
Desc.
AD%
94.2
97.9
100
RBA = AhR Relative Binding Affinity = EC50(TCDD) / EC50(BFR)
PRANT = Progesterone Receptor Antagonism
T4-REP = T4-TTR Relative Competition = IC50(T4) / IC50(BFR)
E2SULT-REP = E2SULT Relative Inhibition = IC50(E2) / IC50(BFR)
E. Papa, S. Kovarich, P. Gramatica, QSAR modeling and prediction of the
Endocrine disrupting potencies of brominated flame retardants,
Submitted to J. Chem. Inf. Mod., 2010.
RESULTS - Model for LogE2SULT-REP
Equation of the “Split Model” (Random 50%):
LogE2SULT-REP = -0.56 + 2.10 B08[C-O] – 2.77 GGI7
1.5
R2 = 0.87
Training set
Prediction Set
1.0
5-OH-BDE-47
Q2EXT = 0.88
4'-OH-BDE-49
TBBPA
0.5
0.0
TBBPA-DBPE
PCP
MORE ACTIVE
THAN PCP!
2,4,6-TBP
-0.5
BDE-19
-1.0
BDE-49
BDE-28
BDE-100
-1.5
BDE-155
3
2'-OH-BDE-66
BDE-127
BDE-47
BDE-169
1
6-OH-BDE-47
BDE-183
-2.0
TBBPA-DBPE
BDE-190
0
BDE-206
BDE-209
-2.5
-2.5
Training set
Prediction set
2
Res
Log E 2SULT-REP Pred.
Q2LOO = 0.73
3-OH-BDE-47
4-OH-BDE-42
-2.0
-1.5
-1
-1.0
-0.5
0.0
0.5
-2
1.0
1.5
Log E 2SULT-REp Exp.
-3
0.0
0.1
0.2
0.3
0.4
0.5
HAT
0.6
0.7
0.8
0.9
RESULTS
QSAR/QSPR models
developed for
Per-fluorinated Chemicals
Barun Bhhatarai
Prof. Paola Gramatica - QSAR Research Unit - DBSF - University of Insubria - Varese (Italy)
Results: QSAR models for LC50 inhalation
Mouse
Inhalation
56
compounds
Splitting
Compounds
SOM
28.5%
Train: 40
Test: 16
Random
by Activity
20%
Train: 44
Test: 12
Variables
selected
X3v;
H-048;
MLOGP;
F01[C-C]
Full model
Rat
Inhalation
52
compounds
SOM
18.9%
Train: 42
Test: 10
Random
Train: 42
by Activity
Test: 10
20%
Full model
Jhetv:
PCR;
MLOGP;
B02[Cl-Cl]
R2 (%)
Q2 LOO Q2BOOT Q2 ext R2-YScrm
82.99
78.09
75.46
71.62
10.32
77.07
71.73
69.89
85.11
8.99
79.83
76.31
75.38
-
7.05
78.36
72.99
71.95
75.47
8.75
80.01
75.21
74.12
66.70
9.91
78.14
73.85
73.26
-
7.64
Barun Bhhatarai and Paola Gramatica, Per- and Poly-fluoro Toxicity
(LC50 inhalation) Study in Rat and Mouse using QSAR Modeling,
Chem.Res. Toxicol, 2010, in press.
Prof. Paola Gramatica - QSAR Research Unit - DBSF - University of Insubria - Varese (Italy)
21
Regression plots for the models on
datasets split by SOM
log 1/LC50 = 4.21 – 1.27 (±0.31) MlogP + 1.43 (±0.46) X3v + 0.38 (±0.13) F01[C-C] –
8
7
7
6
6
5
Rat Inhalation Pred SOM
Mouse Inhalation Pred SOM
1.14 (±0.37) H-048
Mouse
n=56, s=0.72, r2=79.83, F=50.5, Kx=42.34, Kxy=50.40
5
4
3
4
3
2
1
2
0
1
Training
Prediction
Training
Prediction
-1
0
0
1
2
3
4
Mouse Inhalation Exp
5
6
7
-1
0
1
2
3
4
5
6
Rat Inhalation Exp
log 1/LC50 = –12.76 + 1.87 (±0.20) Jhetv + 11.43 (±1.27) PCR – 0.60 (±0.12) MlogP –
1.41 (±0.40) B02[Cl-Cl]
Rat
n=52, s=0.82, r2=78.14, F=41.99, Kx=23.55, Kxy=30.86
22
Prof. Paola Gramatica - QSAR Research Unit - DBSF - University of Insubria - Varese (Italy)
7
Descriptor analysis
RAT
Jhetv
PCR
MlogP
B02[Cl-Cl]
bond multiplicity, the heteroatoms and
the number of atoms
conventional bond-order ID number
(piID) divided by the total path count
MOUSE
hydrophobicity
presence of heteroatom and double
and triple bonds
presence/absence of Cl-Cl at
topological distance 02
total number of C-C bond
MlogP
X3v
F01[C-C]
H-048
formal oxidation number of C-atom which is the sum of the
formal bond orders with electronegative atoms
• Common descriptor characterizing Hydrophobicity was negative for both
species
• JhetV and X3v have similar chemical meanings and are positive for both
species
• B02[Cl-Cl] present for 5 of 52 compounds – fitting (?)
descriptor to include all Freons
23
Prof. Paola Gramatica - QSAR Research Unit - DBSF - University of Insubria - Varese (Italy)
Applicability Domain (AD) study on 250 PFCs
Mouse AD plot
14
Rat AD plot
8
PFOSA
12
6
10
4
8
Y-Pred
Y Pred.
PFOSA
2
6
4
0
2
Compounds Studied
Compounds Predicted
0
0.0
0.267
0.5
1.0
1.5
Hat Values
2.0
2.5
3.0
-2
0.0
Compounds Studied
Compounds Predicted
0.2 0.273
0.4
0.5
0.6
0.8
1.0
1.2
1.4
1.6
Hat values
• 75.6% coverage of PFCs in Mouse model (61 compounds are out of
structural domain) and 76.8% coverage in Rat model (53 out).
•Arbitrary cutoff 0.5 (dotted lines): 11 common compounds are out of domain
Prof. Paola Gramatica - QSAR Research Unit - DBSF - University of Insubria - Varese (Italy)
24
Focus on AD: Common Out-of-domain compounds
• Predicted compounds out of applicability domain of both
Mouse and Rat model are long chain PFCs (>15-Carbon)
• They are probably extrapolated as the longest compounds
in the training sets are with 7-Carbon
25
Prof. Paola Gramatica - QSAR Research Unit - DBSF - University of Insubria - Varese (Italy)
Toxicity Trend
3
Exp.+Pred.=180
Common compounds=28
2
ExpRat
#121
1
PFOA
#1
PC2
0
#165
PFOSA
-1
ExpMus
-2
#176
-3
-4
-4
-3
-2
-1
0
1
2
3
4
PC1
Increasing Toxicity
Prof. Paola Gramatica - QSAR Research Unit - DBSF - University of Insubria - Varese (Italy)
26
More Toxic Chemicals Predicted: by PCA analysis
PFOA
PFOS is under
investigation as toxic
These chemicals have been suggested to the CADASTER
Partners for experimental tests
Prof. Paola Gramatica - QSAR Research Unit - DBSF - University of Insubria - Varese (Italy)
27
QSPR of Melting point: Data splitting
Melting Point
94
SOM split
descriptor
Random split
response
53 Training
48 Training
41 Prediction I
Perfluorinated
chemicals
(PERFORCE)
46 Prediction I
17 compounds
Prediction II
28
Prof. Paola Gramatica - QSAR Research Unit - DBSF - University of Insubria - Varese (Italy)
Results: Melting point (94+17)
Variables
AAC
F02[C-F]
C-013
Train
Set
53
Prediction I
SOM
41 test
48
R2
Q2loo Q2boot
RMSE RMSE
Q2ext*
train
ext
R2Yscr
46.65
70.16
5.18
71.89
77.11
73.35
40.86
Prediction II
17 test
71.90
25.04
91.40
5.16
Prediction I
Response
46 test
77.48
48.52
72.16
5.84
24.60
92.84
6.59
41.86
(cv)
-
2.82
82.85
79.30
Prediction II
17 test
Total 111
38.07
77.36
78.45
76.82
76.60
40.36
*Consonni, V., et al. J. Chem. Inf. Model., 49, 1669-1678.
AAC = mean information index on atomic correlations, information indices
F02[C-F] = frequency of C-F at topological distance 02, 2D frequency fingerprint
C-013 = corresponds to CRX3 (X =electronegative atom), atom-centered fragments
Prof. Paola Gramatica - QSAR Research Unit - DBSF - University of Insubria - Varese (Italy)
29
Analysis of Melting Point Model
MP = 148.81 (±18.43) AAC + 4.03 (±0.66) F02[C-F] – 14.47 (±6.88) C-013 – 269.25
n=111
200
150
50
250
0
200
Av ailable data
Compounds Predicted
150
-50
PFOSA
100
-100
-150
-200
-200
Training
Prediction I (SOM)
Prediction II
-150
-100
-50
0
50
100
150
200
Y-Pred
Y-Pred
100
50
0
PFOA
-50
Y -Exp
-100
-150
-200
0.00
0.02
0.04
0.06
0.10 0.109 0.12
0.08
0.14
0.16
Hat
Prof. Paola Gramatica - QSAR Research Unit - DBSF - University of Insubria - Varese (Italy)
30
0.18
QSPR of Boiling point: Data splitting
Boiling Point
105
SOM split
descriptor
Random split
response
55 Training
53 Training
50 Prediction I
Perfluorinated
chemicals
(PERFORCE)
52 Prediction I
25 compounds
Prediction II
Prof. Paola Gramatica - QSAR Research Unit - DBSF - University of Insubria - Varese (Italy)
31
Results: Boiling point (105+25)
Variables
Train
Set
55
Prediction I
SOM
50 test
R2
Q2loo
53
Prediction I
Response
52 test
87.50
85.25
81.38
86.40
83.55
87.54
Q2ext*
R2Yscr
34.54
75.71
5.73
29.14
85.17
5.55
28.98
87.50
6.12
26.20
89.53
5.35
29.42
(cv)
-
2.41
30.23
80.78
88.54
RMSE
ext
24.78
86.26
Prediction II
25 test
Total 130
RMSE
train
83.16
Prediction II
25 test
Ms
ATS1m
nROH
Q2boot
87.37
28.21
*Consonni, V., et al. J. Chem. Inf. Model., 49, 1669-1678.
Ms = mean electro-topological state, constitutional descriptor
ATS1m = Autocorrelation of a topological structure, 2D autocorrelations
nROH = number of OH groups, functional group counts
Prof. Paola Gramatica - QSAR Research Unit - DBSF - University of Insubria - Varese (Italy)
32
Analysis of Boiling Point Model
BP = 128.43 (±5.295)ATS1m + 93.833 (±5.85)nROH – 54.23 (±4.25)Ms – 43.098
n=130
300
250
200
150
50
Av ailable data
Compounds Predicted
300
PFOSA
0
200
PFOA
-50
Training
Prediction I (SOM)
Prediction II
-100
-150
-150
-100
-50
0
50
100
150
200
250
300
Y-Pred
Y-Pred.
400
100
100
0
Y -Exp.
-100
-200
0.00
0.02
0.04
0.06
0.08 0.09 0.10
0.12
0.14
Hat
Prof. Paola Gramatica - QSAR Research Unit - DBSF - University of Insubria - Varese (Italy)
33
0.16
QSPR of Vapor Pressure: Data splitting
+ PERFORCE
data
Vapor
Pressure 35
SOM split
Random split
24 Training
22 Training
11 Prediction I
13 Prediction I
34
Prof. Paola Gramatica - QSAR Research Unit - DBSF - University of Insubria - Varese (Italy)
Results: Vapor Pressure (35)
Variables
nDB;
AAC;
F03[C-F]
Q2boot
RMSE
train
RMSE
ext
84.33
81.63
0.83
0.97
87.78
12.69
93.75
91.23
82.13
0.64
1.14
80.36
14.08
90.93
88.21
86.06
0.83
0.95
(cv)
-
8.95
Set
R2
Q2loo
Prediction I
SOM
11 test
91.07
Prediction I
Response
13 test
Total 35
Q2ext* R2Yscr
*Consonni, V., et al. J. Chem. Inf. Model., 49, 1669-1678.
nDB = number of double bonds, constitutional descriptor
AAC = mean information index on atomic composition , information indices
F03[C-F] = frequency of C-F at topological distance 03, 2D frequency
fingerprints
Prof. Paola Gramatica - QSAR Research Unit - DBSF - University of Insubria - Varese (Italy)
35
Analysis of Vapour Pressure Model
log VP = –0.642 (±0.405) nDB – 3.164 (±0.924) AAC – 0.165 (±0.025) F03[C-F] + 7.97
n=35
6
4
8.0
Av ailable data
Compounds Predicted
2
0
4.0
2.0
-2
Ypred
Y-Pred.
6.0
-4
Training
Prediction (SOM)
-6
-6
-4
-2
0
Y -Exp.
2
4
0.0
-2.0
-4.0
6
-6.0
-8.0
-10.0
0.0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
Hat
Prof. Paola Gramatica - QSAR Research Unit - DBSF - University of Insubria - Varese (Italy)
36
1.0
Summary of QSPR models on PFCs:
End
point
Melting
Point
Boiling
Point
Vapor
Pressure
Descriptors
AAC
F02[C-F]
C-013
Ms
ATS1m
nROH
CIC0
MATS1v
TPSA(Tot)
n
R2
Q2loo
Q2boot
RMSE
train
RMSE
cv
RMSE
EPI*
(n)
AD%
111
78.5
76.8
76.1
40.36
41.86
46.678
(248)
94.7
130
88.5
87.5
87.3
27.57
29.12
43.046
(290)
97.9
35
90.9
88.2
87.1
0.83
0.95
1.12
(243)
94.2
* http://www.epa.gov/oppt/exposure/pubs/episuite.htm
All our models have smaller RMSE in comparison to EPISUITE models
Prof. Paola Gramatica - QSAR Research Unit - DBSF - University of Insubria - Varese (Italy)
37
Conclusions
•Predictive models were developed ad-hoc for several toxicity endpoints and physico-chemical properties
•‘OECD principles for the validation of QSAR models, for regulatory
applicability’ was strictly followed
•Simplicity (linear analysis, few descriptors, robust models) with
external validation were used
•
Prediction of data for ~250 compounds was done for each set of
chemicals: BFRs and PFCs
•
Applicability domain analysis also for new compounds was done
•
QSA(P)Rs developed could be used to fill data gaps according to the
new REACH regulation, facilitating the screening and prioritization
of chemicals, reducing animal testing as well as for design of
alternative and safer chemicals
Prof. Paola Gramatica - QSAR Research Unit - DBSF - University of Insubria - Varese (Italy)
Acknowledgements
Financial support
by the FP7th-EU Project CADASTER
http://www.qsar.it
Thanks for your attention !!
Prof. Paola Gramatica - QSAR Research Unit - DBSF - University of Insubria - Varese (Italy)
39