QMRF identifier (JRC Inventory):To be entered by JRC
QMRF Title: QSAR Model Reporting Format Prepared for KATE (Fish Acute)Printing Date:

1.QSAR identifier
1.1. QSAR identifier (title)
KATE2020 on NET for acute toxicity to Fish1.2. Other related models
1.3. Software coding the model
Name: KATE2020 on NET
Description: Ecotoxicity prediction system consists of QSAR models
Contact: kate@nies.go.jp
WWW: https://kate2.nies.go.jp/

12.General information
2.1. Date of QMRF
14 January 2020
2.2. QMRF author(s) and contact details
Names: Hiroshi Yamamoto, Koji Imai, Yuto Itami, Ayako Furuhama, and Koichi Ohno
Affiliation: National Institute for Environmental Studies, Japan
Contact: 16-2 Onogawa, Tsukuba, Ibaraki, 305-8506, Japane-mail: kate@nies.go.jp
WWW: https://www.nies.go.jp/index-e.html
2.3. Date of QMRF update(s)2.4. QMRF update(s)2.5. Model developer(s) and contact details
Names: Hiroaki Shiraishi and Koji Imai
Affiliation: National Institute for Environmental Studies, Japan
Contact: 16-2 Onogawa, Tsukuba, Ibaraki, 305-8506, Japane-mail: kate@nies.go.jp
WWW: https://www.nies.go.jp/index-e.html
2.6. Date of model development and/or publication
3 February 2020
2.7. Reference(s) to main scientific papers and/or software package(1) KATE2020 on NET https://kate2.nies.go.jp/(2) A Furuhama, T Toida, N Nishikawa, Y Aoki, Y Yoshioka, and H Shiraishi (2010). Developmentof an ecotoxicity QSAR model for the KAshinhou Tool for Ecotoxicity (KATE) system, March2009 version. SAR and QSAR Environmental Research 21 (5-6) 403–413
http://dx.doi.org/10.1080/1062936X.2010.501815.
(3) A. Furuhama, K. Hasunuma, Y. Aoki, Y. Yoshioka and H. Shiraishi (2011). Application ofchemical reaction mechanistic domains to an ecotoxicity QSAR model, KAshinhou Tool for
Ecotoxicity (KATE). SAR and QSAR Environmental Research 22 (5-6) 505–523
http://dx.doi.org/10.1080/1062936X.2011.569944.
2.8. Availability of information about the model
The model is proprietary, but the training set and algorithm are available.
2.9. Availability of another QMRF for exactly the same model
None to date.

2

3.Defining the endpoint - OECD Principle 1
3.1. Species
Japanese medaka (Oryzias latipes) and Fathead minnow (Pimephales promelas)
3.2. Endpoint
Acute toxicity to fish (lethality)
3.3. Comment on endpoint
The model predicts the 50% lethal concentration (LC50) values in the Fish Acute Toxicity Test
(96-h exposure).
3.4. Endpoint units
mg/L
3.5. Dependent variable
All of the measured endpoint values were converted to mmol/L units, replaced with theirreciprocal, and transformed to logarithmic units. The dependent variable is: log (1/LC50)
3.6. Experimental protocol
Japanese medaka: OECD Test Guideline No. 203 test protocol
Fathead minnow: protocol described at Russom, C.L., S.P. Bradbury, S.J. Broderius, D.E.
Hammermeister and R.A. Drummond. 1997. Predicting modes of action from chemical

structure: Acute toxicity in the fathead minnow (Pimephales promelas). Environmental
Toxicology and Chemistry 16(5): 948-967.
3.7. Endpoint data quality and variability
The data are from the ecotoxicity tests conducted by the Ministry of the Environment,
Government of Japan (Fish acute toxicity tests) according to good laboratory practice (GLP)and the Fish acute toxicity tests from the US Environmental Protection Agency fatheadminnow database.
The former data are available at: http://www.env.go.jp/en/chemi/sesaku/aquatic_Mar_2019.pdf
Fathead minnow database is available at:https://archive.epa.gov/med/med_archive_03/web/html/fathead_minnow.html(Accessed on January 14, 2020)

3

4.Defining the algorithm - OECD Principle 2
4.1. Type of model
QSAR
4.2. Explicit algorithm
The predicted values are calculated with the following algorithm:(1) Extract the information about the number of substructures, which we defined, in the target
compound.

(As

for

the

definition

of

the

substructures,

see

the

spreadsheets“substructure_def” and “substructure_def_for_AD” in the “KATE2020_fish_acute.xlsx”).
(2) Extract the adequate structure classes in the target compound from the information about
the number of the substructure (see 4.5. (3)).
(3) Predict the ecotoxicity of the target compound using the descriptor (log P) and the QSAR
equation (slope and intercept) provided for each structure class.
(4) Convert the unit of the predicted ecotoxicity from log (1/LC50 [mmol/L]) to [mg/L] using themolecular weight (see 3.5).
4.3. Descriptors in the model
Descriptor: log P

Octanol–water partition coefficient

For structural fragments, see the spreadsheets “substructure_def”, “substructure_def_for_AD”
and “structure_def” in the attachment file “KATE2020_fish_acute.xlsx”.
4.4. Descriptor selection
See 4.2.
4.5. Algorithm and descriptor generation(1) Log P can be input by the user, otherwise log P is calculated using KOWWIN from SMILES.
(2) The information about the number of substructures (see 4.2. (1)) is calculated using the
JAVA application developed by NIES using Chemistry Development Kit (CDK) from
inputted SMILES and the definition of the substructure.
(3) The adequacy to each structure class (1:Yes, 0:No) is evaluated by the information about the
number of the substructures and the definition of the structure class (see the spreadsheet“structure_def” in the file “KATE2020_fish_acute.xlsx”) in the KATE2020 on NET.
(4) Molecular weight of the target compound is calculated from SMILES by using Open Babel.
4.6. Software name and version for descriptor generation
Name: KOWWIN
Description: A program to estimate log P values of organic compounds. That is incorporated in
EPI SuiteTM (Estimation Programs Interface: a tool intended for use in applications such as toquickly screen chemicals), which was developed by the US EPA, etc.
KOWWIN v1.69 (April 2015)4

c 2000-2015 U.S. Environmental Protection Agency
KOWWIN is owned by the U.S. Environmental Protection Agency and is protected bycopyright throughout the world.
Permission is granted for individuals to download and use the software on their personaland business computers.
Users may not alter, modify, merge, adapt or prepare derivative works from the software.
Users may not remove or obscure copyright, tradename, or proprietary notices on the
program or related documentation.
KOWWIN contained therein is a tradename owned by the U.S. Environmental Protection
Agency.
KOWWINTM (included in EPI SuiteTM)• https://www.epa.gov/tsca-screening-tools/epi-suitetm-estimation-programinterface
Name: None
Description: An application for SMARTS substructure searching from SMILES that we havedeveloped by using Chemistry Development Kit (CDK) version 2.0
Contact: kate@nies.go.jp
WWW: https://kate2.nies.go.jp/
Name: Open Babel
Description: The Open Source Chemistry Toolbox, version 2.3.1, 2011.
Contact: Contact not available
WWW: http://openbabel.org/wiki/Main_Page
4.7. Chemicals/Descriptors ratio
The ratio (column name: ratio1) for each QSAR class is described in the spreadsheet“qsar_data” in the attachment file “KATE2020_fish_acute.xlsx”.

55.Defining the applicability domain - OECD Principle 3
5.1. Description of the applicability domain of the model(1) Applicability domain of the model for the descriptor (log P) is established for each QSAR
class, which we define as a group of compounds with specific structure classes, and is the rangeof the descriptor, “between the maximum and the minimum” (see the column named “Log P
Range” of the spreadsheet “qsar_data” in the attached file “KATE2020_fish_acute.xlsx”).
(2) Applicability domain of the model for the structure is also established for each QSAR class,and is provided from the list of substructure for the structure judgement of the chemicals in
the training set which belongs to the QSAR class and in the class called narcotic group” (seethe column “FragIDs_for_AD” of the spreadsheet “qsar_data” in the attached file
“KATE2020_fish_acute.xlsx”)
5.2. Method used to assess the applicability domain
The applicability domain of both descriptor and structure is assessed for each QSAR class into
which the target compound is classified.
(1) If the log P value of the target compound is within the applicability domain of the descriptor
as indicated in 5.1. (1), it is judged as “inside of the applicability domain” in terms of thedescriptor.
(2) If the substructure for the structure judgement (see the substructure defined in the
spreadsheet “substructure_def_for_AD” in the file “KATE2020_fish_acute.xlsx”) possessed by
the target compound is all included in the list of the structure judgement of the QSAR class(see 5.1.(2)), it is judged as “inside of the applicability domain” in terms of the structure.
For more details, see Furuhama et al. (2011) (see 2.7 (3)).
5.3. Software name and version for applicability domain assessment
Name: KATE2020 on NET
Contact: kate@nies.go.jp
WWW: https://kate2.nies.go.jp
5.4. Limits of applicability
See 5.1 and 5.2.

6

6.Internal validation - OECD Principle 4
6.1. Availability of the training set
It is available in supporting information.
6.2. Available information for the training set
Chemical Name: Yes
SMILES: Yes
CAS RN: Yes
InChI: No
MOL file: No
Formula: No
6.3. Data for each descriptor variable for the training set
No
6.4. Data for the dependent variable for the training set
The data for the dependent variable for each QSAR class are available in the spreadsheet“training_set” in the attachment file “KATE2020_fish_acute.xlsx”.
6.5. Other information about the training set
The number of the training set for each QSAR class is described on the column named “n” inthe spreadsheet “qsar_data” in the attachment file “KATE2020_fish_acute.xlsx”.
6.6. Pre-processing of data before modelling
The endpoint data LC50 [mg/L] was converted into log (1/LC50 [mmol/L]).
6.7. Statistics for goodness-of-fit
The following statistical values for each QSAR class are obtained and shown in the
spreadsheet “qsar_data” in the attachment file “KATE2020_fish_acute.xlsx”.
R2: square of correlation coefficient
RMSE: Root Mean Squared Error
6.8. Robustness - Statistics obtained by leave-one-out cross-validation
The following statistical value for each QSAR class is obtained and shown in the spreadsheet“qsar_data” in the attachment file “KATE2020_fish_acute.xlsx”.
Q2: square of correlation coefficient
6.9. Robustness - Statistics obtained by leave-many-out cross-validation
No
6.10. Robustness - Statistics obtained by Y-scramblingNo
6.11. Robustness - Statistics obtained by bootstrap
No
6.12. Robustness - Statistics obtained by other methods
No7

7.External validation - OECD Principle 4
7.1. Availability of the external validation set
Available for some QSAR classes, and available in supporting information.
7.2. Available information for the external validation set
Chemical Name: No
SMILES: No
CAS RN: No
InChI: No
MOL file: No
Formula: No
7.3. Data for each descriptor variable for the external validation set
No
7.4. Data for the dependent variable for the external validation set
No
7.5. Other information about the external validation set
No
7.6. Experimental design of test set
No
7.7. Predictivity - Statistics obtained by external validation
No
7.8. Predictivity - Assessment of the external validation set
No
7.9. Comments on the external validation of the model
No8

8.Providing a mechanistic interpretation - OECD Principle 5
8.1. Mechanistic basis of the model
Each QSAR class is a group of compounds with specific structure class. Structure class isdefined by the information about the substructures (see 4.2) and not directly relevant to themode of actions. There are a few classes which can be relevant to the mode of actions. For
example, QSAR ID: 12899941 (in the spreadsheet of “qsar_data”) is a group of chemicals withstructure class: GD_28999 (narcotic group), and considered to exert narcotic actions. This
structure class was created with the integration of the multiple structure classes including
aliphatic hydrocarbons, aliphatic and aromatic ethers, aliphatic and aromatic ketones, andalcohols which are considered to have simple narcotic actions.
8.2. A priori or a posteriori mechanistic interpretation
A priori mechanistic interpretation.
8.3. Other information about the mechanistic interpretation
No9

9.Miscellaneous information
9.1. Comments
This model can output multiple estimations based on the adequacy to the structure classes(see 4.2). There is neither recommended value nor guideline to select one right now and the users
should select by themselves based on R2, Q2, the shape of the regression line (slope and the numberof reference chemicals), and the applicability domain based on descriptor (log P) and structure(structure judgement).
9.2. Bibliography[1] OECD Guideline of Testing of Chemicals, No. 203 (17th July 1992) - Fish, Acute Toxicity Test.
9.3. Supporting information
1. KATE2020_fish_acute.xlsx
The name and content of each spreadsheet are as follows:
substructure_def: definition of substructures
substructure_def_for_AD: definition of substructures (also used for the applicability domain)
structure_def: definition of structure classes
qsar_data: data for each QSAR class (QSAR equation etc.)
training_set: data of the training set

10.Summary (JRC QSAR Model Database)
10.1. QMRF number:
To be entered by JRC

10.2. Publication date:
To be entered by JRC

10.3. Keywords:
To be entered by JRC

10.4. Comments:
To be entered by JRC

10QMRF identifier (JRC Inventory):To be entered by JRC
QMRF Title: QSAR Model Reporting Format Prepared for KATE (Fish Chronic)
Printing Date:

1.QSAR identifier
1.1. QSAR identifier (title)
KATE2020 on NET for chronic toxicity to Fish1.2. Other related models
1.3. Software coding the model
Name: KATE2020 on NET
Description: Ecotoxicity prediction system consists of QSAR models
Contact: kate@nies.go.jp
WWW: https://kate2.nies.go.jp/

112.General information
2.1. Date of QMRF
14 January 2020
2.2. QMRF author(s) and contact details
Names: Hiroshi Yamamoto, Koji Imai, Yuto Itami, Ayako Furuhama, and Koichi Ohno
Affiliation: National Institute for Environmental Studies, Japan
Contact: 16-2 Onogawa, Tsukuba, Ibaraki, 305-8506, Japane-mail: kate@nies.go.jp
WWW: https://www.nies.go.jp/index-e.html
2.3. Date of QMRF update(s)2.4. QMRF update(s)2.5. Model developer(s) and contact details
Names: Hiroaki Shiraishi and Koji Imai
Affiliation: National Institute for Environmental Studies, Japan
Contact: 16-2 Onogawa, Tsukuba, Ibaraki, 305-8506, Japane-mail: kate@nies.go.jp
WWW: https://www.nies.go.jp/index-e.html
2.6. Date of model development and/or publication
3 February 2020
2.7. Reference(s) to main scientific papers and/or software package(1) KATE2020 on NET https://kate2.nies.go.jp/(2) A Furuhama, T Toida, N Nishikawa, Y Aoki, Y Yoshioka, and H Shiraishi (2010). Developmentof an ecotoxicity QSAR model for the KAshinhou Tool for Ecotoxicity (KATE) system, March2009 version. SAR and QSAR Environmental Research 21 (5-6) 403–413
http://dx.doi.org/10.1080/1062936X.2010.501815.
(3) A. Furuhama, K. Hasunuma, Y. Aoki, Y. Yoshioka and H. Shiraishi (2011). Application ofchemical reaction mechanistic domains to an ecotoxicity QSAR model, KAshinhou Tool for
Ecotoxicity (KATE). SAR and QSAR Environmental Research 22 (5-6) 505–523
http://dx.doi.org/10.1080/1062936X.2011.569944.
2.8. Availability of information about the model
The model is proprietary, but the training set and algorithm are available.
2.9. Availability of another QMRF for exactly the same model
None to date.

12

3.Defining the endpoint - OECD Principle 1
3.1. Species
Japanese medaka (Oryzias Latipes)
3.2. Endpoint
Long-term toxicity to fish (early-life stage)
3.3. Comment on endpoint
The model predicts the no-observed-effect concentration (NOEC) values in the Fish Early-Life
Stage Toxicity Test.
3.4. Endpoint units
mg/L
3.5. Dependent variable
All of the measured endpoint values were converted to mmol/L units, replaced with theirreciprocal, and transformed to logarithmic units. The dependent variable is: log (1/NOEC)
3.6. Experimental protocol
OECD Test Guideline No. 210 test protocol
3.7. Endpoint data quality and variability
The data are from the ecotoxicity tests conducted by the Ministry of the Environment,
Government of Japan (Fish early-life stage toxicity tests) according to good laboratory practice(GLP).
This data are available at: http://www.env.go.jp/en/chemi/sesaku/aquatic_Mar_2019.pdf(Accessed on January 14, 2020)

13

4.Defining the algorithm - OECD Principle 2
4.1. Type of model
QSAR
4.2. Explicit algorithm
The predicted values are calculated with the following algorithm:(1) Extract the information about the number of substructures, which we defined, in the target
compound.

(As

for

the

definition

of

the

substructures,

see

the

spreadsheets“substructure_def” and “substructure_def_for_AD” in the “KATE2020_fish_chronic.xlsx”).
(2) Extract the adequate structure classes in the target compound from the information about
the number of the substructure (see 4.5. (3)).
(3) Predict the ecotoxicity of the target compound using the descriptor (log P) and the QSAR
equation (slope and intercept) provided for each structure class.
(4) Convert the unit of the predicted ecotoxicity from log (1/NOEC [mmol/L]) to [mg/L] using
the molecular weight (see 3.5).
4.3. Descriptors in the model
Descriptor: log P

Octanol–water partition coefficient

For structural fragments, see the spreadsheets “substructure_def”, “substructure_def_for_AD”
and “structure_def” in the attachment file “KATE2020_fish_chronic.xlsx”.
4.4. Descriptor selection
See 4.2.
4.5. Algorithm and descriptor generation(1) Log P can be input by the user, otherwise log P is calculated using KOWWIN from SMILES
notation.
(2) The information about the number of substructures (see 4.2. (1)) is calculated using the
JAVA application developed by NIES using Chemistry Development Kit (CDK) from
inputted SMILES and the definition of the substructure.
(3) The adequacy to each structure class (1:Yes, 0:No) is evaluated by the information about the
number of the substructures and the definition of the structure class (see the spreadsheet“structure_def” in the file “KATE2020_fish_chronic.xlsx”) in the KATE2020 on NET.
(4) Molecular weight of the target compound is calculated from SMILES by using Open Babel.
4.6. Software name and version for descriptor generation
Name: KOWWIN
Description: A program to estimate log P values of organic compounds. That is incorporated in
EPI SuiteTM (Estimation Programs Interface: a tool intended for use in applications such as toquickly screen chemicals), which was developed by the US EPA, etc.
KOWWIN v1.69 (April 2015)14

c 2000-2015 U.S. Environmental Protection Agency
KOWWIN is owned by the U.S. Environmental Protection Agency and is protected bycopyright throughout the world.
Permission is granted for individuals to download and use the software on their personaland business computers.
Users may not alter, modify, merge, adapt or prepare derivative works from the software.
Users may not remove or obscure copyright, tradename, or proprietary notices on the
program or related documentation.
KOWWIN contained therein is a tradename owned by the U.S. Environmental Protection
Agency.
KOWWINTM (included in EPI SuiteTM)• https://www.epa.gov/tsca-screening-tools/epi-suitetm-estimation-programinterface
Name: None
Description: An application for SMARTS substructure searching from SMILES that we havedeveloped by using Chemistry Development Kit (CDK) version 2.0
Contact: kate@nies.go.jp
WWW: https://kate2.nies.go.jp/
Name: Open Babel
Description: The Open Source Chemistry Toolbox, version 2.3.1, 2011.
Contact: Contact not available
WWW: http://openbabel.org/wiki/Main_Page
4.7. Chemicals/Descriptors ratio
The ratio (column name: ratio1) for each QSAR class is described in the spreadsheet“qsar_data” in the attachment file “KATE2020_fish_chronic.xlsx”.

15

5.Defining the applicability domain - OECD Principle 3
5.1. Description of the applicability domain of the model(1) Applicability domain of the model for the descriptor (log P) is established for each QSAR
class, which we define as a group of compounds with specific structure classes, and is the rangeof the descriptor, “between the maximum and the minimum” (see the column named “Log P
Range” of the spreadsheet “qsar_data” in the attached file “KATE2020_fish_chronic.xlsx”).
(2) Applicability domain of the model for the structure is also established for each QSAR class,and is provided from the list of substructure for the structure judgement of the chemicals in
the training set which belongs to the QSAR class and in the class called “narcotic group” (seethe column “FragIDs_for_AD” of the spreadsheet “qsar_data” in the attached file
“KATE2020_fish_chronic.xlsx”)
5.2. Method used to assess the applicability domain
The applicability domain of both descriptor and structure is assessed for each QSAR class into
which the target compound is classified.
(1) If the log P value of the target compound is within the applicability domain of the descriptor
as indicated in 5.1.(1), it is judged as “inside of the applicability domain” in terms of thedescriptor.
(2) If the substructure for the structure judgement (see the substructure defined in the
spreadsheet “substructure_def_for_AD” in the file “KATE2020_fish_chronic.xlsx”) possessed by
the target compound is all included in the list of the structure judgement of the QSAR class(see 5.1.(2)), it is judged as “inside of the applicability domain” in terms of the structure.
For more details, see Furuhama et al. (2011) (see 2.7 (3)).
5.3. Software name and version for applicability domain assessment
Name: KATE2020 on NET
Contact: kate@nies.go.jp
WWW: https://kate2.nies.go.jp
5.4. Limits of applicability
See 5.1 and 5.2.

16

6.Internal validation - OECD Principle 4
6.1. Availability of the training set
It is available in the supporting information.
6.2. Available information for the training set
Chemical Name: Yes
SMILES: Yes
CAS RN: Yes
InChI: No
MOL file: No
Formula: No
6.3. Data for each descriptor variable for the training set
No
6.4. Data for the dependent variable for the training set
The data for the dependent variable for each QSAR class are available in the spreadsheet“training_set” in the attachment file “KATE2020_fish_chronic.xlsx”.
6.5. Other information about the training set
The number of the training set for each QSAR class is described on the column named “n” inthe spreadsheet “qsar_data” in the attachment file “KATE2020_fish_chronic.xlsx”.
6.6. Pre-processing of data before modelling
The endpoint data NOEC [mg/L] was converted into log (1/NOEC [mmol/L]).
6.7. Statistics for goodness-of-fit
The following statistical values for each QSAR class are obtained and shown in the
spreadsheet “qsar_data” in the attachment file “KATE2020_fish_chronic.xlsx”.
R2: square of correlation coefficient
RMSE: Root Mean Squared Error
6.8. Robustness - Statistics obtained by leave-one-out cross-validation
The following statistical value for each QSAR class is obtained and shown in the spreadsheet“qsar_data” in the attachment file “KATE2020_fish_chronic.xlsx”.
Q2: square of correlation coefficient
6.9. Robustness - Statistics obtained by leave-many-out cross-validation
No
6.10. Robustness - Statistics obtained by Y-scramblingNo
6.11. Robustness - Statistics obtained by bootstrap
No
6.12. Robustness - Statistics obtained by other methods
No17

7.External validation - OECD Principle 4
7.1. Availability of the external validation set
No.
7.2. Available information for the external validation set
Chemical Name: No
SMILES: No
CAS RN: No
InChI: No
MOL file: No
Formula: No
7.3. Data for each descriptor variable for the external validation set
No
7.4. Data for the dependent variable for the external validation set
No
7.5. Other information about the external validation set
No
7.6. Experimental design of test set
No
7.7. Predictivity - Statistics obtained by external validation
No
7.8. Predictivity - Assessment of the external validation set
No
7.9. Comments on the external validation of the model
No18

8.Providing a mechanistic interpretation - OECD Principle 5
8.1. Mechanistic basis of the model
Each QSAR class is a group of compounds with specific structure class. Structure class isdefined by the information about the substructures (see 4.2) and not directly relevant to themode of actions. There are a few classes which can be relevant to the mode of actions. For
example, QSAR ID: 12899951 (in the spreadsheet of “qsar_data”) is a group of chemicals withstructure class: GD_28999 (narcotic group), and considered to exert narcotic actions. This
structure class was created with the integration of the multiple structure classes including
aliphatic hydrocarbons, aliphatic and aromatic ethers, aliphatic and aromatic ketones, andalcohols which are considered to have simple narcotic actions.
8.2. A priori or a posteriori mechanistic interpretation
A priori mechanistic interpretation.
8.3. Other information about the mechanistic interpretation
No19

9.Miscellaneous information
9.1. Comments
This model can output multiple estimations based on the adequacy to the structure classes(see 4.2). There is neither recommended value nor guideline to select one right now and the users
should select by themselves based on R2, Q2, the shape of the regression line (slope and the numberof reference chemicals), and the applicability domain based on descriptor (log P) and structure(structure judgement).
9.2. Bibliography[1] OECD Guideline for Testing of Chemicals, No. 210 (26th July 2013) - Fish, Early-Life Stage
Toxicity Test.
9.3. Supporting information
1. KATE2020_fish_chronic.xlsx
The name and content of each spreadsheet are as follows:
substructure_def: definition of substructures
substructure_def_for_AD: definition of substructures (also used for the applicability domain)
structure_def: definition of structure classes
qsar_data: data for each QSAR class (QSAR equation etc.)
training_set: data of the training set

10.Summary (JRC QSAR Model Database)
10.1. QMRF number:
To be entered by JRC

10.2. Publication date:
To be entered by JRC

10.3. Keywords:
To be entered by JRC

10.4. Comments:
To be entered by JRC

20

QMRF identifier (JRC Inventory):To be entered by JRC
QMRF Title: QSAR Model Reporting Format Prepared for KATE (Daphnid Acute)Printing Date:

1.QSAR identifier
1.1. QSAR identifier (title)
KATE2020 on NET for acute toxicity to Daphnid
1.2. Other related models
1.3. Software coding the model
Name: KATE2020 on NET
Description: Ecotoxicity prediction system consists of QSAR models
Contact: kate@nies.go.jp
WWW: https://kate2.nies.go.jp/

21

2.General information
2.1. Date of QMRF
14 January 2020
2.2. QMRF author(s) and contact details
Names: Hiroshi Yamamoto, Koji Imai, Yuto Itami, Ayako Furuhama, and Koichi Ohno
Affiliation: National Institute for Environmental Studies, Japan
Contact: 16-2 Onogawa, Tsukuba, Ibaraki, 305-8506, Japane-mail: kate@nies.go.jp
WWW: https://www.nies.go.jp/index-e.html
2.3. Date of QMRF update(s)2.4. QMRF update(s)2.5. Model developer(s) and contact details
Names: Hiroaki Shiraishi and Koji Imai
Affiliation: National Institute for Environmental Studies, Japan
Contact: 16-2 Onogawa, Tsukuba, Ibaraki, 305-8506, Japane-mail: kate@nies.go.jp
WWW: https://www.nies.go.jp/index-e.html
2.6. Date of model development and/or publication
3 February 2020
2.7. Reference(s) to main scientific papers and/or software package(1) KATE2020 on NET https://kate2.nies.go.jp/(2) A Furuhama, T Toida, N Nishikawa, Y Aoki, Y Yoshioka, and H Shiraishi (2010). Developmentof an ecotoxicity QSAR model for the KAshinhou Tool for Ecotoxicity (KATE) system, March2009 version. SAR and QSAR Environmental Research 21 (5-6) 403–413
http://dx.doi.org/10.1080/1062936X.2010.501815.
(3) A. Furuhama, K. Hasunuma, Y. Aoki, Y. Yoshioka and H. Shiraishi (2011). Application ofchemical reaction mechanistic domains to an ecotoxicity QSAR model, KAshinhou Tool for
Ecotoxicity (KATE). SAR and QSAR Environmental Research 22 (5-6) 505–523
http://dx.doi.org/10.1080/1062936X.2011.569944.
2.8. Availability of information about the model
The model is proprietary, but the training set and algorithm are available.
2.9. Availability of another QMRF for exactly the same model
None to date.

22

3.Defining the endpoint - OECD Principle 1
3.1. Species

Daphnia magna
3.2. Endpoint
Short-term toxicity to Daphnid (immobilization)
3.3. Comment on endpoint
The model predicts the 50% effective concentration (EC50) values in the Daphnia sp. Acute
Immobilization Test (48-h exposure).
3.4. Endpoint units
mg/L
3.5. Dependent variable
All of the measured endpoint values were converted to mmol/L units, replaced with theirreciprocal, and transformed to logarithmic units. The dependent variable is: log (1/EC50)
3.6. Experimental protocol
OECD Test Guideline No. 202 test protocol
3.7. Endpoint data quality and variability
The data are from the ecotoxicity tests conducted by the Ministry of the Environment,
Government of Japan (Daphnia sp. immobilization tests) according to good laboratory practice(GLP).
This data are available at: http://www.env.go.jp/en/chemi/sesaku/aquatic_Mar_2019.pdf(Accessed on January 14, 2020)

23

4.Defining the algorithm - OECD Principle 2
4.1. Type of model
QSAR
4.2. Explicit algorithm
The predicted values are calculated with the following algorithm:(1) Extract the information about the number of substructures, which we defined, in the target
compound.

(As

for

the

definition

of

the

substructures,

see

the

spreadsheets“substructure_def” and “substructure_def_for_AD” in the “KATE2020_daphnid_acute.xlsx”).
(2) Extract the adequate structure classes in the target compound from the information about
the number of the substructure (see 4.5. (3)).
(3) Predict the ecotoxicity of the target compound using the descriptor (log P) and the QSAR
equation (slope and intercept) provided for each structure class.
(4) Convert the unit of the predicted ecotoxicity from log (1/EC50 [mmol/L]) to [mg/L] using themolecular weight (see 3.5).
4.3. Descriptors in the model
Descriptor: log P

Octanol–water partition coefficient

For structural fragments, see the spreadsheets “substructure_def”, “substructure_def_for_AD”
and “structure_def” in the attachment file “KATE2020_daphnid_acute.xlsx”.
4.4. Descriptor selection
See 4.2.
4.5. Algorithm and descriptor generation(1) Log P can be input by the user, otherwise log P is calculated using KOWWIN from SMILES
notation.
(2) The information about the number of substructures (see 4.2. (1)) is calculated using the
JAVA application developed by NIES using Chemistry Development Kit (CDK) from
inputted SMILES and the definition of the substructure.
(3) The adequacy to each structure class (1:Yes, 0:No) is evaluated by the information about the
number of the substructures and the definition of the structure class (see the spreadsheet“structure_def” in the file “KATE2020_daphnid_acute.xlsx”) in the KATE2020 on NET.
(4) Molecular weight of the target compound is calculated from SMILES by using Open Babel.
4.6. Software name and version for descriptor generation
Name: KOWWIN
Description: A program to estimate log P values of organic compounds. That is incorporated in
EPI SuiteTM (Estimation Programs Interface: a tool intended for use in applications such as toquickly screen chemicals), which was developed by the US EPA, etc.
KOWWIN v1.69 (April 2015)24

c 2000-2015 U.S. Environmental Protection Agency
KOWWIN is owned by the U.S. Environmental Protection Agency and is protected bycopyright throughout the world.
Permission is granted for individuals to download and use the software on their personaland business computers.
Users may not alter, modify, merge, adapt or prepare derivative works from the software.
Users may not remove or obscure copyright, tradename, or proprietary notices on the
program or related documentation.
KOWWIN contained therein is a tradename owned by the U.S. Environmental Protection
Agency.
KOWWINTM (included in EPI SuiteTM)• https://www.epa.gov/tsca-screening-tools/epi-suitetm-estimation-programinterface
Name: None
Description: An application for SMARTS substructure searching from SMILES that we havedeveloped by using Chemistry Development Kit (CDK) version 2.0
Contact: kate@nies.go.jp
WWW: https://kate2.nies.go.jp/
Name: Open Babel
Description: The Open Source Chemistry Toolbox, version 2.3.1, 2011.
Contact: Contact not available
WWW: http://openbabel.org/wiki/Main_Page
4.7. Chemicals/Descriptors ratio
The ratio (column name: ratio1) for each QSAR class is described in the spreadsheet“qsar_data” in the attachment file “KATE2020_daphnid_acute.xlsx”.

25

5.Defining the applicability domain - OECD Principle 3
5.1. Description of the applicability domain of the model(1) Applicability domain of the model for the descriptor (log P) is established for each QSAR
class, which we define as a group of compounds with specific structure classes, and is the rangeof the descriptor, “between the maximum and the minimum” (see the column named “Log P
Range” of the spreadsheet “qsar_data” in the attached file “KATE2020_daphnid_acute.xlsx”).
(2) Applicability domain of the model for the structure is also established for each QSAR class,and is provided from the list of substructures for the structure judgement of the chemicals in
the training set which belongs to the QSAR class and in the class called “narcotic group” (seethe column “FragIDs_for_AD” of the spreadsheet “qsar_data” in the attached file
“KATE2020_daphnid_acute.xlsx”)
5.2. Method used to assess the applicability domain
The applicability domain of both descriptor and structure is assessed for each QSAR class into
which the target compound is classified.
(1) If the log P value of the target compound is within the applicability domain of the descriptor
as indicated in 5.1. (1), it is judged as “inside of the applicability domain” in terms of thedescriptor.
(2) If the substructure for the structure judgement (see the substructure defined in the
spreadsheet “substructure_def_for_AD” in the file “KATE2020_daphnid_acute.xlsx”) possessedby the target compound is all included in the list of the structure judgement of the QSAR class(see 5.1.(2)), it is judged as “inside of the applicability domain” in terms of the structure. For
more details, see Furuhama et al. (2011) (see 2.7 (3)).
5.3. Software name and version for applicability domain assessment
Name: KATE2020 on NET
Contact: kate@nies.go.jp
WWW: https://kate2.nies.go.jp
5.4. Limits of applicability
See 5.1 and 5.2.

26

6.Internal validation - OECD Principle 4
6.1. Availability of the training set
It is available and attached as supporting information.
6.2. Available information for the training set
Chemical Name: Yes
SMILES: Yes
CAS RN: Yes
InChI: No
MOL file: No
Formula: No
6.3. Data for each descriptor variable for the training set
No
6.4. Data for the dependent variable for the training set
The data for the dependent variable for each QSAR class are available in the spreadsheet“training_set” in the attachment file “KATE2020_daphnid_acute.xlsx”.
6.5. Other information about the training set
The number of the training set for each QSAR class is described on the column named “n” inthe spreadsheet “qsar_data” in the attachment file “KATE2020_daphnid_acute.xlsx”.
6.6. Pre-processing of data before modelling
The endpoint data EC50 [mg/L] was converted into log (1/EC50 [mmol/L]).
6.7. Statistics for goodness-of-fit
The following statistical values for each QSAR class are obtained and shown in the
spreadsheet “qsar_data” in the attachment file “KATE2020_daphnid_acute.xlsx”.
R2: square of correlation coefficient
RMSE: Root Mean Squared Error
6.8. Robustness - Statistics obtained by leave-one-out cross-validation
The following statistical value for each QSAR class is obtained and shown in the spreadsheet“qsar_data” in the attachment file “KATE2020_daphnid_acute.xlsx”.
Q2: square of correlation coefficient
6.9. Robustness - Statistics obtained by leave-many-out cross-validation
No
6.10. Robustness - Statistics obtained by Y-scramblingNo
6.11. Robustness - Statistics obtained by bootstrap
No
6.12. Robustness - Statistics obtained by other methods
No27

7.External validation - OECD Principle 4
7.1. Availability of the external validation set
Available for some QSAR classes, and in supporting information.
7.2. Available information for the external validation set
Chemical Name: No
SMILES: No
CAS RN: No
InChI: No
MOL file: No
Formula: No
7.3. Data for each descriptor variable for the external validation set
No
7.4. Data for the dependent variable for the external validation set
No
7.5. Other information about the external validation set
No
7.6. Experimental design of test set
No
7.7. Predictivity - Statistics obtained by external validation
No
7.8. Predictivity - Assessment of the external validation set
No
7.9. Comments on the external validation of the model
No28

8.Providing a mechanistic interpretation - OECD Principle 5
8.1. Mechanistic basis of the model
Each QSAR class is a group of compounds with specific structure class. Structure class isdefined by the information about the substructures (see 4.2) and not directly relevant to themode of actions. There are a few classes which can be relevant to the mode of actions. For
example, QSAR ID: 22899941 (in the spreadsheet of “qsar_data”) is a group of chemicals withstructure class: GD_28999 (narcotic group), and considered to exert narcotic actions. This
structure class was created with the integration of the multiple structure classes including
aliphatic hydrocarbons, aliphatic and aromatic ethers, aliphatic and aromatic ketones, andalcohols which are considered to have simple narcotic actions.
8.2. A priori or a posteriori mechanistic interpretation
A priori mechanistic interpretation.
8.3. Other information about the mechanistic interpretation
No29

9.Miscellaneous information
9.1. Comments
This model can output multiple estimations based on the adequacy to the structure classes(see 4.2). There is neither recommended value nor guideline to select one right now and the users
should select by themselves based on R2, Q2, the shape of the regression line (slope and the numberof reference chemicals), and the applicability domain based on descriptor (log P) and structure(structure judgement).
9.2. Bibliography[1] OECD Guideline for Testing of Chemicals, No. 202 (13th April 2004) - Daphnia sp., Acute
Immobilization Test.
9.3. Supporting information
1. KATE2020_daphnid_acute.xlsx
The name and content of each spreadsheet are as follows:
substructure_def: definition of substructures
substructure_def_for_AD: definition of substructures (also used for the applicability domain)
structure_def: definition of structure classes
qsar_data: data for each QSAR class (QSAR equation etc.)
training_set: data of the training set

10.Summary (JRC QSAR Model Database)
10.1. QMRF number:
To be entered by JRC

10.2. Publication date:
To be entered by JRC

10.3. Keywords:
To be entered by JRC

10.4. Comments:
To be entered by JRC

30

QMRF identifier (JRC Inventory):To be entered by JRC
QMRF Title: QSAR Model Reporting Format Prepared for KATE (Daphnid Chronic)
Printing Date:

1.QSAR identifier
1.1. QSAR identifier (title)
KATE2020 on NET for chronic toxicity to Daphnid
1.2. Other related models
1.3. Software coding the model
Name: KATE2020 on NET
Description: Ecotoxicity prediction system consists of QSAR models
Contact: kate@nies.go.jp
WWW: https://kate2.nies.go.jp/31

2.General information
2.1. Date of QMRF
14 January 2020
2.2. QMRF author(s) and contact details
Names: Hiroshi Yamamoto, Koji Imai, Yuto Itami, Ayako Furuhama, and Koichi Ohno
Affiliation: National Institute for Environmental Studies, Japan
Contact: 16-2 Onogawa, Tsukuba, Ibaraki, 305-8506, Japane-mail: kate@nies.go.jp
WWW: https://www.nies.go.jp/index-e.html
2.3. Date of QMRF update(s)2.4. QMRF update(s)2.5. Model developer(s) and contact details
Names: Hiroaki Shiraishi and Koji Imai
Affiliation: National Institute for Environmental Studies, Japan
Contact: 16-2 Onogawa, Tsukuba, Ibaraki, 305-8506, Japane-mail: kate@nies.go.jp
WWW: https://www.nies.go.jp/index-e.html
2.6. Date of model development and/or publication
3 February 2020
2.7. Reference(s) to main scientific papers and/or software package(1) KATE2020 on NET https://kate2.nies.go.jp/(2) A Furuhama, T Toida, N Nishikawa, Y Aoki, Y Yoshioka, and H Shiraishi (2010). Developmentof an ecotoxicity QSAR model for the KAshinhou Tool for Ecotoxicity (KATE) system, March2009 version. SAR and QSAR Environmental Research 21 (5-6) 403–413
http://dx.doi.org/10.1080/1062936X.2010.501815.
(3) A. Furuhama, K. Hasunuma, Y. Aoki, Y. Yoshioka and H. Shiraishi (2011). Application ofchemical reaction mechanistic domains to an ecotoxicity QSAR model, KAshinhou Tool for
Ecotoxicity (KATE). SAR and QSAR Environmental Research 22 (5-6) 505–523
http://dx.doi.org/10.1080/1062936X.2011.569944.
2.8. Availability of information about the model
The model is proprietary, but the training set and algorithm are available.
2.9. Availability of another QMRF for exactly the same model
None to date.

32

3.Defining the endpoint - OECD Principle 1
3.1. Species

Daphnia magna
3.2. Endpoint
Long-term toxicity to Daphnid (reproduction output)
3.3. Comment on endpoint
The model predicts the no-observed-effect concentration (NOEC) values in the Daphnia magna
Reproduction Test (21-d exposure).
3.4. Endpoint units
mg/L
3.5. Dependent variable
All of the measured endpoint values were converted to mmol/L units, replaced with theirreciprocal, and transformed to logarithmic units. The dependent variable is: log (1/NOEC)
3.6. Experimental protocol
OECD Test Guideline No. 211 test protocol
3.7. Endpoint data quality and variability
The data are from the ecotoxicity tests conducted by the Ministry of the Environment,
Government of Japan (Daphnia magna reproduction tests) according to good laboratory
practice (GLP).
This data are available at: http://www.env.go.jp/en/chemi/sesaku/aquatic_Mar_2019.pdf(Accessed on January 14, 2020)

33

4.Defining the algorithm - OECD Principle 2
4.1. Type of model
QSAR
4.2. Explicit algorithm
The predicted values are calculated with the following algorithm:(1) Extract the information about the number of substructures, which we defined, in the target
compound.

(As

for

“substructure_def”

thedefinition

of

the

substructures,

see

and

“substructure_def_for_AD”

thespreadsheets
inthe“KATE2020_daphnid_chronic.xlsx”).
(2) Extract the adequate structure classes in the target compound from the information about
the number of the substructure (see 4.5. (3)).
(3) Predict the ecotoxicity of the target compound using the descriptor (log P) and the QSAR
equation (slope and intercept) provided for each structure class.
(4) Convert the unit of the predicted ecotoxicity from log (1/NOEC [mmol/L]) to [mg/L] using
the molecular weight (see 3.5).
4.3. Descriptors in the model
Descriptor: log P

Octanol–water partition coefficient

For structural fragments, see the spreadsheets “substructure_def”, “substructure_def_for_AD”
and “structure_def” in the attachment file “KATE2020_daphnid_chronic.xlsx”.
4.4. Descriptor selection
See 4.2.
4.5. Algorithm and descriptor generation(1) Log P can be input by the user, otherwise log P is calculated using KOWWIN from SMILES
notation.
(2) The information about the number of substructures (see 4.2. (1)) is calculated using the
JAVA application developed by NIES using Chemistry Development Kit (CDK) from
inputted SMILES and the definition of the substructure.
(3) The adequacy to each structure class (1:Yes, 0:No) is evaluated by the information about the
number of the substructures and the definition of the structure class (see the spreadsheet“structure_def” in the file “KATE2020_daphnid_chronic.xlsx”) in the KATE2020 on NET.
(4) Molecular weight of the target compound is calculated from SMILES by using Open Babel.
4.6. Software name and version for descriptor generation
Name: KOWWIN
Description: A program to estimate log P values of organic compounds. That is incorporated in
EPI SuiteTM (Estimation Programs Interface: a tool intended for use in applications such as toquickly screen chemicals), which was developed by the US EPA, etc.

34

KOWWIN v1.69 (April 2015)
c 2000-2015 U.S. Environmental Protection Agency
KOWWIN is owned by the U.S. Environmental Protection Agency and is protected bycopyright throughout the world.
Permission is granted for individuals to download and use the software on their personaland business computers.
Users may not alter, modify, merge, adapt or prepare derivative works from the software.
Users may not remove or obscure copyright, tradename, or proprietary notices on the
program or related documentation.
KOWWIN contained therein is a tradename owned by the U.S. Environmental Protection
Agency.
KOWWINTM (included in EPI SuiteTM)• https://www.epa.gov/tsca-screening-tools/epi-suitetm-estimation-programinterface
Name: None
Description: An application for SMARTS substructure searching from SMILES that we havedeveloped by using Chemistry Development Kit (CDK) version 2.0
Contact: kate@nies.go.jp
WWW: https://kate2.nies.go.jp/
Name: Open Babel
Description: The Open Source Chemistry Toolbox, version 2.3.1, 2011.
Contact: Contact not available
WWW: http://openbabel.org/wiki/Main_Page
4.7. Chemicals/Descriptors ratio
The ratio (column name: ratio1) for each QSAR class is described in the spreadsheet“qsar_data” in the attachment file “KATE2020_daphnid_chronic.xlsx”.

35

5.Defining the applicability domain - OECD Principle 3
5.1. Description of the applicability domain of the model(1) Applicability domain of the model for the descriptor (log P) is established for each QSAR
class, which we define as a group of compounds with specific structure classes, and is the rangeof the descriptor, “between the maximum and the minimum” (see the column named “Log P
Range” of the spreadsheet “qsar_data” in the attached file “KATE2020_daphnid_chronic.xlsx”).
(2) Applicability domain of the model for the structure is also established for each QSAR class,and is provided from the list of substructure for the structure judgement of the chemicals in
the training set which belongs to the QSAR class and in the class called “narcotic group” (seethe column “FragIDs_for_AD” of the spreadsheet “qsar_data” in the attached file
“KATE2020_daphnid_chronic.xlsx”)
5.2. Method used to assess the applicability domain
The applicability domain of both descriptor and structure is assessed for each QSAR class into
which the target compound is classified.
(1) If the log P value of the target compound is within the applicability domain of the descriptor
as indicated in 5.1.(1), it is judged as “inside of the applicability domain” in terms of thedescriptor.
(2) If the substructure for the structure judgement (see the substructure defined in the
spreadsheet“substructure_def_for_AD”

in

the

file

“KATE2020_daphnid_chronic.xlsx”)

possessed by the target compound is all included in the list of the structure judgement of the
QSAR class (see 5.1.(2)), it is judged as “inside of the applicability domain” in terms of thestructure.
For more details, see Furuhama et al. (2011) (see 2.7 (3)).
5.3. Software name and version for applicability domain assessment
Name: KATE2020 on NET
Contact: kate@nies.go.jp
WWW: https://kate2.nies.go.jp
5.4. Limits of applicability
See 5.1 and 5.2.

36

6.Internal validation - OECD Principle 4
6.1. Availability of the training set
It is available and attached as supporting information.
6.2. Available information for the training set
Chemical Name: Yes
SMILES: Yes
CAS RN: Yes
InChI: No
MOL file: No
Formula: No
6.3. Data for each descriptor variable for the training set
No
6.4. Data for the dependent variable for the training set
The data for the dependent variable for each QSAR class are available in the spreadsheet“training_set” in the attachment file “KATE2020_daphnid_chronic.xlsx”.
6.5. Other information about the training set
The number of the training set for each QSAR class is described on the column named “n” inthe spreadsheet “qsar_data” in the attachment file “KATE2020_daphnid_chronic.xlsx”.
6.6. Pre-processing of data before modelling
The endpoint data NOEC [mg/L] was converted into log (1/NOEC [mmol/L]).
6.7. Statistics for goodness-of-fit
The following statistical values for each QSAR class are obtained and shown in the
spreadsheet “qsar_data” in the attachment file “KATE2020_daphnid_chronic.xlsx”.
R2: square of correlation coefficient
RMSE: Root Mean Squared Error
6.8. Robustness - Statistics obtained by leave-one-out cross-validation
The following statistical value for each QSAR class is obtained and shown in the spreadsheet“qsar_data” in the attachment file “KATE2020_daphnid_chronic.xlsx”.
Q2: square of correlation coefficient
6.9. Robustness - Statistics obtained by leave-many-out cross-validation
No
6.10. Robustness - Statistics obtained by Y-scramblingNo
6.11. Robustness - Statistics obtained by bootstrap
No
6.12. Robustness - Statistics obtained by other methods
No37

7.External validation - OECD Principle 4
7.1. Availability of the external validation set
No.
7.2. Available information for the external validation set
Chemical Name: No
SMILES: No
CAS RN: No
InChI: No
MOL file: No
Formula: No
7.3. Data for each descriptor variable for the external validation set
No
7.4. Data for the dependent variable for the external validation set
No
7.5. Other information about the external validation set
No
7.6. Experimental design of test set
No
7.7. Predictivity - Statistics obtained by external validation
No
7.8. Predictivity - Assessment of the external validation set
No
7.9. Comments on the external validation of the model
No38

8.Providing a mechanistic interpretation - OECD Principle 5
8.1. Mechanistic basis of the model
Each QSAR class is a group of compounds with specific structure class. Structure class isdefined by the information about the substructures (see 4.2) and not directly relevant to themode of actions. There are a few classes which can be relevant to the mode of actions. This
structure class was created with the integration of the multiple structure classes including
aliphatic hydrocarbons, aliphatic and aromatic ethers, aliphatic and aromatic ketones, andalcohols which are considered to have simple narcotic actions.
8.2. A priori or a posteriori mechanistic interpretation
A priori mechanistic interpretation.
8.3. Other information about the mechanistic interpretation
No39

9.Miscellaneous information
9.1. Comments
This model can output multiple estimations based on the adequacy to the structure classes(see 4.2). There is neither recommended value nor guideline to select one right now and the users
should select by themselves based on R2, Q2, the shape of the regression line (slope and the numberof reference chemicals), and the applicability domain based on descriptor (log P) and structure(structure judgement).
9.2. Bibliography[1] OECD Guideline for Testing of Chemicals, No. 211 (2nd October 2012) - Daphnia magna
Reproduction Test.
9.3. Supporting information
1. KATE2020_daphnid_chronic.xlsx
The name and content of each spreadsheet are as follows:
substructure_def: definition of substructures
substructure_def_for_AD: definition of substructures (also used for the applicability domain)
structure_def: definition of structure classes
qsar_data: data for each QSAR class (QSAR equation etc.)
training_set: data of the training set

10.Summary (JRC QSAR Model Database)
10.1. QMRF number:
To be entered by JRC

10.2. Publication date:
To be entered by JRC

10.3. Keywords:
To be entered by JRC

10.4. Comments:
To be entered by JRC

40

QMRF identifier (JRC Inventory):To be entered by JRC
QMRF Title: QSAR Model Reporting Format Prepared for KATE (Alga Acute)Printing Date:

1.QSAR identifier
1.1. QSAR identifier (title)
KATE2020 on NET for acute toxicity to Alga
1.2. Other related models
1.3. Software coding the model
Name: KATE2020 on NET
Description: Ecotoxicity prediction system consists of QSAR models
Contact: kate@nies.go.jp
WWW: https://kate2.nies.go.jp/

41

2.General information
2.1. Date of QMRF
14 January 2020
2.2. QMRF author(s) and contact details
Names: Hiroshi Yamamoto, Koji Imai, Yuto Itami, Ayako Furuhama, and Koichi Ohno
Affiliation: National Institute for Environmental Studies, Japan
Contact: 16-2 Onogawa, Tsukuba, Ibaraki, 305-8506, Japane-mail: kate@nies.go.jp
WWW: https://www.nies.go.jp/index-e.html
2.3. Date of QMRF update(s)2.4. QMRF update(s)2.5. Model developer(s) and contact details
Names: Hiroaki Shiraishi and Koji Imai
Affiliation: National Institute for Environmental Studies, Japan
Contact: 16-2 Onogawa, Tsukuba, Ibaraki, 305-8506, Japane-mail: kate@nies.go.jp
WWW: https://www.nies.go.jp/index-e.html
2.6. Date of model development and/or publication
3 February 2020
2.7. Reference(s) to main scientific papers and/or software package(1) KATE2020 on NET https://kate2.nies.go.jp/(2) A Furuhama, T Toida, N Nishikawa, Y Aoki, Y Yoshioka, and H Shiraishi (2010). Developmentof an ecotoxicity QSAR model for the KAshinhou Tool for Ecotoxicity (KATE) system, March2009 version. SAR and QSAR Environmental Research 21 (5-6) 403–413
http://dx.doi.org/10.1080/1062936X.2010.501815.
(3) A. Furuhama, K. Hasunuma, Y. Aoki, Y. Yoshioka and H. Shiraishi (2011). Application ofchemical reaction mechanistic domains to an ecotoxicity QSAR model, KAshinhou Tool for
Ecotoxicity (KATE). SAR and QSAR Environmental Research 22 (5-6) 505–523
http://dx.doi.org/10.1080/1062936X.2011.569944.
2.8. Availability of information about the model
The model is proprietary, but the training set and algorithm are available.
2.9. Availability of another QMRF for exactly the same model
None to date.

42

3.Defining the endpoint - OECD Principle 1
3.1. Species

Pseudokirchneriella subcapitata
3.2. Endpoint
Short-term toxicity to alga (inhibition of the exponential growth rate)
3.3. Comment on endpoint
The model predicts the 50% effective concentration (EC50) values in the Alga Growth Inhibition
Test (72-h exposure).
3.4. Endpoint units
mg/L
3.5. Dependent variable
All of the measured endpoint values were converted to mmol/L units, replaced with theirreciprocal, and transformed to logarithmic units. The dependent variable is: log (1/EC50)
3.6. Experimental protocol
OECD Test Guideline No. 201 test protocol
3.7. Endpoint data quality and variability
The data are from the ecotoxicity tests conducted by the Ministry of the Environment,
Government of Japan (Alga growth inhibition tests) according to good laboratory practice(GLP).
This data are available at: http://www.env.go.jp/en/chemi/sesaku/aquatic_Mar_2019.pdf(Accessed on January 14, 2020)

43

4.Defining the algorithm - OECD Principle 2
4.1. Type of model
QSAR
4.2. Explicit algorithm
The predicted values are calculated with the following algorithm:(1) Extract the information about the number of substructures, which we defined, in the target
compound.

(As

for

the

definition

of

the

substructures,

see

the

spreadsheets“substructure_def” and “substructure_def_for_AD” in the “KATE2020_alga_acute.xlsx”).
(2) Extract the adequate structure classes in the target compound from the information about
the number of the substructure (see 4.5. (3)).
(3) Predict the ecotoxicity of the target compound using the descriptor (log P) and the QSAR
equation (slope and intercept) provided for each structure class.
(4) Convert the unit of the predicted ecotoxicity from log (1/EC50 [mmol/L]) to [mg/L] using themolecular weight (see 3.5).
4.3. Descriptors in the model
Descriptor: log P

Octanol–water partition coefficient

For structural fragments, see the spreadsheets “substructure_def”, “substructure_def_for_AD”
and “structure_def” in the attachment file “KATE2020_alga_acute.xlsx”.
4.4. Descriptor selection
See 4.2.
4.5. Algorithm and descriptor generation(1) Log P can be input by the user, otherwise log P is calculated using KOWWIN from SMILES
notation.
(2) The information about the number of substructures (see 4.2. (1)) is calculated using the
JAVA application developed by NIES using Chemistry Development Kit (CDK) from
inputted SMILES and the definition of the substructure.
(3) The adequacy to each structure class (1:Yes, 0:No) is evaluated by the information about the
number of the substructures and the definition of the structure class (see the spreadsheet“structure_def” in the file “KATE2020_alga_acute.xlsx”) in the KATE2020 on NET.
(4) Molecular weight of the target compound is calculated from SMILES by using Open Babel.
4.6. Software name and version for descriptor generation
Name: KOWWIN
Description: A program to estimate log P values of organic compounds. That is incorporated in
EPI SuiteTM (Estimation Programs Interface: a tool intended for use in applications such as toquickly screen chemicals), which was developed by the US EPA, etc.
KOWWIN v1.69 (April 2015)44

c 2000-2015 U.S. Environmental Protection Agency
KOWWIN is owned by the U.S. Environmental Protection Agency and is protected bycopyright throughout the world.
Permission is granted for individuals to download and use the software on their personaland business computers.
Users may not alter, modify, merge, adapt or prepare derivative works from the software.
Users may not remove or obscure copyright, tradename, or proprietary notices on the
program or related documentation.
KOWWIN contained therein is a tradename owned by the U.S. Environmental Protection
Agency.
KOWWINTM (included in EPI SuiteTM)• https://www.epa.gov/tsca-screening-tools/epi-suitetm-estimation-programinterface
Name: None
Description: An application for SMARTS substructure searching from SMILES that we havedeveloped by using Chemistry Development Kit (CDK) version 2.0
Contact: kate@nies.go.jp
WWW: https://kate2.nies.go.jp/
Name: Open Babel
Description: The Open Source Chemistry Toolbox, version 2.3.1, 2011.
Contact: Contact not available
WWW: http://openbabel.org/wiki/Main_Page
4.7. Chemicals/Descriptors ratio
The ratio (column name: ratio1) for each QSAR class is described in the spreadsheet“qsar_data” in the attachment file “KATE2020_alga_acute.xlsx”.

45

5.Defining the applicability domain - OECD Principle 3
5.1. Description of the applicability domain of the model(1) Applicability domain of the model for the descriptor (log P) is established for each QSAR
class, which we define as a group of compounds with specific structure classes, and is the rangeof the descriptor, “between the maximum and the minimum” (see the column named “Log P
Range” of the spreadsheet “qsar_data” in the attached file “KATE2020_alga_acute.xlsx”).
(2) Applicability domain of the model for the structure is also established for each QSAR class,and is provided from the list of substructure for the structure judgement of the chemicals in
the training set which belongs to the QSAR class and in the class called “narcotic group” (seethe column “FragIDs_for_AD” of the spreadsheet “qsar_data” in the attached file
“KATE2020_alga_acute.xlsx”)
5.2. Method used to assess the applicability domain
The applicability domain of both descriptor and structure is assessed for each QSAR class into
which the target compound is classified.
(1) If the log P value of the target compound is within the applicability domain of the descriptor
as indicated in 5.1.(1), it is judged as “inside of the applicability domain” in terms of thedescriptor.
(2) If the substructure for the structure judgement (see the substructure defined in the
spreadsheet “substructure_def_for_AD” in the file “KATE2020_alga_acute.xlsx”) possessed by
the target compound is all included in the list of the structure judgement of the QSAR class(see 5.1.(2)), it is judged as “inside of the applicability domain” in terms of the structure.
For more details, see Furuhama et al. (2011) (see 2.7 (3)).
5.3. Software name and version for applicability domain assessment
Name: KATE2020 on NET
Contact: kate@nies.go.jp
WWW: https://kate2.nies.go.jp
5.4. Limits of applicability
See 5.1 and 5.2.

46

6.Internal validation - OECD Principle 4
6.1. Availability of the training set
It is available in the supporting information.
6.2. Available information for the training set
Chemical Name: Yes
SMILES: Yes
CAS RN: Yes
InChI: No
MOL file: No
Formula: No
6.3. Data for each descriptor variable for the training set
No
6.4. Data for the dependent variable for the training set
The data for the dependent variable for each QSAR class are available in the spreadsheet“training_set” in the attachment file “KATE2020_alga_acute.xlsx”.
6.5. Other information about the training set
The number of the training set for each QSAR class is described on the column named “n” inthe spreadsheet “qsar_data” in the attachment file “KATE2020_alga_acute.xlsx”.
6.6. Pre-processing of data before modelling
The endpoint data EC50 [mg/L] was converted into log (1/EC50 [mmol/L]).
6.7. Statistics for goodness-of-fit
The following statistical values for each QSAR class are obtained and shown in the
spreadsheet “qsar_data” in the attachment file “KATE2020_alga_acute.xlsx”.
R2: square of correlation coefficient
RMSE: Root Mean Squared Error
6.8. Robustness - Statistics obtained by leave-one-out cross-validation
The following statistical value for each QSAR class is obtained and shown in the spreadsheet“qsar_data” in the attachment file “KATE2020_alga_acute.xlsx”.
Q2: square of correlation coefficient
6.9. Robustness - Statistics obtained by leave-many-out cross-validation
No
6.10. Robustness - Statistics obtained by Y-scramblingNo
6.11. Robustness - Statistics obtained by bootstrap
No
6.12. Robustness - Statistics obtained by other methods
No47

7.External validation - OECD Principle 4
7.1. Availability of the external validation set
No.
7.2. Available information for the external validation set
Chemical Name: No
SMILES: No
CAS RN: No
InChI: No
MOL file: No
Formula: No
7.3. Data for each descriptor variable for the external validation set
No
7.4. Data for the dependent variable for the external validation set
No
7.5. Other information about the external validation set
No
7.6. Experimental design of test set
No
7.7. Predictivity - Statistics obtained by external validation
No
7.8. Predictivity - Assessment of the external validation set
No
7.9. Comments on the external validation of the modelNo48

8.Providing a mechanistic interpretation - OECD Principle 5
8.1. Mechanistic basis of the model
Each QSAR class is a group of compounds with specific structure class. Structure class isdefined by the information about the substructures (see 4.2) and not directly relevant to themode of actions. There are a few classes which can be relevant to the mode of actions. For
example, QSAR ID: 32899941 (in the spreadsheet of “qsar_data”) is a group of chemicals withstructure class: GD_28999 (narcotic group), and considered to exert narcotic actions. This
structure class was created with the integration of the multiple structure classes including
aliphatic hydrocarbons, aliphatic and aromatic ethers, aliphatic and aromatic ketones, andalcohols which are considered to have simple narcotic actions.
8.2. A priori or a posteriori mechanistic interpretation
A priori mechanistic interpretation.
8.3. Other information about the mechanistic interpretation
No49

9.Miscellaneous information
9.1. Comments
This model can output multiple estimations based on the adequacy to the structure classes(see 4.2). There is neither recommended value nor guideline to select one right now and the users
should select by themselves based on R2, Q2, the shape of the regression line (slope and the numberof reference chemicals), and the applicability domain based on descriptor (log P) and structure(structure judgement).
9.2. Bibliography[1] OECD Guideline for Testing of Chemicals, No. 201 (23rd March 2006) - Annex 5 corrected:
28th July 2011) - Freshwater Alga and Cyanobacteria, Growth Inhibition Test.
9.3. Supporting information
1. KATE2020_alga_acute.xlsx
The name and content of each spreadsheet are as follows:
substructure_def: definition of substructures
substructure_def_for_AD: definition of substructures (also used for the applicability domain)
structure_def: definition of structure classes
qsar_data: data for each QSAR class (QSAR equation etc.)
training_set: data of the training set

10.Summary (JRC QSAR Model Database)
10.1. QMRF number:
To be entered by JRC

10.2. Publication date:
To be entered by JRC

10.3. Keywords:
To be entered by JRC

10.4. Comments:
To be entered by JRC

50

QMRF identifier (JRC Inventory):To be entered by JRC
QMRF Title: QSAR Model Reporting Format Prepared for KATE (Alga Chronic)
Printing Date:

1.QSAR identifier
1.1. QSAR identifier (title)
KATE2020 on NET for chronic toxicity to Alga
1.2. Other related models
1.3. Software coding the model
Name: KATE2020 on NET
Description: Ecotoxicity prediction system consists of QSAR models
Contact: kate@nies.go.jp
WWW: https://kate2.nies.go.jp/51

2.General information
2.1. Date of QMRF
14 January 2020
2.2. QMRF author(s) and contact details
Names: Hiroshi Yamamoto, Koji Imai, Yuto Itami, Ayako Furuhama, and Koichi Ohno
Affiliation: National Institute for Environmental Studies, Japan
Contact: 16-2 Onogawa, Tsukuba, Ibaraki, 305-8506, Japane-mail: kate@nies.go.jp
WWW: https://www.nies.go.jp/index-e.html
2.3. Date of QMRF update(s)2.4. QMRF update(s)2.5. Model developer(s) and contact details
Names: Hiroaki Shiraishi and Koji Imai
Affiliation: National Institute for Environmental Studies, Japan
Contact: 16-2 Onogawa, Tsukuba, Ibaraki, 305-8506, Japane-mail: kate@nies.go.jp
WWW: https://www.nies.go.jp/index-e.html
2.6. Date of model development and/or publication
3 February 2020
2.7. Reference(s) to main scientific papers and/or software package(1) KATE2020 on NET https://kate2.nies.go.jp/(2) A Furuhama, T Toida, N Nishikawa, Y Aoki, Y Yoshioka, and H Shiraishi (2010). Developmentof an ecotoxicity QSAR model for the KAshinhou Tool for Ecotoxicity (KATE) system, March2009 version. SAR and QSAR Environmental Research 21 (5-6) 403–413
http://dx.doi.org/10.1080/1062936X.2010.501815.
(3) A. Furuhama, K. Hasunuma, Y. Aoki, Y. Yoshioka and H. Shiraishi (2011). Application ofchemical reaction mechanistic domains to an ecotoxicity QSAR model, KAshinhou Tool for
Ecotoxicity (KATE). SAR and QSAR Environmental Research 22 (5-6) 505–523
http://dx.doi.org/10.1080/1062936X.2011.569944.
2.8. Availability of information about the model
The model is proprietary, but the training set and algorithm are available.
2.9. Availability of another QMRF for exactly the same model
None to date.

52

3.Defining the endpoint - OECD Principle 1
3.1. Species

Pseudokirchneriella subcapitata
3.2. Endpoint
Short-term toxicity to alga (inhibition of the exponential growth rate)
3.3. Comment on endpoint
The model predicts the no-observed-effect concentration (NOEC) values in the Alga Growth
Inhibition Test (72-h exposure).
3.4. Endpoint units
mg/L
3.5. Dependent variable
All of the measured endpoint values were converted to mmol/L units, replaced with theirreciprocal, and transformed to logarithmic units. The dependent variable is: log (1/NOEC)
3.6. Experimental protocol
OECD Test Guideline No. 201 test protocol
3.7. Endpoint data quality and variability
The data are from the ecotoxicity tests conducted by the Ministry of the Environment,
Government of Japan (Alga growth inhibition tests) according to good laboratory practice(GLP).
This data are available at: http://www.env.go.jp/en/chemi/sesaku/aquatic_Mar_2019.pdf(Accessed on January 14, 2020)

53

4.Defining the algorithm - OECD Principle 2
4.1. Type of model
QSAR
4.2. Explicit algorithm
The predicted values are calculated with the following algorithm:(1) Extract the information about the number of substructures, which we defined, in the target
compound.

(As

for

the

definition

of

the

substructures,

see

the

spreadsheets“substructure_def” and “substructure_def_for_AD” in the “KATE2020_alga_chronic.xlsx”).
(2) Extract the adequate structure classes in the target compound from the information about
the number of the substructure (see 4.5. (3)).
(3) Predict the ecotoxicity of the target compound using the descriptor (log P) and the QSAR
equation (slope and intercept) provided for each structure class.
(4) Convert the unit of the predicted ecotoxicity from log (1/NOEC [mmol/L]) to [mg/L] using
the molecular weight (see 3.5).
4.3. Descriptors in the model
Descriptor: log P

Octanol–water partition coefficient

For structural fragments, see the spreadsheets “substructure_def”, “substructure_def_for_AD”
and “structure_def” in the attachment file “KATE2020_alga_chronic.xlsx”.
4.4. Descriptor selection
See 4.2.
4.5. Algorithm and descriptor generation(1) Log P can be input by the user, otherwise log P is calculated using KOWWIN from SMILES
notation.
(2) The information about the number of substructures (see 4.2. (1)) is calculated using the
JAVA application developed by NIES using Chemistry Development Kit (CDK) from
inputted SMILES and the definition of the substructure.
(3) The adequacy to each structure class (1:Yes, 0:No) is evaluated by the information about the
number of the substructures and the definition of the structure class (see the spreadsheet“structure_def” in the file “KATE2020_alga_chronic.xlsx”) in the KATE2020 on NET.
(4) Molecular weight of the target compound is calculated from SMILES by using Open Babel.
4.6. Software name and version for descriptor generation
Name: KOWWIN
Description: A program to estimate log P values of organic compounds. That is incorporated in
EPI SuiteTM (Estimation Programs Interface: a tool intended for use in applications such as toquickly screen chemicals), which was developed by the US EPA, etc.
KOWWIN v1.69 (April 2015)54

c 2000-2015 U.S. Environmental Protection Agency
KOWWIN is owned by the U.S. Environmental Protection Agency and is protected bycopyright throughout the world.
Permission is granted for individuals to download and use the software on their personaland business computers.
Users may not alter, modify, merge, adapt or prepare derivative works from the software.
Users may not remove or obscure copyright, tradename, or proprietary notices on the
program or related documentation.
KOWWIN contained therein is a tradename owned by the U.S. Environmental Protection
Agency.
KOWWINTM (included in EPI SuiteTM)• https://www.epa.gov/tsca-screening-tools/epi-suitetm-estimation-programinterface
Name: None
Description: An application for SMARTS substructure searching from SMILES that we havedeveloped by using Chemistry Development Kit (CDK) version 2.0
Contact: kate@nies.go.jp
WWW: https://kate2.nies.go.jp/
Name: Open Babel
Description: The Open Source Chemistry Toolbox, version 2.3.1, 2011.
Contact: Contact not available
WWW: http://openbabel.org/wiki/Main_Page
4.7. Chemicals/Descriptors ratio
The ratio (column name: ratio1) for each QSAR class is described in the spreadsheet“qsar_data” in the attachment file “KATE2020_alga_chronic.xlsx”.

55

5.Defining the applicability domain - OECD Principle 3
5.1. Description of the applicability domain of the model(1) Applicability domain of the model for the descriptor (log P) is established for each QSAR
class, which we define as a group of compounds with specific structure classes, and is the rangeof the descriptor, “between the maximum and the minimum” (see the column named “Log P
Range” of the spreadsheet “qsar_data” in the attached file “KATE2020_alga_chronic.xlsx”).
(2) Applicability domain of the model for the structure is also established for each QSAR class,and is provided from the list of substructure for the structure judgement of the chemicals in
the training set which belongs to the QSAR class and in the class called “narcotic group” (seethe column “FragIDs_for_AD” of the spreadsheet “qsar_data” in the attached file
“KATE2020_alga_chronic.xlsx”)
5.2. Method used to assess the applicability domain
The applicability domain of both descriptor and structure is assessed for each QSAR class into
which the target compound is classified.
(1) If the log P value of the target compound is within the applicability domain of the descriptor
as indicated in 5.1.(1), it is judged as “inside of the applicability domain” in terms of thedescriptor.
(2) If the substructure for the structure judgement (see the substructure defined in the
spreadsheet “substructure_def_for_AD” in the file “KATE2020_alga_chronic.xlsx”) possessedby the target compound is all included in the list of the structure judgement of the QSAR class(see 5.1.(2)), it is judged as “inside of the applicability domain” in terms of the structure.
For more details, see Furuhama et al. (2011) (see 2.7 (3)).
5.3. Software name and version for applicability domain assessment
Name: KATE2020 on NET
Contact: kate@nies.go.jp
WWW: https://kate2.nies.go.jp
5.4. Limits of applicability
See 5.1 and 5.2.

56

6.Internal validation - OECD Principle 4
6.1. Availability of the training set
It is available in the supporting information.
6.2. Available information for the training set
Chemical Name: Yes
SMILES: Yes
CAS RN: Yes
InChI: No
MOL file: No
Formula: No
6.3. Data for each descriptor variable for the training set
No
6.4. Data for the dependent variable for the training set
The data for the dependent variable for each QSAR class are available in the spreadsheet“training_set” in the attachment file “KATE2020_alga_chronic.xlsx”.
6.5. Other information about the training set
The number of the training set for each QSAR class is described on the column named “n” inthe spreadsheet “qsar_data” in the attachment file “KATE2020_alga_chronic.xlsx”.
6.6. Pre-processing of data before modelling
The endpoint data NOEC [mg/L] was converted into log (1/NOEC [mmol/L]).
6.7. Statistics for goodness-of-fit
The following statistical values for each QSAR class are obtained and shown in the
spreadsheet “qsar_data” in the attachment file “KATE2020_alga_chronic.xlsx”.
R2: square of correlation coefficient
RMSE: Root Mean Squared Error
6.8. Robustness - Statistics obtained by leave-one-out cross-validation
The following statistical value for each QSAR class is obtained and shown in the spreadsheet“qsar_data” in the attachment file “KATE2020_alga_chronic.xlsx”.
Q2: square of correlation coefficient
6.9. Robustness - Statistics obtained by leave-many-out cross-validation
No
6.10. Robustness - Statistics obtained by Y-scramblingNo
6.11. Robustness - Statistics obtained by bootstrap
No
6.12. Robustness - Statistics obtained by other methods
No57

7.External validation - OECD Principle 4
7.1. Availability of the external validation set
No.
7.2. Available information for the external validation set
Chemical Name: No
SMILES: No
CAS RN: No
InChI: No
MOL file: No
Formula: No
7.3. Data for each descriptor variable for the external validation set
No
7.4. Data for the dependent variable for the external validation set
No
7.5. Other information about the external validation set
No
7.6. Experimental design of test set
No
7.7. Predictivity - Statistics obtained by external validation
No
7.8. Predictivity - Assessment of the external validation set
No
7.9. Comments on the external validation of the model
No58

8.Providing a mechanistic interpretation - OECD Principle 5
8.1. Mechanistic basis of the model
Each QSAR class is a group of compounds with specific structure class. Structure class isdefined by the information about the substructures (see 4.2) and not directly relevant to themode of actions. There are a few classes which can be relevant to the mode of actions. This
structure class was created with the integration of the multiple structure classes including
aliphatic hydrocarbons, aliphatic and aromatic ethers, aliphatic and aromatic ketones, andalcohols which are considered to have simple narcotic actions.
8.2. A priori or a posteriori mechanistic interpretation
A priori mechanistic interpretation.
8.3. Other information about the mechanistic interpretation
No59

9.Miscellaneous information
9.1. Comments
This model can output multiple estimations based on the adequacy to the structure classes(see 4.2). There is neither recommended value nor guideline to select one right now and the users
should select by themselves based on R2, Q2, the shape of the regression line (slope and the numberof reference chemicals), and the applicability domain based on descriptor (log P) and structure(structure judgement).
9.2. Bibliography[1] OECD Guideline for Testing of Chemicals, No. 201 (23rd March 2006) - Annex 5 corrected:
28th July 2011) - Freshwater Alga and Cyanobacteria, Growth Inhibition Test.
9.3. Supporting information
1. KATE2020_alga_chronic.xlsx
The name and content of each spreadsheet are as follows:
substructure_def: definition of substructures
substructure_def_for_AD: definition of substructures (also used for the applicability domain)
structure_def: definition of structure classes
qsar_data: data for each QSAR class (QSAR equation etc.)
training_set: data of the training set

10.Summary (JRC QSAR Model Database)
10.1. QMRF number:
To be entered by JRC

10.2. Publication date:
To be entered by JRC

10.3. Keywords:
To be entered by JRC

10.4. Comments:
To be entered by JRC

60