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EC number: 904-139-6 | CAS number: -
- Life Cycle description
- Uses advised against
- Endpoint summary
- Appearance / physical state / colour
- Melting point / freezing point
- Boiling point
- Density
- Particle size distribution (Granulometry)
- Vapour pressure
- Partition coefficient
- Water solubility
- Solubility in organic solvents / fat solubility
- Surface tension
- Flash point
- Auto flammability
- Flammability
- Explosiveness
- Oxidising properties
- Oxidation reduction potential
- Stability in organic solvents and identity of relevant degradation products
- Storage stability and reactivity towards container material
- Stability: thermal, sunlight, metals
- pH
- Dissociation constant
- Viscosity
- Additional physico-chemical information
- Additional physico-chemical properties of nanomaterials
- Nanomaterial agglomeration / aggregation
- Nanomaterial crystalline phase
- Nanomaterial crystallite and grain size
- Nanomaterial aspect ratio / shape
- Nanomaterial specific surface area
- Nanomaterial Zeta potential
- Nanomaterial surface chemistry
- Nanomaterial dustiness
- Nanomaterial porosity
- Nanomaterial pour density
- Nanomaterial photocatalytic activity
- Nanomaterial radical formation potential
- Nanomaterial catalytic activity
- Endpoint summary
- Stability
- Biodegradation
- Bioaccumulation
- Transport and distribution
- Environmental data
- Additional information on environmental fate and behaviour
- Ecotoxicological Summary
- Aquatic toxicity
- Endpoint summary
- Short-term toxicity to fish
- Long-term toxicity to fish
- Short-term toxicity to aquatic invertebrates
- Long-term toxicity to aquatic invertebrates
- Toxicity to aquatic algae and cyanobacteria
- Toxicity to aquatic plants other than algae
- Toxicity to microorganisms
- Endocrine disrupter testing in aquatic vertebrates – in vivo
- Toxicity to other aquatic organisms
- Sediment toxicity
- Terrestrial toxicity
- Biological effects monitoring
- Biotransformation and kinetics
- Additional ecotoxological information
- Toxicological Summary
- Toxicokinetics, metabolism and distribution
- Acute Toxicity
- Irritation / corrosion
- Sensitisation
- Repeated dose toxicity
- Genetic toxicity
- Carcinogenicity
- Toxicity to reproduction
- Specific investigations
- Exposure related observations in humans
- Toxic effects on livestock and pets
- Additional toxicological data
Toxicity to microorganisms
Administrative data
Link to relevant study record(s)
- Endpoint:
- activated sludge respiration inhibition testing
- Type of information:
- (Q)SAR
- Adequacy of study:
- weight of evidence
- Study period:
- January 25, 2018
- Reliability:
- 2 (reliable with restrictions)
- Rationale for reliability incl. deficiencies:
- results derived from a (Q)SAR model, with limited documentation / justification, but validity of model and reliability of prediction considered adequate based on a generally acknowledged source
- Justification for type of information:
- The predictive ability of each of the QSAR methodologies was evaluated using statistical external validation.
A QSAR model has acceptable predictive power if the following conditions are satisfied:
q^2 > 0.5 (1)
R^2 > 0.6 (2)
(R^2-Ro^2)/R^2 < 0.1 and 0.85 <= k <= 1.15 (3)
where:
q^2 is the leave one out correlation coefficient for the training set
R^2 is correlation coefficient between the observed and predicted toxicities for the test set
Ro^2 is correlation coefficient between the observed and predicted toxicities for the test set with the Y-intercept set to zero (where the regression line is given by Y=kX)
The prediction accuracy will be evaluated in terms of equations (2) and (3).
In addition, the accuracy will be evaluated in terms of the RMSE (root mean square error), and the MAE (mean absolute error) for the test set.
It has been demonstrated that q^2 is not correlated with R^2 for the test set.
The prediction coverage (fraction of chemicals predicted) must be considered because the prediction accuracy (in terms of R^2 and RMSE) can sometimes be improved at the sacrifice of the prediction coverage.
For binary (active/inactive) toxicity endpoints such as developmental toxicity, the prediction accuracy is evaluated in terms of the fraction of compounds that are predicted accurately.
The prediction accuracy is evaluated in terms of three different statistics: concordance, sensitivity, and specificity.
Concordance is the fraction of all compounds that are predicted correctly (i.e. experimentally active compounds that are predicted to be active and experimentally inactive compounds that are predicted to be inactive). Sensitivity is the fraction of experimentally active compounds that are predicted to be active.
Specificity is the fraction of experimentally inactive compounds that are predicted to be inactive. - Qualifier:
- equivalent or similar to guideline
- Guideline:
- other: ECHA Guidance on information requirements and chemical safety assessment - Chapter R.06: QSARs and grouping of chemicals
- Principles of method if other than guideline:
- T.E.S.T. has been developed to allow users to easily estimate toxicity using a variety of QSAR methodologies. T.E.S.T provides multiple prediction methodologies so one can have greater confidence in the predicted toxicities (assuming the predicted toxicities are similar from different methods).
(Q)SAR PREDICTION METHODOLOGIES
• Hierarchical method
The toxicity for a given query compound is estimated using the weighted average of the predictions from several different models. The different models are obtained by using Ward’s method to divide the training set into a series of structurally similar clusters. A genetic algorithm based technique is used to generate models for each cluster. The models are generated prior to runtime.
• FDA method
The prediction for each test chemical is made using a new model that is fit to the chemicals that are most similar to the test compound. Each model is generated at runtime.
• Single model method
Predictions are made using a multilinear regression model that is fit to the training set (using molecular descriptors as independent variables) using a genetic algorithm based approach. The regression model is generated prior to runtime.
• Group contribution method
Predictions are made using a multilinear regression model that is fit to the training set (using molecular fragment counts as independent variables). The regression model is generated prior to runtime.
• Nearest neighbor method
The predicted toxicity is estimated by taking an average of the three chemicals in the training set that are most similar to the test chemical.
• Consensus method
The predicted toxicity is estimated by taking an average of the predicted toxicities from the above (Q)SAR methods (provided the predictions are within the respective applicability domains).
• Random forest method
The predicted toxicity is estimated using a decision tree which bins a chemical into a certain toxicity score (i.e. positive or negative developmental toxicity) using a set of molecular descriptors as decision variables. The random forest method is currently only available for the developmental toxicity endpoint. The random forest models for the developmental toxicity endpoint were developed by researchers at Mario Negri Institute for Pharmacological Research as part of the CAESAR project (CAESAR 2009). - Analytical monitoring:
- not specified
- Vehicle:
- not specified
- Test organisms (species):
- Tetrahymena pyriformis
- Test type:
- other: in silico estimation
- Water media type:
- freshwater
- Total exposure duration:
- 48 h
- Test temperature:
- 25 deg C
- Reference substance (positive control):
- not specified
- Duration:
- 48 h
- Dose descriptor:
- IC50
- Effect conc.:
- 1 742 mg/L
- Nominal / measured:
- estimated
- Conc. based on:
- test mat.
- Basis for effect:
- growth inhibition
- Remarks on result:
- other: Consensus method
- Reported statistics and error estimates:
- Statistical external validation
The prediction results for the IGC50 test set were as follows:
Method R2 (R^2-R0^2)/R^2 k RMSE MAE Coverage
Hierarchical 0.719 0.023 0.978 0.539 0.358 0.933
FDA 0.747 0.056 0.988 0.489 0.337 0.978
Group contribution 0.682 0.065 0.994 0.575 0.411 0.955
Nearest neighbor 0.600 0.170 0.976 0.638 0.451 0.986
Consensus 0.764 0.065 0.983 0.475 0.332 0.983
The Consensus method achieved the best results. The R2 value for the consensus method in version 4.1 of TEST was slightly lower than the value for version 4.0. This is due to the fact that the data set has been expanded to include a wider variety of chemical classes. - Validity criteria fulfilled:
- yes
- Conclusions:
- The IC50, on the basis of growth inhibition effect (48h) of 1,2-diethyl citrate on Tetrahymena pyriformis, was estimated to be 1742 mg/L.
- Executive summary:
The IC50, on the basis of growth inhibition effect (48h) of 1,2-diethyl citrate on Tetrahymena pyriformis, was estimated to be 1742 mg/L.
- Endpoint:
- activated sludge respiration inhibition testing
- Type of information:
- (Q)SAR
- Adequacy of study:
- weight of evidence
- Study period:
- January 25, 2018
- Reliability:
- 2 (reliable with restrictions)
- Rationale for reliability incl. deficiencies:
- results derived from a (Q)SAR model, with limited documentation / justification, but validity of model and reliability of prediction considered adequate based on a generally acknowledged source
- Justification for type of information:
- The predictive ability of each of the QSAR methodologies was evaluated using statistical external validation.
A QSAR model has acceptable predictive power if the following conditions are satisfied:
q^2 > 0.5 (1)
R^2 > 0.6 (2)
(R^2-Ro^2)/R^2 < 0.1 and 0.85 <= k <= 1.15 (3)
where:
q^2 is the leave one out correlation coefficient for the training set
R^2 is correlation coefficient between the observed and predicted toxicities for the test set
Ro^2 is correlation coefficient between the observed and predicted toxicities for the test set with the Y-intercept set to zero (where the regression line is given by Y=kX)
The prediction accuracy will be evaluated in terms of equations (2) and (3).
In addition, the accuracy will be evaluated in terms of the RMSE (root mean square error), and the MAE (mean absolute error) for the test set.
It has been demonstrated that q^2 is not correlated with R^2 for the test set.
The prediction coverage (fraction of chemicals predicted) must be considered because the prediction accuracy (in terms of R^2 and RMSE) can sometimes be improved at the sacrifice of the prediction coverage.
For binary (active/inactive) toxicity endpoints such as developmental toxicity, the prediction accuracy is evaluated in terms of the fraction of compounds that are predicted accurately.
The prediction accuracy is evaluated in terms of three different statistics: concordance, sensitivity, and specificity.
Concordance is the fraction of all compounds that are predicted correctly (i.e. experimentally active compounds that are predicted to be active and experimentally inactive compounds that are predicted to be inactive). Sensitivity is the fraction of experimentally active compounds that are predicted to be active.
Specificity is the fraction of experimentally inactive compounds that are predicted to be inactive. - Qualifier:
- equivalent or similar to guideline
- Guideline:
- other: ECHA Guidance on information requirements and chemical safety assessment - Chapter R.06: QSARs and grouping of chemicals
- Principles of method if other than guideline:
- T.E.S.T. has been developed to allow users to easily estimate toxicity using a variety of QSAR methodologies. T.E.S.T provides multiple prediction methodologies so one can have greater confidence in the predicted toxicities (assuming the predicted toxicities are similar from different methods).
(Q)SAR PREDICTION METHODOLOGIES
• Hierarchical method
The toxicity for a given query compound is estimated using the weighted average of the predictions from several different models. The different models are obtained by using Ward’s method to divide the training set into a series of structurally similar clusters. A genetic algorithm based technique is used to generate models for each cluster. The models are generated prior to runtime.
• FDA method
The prediction for each test chemical is made using a new model that is fit to the chemicals that are most similar to the test compound. Each model is generated at runtime.
• Single model method
Predictions are made using a multilinear regression model that is fit to the training set (using molecular descriptors as independent variables) using a genetic algorithm based approach. The regression model is generated prior to runtime.
• Group contribution method
Predictions are made using a multilinear regression model that is fit to the training set (using molecular fragment counts as independent variables). The regression model is generated prior to runtime.
• Nearest neighbor method
The predicted toxicity is estimated by taking an average of the three chemicals in the training set that are most similar to the test chemical.
• Consensus method
The predicted toxicity is estimated by taking an average of the predicted toxicities from the above (Q)SAR methods (provided the predictions are within the respective applicability domains).
• Random forest method
The predicted toxicity is estimated using a decision tree which bins a chemical into a certain toxicity score (i.e. positive or negative developmental toxicity) using a set of molecular descriptors as decision variables. The random forest method is currently only available for the developmental toxicity endpoint. The random forest models for the developmental toxicity endpoint were developed by researchers at Mario Negri Institute for Pharmacological Research as part of the CAESAR project (CAESAR 2009). - Analytical monitoring:
- not specified
- Vehicle:
- not specified
- Test organisms (species):
- Tetrahymena pyriformis
- Test type:
- other: in silico estimation
- Water media type:
- freshwater
- Total exposure duration:
- 48 h
- Test temperature:
- 25 deg C
- Reference substance (positive control):
- not specified
- Duration:
- 48 h
- Dose descriptor:
- IC50
- Effect conc.:
- 2 858 mg/L
- Nominal / measured:
- estimated
- Conc. based on:
- test mat.
- Basis for effect:
- growth inhibition
- Remarks on result:
- other: Consensus method
- Reported statistics and error estimates:
- Statistical external validation
The prediction results for the IGC50 test set were as follows:
Method R2 (R^2-R0^2)/R^2 k RMSE MAE Coverage
Hierarchical 0.719 0.023 0.978 0.539 0.358 0.933
FDA 0.747 0.056 0.988 0.489 0.337 0.978
Group contribution 0.682 0.065 0.994 0.575 0.411 0.955
Nearest neighbor 0.600 0.170 0.976 0.638 0.451 0.986
Consensus 0.764 0.065 0.983 0.475 0.332 0.983
The Consensus method achieved the best results. The R2 value for the consensus method in version 4.1 of TEST was slightly lower than the value for version 4.0. This is due to the fact that the data set has been expanded to include a wider variety of chemical classes. - Validity criteria fulfilled:
- yes
- Conclusions:
- The IC50, on the basis of growth inhibition effect (48h) of 1-ethyl citrate on Tetrahymena pyriformis, was estimated to be 2858 mg/L.
- Executive summary:
The IC50, on the basis of growth inhibition effect (48h) of 1-ethyl citrate on Tetrahymena pyriformis, was estimated to be 2858 mg/L.
- Endpoint:
- activated sludge respiration inhibition testing
- Type of information:
- (Q)SAR
- Adequacy of study:
- weight of evidence
- Study period:
- January 25, 2018
- Reliability:
- 2 (reliable with restrictions)
- Rationale for reliability incl. deficiencies:
- results derived from a (Q)SAR model, with limited documentation / justification, but validity of model and reliability of prediction considered adequate based on a generally acknowledged source
- Justification for type of information:
- The predictive ability of each of the QSAR methodologies was evaluated using statistical external validation.
A QSAR model has acceptable predictive power if the following conditions are satisfied:
q^2 > 0.5 (1)
R^2 > 0.6 (2)
(R^2-Ro^2)/R^2 < 0.1 and 0.85 <= k <= 1.15 (3)
where:
q^2 is the leave one out correlation coefficient for the training set
R^2 is correlation coefficient between the observed and predicted toxicities for the test set
Ro^2 is correlation coefficient between the observed and predicted toxicities for the test set with the Y-intercept set to zero (where the regression line is given by Y=kX)
The prediction accuracy will be evaluated in terms of equations (2) and (3).
In addition, the accuracy will be evaluated in terms of the RMSE (root mean square error), and the MAE (mean absolute error) for the test set.
It has been demonstrated that q^2 is not correlated with R^2 for the test set.
The prediction coverage (fraction of chemicals predicted) must be considered because the prediction accuracy (in terms of R^2 and RMSE) can sometimes be improved at the sacrifice of the prediction coverage.
For binary (active/inactive) toxicity endpoints such as developmental toxicity, the prediction accuracy is evaluated in terms of the fraction of compounds that are predicted accurately.
The prediction accuracy is evaluated in terms of three different statistics: concordance, sensitivity, and specificity.
Concordance is the fraction of all compounds that are predicted correctly (i.e. experimentally active compounds that are predicted to be active and experimentally inactive compounds that are predicted to be inactive). Sensitivity is the fraction of experimentally active compounds that are predicted to be active.
Specificity is the fraction of experimentally inactive compounds that are predicted to be inactive. - Qualifier:
- equivalent or similar to guideline
- Guideline:
- other: ECHA Guidance on information requirements and chemical safety assessment - Chapter R.06: QSARs and grouping of chemicals
- Principles of method if other than guideline:
- T.E.S.T. has been developed to allow users to easily estimate toxicity using a variety of QSAR methodologies. T.E.S.T provides multiple prediction methodologies so one can have greater confidence in the predicted toxicities (assuming the predicted toxicities are similar from different methods).
(Q)SAR PREDICTION METHODOLOGIES
• Hierarchical method
The toxicity for a given query compound is estimated using the weighted average of the predictions from several different models. The different models are obtained by using Ward’s method to divide the training set into a series of structurally similar clusters. A genetic algorithm based technique is used to generate models for each cluster. The models are generated prior to runtime.
• FDA method
The prediction for each test chemical is made using a new model that is fit to the chemicals that are most similar to the test compound. Each model is generated at runtime.
• Single model method
Predictions are made using a multilinear regression model that is fit to the training set (using molecular descriptors as independent variables) using a genetic algorithm based approach. The regression model is generated prior to runtime.
• Group contribution method
Predictions are made using a multilinear regression model that is fit to the training set (using molecular fragment counts as independent variables). The regression model is generated prior to runtime.
• Nearest neighbor method
The predicted toxicity is estimated by taking an average of the three chemicals in the training set that are most similar to the test chemical.
• Consensus method
The predicted toxicity is estimated by taking an average of the predicted toxicities from the above (Q)SAR methods (provided the predictions are within the respective applicability domains).
• Random forest method
The predicted toxicity is estimated using a decision tree which bins a chemical into a certain toxicity score (i.e. positive or negative developmental toxicity) using a set of molecular descriptors as decision variables. The random forest method is currently only available for the developmental toxicity endpoint. The random forest models for the developmental toxicity endpoint were developed by researchers at Mario Negri Institute for Pharmacological Research as part of the CAESAR project (CAESAR 2009). - Analytical monitoring:
- not specified
- Vehicle:
- not specified
- Test organisms (species):
- Tetrahymena pyriformis
- Test type:
- other: in silico estimation
- Water media type:
- freshwater
- Total exposure duration:
- 48 h
- Test temperature:
- 25 deg C
- Reference substance (positive control):
- not specified
- Duration:
- 48 h
- Dose descriptor:
- IC50
- Effect conc.:
- 642 mg/L
- Nominal / measured:
- estimated
- Conc. based on:
- test mat.
- Basis for effect:
- growth inhibition
- Remarks on result:
- other: Consensus method
- Reported statistics and error estimates:
- Statistical external validation
The prediction results for the IGC50 test set were as follows:
Method R2 (R^2-R0^2)/R^2 k RMSE MAE Coverage
Hierarchical 0.719 0.023 0.978 0.539 0.358 0.933
FDA 0.747 0.056 0.988 0.489 0.337 0.978
Group contribution 0.682 0.065 0.994 0.575 0.411 0.955
Nearest neighbor 0.600 0.170 0.976 0.638 0.451 0.986
Consensus 0.764 0.065 0.983 0.475 0.332 0.983
The Consensus method achieved the best results. The R2 value for the consensus method in version 4.1 of TEST was slightly lower than the value for version 4.0. This is due to the fact that the data set has been expanded to include a wider variety of chemical classes. - Validity criteria fulfilled:
- yes
- Conclusions:
- The IC50, on the basis of growth inhibition effect (48h) of triethyl citrate on Tetrahymena pyriformis, was estimated to be 642 mg/L.
- Executive summary:
The IC50, on the basis of growth inhibition effect (48h) of triethyl citrate on Tetrahymena pyriformis, was estimated to be 642 mg/L.
Referenceopen allclose all
Predicted T. pyriformis IGC50 (48 hr) for 1,2-DEC from Consensus method:
Prediction results |
||
Endpoint |
Experimental value |
Predicted value |
T. pyriformis IGC50(48 hr) -Log10(mol/L) |
N/A |
2.23 |
T. pyriformis IGC50(48 hr) mg/L |
N/A |
1741.88 |
Individual Predictions |
|
Method |
Predicted value -Log10 (mol/L) |
Hierarchical clustering |
N/A |
Group contribution |
2.01 |
FDA |
1.82 |
Nearest neighbor |
2.85 |
Predicted T. pyriformis IGC50 (48 hr) for 1-MEC citrate from Consensus method:
Prediction results |
||
Endpoint |
Experimental value |
Predicted value |
T. pyriformis IGC50(48 hr) -Log10(mol/L) |
N/A |
1.89 |
T. pyriformis IGC50(48 hr) mg/L |
N/A |
2858 |
Individual Predictions |
|
Method |
Predicted value -Log10 (mol/L) |
Hierarchical clustering |
N/A |
Group contribution |
1.48 |
FDA |
1.60 |
Nearest neighbor |
2.58 |
Predicted T. pyriformis IGC50 (48 hr) for diethyl citrate (TEC) from Consensus method:
Prediction results |
||
Endpoint |
Experimental value |
Predicted value |
T. pyriformis IGC50(48 hr) -Log10(mol/L) |
N/A |
2.63 |
T. pyriformis IGC50(48 hr) mg/L |
N/A |
641.80 |
Individual Predictions |
|
Method |
Predicted value -Log10 (mol/L) |
Hierarchical clustering |
N/A |
Group contribution |
2.54 |
FDA |
2.13 |
Nearest neighbor |
3.23 |
Description of key information
Inhibition growth concentration (IGC50): 642 ÷ 1742 mg/L
Key value for chemical safety assessment
- EC50 for microorganisms:
- 642 mg/L
Additional information
The inhibition growth concentration (IGC50) value of diethyl citrate technical in microorganisms (tetrahymena pyriformis) in a 48 h study, on the basis of growth inhibition effect, was estimated to be in range 642 ÷ 1742 mg/L.
For the assessment was conservatively selected the lowest value.
Information on Registered Substances comes from registration dossiers which have been assigned a registration number. The assignment of a registration number does however not guarantee that the information in the dossier is correct or that the dossier is compliant with Regulation (EC) No 1907/2006 (the REACH Regulation). This information has not been reviewed or verified by the Agency or any other authority. The content is subject to change without prior notice.
Reproduction or further distribution of this information may be subject to copyright protection. Use of the information without obtaining the permission from the owner(s) of the respective information might violate the rights of the owner.