Benzene-1,3,5-tricarbonyl trichloride

Partition coefficient

Link to relevant study record(s)

Reference

Reference 1

Endpoint:

partition coefficient

Data waiving:

study technically not feasible

Justification for data waiving:

the study does not need to be conducted because the substance decomposes

the study does not need to be conducted because the substance reacts violently during the performance of the test

other:

Justification for type of information:

JUSTIFICATION FOR DATA WAIVING
In accordance with REACH Regulation (EC) No. 1907/2006 Annex VII, column 2 section 7.8 the study does not need to be conducted if the test cannot be performed (e.g. the substance decomposes or the substance reacts violently during the performance of the test). A calculated value for log Pow as well as details of the calculation method shall be provided. The substance is rapidly degraded at all relevant pH in the presence of water (via hydrolysis). At pH 7.0 the half life is << 10 minutes (ref: Benzoyl Chloride : t1/2 = 16 seconds). The hydrolysis reaction is exothermic and/or has the potential to be violent due to rapid increases in water temperature. The applicant adapts the information by providing (1) reliable (Q)SAR predictions/experimental reference citations for hydrolysis and (2) accepted calculation/(Q)SAR methods for Log Pow within a weight of evidence approach (i.e. comparable to ‘consensus modelling’) for both the parent and/or transformation (hydrolysis) product. According to ECHA Guidance on Information Requirements and Chemical Safety Assessment (Chapter R.7a: Endpoint Specific Guidance, R.7.1.8.4, v6.0, July 2017) the study does not need to be conducted.

Reference 2

Endpoint:

partition coefficient

Type of information:

(Q)SAR

Adequacy of study:

weight of evidence

Study period:

08-04-2022

Reliability:

2 (reliable with restrictions)

Rationale for reliability incl. deficiencies:

results derived from a valid (Q)SAR model and falling into its applicability domain, with limited documentation / justification

Justification for type of information:

1. SOFTWARE
ACD/Labs Preceptor Platform : ACD/LogP ; embedded into US EPA COMPTOX CHEMICALS DASHBOARD (url: https://comptox.epa.gov/)
ACD/Labs Preceptor software/model developer: (url: https://www.acdlabs.com/products/percepta/predictors/logP/)

2. MODEL (incl. version number)
(i) ACD/Labs Preceptor Classic – sub-model
(ii) ACD/Labs Preceptor Consensus – sub-model (combination of Classic and GALAS sub-model/algorithms)
Software version is that embedded into US EPA COMPTOX CHEMICALS DASHBOARD (url: https://comptox.epa.gov/ ; considered to be latest version as of date: 08 April 2022)

3. SMILES OR OTHER IDENTIFIERS USED AS INPUT FOR THE MODEL
SMILES used for model input:
(i) Parent substance : benzene-1,3,5-tricarbonyl trichloride ; SMILES : ClC(=O)C1=CC(=CC(=C1)C(Cl)=O)C(Cl)=O
(ii) Transformation product: benzene-1,3,5-tricarboxylic acid ; SMILES ; OC(=O)C1=CC(=CC(=C1)C(O)=O)C(O)=O

4. SCIENTIFIC VALIDITY OF THE (Q)SAR MODEL
- Defined endpoint: Yes. Equivalent or similar to:
QMRF 1. Physical Chemical Properties
QMRF 1.6. Octanol-water partition coefficient (Kow)
- Unambiguous algorithm: Yes. See sources of information for further information. The model is a Fragment or Compound ‘ClogP’ type algorithm.
- Defined domain of applicability: Yes. However, no formal QMRF is available.
- Appropriate measures of goodness-of-fit and robustness and predictivity: See below. The model has been peer reviewed by Mannhold et al (2009) and/or ECETOC (2013).
- Mechanistic interpretation: Not applicable.
- Other:
(i) Petrauskas AA, Kolovanov EA. 2000. ACD/LogP method description. Perspect Drug Discov Des 19: 99–116.
(ii) ACD/LogP, www.acdlabs.com/logp/ Advanced Chemistry Development, Inc., Toronto, ON, Canada.
(iii) ECETOC Technical Report 123 (2013):
According to ECETOC taskforce:
"ALogPS v. 2.1 : The ACD/logP estimation method uses a fragment-based approach, whereby the contributions of separate atoms, structural fragments, and intramolecular interactions between different fragments are considered (Petrauskas and Kolovanov, 2000). The individual contributions have been quantified based on a database of about 18,400 structures having experimental log KOW data, which resulted in over 1,200 different functional groups being defined with fragment contributions. The database for intramolecular interaction contributions contains increments for over 2400 different types of pair-wise group interactions. If a fragment or intramolecular interaction contribution for a molecule that is being estimated are not found, then the ACD/LogP program will calculate it through the use of a special secondary algorithm, and a larger uncertainty against the log KOW estimate is defined…
… the results reported by Mannhold et al (2009) suggest that of the methods reviewed by the Task Force, the ACD/LogP performs best, followed by ALogPS and KOWWIN, both of which show similar performance."
(iv) Mannhold R, Poda GI, Ostermann C, Tetko IV. 2009. Calculation of Molecular Lipophilicity: State-of-the-Art and Comparison of log P Methods on More Than 96,000 Compounds. J Pharm Sci 98:861-893.
From the peer review evaluation of ECETOC (2013), it should be considered that the model is a generally acceptable calculation method, consistent with OECD principles. ACD/Labs Preceptor Platform : ACD/LogP is explicitly cited within OECD 194 (2014): Guidance on Grouping of Chemicals, Second Edition, for physico-chemical parameter prediction. As such it should be considered a recognised calculation method.

5. APPLICABILITY DOMAIN
- Descriptor domain: Should be considered in domain (given the size of the proprietary training sets ; which will very likely include carbonyl and/or chloride substances). However it is noted no formal QMRF is available.
- Structural and mechanistic domains: (i) ACD/Labs Preceptor Classic – sub-model is based upon over 12000 experimental log P values. The (ii) ACD/Labs Preceptor Consensus – sub-model (combination of Classic and GALAS sub-model/algorithms which are individually based upon over 11000 and 12000 substances, each according to the model developer). The consensus model uses both Classic and GALAS algorithms and weights the calculation to the model best suited for the structure concerned.
- Similarity with analogues in the training set: Not reported. no formal QMRF is available.
- Other considerations (as appropriate): The calculated prediction will be utilised in a weight of evidence, based upon consensus evaluation. Similar to consensus (Q)SAR modelling approaches as suggested in ECHA R.6 (2008).

6. ADEQUACY OF THE RESULT
The calculated prediction will be utilised in a weight of evidence, based upon consensus evaluation. Similar to consensus (Q)SAR modelling approaches as suggested in ECHA R.6 (2008). From the peer review evaluation of ECETOC (2013), it should be considered that the model is a generally acceptable calculation method, consistent with OECD principles. ACD/Labs Preceptor Platform : ACD/LogP is explicitly cited within OECD 194 (2014): Guidance on Grouping of Chemicals, Second Edition, for physico-chemical parameter prediction.

Guideline:

other: REACH Guidance on QSARs R.6, May/July 2008

Principles of method if other than guideline:

- Software tool(s) used including version: (i) ACD/Labs Preceptor Classic – sub-model and/or (ii) ACD/Labs Preceptor Consensus – sub-model (combination of Classic and GALAS sub-model/algorithms)
Software version is that embedded into US EPA COMPTOX CHEMICALS DASHBOARD (url: https://comptox.epa.gov/ ; considered to latest version as of date: 08 April 2022
- Model(s) used: See above.
- Model description: see field 'Justification for type of information'
- Justification of QSAR prediction: see field 'Justification for type of information'
- Other: (i) No formal QMRF is available for the model. The model has been peer reviewed by Mannhold et al (2009) and/or ECETOC (2013). The calculated prediction will be utilised in a weight of evidence, based upon consensus evaluation. Similar to consensus (Q)SAR modelling approaches as suggested in ECHA R.6 (2008). From the peer review evaluation of ECETOC (2013), it should be considered that the model is a generally acceptable calculation method, consistent with OECD principles. ACD/Labs Preceptor Platform : ACD/LogP is explicitly cited within OECD 194 (2014): Guidance on Grouping of Chemicals, Second Edition, for physico-chemical parameter prediction. As such it should be considered a recognised calculation method. (ii) calculations were generated 08 April 2022 using the online web app. : US EPA COMPTOX CHEMICALS DASHBOARD (url: https://comptox.epa.gov/)

Partition coefficient type:

octanol-water

Key result

Type:

log Pow

Partition coefficient:

3.02

Temp.:

25 °C

pH:

7

Remarks on result:

other: Parent substance (benzene-1,3,5-tricarbonyl trichloride)

Remarks:

ACD/Labs Preceptor Classic – sub-model

Key result

Type:

log Pow

Partition coefficient:

2.88

Temp.:

25 °C

pH:

7

Remarks on result:

other: Parent substance (benzene-1,3,5-tricarbonyl trichloride)

Remarks:

ACD/Labs Preceptor Consensus – sub-model (combination of Classic and GALAS sub-model/algorithms)

Type:

log Pow

Partition coefficient:

1.51

Temp.:

25 °C

pH:

7

Remarks on result:

other: Transformation product (benzene-1,3,5-tricarboxylic acid)

Remarks:

ACD/Labs Preceptor Classic – sub-model

Type:

log Pow

Partition coefficient:

1.72

Temp.:

25 °C

pH:

7

Remarks on result:

other: Transformation product (benzene-1,3,5-tricarboxylic acid)

Remarks:

ACD/Labs Preceptor Consensus – sub-model (combination of Classic and GALAS sub-model/algorithms)

Conclusions:

The results are adequate for the for the regulatory purpose when used in a weight of evidence, based upon consensus evaluation.

Executive summary:

ACD/Labs Preceptor Platform : ACD/LogP (model initially published: September 2000)

(i) ACD/Labs Preceptor Classic – sub-model

Parent substance (benzene-1,3,5-tricarbonyl trichloride) : Log Kow = 3.02

Transformation product (benzene-1,3,5-tricarboxylic acid) : Log Kow = 1.51

(ii) ACD/Labs Preceptor Consensus – sub-model (combination of Classic and GALAS sub-model/algorithms)

Parent substance (benzene-1,3,5-tricarbonyl trichloride) : Log Kow = 2.88

Transformation product (benzene-1,3,5-tricarboxylic acid) : Log Kow = 1.72

 

No constituents have predictions for Log Kow > 4.0.

The hydrolysis products have a decreasing Log Kow << 4.0

 

Adequacy of the QSAR:

The calculated prediction will be utilised in a weight of evidence, based upon consensus evaluation. Similar to consensus (Q)SAR modelling approaches as suggested in ECHA R.6 (2008). From the peer review evaluation of ECETOC (2013), it should be considered that the model is a generally acceptable calculation method, consistent with OECD principles. ACD/Labs Preceptor Platform : ACD/LogP is explicitly cited within OECD 194 (2014): Guidance on Grouping of Chemicals, Second Edition, for physico-chemical parameter prediction.

Reference 3

Endpoint:

partition coefficient

Type of information:

(Q)SAR

Adequacy of study:

weight of evidence

Study period:

08-04-2022

Reliability:

2 (reliable with restrictions)

Rationale for reliability incl. deficiencies:

results derived from a valid (Q)SAR model and falling into its applicability domain, with limited documentation / justification

Justification for type of information:

1. SOFTWARE
ALOGPS 2.1, VCCLAB, Virtual Computational Chemistry Laboratory
Available from: http://www.vcclab.org and/or also available at OCHEM (web platform) : https://ochem.eu/model/535

2. MODEL (incl. version number)
ALOGPS v. 2.1

3. SMILES OR OTHER IDENTIFIERS USED AS INPUT FOR THE MODEL
SMILES used for model input:
(i) Parent substance : benzene-1,3,5-tricarbonyl trichloride ; SMILES : ClC(=O)C1=CC(=CC(=C1)C(Cl)=O)C(Cl)=O
(ii) Transformation product: benzene-1,3,5-tricarboxylic acid ; SMILES ; OC(=O)C1=CC(=CC(=C1)C(O)=O)C(O)=O

4. SCIENTIFIC VALIDITY OF THE (Q)SAR MODEL
- Defined endpoint: Yes. Equivalent or similar to:
QMRF 1. Physical Chemical Properties
QMRF 1.6. Octanol-water partition coefficient (Kow)
- Unambiguous algorithm: Yes. See sources of information for further information. The model is a ‘hybrid AlogP’ type algorithm.
- Defined domain of applicability: Yes. However, no formal QMRF is available.
- Appropriate measures of goodness-of-fit and robustness and predictivity: See below. The model has been peer reviewed by Mannhold et al (2009) and/or ECETOC (2013).
- Mechanistic interpretation: Not applicable.
- Other:
1-octanol/water calculation: ALOGPs was developed with 12908 molecules from the PHYSPROP database using 75 E-state indices. 64 neural networks were trained using 50% of molecules selected by chance from the whole set. The logP prediction accuracy has root mean squared error RMS=0.35 and standard mean error s=0.26
Primary references:
(i) Tetko, I. V.; Gasteiger, J.; Todeschini, R.; Mauri, A.; Livingstone, D.; Ertl, P.; Palyulin, V. A.; Radchenko, E. V.; Zefirov, N. S.; Makarenko, A. S.; Tanchuk, V. Y.; Prokopenko, V. V. Virtual computational chemistry laboratory - design and description, J. Comput. Aid. Mol. Des., 2005, 19, 453-63
(ii) VCCLAB, Virtual Computational Chemistry Laboratory, http://www.vcclab.org, 2005.
(iii) Tetko, I. V.; Tanchuk, V. Y. Application of associative neural networks for prediction of lipophilicity in ALOGPS 2.1 program, J. Chem. Inf. Comput. Sci., 2002, 42, 1136-45
(iv) Tetko, I. V.; Tanchuk, V. Y.; Villa, A. E. Prediction of n-octanol/water partition coefficients from PHYSPROP database using artificial neural networks and E-state indices, J. Chem. Inf. Comput. Sci., 2001, 41, 1407-21
Secondary references/reviews:
(v) ECETOC Technical Report 123 (2013):
According to ECETOC taskforce:
"ALogPS v. 2.1 : Using 75 descriptors, the ALogPS v. 2.1, freely available at http://www.vcclab.org/, uses a neural network method, based on 12908 molecules, with a RMSE of 0.35 (Tetko and Tanchuk, 2002; Tetko, 2002). The method used in ALogPS is based on E-state indices, which were developed to cover both topological and valence states of atoms, and have been used to develop QSARs for a number of physical-chemical and biological properties (Mannhold et al, 2009). The Task Force has included this method in their review based on results reported by Mannhold et al (2009), where the method is shown to perform relatively well on an extensive dataset. Indeed the results reported by Mannhold et al (2009) suggest that of the methods reviewed by the Task Force, the ACD/LogP performs best, followed by ALogPS and KOWWIN, both of which show similar performance."
(vi) Mannhold R, Poda GI, Ostermann C, Tetko IV. (2009). Calculation of Molecular Lipophilicity: State-of-the-Art and Comparison of log P Methods on More Than 96,000 Compounds. J Pharm Sci 98:861-893.
From the peer review evaluation of ECETOC (2013), it should be considered that the model is a generally acceptable calculation method, consistent with OECD principles and with a performance comparable to those physico-chemical parameter prediction tools cited by OECD 194 (2014): Guidance on Grouping of Chemicals, Second Edition.
The most up to date information on the training set is available at the OCHEM (web platform) : https://ochem.eu/model/535. Further references on OCHEM:
(vii) Sushko I, et al., Online chemical modelling environment (OCHEM): web platform for data storage, model development and publishing of chemical information. J Comput Aided Mol Des. 2011; 25(6):533-54

5. APPLICABILITY DOMAIN
- Descriptor domain: Should be considered in domain (based on consideration of substances within the PHYSPROP database ; which include carbonyl and/or chloride substances).
- Structural and mechanistic domains: The 12908 molecules utilised in the model, are available for download from VCCLAB, Virtual Computational Chemistry Laboratory, http://www.vcclab.org and/or http://www.vcclab.org/lab/alogps/ ; further information is also available at: https://ochem.eu/model/535
- Similarity with analogues in the training set: Further information is given in ‘attached background information’ on nearest neighbours in the training set (similarity scores range from: 0.76 to 0.81).
- Other considerations (as appropriate): The calculated prediction will be utilised in a weight of evidence, based upon consensus evaluation. Similar to consensus (Q)SAR modelling approaches as suggested in ECHA R.6 (2008). It is noted that a newer version of the model (significant increase in training set size): ALOGPS 3.0 generates a calculated log P = 2.7. The ALGOPS 3.0 model update was not included in Mannhold (2009) or ECETOC (2013) peer reviews and was therefore not formally included at this time. It is acknowledged however that recent updates to the ALOGPS training set, yield a prediction close to current weight of evidence conclusion based on consensus (+/- 0.1 log units).

6. ADEQUACY OF THE RESULT
The calculated prediction will be utilised in a weight of evidence, based upon consensus evaluation. Similar to consensus (Q)SAR modelling approaches as suggested in ECHA R.6 (2008). From the peer review evaluation of ECETOC (2013), it should be considered that the model is a generally acceptable calculation method, consistent with OECD principles and with a performance comparable to those physico-chemical parameter prediction tools cited by OECD 194 (2014): Guidance on Grouping of Chemicals, Second Edition.

Guideline:

other: REACH Guidance on QSARs R.6, May/July 2008

Principles of method if other than guideline:

- Software tool(s) used including version: ALOGPS 2.1, VCCLAB, Virtual Computational Chemistry Laboratory
- Model(s) used: ALOGPS v. 2.1
- Model description: see field 'Justification for type of information'
- Justification of QSAR prediction: see field 'Justification for type of information'
- Other: (i) No formal QMRF is available for the model. The model has been peer reviewed by Mannhold et al (2009) and/or ECETOC (2013). The calculated prediction will be utilised in a weight of evidence, based upon consensus evaluation. Similar to consensus (Q)SAR modelling approaches as suggested in ECHA R.6 (2008). From the peer review evaluation of ECETOC (2013), it should be considered that the model is a generally acceptable calculation method, consistent with OECD principles and to those physico-chemical parameter prediction tools cited by OECD 194 (2014): Guidance on Grouping of Chemicals, Second Edition ; (ii) calculations were generated 08 April 2022 using the online web app. URL: http://www.vcclab.org/lab/alogps/

Partition coefficient type:

octanol-water

Key result

Type:

log Pow

Partition coefficient:

3.12

Temp.:

25 °C

pH:

7

Remarks on result:

other: Parent substance (benzene-1,3,5-tricarbonyl trichloride)

Type:

log Pow

Partition coefficient:

0.87

Temp.:

25 °C

pH:

7

Remarks on result:

other: Transformation product (benzene-1,3,5-tricarboxylic acid)

Conclusions:

The results are adequate for the for the regulatory purpose when used in a weight of evidence, based upon consensus evaluation.

Executive summary:

ALOGPS 2.1, VCCLAB, Virtual Computational Chemistry Laboratory (model publication: March 2005)

Parent substance (benzene-1,3,5-tricarbonyl trichloride) : Log Kow = 3.12

Transformation product (benzene-1,3,5-tricarboxylic acid) : Log Kow = 0.87

No constituents have predictions for Log Kow > 4.0.

The hydrolysis products have a decreasing Log Kow << 4.0

Adequacy of the QSAR:

The calculated prediction will be utilised in a weight of evidence, based upon consensus evaluation. Similar to consensus (Q)SAR modelling approaches as suggested in ECHA R.6 (2008). From the peer review evaluation of ECETOC (2013), it should be considered that the model is a generally acceptable calculation method, consistent with OECD principles and with a performance comparable to those physico-chemical parameter prediction tools cited by OECD 194 (2014): Guidance on Grouping of Chemicals, Second Edition.

Reference 4

Endpoint:

partition coefficient

Type of information:

(Q)SAR

Adequacy of study:

weight of evidence

Study period:

08-04-2022

Reliability:

2 (reliable with restrictions)

Rationale for reliability incl. deficiencies:

results derived from a valid (Q)SAR model and falling into its applicability domain, with limited documentation / justification

Justification for type of information:

1. SOFTWARE
ChemAxon Log P Plugin : ChemAxon sub-model ; Model version not specified
Software Package: chemicalize.com ; web-programme (url: www. https://chemicalize.com/)
Plugin sub-model is equivalent to that utilized within Marvin version 22.9, ChemAxon (url: https://www.chemaxon.com).

2. MODEL (incl. version number)
ChemAxon Log P Plugin : ChemAxon sub-model ; Model version not specified
Software version is that embedded into chemicalize.com ; web-programme (url: www. https://chemicalize.com/ ; considered to latest version as of date:
08 April 2022)

3. SMILES OR OTHER IDENTIFIERS USED AS INPUT FOR THE MODEL
SMILES used for model input:
(i) Parent substance : benzene-1,3,5-tricarbonyl trichloride ; SMILES : ClC(=O)C1=CC(=CC(=C1)C(Cl)=O)C(Cl)=O
(ii) Transformation product: benzene-1,3,5-tricarboxylic acid ; SMILES ; OC(=O)C1=CC(=CC(=C1)C(O)=O)C(O)=O

4. SCIENTIFIC VALIDITY OF THE (Q)SAR MODEL
- Defined endpoint: Yes. Equivalent or similar to:
QMRF 1. Physical Chemical Properties
QMRF 1.6. Octanol-water partition coefficient (Kow)
- Unambiguous algorithm: Yes. See sources of information for further information. The model is a ‘hybrid AlogP’ type algorithm.
- Defined domain of applicability: Yes. However, no formal QMRF is available.
- Appropriate measures of goodness-of-fit and robustness and predictivity: See below. Internal validation is available on the model developer website (url: https://www.chemaxon.com). Specifically, the internal validation of predicted log Kow against (n=) 1639 experimental log Kow indicates r2 = 0.91. It is probable that the experimental log Kow values used for validation were taken from those included in the PHYSPROP© database ; SRC Inc. (USA).
- Mechanistic interpretation: Not applicable.
- Other: The ChemAxon Log P Plugin is a computerised predictive system that estimates the n-Octanol/water partition coefficient (Log Kow or Log P) of organic substances. The program estimates this physico-chemical property using three different sub-models (i) Consensus ; (ii) ChemAxon and/or (iii) User Defined. In calculation only the (ii) ChemAxon Log P sub-model is considered. This sub-model is based upon a hybrid atomic log P (AlogP) type-method based on the contribution of each atom and/or contributions from near neighbours as well as applicable correction factors. It is built upon the work of Viswanadhan et al. (1989). Further details on the initial development of the model are given in Csizmadia et al. (1997).
References:
(i) Viswanadhan, V. N.; Ghose, A. K.; Revankar, G. R.; Robins, R. K, J. Chem. Inf. Comput. Sci., 1989, 29 , 163-172; doi: https://dx.doi.org/10.1021/ci00063a006
(ii) Csizmadia, F; Tsantili-Kakoulidou, A.; Pander, I.; Darvas, F., J. Pharm. Sci., 1997, 86, 865-871; doi: https://dx.doi.org/10.1021/js960177k
(iii) ChemAxon Kft, Váci út 133., 1138 Budapest, Hungary. (url: https://www.chemaxon.com).
(iv) Klopman, G.; Li, Ju-Yun.; Wang, S.; Dimayuga, M.: J.Chem. Inf. Comput. Sci., 1994, 34, 752; doi: https://dx.doi.org/10.1021/ci00020a009
It should be considered that the model is a generally acceptable calculation method, consistent with OECD principles. ChemAxon Log P is explicitly cited within OECD 194 (2014): Guidance on Grouping of Chemicals, Second Edition, for physico-chemical parameter prediction. As such it should be considered a recognised calculation method.

5. APPLICABILITY DOMAIN
- Descriptor domain: Should be considered in domain. However it is noted no formal QMRF is available.
- Structural and mechanistic domains: Refer to Viswanadhan et al. (1989) and Csizmadia et al. (1997) for further information.
- Similarity with analogues in the training set: Not reported. no formal QMRF is available.
- Other considerations (as appropriate): The calculated prediction will be utilised in a weight of evidence, based upon consensus evaluation. Similar to consensus (Q)SAR modelling approaches as suggested in ECHA R.6 (2008).

6. ADEQUACY OF THE RESULT
The calculated prediction will be utilised in a weight of evidence, based upon consensus evaluation. Similar to consensus (Q)SAR modelling approaches as suggested in ECHA R.6 (2008). It should be considered that the model is a generally acceptable calculation method, consistent with OECD principles. ChemAxon Log P is explicitly cited within OECD 194 (2014): Guidance on Grouping of Chemicals, Second Edition, for physico-chemical parameter prediction. As such it should be considered a recognised calculation method.

Guideline:

other: REACH Guidance on QSARs R.6, May/July 2008

Principles of method if other than guideline:

- Software tool(s) used including version: ChemAxon Log P Plugin
Software version is that embedded into chemicalize.com ; web-programme (url: www. https://chemicalize.com/ ; considered to latest version as of date: 08 April 2022
Plugin sub-model is equivalent to that utilized within Marvin version 22.9, ChemAxon (url: https://www.chemaxon.com).
- Model(s) used: See above.
- Model description: see field 'Justification for type of information'
- Justification of QSAR prediction: see field 'Justification for type of information'
- Other: (i) No formal QMRF is available for the model. The calculated prediction will be utilised in a weight of evidence, based upon consensus evaluation. Similar to consensus (Q)SAR modelling approaches as suggested in ECHA R.6 (2008). It should be considered that the model is a generally acceptable calculation method, consistent with OECD principles. ChemAxon Log P is explicitly cited within OECD 194 (2014): Guidance on Grouping of Chemicals, Second Edition, for physico-chemical parameter prediction. As such it should be considered a recognised calculation method. (ii) calculations were generated 08 April 2022 using the online web app.

Partition coefficient type:

octanol-water

Key result

Type:

log Pow

Partition coefficient:

2.546

Temp.:

25 °C

pH:

7

Remarks on result:

other: Parent substance (benzene-1,3,5-tricarbonyl trichloride)

Type:

log Pow

Partition coefficient:

0.946

Temp.:

25 °C

pH:

7

Remarks on result:

other: Transformation product (benzene-1,3,5-tricarboxylic acid)

Conclusions:

The results are adequate for the for the regulatory purpose when used in a weight of evidence, based upon consensus evaluation.

Executive summary:

ChemAxon Log P Plugin (model initially published: 1998)

ChemAxon sub-model

Parent substance (benzene-1,3,5-tricarbonyl trichloride) : Log Kow = 2.546

Transformation product (benzene-1,3,5-tricarboxylic acid) : Log Kow = 0.946

 

No constituents have predictions for Log Kow > 4.0.

The hydrolysis products have a decreasing Log Kow << 4.0

 

Adequacy of the QSAR:

The calculated prediction will be utilised in a weight of evidence, based upon consensus evaluation. Similar to consensus (Q)SAR modelling approaches as suggested in ECHA R.6 (2008). It should be considered that the model is a generally acceptable calculation method, consistent with OECD principles. ChemAxon Log P is explicitly cited within OECD 194 (2014): Guidance on Grouping of Chemicals, Second Edition, for physico-chemical parameter prediction. As such it should be considered a recognised calculation method.

Description of key information

Weight of evidence: Log Kow = 2.85 (based on consensus modelling by calculation) at 25 °C and pH 7, 2022

Note: The substance rapidly hydrolyses at all relevant pH 4, 7 and 9. Refer to ‘Hydrolysis’ and/or ‘Biodegradation’ sections for further information.

Key value for chemical safety assessment

Log Kow (Log Pow):

2.85

at the temperature of:

25 °C

Additional information

In accordance with REACH Regulation (EC) No. 1907/2006 Annex VII, column 2 section 7.8 the study does not need to be conducted if the test cannot be performed (e.g. the substance decomposes or the substance reacts violently during the performance of the test). A calculated value for log Pow as well as details of the calculation method shall be provided. The substance is rapidly degraded at all relevant pH in the presence of water (via hydrolysis). At pH 7.0 the half life is << 10 minutes (ref: Benzoyl Chloride : t1/2 = 16 seconds). The hydrolysis reaction is exothermic and/or has the potential to be violent due to rapid increases in water temperature. The applicant adapts the information by providing (1) reliable (Q)SAR predictions/experimental reference citations for hydrolysis and (2) accepted calculation/(Q)SAR methods for Log Pow within a weight of evidence approach (i.e. comparable to ‘consensus modelling’) for both the parent and/or transformation (hydrolysis) product. According to ECHA Guidance on Information Requirements and Chemical Safety Assessment (Chapter R.7a: Endpoint Specific Guidance, R.7.1.8.4, v6.0, July 2017) the study does not need to be conducted.

 

Weight of evidence conclusion:

The Log Pow of the substance has been assessed by several calculation/(Q)SAR methods, these are detailed below. The mean (n=3) log Pow of all methods using a ‘consensus’ modelling approach indicates that the substance has a log Pow = 2.85

Specifically from:

(1) ACD/Labs Preceptor Platform : ACD/LogP

Consensus – sub-model (combination of Classic and GALAS sub-model/algorithms)

Parent substance (benzene-1,3,5-tricarbonyl trichloride) : Log Kow = 2.88

The ‘Classic model’ prediction per se, was discarded as was already considered in the Consensus output.

(2) ALOGPS 2.1, VCCLAB

Parent substance (benzene-1,3,5-tricarbonyl trichloride) : Log Kow = 3.12

(3) ChemAxon Log P Plugin

ChemAxon sub-model

Parent substance (benzene-1,3,5-tricarbonyl trichloride) : Log Kow = 2.546

 

Conclusion: the substance mean (n=3) calculated Log Pow = 2.85

The parent substance can be expected to have a log Pow 2.85 +/- 0.5 log units (which is within the range of the experimental method e.g. OECD TG 117).

 

Under REACH Regulation No. (EC) 1907/2006: Annex XI: section 1.2 - weight of evidence, adequate information is available for environmental hazard and risk assessment and therefore further testing is omitted. Specifically, the parent substance and/or its transformation product have log Pow < 4.0 and/or the transformation product is (biotically) rapidly degradable. Furthermore, there are no identified (eco)toxicities triggering classification and labelling.