N-[2-[(2-chloro-4,6-dinitrophenyl)azo]-5-(diallylamino)-4-methoxyphenyl]acetamide

Partition coefficient

Link to relevant study record(s)

Reference

Reference 1

Endpoint:

partition coefficient

Type of information:

experimental study

Adequacy of study:

key study

Study period:

February 09, 2017 - February 10, 2017

Reliability:

1 (reliable without restriction)

Rationale for reliability incl. deficiencies:

guideline study

Qualifier:

according to guideline

Guideline:

OECD Guideline 117 (Partition Coefficient (n-octanol / water), HPLC Method)

Deviations:

no

Qualifier:

according to guideline

Guideline:

EU Method A.8 (Partition Coefficient - HPLC Method)

Deviations:

no

GLP compliance:

no

Type of method:

HPLC method

Partition coefficient type:

octanol-water

Analytical method:

high-performance liquid chromatography

Type:

log Pow

Partition coefficient:

6.9

Temp.:

20 °C

pH:

5.8

 Individual results

Calculation of the partition coefficient log P_OW from the preliminary test

The program KOWWIN (part of EPI Suite) was used for calculating the log P_OW of the test item. The calculated value was 6.7.

Therefore according to the guidelines the partition coefficient n-octanol/water of the test item at room temperature was determined by the HPLC method.

HPLC method

The results from the HPLC method are summarized in Table2 to Table6.

Table2: Determination of the dead time t₀

Dead time marker	tR / min 1^stinjection	tR / min 2^stinjection	tR / min mean
Formamide	1.085	1.086	1.086

Dead time:     t₀ = 1.086 min

Table3: Calibration data 1^st injection

Calibration substance	tR / min	k	log k	log POW^1)
2-Butanone	1.282	0.18	-0.74	0.3
Acetanilide	1.355	0.25	-0.61	1.0
Cinnamyl alcohol	1.611	0.48	-0.32	1.9
2,6-Dichlorobenzonitrile	2.029	0.87	-0.06	2.6
Allyl phenyl ether	2.856	1.63	0.21	2.9
Benzophenone	2.592	1.39	0.14	3.2
Cumene	5.138	3.73	0.57	3.7
Diphenyl ether	4.706	3.34	0.52	4.2
Fluoranthene	9.370	7.63	0.88	5.1
4,4’-DDT	22.464	19.69	1.29	6.5

¹⁾OECD guideline 117, adopted 2004

Regression:        

                           Parameters:        a = 2.599

                                                        b = 2.842

                                                        r²= 0.9811

 

Table4: log P_OW of the test item – 1^st injection

Test item	tR/ min	k	log k	log POW
Disperse Blue 291/1 Br	36.468	32.60	1.51	> 6.5^2) (6.9)^3)

²⁾above the highest log P_OW value of calibration substances (4,4’-DDT)

³⁾via extrapolation

Table5: Calibration data 2^nd injection

Calibration substance	tR / min	k	log k	log POW^4)
2-Butanone	1.280	0.18	-0.75	0.3
Acetanilide	1.354	0.25	-0.61	1.0
Cinnamyl alcohol	1.609	0.48	-0.32	1.9
2,6-Dichlorobenzonitrile	2.025	0.87	-0.06	2.6
Allyl phenyl ether	2.853	1.63	0.21	2.9
Benzophenone	2.587	1.38	0.14	3.2
Cumene	5.124	3.72	0.57	3.7
Diphenyl ether	4.693	3.32	0.52	4.2
Fluoranthene	9.358	7.62	0.88	5.1
4,4’-DDT	22.358	19.60	1.29	6.5

⁴⁾OECD guideline 117, adopted 2004

Regression:        

                           Parameters:        a = 2.604

                                                        b = 2.840

                                                        r²= 0.9811

Table6: log P_OW of the test item – 2^nd injection

Test item	tR/ min	k	log k	log POW
Disperse Blue 291/1 Br	36.415	32.55	1.51	> 6.5^5) (6.9)^6)

⁵⁾above the highest log P_OW value of calibration substances (4,4’-DDT)

⁶⁾via extrapolation

The results of both measurements were averaged. The partition coefficient P_OW of the test item was determined to be:

log P_OW > 6.5⁷⁾

log P_OW = 6.9⁸⁾

⁷⁾above the highest log P_OWvalue of calibration substances (4,4’-DDT)

⁸⁾via extrapolation

 

Executive summary:

The partition coefficient n-octanol/water of the test item Disperse Blue 291:1 Br was determined according to the HPLC method.

It was found to be:

log P_OW> 6.5⁷⁾

log P_OW=6.9⁸⁾

⁷⁾above the highest log P_OW value of calibration substances (4,4’-DDT)

⁸⁾via extrapolation

 

Reference 2

Endpoint:

partition coefficient

Type of information:

calculation (if not (Q)SAR)

Adequacy of study:

weight of evidence

Study period:

2020

Reliability:

2 (reliable with restrictions)

Rationale for reliability incl. deficiencies:

accepted calculation method

Justification for type of information:

1. SOFTWARE
EpiSuite / KOWWIN v1.68

2. MODEL (incl. version number)
EpiSuite 4.1
KOWWIN v1.68

3. SMILES OR OTHER IDENTIFIERS USED AS INPUT FOR THE MODEL
See attachment

4. SCIENTIFIC VALIDITY OF THE (Q)SAR MODEL
See attachment

5. APPLICABILITY DOMAIN
See attachment

6. ADEQUACY OF THE RESULT
See attachment

Principles of method if other than guideline:

KOWWIN uses a "fragment constant" methodology to predict log P.  In a "fragment constant" method, a structure is divided into fragments (atom or larger functional groups) and coefficient values of each fragment or group are summed together to yield the log P estimate.   KOWWIN’s methodology is known as an Atom/Fragment Contribution (AFC) method.  Coefficients for individual fragments and groups were derived by multiple regression of 2447 reliably measured log P values.  KOWWIN’s "reductionist" fragment constant methodology (i.e. derivation via multiple regression) differs from the "constructionist" fragment constant methodology of Hansch and Leo (1979) that is available in the CLOGP Program (Daylight, 1995).  See the Meylan and Howard (1995) journal article for a more complete description of KOWWIN’s methodology.

To estimate log P, KOWWIN initially separates a molecule into distinct atom/fragments.  In general, each non-hydrogen atom (e.g. carbon, nitrogen, oxygen, sulfur, etc.) in a structure is a "core" for a fragment; the exact fragment is determined by what is connected to the atom.  Several functional groups are treated as core "atoms"; these include carbonyl (C=O), thiocarbonyl (C=S), nitro (-NO2), nitrate (ONO2), cyano (-C/N), and isothiocyanate (-N=C=S).  Connections to each core "atom" are either general or specific; specific connections take precedence over general connections.  For example, aromatic carbon, aromatic oxygen and aromatic sulfur atoms have nothing but general connections; i.e., the fragment is the same no matter what is connected to the atom.  In contrast, there are 5 aromatic nitrogen fragments: (a) in a five-member ring, (b) in a six-member ring, (c) if the nitrogen is an oxide-type {i.e. pyridine oxide}, (d) if the nitrogen has a fused ring location {i.e. indolizine}, and (e) if the nitrogen has a +5 valence {i.e. N-methyl pyridinium iodide}; since the oxide-type is most specific, it takes precedence over the other four.  The aliphatic carbon atom is another example; it does not matter what is connected to -CH3, -CH2-, or -CH< , the  fragment is the same; however, an aliphatic carbon with no hydrogens has two possible fragments: (a) if there are four single bonds with 3 or more carbon connections and (b) any other not meeting the first criteria.

It became apparent, for various types of structures, that log P estimates made from atom/fragment values alone could or needed to be improved by inclusion of  substructures larger or more complex than "atoms"; hence, correction factors were added to the AFC method.  The term "correction factor" is appropriate because their values are derived from the differences between the log P estimates from atoms alone and the measured log P values.  The correction factors have two main groupings: first, factors involving aromatic ring substituent positions and second,  miscellaneous factors.  In general, the correction factors are values for various steric interactions, hydrogen-bondings, and effects from polar functional substructures.  Individual correction factors were selected through a tedious process of correlating the differences (between log P estimates from atom/fragments alone and measured log P values) with common substructures.

Two separate regression analyses were performed.  The first regression related log P to atom/fragments of compounds that do not require correction factors (i.e., compounds estimated adequately by fragments alone).  The general regression equation has the following form:

 log P  = Σ(fini ) +  b     (Equation 1)

where Σ(fini )  is the summation of fi (the coefficient for each atom/fragment) times ni (the number of times the atom/fragment occurs in the structure) and b  is the linear equation constant.  This initial regression used 1120 compounds of the 2447 compounds in the total training dataset.

The correction factors were then derived from a multiple linear regression that correlated differences between the experimental (expl) log P and the log P estimated by Equation 1 above with the correction factor descriptors.  This regression did not utilize an additional equation constant.  The equation for the second regression is:

 lop P (expl)  -  log P (eq 1)  = Σ(cjnj )       (Equation 1)

where Σ(cjnj )  is the summation of cj (the coefficient for each correction factor) times nj  (the number of times the correction factor occurs (or is applied) in the molecule).

 

Regression Results

Results of the two successive multiple regressions (first for atom/fragments and second for correction factors) yield the following general equation for estimating log P of any organic compound:

log P  = Σ(fini ) + Σ(cjnj ) + 0.229     (Equation 3)

(num = 2447,   r2 = 0.982,  std dev = 0.217,  mean error = 0.159)

Type of method:

calculation method (fragments)

Partition coefficient type:

octanol-water

Key result

Type:

log Pow

Partition coefficient:

6.67

Temp.:

25 °C

pH:

7.4

Remarks on result:

other: assumed values for modelling

Conclusions:

Log Kow = 6.67

Executive summary:

Log Kow (version 1.68 estimate): 6.67

Reference 3

Endpoint:

partition coefficient

Type of information:

calculation (if not (Q)SAR)

Adequacy of study:

weight of evidence

Study period:

2020

Reliability:

2 (reliable with restrictions)

Rationale for reliability incl. deficiencies:

accepted calculation method

Justification for type of information:

1. SOFTWARE
EpiSuite / KOWWIN v1.68

2. MODEL (incl. version number)
EpiSuite 4.1
KOWWIN v1.68

3. SMILES OR OTHER IDENTIFIERS USED AS INPUT FOR THE MODEL
See attachment

4. SCIENTIFIC VALIDITY OF THE (Q)SAR MODEL
See attachment

5. APPLICABILITY DOMAIN
See attachment

6. ADEQUACY OF THE RESULT
See attachment

Principles of method if other than guideline:

KOWWIN uses a "fragment constant" methodology to predict log P.  In a "fragment constant" method, a structure is divided into fragments (atom or larger functional groups) and coefficient values of each fragment or group are summed together to yield the log P estimate.   KOWWIN’s methodology is known as an Atom/Fragment Contribution (AFC) method.  Coefficients for individual fragments and groups were derived by multiple regression of 2447 reliably measured log P values.  KOWWIN’s "reductionist" fragment constant methodology (i.e. derivation via multiple regression) differs from the "constructionist" fragment constant methodology of Hansch and Leo (1979) that is available in the CLOGP Program (Daylight, 1995).  See the Meylan and Howard (1995) journal article for a more complete description of KOWWIN’s methodology.

To estimate log P, KOWWIN initially separates a molecule into distinct atom/fragments.  In general, each non-hydrogen atom (e.g. carbon, nitrogen, oxygen, sulfur, etc.) in a structure is a "core" for a fragment; the exact fragment is determined by what is connected to the atom.  Several functional groups are treated as core "atoms"; these include carbonyl (C=O), thiocarbonyl (C=S), nitro (-NO2), nitrate (ONO2), cyano (-C/N), and isothiocyanate (-N=C=S).  Connections to each core "atom" are either general or specific; specific connections take precedence over general connections.  For example, aromatic carbon, aromatic oxygen and aromatic sulfur atoms have nothing but general connections; i.e., the fragment is the same no matter what is connected to the atom.  In contrast, there are 5 aromatic nitrogen fragments: (a) in a five-member ring, (b) in a six-member ring, (c) if the nitrogen is an oxide-type {i.e. pyridine oxide}, (d) if the nitrogen has a fused ring location {i.e. indolizine}, and (e) if the nitrogen has a +5 valence {i.e. N-methyl pyridinium iodide}; since the oxide-type is most specific, it takes precedence over the other four.  The aliphatic carbon atom is another example; it does not matter what is connected to -CH3, -CH2-, or -CH< , the  fragment is the same; however, an aliphatic carbon with no hydrogens has two possible fragments: (a) if there are four single bonds with 3 or more carbon connections and (b) any other not meeting the first criteria.

It became apparent, for various types of structures, that log P estimates made from atom/fragment values alone could or needed to be improved by inclusion of  substructures larger or more complex than "atoms"; hence, correction factors were added to the AFC method.  The term "correction factor" is appropriate because their values are derived from the differences between the log P estimates from atoms alone and the measured log P values.  The correction factors have two main groupings: first, factors involving aromatic ring substituent positions and second,  miscellaneous factors.  In general, the correction factors are values for various steric interactions, hydrogen-bondings, and effects from polar functional substructures.  Individual correction factors were selected through a tedious process of correlating the differences (between log P estimates from atom/fragments alone and measured log P values) with common substructures.

Two separate regression analyses were performed.  The first regression related log P to atom/fragments of compounds that do not require correction factors (i.e., compounds estimated adequately by fragments alone).  The general regression equation has the following form:

 log P  = Σ(fini ) +  b     (Equation 1)

where Σ(fini )  is the summation of fi (the coefficient for each atom/fragment) times ni (the number of times the atom/fragment occurs in the structure) and b  is the linear equation constant.  This initial regression used 1120 compounds of the 2447 compounds in the total training dataset.

The correction factors were then derived from a multiple linear regression that correlated differences between the experimental (expl) log P and the log P estimated by Equation 1 above with the correction factor descriptors.  This regression did not utilize an additional equation constant.  The equation for the second regression is:

 lop P (expl)  -  log P (eq 1)  = Σ(cjnj )       (Equation 1)

where Σ(cjnj )  is the summation of cj (the coefficient for each correction factor) times nj  (the number of times the correction factor occurs (or is applied) in the molecule).

 

Regression Results

Results of the two successive multiple regressions (first for atom/fragments and second for correction factors) yield the following general equation for estimating log P of any organic compound:

log P  = Σ(fini ) + Σ(cjnj ) + 0.229     (Equation 3)

(num = 2447,   r2 = 0.982,  std dev = 0.217,  mean error = 0.159)

Type of method:

calculation method (fragments)

Partition coefficient type:

octanol-water

Key result

Type:

log Pow

Partition coefficient:

6.43

Temp.:

25 °C

pH:

7.4

Remarks on result:

other: assumed values for modelling

Conclusions:

Log Kow = 6.43

Executive summary:

Log Kow (version 1.68 estimate): 6.43

Description of key information

The partition coefficient n-octanol/water of the test item Disperse Blue 291:1 Br was determined according to the HPLC method.

It was found to be above the highest log Kow value of calibration substances (4,4’-DDT; log Kow= 6.5) and was hence extrapolated to be: log Kow= 6.9.

 

The study to determine the partition coefficient n-octanol/water of the test item Disperse Blue 291.1 Cl was using an incorrect testing method, i.e. the shake flask method (not a suitable method for poorly water soluble substances) instead of the HPLC method, and lead to a log Kow of 1.9.

 

As this value is clearly wrong for a poorly water soluble substance, the n-octanol/water partition coefficient was calculated using KOWWIN v1.68 in EpiSuite 4.1 for Disperse Blue 291:1 Br and Disperse Blue 291.1 Cl, to compare both calculated values, as a valid experimental study exists for Disperse Blue 291:1 Br.

The calculated log Kow for Disperse Blue 291:1 Br is 6.67, the calculated log Kow for Disperse Blue 291:1 Cl is 6.43. Based on the results of the experimental study with Disperse Blue 291:1 Br, these values seem both to be reasonable and are hence taken for further assessment of the test substances.

Key value for chemical safety assessment

Log Kow (Log Pow):

6.4

at the temperature of:

25 °C