Methyl α-D-glucoside

Partition coefficient

Link to relevant study record(s)

Reference

Reference 1

Endpoint:

partition coefficient

Type of information:

(Q)SAR

Adequacy of study:

weight of evidence

Study period:

2016-12-30

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 adequate and reliable documentation / justification

Justification for type of information:

1. SOFTWARE
ACD/Labs

2. MODEL (incl. version number)
ACD/Percepta 14.1.0 (Build 2911)

3. SMILES OR OTHER IDENTIFIERS USED AS INPUT FOR THE MODEL
SMILES: C1(OC)C(O)C(O)C(O)C(CO)O1
CAS: 97-30-3

4. SCIENTIFIC VALIDITY OF THE (Q)SAR MODEL

- Defined endpoint:
Partition coefficient

- Unambiguous algorithm:
The ACD/Labs algorithm performs the following procedures in the course of calculating logP:
1. Splits the structure into fragments.
2. Searches for identical fragments in the internal databases:
• The database of Fragmental Increments contains well-characterized increments for over 500 different functional groups. These differ from each other by their chemical structure (e.g., amide, carboxy, ester, etc.), attachment to the hydrocarbon skeleton (aliphatic, vinylic, or aromatic), cyclization (cyclic or non-cyclic), and aromaticity (non-aromatic, aromatic, or fused aromatic).
• The database of Carbon Atom Increments contains well-characterized increments for different types of carbons that are not involved in any functional group. They differ from each other by their state of hybridization (sp, sp2, or sp3), number of attached hydrogens, branching (primary, secondary, tertiary, or quaternary), cyclization (cyclic or non-cyclic), and aromaticity (non-aromatic, aromatic, or fused aromatic).
• The database of the Intramolecular Interaction Increments contains well-characterized increments for over 2,000 different types of pair-wise group interactions. They differ from each other by the type of the interacting terminal groups (see the differences among functional groups above), and the length and type of the fragmental system in-between the interacting groups (aliphatic, aromatic, and vinylic).
3. If some fragments are not found in neither of the above mentioned databases, the program estimates their increments (as well as increments of inter-fragmental interactions) using Secondary Algorithms.
Lastly, the algorithm estimates the probability of tautomeric and ionic equilibria, calculation error and displays the results.

- Defined domain of applicability:
Currently there is no universally accepted definition of model domain. However, users may wish to consider the possibility that property estimates are less accurate for certain compounds. The general ACD/LogP algorithm does not calculate the logP values for the following chemical structures:
• Charged structures other than the zwitterionic alpha-amino acids and their peptide derivatives;
• Structures containing atoms other than C, H, O, S, N, and F in possible chemical surroundings or structures containing atoms P, Cl, Br, I, Se, Si, Ge, Pb, Sn, As, and B that are not within the chemical surroundings.
• Structures that contain elements in their non-typical valence;
• Structures with coordinating bonds; and
• Structures containing more than 255 atoms excluding hydrogen.
ACD/LogP does not take into account the specific features of different geometric isomers, stereoisomers, conformers, isotopes, and structures with non-covalent bonds. It predicts logP values so that in most cases the reliable experimental measurements lie within the calculated ± logP interval. However, it is still possible that some new chemical structures might possess new specific structural features, such as far-range non-covalent bonding, intra-
molecular shielding, or inter-molecular association. In such cases the discrepancy between a newly measured experimental value and the calculated ± logP interval might occur. Plots of chemical structures on the logP scales indicate the changes in other properties only when these properties correlate with logP. Usually, this is only the case for a set of compounds with closely related structures. These points should be taken into consideration when interpreting model results.

- Appropriate measures of goodness-of-fit and robustness and predictivity:
In most cases, ACD/LogP DB calculates logP values with an accuracy ±0.3 or better. Such an accuracy has been demonstrated for many different classes of compounds (including acridines, amino acids and peptides, amphenicoles, barbituric acids, benzimidazoles, benzotriazoles, carbamates, furans, furazans, furoxans, imidazoles, indoles, β-lactams, nucleosides and nucleotides, phenothiazines, phosphoroorganics, polyhalogenated aromatic and aliphatic compounds, purines, pyrazines, pyrazoles, pyridazines, pyridines, pyrimidines, quinolines, quinoxalines, saccharides, steroids, thiophenes, and many others).
However, the exact goodness of fit measurements are proprietary of ACDLabs.

- Mechanistic interpretation:
The underlying mechanistic considerations are proprietary of ACDLabs.

5. APPLICABILITY DOMAIN
- Descriptor domain:
structural fragments

- Similarity with analogues in the training set:
For similarities to training set substances please refer to the "attached background material" section.

6. ADEQUACY OF THE RESULT
Alpha methyl glucoside contains a very common core structure. Since very similar structures were identified in the training sets of the program used, the substance falls into the applicability domain and therefore, the results are highly reliable.

Qualifier:

no guideline followed

Principles of method if other than guideline:

- Software tool(s) used including version:ACD/Labs Percepta 14.1.0 (Built 2911)
- Model description: see field 'Justification for non-standard information', 'Attached justification'
- Justification of QSAR prediction: see field 'Justification for type of information', 'Attached justification'

GLP compliance:

no

Type of method:

other: QSAR prediction using ACD/Percepta 14.1.0

Partition coefficient type:

other: LogP/Classic module and LogP GALAS module

Key result

Type:

log Pow

Partition coefficient:

-2.19

Temp.:

25 °C

Remarks on result:

other: pH was not reported

The results of the prediction are attached in the "overall remarks, attachment" section.

Conclusions:

According to ACD/Percepta the logP of alpha methyl glucoside is -2.19. Therefore the substance is considered to be hydrophilic.

Executive summary:

The partition coefficient of alpha methyl glucoside was determined by QSAR prediction with ACD/Percepta was performed. The model estimates the partition coefficient using the fragment constant methodology and is validated by a huge training set of substances. The partition coefficient of the structurally very similar beta-D-Galactopyranoside, ethyl is available in the dataset, thus, alpha methyl glucoside is considered to fall into the applicability domain of the model. Furthermore, predictions for substances which are listed in the model description are considered to be less accurate. However, the test items fragments can be found in the validation and training sets of the model, thus, the results obtained by the present prediction are considered valid and sufficient to fulfil the requirements of Regulation (EC) No 1907/2006 (REACH).

Reference 2

Endpoint:

partition coefficient

Type of information:

(Q)SAR

Adequacy of study:

weight of evidence

Study period:

17-01-17

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 adequate and reliable documentation / justification

Justification for type of information:

1. SOFTWARE
Estimation Programs Interface Suite™ for Microsoft® Windows v4.11. US EPA, United States Environmental Protection Agency, Washington, DC, USA.

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

3. SMILES OR OTHER IDENTIFIERS USED AS INPUT FOR THE MODEL
CAS-no.: 97-30-3; C1(OC)C(O)C(O)C(O)C(CO)O1; 194.19 g/mol

4. SCIENTIFIC VALIDITY OF THE (Q)SAR MODEL
- Defined endpoint:
log Kow; (log P)- logarithmic octanol/water partition coefficient. The partition coefficient Kow (P) is the ratio of concentrations of a chemical in n-octanol and in water at equilibrium at a specified temperature (typically 25 °C, although partition coefficient is not usually very temperature dependent and training data for KOWWIN are collected at different temperatures).

- Unambiguous algorithm:
KOWWIN uses a "fragment constant" methodology to predict log P. 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.
To estimate log P, KOWWIN initially separates a molecule into distinct atom/fragments. In general, each non-hydrogen atom 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. Connections to each core "atom" are either general or specific;specific connections take precedence over general connections.
Log P estimates made from atom/fragment values alone could or need to be improved by inclusion of substructures larger or more complex than "atoms"; hence, correction factors were added to the AFC method. They are either factors involving aromatic ring substituent positions, or miscellaneous factors. 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.
The general regression equation has the following form:

log P = ∑(f(i)*n(i)) + ∑(c(j)*n(j)) + b

where f(i) is the coefficient of atom/fragment i, n(i) – the number of times the fragment i occurs in the molecule, c(j) is the coefficient for the correction factor j, and n(j) the number of times the factor j occurs (or is applied) in the molecule. b is the linear equation constant; b = 0.229.
Values of f and c coefficients are available.

- Defined domain of applicability: Currently there is no universally accepted definition of model domain. However, it should be considered that log P estimates may be less accurate for compounds outside the molecular weight range of the training set compounds, and/or that have more instances of a given fragment than the maximum for all training set compounds. Although the training set of the model contains a large number of diverse molecules and can be considered abundant, it is also possible that a compound may be characterised by structural features (e.g. functional groups) not represented in the training set, with no respective fragment/correction coefficient developed. These points should be taken into consideration when interpreting model results.
Training set molecular weights:
Minimum MW: 18.02
Maximum MW: 719.82 (in the validation set: 991.15)
Average in the training set: 199.98.

- Appropriate measures of goodness-of-fit and robustness and predictivity:
Training set statistics:
N = 2447 compounds
correlation coefficient R2= 0.982
standard deviation = 0.217
absolute deviation = 0.159
 
Training set estimation error:
within ≤ 0.10 – 45.0%
within ≤ 0.20 – 72.5%
within ≤ 0.40 – 92.4%
within ≤ 0.50 – 96.4%
within ≤ 0.60 – 98.2%
 
External validation set statistics:
N = 10946 compounds
correlation coefficient R2= 0.943
standard deviation = 0.479
absolute deviation = 0.356
 
Validation set estimation error:
within ≤ 0.20 – 39.6%
within ≤ 0.40 – 66.0%
within ≤ 0.50 – 75.6%
within ≤ 0.60 – 82.5%
within ≤ 0.80 – 91.6%
within ≤ 1.00 – 95.6%

- Mechanistic interpretation:
KOWWIN’s "reductionist" fragment constant methodology (i.e. derivation via multiple regression) differs from the "constructionist" fragment constant methodology of Hansch and Leo (Hansch, C. and Leo, A.J., Substituent Constants for Correlation Analysis in Chemistry and Biology, Wiley, New York, 1979). More complete description of KOWWIN methodology is described in: Meylan, W.M., and Howard, P.H., Atom/Fragment Contribution Method for Estimating Octanol-Water Partition Coefficients, J. Pharm. Sci 84: 83-92, 1995.

5. APPLICABILITY DOMAIN
- Descriptor domain:
molecular weight, structure fragments
- Structural and mechanistic domains:
KOWWIN’s "reductionist" fragment constant methodology (i.e. derivation via multiple regression) differs from the "constructionist" fragment constant methodology of Hansch and Leo (Hansch, C. and Leo, A.J., Substituent Constants for Correlation Analysis in Chemistry and Biology, Wiley, New
York, 1979). More complete description of KOWWIN methodology is described in: Meylan, W.M., and Howard, P.H., Atom/Fragment Contribution Method for Estimating Octanol-Water Partition Coefficients, J. Pharm. Sci 84: 83-92, 1995.
- Similarity with analogues in the training set:
Since KOWWIN uses a "fragment constant" methodology to predict log P, the functional groups within arginine, i.e. the amino- and the carboxy-group can be found in the training set provided by the KOWWIN help section.


6. ADEQUACY OF THE RESULT
2-oxoglutaric acid contains a very common structure with two terminal carboxy-groups and one carbonyl group. Since these functional groups are part of the training sets for the fragment constant methodology, the substance falls within the applicability domain and therefore, the results are highly reliable.

For a more detailed explanation please refer to the "attached justification" section.

Qualifier:

no guideline followed

Principles of method if other than guideline:

-- Software tool(s) used including version: EPISuite v4.11; KOWWIN v1.68
- Model(s) used: KOWWIN v1.68
- Model description: see field 'Justification for non-standard information', 'Attached justification' and/or 'Cross-reference'
- Justification of QSAR prediction: see field 'Justification for type of information', 'Attached justification' and/or 'Cross-reference'

GLP compliance:

no

Type of method:

other: QSAR prediction using EPISuite/KOWWIN

Key result

Type:

log Pow

Partition coefficient:

-2.5

Temp.:

25 °C

Remarks on result:

other: pH was not estimated

Log Kow(version 1.68 estimate): -2.50

     

     SMILES : C1(OC)C(O)C(O)C(O)C(CO)O1

     CHEM   : alpha methyl glucoside

     MOL FOR: C7 H14 O6

     MOL WT : 194.19

     -------+-----+--------------------------------------------+---------+--------

      TYPE  | NUM |        LOGKOW FRAGMENT DESCRIPTION         |  COEFF  |  VALUE

     -------+-----+--------------------------------------------+---------+--------

      Frag  |  1  |  -CH3    [aliphatic carbon]                       | 0.5473  |  0.5473

      Frag  |  1  |  -CH2-   [aliphatic carbon]                       | 0.4911  |  0.4911

      Frag  |  5  |  -CH     [aliphatic carbon]                        | 0.3614  |  1.8070

      Frag  |  4  |  -OH     [hydroxy, aliphatic attach]           |-1.4086  | -5.6344

      Frag  |  2  |  -O-     [oxygen, aliphatic attach]             |-1.2566  | -2.5132

      Factor|  1  |  C-O-C-O-C  structure  correction           | 0.5036  |  0.5036

      Factor|  1  |  Multi-alcohol correction                         | 0.4064  |  0.4064

      Factor|  1  |  HO-CH-C(-O-)-CH-OH   structure correction | 1.0649  |  1.0649

      Factor|  1  |  HO-CH-C(-OH)-CH-OH   structure correction | 0.5944  |  0.5944

      Const |     |  Equation Constant                         |         |  0.2290

     -------+-----+--------------------------------------------+---------+--------

                                                              Log Kow   =  -2.5039

Conclusions:

According to KOWWIN v.1.68 the logKow of alpha methyl glucoside is -2.50. Therefore the substance is considered to be hydrophilic.

Executive summary:

The partition coefficient of alpha methyl glucoside was determined by QSAR prediction with EpiSuite TM; KOWWIN v.1.68 was performed. The model estimates the partition coefficient using the fragment constant methodology and is validated by a huge training set of substances with correct estimated logKow. The partition coefficient of D-Glucose is available in the dataset, thus, falling into the applicability domain of the model. Furthermore, since there is currently no defined and universally accepted applicability domain predictions with substances which are not in the defined range of molecular weight are considered to be less accurate. However, the test items fragments can be found in the validation and training sets of the model and its molecular weight is also inside the defined range of molecular weight, thus, the results obtained by the present prediction are considered valid and sufficient to fulfil the requirements of Regulation (EC) No 1907/2006 (REACH).

Description of key information

According to KOWWIN v.1.68 the logKow of alpha methyl glucoside is -2.50. Therefore the substance is considered to be hydrophilic.

According to ACD/Percepta the logP of alpha methyl glucoside is -2.19. Therefore the substance is considered to be hydrophilic.

Since both values are in the same range the mean value was determined for further calculations. The mean value for the partition coefficient is: -2.35.

Key value for chemical safety assessment

Log Kow (Log Pow):

-2.35

at the temperature of:

25 °C

Additional information

The partition coefficient of alpha methyl glucoside was determined by QSAR prediction with EpiSuite TM; KOWWIN v.1.68 and ACD/Percepta.

The first model estimates the partition coefficient using the fragment constant methodology and is validated by a huge training set of substances with correct estimated logKow. The partition coefficient of structurally similar D-Glucose is available in the dataset, thus, the substance falls into the applicability domain of the model.

However, since there is currently no defined and universally accepted applicability domain predictions with substances which are not in the defined range of molecular weight are considered to be less accurate. However, the test items fragments can be found in the validation and training sets of the model and its molecular weight is also inside the defined range of molecular weight, thus, the results obtained by the present prediction are considered valid and sufficient to fulfil the requirements of Regulation (EC) No 1907/2006 (REACH).

The second model estimates the partition coefficient using the fragment constant methodology as well and is validated by a huge training set of substances. The partition coefficient of the structurally very similar beta-D-Galactopyranoside, ethyl is available in the dataset, thus, alpha methyl glucoside is considered to fall also into the applicability domain of the second model. Furthermore, predictions for substances which are listed in the model description are considered to be less accurate. However, the test items fragments can be found in the validation and training sets of the model, thus, the results obtained by the present prediction are considered valid and sufficient to fulfil the requirements of Regulation (EC) No 1907/2006 (REACH).