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Partition coefficient

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Endpoint:
partition coefficient
Type of information:
(Q)SAR
Adequacy of study:
weight of evidence
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 / Percepta
2. MODEL (incl. version number)
ACD /Labs 2015 Release (Build 2726. 27 Nov 2014)
3. SMILES OR OTHER IDENTIFIERS USED AS INPUT FOR THE MODEL
Smiles: C12=C(NC(N)=NC1=O)NCC(CNc1ccc(C(=O)NC(C(=O)O)CCC(=O)O)cc1)N2
Default logP model used:
ACD/LogP GALAS
Default pKa model used:
ACD/pKa GALAS

4. SCIENTIFIC VALIDITY OF THE (Q)SAR MODEL
- Defined endpoint: Log D
- Unambiguous algorithm: The octanol-water partition coefficient, logP, is a measure of compound hydrophobicity, which in many cases correlates well with the above chemical and biological properties. However, the logP can be accurately calculated only for uncharged substances. If the chemical compound contains one or more ionogenic groups (i.e., functional groups which can easily form ions), it may exist as a mixture of different ionic forms. The composition of this mixture depends strongly on pH. In such cases the apparent partition coefficient D for dissociative systems (or logD) gives a more appropriate description than logP of the complex partitioning equilibria. So, if such complex equilibria exist, then the partitioning of chemicals between water and organic phase must be a function of:
(i) The extent of ionization, and
(ii) The partition constants.
of the numerous different microspecies shown in the diagram above. These problems have already been tackled, and to a good degree of success, by two ACD/Labs algorithms:
(i) The acid-base ionization coefficient, pK a , can be predicted normally to within ±0.2 pK a units for most functional groups by ACD/pK a.
(ii) The octanol-water partition coefficients can be predicted normally to within ±0.3 logP units by ACD/LogP DB.
For more detailed information please refer to 'attached justification'
- Defined domain of applicability: Currently there is no universally accepted definition of model domain. However, the algorithm for logD predictions is dependent on the microspecies of the training and validation dataset. Thus, one may wish to consider the possibility that property estimates are less accurate for compounds that do not exhibit such microspecies. A compound may have a functional group(s) or other structural features not represented in the dataset, and for which no additive function was developed. The ACD/LogD algorithm does not calculate the logD values for the following chemical structures:
• 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 shown below (note that A denotes any atom out of C, O, S, N, F
• Structures containing atoms in non-typical valence states;
• Structures containing non-covalent (co-ordination) bonds;
• Structures containing more than 255 heavy atoms or 10 ionization centers; and
• Structures containing more than 10 exchangeable protons.
Additionally, the ACD/LogD algorithm does not take into account the specific features of different geometric isomers, stereomers, conformers, and isotopes. The algorithm does not correct its prediction for long-range non-covalent bonding, intra-molecular shielding, or inter-molecular association.These points should be taken into consideration when interpreting model results.

5. APPLICABILITY DOMAIN
- Descriptor domain: Structural fragments
Microspecies

6. ADEQUACY OF THE RESULT
For more detailed information please refer to 'attached justification'
Qualifier:
no guideline followed
Principles of method if other than guideline:
- Software tool(s) used including version: ACD / Percepta ACD /Labs 2015 Release (Build 2726. 27 Nov 2014)
- Model description: see field 'Attached justification'
- Justification of QSAR prediction: see field 'Attached justification'
GLP compliance:
no
Type of method:
other: QSAR estimation
Partition coefficient type:
octanol-water
Key result
Type:
log Pow
Partition coefficient:
-4.06
Temp.:
25 °C
pH:
1.7
Key result
Type:
log Pow
Partition coefficient:
-3.08
Temp.:
25 °C
pH:
4.6
Key result
Type:
log Pow
Partition coefficient:
-5.11
Temp.:
25 °C
pH:
6.5
Key result
Type:
log Pow
Partition coefficient:
-5.74
Temp.:
25 °C
pH:
7.4
Key result
Type:
log Pow
Partition coefficient:
-5.84
Temp.:
25 °C
pH:
8
Key result
Type:
log Pow
Partition coefficient:
-2.99
Temp.:
25 °C
pH:
4
Key result
Type:
log Pow
Partition coefficient:
-5.56
Temp.:
25 °C
pH:
7
Key result
Type:
log Pow
Partition coefficient:
-5.9
Temp.:
25 °C
pH:
9
Conclusions:
The partition coefficient of Tetrahydrofolic acid at different pH values was estimated with ACD / Percepta, ACD /Labs 2015 Release (Build 2726. 27 Nov 2014). The log D reached a maximum of -2.99 at ph 4 and subsequently declined continuously to -5.9 at pH 9.
Endpoint:
partition coefficient
Type of information:
(Q)SAR
Adequacy of study:
weight of evidence
Reliability:
2 (reliable with restrictions)
Rationale for reliability incl. deficiencies:
results derived from a valid (Q)SAR model, but not (completely) falling into its applicability domain, with adequate and reliable documentation / justification
Justification for type of information:
1. SOFTWARE
ACD / Percepta
2. MODEL (incl. version number)
ACD / Percepta 14.0.0 (Build 2726. 27 Nov 2014)/ ACD/LogP GALAS 
3. SMILES OR OTHER IDENTIFIERS USED AS INPUT FOR THE MODEL
Smiles: C12=C(NC(N)=NC1=O)NCC(CNc1ccc(C(=O)NC(C(=O)O)CCC(=O)O)cc1)N2
4. SCIENTIFIC VALIDITY OF THE (Q)SAR MODEL
- Defined endpoint: Octanol/water partition coefficient (logP)
- Unambiguous algorithm:
ACD/LogP GALAS module provides the estimate of the octanol-water partitioning coefficient for neutral species derived on the basis of GALAS (Global, Adjusted Locally According to Similarity) modeling methodology
Each GALAS model consists of two parts:
• Global (baseline) statistical model that reflects general trends in the variation of the property of interest.
Similarity-based routine that performs local correction of baseline predictions taking into account the differences between baseline and experimental LogP values for the most similar training set compounds.
GALAS methodology also provides the basis for estimating reliability of predictions by the means of calculated Reliability Index (RI) value that takes into account:
• Similarity of tested compound to the training set molecules.
• Consistence of experimental LogP values and baseline model prediction for the most similar similar compounds from the training set.
Reliability Index ranges from 0 to 1 (0 corresponds to a completely unreliable, and 1 - a highly reliable prediction) and serves as an indication whether a submitted compound falls within the Model Applicability Domain. Compounds obtaining predictions RI < 0.3 are considered outside of the Applicability Domain of the model.
- Defined domain of applicability: Currently there is no universally accepted definition of model domain. However, the algorithm for logP predictions is dependent on the training and validation dataset. Thus, one may wish to consider the possibility that property estimates are less accurate for compounds outside the molecular weight range of the dataset compounds and for substances with rare structural fragments. A compound may have a functional group(s) or other structural features not represented in the dataset, and for which no additive function was developed. These points should be taken into consideration when interpreting model results.
- Appropriate measures of goodness-of-fit and robustness and predictivity: Prior to model development, the compounds comprising the ACD/LogP DB were randomly split into a training set used for building the model, and a test set reserved for validation purposes:
• Training set size: 11,387
• Internal validation set size: 4,890
For more detailed information please refer to the 'attached justification' section

5. APPLICABILITY DOMAIN
- Descriptor domain: Structural fragments
molecular weight
- Similarity with analogues in the training set: In the documentation of the estimation results the first five substances exhibiting the closest similarity with the test item are listed. Since the reliability index is used and at least a moderate reliability was shown the results are considered to be adequate.

6. ADEQUACY OF THE RESULT
The reliability index indicated a borderline reliability. This should be taken into account while interpreting the results.
Qualifier:
no guideline followed
Principles of method if other than guideline:
- Software tool(s) used including version: ACD / Percepta, ACD / Percepta 14.0.0 (Build 2726. 27 Nov 2014)
- Model(s) used: ACD/LogP GALAS 
- Model description: see field 'Attached justification'
- Justification of QSAR prediction: see field 'Attached justification'
GLP compliance:
no
Type of method:
other: QSAR estimation
Partition coefficient type:
octanol-water
Key result
Type:
log Pow
Partition coefficient:
-0.88
Temp.:
25 °C
Remarks on result:
other: QSAR estimation; pH not reported; Reliability index = 0.44 (borderline prediction)
Details on results:
Most similar structures found within the training and validation set:
1) 4(1H)-Pyrimidinone, 6-amino-; LogP (used in model): -0,99; Similarity: 0,58
2) 2,5-Cyclohexadiene-1,4-dione, 2,5-bis(1-aziridinyl)-3,6-bis(methylamino)-; LogP (used in model): 0,13; Similarity: 0,53; LogP: 0,13 (presented in BioByte Star List) Moret,E.; de Boer, M.; Hilbers,H.; Tollenaere, J.; et al. J. Med.Chem. 1996, 39, 720.
3) Uracil, 1-methyl, 5,6-diamino-; LogP (used in model): -1,40; Similarity: 0,52; LogP: -1,40 (presented in BioByte Star List) Fraisse,L.; Verlhac,J-B.; Roche,B.; Rascle,M.; Rabion,A.; Seris,J. J.Med.Chem. 1993, 36, 1465.
4) 4(1H)-Pyridinone, 2-[[2-[[(5-methyl-1H-imidazol-4-yl)methyl]thio]ethyl]amino]-; LogP (used in model): 0,80; Similarity: 0,50; LogP: 0,80 R.C. Young, M.J. Graham and M.L. Roantree, Pharmacochem. Library, 1987, 10, 91-95.
LogP: 0,80 (presented in BioByte Star List) Young,R.C.; Graham,M.J.; Roantree,M.L. "QSAR in Drug Design & Toxicology", Hadzi & Jerman-Blazic Eds., Elsevier, Amsterdam, 1987, p.91.
5) Thiazole, 2-chloro-5-[[2-(nitromethylene)imidazolidinyl]methyl]-; LogP (used in model): -0,04; Similarity: 0,47
Conclusions:
In this study report the partition coefficient of Tetrahydrofolic acid was estimated by calculating the logP using ACD / Percepta 14.0.0 . The logP of Tetrahydrofolic acid is considered to be -0.88 at 25°C.
Endpoint:
partition coefficient
Type of information:
(Q)SAR
Adequacy of study:
weight of evidence
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
EPISuite v4.11
2. MODEL (incl. version number)
KOWWIN v1.68
3. SMILES OR OTHER IDENTIFIERS USED AS INPUT FOR THE MODEL
CAS: 135-16-0
4. SCIENTIFIC VALIDITY OF THE (Q)SAR MODEL
- Defined endpoint: Partion coefficient octanol/water (log Kow)
- Unambiguous algorithm: 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.
- 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.
- Appropriate measures of goodness-of-fit and robustness and predictivity: Please refer to 'attached justification' for more detailed information.
- 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, type of „fragment“
- Similarity with analogues in the training set:
APPENDIX D of the HELP section in KOWWIN v1.66 contains the fragments used in the training set. The substance consists of fragments which are part of the training set. Moreover, as depicted above also the logKow´s of chemicals exceeding the molecular weight of the training set and/or exceeding the complexicity of the training set fragments are predicted with sufficient accuracy.

6. ADEQUACY OF THE RESULT
As explained in detail in the sections above the substance falls within the range of reliable predictivity. The substance falls within the molecular weight range of the model. furthermore, the substance consists of common functional groups, thus, the results of the estimation are considered to be sufficient to fulfil the information requirements for registration.
Qualifier:
no guideline followed
Principles of method if other than guideline:
- Software tool(s) used including version: EPISuitev 4.11
- Model(s) used:KOWWIN v1.68
- Model description: see field 'Attached justification'
- Justification of QSAR prediction: see field 'Attached justification'
GLP compliance:
no
Type of method:
other: QSAR estimation
Partition coefficient type:
octanol-water
Key result
Type:
log Pow
Partition coefficient:
-3.685
Temp.:
25 °C
Remarks on result:
other: pH value not reported
Conclusions:
In this study report the partition coefficient of Tetrahydrofolic acid was estimated using EPISuite/KOWWIN v1.68. Based on the results the logKow is considered to be -3.6847 at 25°C.

Description of key information

- QSAR estimation of the partition coefficient using ACD /Labs 2015 Release (Build 2726. 27 Nov 2014) / ACD/LogP GALAS, log D maximum -2.99 at pH 4 to -5.9 at pH 9 at 25 °C

- QSAR estimation of the partition coefficient using ACD /Labs 2015 Release (Build 2726. 27 Nov 2014) / ACD/LogP GALAS, log P -0.88 at 25°C

- QSAR estimation of the partition coefficient using EPISuite v4.11/ KOWWIN v1.68, log P -3.6847 at 25°C

Key value for chemical safety assessment

Log Kow (Log Pow):
-3.685
at the temperature of:
25 °C

Additional information

There are no experimental data for the partition coefficient of Tetrahydrofolic acid. Three QSAR estimations were performed usind three different models. Tetrahydrofolic acid and its structural fragments respectively are within the applicability domain of each of the used models.