(hexadecylamidopropyl)trimethylammonium chloride

Partition coefficient

Link to relevant study record(s)

Reference

Endpoint:

partition coefficient

Type of information:

(Q)SAR

Adequacy of study:

key study

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

Remarks:

Calculated value with limited number of experimental data for similar compounds

Justification for type of information:

1. SOFTWARE
ACD/Percepta 14.0.0 (Build 2726)

2. MODEL (incl. version number)
GALAS, Classic and Consensus module

3. SMILES OR OTHER IDENTIFIERS USED AS INPUT FOR THE MODEL
CCCCCCCCCCCCCC(NCCC[N+](C)(C)C)=O.[Cl-]
CCCCCCCCCCCCCCCC(NCCC[N+](C)(C)C)=O.[Cl-]
CCCCCCCCCCCCCCCCCC(NCCC[N+](C)(C)C)=O.[Cl-]

4. SCIENTIFIC VALIDITY OF THE (Q)SAR MODEL
see attached information

5. APPLICABILITY DOMAIN
see attached information

6. ADEQUACY OF THE RESULT
see attached information

Explanation Algorithms for Log P:
 
The logP prediction module offers two different predictive algorithms within ACD/Percepta software—Classic and GALAS (Global, Adjusted Locally According to Similarity). A Consensus logP based on these two models is also available. Experts can investigate each model manually to decide which is more appropriate for particular chemical space, and provide colleagues with guidelines for use.

Classic
The primary algorithm calculates logP using the principle of isolating carbons. Well-characterized logP contributions have been compiled for atoms, structural fragments, and intramolecular interactions derived from >12,000 experimental logP values. A secondary algorithm is applied when unknown fragments are presented. A detailed description of the original algorithm may be found at "Petrauskas, A., Kolovanov, E., ACD/Log P Method Description. Persp. in Drug Design, 19:1–19,2000".
Source of experimental data—peer-reviewed scientific journals and the BioByte Star list.
Provides a detailed calculation protocol with references for known fragments, and indication of approximated contributions, with mapping onto the structure for easy interpretation.

GALAS
Training set: 11,387 compounds; Internal validation: 4890 compounds
Source of experimental data—reference books (the Merck index, Therapeutic Drugs, Clarke's Isolation and Identification of Drugs), peer-reviewed scientific journals, and other public data sources such as handbooks and online databases.
Offers color-coded representation of lipophilic and hydrophilic parts of the compound structure.
Provides a quantitative estimate of reliability of prediction through the Reliability Index (RI). This number, between 0 and 1, allows you to judge the relevance of the internal training set to the chemical space being investigated by looking for similar structures and evaluating how well the model performs in the local chemical environment of the compound (0=poor reliability, either nothing similar exists in the training set, or the model produces inconsistent predictions for similar compounds; 1=excellent reliability, several identical entries present, and model predictions precisely match given experimental values).
Shows up to five of the most similar structures in the internal training set, to help further gauge relevance of the training set to your chemical space.

Consensus LogP
Uses both the Classic and GALAS algorithms.
Assigns dynamic adaptive coefficients to each model according to the corresponding indications of prediction quality. As a result, each model obtains larger weight in those regions of chemical space where it performs most reliably. This allows maximizing the Applicability Domain of the final model and obtaining maximal overall accuracy for the predicted result.
Provides the equation used for calculation with dynamic coefficients of both models.

Qualifier:

no guideline available

Principles of method if other than guideline:

Calculation of log POW using the software ACD / Labs

GLP compliance:

no

Type of method:

other: Calculation

Partition coefficient type:

octanol-water

Type:

log Pow

Partition coefficient:

1.74

Temp.:

25 °C

Remarks on result:

other: Value for C14 quaternised ammoniumconstituent

Type:

log Pow

Partition coefficient:

2.81

Temp.:

25 °C

Remarks on result:

other: Value for C16 quaternised ammoniumconstituent

Type:

log Pow

Partition coefficient:

3.87

Temp.:

25 °C

Remarks on result:

other: Value for C18 quaternised ammoniumconstituent

	Log POW/ Classic	Log POW/ Galas	Consensus log POW*	Conc. % (approx.)	Conc. % (normalised)
1-Propanaminium-N,N,N-trimethyl-3-[(1-oxotetradecyl)amino	1.74 ± 0.33	1.38	1.56	2.4	2.5
1-Propanaminium-N,N,N-trimethyl-3-[(1-oxohexadecyl)amino (fig. 1)	2.81 ± 0.34	2.29	2.47	89.9	92
1-Propanaminium-N,N,N-trimethyl-3-[(1-oxooctadecyl)amino	3.87 ± 0.34	2.76	3.15	5.4	5.4
N-[3-(Dimethylamino)propyl] myristamide	5.70 ± 0.33	6.09	5.95
N-[3-(Dimethylamino)propyl] hexadecan-1-amide  (fig. 2)	6.76 ± 0.33	6.96	6.89
N-[3-(Dimethylamino)propyl] stearamide	7.82 ± 0.33	7.94	7.90
Myristic acid	6.09 ± 0.19	6.23	6.19
Palmitic acid	7.15 ± 0.19	6.96	7.02
Stearic acid	8.22 ± 0.19	7.98	8.06

Weighted log POW:

1.56*0.025 + 2.47*0.92 + 3.15 +0.055 = 2.49

Explanation Algorithms for Log P:

 

The logP prediction module offers two different predictive algorithms within ACD/Percepta software—Classic and GALAS (Global, Adjusted Locally According to Similarity). A Consensus logP based on these two models is also available. Experts can investigate each model manually to decide which is more appropriate for particular chemical space, and provide colleagues with guidelines for use.

Classic

The primary algorithm calculates logP using the principle of isolating carbons. Well-characterized logP contributions have been compiled for atoms, structural fragments, and intramolecular interactions derived from >12,000 experimental logP values. A secondary algorithm is applied when unknown fragments are presented. A detailed description of the original algorithm may be found at "Petrauskas, A., Kolovanov, E., ACD/Log P Method Description. Persp. in Drug Design, 19:1–19,2000".

Source of experimental data—peer-reviewed scientific journals and the BioByte Star list.

Provides a detailed calculation protocol with references for known fragments, and indication of approximated contributions, with mapping onto the structure for easy interpretation.

GALAS

Training set: 11,387 compounds; Internal validation: 4890 compounds

Source of experimental data—reference books (the Merck index, Therapeutic Drugs, Clarke's Isolation and Identification of Drugs), peer-reviewed scientific journals, and other public data sources such as handbooks and online databases.

Offers color-coded representation of lipophilic and hydrophilic parts of the compound structure.

Provides a quantitative estimate of reliability of prediction through the Reliability Index (RI). This number, between 0 and 1, allows you to judge the relevance of the internal training set to the chemical space being investigated by looking for similar structures and evaluating how well the model performs in the local chemical environment of the compound (0=poor reliability, either nothing similar exists in the training set, or the model produces inconsistent predictions for similar compounds; 1=excellent reliability, several identical entries present, and model predictions precisely match given experimental values).

Shows up to five of the most similar structures in the internal training set, to help further gauge relevance of the training set to your chemical space.

Consensus LogP

Uses both the Classic and GALAS algorithms.

Assigns dynamic adaptive coefficients to each model according to the corresponding indications of prediction quality. As a result, each model obtains larger weight in those regions of chemical space where it performs most reliably. This allows maximizing the Applicability Domain of the final model and obtaining maximal overall accuracy for the predicted result.

Provides the equation used for calculation with dynamic coefficients of both models.

Conclusions:

The weighted log POW for the a.i. of the substance is 2.49

Executive summary:

The weighted log POW was calculated for the quaternised ammonium constituents of the substance using the software ACD /Labs, Release 14.0.0 (Build 272627, Nov. 2014). A value of 2.49 was obtained.

Description of key information

log Kow: 2.49 ; calculation (ACD /Labs, Release 14.0.0 (Build 272627, Nov. 2014)); RL2

Key value for chemical safety assessment

Log Kow (Log Pow):

2.49

at the temperature of:

25 °C

Additional information

The weighted log Kow was calculated for the quaternised ammonium constituents of the substance using the software ACD /Labs, Release 14.0.0 (Build 272627, Nov. 2014). A value of 2.49 was obtained.