Registration Dossier
Registration Dossier
Data platform availability banner - registered substances factsheets
Please be aware that this old REACH registration data factsheet is no longer maintained; it remains frozen as of 19th May 2023.
The new ECHA CHEM database has been released by ECHA, and it now contains all REACH registration data. There are more details on the transition of ECHA's published data to ECHA CHEM here.
Diss Factsheets
Use of this information is subject to copyright laws and may require the permission of the owner of the information, as described in the ECHA Legal Notice.
EC number: 257-104-6 | CAS number: 51277-96-4
- Life Cycle description
- Uses advised against
- Endpoint summary
- Appearance / physical state / colour
- Melting point / freezing point
- Boiling point
- Density
- Particle size distribution (Granulometry)
- Vapour pressure
- Partition coefficient
- Water solubility
- Solubility in organic solvents / fat solubility
- Surface tension
- Flash point
- Auto flammability
- Flammability
- Explosiveness
- Oxidising properties
- Oxidation reduction potential
- Stability in organic solvents and identity of relevant degradation products
- Storage stability and reactivity towards container material
- Stability: thermal, sunlight, metals
- pH
- Dissociation constant
- Viscosity
- Additional physico-chemical information
- Additional physico-chemical properties of nanomaterials
- Nanomaterial agglomeration / aggregation
- Nanomaterial crystalline phase
- Nanomaterial crystallite and grain size
- Nanomaterial aspect ratio / shape
- Nanomaterial specific surface area
- Nanomaterial Zeta potential
- Nanomaterial surface chemistry
- Nanomaterial dustiness
- Nanomaterial porosity
- Nanomaterial pour density
- Nanomaterial photocatalytic activity
- Nanomaterial radical formation potential
- Nanomaterial catalytic activity
- Endpoint summary
- Stability
- Biodegradation
- Bioaccumulation
- Transport and distribution
- Environmental data
- Additional information on environmental fate and behaviour
- Ecotoxicological Summary
- Aquatic toxicity
- Endpoint summary
- Short-term toxicity to fish
- Long-term toxicity to fish
- Short-term toxicity to aquatic invertebrates
- Long-term toxicity to aquatic invertebrates
- Toxicity to aquatic algae and cyanobacteria
- Toxicity to aquatic plants other than algae
- Toxicity to microorganisms
- Endocrine disrupter testing in aquatic vertebrates – in vivo
- Toxicity to other aquatic organisms
- Sediment toxicity
- Terrestrial toxicity
- Biological effects monitoring
- Biotransformation and kinetics
- Additional ecotoxological information
- Toxicological Summary
- Toxicokinetics, metabolism and distribution
- Acute Toxicity
- Irritation / corrosion
- Sensitisation
- Repeated dose toxicity
- Genetic toxicity
- Carcinogenicity
- Toxicity to reproduction
- Specific investigations
- Exposure related observations in humans
- Toxic effects on livestock and pets
- Additional toxicological data
Partition coefficient
Administrative data
Link to relevant study record(s)
- 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
- 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.
Reference
|
Log POW/ Classic |
Log POW/ Galas |
Consensus log POW* |
Conc. |
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.
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.
Information on Registered Substances comes from registration dossiers which have been assigned a registration number. The assignment of a registration number does however not guarantee that the information in the dossier is correct or that the dossier is compliant with Regulation (EC) No 1907/2006 (the REACH Regulation). This information has not been reviewed or verified by the Agency or any other authority. The content is subject to change without prior notice.
Reproduction or further distribution of this information may be subject to copyright protection. Use of the information without obtaining the permission from the owner(s) of the respective information might violate the rights of the owner.