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EC number: 202-571-3 | CAS number: 97-30-3
- 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
Vapour pressure
Administrative data
Link to relevant study record(s)
- Endpoint:
- vapour pressure
- Type of information:
- (Q)SAR
- Adequacy of study:
- key study
- 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)
MPBPWIN v1.43 (September 2010)
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
Melting Point: 168°C
4. SCIENTIFIC VALIDITY OF THE (Q)SAR MODEL
- Defined endpoint: Vapor pressure (VP)
- Unambiguous algorithm: MPBPWIN estimates vapor pressure (VP) by three separate methods: (1) the Antoine method, (2) the modified Grain method, and (3) the Mackay method. Antoine Method: Chapter 14 of Lyman et al (1990) includes the description of the Antoine method used by MPBPWIN. It was developed for gases and liquids. The Antoine equation uses the normal boiling (Tb) to estimate vapor pressure. MPBPWIN has extended the Antoine method to make it applicable to solids by using the same methodology as the modified Grain method to convert a super-cooled liquid VP to a solid-phase VP as shown below.
Modified Grain Method: Chapter 2 of Lyman (1985) describes the modified Grain method used by MPBPWIN. This method is a modification and significant improvement of the modified Watson method. It is applicable to solids, liquids and gases. The modified Grain method may be the best all-around VP estimation method currently available. Mackay Method: Mackay derived an equation to estimate VP which contains also the melting point. The melting point term is ignored for liquids. It was derived from two chemical classes: hydrocarbons (aliphatic and aromatic) and halogenated compounds (again aliphatic and aromatic).
- 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 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.
- Appropriate measures of goodness-of-fit and robustness and predictivity: The accuracy of MPBPWIN's "suggested" VP estimate was tested on a dataset of 3037 compounds with known, experimental VP values between 15 and 30 deg C (the vast majority at 25 or 20 deg C). The experimental values were taken from the PHYSPROP Database that is part of the EPI Suite. For this test, the CAS numbers were run through MPBPWIN as a standard batch-mode run (using the default VP estimation temperature of 25 °C) and the batch estimates were compared to PHYSPROP's experimental VP. Plotting the results clearly indicates that the estimation error increases as the vapor pressure (both experimental and estimated) decreases, especially when the vapor pressure decreases below 1E-006 mmHg (0.0001333 Pascals). For maximum VP accuracy, good experimental Boiling Points and/or Melting Points should be entered on the data entry screen
- Mechanistic interpretation: MPBPWIN estimates vapor pressure (VP) by three separate methods: (1) the Antoine method, (2) the modified Grain method, and (3) the Mackay method. All three methods use the normal boiling point to estimate VP. Unless the user enters a boiling point on the data entry screen, MPBPWIN uses the estimated boiling point from the adapted Stein and Brown method (For more information see: Stein, S.E. and Brown, R.L.; 1994. Estimation of normal boiling points from group contributions. J. Chem. Inf. Comput. Sci. 34: 581-7). MPBPWIN reports the VP estimate from all three methods. It then reports a "suggested" VP. For liquids and gases, the suggested VP is the average of the Antoine and the modified Grain estimates. The Mackay method is not used in the suggested VP because its application is currently limited to its derivation classes.
5. APPLICABILITY DOMAIN
- Descriptor domain: molecular weight, structure available in the training data sets, boiling point, melting point
- Structural and mechanistic domains: For more detailed information about the methodologies please refer also to Lyman, W.J. 1985. In: Environmental Exposure From Chemicals. Volume I., Neely,W.B. and Blau,G.E. (eds), Boca Raton, FL: CRC Press, Inc., Chapter 2. and Lyman, W.J., Reehl, W.F. and Rosenblatt, D.H. 1990. Handbook of Chemical Property Estimation Methods. Washington, DC: American Chemical Society, Chapter 14.
- Similarity with analogues in the training set: The core structure a-D-glucose is available in thr training data sets and its molecular weight is in the recommended range for reliable predictions.
6. ADEQUACY OF THE RESULT
Since the substance falls within the recommended molecular weight range and its core structure is part of the training sets, predictivity of the model used is sufficient to provide reliable results for classification and labelling and/or risk assessment. Furthermore, the vapour pressure obtained from the QSAR prediction is very low which substantiates the assumption that testing of vapour pressure is technically not feasible without reasonable effort. - 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: MPBPWIN v1.43
- Model description: see field 'Justification for non-standard information' and 'Attached justification'
- Justification of QSAR prediction: see field 'Justification for type of information' and 'Attached justification' - GLP compliance:
- no
- Type of method:
- other: QSAR estimation
- Key result
- Temp.:
- 25 °C
- Vapour pressure:
- 0 Pa
- Conclusions:
- According to MPBPWIN v1.43 the vapour pressure of alpha methyl glucoside is 1.89E-006 Pa at 25°C.
- Executive summary:
The vapour pressure of alpha methyl glucoside was determined by QSAR prediction with EpiSuite TM; MPBPWIN v1.43 was performed. The model estimates the vapour pressure using the boiling point and the melting point and is validated by a huge training set of substances with experimentally determined vapour pressures. The vapour pressure 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 core structure can be found in the 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).
Reference
Description of key information
According to MPBPWIN v1.43 the vapour pressure of alpha methyl glucoside is 1.89E-006 Pa at 25°C.
Key value for chemical safety assessment
- Vapour pressure:
- 0 Pa
- at the temperature of:
- 25 °C
Additional information
The vapour pressure of alpha methyl glucoside was determined by QSAR prediction with EpiSuite TM; MPBPWIN v1.43 was performed. The model estimates the vapour pressure using the boiling point and the melting point and is validated by a huge training set of substances with experimentally determined vapour pressures. The vapour pressure 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 core structure can be found in the 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).
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.
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