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Diss Factsheets
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EC number: 202-443-7 | CAS number: 95-71-6
- 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
Basic toxicokinetics
Administrative data
- Endpoint:
- basic toxicokinetics, other
- Remarks:
- blood-brain barrier penetration
- 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
Data source
Reference
- Reference Type:
- study report
- Title:
- Unnamed
- Year:
- 2 017
- Report date:
- 2017
Materials and methods
- Objective of study:
- distribution
Test guideline
- Qualifier:
- equivalent or similar to guideline
- Guideline:
- OECD Guideline 417 (Toxicokinetics)
- GLP compliance:
- no
Test material
- Reference substance name:
- 2-methylhydroquinone
- EC Number:
- 202-443-7
- EC Name:
- 2-methylhydroquinone
- Cas Number:
- 95-71-6
- Molecular formula:
- C7H8O2
- IUPAC Name:
- 2-methylbenzene-1,4-diol
- Test material form:
- solid: particulate/powder
Constituent 1
- Radiolabelling:
- other: not applicable
Test animals
- Species:
- rat
Results and discussion
- Preliminary studies:
- Applicability domain (OECD principle 3)
a. Domains:
i. descriptor domain
All descriptor values for methylhydroquinone fall in the applicability domain (training set value ±30%).
ii. structural fragment domain
Methylhydroquinone is structurally relatively similar to the model compounds. The training set contains compounds of similar size to the studied molecule.
iii. mechanism domain
Methylhydroquinone is considered to be in the same mechanistic domain as the molecules in the training set as it is structurally similar to the model compounds.
iv. metabolic domain, if relevant
Methylhydroquinone e is considered to be in the same metabolic domain as themolecules in the training set of the model due to the structural similarity (i.e. CAS-No. 59-92-7, CAS-No. 50-78-2).
Considerations on structural analogues:
The structural analogues are relatively similar to the studied compound, and are considered to be within the same mechanistic domain. The descriptor values of the
analogues are close to those of the studied compound. The analogues are evaluated correctly within the model. The following aspects have been considered for the selection and analysis of structural analogues:
Presence and number of common functional groups;
Presence and relevance of non-common functional groups;
Similarity of the ‘core structure’ apart from the (non-)common functional groups;
Potential differences due to reactivity;
Potential differences due to steric hindrance;
Presence of structural alerts;
Position of the double bonds;
Presence of stereoisomers.
Toxicokinetic / pharmacokinetic studies
Transfer into organs
- Transfer type:
- blood/brain barrier
- Observation:
- slight transfer
- Remarks:
- logP(BB) = 0.07
Metabolite characterisation studies
- Metabolites identified:
- no
Any other information on results incl. tables
The uncertainty of the prediction (OECD principle 4)
The training set is assembled from different sources, but the previous successful modelling (ref 1,2) supports the consistency of the present model. While the blood-brain barrier penetration is in principle relatively simple to measure, in rats for instance, it requires the sacrifice of the rat, therefore, reliable and large data-series are not common. Due to the complexity of the mechanism, the modelling has been relatively limited. While the size of the dataset is moderate, the endpoint values are very well distributed (roughly equal positive and negative logP values), the statistical quality (RMS, correlation coefficients etc.) of the model supports reliable predictions within the margins of the experimental error. The similarity of the analogues to the studied molecule supports prediction consistency. All similar compounds have been evaluated correctly within the model, also adding to consistency.
Considering the dataset, model statistical quality and prediction reliability, a reliability score (Klimisch score) “2” could be assigned to the present prediction.
The prediction reliability in terms of classification is estimated as 78 %.
Applicant's summary and conclusion
- Conclusions:
- Based on a QSAR calculation, methylhydroquinone is able to penetrate the Blood-brain barrier.
- Executive summary:
Based on a QSAR calculation, methylhydroquinone is able to penetrate the Blood-brain barrier.
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