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Please be aware that this old REACH registration data factsheet is no longer maintained; it remains frozen as of 19th May 2023.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

Administrative data

respiratory sensitisation, other
in silico
Type of information:
Adequacy of study:
weight of evidence
Study period:
August 5, 2016
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:
The model is been assessed according to the OECD principles for the validation of QSAR, to generate a transparent, understandable, reproducible and verifiable result.

Data source

Referenceopen allclose all

Reference Type:
other: Authoritative data base
Danish (Q)AR study for CAS no. 70969-58-3
Danish EPA
Bibliographic source:
Danish (Q)SAR Database, Division of Diet, Disease Prevention and Toxicology, National Food Institute, Technical University of Denmark,
Reference Type:
other company data
Report date:

Materials and methods

Test guideline
other: ECHA Guidance on information requirements and chemical safety assessment - Chapter R.06: QSARs and grouping of chemicals
Principles of method if other than guideline:
Battery algorithm
The models are made in three independent systems: CASE Ultra (CU), Leadscope Predictive Data Miner (LS) and SciQSAR (SQ). Based on predictions from each of the applied systems, a battery prediction is made using a so-called battery algorithm. The battery approach can give more reliable predictions and can also expand the applicability domain, which was shown in a previous pilot project including 32 different models and the three systems mentioned above (not published).

For the sensitisation endpoints, QSAR predictions are made in each of the independent QSAR model systems and combined into a battery prediction by using the criteria shown in the following table. The first column shows the total number of predictions (positive/negative) in domain. The next two columns show the number of positive and negative predictions, respectively. The final battery prediction based on the individual predictions is shown in the fourth column.

Total POS/NEG POS NEG Battery prediction (a) Remarks
in domain in domain in domain
3 3 0 POS_IN
3 0 3 NEG_IN
3 2 1 POS_IN
3 1 2 INC_OUT EXCEPT when CU and LS are both NEG_IN,
or (see remark) in this case the battery call is NEG_IN

(a) POS, positive; NEG, negative; INC, inconclusive; IN, inside applicability domain; OUT, outside applicability domain.
(b) Less weight is put on an SQ POS compared to LS or CU POS in cases where LS and CU agree on a NEG in AD prediction, because SQ in many cases has lower specificity than LS and CU.

Test material

Constituent 1
Chemical structure
Reference substance name:
Trioctyl benzene-1,2,4-tricarboxylate
EC Number:
EC Name:
Trioctyl benzene-1,2,4-tricarboxylate
Cas Number:
Molecular formula:
1,2,4-trioctyl benzene-1,2,4-tricarboxylate

Results and discussion

Any other information on results incl. tables




CASE Ultra



Respiratory Sensitisation in Humans






Applicant's summary and conclusion

Interpretation of results:
GHS criteria not met
(Q)SAR predictions by Danish Environmental Protection Agency models indicates that the substance is not predicted to exhibit respiratory sensitisation.
Executive summary:

(Q)SAR predictions by Danish Environmental Protection Agency models indicates that the substance is not predicted to exhibit respiratory sensitisation.