Advisory self-classifications for 54,135 substances based on (Q)SAR predictions from the Danish (Q)SAR database, VEGA QSAR and the OECD QSAR Toolbox

Nikolai Georgiev Nikolov, Henrik Tyle, Magnus Løfstedt, Eva Bay Wedebye

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Lack of experimental data on toxicological properties make it difficult for companies to self-classify the chemical substances they import or produce. To address this issue, 80,085 pre-registered and/or registered REACH substances from the Danish (Q)SAR Database were evaluated by (Q)SAR. For this purpose predictions primarily from the Danish (Q)SAR Database were used, supplemented with predictions from VEGA QSAR and a number of the profilers available from the OECD QSAR Application Toolbox. The following classification endpoints were addressed:

• Mutagenicity: Muta. 2
• Carcinogenicity: Carc. 2
• Reproductive toxicity (possible harm to the unborn child): Repr. 2
• Acute oral toxicity: Acute Tox.1-4
• Skin irritation: Skin Irrit. 2
• Skin sensitisation: Skin Sens. 1
• Danger to the aquatic environment: Acute 1, Chronic 1-3

Algorithms were developed for each classification endpoints to combine predictions to reach a final call in an attempt to reach further reliability and to best comply with the classification criteria. No advisory predictions were based on a positive prediction from a single system, and if only based on a battery prediction (majority vote from 3 systems) the third system was required not to give a negative prediction in applicability domain. This resulted in a list with a total of 54,135 substances with one or more advisory self-classifications. The list is available from the Danish Environmental Protection Agency homepage.
Original languageEnglish
Publication date2018
Number of pages2
Publication statusPublished - 2018
EventQSAR2018: 18th International Conference on QSAR in Environmental and Health Sciences - Rikli balance hotel , Bled, Slovenia
Duration: 11 Jun 201815 Jun 2018
Conference number: 18th


LocationRikli balance hotel
Internet address


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