- Another successful Stakeholders' Day
- Sixth Stakeholders' Day: Start preparing registrations for 2013 now!
- Still a long way to go for downstream users
- ECHA reports to the Commission on the operation of the RE ACH and CLP Regulations and on non-animal testing
- QSAR Toolbox - increasing confidence in computational assessment
- ECHA Unit for Guidance and Forum Secretariat: Supporting the Forum activities
- Risk Management Interview - Part 2: Authorisation and restriction start to work
- Mr Dan Jørgensen, Vice-Chair of the Environment Committee: Happy with progress but some reason for criticism
- Dr Marion Healy: Non-animal testing methods and the assessment of nanomaterials are examples of special areas of interest
- Mr Benedikt Vogt, Enforcer in Freiburg, Germany: Overall situation is positive
- REACH at Helsinki Chemicals Forum
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Article related to: News from ECHA
QSAR Toolbox - increasing confidence in computational assessment
The REACH regulation requires registrants to collect and assess all data available for the substance they intend to register under REACH with regard to substance properties, exposure, use and risk management measures. This information must be documented in a registration dossier submitted to ECHA.
After collecting existing information the registrants need to identify data gaps and consider whether these gaps can be filled by using non-standard data before any new tests are conducted. This means that all available information is collected: in vivo and in vitro studies, information from human exposure, information from structurally-related substances (i.e. ‘read-across' and ‘chemical categories') and predictions from valid (Q)SARs*.
When collecting and assessing data, grouping (chemical categories) and read-across approaches have proven to be very useful for filling data gaps. These approaches rely on the fact that the substances in the group have physicochemical, toxicological and ecotoxicological properties that are likely to be similar or follow a regular pattern as a result of structural similarity. The QSAR Toolbox software developed by OECD and ECHA, in close cooperation, is a valuable tool for building meaningful categories and applying read-across approaches. It helps registrants to fill data gaps, and to assess the (eco)toxicity hazards without new testing under REACH. "The QSAR Toolbox aims to make (Q)SAR methodologies more acceptable by exploiting the success of grouping and building categories", says Doris Hirmann, the QSAR Toolbox project manager at ECHA. "One of the objectives of REACH is to promote the development of alternative methods for the assessment of hazards of substances. The development of the QSAR Toolbox supports this objective. Using alternative approaches also lowers the costs", Ms Hirmann continues.
The Toolbox can be used not only by the chemical industry but also by authorities and other stakeholders to identify the substances most likely to be hazardous and to verify the quality of non-test data. "At ECHA, for example, the tool supports the evaluation process", Ms Hirmann says.
Together with the OECD
The QSAR Toolbox project was initiated by the OECD member countries with the aim of improving the regulatory use of (Q)SAR methodologies. The first version of the Toolbox which emphasises the technological proof-of-concept was released in March 2008 with an update following soon after in December 2008. Version 2.0 was released in October 2010 and the current version 2.1 in February 2011. "We are now in phase two of the development process. ECHA has decided to support the further development of the Toolbox; we have signed a joint development agreement with the OECD meaning that we are the co-owners of the tool", says Ms Hirmann. OECD has the scientific lead in the development of the project and does a lot of the day-to-day management. "We contribute a lot in the IT area, and also offer our scientific knowledge", Ms Hirmann explains.
A chemical category is a group of chemicals which can be expected to behave in a similar manner. To explain the concept of grouping, Ms Hirmann has used a real life example with people:"First you identify a certain aim to group people, let's say I want to find people who share my dancing hobby. Then you look for certain characteristics like outgoing, nice and sporty from the group of people and identify those individuals who meet these characteristics. You can basically do the same with chemicals. You look for certain characteristics that relate to a certain endpoint. For example if you look for an endpoint related to genotoxicity you may want to identify those chemicals that react with DNA. Then you look for the chemicals that have experimental data available and apply a read-across. Read-across means that information on an endpoint for an untested chemical is predicted from data from a similar tested chemical. The basic idea is that entire categories of chemicals can be assessed when only a few are tested.
The similarities for grouping may be based on a common functional group, common constituents or chemical classes or similar carbon range numbers, an incremental and constant change across the category or the likelihood of common precursors and/or the breakdown products which result in structurally similar chemicals.
Dealing with uncertainty
How sure can one be that predictions based on the similarities of substances reflect reality? Ms Hirmann says that dealing with uncertainty is the most interesting and challenging question. "It is also interesting in terms of what you compare with. We would like to predict toxicity to human health and to the environment, but we don't have a golden standard for that, so we model. What we used to do was to model with experimental data. Now we try to compare the predicted values to the experimental values generated for example with rats, fish and daphnia. But still it's not reality; it is again a comparison to another model."
Ms Hirmann thinks that the regulators feel more confident with the read-across and grouping approach than with the statistical (Q)SAR approach. "In the statistical (Q)SAR prediction you have quite a lot of data and you can relate the data to a descriptor. Then you can see a trend and make a trend analysis for your substance and predict. But at the end you don't understand why. And you have outliers. It gets even more difficult when the endpoint is complex. The approach is considered to be a kind of black box, where you don't know exactly what is happening." In the read-across and categories approach one deals with fewer substances but there is more information available about each substance. "More attention is paid to the mechanisms inside the human body, than to the understanding of why those ten or fifteen substances belong to this one group. This gives confidence in the result", says Ms Hirmann. Ms Hirmann emphasises that statistical (Q)SAR models are not bad and that there are actually some that work very well: "We have received dossiers with predictions that could be accepted."
The OECD has created a list of five key validation principles for (Q)SAR models, and several guidelines on the application of (Q)SARs. These five principles and the guidelines also provide a basis for ECHA guidance and other supporting documents about the basic concepts of validity, applicability and acceptance of (Q)SAR models.
How does the Toolbox work in practice?
The Toolbox incorporates information and tools from various databases into a logical workflow. "Basically it has a workflow with six steps. First you enter your chemical into the database and retrieve the characteristics for this substance. This is called profiling. After that you retrieve any data that is available on your substance and/or use the information available in the tool. Then you start to build your category based on the characteristics that you identified before. Eventually you will find similar substances and hopefully these similar substances have experimental data available. The next step is that you apply either read-across or trend analysis to fill the data gap. The last step is the generation of reports", Ms Hirmann explains. The Toolbox software can be downloaded free of charge with the various databases, stored models, profiles and rule basis from the QSAR Toolbox website http://www.qsartoolbox.org.
Ms Hirmann says that the Toolbox can be of great assistance when registrants prepare for the upcoming 2013 and 2018 REACH deadlines. "For the first deadline there was a lot more data available on substances. For the 2013 and 2018 deadlines substances with less data will have to be registered. This means more experimental testing, in-vitro testing or using these non-test methods to fill the data gaps."
"You can do a lot with the Toolbox and I encourage registrants to use it. However, it's not the most simple software. In the end, the quality of information you get out of the Toolbox depends on how you build your prediction. This requires expertise and experience", Ms Hirmann says.
The QSAR Toolbox project is appreciated among the OECD member countries, and both the OECD and ECHA are committed to further developing the tool in a four-year collaborative project. "The development is progressing very well. One of the future challenges is to improve the methodology of the tool to be able to reduce uncertainty", says Ms Hirmann. The release of the next version is scheduled for October 2012. "Version 3.0 is envisioned to have more databases incorporated, improved rule basis and profilers to identify characteristics of chemicals, especially profilers that identify specific mechanisms or modes of action."
Evaluation of non-test data
The non-standard information has to be equivalent to the information obtained from the standard test data. The key point is that the non-standard data must be suitable for an adequate risk assessment to ensure the substance can be used safely and also for adequate classification for hazard communication.
Registrants have to justify these adaptations of the standard information requirements in the registration dossier and provide scientific explanations why the non-standard data is nevertheless adequate. Within this context it should be noted that industry remains responsible for assessing the intrinsic properties for hazard and/or risk assessment and classification; hence they are responsible for making the technical and scientific judgments. However, ECHA can require missing information to be provided, including tests if the data waivers or non-standard data do not meet the information needed for registration, as an outcome of the dossier evaluation processes.
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