2011 Patlewicz - Non Testing Approaches Under REACH Help or Hindrance Perspectives From Practitioner

8/13/2019 2011 Patlewicz - Non Testing Approaches Under REACH Help or Hindrance Perspectives From Practitioner

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SAR and QSAR in Environmental ResearchVol. 22, Nos. 12, JanuaryMarch 2011, 6788

Non-testing approaches under REACH help or hindrance? Perspectivesfrom a practitioner within industry yG. Patlewicz *, M.W. Chen and C.A. Bellin

DuPont Haskell Global Centers for Health and Environmental Sciences, Newark, USA

(Received 7 July 2010; in final form 26 August 2010 )

Legislation such as REACH strongly advocates the use of alternative approachesincluding in vitro , (Q)SARs, and chemical categories as a means to satisfy theinformation requirements for risk assessment. One of the most promisingalternative approaches is that of chemical categories, where the underlyinghypothesis is that the compounds within the category are similar and thereforeshould have similar biological activities. The challenge lies in characterizing thechemicals, understanding the mode/mechanism of action for the activity of interest and deriving a way of relating these together to form inferences about thelikely activity outcomes. (Q)SARs are underpinned by the same hypothesis butare packaged in a more formalized manner. Since the publication of the WhitePaper for REACH, there have been a number of efforts aimed at developingtools, approaches and techniques for (Q)SARs and read-across for regulatorypurposes. While technical guidance is available, there still remains little practicalguidance about how these approaches can or should be applied in either theevaluation of existing (Q)SARs or in the formation of robust categories. Here we

provide a perspective of how some of these approaches have been utilized toaddress our in-house REACH requirements.

Keywords: REACH; (Q)SAR; chemical category; QMRF; QPRF

1. Introduction

REACH (Registration, Evaluation, Authorisation and restriction of CHemicals) [1] is thenew EU legislation that came into force in June 2008 which superseded Directive 79/831/EEC [2]. It calls for equivalent information requirements for all new and existing chemicalsmanufactured or imported at quantities of 1 tonne or greater per annum. Under Directive79/831/EEC [2], Existing chemicals comprised substances introduced between January1971 and September 1981 and were listed on EINECS (European INventory of Existingcommercial Chemical Substances), whereas New Chemicals were those substancesintroduced subsequently and listed on ELINCS (European LIst of Notified ChemicalSubstances).

The specific information requirements for REACH depend on tonnage bands whichare described in Annexes VII-X of the REACH legal text [1]. Annex XI provides a

*Corresponding author. Email: [email protected] at the 14th International Workshop on Quantitative StructureActivity Relationshipsin Environmental and Health Sciences (QSAR2010), 2428 May 2010, Montreal, Canada and the

ICCA-LRI & JRC Workshop Integrating New Advances in Exposure Science and Toxicity Testing:Next Steps, 1617 June 2010, Stresa, Italy.

ISSN 1062936X print/ISSN 1029046X online 2011 Taylor & Francis

DOI: 10.1080/1062936X.2010.528448http://www.informaworld.com


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framework for fulfilling these information requirements by other means (examples includein vitro , Weight of Evidence (WOE), (Q)SARs, chemical grouping, etc.), thus limitingvertebrate testing to the fullest extent possible. There is an obligation to carry outvertebrate testing only as a last report and to consider all other options before performingor requiring testing as described by Articles 13(1) and 25(1).

The legal text of REACH [2] is a starting point in addressing practically theinformation requirements. The REACH technical guidance provides endpoint-specificguidance [3,4] by providing a workflow, the Intelligent Testing Strategy (ITS), for how tomeet the hazard characterization requirements for a specific endpoint. The technicalguidance identifies the available approaches, e.g. in vivo test methods, in vitro , (Q)SARsetc., in brief for that endpoint and how to evaluate their respective outputs. The workflow(the ITS) provides the framework for how the information available/generated should beintegrated to arrive at an overall conclusion for hazard characterization purposes(i.e. Classification & Labelling (C&L) and Risk Assessment.) The ITS does not address allthe practical aspects of how to perform an integrated assessment, but it does nonetheless

provide a framework to encourage assessors to shift from a checkbox mode of generatingexperimental data in a vacuum to one where there is a consideration of the synergiesbetween different data, endpoints and their respective modes/mechanisms of action.

Annex XI describes the potential application of (Q)SAR results when testing does notappear to be necessary as the same level of information could be potentially obtained bymeans other than vertebrate testing. Annex XI contains the following wording for (Q)SARuse:

Results obtained from valid qualitative or quantitative structureactivity relationshipmodels (Q)SARs may indicate the presence or absence of a certain dangerous properties.Results of (Q)SARs may be used instead of testing when the following conditions are met:

. Results are derived from a (Q)SAR model whose scientific validity has beenestablished

. The substance falls within the applicability domain of the QSAR model

. Results are adequate for the purpose of classification and labelling and/or riskassessment, and

. Adequate and reliable documentation of the applied method is provided.

The Agency in collaboration with the Commission, Member States and interestedparties shall develop and provide guidance in assessing which (Q)SARs will meet theseconditions and provide examples.

The wording really emphasizes how information provided by (Q)SARs may be used in

place of experimental data provided certain conditions are met. In practice, it could beforeseen that (Q)SAR information will be used as one part of a WOE evaluation ratherthan simply a direct replacement for an experimental test. More detailed technicalguidance for the use of (Q)SARs is available from EChA [5].

It is perhaps useful to clarify the terminology of these conditions. Scientific validity of the (Q)SAR makes reference to the internationally agreed OECD principles for thevalidation of (Q)SARs. These were adopted by the 37th Joint Meeting of the ChemicalsCommittee and Working Party on Chemicals, Pesticides and Biotechnology in November2004 [6]. Preliminary guidance developed by the former European Chemicals Bureau(ECB) provided some context to interpret these different principles [7] which was

subsequently taken up by the OECD (Q)SAR group, expanded and published as anOECD Guidance document in 2007 [8].

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Scientific validity is just one condition that needs to be satisfied for a (Q)SAR to bepotentially used under REACH. A major consideration is the domain of applicability,i.e. demonstrating and justifying that the (Q)SAR model of interest is relevant for thechemical under evaluation. The concept of the domain of applicability was defined as partof an ECVAM workshop in 2004. The definition proposed [9] was as follows:

The applicability domain of a (Q)SAR model is the response and chemical structure space inwhich the model makes predictions with a given reliability.

Indeed, there are many different approaches to characterizing a domain, and whethera substance lies within the domain or not will ultimately depend on the approach applied.One methodology may categorize a substance of interest as being within the domain,whereas another might conclude that it is out of domain. The question of which domainapproach to use requires careful examination of the methodology used to characterize thedomain, and to what extent the model being used provides reasonable predictions forother related substances. The common means of characterizing a domain for a QSAR

model is on the basis of its training set of chemicals, i.e. those substances that were used toderive the QSAR model. This type of information can be encoded in the form of structuralkeys, fragments or fingerprints effectively these provide a means of characterizing thedomain in terms of its representative structural components. It also helps to address thequestion of whether the chemical of interest resembles any of the training set chemicals.Another means of characterizing the domain might be in terms of the training set rangesof the descriptors used in the derivation of the model. An example where the training setranges of the descriptors could be utilized are for the Potts and Guy skin permeability(Kp) QSAR model [10] where log Kow and molecular weight (MW) form the inputdescriptors. Alternatively, the domain could be characterized on the basis of commonmode/mechanism of action. The ECOSAR [11] program is able to identify substanceslikely to exert their aquatic acute toxicity effects via general narcosis [12]. In this case,neutral organics is triggered as a class within the program.

Many of these types of methodologies have been implemented into software for moreroutine use. AMBIT Discovery v0.04 (released May 2006 by Ideaconsult Ltd, Bulgaria)and freely accessible from ambit.acad.bg/downloads/AmbitDiscovery/ provides a numberof algorithms to characterize the domain of a QSAR model. The actual statisticalapproaches are described in more detail by Netzeva et al . [9]. Domain Manager v1.0,developed and commercialized by the Laboratory of Mathematical Chemistry (LMC)(University Prof. As. Zlatarov, Bourgas, Bulgaria), provides the methodology to codifydomains on the basis of training set descriptor values and/or their structural fragments

(e.g. by using atom-centred fragments). Multi-component approaches where severaldomains are characterized to capture the descriptor ranges, the structural space, themechanistic domain and the metabolic space are also feasible. Such an approach wasoutlined by Dimitrov et al . [13] and is in fact practically implemented for the models withinthe TIMES system (http://oasis-lmc.org/?section=software&swid=4). TIMES is a hybridexpert system developed by LMC to make predictions for a wide range of chemicals usinga framework of interconnected models. TIMES incorporates structuretoxicity andstructuremetabolism relationships through a number of transformations, simulatingmetabolism and interaction of the generated reactive metabolites with nucleophiles, eitherskin proteins or DNA. The metabolism simulator mimics metabolism using 2D structural

information. Metabolic pathways are generated based on a set of hierarchically orderedprincipal transformations including spontaneous reactions and enzyme-catalysed

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biotransformation reactions (phase I and II). The platform includes models for skinsensitization, Ames mutagenicity and in vitro chromosomal aberration.

Characterizing the domain is one aspect of defining the models applicability; thesecond is in determining how well the substance of interest is predicted relative to othersimilar analogues. This is critical to establish whether related substances are reasonablypredicted relative to their experimental results, and to provide the confidence that a robustand reliable prediction is feasible for the substance of interest. Tools for identifyingsuitable analogues include the US EPAs Analog Identification Methodology (AIM)(http://www.epa.gov/oppt/sf/tools/aim.htm), Leadscope (www.leadscope.com), Toxmatch[14] (http://ecb.jrc.ec.europa.eu/qsar/qsar-tools/index.php?c=TOXMATCH), OECDToolbox (http://www.oecd.org/document/54/0,3343,en_2649_34379_42923638_1_1_1_ 1,00.html) (all referenced in EChA Guidance, Chapter R6 [5]).

The technical guidance on (Q)SARs addresses the principles, and the need torationalize the reliability and adequacy of a (Q)SAR result, as well as some of the availablesoftware tools in some detail in Chapter R6 [5]. The conditions for (Q)SAR use under

REACH are well summarized in the following graphic (Figure 1), such that an Adequate(Q)SAR result is one derived from a (Q)SAR model whose scientific validity has beenestablished, where the substance lies within the domain of the (Q)SAR model andaddresses an endpoint that is relevant for REACH.

Of particular note is the importance of documenting the characteristics of a model andits prediction. Work under the former QSAR Working Group, a subgroup under the EUsTechnical Committee for New and Existing Substances (TCNES) agreed on reportingformats (templates) to capture the key pieces of information needed for REACH for botha (Q)SAR model and its prediction. Two formats were proposed, and these are known as

Figure 1. Adequacy of a QSAR result for use under REACH.



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the QSAR Model Reporting Format and QSAR Prediction Reporting Format, abbre-viated as QMRF and QPRFs. Both of these formats are structured on the OECDValidation Principles. Several completed examples of these formats are provided on thewebsite of the former ECB (see http://ecb.jrc.ec.europa.eu/qsar/qsar-tools/index.php?c=QRF for illustrative examples).

Annex XI also contains the specific wording for the use of grouping methods (read-across and chemical categories). Specifically:

Substances whose physicochemical, toxicological and ecotoxicological properties arelikely to be similar or follow a regular pattern as a result of structural similarity may beconsidered as a group or category of substances. Application of the group conceptrequires that physicochemical properties, human health effects and environmental effectsor environmental fate may be predicted from data for a reference substance within thegroup by interpolation to other substances in the group (read-across approach).This avoids the need to test every substance for every endpoint. The similarities may bebased on:

(1) a common functional group(2) the common precursors and/or the likelihood of common breakdown products via

physical and biological processes, which result in structurally similar chemicals, or(3) a constant pattern in the changing of the potency of the properties across the

category . . .

While the development of chemical categories and (Q)SARs is underpinned by thesame principles of chemical similarity, there is no specific requirement to validate acategory. Most likely this is because ad hoc categories have been routinely used under theHigh Production Volume (HPV) programmes within the US and under the OECD. UnderREACH, the adequacy and reliability of the category approach must be substantiated anddocumented in a format known as the Category (Analogue) Reporting Format(CRF/ARF). An ARF is used when a read-across is carried out for one substance toanother. A CRF considers a group of three of more substances. The technical guidance forchemical grouping is described in more detail in Chapter R6. Case studies [15] developedby the drafting group authors of Chapter R6 are available at the former ECB website(http://ecb.jrc.ec.europa.eu/DOCUMENTS/QSAR/EUR_22481_EN.pdf).

Read-across is the data gap filling mechanism used to interpolate predictions as part of the category. The approach can be qualitative or quantitative depending on the typeof data available. Quantitative approaches might make use of a trend analysis (derivationof a local QSAR) using the category members themselves or rely on external QSARs or

expert systems to derive the necessary predictions. It is beyond the scope to discuss theexpert systems and other (Q)SARs here. There have been several reviews that describethe state of the art of (Q)SARs for a number of REACH endpoints [1619]. Moreover, thetechnical guidance for the different endpoints under REACH describes the availability of different (Q)SARs for each of the endpoints in turn. These (Q)SARs are not accepted forregulatory use; they are simply provided as a source of available information.

For categories, the OECD Application Toolbox is probably the best-known tool sincethe prototype release in early 2008. Phase 2, a 4-year project funded by the EuropeanChemicals Agency, EChA, commenced late 2008. The Toolbox aids in the development,evaluation, justification and documentation of chemical categories. It can verify whether

a substance is part of an existing established category e.g. EPA, OECD HPV category. Italso possesses the functionality to develop endpoint-specific categories, making use of



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mode/mechanistic/empirical/structural profilers. It is envisaged that the OECD Toolboxwill be extensively used in evaluating the categories and (Q)SARs submitted for REACH.The OECD Toolbox v1.1.02 contains a handful of external QSARs that can be used to filldata gaps as part of the category implementation process. Currently only a limitedselection of these exist notably the modules from EPIWIN such as KOWWIN, ECOSAR but neither the validity of these specific models nor their domain have been fullycharacterized to meet the conditions of REACH.

Other tools that can play a role in evaluating categories include the industry-fundedAMBIT, and the Toxmatch v1.06 chemical similarity tool both of which were developedby Ideaconsult Ltd and have been previously cited.

While there is extensive technical guidance for the development of chemical categoriesand the use of (Q)SARs for regulatory purposes such as REACH, there remains littlepractical guidance or example case studies for how some of these tools can or should beapplied in either the evaluation of existing (Q)SARs or the formation of robust categories.The study here aims to illustrate, with several examples, some of the practical steps and

challenges faced in trying to address the information requirements under REACH.

2. Methods

Here we describe some of the activities conducted in-house in a bid to exploit the use of non-testing approaches. The objective of this study was to illustrate through severaldifferent case studies some of the practicalities of using non-testing approaches.

Our starting point was to evaluate what opportunities existed for (Q)SAR use thatcould be readily applied in-house. An extensive summary of appropriately documented

(Q)SARs (i.e. QMRFs) is not currently available externally. The former ECB, inconsultation with the EU QSAR Working Group, took the initiative to start buildinga database of evaluated (Q)SARs, known as the JRC QSAR Model Database (http://qsardb.jrc.ec.europa.eu/qmrf/), but this currently stands at 34 published (Q)SARs. To thatend, we collected a list of available (Q)SAR models for each of the different REACHendpoints based on what was described in the Endpoint Technical guidance for REACH,as described in Chapters R7a, R7b and R10. Each of the models was briefly examinedto determine their potential application in-house: to what extent was the training setpublished and therefore could be readily re-created, to what extent any descriptors listed inthe models could be calculated with available software programs in-house, and to what

extent were any of the expert systems listed already under licence or freely available.Our list of models comprised local QSAR models, global QSAR models, and expert

systems as well as classification schemes which addressed 27 super endpointsencompassing a range of mammalian toxicity endpoints (predominantly acute toxicityendpoints), environmental fate, ecotoxicity and physical properties. In total, 152individual models were identified that were of potential utility.

Characterizing each of these with respect to the OECD principles and documentingthem in associated QMRFs for submission to the JRC Inventory was not practicallyfeasible given resource constraints. Instead, effort was focused on endpoints where thedata gaps were most common for the substances undergoing registration; that is, if there

was a data gap for bacterial mutagenicity for one substance vs. a log Kow need for manysubstances, preference was given to evaluating a log Kow model.



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Three different case studies are described to illustrate the scope of some of theevaluation efforts made.

3. Results and discussion

The first two case studies explore how (Q)SAR models can be characterized and appliedfor specific substances as replacements for experimental testing. The first of these usesa well-established (Q)SAR model already in existence. The second example illustrates howa new (Q)SAR model can be derived using the OECD Toolbox. The third case studyinvestigates how (Q)SAR approaches are useful in the evaluation and substantiation of chemical categories.

3.1 Case study 1: characterization of KOWWIN v1.67 with respect to the OECD principles for the purposes of drafting a QMRF

This case study focuses on a well-established QSAR model for log Kow, KOWWIN v1.67(US EPA). Log Kow was a significant data gap for a number of substances undergoingregistration. This example aims to provide some practical insights of how model validitycan be potentially addressed and how the information generated can be documented in aQMRF. An example compound is used to show how a prediction from this model could bepotentially documented in a QPRF. Note the intent here is not to be prescriptive abouta given approach or to endorse a specific methodology. We merely aim to provide apractical example of how we chose to address the conditions under Annex XI for thismodel. To establish scientific validity, each of the OECD principles was considered in turn.Suggestions for how the information could be represented in a QMRF are provided asappropriate (note a QMRF itself is not provided).

Defined Endpoint (Principle 1): log Kow, the log to the base ten of the octanolwaterpartition coefficient, is an endpoint that needs to be addressed under Annex VII for allsubstances imported and manufactured at levels greater than 1 tonne per annum. Of theavailable models for log Kow, KOWWIN v1.67 (which is freely available from the US EPAas part of the EPIWIN v4 program) was by far the most convenient to apply and thereforemerited further evaluation to address the conditions for use under REACH. While thismodel is routinely used and well established under the PMN (premanufacture notice)process and the HPV programme under the US EPA, to our knowledge no QMRF hasbeen published or is available. While the documentation in the help manual is extensive

and significantly improved from previous versions, an evaluation exercise to characterizethe model was undertaken.

The training set of compounds for KOWWIN v1.67 is made available by thedevelopers SRC, Inc., and the US EPA at http://esc.syrres.com/interkow/KowwinData.htm. This training set as provided was used to verify the model algorithmand extract an applicability domain. The latter would be used to determine whichsubstances of interest for registration nominally fell within the KOWWIN domain or not.A substance found to fall within the domain was categorized as preliminarily valid. Foractual use under REACH, the substance would be evaluated further to address theinformation requirements laid out in the associated QPRF.

Defined Algorithm (Principle 2): based on the definition of Dearden et al . [19] of an expert system where any formalized system, not necessarily computer-based,



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which enables a user to obtain rational predictions about the toxicity of chemicals,KOWWIN v1.67 was categorized as an expert system since it readily generates predictionsfrom simply entering a chemical structure. The algorithm contained within KOWWINis a regression equation of the form

Log Kow X f i ni X c j n j 0:229

where ( f i ni ) is the summation of f i (the coefficient for each atom/fragment) times ni (thenumber of times the atom/fragment occurs in the structure) and (c j n j ) is the summationof c j (the coefficient for each correction factor) times n j (the number of times the correctionfactor occurs (or is applied) in the molecule).

Domain of Applicability (Principle 3): KOWWIN does not provide any explicitmethodology of evaluating the domain. Chapter 6.2 in the KOWWIN user manualprovides some guidance in terms of considering the MW of the chemical of interest andwhether it is represented by the fragment descriptors as listed in the user manualsAppendix, specifically Appendix D. In this case study, the Domain Manager v1.0software, a commercial program developed by LMC, was used to extract a structuraldomain from the KOWWIN training set on the basis of atom-centred fragments. Theapproach is described in more detail by Dimitrov et al. [13]. A set of rules is used to reflectthe effect of different neighbours on a specified atom. The application of these rules allowsthe extraction of a set of atom-centred fragments that can be used to characterize thestructural domain of the atoms presented in a certain set of chemicals. The maximum andminimum values of MW were also determined as a second criterion for assessing theKOWWIN domain. The MW range was as follows: minimum MW 18.02, maximum MW719.92. In other words, for a substance to be considered within the domain of KOWWIN

for our purposes, it had to be 100% within the structural domain as determined byatom-centred fragments and have a MW value between 18 and 719.

Appropriate measures of goodness-of-fit, robustness and predictivity to satisfyPrinciple 4 were taken directly from the KOWWIN help manual, Chapter 6.2. Theinformation on internal performance was given as n 2447, correlation coefficient(r 2 ) 0.982, standard deviation (stdev) 0.217, whereas external validation for predictiv-ity on a large test set comprised the following information: n 10946, correlationcoefficient ( r2 ) 0.943, standard deviation (stdev) 0.479, average MW 258.98.

Principle 5 addresses the mechanistic interpretation of a model. Since the KOWWINmodel was derived empirically, the only inference that could be readily made was with

respect to the contribution the individual fragment descriptors made towards hydropho-bicity. Fragments with positive coefficients, e.g. aliphatic C CH2, contributed toincreasing the log Kow, where as fragments with negative coefficients such as CHO(aliphatic aldehyde), contributed to lowering the log Kow.

Demonstrating scientific validity with respect to the OECD principles only addressespart of the conditions of (Q)SAR use under Annex XI. For a substance of interest,assessment of the domain of applicability and documentation in a QPRF is also required.To illustrate the approach adopted, an ether (1-propoxybutane as (CAS# 3073-92-5),shown in Figure 2) was chosen as an example substance of interest. This was introducedinto the Domain Manager v1.0 software as a test set versus the KOWWIN dataset as the

training set. Figure 3 shows a screen shot of the Domain Manager interface where theether satisfies the structural domain criterion of KOWWIN as described above. The MW



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of this substance is 116.2, which is well within the MW range of the KOWWINtraining set.

The ether meets the applicability domain conditions as defined by the structuralfragments and MW range. Accordingly, we would expect a log Kow estimate for this etherto be reasonable. This information could be captured in Section 3.3, Applicability domain(OECD principle 3) a. of a QPRF.

Belonging to an applicability domain of a model is no guarantee of a reasonableprediction. Within the QPRF, some evaluation of related analogues and their predictionsrelative to their experimental values needs to be captured. These analogues may be takenfrom the training set of the (Q)SAR model itself, i.e. identifying whether there are anyrelated ethers within KOWWIN or from other sources, and how their predicted/experimental log Kow values compare. In this case study, Toxmatch v1.06 was used tosearch and retrieve analogues from the KOWWIN training set to enable a comparison of experimental and estimated log Kow values. The training set of the KOWWIN model wasimported into the Toxmatch v1.06 software. The Tanimoto distance (fingerprints, kNN)was chosen as the similarity index approach. The Tanimoto distance (fingerprints, kNN)

method calculates the average Tanimoto index between the fingerprints for each querychemical within the KOWWIN dataset and the fingerprints for the k most similar

Figure 3. Domain Manager Interface demonstrating how the substance of interest from Figure 2is within the structural domain of KOWWIN.

Figure 2. Example substance of interest.



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chemicals from the set (where k 10 as default). The most similar chemicals are those with

the highest Tanimoto index values. 1-propoxybutane, our substance of interest, was thenimported as the test set chemical and a pairwise similarity performed using the Tanimotodistance relative to the training set (as shown in Figure 4). The most similar analoguesaccording to this Tanimoto distance index were then reviewed, where an index of 0.7 wasarbitrarily taken as a quantitative measure of most similar. Four other analogues wereidentified which are shown in Table 1 together with their respective similarity index values,experimental log Kow values and estimated log Kow values (by KOWWIN). With theexception of tetahydrofuran, which is cyclic ether, all the acyclic ethers were reasonablypredicted by KOWWIN v1.67 compared with their reported experimental log Kow values.Accordingly, the estimated log Kow of 1-propoxybutane, our test substance, is expected to

be a realistic prediction. This information can be inserted into Section 3.3 Applicabilitydomain (OECD principle 3) of a QPRF.

Section 4. Adequacy of a QPRF within a QPRF document is optional but provides auseful placeholder to summarize and substantiate why a prediction can be relied upon fora given substance. In the example here, the following text could be inserted in the QPRFby way of conclusion:

KOWWIN v1.67 is a valid model in accordance with the OECD principles. Anevaluation of its training set was made for the purposes of extracting a structural domain.1-propoxybutane (CAS# 3073-92-5) satisfies this structural domain requirement 100%and lies within the MW range of the training set. In addition, an evaluation of related

analogues within the training set using Toxmatch v1.06 identified four other analogueswith predicted and experimental values in good agreement. This demonstrated that acylic

Figure 4. Toxmatch v1.06 interface showing the training set of KOWWIN and the test set substanceof interest.



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ethers such as 1-propoxybutane are well represented as a chemical class in the training setwith robust predictions. Thus 1-propoxybutane satisfies the applicability domainrequirement. The prediction satisfies the needs for both C&L and Risk Assessment.Adequate documentation has been provided by way of this QPRF and the associatedQMRF for KOWWIN. In accordance with Section 1.3 for (Q)SARs within Annex XI of the REACH text, all the conditions have been satisfied.

3.2 Case study 2: demonstration on how a local QSAR can be developed using the OECDToolbox to address a data gap for short-term toxicity to aquatic invertebrates

This case study illustrates how the OECD Toolbox can be used to derive to fill a data gapfor aquatic toxicity to aquatic invertebrates for a given substance of interest(1-propoxybutane (CAS# 3073-92-5)). An endpoint-specific category is derived and atrend analysis performed to estimate the aquatic toxicity to Daphnia magna (specificallythe 48 h EC50). 1-propoxybutane (CAS# 3073-92-5) was imported into the OECDToolbox v2.0 (currently undergoing beta-testing) using the CAS number as a search term.A series of different profilers housed within the Toolbox, which aim to encode informationabout likely modes/mechanisms of action, were applied to the substance of interest. Theether possessed no overt electrophilic groups, as demonstrated by the lack of bindingflagged by the protein binding profiler. In addition, it triggered general narcosis as themode of action for acute aquatic toxicity on the basis of three separate profilers(OASIS Mode of Action, ECOSAR and Verhaar [20]). Figure 5 shows the Toolboxinterface.

In general, chemicals which are of low reactivity exert their toxic effects towards

aquatic organisms by narcosis mechanisms. Hydrophobicity is the key chemical feature of narcotic organic compounds in determining their effects in aquatic systems [21]. The mostcommon measure of hydrophobicity is log Kow. The classic general narcosis QSAR wasdeveloped by Konemann [22] for toxicity to Poecilia reticulate (guppy) which relatedlog Kow to LC50. This was subsequently found to be applicable for prediction of toxicityin a range of aquatic species, not only other fish species but other organisms such asD. magna . It is often referred to as the baseline toxicity equation. Compounds which fitthis equation are usually referred to as general narcotics. Evaluation of the profilers foraquatic toxicity shows that this acyclic ether flags the same mode of action from threeseparate classification schemes i.e. its toxicity will be exerted by a narcotic mode of

action. Therefore, the toxicity in Daphnia is expected to be well correlated withthe hydrophobicity as modelled by log Kow. Accordingly, it would be expected that a

Table 1. Related analogues with their associated estimated and experimental log Kow values.

SMILES CAS number Chemical nameKowwin

Est Exp log KowTanimotodistance

C1CCCO1 109-99-9 Tetrahydrofuran 0.94 0.46 0.765CCCOCCC 111-43-3 Di( n-propyl) ether 2.03 2.03 0.706CCCCOCCO 111-76-2 Ethylene glycol n-butyl ether 0.57 0.83 0.727CCCCOCCCC 142-96-1 Di- n-butyl ether 3.01 3.21 0.944CCCCOCC 628-81-9 Butyl ethyl ether 2.03 2.03 0.941



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read-across could be performed from other substances acting by a narcosis mode of actionwhere log Kow provided the context of similarity.

No data were available for the ether, hence an initial category was developed using theECOSAR Neutral Organics profiler. A set of 2326 substances including 1-propoxybutane(CAS# 3073-92-5) was identified. Of those, only 167 substances had EC50 values at the48 h time point for D. magna . Removal of substances with low water solubility usingthe EPIWIN water solubility model left 157 substances. Further sub-categorization on thebasis of substance type, protein binding and MOA OASIS gave rise to 102 substances. Thefinal sub-categorization chosen was to retain only substances with C and O as chemicalelements. A dataset of 67 analogues with 48 h EC50 values resulted in our refined category.The sub-categorizations performed assured similarity in a structural and mechanisticcontext to the substance of interest: only substances that were baseline narcotics, i.e. werenot electrophilic and were structurally related to substance of interest, were included in thecategory. A trend analysis was performed which resulted in a good correlation beingestablished between the EC50 values (as a log molar equivalent) and the log Kow(as estimated by KOWWIN) (see Figure 6). The resulting algorithm for the 48 h EC50took the form:

Y 1:23 0:990 log Kow

where Y was the EC50 expressed as log(1 mol L 1 )

n 67, r2 0:925, r2adj 0:923, std dev 0:511, F 797

The predicted EC50 (mg L1

) of 1-propoxybutane (CAS# 3073-92-5) was determinedto be 21.6 mg L 1 . The training set of the derived model is shown in Table 2.

Figure 5. OECD Toolbox interface for the substance of interest.



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The trend analysis (local QSAR) generated would be documented in a QMRF and theprediction for 1-propoxybutane would be summarized in a QPRF. The OECD Toolboxv2.0 has established customized formats (TMRF and TPRF) that are based on theQMRFs and QPRFs (not provided) which can be automatically generated as part of thereport functionality within the Toolbox.

Rather than use a previously developed QSAR for acute aquatic toxicity such as thosecontained within ECOSAR (US EPA), we have shown that it is feasible to address a datagap for a substance by developing an endpoint-specific category and performing a trend

analysis. The local QSAR derived based on the category members was used to estimate theacute toxicity to Daphnia for substance 1-propoxybutane (CAS# 3073-92-5). The QSARderived can be shown to be valid in accordance with the OECD principles. The endpoint isdefined; an EC50, the concentration estimated to immobilize 50% of Daphnia after 48 hexposure in accordance with OECD Test Guideline 202 or equivalent was used as thesource of experimental data. An unambiguous algorithm was derived where there is a clearinterpretable relationship between a log molar equivalent of EC50 and the log Kow.Appropriate measures of goodness-of-fit have been provided to demonstrate that therelationship is robust. A mechanistic interpretation is feasible since substances selected forinclusion to the category were similar with respect to their structural features and mode of

action. The log Kow used in the relationship is a well-established descriptor used to modelhydrophobicity which is relevant for estimating general narcotics. A domain of applica-bility was automatically created within the Toolbox by virtue of the sub-categorizationsperformed. These are automatically translated as logic rules when documented in theTMRF. Within the Toolbox program, the domain description forms a key component of the QSAR model such that the domain would be automatically verified for any newsubstance of interest requiring a 48 h EC50 prediction.

3.3 Case study 3: development of categories

Under the HPV programmes, categories are typically constructed in an ad hoc fashionusing structural similarity or chemical class as a basis. Under REACH, deriving larger

Figure 6. Trend analysis for case study 2.



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Table 2. Training set of 67 analogues for case study 2.

CAS SMILES EC50_mg L 1(exp)logKow EC50_mg L 1(est) MW

95-63-6 c1(C)c(C)cc(C)cc1 3.60 3.64 1.78 120.19110-54-3 C(C)CCCC 3.88 3.29 2.82 86.18110-82-7 C1CCCCC1 3.79 3.18 3.56 84.1660-29-7 C(C)OCC 1071.38 1.05 399.42 74.1267-64-1 C(C)(C) O 13964.33 0.24 5843.46 58.0891-20-3 c12c(cccc1)cccc2 3.86 3.17 5.50 128.1798-82-8 c1(C(C)C)ccccc1 4.15 3.45 2.72 120.1971-43-2 c1ccccc1 16.10 1.99 48.94 78.1178-93-3 C(C)( O)CC 5092.95 0.26 2368.81 72.11106-42-3 c1(C)ccc(C)cc1 5.87 3.09 5.48 106.17108-38-3 c1(C)cc(C)ccc1 5.52 3.09 5.48 106.17108-88-3 c1(C)ccccc1 10.73 2.54 16.59 92.14111-76-2 C(CCC)OCCO 998.90 0.57 1910.70 118.1767-56-1 CO 14952.63 0.63 7968.89 32.04

107-41-5 C(C)(C)(O)CC(C)O 3202.75 0.58 1854.91 118.17109-66-0 C(C)CCC 9.73 2.80 7.23 72.15120-12-7 c12c(cc3c(cccc3)c1)cccc2 0.05 4.35 0.52 178.2371-23-8 C(O)CC 4215.40 0.35 1593.45 60.1078-83-1 C(C)(C)CO 1367.55 0.77 757.92 74.1291-57-6 c12c(cc(C)cc1)cccc2 1.60 3.72 1.75 142.2090-12-0 c1(C)c2c(cccc2)ccc1 1.42 3.72 1.75 142.20123-51-3 C(C)(C)CCO 350.92 1.26 294.31 88.15110-88-3 C1OCOCO1 15192.12 0.56 18882.02 90.08592-41-6 C( C)CCCC 30.00 3.15 3.76 84.16115-77-5 C(CO)(CO)(CO)CO 33574.23 1.77 450128.07 136.15100-41-4 c1(CC)ccccc1 2.64 3.03 6.24 106.1777-99-6 C(CC)(CO)(CO)CO 12991.73 0.19 5135.19 134.17

71-36-3 C(O)CCC 1922.85 0.84 641.73 74.1257-55-6 C(C)(O)CO 3164.85 0.78 26639.91 76.0964-17-5 C(C)O 9781.46 0.14 3741.05 46.0775-65-0 C(C)(C)(C)O 5507.38 0.73 826.50 74.12112-27-6 C(O)COCCOCCO 49384.62 1.75 475457.51 150.1783-32-9 c12c3c(cccc3ccc1)CC2 1.27 4.15 0.71 154.21104-76-7 C(CCCC)(CC)CO 25.98 2.73 15.14 130.2392-52-4 c1(-c2ccccc2)ccccc1 1.55 3.76 1.73 154.21206-44-0 c12c3c(c4c(c3ccc1)cccc4)ccc2 0.02 4.93 0.16 202.2586-73-7 c12-c3c(cccc3)Cc1cccc2 0.43 4.02 1.03 166.22129-00-0 c12c3c4c(ccc3ccc1)cccc4cc2 0.09 4.93 0.16 202.2578-92-2 C(C)(O)CC 4226.15 0.77 757.92 74.1271-41-0 C(O)CCCC 421.90 1.33 249.19 88.15

85-01-8 c12c(c3c(cccc3)cc1)cccc2 0.29 4.35 0.52 178.2395-47-6 c1(C)c(C)cccc1 2.13 3.09 5.48 106.1795-93-2 c1(C)c(C)cc(C)c(C)c1 0.47 4.18 0.57 134.22104-51-8 c1(CCCC)ccccc1 0.39 4.01 0.84 134.22110-83-8 C1 CCCCC1 5.30 2.96 5.66 82.14111-65-9 C(C)CCCCCC 0.38 4.27 0.40 114.23111-70-6 C(O)CCCCCC 82.26 2.31 35.02 116.20111-87-5 C(O)CCCCCCC 20.17 2.81 12.82 130.23112-30-1 C(O)CCCCCCCCC 10.95 3.79 1.66 158.28124-18-5 C(C)CCCCCCCC 0.03 5.25 0.05 142.28504-63-2 C(O)CCO 7419.13 0.71 22504.68 76.09584-02-1 C(O)(CC)CC 350.92 1.26 294.31 88.15779-02-2 c12c(c(C)c3c(cccc3)c1)cccc2 0.12 4.89 0.16 192.26

994-05-8 C(C)(C)(CC)OC 100.08 1.92 75.60 102.172517-43-3 C(C)(CCO)OC 999.19 0.00 6104.77 104.15

(continued )



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categories is being strongly encouraged to increase the robustness of the group and tominimize experimental testing to the fullest extent possible. Experience so far suggests thatcategories being currently proposed remain conservative: small groups (23 members) of very closely related analogues driven by a minimum number of data gaps. There areimportant downstream consequences if classification and labelling is driven by a moreconservative category member: indeed, the perception of increased hazard or the risk of over labelling to obviate testing is a business issue. To date, industry practice appears to beone of considering whether to group a set of compounds together on a case-by-case basis,factoring in the business risk and the number of data gaps that need to be fulfilled. Thechallenge for the QSAR practitioner becomes one of trying to justify whether there is anadequate rationale to group the substances of interest and whether this is supported by theavailable experimental data for the different endpoints. Here QSARs, SARs and profilerssuch as those that are included in the OECD Toolbox or Toxtree play a major role inrationalizing whether there is an underlying basis for grouping for a specific endpoint.A category-reporting format is the means to document a grouping, and this requires a

strong justification to describe both the underlying rationale for forming the category andhow this is supported for all the endpoints covered (conceivably certain endpoints may notbe read-across whereas others can be adequately covered).

The following example addresses an arbitrary category that has been discussedpreviously at SIAM. The intent here is not to modify the existing category but to illustratefor a handful of endpoints how a read-across can potentially work in practice when takinginto account the structural, mechanistic and metabolic contexts of similarity.

The ethylene glycol (EG) category, first discussed at SIAM 18, 2004, was chosen as anexample of a good category, based on the review conducted as part of REACH technicalguidance development (see Worth and Patlewicz [15]). Table 3 extracts a handful of

selected endpoint results for the full category for illustrative purposes. Data for acute fishtoxicity and carcinogenicity were extracted from the OECD Application Toolbox 2.0.

Table 2. Continued.

CAS SMILES EC50_mg L 1(exp)logKow EC50_mg L 1(est) MW

13343-98-1 C(CCC)OCCOC 1000.54 1.27 434.40 132.20287-92-3 C1CCCC1 10.52 2.68 9.09 70.13646-06-0 C1COCO1 6945.32 0.31 8782.59 74.08108-87-2 C1(C)CCCCC1 1.47 3.59 1.60 98.19111-27-3 C(O)CCCCC 199.22 1.82 94.31 102.17108-67-8 c1(C)cc(C)cc(C)c1 6.01 3.64 1.78 120.1926760-64-5 C( C)(C)CC 50.23 2.72 8.44 70.1325013-15-4 c1(C)c(C C)cccc1 31.59 3.44 2.73 118.1834398-01-1 C(CCCCCCCCCC)

OCCO3.76 4.00 1.40 216.36

66455-14-9 C(CCCCCCCCCCC)OCCOCCOCCOCCOCCO

0.59 3.40 10.31 406.60

68439-46-3 C(CCCCCCCCC)OCCOCCOCCOCCOCCO

7.55 2.42 89.60 378.54

68951-67-7 C(CCCCCCCCCCCCC)OCCOCCOCCOCCOCCO 0.35 4.38 1.18 434.65



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Table 3. Ethylene glycols category data matrix, subset only.

CAS 107-21-1 111-46-6 112-27-6 112-60-7

Structure

Name EG DEG TEG TetraEG log K ow (est) 1.2 1.47 1.75 2.02Skin irritation Irritant Minimal irritation Minimal irritation Minimal irritation Sensitisation Negative Negative Negative Negative Mutagenicity (Ames)Negative Negative Negative Negative Mutagenicity

(mammalianin vitro )

Negative Negative Negative Negative

Mutagenicity(in vitrochrom abs)

Negative Negative Negative No data

Carcinogenicity ve M/F mouse ve M/F rat ve M rat No data Acute fish toxicity

(96hr LC50in Pimephala promelas )OASIS OECD TB

5.28E 04mgL 1 7.51E 04mgL 1 6.86E 04mgL 1 No data Read-acfrom remainicategory 8.16E 04mLarger catego6.54E 05m

Acute fish toxicity

(ECOSAR v1.0)

3.13E 04mgL 1 9.3E 04mgL 1 2.3E 05mgL 1 5.19E 05mgL


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The rationale for the category as summarized in the original SIAR document is asfollows:

Category members are represented by the generic molecular structure, HO(CH2 CH2O) n H, where n 15. All category members therefore possess two terminal hydroxygroups and the members differ from each other only in the number of oxyethylene units.Because of this it is appropriate to classify EG and the higher glycols (up to and includingn 5) as a single group. In fact, in the manufacture of EG, which is a catalysedcondensation reaction between ethylene oxide and a controlled amount of water, higherglycols i.e. DEG, TEG and TetraEG etc., are in fact by-products. At n 68, absorptionfrom ingestion decreases and certain physicochemical attributes change significantly.Category members EG and the higher glycols (di-, tri-, tetra-, and penta-) are closelyrelated in structure and have physicochemical properties which differ in a regular andexpected way as a result of increasing molecular weight and consistent functionality of arelatively less stable hydroxy moiety on each end of the molecule e.g. log Kow. Thus, thehazard profile and dose response are also expected to change consistently, with decreasing

potential for adverse effect with increasing molecular weight.The rationale provides information on the applicability domain of the category.Inspection of the extracted data matrix shows that there are several gaps which couldpotentially be fulfilled by applying the category approach, hence there is significant meritin grouping these glycols together rather than considering them as individual substancesand filling their data gaps independently. Results can be inferred for TetraEG andPentaEG based on the consistent trend within the category.

EG can produce skin irritation, but the other EGs tested on humans (DEG, TEG andTetraEG) produced minimal irritation and the human skin primary irritation index wasfound to decrease with an increasing number of oxyethylene units. From theseobservations, it would be inferred that PentaEG would be a minimal irritant, if at all,in skin. This observation is also consistent with the output of the BfR rulebase,(the skin/eye irritation rulebase developed by the German authorities [23,24]) asimplemented in the OECD Toolbox. No skin or eye structural inclusion rules areidentified for any of the substances.

EG, DEG, TEG and TetraEG did not induce skin sensitization. This is consistent withan absence of structural alerts for skin sensitization as encoded in the OECD Toolbox andthe mechanistic domains outlined in Aptula and Roberts (2006) [25]. The rate-determiningstep for skin sensitization is considered to be the covalent binding between a proteinnucleophile and the electrophilic chemical [25]. EGs are not overtly electrophilic, hencethere is no expectation that these substances will be sensitizers. Based on a qualitative

read-across, PentaEG would not be expected to induce skin sensitization.Similarly, evidence of in vitro mutagenicity for EG, EG, TEG and TetraEG and the

absence of structural alerts for genotoxic mutagenicity/carcinogenicity is determined bythe three relevant profilers within the Toolbox, namely: the Benigni-Bossa rulebase formutagenicity and carcinogenicity, the OASIS DNA binding profiler, and the LJMU DNAbinding profiler. The absence of alerts as identified for all the substances and the evidenceof in vitro mutagenicity implies an absence of mutagenic effects for PentaEG for whichthere are no experimental data. The absence of alerts for mutagenicity/carcinogenicity isconsistent across the category.

There is a concordance between unspecific reactivity for acute aquatic toxicity and skin

sensitization the molecular initiating event is common even though the downstreameffects are different [25]. The OASIS Mode of Action profiler within the OECD Toolbox



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shows that all EGs exert their acute aquatic toxicity by narcosis, which in turn is correlatedwith hydrophobicity. In this instance, a quantitative read-across can be performed

whereby a local QSAR is derived for the three substances with acute data in fish anda trend analysis using log Kow as a model for hydrophobicity could be used to estimatethe likely LC50 for TetraEG and PentaEG. Based on the three substances, a LC50 of 8.16E 04mgL 1 and 7.25E 04mgL 1 (as shown by the trendlines in Figures 7(a)and 7(b)) can be inferred for these two substances, thus demonstrating a low order of toxicity to fish. Predictions made using ECOSAR v1.0 (US EPA) demonstrate goodconcordance with the experimental values also, namely that all the glycols exhibit a loworder of toxicity, with 96 h LC50 estimates for TetraEG and PentaEG being 5.19E 05and 1.11E 06mgL 1 , respectively.

Available data and modelling confirm that as the MW increases, the potential for

aquatic acute fish toxicity decreases, since hydrophobicity which drives the narcoticsresponse decreases with addition of additional oxyethylene units.

Figure 7. Trend analysis for fish LC50 prediction for (a) TetraEG and (b) PentaEG.



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A more robust estimate of LC50 of TetraEG and PentaEG can be derived throughdeveloping a larger endpoint-specific category within the OECD Toolbox. Rather thanextrapolate a LC50 from the three members of this predefined category, an expandedcategory on the basis of common mode of action and exploiting other available acutetoxicity data in fish could be investigated. A trend analysis (thus a local QSAR for thecategory members) on the basis of a larger set of data should give rise to a more robustestimate. The four original category members were imported into the OECD Toolbox andprofiled on the basis of their mode of action for aquatic toxicity. A category was thendefined on the basis of a neutral organics mode of action using the ECOSAR profiler. Thiswas in order to generate a pragmatic starting group of substances that could besubsequently profiled and evaluated. The ECOSAR profiler identified 8815 substancesthat triggered the neutral organics mode of action, including the four original categorymembers. This set of substances was then sub-categorized on the basis of a number of different profilers with the aim of retaining in a set of category members that were similarin terms of their structural features and mode of action for aquatic toxicity. The firstsub-categorization was for protein binding, thus any substance that triggered an alert forprotein binding using the OASIS protein binding profiler was removed. An alert forprotein binding would be an indicator of electrophilic reactivity, i.e. greater toxicity thanwhat would be expected for a narcotic substance alone. Sub-categorization on the basis of protein binding left 7919 substances. Sub-categorization on the basis of substance typeremoved hydrolysing and inorganic substances, resulting in 7128 substances remaining.Profiling on the basis of OASIS Mode of Action Acute Aquatic Toxicity (a differentapproach for assigning aquatic modes of action) removed amines, aldehydes and otherunspecific reactive substances; 5140 substances remained. Removal of substances thatpossessed structural features indicative of DNA binding potential resulted in 4456

substances. The final sub-categorization aimed to retain only substances containing thesame two chemical elements carbon and oxygen, resulting in a final set of 2519substances. Trend analysis was then performed on the 2519 substances with actualexperimental data. Substances with low water solubilities were removed from the dataset.Average LC50 experimental data values were used in the trend analysis. A LC50 valueof 6.54E 05mgL 1 was estimated for TetraEG and 1.39E 06mgL 1 for PentaEG;the trend lines are shown in Figures 8(a) and 8(b). These refined estimates demonstrate thesame order of low toxicity for TetraEG and PentaEG and are consistent with the othermodel estimates.

This case study shows that in practice a category might be already pre-selected/defined

for a number of different endpoints. The value of QSAR approaches comes inrationalizing and substantiating the basis for the category members. The alerting profilerswithin the OECD Toolbox can provide useful insights to justify the context of similarityfor the endpoints. These are useful to fill data gaps, or to simply demonstrate that categoryis valid since the experimental data is consistent across all category members. Filling datagaps within the category can be conducted by read-across using the category membersalone, or can be achieved through the use of external QSARs or trend analysis derivedfrom extending the original category. In this example, we have demonstrated the value of the OECD profilers for a handful of endpoints such as mutagenicity and sensitization. Inaddition, data gaps for acute aquatic toxicity to fish have been filled through three

separate means (read-across, QSAR, trend analysis), and in each case the outcomes thathave resulted have been consistent with each other.



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4. Conclusions

There is a real drive to address information requirements for regulatory programmes,notably REACH, through the use of non-testing approaches such as (Q)SARs andchemical categories. The conditions for use of these approaches are well described in thelegal text in Annex XI [1] and the accompanying Technical Guidance [5], but there remainsa lack of practical examples to truly illustrate how these approaches can be applied andhow to report their outcomes. The three case studies here provide some representativeexamples drawing from our in-house experiences so far. They aim to demonstrate the levelof effort (both resource and technical competence) required to address the conditions inAnnex XI, yet at the same time illustrate the degree of flexibility that is afforded by theRegulation when attempting to exploit these types of non-testing approaches.

Disclaimer

This article reflects the views and understanding of the authors at the time of writing, anddoes not necessarily reflect the position of the institution they represent.

Figure 8. Trend analysis for fish LC50 for (a) TetraEG and (b) PentaEG.



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References

[1] European Union, Regulation (EC) No 1907/2006 of the European Parliament and of the Council of 18 December 2006 concerning the Registration, Evaluation, Authorisation and Restriction of Chemicals (REACH), establishing a European Chemicals Agency, amending Directive 1999/45/EC

and repealing Council Regulation (EEC) No 793/93 and Commission Regulation (EC) No 1488/94as well as Council Directive 76/769/EEC and Commission Directives 91/155/EEC, 93/67/EEC,93/105/EC and 2000/21/EC , Official Journal, L 396 (2006), pp. 1850.

[2] European Union,, Council Directive 79/831/EEC of 18 September 1979 amending for the sixthtime Directive 67/548/EEC on the approximation of the laws, regulations and administrative provisions relating to the classification, packaging and labelling of dangerous substances , OfficialJournal L 259 (1979), pp. 1028.

[3] ECHA, Guidance on information requirements and chemical safety assessment Chapter R.7a: End- point specific guidance , May 2008. Available at: http://guidance.echa.europa.eu/docs/guidance_ document/information_requirements_r7a_en.pdf?vers=02_02_10 (Accessed 7 July 2010).

[4] ECHA, Guidance on information requirements and chemical safety assessment Chapter R.7b: End-

point specific guidance , May 2008. Available at: http://guidance.echa.europa.eu/docs/guidance_ document/information_requirements_r7b_en.pdf?vers=20_08_08 (Accessed 7 July 2010).[5] ECHA, Guidance on information requirements and chemical safety assessment Chapter R.6:

QSARs and grouping of chemicals , May 2008. Available at: http://guidance.echa.europa.eu/docs/guidance_document/information_requirements_r6_en.pdf?vers=20_08_08 (Accessed 7 July2010).

[6] OECD, The Report from the Expert Group on (Quantitative) StructureActivity Relationships[(Q)SARs] on the Principles for the Validation of (Q)SARs. ENV/JM/TG(2004)27/REV ,Organisation for Economic Cooperation and Development, Paris, 2004.

[7] A.P Worth, A. Bassan, A. Gallegos, T.I. Netzeva, G. Patlewicz, M. Pavan, I.Tsakovska, andM. Vracko, The Characterisation of (Quantitative) StructureActivity Relationships: PreliminaryGuidance , EUR 21866 EN, European Commission-Joint Research Centre, Ispra, Italy, 2005.Available at: http://ecb.jrc.ec.europa.eu/DOCUMENTS/QSAR/QSAR_characterisation_ EUR_21866_EN.pdf (Accessed 7 July 2010).

[8] OECD, Guidance Document on the Validation of (Quantitative) StructureActivity Relationships[(Q)SAR] Models. ENV/JM/MONO(2007)2 . Organisation for Economic Cooperation andDevelopment, Paris, 2007. Available at: http://www.oecd.org/dataoecd/55/35/38130292.pdf (Accessed 7 July 2010).

[9] T.I. Netzeva, A. Worth, T. Aldenberg, R. Benigni, M.T. Cronin, P. Gramatica, J.S. Jaworska,S. Kahn, G. Klopman, C.A. Marchant, G. Myatt, N. Nikolova-Jeliazkova, G.Y. Patlewicz,R. Perkins, D. Roberts, T. Schultz, D.W. Stanton, J.J. van de Sandt, W. Tong, G. Veith, andC. Yang, Current status of methods for defining the applicability domain of (quantitative)structureactivity relationships. The report and recommendations of ECVAM Workshop 52 , Altern.Lab. Anim. 33 (2005), pp. 155173.

[10] R.O. Potts and R.H. Guy, Predicting skin permeability , Pharm. Res. 9 (1992), pp. 663669.[11] US EPA. Ecological Structure Activity Relationships (ECOSAR) v1.0 September 2008.

Available at: http://www.epa.gov/oppt/newchems/tools/21ecosar.htm.[12] G.D Veith and S.J Broderius, Rules for distinguishing toxicants that cause type I and type II

narcosis syndromes , Environ Health Perspect. 87 (1990), pp. 207211.[13] S. Dimitrov, G. Dimitrova, T. Pavlov, G. Dimitrova, G. Patlewicz, J. Niemela, and

O. Mekenyan, A stepwise approach for defining the applicability domain of SAR and QSARmodels , J. Chem. Inf. Model. 45 (2005), pp. 839849.

[14] G. Patlewicz, N. Jeliazkova, A. Gallegos Saliner, and A.P. Worth, Toxmatch a new softwaretool to aid in the development and evaluation of chemically similar groups , SAR QSAR Environ.Res. 19 (2008), pp. 397412.



22/22

[15] Worth and G. Patlewicz, A compendium of case studies that helped to shape the REACH Guidance on Chemical Categories and Read Across, European Commission report EUR 22481EN. European Commission-Joint Research Centre, Ispra, Italy, 2007. Available at: http://ecb.jrc.ec.europa.eu/DOCUMENTS/QSAR/EUR_22481_EN.pdf (Accessed 7 July 2010).

[16] A. Gallegos Saliner, G. Patlewicz, and A.P. Worth, A review of (Q)SAR models for skin and eye

irritation and corrosion , QSAR & Comb Sci. 27 (2008), pp. 4959.[17] G. Patlewicz, A.O. Aptula, D.W. Roberts, and E. Uriarte, A mini review of available (Q)SARsand Expert systems for skin sensitisation , QSAR & Comb Sci. 27 (2008), pp. 6076.

[18] A.P. Worth, T.I. Netzeva, and G. Patlewicz, Predicting toxicological and ecotoxicological endpoints , in Risk Assessment of Chemicals , C.J. Leeuwen and T. Vermeire, eds., Springer, 2007,pp. 427465.

[19] J.C. Dearden, M.D. Barratt, R. Benigni, D.W. Bristol, R.D. Combes, M.T.D. Cronin,P.N. Judson, M.P. Payne, A.M. Richard, M. Tichy, A.P. Worth, and J.J. Yourick, Thedevelopment and validation of expert systems for predicting toxicity , Altern. Lab. Anim. 25(1997), pp. 223252.

[20] H.J.M. Verhaar, C.J. van Leeuwen, and J.L.M. Hermens, Classifying environmental pollutants.1. Structure-activity relationships for prediction of aquatic toxicity , Chemosphere 25 (1992),pp. 471491.

[21] P. Donkin, Quantitative structure-activity relationships , in Handbook of Ecotoxicology vol. 2 ,P. Calow, ed., Blackwell Scientific Publications, London, 1994.

[22] H. Konemann, Quantitative structure-activity relationships in fish toxicity studies 1. Relationship for 50 industrial pollutants , Toxicology 19 (1981), pp. 209221.

[23] I. Gerner, M. Liebsch, and H. Spielmann, Assessment of the eye irritating properties of chemicalsby applying alternatives to the Draize rabbit eye test: The use of QSARs and in vitro tests forthe classification of eye irritation , ATLA 33 (2005), pp. 215237.

[24] J.D. Walker, I. Gerner, E. Hulzebos, and K. Schlegel, The Skin Irritation Corrosion RulesEstimation Tool (SICRET) , QSAR Comb. Sci. 24 (2005), pp. 378384.

[25] A.O. Aptula and D.W. Roberts, Mechanistic applicability domains for nonanimal-based

prediction of toxicological end points: General principles and application to reactive toxicity ,Chem. Res. Toxicol. 19 (2006), pp. 10971105.


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