This paper introduces the basic concepts of quantitative structure–activity relationship (QSAR), expert system and integrated testing strategy, and explains how the analogy between QSARs and prediction models leads naturally to criteria for the validation of QSARs. ECVAM's in-house research programme on QSAR modelling and integrated testing is summarised, along with plans for the validation of QSAR models and expert system rulebases at the European Union level.
LivingstoneD. (1995). Data Analysis for Chemists. Applications to QSAR and Chemical Product Design.Oxford, UK: Oxford University Press.
2.
WorthA.P. (2000). The Integrated Use of Physicochemical and In Vitro Data for Predicting Chemical Toxicity. PhD Thesis, Liverpool John Moores University, UK.
3.
CroninM.T.D., DeardenJ.C., MossG.P., & Murray-DicksonG. (1999). Investigation of the mechanism of flux across human skin in vitro by quantitative structure-permeability relationships. European Journal of Pharmaceutical Sciences7, 325–330.
4.
WorthA.P., & BallsM. (2001). The importance of the prediction model in the development and validation of alternative tests. ATLA29, 135–143.
5.
DeardenJ.C., BarrattM.D., BenigniR., BristolD.W., CombesR.D., CroninM.T.D., JudsonP.N., PayneM.P., RichardA.M., TichyM., WorthA.P., & YourickJ.J. (1997). The development and validation of expert systems for predicting toxicity. The report and recommendations of an ECVAM/ECB workshop (ECVAM workshop 24). ATLA25, 223–252.
6.
GreeneN., JudsonP.N., LangowskiJ.J., & MarchantC.A. (1999). Knowledge-based expert systems for toxicity and metabolism prediction: DEREK, StAR, METEOR. SAR and QSAR in Environmental Research10, 299–314.
7.
WorthA.P., & CroninM.T.D. (2001). The use of pH measurements to predict the potential of chemicals to cause acute dermal and ocular toxicity. Toxicology169, 119–131.
8.
WorthA.P., & CroninM.T.D. (2001). Prediction models for eye irritation potential based on endpoints of the HETCAM and neutral red assays. In Vitro and Molecular Toxicology14, 143–156.
9.
WorthA.P., FentemJ.H., BallsM., BothamP.A., CurrenR.D., EarlL.K., EsdaileD.J., & LiebschM. (1998). An evaluation of the proposed OECD testing strategy for skin corrosion. ATLA26, 709–720.
10.
WorthA.P., & FentemJ.H. (1999). A general approach for evaluating stepwise testing strategiesATLA27, 161–177.
WorthA.P., & CroninM.T.D. (1999). Embedded cluster modelling: a novel method for analysing embedded data sets. Quantitative Structure–Activity Relationships18, 229–235.
13.
WorthA.P., & CroninM.T.D. (2000). Embedded cluster modelling: a novel QSAR method for generating elliptic models of biological activity. In Progress in the Reduction, Refinement and Replacement of Animal Experimentation (ed. BallsM., van ZellerA-M., & HalderM.E.), pp. 479–491. Amsterdam, The Netherlands: Elsevier.
14.
EfronB., & TibshiraniR.J. (1993). An Introduction to the Bootstrap, 436 pp. London, UK: Chapman & Hall.
15.
WehrensR., PutterH., & BuydensL.M.C. (2000). The bootstrap: a tutorial. Chemometrics and Intelligent Laboratory Systems54, 35–52.
16.
WorthA.P., & CroninM.T.D. (2001). The use of bootstrap resampling to assess the uncertainty of Cooper statistics. ATLA29, 447–459.
17.
WorthA.P., & CroninM.T.D. (2001). The use of bootstrap resampling to assess the variability of Draize tissue scores. ATLA29, 557–573.
18.
Anon. (1996). In Technical Guidance Documents in Support of the Commission Directive 93/67/EEC on Risk Assessment for New Notified Substances and the Commission Regulation (EC) 1488/94 on Risk Assessment for Existing Substances, pp. 517–526. Luxembourg: CEC.
19.
WorthA.P., BarrattM.D., & HoustonJ.B. (1998). The validation of computational prediction techniques. ATLA26, 241–247.
20.
CroninM.T.D., JaworskaJ.S., WalkerJ.D., ComberM.H.I., WattsC.D., & WorthA.P. (2003). Use of QSARs in international decision-making frameworks to predict health effects of chemical substances. Environmental Health Perspectives, in press.
21.
CroninM.T.D., WalkerJ.D., JaworskaJ.S., ComberM.H.I., WattsC.D., & WorthA.P. (2003). Use of QSARs in international decision-making frameworks to predict ecological effects and environmental fate of chemical substances. Environmental Health Perspectives, in press.
22.
ErikssonL., JaworskaJ.S., WorthA.P., CroninM.T.D., McDowellR.M., & GramaticaP. (2003). Methods for reliability, uncertainty assessment, and applicability evaluations of regression based and classification QSARs. Environmental Health Perspectives, in press.
23.
JaworskaJ.S., ComberM., AuerC., & Van LeeuwenC.J. (2003). Summary of a workshop on regulatory acceptance of (Q)SARS for human health and environmental endpoints. Environmental Health Perspectives, in press.
24.
BallsM., BlaauboerB.J., FentemJ.H., BrunerL., CombesR.D., EkwallB., FielderR.J., GuillouzoA., LewisR.W., LovellD.P., ReinhardtC.A., RepettoG., SladowskiD., SpielmannH., & ZuccoF. (1995). Practical aspects of the validation of toxicity test procedures. The report and recommendations of ECVAM workshop 5. ATLA23, 129–147.
25.
WorthA.P., & BallsM. (2001). The role of ECVAM in promoting the regulatory acceptance of alternative methods in the European Union. ATLA29, 525–535.
26.
CroninM.T.D., DeardenJ.C., DuffyJ.C., EdwardsR., MangaN., WorthA.P., & WorganA.D.P. (2002). The importance of hydrophobicity and electrophilicity descriptors in mechanistically-based QSARs for toxicological endpoints. SAR and QSAR in Environmental Research13, 167–176.
27.
SandersonD.M., & EarnshawC.G. (1991). Computer prediction of possible toxic action from chemical structure; the DEREK system. Human and Experimental Toxicology10, 261–273.
28.
BothamP. (2002). ECVAM, ECETOC and the EU chemicals policy. ATLA30, Suppl. 2, 185–187.