Building on a solid foundation: SAR and QSAR as a fundamental strategy to reduce animal testingFootnote

^$Dedicated to the memory of Dr. Gilman D. Veith (1944–2013).

References

• Regulation (EC) No 1907/2006 of the European Parliament and of the Council concerning the Registration, Evaluation, Authorization and Restriction of Chemicals (REACH), establishing a European Chemicals Agency, amending Directive 1999/45/EC of the European Parliament and of the Council and repealing Council Regulation (EEC) No 793/93 and Commission Regulation (EC) No 1488/94, as well as Council Directive 76/769/EEC and Commission Directives 91/155/EEC, 93/67/EEC, 93/105/EC, and 2000/21/EC.
   Google Scholar
• D.M. Reif, M. Sypa, E.F. Lock, F.A. Wright, A. Wilson, T. Cathey, R.R. Judson, and I. Rusyn, ToxPi GUI: An interactive visualization tool for transparent integration of data from diverse sources of evidence, Bioinformatics 29 (2013), pp. 402–403.
   PubMed Web of Science ®Google Scholar
• OECD QSAR Toolbox 3.2. Organisation for Economic Cooperation and Development, Paris, 2013; software available at http://www.qsartoolbox.org.
   Google Scholar
• D. Morgott, C. Lewis, J. Bootman, and M. Banton, Disulfide oil hazard assessment using categorical analysis and a mode of action determination, Int. J. Toxicol. 33 (2013), pp. 181S–198S.
   Google Scholar
• Ecological Structure Activity Relationships (ECOSAR) v. 1.11, Environmental Protection Agency, Washington, USA, 2012; software available at http://www.epa.gov/oppt/newchems/tools/21ecosar.htm.
   Google Scholar
• OncoLogic™ – A computer system to evaluate the carcinogenic potential of chemicals, Version 8.0, Environmental Protection Agency, Washington, USA, 2013; software available at http://www.epa.gov/oppt/sf/pubs/oncologic.htm.
   Google Scholar
• K. van Leeuwen, T.W. Schultz, T. Henry, B. Diderich, and G.D. Veith, Using chemical categories to fill data gaps in hazard assessment, SAR QSAR Environ. Res. 20 (2009), pp. 207–220.
   PubMed Web of Science ®Google Scholar
• C.L. Russom, S.P. Bradbury, S.J. Broderius, D.E. Hammermeister, R.A. Drummond, and L. Christine, Predicting modes of toxic action from chemical structure: Acute toxicity in the fathead minnow (Pimephales promelas), Environ. Toxicol. Chem. 16 (1997), pp. 948–967.
   Web of Science ®Google Scholar
• P.L. Bishop, J.R. Manuppello, C.E. Willett, and J.T. Sandler, Animal use and lessons learned in the US High Production Volume Chemicals Challenge Program, Environ. Health Perspect. 120 (2012), pp. 1631–1639.
   PubMed Web of Science ®Google Scholar
• European Chemicals Agency, press memo: The results of ECHA’s examination of testing proposals, ECHA, Helsinki, Finland, 2012. Available at http://echa.europa.eu/documents/10162/13585/press+memo_testing_proposals_deadline_20121201_en.pdf.
   Google Scholar
• K. van der Jagt, S. Munn, J. Tørsløv, and J. de Bruijn, Alternative approaches can reduce the use of test animals under REACH. Addendum to the report, Assessment of additional testing needs under REACH: Effects of (Q)SARS, risk-based testing and voluntary industry initiatives. European Commission Joint Research Centre, Ispra, Italy, 2004. Available at http://home.kpn.nl/reach/downloads/reducingtheuseoftestanimalsunderreachihcprepor.pdf.
   Google Scholar
• G. Schaafsma, E.D. Kroese, E.L.J.P. Tielemans, J.J.M. van de Sandt, and C.J. van Leeuwen, REACH, non-testing approaches and the urgent need for a change in mind set, Regul. Toxicol. Pharm. 53 (2009), pp. 70–80.
   PubMed Web of Science ®Google Scholar
• H. Spielmann, U.G. Sauer, and O. Mekenyan, A critical evaluation of the 2011 ECHA reports on compliance with the REACH and CLP regulations and on the use of alternatives to testing on animals for compliance with the REACH regulation, ATLA–Altern. Lab. Anim. 39 (2011), pp. 481–493.
   PubMed Web of Science ®Google Scholar
• K. Taylor, W. Stengel, C. Casalegno, and D. Andrew, Experiences of the REACH testing proposals system to reduce animal testing, ALTEX–Altern. Tierexp. Available at http://dx.doi.org/10.14573/altex.1311151.
   Google Scholar
• C. Rovida, How many animals have been really used for REACH purposes? Appraisal after the second deadline. EUSAAT poster presentation, Linz, Austria, 2013.
   Google Scholar
• J. Jaworska, S. Dimitrov, N. Nikolova, and O. Mekenyan, Probabilistic assessment of biodegradability based on metabolic pathways: CATABOL system, SAR QSAR Environ. Res. 13 (2002), pp. 307–323.
   PubMed Web of Science ®Google Scholar
• TIMES–SS, Laboratory of Mathematical Chemistry, Bourgas, Bulgaria; software available at http://oasis-lmc.org/products/software/times.aspx.
   Google Scholar
• G. Patlewicz, S.D. Dimitrov, L.K. Low, P.S. Kern, G.D. Dimitrova, M.I. Comber, A.O. Aptula, R.D. Phillips, J. Niemelä, C. Madsen, E.B. Wedebye, D.W. Roberts, P.T. Bailey, and O.G. Mekenyan, TIMES–SS – A promising tool for the assessment of skin sensitization hazard: A characterization with respect to the OECD validation principles for (Q)SARs and an external evaluation for predictivity, Regul. Toxicol. Pharm. 48 (2007), pp. 225–239.
   PubMed Web of Science ®Google Scholar
• D.W. Roberts, G. Patlewicz, S.D. Dimitrov, L.K. Low, A.O. Aptula, P.S. Kern, G.D. Dimitrova, M.I. Comber, R.D. Phillips, J. Niemelä, C. Madsen, E.B. Wedebye, P.T. Bailey, and O.G. Mekenyan, TIMES–SS – A mechanistic evaluation of an external validation study using reaction chemistry principles, Chem. Res. Toxicol. 20 (2007), pp. 1321–1330.
   PubMed Web of Science ®Google Scholar
• T.W. Schultz, R.E. Carlson, M.T. Cronin, J.L. Hermens, R. Johnson, P.J. O’Brien, D.W. Roberts, A. Siraki, K.D. Wallace, and G.D. Veith, A conceptual framework for predicting the toxicity of reactive chemicals: Modeling soft electrophilicity, SAR QSAR Environ. Res. 17 (2006), pp. 413–428.
   PubMed Web of Science ®Google Scholar
• G.D. Veith, On the nature, evolution and future of quantitative structure–activity relationships (QSAR) in toxicology, SAR QSAR Environ. Res. 15 (2004), pp. 323–330.
   PubMed Web of Science ®Google Scholar
• National Research Council, Toxicity testing in the 21st Century: A vision and a strategy, National Academy of Sciences, Washington, DC, USA, 2007.
   Google Scholar
• G.D. Veith, E.P. Petkova, and K.B. Wallace, A baseline inhalation toxicity model for narcosis in mammals, SAR QSAR Environ. Res. 20 (2009), pp. 567–578.
   PubMed Web of Science ®Google Scholar
• P.K. Schmieder, M.A. Tappers, J.S. Denny, R.C. Kolanczyk, B.R. Sheedy, T.R. Henry, and G.D. Veith, Use of trout liver slices to enhance mechanistic interpretation of estrogen receptor binding for cost-effective prioritization of chemicals within large inventories, Environ. Sci. Technol. 38 (2004), pp. 6333–6342.
   PubMed Web of Science ®Google Scholar
• Effectopedia: The online encyclopedia of adverse effect pathways (Alpha), International QSAR Foundation, Two Rivers, MN, USA, 2013; software available at http://sourceforge.net/projects/effectopedia/.
   Google Scholar
• R. Benigni, C. Bossa, C.L. Battistelli, and O. Tcheremenskaia, IARC Classes 1 and 2 carcinogens are successfully identified by an alternative strategy that detects DNA-reactivity and cell transformation ability of chemicals, Mutat. Res. 758 (2013), pp. 56–61.
   PubMed Web of Science ®Google Scholar
• OECD Extended Advisory Group for Molecular Screening and Toxicogenomics, 2013 Workplan, Project 1.8: The adverse outcome pathways for mutagenic modes of action for cancer. Available at http://www.oecd.org/env/ehs/testing/listsofprojectsontheaopdevelopmentprogrammeworkplan.htm.
   Google Scholar
• Organization for Economic Coordination and Development (OECD), Environment Health and Safety Publications Series on Testing and Assessment No. 69: OECD Guidance Document on the Validation of (Quantitative) Structure–Activity Relationships, OECD, Environment Directorate, Paris, France, 2007.
   Google Scholar
• QMRF Editor 2.0.0, Joint Research Centre, Ispra, Italy, 2013; software available at http://sourceforge.net/apps/mediawiki/qmrf/index.php?title=Main_Page.
   Google Scholar
• O. Mekenyan, S. Dimitrov, P. Schmieder, and G. Veith, In silico modelling of hazard endpoints: Current problems and perspectives, SAR QSAR Environ. Res. 14 (2003), pp. 361–371.
   PubMed Web of Science ®Google Scholar
• D.J. Dix, K.A. Houck, M.T. Martin, A.M. Richard, R.W. Setzer, and R.J. Kavlock, The ToxCast program for prioritizing toxicity testing of environmental chemicals, Tox. Sci. 95 (2007), pp. 5–12.
   PubMed Web of Science ®Google Scholar
• S. Bhattacharya, Q. Zhang, P.L. Carmichael, K. Boekelheide, and M.E. Andersen, Toxicity testing in the 21st century: Defining new risk assessment approaches based on perturbation of intracellular toxicity pathways, PLoS One. 6 (2011), e20887. Available at http://www.ncbi.nlm.nih.gov/pubmed/21701582.
   PubMed Web of Science ®Google Scholar
• M. Vinken, The adverse outcome pathway concept: A pragmatic tool in toxicology, Toxicology 312 (2013), pp. 158–165.
   PubMed Web of Science ®Google Scholar
• M. Hewitt, S.J. Enoch, J.C. Madden, K.R. Przybylak, and M.T.D. Cronin, Hepatotoxicity: A scheme for generating chemical categories for read-across, structural alerts and insights into mechanism(s) of action, Crit. Rev. Toxicol. 43 (2013), pp. 537–558.
   PubMed Web of Science ®Google Scholar
• E. Fioravanzano, A. Bassan, M.T.D. Cronin, S. Kovarich, and C. Manelfi, A-N. Richarz, I. Tsakovska, and A.P. Worth, Molecular modeling of LXR binding to evaluate the potential for liver steatosis, Toxicol. Lett. 221 (2013), p. S83.
   PubMed Web of Science ®Google Scholar
• M. Al Sharif, P. Alov, M.T.D Cronin, E. Fioravanzo, I. Tsakovska, V. Vitcheva, A. Worth, C. Yang, and I. Pajeva, Towards better understanding of liver steatosis MoA: Molecular modeling study of PPAR gamma receptor, Toxicol. Lett. 221 (2013), S85.
   Web of Science ®Google Scholar
• C.M. Ellison, S.J. Enoch, and M.T. Cronin, A review of the use of in silico methods to predict the chemistry of molecular initiating events related to drug toxicity, Expert Opin. Drug Met. 7 (2011), pp. 1481–1495.
   PubMed Web of Science ®Google Scholar
• S. Wu, K. Blackburn, J. Amburgey, J. Jaworska, and T. Federle, A framework for using structural, reactivity, metabolic and physicochemical similarity to evaluate the suitability of analogs for SAR-based toxicological assessments, Regul. Toxicol. Pharm. 56 (2010), pp. 67–81.
   PubMed Web of Science ®Google Scholar
• K. Blackburn, D. Bjerke, G. Daston, S. Felter, C. Mahony, J. Naciff, S. Robison, and S. Wu, Case studies to test: A framework for using structural, reactivity, metabolic and physicochemical similarity to evaluate the suitability of analogs for SAR-based toxicological assessments, Regul. Toxicol. Pharm. 60 (2011), pp. 120–135.
   PubMed Web of Science ®Google Scholar
• A. Sedykh, H. Zhu, H. Tang, L. Zhang, A. Richard, I. Rusyn, and A. Tropsha, Use of in vitro HTS-derived concentration-response data as biological descriptors improves the accuracy of QSAR models of in vivo toxicity, Environ. Health Perspect. 119 (2011), pp. 364–370.
   PubMed Web of Science ®Google Scholar
• C.L. Russom, S.P. Bradbury, S.J. Broderius, D.J. Hammermeister, R.A. Drummond, and G.D. Veith, Predicting modes of toxic action from chemical structure, Environ. Toxicol. Chem. 32 (2013), pp. 1441–1442.
   PubMed Web of Science ®Google Scholar