1. Chemical Category Formation: Toxicology and REACH Dr Steven Enoch Liverpool John Moores University 14 th May 2009

2. Is Regulatory Toxicology Important? Number of stories about the toxicity of chemicals

3. Is Regulatory Toxicology Important? Number of stories about the toxicity of chemicals Many chemicals have little or no toxicological data Concerns about the potential toxicity of chemicals New REACH legislation regarding chemical safety Applies to excipients, intermediates etc Cosmetics directive prohibits animal testing

4. REACH and Intelligent Testing Strategies Risk Assessment In vivo In silicoIn chemico In vitro

5. In-silico Category Formation Structural Mechanistic Toxicological Qualitative and quantitative predictions

6. In-silico Category Formation Structural Mechanistic Toxicological Qualitative and quantitative predictions

7. Mechanistic Category Formation – Skin Sensitisation

8. Six key chemical reactions have been defined for protein reactivity 1 All known skin sensitising chemicals can be assigned to one of these mechanisms SMARTS based rules have been developed 2 1 Aptula AO and Roberts DW (2006) Chem Res Toxicol 19; 1097-1105 2 Enoch SJ et al (2008) SAR QSAR Environ Sci 19; 555-578 Electrophilic Reaction Chemistry

9. X = electron withdrawing substituent e.g. CO, CHO, NO 2, CO 2 R. Mechanism for Michael Addition

10. Mechanistic Category Formation

11. Qualitative read-across using only mechanistic assignment Quantitative read-across using the electrophilicity index (  ) to model protein reactivity within a category 3 Electrophilic index calculated from HOMO and LUMO using DFT 3 Enoch SJ et al (2008) Chem Res Toxicol 21; 513-520 Read-Across within a Mechanistic Category

12. Increasing electrophilicity (  ) Increasing skin sensitising potential (pEC3) pEC3 = NC,  = 1.10 pEC3 = 0.55,  = 1.49pEC3 = 1.82,  = 1.55 pEC3 = 1.25,  = 1.61pEC3 = 1.64,  = 2.10pEC3 = 4.04,  = 3.90 Quantitative Electrophilicity (  ) Ranking

13. Chemical A:  = 1.61, EC3 = 5.5, pEC3 = 1.25 Chemical B:  = 1.80, EC3 = 7.5, pEC3 = 1.30 Chemical X:  = 1.73 Pred. pEC3 = 1.29 (1.31) Pred. EC3 = 9.87 (9.30) Quantitative Read-Across Predictions

14. Toxicological Category Formation - Developmental Toxicity

15. Mechanistic read-across requires a priori mechanistic knowledge What about category formation when we don’t know about the mechanism of action? Can we use chemical similarity to form categories? 4 Read-Across within a Toxicological Category 4 Enoch SJ et al (2009) QSAR Comb Sci in-press

16. Read-across prediction (atom environment similarity): D / X Actual classification: D Qualitative Read-Across

17. Read-across prediction (fingerprint similarity): B Actual classification: B Qualitative Read-Across

18. Regulatory QSAR Tools 4 http://www.oecd.org/document/23/0,3343,en_2649_34377_33957015_1_1_1_1,00.html 5 http://ecb.jrc.ec.europa.eu/qsar/qsar-tools / OECD QSAR Application Toolbox 4 Chemical category formation Read-across and trend analysis Regulatory reporting for ECHA Toxmatch and Toxtree 5 Similarity based category formation Rule based category formation

19. Conclusions REACH envisages intelligent testing of chemicals In silico developed chemical categories play a central role Qualitative and quantitative predictions of toxicity used to fill data gaps In silico methods must be transparent and simple in order for regulatory acceptance from EChA

20. The Future – Intelligent Testing Strategies Risk Assessment In vivo In silicoIn chemico In vitro ?

21. Acknowledgements The funding of the European Chemicals Agency (EChA) Service Contract No. ECHA/2008/20/ECA.203 is gratefully acknowledged