TIMES-SS Application of Reactivity Principles in Screening for Skin Sensitisers

1. TIMES-SS Application of Reactivity Principles in Screening for Skin Sensitisers Presented on behalf of the TIMES-SS consortia & International QSAR Foundation Mike Comber

2. Outline • Goal – to apply the reactivity knowledge base to an economically important hazard assessment endpoint • Why TIMES-SS? • Refining TIMES-SS • Consortium & Aims • Brief overview of the model • Performance & issues • Current programme & developments • Future needs

3. Skin sensitisation • There are in-vitro alternatives & other models….. but none are adequate for all classes of sensitisers • Greater regulatory acceptance requires greater mechanistic transparency- “no black boxes” • Increasing regulatory acceptance • Avoiding the test saves industry money! • Screening chemicals helps direct research

4. TIMES-SS :Consortium • Coordination – Mike Comber – on behalf of the IQF • Research team • Laboratory of Mathematical Chemistry, University Bourgas • Dr Dave Roberts • Consortium • ExxonMobil • Procter & Gamble • Unilever • Research Institute for Fragrance Materials (RIVM) • Dow • Danish National Food Institute • Funding & data sharing + sweat equity

5. Aims for TIMES-SS • To develop a skin sensitisation (Q)SAR model that: • Potentially minimises the need for animal testing • Is scientifically credible and valid to Industry and Regulatory bodies • Agrees with the OECD principles for (Q)SAR validation • Mechanistically defensible • Hence has high potential for acceptance under REACH in place of animal tests

6. Characterisation with the OECD principles • OECD principles for (Q)SAR validation: • a defined endpoint  • an unambiguous algorithm  • a defined domain of applicability  • appropriate measures of goodness-of-fit, robustness and predictivity • a mechanistic interpretation  where  demonstrates the concordance between TIMES and the OECD principles For full evaluations - see : Patlewicz et al., 2007 RegToxPharm, 48, 225–239 & Roberts et al., 2007, Chem Res Toxicol,20 (9), 1321–1330

7. Identifying the cause of an effect • Complex toxicological endpoints are biological responses to: • Direct molecular interactions dependent on chemical structure • Indirect molecular interactions which are dependent on the chemical structure of metabolites • AND Biological processes dependent on other properties e.g. pH/chemical reactivity • In TIMES-SS - trying to separate the unknowns associated with metabolism from the unknowns associated with chemical reactivity itself. • There has been a continual effort to make sure any plausible mechanism of interactions leading to a protein adduct is consistent: • With the literature on that specific reaction mechanism, and • With the more general understanding chemical reactivity.

8. Process for TIMES-SS What is the hypothesis? How do we then model the endpoint? What is the data required to then build the model? What are the shortcomings and the gaps?

9. Hypothesis for skin sensitization • Assumptions: • Chemicals always penetrate stratum corneum • Formation of protein conjugates is a premise for ultimate effect • Metabolism may play significant role in skin sensitization Penetration Subject of modeling Epidermis Protein conjugates Protein conjugates Metabolism Dermis Hypodermis Lymph Vein

10. Modeling the hypothesis • Modeling skin metabolism • Skin metabolic simulator contains 336 hierarchically ordered spontaneous and enzyme controlled reactions. • Covalent interactions of chemicals/metabolites with skin proteins are described by 67 alerting groups. • 3D-QSARs are applied for some of these alerting groups to improve the associated predictability.

11. Different type of principal transformations • Phase I reactions • Ester hydrolysis • Phase II reactions • Glucoronidation • Interactions with proteins • Nucleophilic substitution on halogenated C sp3 atom Schiff base formation with aldehydes

12. Protein binding alerts requiring 3D QSAR (COREPA) models 1. Aldehydes R = alkyl, H 2. α,β- unsaturated carbonyl compoundsacting by Michael addition R = OC, C, N, S

13. What is the data required to then build the model? • Training set of 885 chemicals with experimental data from three sources (LLNA, GPMT, BfR). • Experimental skin sensitisation data • Reaction data – data on reactivity is not available. Discrimination by chemical reactivity is semi qualitative

14. Ranges of chemical reactivity are introduced - Examples Nucleophilic acyl substitution in azalactones causing Strong skin sensitization effect Chemicals from the training set having observed Strong sensitization effect

15. Ranges of chemical reactivity are introduced – Examples (Contd.) Nucleophilic acyl substitution in azalactones causing Weak skin sensitization effect Chemicals from the training set having observed Weak sensitization effect

16. Ranges of chemical reactivity are introduced – Examples (Contd.) Michael addition on a,b - aldehydescausing Strong skin sensitization effect Chemicals from the training set having observed Strong sensitization effect

17. Ranges of chemical reactivity are introduced – Examples (Contd.) Michael addition on a,b - aldehydescausing Weak skin sensitization effect Chemicals from the training set having observed Weak sensitization effect

18. Current shortcomings and needs • The level of support for reactions is variable • Some reactions have 1 supporting chemical • New reactions are being generated as new data is reviewed – requiring further assessments • Reactivity data for the reactions/chemical groups is not available • A semi qualitative potency scale is used for some of the reactions/chemical groups