TIMES-SS Assessment of skin sensitisation hazard

1. TIMES-SS Assessment of skin sensitisation hazard Presented on behalf of the TIMES-SS consortia Mike Comber Consulting

2. Outline • Why TIMES-SS? • TIMES-SS v1 • - Brief overview of the model • Performance & issues • The need for further work - TIMES-SS v2 • Consortium & Aims • Current issues

3. Why TIMES-SS? • There are in-vitro alternatives & other models….. • But no one model is helpful on its own • TIMES-SS attempts to be mechanistic • Improves understanding • More acceptable to regulators and toxicologists • LMC are very open to development • TIMES-SS is available to the consortia during the development of v2

4. 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

5. TIMES-SS v1 • TIssueMEtabolism Simulator • Estimates skin sensitisation potency using a simulator for skin metabolism with (Q)SARs. • Training set of 729 chemicals with experimental data from three sources (LLNA, GPMT, BgVV). • Predicts potency as one of three classes: significant, weak or non sensitising.

6. TIMES-SS v1 • Skin metabolic simulator contains over 236 hierarchically ordered spontaneous and enzyme controlled reactions. • Covalent interactions of chemicals/metabolites with skin proteins are described by 47 alerting groups. • A multi-step applicability domain is incorporated into the model.

7. External validation exercise • Applicability domain of TIMES-SS defined • Identified EINECS chemicals that fell within TIMES domain (6000 out of EINECS (60311)) • “Randomly” chose 40 chemicals to test in the LLNA (12 predicted sensitisers & 28 predicted non-sensitisers) • Tested blind • Observed skin sensitisation effect was compared with TIMES model prediction. Results were evaluated in light of reaction chemistry principles.

8. Evaluation • For each of the test results • TIMES-SS predicted a result, but also • Dave Roberts - expert assessment also used • Evaluated each of the data points where • TIMES-SS differed from the data • Using the DR expert assessment • Assessed on mechanistic grounds

9. 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

10. Conclusions – TIMES-SS v1 • Development of a QSAR with external validation to meet OECD principles • 40 chemicals tested resulting in new LLNA data • The results were promising (initial concordance 75%) • Extensive evaluation to assess results in light of reaction chemistry principles • To check hypotheses, 4 further compounds were tested • The insights derived from all 44 compounds have helped to define: • short-term modifications/refinements for TIMES-SS • mid-long term targets for new research work

11. TIMES-SS v2 :Consortium • Research team • Laboratory of Mathematical Chemistry, University Bourgas • Dr Dave Roberts • Consortium • ExxonMobil • Procter & Gamble • Research Institute for Fragrance Materials • Unilever • Danish National Food Institute • ECB - JRC • Funding & data sharing + sweat equity

12. TIMES-SS v2 • Under the auspices of the International QSAR Foundation • 9 milestones agreed for a 3 year programme • Milestone 1 – Identify a process for dealing with the test/training set chemicals which are identified out of domain • Milestone 2 – Address chemicals where data conflicts • Milestone 3 – Provide guidance on how to address charged molecules • Milestone 4 – Assess the categories of sensitisation (strong/weak/non)  • Milestone 5 –Implement modifications for inclusion in TIMES-SS v2 • Milestones 6/7 – Assess missing/inaccurate mechanisms via literature • Milestone 8 – Gather new LLNA data for TIMES-SS v2 model. • Milestones 9/10 – Assessment of abiotic/protein reactivity

13. Issues being addressed • Improving TIMES-SS • How to deal with conflicting test data • Within test systems & across test systems • Using expert judgement to build/refine the model • Including new or refined mechanistic descriptions for reactions • How to describe chemical reactivity in a model • Providing the literature support • Are they better ways of describing reactivity and incorporating the information in TIMES-SS?

14. 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.