1. G. Frank Gerberick 1, Leslie M. Foertsch,, John Troutman 1 and Jean- Pierre Lepoittevin 2 1 The Procter & Gamble Company 2 Université of Strasbourg Utilization of cysteine and lysine peptides for screening reactivity of skin sensitizers Logan Reactivity Workshop Utah State University March 23-24, 2010

2. Acknowledgements  Cindy Ryan – P&G  Petra Kern – P&G  Joanna Jaworska – P&G  Joel Chaney – P&G  Leslie Foertsch – P&G  John Troutman – P&GP  Roy Dobson – P&GP  Mike Quijano – P&GP  Jean-Pierre Lepoittevin – ULP  Elena Gimenez-Arnau - ULP  Carsten Goebel – Wella  Andreas Natsch – Givaudan  COLIPA Skin Tolerance TF

3. Allergic Contact Dermatitis: Immunologic Mechanism of Contact Sensitization Elicitation Phase KC TNF -  IL-1  GM-CSF IL1-  Allergen Epidermis Dermis LC Afferent Lymphatics Draining Lymph Node Naive T-cell SC IL-2 Sensitization Phase Antigen-induced clonal expansion Sensitized Memory / Effector T-cell Dissemination into peripheral circulation Allergen Inflammatory mediator release Erythema Edema Vesiculation

4. How do we currently perform skin sensitisation risk assessments?  If significant skin exposure is predicted then the sensitisation hazard of the ingredient needs to be characterised Mouse Local Lymph Node Assay (LLNA) allows skin sensitisation potency and dose response information to be generated  Reduction and Refinement alternative to Guinea Pig test methods (GPMT and Buehler test)  LLNA data used to predict a Human repeat insult patch test (HRIPT) No Adverse Effect Level (NOAEL)  HRIPT NOAEL data used to predict a theoretical Acceptable Exposure Level (AEL) using a Quantitative Risk Assessment (QRA) approach Risk Assessment decision on safety of ingredient in product taken using AEL, product-specific exposure data & any other relevant information

5. Relative Potency Classification by LLNA EC3 Values ChemicalEC3 (µg/cm 2 ) Potency Category (ECETOC) EC3 (%) MCI/MI1.25Extreme0.005 DNCB10Extreme0.04 Lauryl gallate75Strong0.3 Cinnamaldehyde750Moderate3 Isoeugenol450Moderate1.8 Eugenol3250Weak13 Penicillin G11,500Weak46 1 Gerberick et al. 2005. Dermatitis, Vol. 16 (4), pp1-46. 2 Kern et al. 2010. Dermatitis, Vol 21(1), pp8-32. Initially > 200 chemicals 1 of all potencies, chemical and biological diversity, quality controlled. Now ~ 320 entries 2. Data from UL, P&G, Syngenta, RIFM, public literature..

6. Skin Sensitization Endpoint Evolution DC Gene Expression (HTS) allergen protein E :Nu Molecular Modeling Reactivity Assays 1970s<20101990s Local Lymph Node Assay Guinea Pig Test Methods Lymph Nodes

7. Chemical-Protein Reactivity, Metabolism and Skin Sensitization Nucleophilic-electrophilic interaction: Hapten E :Nu The correlation of skin protein reactivity and skin sensitization is well established and has been known for many years. (Landsteiner and Jacobs, 1936; Dupuis and Benezra, 1982; Lepoittevin et al, 1998) Hapten Pro/Pre-Hapten Protein

8. Test chemical in acetonitrile. Cys peptide (Ac-RFAACAA-COOH) in phosphate buffer, pH 7.5. Lys peptide (Ac-RFAAKAA-COOH) in ammonium acetate, pH 10.2. Test chemical reacted with peptide (10:1 or 50:1) for 24 hours. Peptide depletion monitored by HPLC at 220 nm. Un-reacted Peptide Test Chemical Reaction Mixture Readout for Direct Peptide Reactivity Assay (DPRA): Peptide Depletion

9. How to best analyze peptide reactivity data? Extreme/Strong17 Moderate20 Weak15 Non-Sensitizers30 Total82

10. Use of Classification Tree Approach for Analysis of GSH, Cys and Lys Data  A form of binary recursive partitioning  Used when observations need to be assigned to a category based on a number of predictor variables  Predictor variables can be nominal, ordinal, or continuous  Used in situations where discriminant analysis or logistic regression analysis may be used  Used peptide depletion data and LLNA potency data to generate models

11. Prediction Model Based on Sum of Cys 1:10 and Lys 1:50 (n=81) NS/W/M/S Test (29 / 11 / 3 / 0) Total Sample (29 / 15 / 20 / 17) Minimal Reactivity (26 / 5 / 1 / 0) Low Reactivity (3 / 6 / 2 / 0) Avg Score < 6.376% Avg Score < 22.62%Avg Score > 22.62% Test (0 / 4 / 17 / 17) Moderate Reactivity (0 / 1 / 6 / 3) High Reactivity (0 / 3 / 11 / 14) Avg Score < 42.47% Avg Score > 6.376% Avg Score > 42.47%

12. Cooper Statistics Analysis is Based on Classification Tree Model Results  Minimal Reactivity = Non-sensitizing  Low Reactivity = Sensitizing  Moderate Reactivity = Sensitizing  High Reactivity = Sensitizing

13. Cooper Statistics for Prediction Model based on Cys 1:10 and Lys 1:50 (n=81) Sensitivity:88% Specificity:90% Accuracy:89% Predicted Classification (based on classification tree model) Non-SensitizerSensitizertotal Chemical Classification Non-Sensitizer26329 (based primarily on LLNA) Sensitizer64652 total324981

14. Inter-laboratory studies to evaluate direct peptide reactivity assay  We have completed 2 Inter-laboratory studies to evaluate the transferability of the DPRA.  Scientists from Kao, L’Oreal and Givaudan visited P&G for “hands on” training  Ring Trial 1 consisted of 15 chemicals with very good results  Ring Trial 2 consisted of 28 chemicals  The chemicals of Ring Trial 2 proved to be a bit more challenging but provided us with an opportunity to improve the SOP  The 2 successful inter-laboratory studies encouraged us to move forward with ECVAM for validation of the assay.

15. Chemicals Prediction Model Results Cys (1:10) and Lys (1:50 P&G Givaudan L'Oreal Kao p-BenzoquinoneHigh 2,4-DinitrochlorobenzeneHigh OxazoloneHigh FormaldehydeModerate LowModerate 2-PhenylpropionaldehydeHigh Moderate Diethyl maleateHigh BenzylideneacetoneHigh FarnesalLow Moderate 2,3-ButanedioneHigh 4-AllylanisoleLow Moderate HydroxycitronellalLowModerateLow ButanolMinimal 6-MethylcoumarinMinimal Lactic acidMinimal 4-MethoxyacetophenoneMinimal Interlaboratory studies to evaluate direct peptide reactivity assay

16. 135 chemicals tested to date (33 extreme/strong; 36 moderate; 29 weak and 37 non- sensitizers)

17. Assay Reproducibility (2005 – present )

18. DPRA ECVAM Test Submission  Test Submission to ECVAM – February, 2009  DPRA SOP finalized – December, 2009  Participating labs for pre-validation study identified – January, 2010  Training and Transfer plan approved – February 2010  Training begins – March 2010  Aside from Skin Sensitization, the DPRA is also used as part of the Eye Irritation Validation Study (led by Pauline Mcnamee)

19. ECVAM Pre-validation Timeline PhaseEstimated Completion Date Phase A, Stage I: SOP trainingMarch 31, 2010 Phase A, Stage II: SOP transferJune 30, 2010 Phase B, Stage I: 9 ChemicalsJuly 31, 2010 Phase B, Stage II: Additional 15 Chemicals September 15, 2010 Data Analysis (ECVAM biostatistician) March 31, 2011 Final Pre-Validation ReportMay 31, 2011

20. Analysis of Direct Peptide Reactivity Data Using GHS Classification Cutoff

21. Analysis of Direct Peptide Reactivity Data Using GHS Classification Cutoff The classification is generally an over classification (1B chemicals classified as 1A)

22. Test Chemical Peptide-chemical ADDUCT Readout = Non-adducted peptide monomer measured by LC/MS/MS Monitored but not quantified vs monomer X Blocked by DTT Auto-oxidation Not quantified but can be observed using MALDI Reactive Metabolite (electrophilic hapten) Peroxidase(O)Peroxidase(R) H 2 O 2 H 2 O Fe 2+ + H 2 O 2 Oxidant Blocked by desferroxamine X Fenton Chemistry Peptide (nucleophile) Principle of the Assay Objective: To incorporate an enzyme- mediated activation step into an LC/MS-based in chemico model for the quantitative prediction of a chemical’s reactivity potential. Aim 1: Optimize assay parameters with cysteine and lysine peptides for direct and enzyme-mediated reactivity assessments. Aim 2: Develop simple methods for estimating ‘reactivity potency’ for use as a parameter in the development of an integrated testing strategy for predicting skin sensitization potential. Next Generation Peptide Reactivity Assay Peptide Dimer

23. Optimization of Assay Conditions with a Cysteine-containing peptide Peroxide concentration Peroxidase concentration Incubation time Test Chemicals: o2-Aminophenol oEugenol o1,4-Phenylenediamine o2-Methoxy-4- methylphenol o3-Methylcatechol

24. Effect of Peroxide Concentration

25. Non-sensitizersPro-haptensPre-haptens Reactivity Screen Under Optimized Conditions with Cysteine Summary: Peptide depletion values for the non-sensitizers was generally < 10%, Peptide depletion values for sensitizers ranged from approx. 30% to nearly 80%, Prohapten sensitizers showed minimal to no peptide depletion in the absence of HRP/P, Peptide depletion for pre-haptens (catechols, hydroquinones, pPD) ranged from 40-80%.

26. Lysine Method Development Primary Objectives: –To demonstrate direct peptide reactivity using LC/MS detection methods with lysine. –To understand the relationship between peptide depletion and: peptide concentration test chemical concentration incubation temperature –The focus for subsequent studies included: Testing for lysine reactivity + HRP/P Concentration-response testing with cysteine and lysine Incorporation of a curve-fitting model for RC50 determination

27. Direct reactivity with known lysine reactive test chemicals was observed with LC/MS detection. For each test chemical examined, direct peptide depletion appeared to be test chemical concentration dependent. Although peptide concentration appeared to have much less of an effect on overall depletion, loss of peptide was highest at the lowest peptide concentration tested (i.e., 0.005 mM; reactions with 2.5 mM 3-MC was a noted exception). These trends suggested that the change in peptide concentration per unit time would be greatest at the lowest peptide concentration tested. The lysine peptide concentration chosen for subsequent assay development was 0.005 mM. Effect of peptide and test chemical concentration

28. Method Development – Lysine Peptide

29. Method Development Effect of incubation temperature on Lysine Peptide Reactivity: Results: No significant increase in depletion was noted for p-benzoquinone and 3-MC at elevated temperature. Subtle increases were observed for the aldehydes, which may be attributed to an increase in the rate of reaction and/or solubility of the test chemical (?). The incubation temperature selected for subsequent studies was ambient lab conditions.

30. Summary of method development work Optimization studies with direct lysine-reactive chemicals resulted in a defined set of incubation conditions for further testing with HRP/P. The focus for subsequent studies included: –Testing for lysine reactivity + HRP/P –Concentration-response testing with cysteine and lysine –Incorporation of a curve-fitting model for RC50 determination

31. Concentration-response testing using ‘optimized’ conditions ParameterCysteine PeptideLysine Peptide Peptide concentration20 uM5 uM Test chemical concentration0.003, 0.03, 0.3, 3.0 and 30 mM Desferroxamine10 uM- HRP+ 3.0 U/mL Hydrogen peroxide+ 100 uM Buffer0.1 M potassium phosphate (pH 7.4)0.1 M Ammonium acetate (pH 10.2) Incubation period24 hr Incubation temperatureambient Number chemicals/experiment 13 unknowns plus 3 control chemicals (N=1 replicate/concentration)

32. Criteria for characterizing and reporting peptide reactivity Prior to analysis, all negative peptide depletion values were set to ‘zero.’ RC50 was estimated for each test chemical using RExcel, which is a tool that fits a two-parameter log-logistic model to peptide depletion data and graphs the raw data and fitted curves. RC50 values were estimated and reported as described above except for the following: –If peptide depletion did not exceed 10% across the concentration range, peptide reactivity was designated as ‘NC’ (not calculated). –If peptide depletion did not exceed 10% for the highest two concentrations tested, or depletion was low (< 20%) and did not increase with an increase in test chemical concentration, peptide reactivity was designated as ‘NR’ (not reported). Note: the ‘NR’ designation can serve as an alert by distinguishing non-reactive (‘NC’) test chemicals from those that show reactivity that is insufficient for estimating the RC50.

33. Characterize peptide depletion as a function of test chemical concentration with cysteine and lysine + HRP/P Quantify reactivity by estimating the concentration of test chemical that depletes the peptide by 50% (RC50) within 24 hrs Current Process being evaluated for Reactivity Assessments Correlate peptide reactivity with chemical sensitization data for evaluating its use in an integrated testing strategy for predicting skin sensitization potential

34. Representative chemicals that are well characterized with Cysteine

35. Representative chemicals that are well characterized with Lysine

36. * RC50 values were estimated using RExcel which fits a two-parameter log-logistic model to peptide reactivity data, and graphs the raw data and fitted curves. NC = not calculated (peptide depletion did not exceed 10% across concentration range) NR = not reported (peptide depletion did not exceed 10% for the two highest concentrations tested, or depletion was low (< 20%) and did not increase with an increase in test chemical concentration) ND = not determined (not tested to date) Preliminary Results with Cysteine and Lysine + HRP/P with dose-response Trends in peptide reactivity appear to coincide with general trends in LLNA-based potency classifications Rank order from low to high for the most reactive nucleophile

37. Summary and Next Steps Optimization of a peroxidase-based peptide reactivity assay for screening pro-hapten, pre-hapten and hapten chemical sensitizers have been completed with cysteine and lysine peptides, A total of 133 test chemicals have been evaluated to date, with plans to complete testing of approx. 150 chemicals by mid-April, RC50 values show good correlation with LLNA-based potency data, which supports further the hypothesis that protein reactivity of chemical allergens (or their metabolites) is a key component of the induction of skin sensitization, Next steps include submitting a manuscript to a peer-reviewed scientific journal on the development work for lysine, Continue to evaluate the criteria for RC50 data reporting and its use in skin sensitization prediction models.

38. T DC T T T Lymph Node Skin Understanding epidermal bioavailability of chemical sensitizers Understanding chemical sensitizer-induced Dendritic cell (DC) activation –Intracellular signalling pathways –Gene expression changes How can all data be combined? Understanding metabolism of chemical sensitizers in skin Predicting modification of proteins by chemical sensitizers In vitro T cell activation

39. Acknowledgements Cindy Ryan – P&G Petra Kern – P&G Joanna Jaworska – P&G Joel Chaney – P&G Leslie Foertsch – P&G John Troutman – P&GP Roy Dobson – P&GP Mike Quijano – P&GP Jean-Pierre Lepoittevin – ULP Elena Gimenez-Arnau - ULP Carsten Goebel – Wella Andreas Natsch – Givaudan COLIPA Skin Tolerance TF Thanks for your attention