1. Health Canada experiences with early identification of potential carcinogens - An Existing Substances Perspective Sunil Kulkarni Hazard Methodology Division, Existing Substances Risk Assessment Bureau Health Canada, Ottawa, ON

2. Outline Brief introduction DSL - Categorization – Tools/Approaches Chemicals Management Plan – Phase I & II Remaining priorities (Q)SAR tools we use Challenges of (Q)SAR models & modelable endpoints (Q)SAR results/analyses

3. Existing Substances under CEPA 1999 Approximately 23,000 substances (e.g., industrial chemicals) on the Domestic Substances List (DSL) Includes substances used for commercial manufacturing or manufactured or imported in Canada at >100 kg/year between Jan 1, 1984 and Dec 31, 1986

4. Categorization Identify substances on the basis of exposure or hazard to consider further for screening assessment and to determine if they pose “harm to human health” or not A variety of tools including those based on (Q)SAR approaches were applied

5. ~3200 remaining priorities Categorization 23,000 DSL chemicals 4,300 priorities Chemicals Management Plan

6. Chemicals Management Plan (CMP) To assess and manage the risks associated with 4300 legacy substances identified through categorization by 2020 4300 substances were prioritized into high (~500), medium (~3200) and low concern substances (~550) CMP brings all existing federal programs together into a single strategy to ensure that chemicals are managed appropriately to prevent harm to Canadians and their environment It is science-based and specifically designed to protect human health and the environment through four major areas of action: Taking action on chemical substances of high concern Taking action on specific industry sectors Investing in research and biomonitoring Improving the information base for decision-making through mandatory submission of use and volume information

7. DSL Categorization Commercial (Q)SAR models; basis for decision making (prioritization) 2000-06 Commercial and some public domain (Q)SAR models, Metabolism, Analogue identification, Read-across; basis for decision making but mainly supportive evidence Ministerial Challenge Phase CMP (high priorities) 2006-11 2011-CMP II (includes data poor substances) Commercial and public domain (Q)SAR models, Analogue identification, Chemical categories, Read-across, Metabolism, in-house models/tools Historical use of (Q)SAR applications

8. Universe of chemicals in work plan 4300 existing chemical substances to be addressed by 2020: ~1500 to be addressed by 2016 through the groupings initiative, rapid screening and other approaches

9. Remaining Priorities - Scope

10. (Q)SAR tools are generally only applicable to discrete organics!

11. Remaining Priorities – Data availability Are there enough data-rich analogues? (Q)SAR opportunities? 58% 4% 15% 23%

12. Approach

13. Human health risk assessment Chemical’s inherent toxicity & potential human exposure Assess a range of endpoints including genotoxicity, carcinogenicity, developmental toxicity, reproductive toxicity & skin sensitization (Q)SAR approaches, including analogue/chemical category read across are used to support our assessments (line of evidence) Apply weight of evidence and precaution in our decision-making

14. Hierarchical consideration of sources of information Chemical Hazard Assessment

15. Predictive tools for hazard assessment Commercial Casetox Topkat Derek Model Applier Oasis Times Non-commercial OECD QSAR Toolbox Toxtree OncoLogic Caesar (Vega) lazar Supporting tools Leadscope Hosted - chemical data miner Pipeline Pilot – cheminformatics and workflow builder

16. Identifying toxic potential Relevance to humans Essential to have a balanced judgement of the totality of available evidence Consider strengths & weaknesses of evidence Hazard assessment

17. Reliability of estimations Minimizing uncertainties and maximizing confidence in predictions considering multiple factors: - OECD QSAR Validation principles - accuracy of input - quality of underlying biological data - multiple models based on different predictive paradigms or methodologies - mechanistic understanding - inputs from in vitro/in vivo tests (if available) Professional judgement of expert(s)

18. (Q)SAR tools/approaches to identify potential genotoxic carcinogens QSAR Toolbox profiler flags- DNA/Protein binding, Benigni-Bossa, OncoLogic Metabolic simulators (Toolbox/TIMES) + DNA/Protein binding/Benigni-Bossa flags Combination of (Q)SAR models for genotoxicity & carcinogenicity (Casetox, Model Applier, Derek, Times, Toxtree, Caesar, Topkat) Genotox - Salmonella (Ames) models for different strains, Chrom ab, Micronuclei Ind, Mouse Lymphoma mut with metabolic activation Carcinogenicity – Male & female rats, mice, rodent

19. (Q)SAR tools/approaches to identify potential non-genotoxic carcinogens Flags from QSAR Toolbox profilers – Benigni-Bossa flags QSAR models based on in vitro Cell Transformation assays such as Syrian Hamster Embryo, BALB/c-3T3, C3H10T1/2 Expert rule based systems Derek and Toxtree

20. Holds potential to form part of hazard identification strategy

21. Helpful to have a better understanding of Cell Transformation information in mechanistic interpretation of (non-genotoxic) carcinogenicity

22. Domain of most (Q)SAR models Few or no robust (Q)SAR models Ashby (1992), Prediction of non-genotoxic carcinogenesis. Toxicology Letters, 64/65, 605-612.

23. Few or no (Q)SAR models

24. Basis of non-empirical approaches PhysChemBio activityFunction ofAbility to model/ Use in decision-making SimpleMolecular structureGood Complex Molecular structure Mechanism Metabolism Multi-step Challenging (uncertainty ↑) Complex BA not easily translated/explainable in terms of simple molecular structure/fragments to enable building a robust QSAR For instance, a QSAR model for carcinogenicity only predicts Yes/No without any information about its mechanism Availability of data rich analogues is essential for read-across approaches

25. (Q)SAR analysis

26. Performance of some (Q)SAR models A set of chemicals with in vitro and in vivo data on genotoxicity and carcinogenicity was chosen Predictions were obtained for different human health relevant endpoints by running these through a variety of (Q)SAR models Performance of models to discriminate carcinogenic and non-carcinogenic chemicals was evaluated by analysing the results Structural analysis of chemicals incorrectly classified by all models revealed a diverse group of chemicals with few trends (we are working on that) Failure of models/expert systems to flag them as “Out of domain”

27. Prediction results/analysis Dataset of approx. 100 chemicals : Ames PN ratio=55:46 Carc PN ratio: 49:52. 23 are positive in both Carc and Ames 20 are negative in both; 32 are only Ames positive 26 are Carc positive but Ames negative (non-Gtx Carc?)

28. Performance of QSAR models to discriminate carcinogenic/non-carcinogenic chemicals (n=100) Models Casetox 2.4 Model Applier 1.4 Topkat 6.2 Toxtree 2.5 SHE=Syrian Hamster Embryo model NgC=Non-genotoxic carcinogenicity a1 (96) a2 (98) b1 (73) c1 (68) b2 (76) c2 (29) SHE carc(68) d (37)

29. Performance of in vitro Cell Transformation QSAR models to discriminate carcinogenic/non- carcinogenic chemicals (n=130) Legend CTA=Cell Transformation assay based model SHE=Syrian Hamster Embryo BALB/c 3T3 C3H 10T1/2 CTA models exhibit potential but there is scope for improvement

30. Performance of some (Q)SAR models to identify non-genotoxic carcinogens Current cancer models aren’t designed to inform about genotoxic or non-genotoxic events in the carcinogenesis process SHE(31) a1(43) a2(44) c2 (10) b2(42) c1(33) b1(41) d1(6) e(20) d2(46)

31. Data analysis

32. Comparative ability of Ames & SHE tests to discriminate carcinogens/non-carcinogens SHE (150) SHE+Ames (70) Ames (700)

33. MN (190) CA (300) MLm (220) SHE (55) Performance of genotoxicity and CT tests to discriminate (Ames -) carcinogens/non-carcinogens Legend SHE=Syrian Hamster Embryo MLm=Mouse Lymphoma mutation CA=Chromosomal Aberration MN=Micronuclei induction

34. Performance of genotoxicity and CT tests to discriminate (Ames +) carcinogens/non-carcinogens

35. Ability of reprotoxicity data to discriminate carc/non-carc chemicals Legend FRR=female rat reproductive FRodR=female rodent repro MMR=male mice repro FMR=female mice repro MRodR=male rodent repro MRR=male rat repro

36. Current performance Scope for improvement Finally……….. fpr tpr

37. Examples from CMP I where (Q)SAR or analogue-read across approaches were used as supporting information n-butyl glycidyl ether (CAS 2426-08-6 ) MAPBAP acetate (CAS 72102-55-7) DAPEP (CAS 25176-89-0 ) Disperse Red 179 (CAS 16586-42-8) http://www.chemicalsubstanceschimiques.gc.ca/challenge-defi/index-eng.php