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S unil Kulkarni Hazard Methodology Division, Existing Substances Risk Assessment Bureau PowerPoint Presentation
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S unil Kulkarni Hazard Methodology Division, Existing Substances Risk Assessment Bureau

S unil Kulkarni Hazard Methodology Division, Existing Substances Risk Assessment Bureau

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S unil Kulkarni Hazard Methodology Division, Existing Substances Risk Assessment Bureau

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  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 23,000 DSL chemicals 4,300 priorities Categorization 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. Historical use of (Q)SAR applications Commercial (Q)SAR models; basis for decision making (prioritization) 2000-06 DSL Categorization 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 Commercial and public domain (Q)SAR models, Analogue identification, Chemical categories, Read-across, Metabolism, in-house models/tools 2011- CMP II (includes data poor substances)

  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 (Q)SAR opportunities? 4% 15% 58% 23% Are there enough data-rich analogues?

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

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

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

  23. Few or no (Q)SAR models

  24. Basis of non-empirical approaches 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) a1 (96) d (37) c2 (29) SHE carc(68) a2 (98) Models Casetox 2.4 Model Applier 1.4 Topkat 6.2 Toxtree 2.5 SHE=Syrian Hamster Embryo model NgC=Non-genotoxic carcinogenicity b1 (73) b2 (76) c1 (68)

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

  31. Data analysis

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

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

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

  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 ) DAPEP (CAS 25176-89-0 ) Disperse Red 179 (CAS 16586-42-8) MAPBAP acetate (CAS 72102-55-7)