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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
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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
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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
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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
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~3200 remaining priorities Categorization 23,000 DSL chemicals 4,300 priorities Chemicals Management Plan
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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
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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
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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
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Remaining Priorities - Scope
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(Q)SAR tools are generally only applicable to discrete organics!
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Remaining Priorities – Data availability Are there enough data-rich analogues? (Q)SAR opportunities? 58% 4% 15% 23%
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Approach
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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
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Hierarchical consideration of sources of information Chemical Hazard Assessment
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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
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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
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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)
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(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
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(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
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Holds potential to form part of hazard identification strategy
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Helpful to have a better understanding of Cell Transformation information in mechanistic interpretation of (non-genotoxic) carcinogenicity
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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.
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Few or no (Q)SAR models
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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
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(Q)SAR analysis
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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”
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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?)
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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)
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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
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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)
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Data analysis
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Comparative ability of Ames & SHE tests to discriminate carcinogens/non-carcinogens SHE (150) SHE+Ames (70) Ames (700)
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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
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Performance of genotoxicity and CT tests to discriminate (Ames +) carcinogens/non-carcinogens
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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
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Current performance Scope for improvement Finally……….. fpr tpr
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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
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