Health Canada experiences with early Health Canada experiences with early identification of potential carcinogensidentification of potential carcinogens- An Existing Substances Perspective- An Existing Substances Perspective

SSunil Kulkarniunil KulkarniHazard Methodology Division, Hazard Methodology Division,

Existing Substances Risk Assessment BureauExisting Substances Risk Assessment Bureau

Health Canada, Ottawa, ONHealth Canada, Ottawa, ON

OutlineOutline

• 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

Existing Substances under CEPA 1999Existing 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

Categorization 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

~3200 ~3200 remaining remaining prioritiespriorities

Categorization

23,00023,000DSL DSL chemicalchemicals s

4,300 priorities

Chemicals Management Plan

Chemicals Management Plan (CMP)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

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 PhaseCMP (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 applicationsHistorical use of (Q)SAR applications

Included in Rapid Screening: 545

Addressed through PBiT SNAcs: 145

Being addressed in Petroleum Sector Stream Approach: 164

Addressed in the Challenge: 200

Remaining priorities to be addressed by 2020: 3200

Universe of chemicals in work planUniverse of chemicals in work plan4300 existing chemical substances to be addressed by 2020:

~1500 to be addressed by 2016 through the groupings initiative, rapid screening and other approaches

Remaining Priorities - ScopeRemaining Priorities - Scope

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

Remaining Priorities – Data availabilityRemaining Priorities – Data availability

Are there enough data-rich analogues?

(Q)SAR opportunities?

58%

4%15%

23%

ApproachApproach

Human health risk assessmentHuman 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

Hierarchical consideration of sources Hierarchical consideration of sources of informationof information

Chemical

Predictive tools for hazard assessmentPredictive 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

Identifying toxic potentialIdentifying toxic potential

Relevance to humans

Relevance to humans

Essential to have a balanced judgement of the totality of available evidence

Expert systems

Chemical of interest

In vitrodata

In vivo mammalian

data

QSAR models

Analogue/Chemical

category read across

Toxic potential

Sufficient information

Insufficient information

Sufficient information

Hazard assessment

Reliability of estimationsReliability 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)

(Q)SAR tools/approaches to identify (Q)SAR tools/approaches to identify potential potential genotoxicgenotoxic carcinogens 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

(Q)SAR tools/approaches to identify (Q)SAR tools/approaches to identify potential potential non-genotoxicnon-genotoxic carcinogens 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

In vitro CTA

In vivomammalian

(Q)SAR/Read across

In vivo/in vitro

Genotox

Expert rules/knowledge

Male rat

Female rat

Male mice

Female mice

ChromAber

Micronuclei Ind

Mouse Lymphoma

Salmonella Ames

Drosophila

Unsch DNA Syn

SisterChrExc

SHE

BALB/c-3T3

C3H10T1/2

Non-genotoxic

DNA binding

Protein binding

Metabolism

Genotoxic

Holds potential to Holds potential to form part of hazard form part of hazard identification identification strategystrategy

Helpful to have a better Helpful to have a better understanding of Cell understanding of Cell

Transformation information in Transformation information in mechanistic interpretation of mechanistic interpretation of

(non-genotoxic) carcinogenicity(non-genotoxic) carcinogenicity

Domain of Domain of most most

(Q)SAR (Q)SAR modelsmodels

Few or no Few or no robust robust (Q)SAR (Q)SAR modelsmodels

Ashb

y (1

992)

, Pre

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f non

-gen

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ic c

arci

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s. T

oxic

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, 605

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.

Few or no (Q)SAR modelsFew or no (Q)SAR models

Basis of non-empirical approachesBasis of non-empirical approachesPhysChemBio activity Function of Ability to model/

Use in decision-making

Simple Molecular structure Good

Complex

Molecular structureMechanism MetabolismMulti-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

(Q)SAR analysis(Q)SAR analysis

Performance of some (Q)SAR modelsPerformance 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”

Prediction results/analysisPrediction results/analysis

Dataset of approx. 100 chemicals:

Ames PN ratio=55:46 Carc PN ratio: 49:52. 23 are positive in both Carc and Ames20 are negative in both; 32 are only Ames positive26 are Carc positive but Ames negative (non-Gtx Carc?)

Model a1 a2 b1 b2 c1 c2 d SHE-NgC SHE-Carc

TP 41 35 21 21 16 12 16 11 27

TN 32 46 32 35 30 13 8 9 17

FP 18 5 6 4 6 2 11 9 17

FN 5 12 14 16 16 2 2 4 7

total 96 98 73 76 68 29 37 33 68

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1

True

pos

itive

rate

False positive rate

Performance of QSAR models to discriminate Performance of QSAR models to discriminate carcinogenic/non-carcinogenic chemicals (n=100)carcinogenic/non-carcinogenic chemicals (n=100)

Models Casetox 2.4Model Applier 1.4Topkat 6.2Toxtree 2.5SHE=Syrian Hamster Embryo modelNgC=Non-genotoxic carcinogenicity

a1 (96)

a2 (98)

b1 (73)

c1 (68)b2 (76)

c2 (29)

SHE carc(68)

d (37)

Performance of Performance of in vitroin vitro Cell Transformation QSAR Cell Transformation QSAR models to discriminate carcinogenic/non-models to discriminate carcinogenic/non-

carcinogenic chemicals (n=130)carcinogenic chemicals (n=130)

LegendCTA=Cell Transformation assay based modelSHE=Syrian Hamster EmbryoBALB/c 3T3C3H 10T1/2

BALBc (115)

C3H10T1 (50)

SHE (96)

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1

TPR

FPR

CTA models exhibit potential but there is scope for improvement

Performance of some (Q)SAR models to Performance of some (Q)SAR models to identify non-genotoxic carcinogensidentify non-genotoxic carcinogens

Current cancer models aren’t designed to inform about genotoxic or non-genotoxic events in the carcinogenesis process

0

0.2

0.4

0.6

0.8

1

0 0.2 0.4 0.6 0.8 1

TPR

FPR

SHE(31)

a1(43)

a2(44)

c2(10)

b2(42)

c1(33)

b1(41)

d1(6) e(20)

d2(46)

Data analysisData analysis

Comparative ability of Ames & SHE tests to Comparative ability of Ames & SHE tests to discriminate carcinogens/non-carcinogensdiscriminate carcinogens/non-carcinogens

SHE (150)

SHE+Ames (70)

Ames (700)

0.00

0.20

0.40

0.60

0.80

1.00

0.00 0.20 0.40 0.60 0.80 1.00

TPR

FPR

MN (190)

CA (300)

MLm (220)

SHE (55)

Performance of genotoxicity and CT tests to Performance of genotoxicity and CT tests to discriminate (Ames -) carcinogens/non-carcinogensdiscriminate (Ames -) carcinogens/non-carcinogens

LegendSHE=Syrian Hamster EmbryoMLm=Mouse Lymphoma mutationCA=Chromosomal AberrationMN=Micronuclei induction

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

carcinogenscarcinogens

0.00

0.20

0.40

0.60

0.80

1.00

0.00 0.20 0.40 0.60 0.80 1.00

TPR

FPR

SHE (60)

MLm(155)

CA (245)

MN (110)

Ability of reprotoxicity data to Ability of reprotoxicity data to discriminate carc/non-carc chemicalsdiscriminate carc/non-carc chemicals

FMR (27)

FRR (107)

FRodR (118)

MMR (29)

MRR (72)

MRodR (83)

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1

TPR

FPR

LegendFRR=female rat reproductiveFRodR=female rodent reproMMR=male mice reproFMR=female mice reproMRodR=male rodent reproMRR=male rat repro

Scope for improvementScope for improvement

Finally………..Finally………..

fpr

tpr

Examples from CMP I where (Q)SAR Examples from CMP I where (Q)SAR or analogue-read across approaches or analogue-read across approaches

were used as supporting were used as supporting informationinformation

n-butyl glycidyl ether(CAS 2426-08-6 )

N

N

S

N

N

N Cl

Cl

CH3

O

N+O–

N

N

NN

H2

CN

CH3

CH3

S

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