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
Outline
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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 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
• 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
23,000
DSL
chemicals
Categorization
4,300
priorities
~3200
remaining
priorities
Chemicals
Management Plan
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:
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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
Historical use of (Q)SAR applications
2000-06
DSL Categorization
Commercial (Q)SAR models; basis for
decision making (prioritization)
2006-11
Ministerial
Challenge Phase
CMP (high priorities)
Commercial and some public domain
(Q)SAR models, Metabolism, Analogue
identification, Read-across; basis for
decision making but mainly supportive
evidence
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
Universe of chemicals in work plan
4300 existing chemical substances to be addressed by 2020:
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
~1500 to be addressed by 2016
through the groupings initiative,
rapid screening and other
approaches
Remaining Priorities - Scope
Organometallic Unknown
3%
4%
Organic metal salts
5%
Organic
36%
Inorganic
11%
Polymers
14%
UVCB
27%
(Q)SAR tools are generally only
applicable to discrete organics!
Remaining Priorities – Data availability
(Q)SAR
opportunities?
15%
23%
4%
58%
Are there enough
data-rich analogues?
Approach
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
Hierarchical consideration of sources
of information
Hazard Assessment
Chemical
Predictive tools for hazard assessment
Non-commercial
Commercial
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Casetox
Topkat
Derek
Model Applier
Oasis Times
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OECD QSAR Toolbox
Toxtree
OncoLogic
Caesar (Vega)
lazar
Supporting tools
• Leadscope Hosted - chemical data miner
• Pipeline Pilot – cheminformatics and workflow builder
Identifying toxic potential
In vivo
mammalian
data
Sufficient information
Relevance to humans
Insufficient
information
In vitro
data
Expert
systems
QSAR
models
Relevance to Sufficient
information
humans
Hazard
Toxic
potential
Analogue/
Chemical
category read
across
Consider strengths & weaknesses of evidence
Chemical of
interest
assessment
Essential to have a balanced judgement of the totality of available evidence
Reliability of estimations
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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)
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Professional judgement of expert(s)
(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
(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
(Q)SAR/
Read across
In vivo/
in vitro
Genotox
In vivo
mammalian
Male rat
Female rat
Male mice
Female mice
ChromAber
In vitro
CTA
SHE
Salmonella Ames
Drosophila
Unsch DNA Syn
SisterChrExc
Expert rules/
knowledge
Non-genotoxic
Genotoxic
Micronuclei Ind
Mouse Lymphoma
Holds potential to
form part of hazard
identification strategy
BALB/c-3T3
DNA binding
C3H10T1/2
Metabolism
Protein binding
Helpful to have a better
understanding of Cell
Transformation information in
mechanistic interpretation of
(non-genotoxic) carcinogenicity
Few or no
robust
(Q)SAR
models
Ashby (1992), Prediction of non-genotoxic carcinogenesis. Toxicology Letters, 64/65, 605-612.
Domain of
most
(Q)SAR
models
Few or no (Q)SAR models
Basis of non-empirical approaches
PhysChemBio activity
Function of
Ability to model/
Use in decision-making
Simple
Molecular structure
Good
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
(Q)SAR analysis
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”
Prediction results/analysis
Model
a1
a2
b1
b2
c1
c2
d
SHE-NgC SHE-Carc
TP
TN
41
32
35
46
21
32
21
35
16
30
12
13
16
8
11
9
27
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
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?)
Performance of QSAR models to discriminate
carcinogenic/non-carcinogenic chemicals (n=100)
1
0.9
a1 (96)
c2 (29)
d (37)
0.8
SHE carc(68)
a2 (98)
True positive rate
0.7
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)
0.6
b2 (76)
0.5
c1 (68)
0.4
0.3
0.2
0.1
0
0
0.1
0.2
0.3
0.4
0.5
0.6
False positive rate
0.7
0.8
0.9
1
Performance of in vitro Cell Transformation QSAR
models to discriminate carcinogenic/noncarcinogenic chemicals (n=130)
1
C3H10T1 (50)
0.9
BALBc (115)
0.8
SHE (96)
0.7
Legend
CTA=Cell Transformation
assay based model
SHE=Syrian Hamster
Embryo
BALB/c 3T3
C3H 10T1/2
TPR
0.6
0.5
0.4
0.3
0.2
0.1
0
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1
FPR
CTA models exhibit potential but there is scope for improvement
Performance of some (Q)SAR models to
identify non-genotoxic carcinogens
1
e(20)
d1(6)
c2
(10)
0.8
a1(43)
a2(44)
SHE(31)
TPR
0.6
b1(41)
0.4
b2(42)
c1(33)
0.2
d2(46)
0
0
0.2
0.4
0.6
0.8
1
FPR
Current cancer models aren’t designed to inform about genotoxic
or non-genotoxic events in the carcinogenesis process
Data analysis
Comparative ability of Ames & SHE tests to
discriminate carcinogens/non-carcinogens
SHE
(150)
Ames
(700)
SHE+Ames
(70)
Performance of genotoxicity and CT tests to
discriminate (Ames -) carcinogens/non-carcinogens
1.00
0.80
SHE
(55)
MLm
(220)
Legend
SHE=Syrian Hamster
Embryo
MLm=Mouse Lymphoma
mutation
CA=Chromosomal
Aberration
MN=Micronuclei induction
TPR
0.60
0.40
CA
(300)
MN
(190)
0.20
0.00
0.00
0.20
0.40
0.60
FPR
0.80
1.00
Performance of genotoxicity and CT tests to
discriminate (Ames +) carcinogens/non-carcinogens
1.00
MLm
(155)
SHE
(60)
0.80
CA
(245)
TPR
0.60
MN
(110)
0.40
0.20
0.00
0.00
0.20
0.40
0.60
FPR
0.80
1.00
Ability of reprotoxicity data to
discriminate carc/non-carc chemicals
1
0.9
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
0.8
0.7
MRR (72)
TPR
0.6
MRodR (83)
FMR (27)
0.5
MMR (29)
0.4
FRodR (118)
0.3
FRR (107)
0.2
0.1
0
0
0.1
0.2
0.3
0.4
0.5
FPR
0.6
0.7
0.8
0.9
1
Finally………..
tpr
Scope for improvement
fpr
Examples from CMP I where (Q)SAR or
analogue-read across approaches were
used as supporting information
CH3
Cl
N
N
N
N
S
Cl
N
N
H2
C
N
N
N
N
CH 3
CH 3
S
O–
N+
O
MAPBAP acetate
(CAS 72102-55-7)
DAPEP (CAS 25176-89-0 )
n-butyl glycidyl ether
(CAS 2426-08-6 )
Disperse Red 179
(CAS 16586-42-8)
http://www.chemicalsubstanceschimiques.gc.ca/challenge-defi/index-eng.php