S unil Kulkarni Hazard Methodology Division, Existing Substances Risk Assessment Bureau
description
Transcript of S unil Kulkarni Hazard Methodology Division, Existing Substances Risk Assessment Bureau
![Page 1: S unil Kulkarni Hazard Methodology Division, Existing Substances Risk Assessment Bureau](https://reader036.fdocuments.us/reader036/viewer/2022081419/56815055550346895dbe565f/html5/thumbnails/1.jpg)
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
![Page 2: S unil Kulkarni Hazard Methodology Division, Existing Substances Risk Assessment Bureau](https://reader036.fdocuments.us/reader036/viewer/2022081419/56815055550346895dbe565f/html5/thumbnails/2.jpg)
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
![Page 3: S unil Kulkarni Hazard Methodology Division, Existing Substances Risk Assessment Bureau](https://reader036.fdocuments.us/reader036/viewer/2022081419/56815055550346895dbe565f/html5/thumbnails/3.jpg)
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
![Page 4: S unil Kulkarni Hazard Methodology Division, Existing Substances Risk Assessment Bureau](https://reader036.fdocuments.us/reader036/viewer/2022081419/56815055550346895dbe565f/html5/thumbnails/4.jpg)
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
![Page 5: S unil Kulkarni Hazard Methodology Division, Existing Substances Risk Assessment Bureau](https://reader036.fdocuments.us/reader036/viewer/2022081419/56815055550346895dbe565f/html5/thumbnails/5.jpg)
~3200 ~3200 remaining remaining prioritiespriorities
Categorization
23,00023,000DSL DSL chemicalchemicals s
4,300 priorities
Chemicals Management Plan
![Page 6: S unil Kulkarni Hazard Methodology Division, Existing Substances Risk Assessment Bureau](https://reader036.fdocuments.us/reader036/viewer/2022081419/56815055550346895dbe565f/html5/thumbnails/6.jpg)
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
![Page 7: S unil Kulkarni Hazard Methodology Division, Existing Substances Risk Assessment Bureau](https://reader036.fdocuments.us/reader036/viewer/2022081419/56815055550346895dbe565f/html5/thumbnails/7.jpg)
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
![Page 8: S unil Kulkarni Hazard Methodology Division, Existing Substances Risk Assessment Bureau](https://reader036.fdocuments.us/reader036/viewer/2022081419/56815055550346895dbe565f/html5/thumbnails/8.jpg)
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
![Page 9: S unil Kulkarni Hazard Methodology Division, Existing Substances Risk Assessment Bureau](https://reader036.fdocuments.us/reader036/viewer/2022081419/56815055550346895dbe565f/html5/thumbnails/9.jpg)
Remaining Priorities - ScopeRemaining Priorities - Scope
![Page 10: S unil Kulkarni Hazard Methodology Division, Existing Substances Risk Assessment Bureau](https://reader036.fdocuments.us/reader036/viewer/2022081419/56815055550346895dbe565f/html5/thumbnails/10.jpg)
(Q)SAR tools are generally only (Q)SAR tools are generally only applicable to discrete organics!applicable to discrete organics!
![Page 11: S unil Kulkarni Hazard Methodology Division, Existing Substances Risk Assessment Bureau](https://reader036.fdocuments.us/reader036/viewer/2022081419/56815055550346895dbe565f/html5/thumbnails/11.jpg)
Remaining Priorities – Data availabilityRemaining Priorities – Data availability
Are there enough data-rich analogues?
(Q)SAR opportunities?
58%
4%15%
23%
![Page 12: S unil Kulkarni Hazard Methodology Division, Existing Substances Risk Assessment Bureau](https://reader036.fdocuments.us/reader036/viewer/2022081419/56815055550346895dbe565f/html5/thumbnails/12.jpg)
ApproachApproach
![Page 13: S unil Kulkarni Hazard Methodology Division, Existing Substances Risk Assessment Bureau](https://reader036.fdocuments.us/reader036/viewer/2022081419/56815055550346895dbe565f/html5/thumbnails/13.jpg)
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
![Page 14: S unil Kulkarni Hazard Methodology Division, Existing Substances Risk Assessment Bureau](https://reader036.fdocuments.us/reader036/viewer/2022081419/56815055550346895dbe565f/html5/thumbnails/14.jpg)
Hierarchical consideration of sources Hierarchical consideration of sources of informationof information
Chemical
![Page 15: S unil Kulkarni Hazard Methodology Division, Existing Substances Risk Assessment Bureau](https://reader036.fdocuments.us/reader036/viewer/2022081419/56815055550346895dbe565f/html5/thumbnails/15.jpg)
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
![Page 16: S unil Kulkarni Hazard Methodology Division, Existing Substances Risk Assessment Bureau](https://reader036.fdocuments.us/reader036/viewer/2022081419/56815055550346895dbe565f/html5/thumbnails/16.jpg)
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
![Page 17: S unil Kulkarni Hazard Methodology Division, Existing Substances Risk Assessment Bureau](https://reader036.fdocuments.us/reader036/viewer/2022081419/56815055550346895dbe565f/html5/thumbnails/17.jpg)
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)
![Page 18: S unil Kulkarni Hazard Methodology Division, Existing Substances Risk Assessment Bureau](https://reader036.fdocuments.us/reader036/viewer/2022081419/56815055550346895dbe565f/html5/thumbnails/18.jpg)
(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
![Page 19: S unil Kulkarni Hazard Methodology Division, Existing Substances Risk Assessment Bureau](https://reader036.fdocuments.us/reader036/viewer/2022081419/56815055550346895dbe565f/html5/thumbnails/19.jpg)
(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
![Page 20: S unil Kulkarni Hazard Methodology Division, Existing Substances Risk Assessment Bureau](https://reader036.fdocuments.us/reader036/viewer/2022081419/56815055550346895dbe565f/html5/thumbnails/20.jpg)
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
![Page 21: S unil Kulkarni Hazard Methodology Division, Existing Substances Risk Assessment Bureau](https://reader036.fdocuments.us/reader036/viewer/2022081419/56815055550346895dbe565f/html5/thumbnails/21.jpg)
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
![Page 22: S unil Kulkarni Hazard Methodology Division, Existing Substances Risk Assessment Bureau](https://reader036.fdocuments.us/reader036/viewer/2022081419/56815055550346895dbe565f/html5/thumbnails/22.jpg)
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
dicti
on o
f non
-gen
otox
ic c
arci
noge
nesi
s. T
oxic
olog
y Le
tter
s, 6
4/65
, 605
-612
.
![Page 23: S unil Kulkarni Hazard Methodology Division, Existing Substances Risk Assessment Bureau](https://reader036.fdocuments.us/reader036/viewer/2022081419/56815055550346895dbe565f/html5/thumbnails/23.jpg)
Few or no (Q)SAR modelsFew or no (Q)SAR models
![Page 24: S unil Kulkarni Hazard Methodology Division, Existing Substances Risk Assessment Bureau](https://reader036.fdocuments.us/reader036/viewer/2022081419/56815055550346895dbe565f/html5/thumbnails/24.jpg)
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
![Page 25: S unil Kulkarni Hazard Methodology Division, Existing Substances Risk Assessment Bureau](https://reader036.fdocuments.us/reader036/viewer/2022081419/56815055550346895dbe565f/html5/thumbnails/25.jpg)
(Q)SAR analysis(Q)SAR analysis
![Page 26: S unil Kulkarni Hazard Methodology Division, Existing Substances Risk Assessment Bureau](https://reader036.fdocuments.us/reader036/viewer/2022081419/56815055550346895dbe565f/html5/thumbnails/26.jpg)
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”
![Page 27: S unil Kulkarni Hazard Methodology Division, Existing Substances Risk Assessment Bureau](https://reader036.fdocuments.us/reader036/viewer/2022081419/56815055550346895dbe565f/html5/thumbnails/27.jpg)
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
![Page 28: S unil Kulkarni Hazard Methodology Division, Existing Substances Risk Assessment Bureau](https://reader036.fdocuments.us/reader036/viewer/2022081419/56815055550346895dbe565f/html5/thumbnails/28.jpg)
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)
![Page 29: S unil Kulkarni Hazard Methodology Division, Existing Substances Risk Assessment Bureau](https://reader036.fdocuments.us/reader036/viewer/2022081419/56815055550346895dbe565f/html5/thumbnails/29.jpg)
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
![Page 30: S unil Kulkarni Hazard Methodology Division, Existing Substances Risk Assessment Bureau](https://reader036.fdocuments.us/reader036/viewer/2022081419/56815055550346895dbe565f/html5/thumbnails/30.jpg)
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)
![Page 31: S unil Kulkarni Hazard Methodology Division, Existing Substances Risk Assessment Bureau](https://reader036.fdocuments.us/reader036/viewer/2022081419/56815055550346895dbe565f/html5/thumbnails/31.jpg)
Data analysisData analysis
![Page 32: S unil Kulkarni Hazard Methodology Division, Existing Substances Risk Assessment Bureau](https://reader036.fdocuments.us/reader036/viewer/2022081419/56815055550346895dbe565f/html5/thumbnails/32.jpg)
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)
![Page 33: S unil Kulkarni Hazard Methodology Division, Existing Substances Risk Assessment Bureau](https://reader036.fdocuments.us/reader036/viewer/2022081419/56815055550346895dbe565f/html5/thumbnails/33.jpg)
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
![Page 34: S unil Kulkarni Hazard Methodology Division, Existing Substances Risk Assessment Bureau](https://reader036.fdocuments.us/reader036/viewer/2022081419/56815055550346895dbe565f/html5/thumbnails/34.jpg)
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)
![Page 35: S unil Kulkarni Hazard Methodology Division, Existing Substances Risk Assessment Bureau](https://reader036.fdocuments.us/reader036/viewer/2022081419/56815055550346895dbe565f/html5/thumbnails/35.jpg)
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
![Page 36: S unil Kulkarni Hazard Methodology Division, Existing Substances Risk Assessment Bureau](https://reader036.fdocuments.us/reader036/viewer/2022081419/56815055550346895dbe565f/html5/thumbnails/36.jpg)
Scope for improvementScope for improvement
Finally………..Finally………..
fpr
tpr
![Page 37: S unil Kulkarni Hazard Methodology Division, Existing Substances Risk Assessment Bureau](https://reader036.fdocuments.us/reader036/viewer/2022081419/56815055550346895dbe565f/html5/thumbnails/37.jpg)
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