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Fighting unknown chemicals: analytical strategies for risk prioritization
Eelco Nicolaas Pieke
Research Group for Analytical Food ChemistryNational Food Institute of Denmark
Berlin, Germany30 November 2017
DTU Food, Technical University of Denmark Berlin, Germany 30 November 2017
In this talk:
• Introduction to IAS & NIAS: the chemical headache in foodo Knowledge gapso The analytical “Pillars of Knowledge”o Difference between IAS and NIASo Risk-assessing IAS and NIAS
• Proposed methods for closing the knowledge gap (not detailed)o Quantification (how much?)o Identification (what?)o Hazard character (how bad?)o Preliminary risk (priority?)
2
DTU Food, Technical University of Denmark Berlin, Germany 30 November 2017
Knowledge gaps in Risk Assessment
3
Intentionalor not?
Chemicalidentity
Knowledge on chemical intake
DTU Food, Technical University of Denmark Berlin, Germany 30 November 2017
Knowledge gaps in Risk Assessment
4
Intentionalor not?
Chemicalidentity
Knowledge on chemical intake
DTU Food, Technical University of Denmark Berlin, Germany 30 November 2017
Introducing the Pillars
5
[Identity & Potential] + [Quantity]
[Toxicity] + [Identity & Potential]
[Toxicity] + [Quantity]
= Exposure
= Hazard
= Effect level
Risk = Exposure + Hazard + Effect level
Identity &Potential
RISK
DTU Food, Technical University of Denmark Berlin, Germany 30 November 2017
• Intentionally Added Substances (IAS):
– Limited number
– Pillars OK (well-defined data)
– Validated methods
– Substances are regulated
Existing tools & knowledge
Rarely problematic
A story of IAS and NIAS
6
?
IAS
Che
mic
als
in foo
d
DTU Food, Technical University of Denmark Berlin, Germany 30 November 2017
• Non-Intentionally Added Substances (NIAS):
– Virtually unlimited number
– Pillars NOT OK (missing data)
– No formalized methodology
– No effective regulation
Lack of tools & knowledge
Unknown & undefined risk!
A story of IAS and NIAS
7
Unknown NIAS
Known NIAS + IAS
Che
mic
als
in foo
d
DTU Food, Technical University of Denmark Berlin, Germany 30 November 2017
Risk Assessment
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Suspect chemical
Exposure assessment
Hazard assessment
Risk Assessment
Existing tox. data Animal testing
Assay testing
Blood/urine tests
Consumption Surveys
Sample analysis
Structure assessment
DTU Food, Technical University of Denmark Berlin, Germany 30 November 2017
Risk Assessment for unknown NIAS
9
Unknown NIAS
Exposure assessment
Hazard assessment
Risk assessment
Existing tox. data
Animal testing
Assay testing
Blood/urine tests
Consumption Surveys
Sample analysis
Structure assessment
No chemical structure
No availableexposure data
DTU Food, Technical University of Denmark Berlin, Germany 30 November 2017
Making informed decisions without tools?
10
Because of inadequate tools, there is no choice
DTU Food, Technical University of Denmark Berlin, Germany 30 November 2017
What is needed?
11
• Methods to explore what is present & any effect on human health
• Decisions on unknowns should be information-based
• Data must be available fast & nonexhaustive:
» time is precious;
» information is expensive.
(standard matching, animal tests, identification studies, …)
DTU Food, Technical University of Denmark Berlin, Germany 30 November 2017
Chemicals in the tip of the iceberg
12
IASKnown NIAS
DTU Food, Technical University of Denmark Berlin, Germany 30 November 2017
Chemicals in the bottom of the iceberg
13
Lots of
Unknown NIAS
DTU Food, Technical University of Denmark Berlin, Germany 30 November 2017
New tools to explore unknown chemicals
14
Quantitative data
LC-QTOF-MSLC Liquid Chromatography QTOF Quadrupole x Time of Flight MS Mass Spectrometry
TCMTotal Migratable Content: The chemical portion of a sample that has potential to migrate to food.
Quantity
DTU Food, Technical University of Denmark Berlin, Germany 30 November 2017
Semi-quantitative tools
15
0
20
40
60
80
100
120
140
1 2 3 4 5 6 7 8
[C] ± 3-fold error
Pieke et al., Anal Chim Acta. 2017 vol. 975, pp. 30-41.DOI: 10.1016/j.aca.2017.03.054.
Semi-quantify
Marker
Analyte Analyte
Quantity
DTU Food, Technical University of Denmark Berlin, Germany 30 November 2017
Semi-quantitative tools
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0
2
4
6
8
10
12
14
16
18
20
1 51 101 151 201 251 301 351 401 451 501 551
Concentration (µmol/L) -> 1 sample (recycled cardboard)
-> 1 analytical run
-> No identification needed
-> 550+ semi-quantified compounds0
5
10
15
20 Max. Conc.
Min. Conc.
Features
Pieke et al., Anal Chim Acta. 2017 vol. 975, pp. 30-41.DOI: 10.1016/j.aca.2017.03.054.
Quantity
DTU Food, Technical University of Denmark Berlin, Germany 30 November 2017
Exploring the world of unknown chemicals
17
Semi-quantitative data
0
20
40
60
80
100
120
140
1 2 3 4 5 6 7 8
Conc.
Quantity
DTU Food, Technical University of Denmark Berlin, Germany 30 November 2017
Exploring the world of unknown chemicals
18
0
20
40
60
80
100
120
140
1 2 3 4 5 6 7 8
Conc.
Chemical structure data Semi-quantitative data
Identity Quantity
DTU Food, Technical University of Denmark Berlin, Germany 30 November 2017
Ambiguous structure assessment
19
Controlled FragmentationSimilarity Correlations
?First moleculein DB1
Second molecule in DB1
Third molecule in DB3
Database #1
First molecule in DB2
Second molecule in DB2
Database #2?
Pieke et al. (in press), J. Mass Spectrom.
Outputs
Identity
DTU Food, Technical University of Denmark Berlin, Germany 30 November 2017
Ambiguous structure assessment
20
• Result: best-matching similar compound from available databases:
Identity
Pieke et al. (in press), J. Mass Spectrom.
DTU Food, Technical University of Denmark Berlin, Germany 30 November 2017
The Three Pillars: incomplete
21
[Quantity] Semi-quantification
[Identity] Structure prediction
[Toxicity] ???
Identity &Potential
RISK
DTU Food, Technical University of Denmark Berlin, Germany 30 November 2017
Developmental Toxicity
The toxicity of tentative chemicals?
22
Carcinogenicity
Mutagenicity
Cramer Classification
???Q SAR–
Computer-based toxicity predictions
Model evaluation
Toxicity
DTU Food, Technical University of Denmark Berlin, Germany 30 November 2017
The Three Pillars: nonexact data
23
Risk = Hazard * Exposure
Placeholders…
Identity &Potential
DTU Food, Technical University of Denmark Berlin, Germany 30 November 2017
• We may not be able to calculate the true risk from approximated data, but …
• We can still get a nonconclusive perception of risk: preliminary risk assessment
Risk: starting from tentative data
24
Estimated Hazard Exposure
Risk Likeliness
Toxicity (QSAR) +Identity (Pred.) =
Quantity (Semi-Q) +Identity (Pred.) =
DTU Food, Technical University of Denmark Berlin, Germany 30 November 2017
Risk: a classification method
25
Rule-based decisions
Expertise decisions
Are exposure thresholds exceeded?”
Is the presence of this compound acceptable?
Classification
Identity &Potential
RISK
DTU Food, Technical University of Denmark Berlin, Germany 30 November 2017
Risk: the first things first
26
Identification
QSAR alerts
Exposure
FACTORS
Decision
A: Substances of direct concern
B: Substances of lesser concern
C: Insufficient or incomplete data
CLASSIFICATION
DTU Food, Technical University of Denmark Berlin, Germany 30 November 2017
The purpose of tentative data
27
We needtentative data
… because the alternative isno data!
DTU Food, Technical University of Denmark Berlin, Germany 30 November 2017
The purpose of tentative data
28
The choice to do nothing should be deliberate
DTU Food, Technical University of Denmark Berlin, Germany 30 November 2017
Thank you for your attention
29
Thanks to:
• Kit Granby• Jørn Smedsgaard
• Gilles Riviere• Bruno Teste
Eelco Nicolaas Pieke enpi(at)food.dtu.dk
• Elena Boriani
DTU Food, Technical University of Denmark Berlin, Germany 30 November 2017
Wish-list for NIAS screening
• Better identification methods and tools – more knowledge on HRMS needed
• Greatly improved databases for possible IAS & NIAS – ideally from the source
• More comprehensive tools for hazard – QSAR is not mature and subject to discussion
• More work on sampling & semi-quantification – reduce the uncertainty in the assessment
30
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