Development of a New Tool for Modelling Potential Risks from Food ...

Development of a New Tool for
Modelling Potential Risks from
Food Contact Materials
P.S. Price MS, Dr. J.M. Zabik, G.M. Wiltshire, and
Dr. H.M. Hollnagel
The Dow Chemical Company
ILSI 4 th International Symposium on
Food Packaging
Prague, Czech Republic
1

Project Goals
• Characterize risks posed by mixtures of migrants rather than single
substances
• Create a screening tool for non-carcinogenic effects to identify
mixtures that have a low potential for toxicity
• Develop a tool that:
– Can be applied to a wide range of mixtures;
– Does not require toxicity data on mixture components; but
– Can make effective use of toxicity data when available
• Build on work done in the Threshold of Toxicological Concern (TTC)
• Use the latest guidance on assessing chemical toxicity (ECHA and
other sources)
2

Challenges Posed by Mixtures
• Complexity:
– Infinite number of mixtures (can’t test them all)
• Little or no toxicity data for many components of mixtures.
• Traditional approaches for assessing mixture toxicity
– Additive models too conservative (not all effects add);
– Independence models not sufficiently conservative (some might
add);
– Both approaches require toxicity data on all components
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Components of Tool
• Use existing models of mixture toxicity
– To develop estimates of safe exposures under different
assumptions
– To identify the mixture components that drive toxicity
• Use Cramer classes to fill data gaps
• Monte Carlo modeling of uncertainty in estimates
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Limitations to the Tool
• Does not address carcinogenic / genotoxic effects
– A separate assessment of carcinogenic effects is required
• The approach requires information on the structure of the compounds
– To assign the compounds to a Cramer class
– To confirm that the Munro et al. 1996 data set has similar compounds
– To determine that there are no structural alerts for carcinogenicity and
that they are not organophosphorous compounds
• Synergy / Potentiation
– Not modeled directly
– Proposed approach minimizes the potential for synergistic effects by
restricting exposures to mixture components to levels where synergy is
unlikely to occur (Konemann and Pieters, 1996)
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Models for Mixture Toxicity
Derived No Effect Level (DNEL) for single compound:
DNEL =
POD
AF A AF H
POD = Point of departure (NOAEL, NOEL, LOAEL, BMD, etc.)
AF = Adjustment Factor (interspecies or interindividual uncertainty)
DNEL for mixture assuming additivity:
Mixture DNEL =
-1 -1
POD
Σ F i
i
AF Ai
AF Hi
F i = Weight fraction of the i th chemical in the mixture
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Models for Mixture Toxicity (Cont.)
DNEL for mixture assuming independence:
Mixture DNEL =
Min
POD i
AF Ai AF Hi
F i
Contributions to Additive models of mixture toxicity can be
ranked based on:
Toxicity
Weight of
Compound
=
F i
AF Ai AF Hi
POD i
Mixture components with the highest weights will drive the
mixture’s toxicity and can be investigated further.
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Using the Munro et al. Analysis of
Cramer Classes to Fill Data Gaps
• Cramer 1 classes associate structural properties with
toxicological potency
• Assignment to the three Cramer classes is made based on
structure
• Use the lower 5 th percentile of the distribution or NOELs
as the POD
• Use 100 fold adjustment factor proposed by Munro et al.
1996 2 .
1
Cramer et al. 1978 Food Cosmetic Toxicology Vol. 16. pp.255-278
2
Munroe et al. 1996 Food and Chemical Toxicology Vol 34. pp 829-867
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The Cramer Class Approach
Class
II
Class
I
5%ile NOEL
(mg/kg/day)
3.0
Class
III
Class I
II
0.91
III
0.15
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Probabilistic Non-cancer Risk
Assessment
• Concept first raised in the early 1990s
• Groups that have published in this area
– RIVM, TNO, FoBiG, and BAuG
– U.S. EPA
– Harvard Center for Risk Analysis
• Cited in ECHA guidance for REACH
• Benefits
– Allows the investigation of the impact of making multiple conservative
assumptions for each component
– The approach gives:
• A single value (lower 5 th percentile)
• Complete distribution to provide a context to the standard
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Why Perform a Probabilistic
Analysis?
• The use of the Cramer classes/Munro et al. approach for a
single chemical gives a conservative assumption of
toxicity
• Applying the same approach to multiple chemicals can
result in an over-estimation
– It is unlikely that all chemicals in a mixture will be as toxic as the
5 th percentile
• The conservative adjustment factors have a similar
problem
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Probabilistic Noncancer Risk
Assessment
• Uses Monte Carlo analysis
• Based on the same equations
• Requires uncertainty distributions for POD and each
adjustment factor
• Currently ECHA guidance on DNELs is most closely
aligned to published probabilistic assessments
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Probabilistic Additive Model of
Mixture Risk
-1 -1
Uncertainty
distribution of
NOAEL in
Sensitive Humans
=
Σ F i
AF Ai
POD i
AF Hi
Fifth percentile
provides a basis for a
point estimate of the
Mixture DNEL
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Application of the Tool to Food
Contact Materials
• Chemicals detected in water stored in
polypropylene bottles (Skjevrak et al. 2005)
• Chemicals identified and concentrations
measured
– 72 hr duration ambient temperatures
– Highest values from multiple bottles reported in
paper
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Residue Data
Compound
Concentration in Water ug/l
Di isobutyl phthalate 36
Dibutyl phthalate 9
Ethyl-4-Ethoxybenzoate 101
2,4-di-tert-butylphenol 25
Ethylbenzoate 15
4-Methylbenzaldehyde 37
Toxicity Data
Compound Cramer Class Safe Level of Intake Reference
Di isobutyl phthalate 1
Dibutyl phthalate 1 1 EPA IRIS 1990
Ethyl-4-Ethoxybenzoate 2
2,4-di-tert-butylphenol 1
Ethylbenzoate 1
4-Methylbenzaldehyde 1
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Revised Toxicity Data
Compound
Estimate of Safe Dose mg/kg/d
Di isobutyl phthalate 0.03
Dibutyl phthalate 0.2
Ethyl-4-Ethoxybenzoate 0.0091
2,4-di-tert-butylphenol 0.03
Ethylbenzoate 0.03
4-Methylbenzaldehyde 0.03
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Results
0.05
Safe Dose mg/kg/d
0.04
0.03
0.02
0.01
0.00
Additive Independence MC Additive MC Independence
Estimates of Mixture Toxicity
• Estimates are similar for independence and additive models – one
compound drives the mixture’s toxicity
• Probabilistic models give values 2-3 fold higher than deterministic
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Drivers of Mixture Toxicity
• One compound, Ethyl-4-Ethoxybenzoate, drives toxicity
Compound
Toxicity
Contribution
Weight
Di isobutyl phthalate 5.4
Dibutyl phthalate 0.2
Ethyl-4-Ethoxybenzoate 49.8
2,4-di-tert-butylphenol 3.7
Ethylbenzoate 2.2
4-Methylbenzaldehyde 5.5
Toxicity Driver
• Additional data on this compound could have a major
impact on the estimate of mixture toxicity
• Removal of this compound would raise the estimate of a
safe dose of the mixture 4 fold!
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Estimates of Safe Doses in Bottled Water
• Assuming 2 l/d intake and 60 kg body weight, the estimates of safe levels of the
mixture in the water range from 0.5 – 1.5 mg/l
• The highest level of the mixture found in the study is less than 0.25 mg/l
3.00
Water Conc. Of Mixture mg/l
2.50
2.00
1.50
1.00
0.50
Estimates of Safe Level of Mixture in Water
0.00
Additive Independence MC Additive MC
Independence
Higest
Reported
Level of
Mixture
Therefore, total toxicity of the migrants at the concentrations reported in
this study does not pose a risk for individuals consuming water from the
polypropylene bottles.
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Summary
• The approach is allows estimates to be made on any
mixture of migrants where:
– the relative amounts, and
– structures of the migrants are known;
• Identifies the migrants that drive mixtures’ toxicities
– Provides guidance for risk management of migrants
• Estimates take advantage of toxicity data when available
• Probabilistic approaches show that deterministic values
overestimated toxicity by a factor of 2-3 in this example
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Questions
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