computational modelling for ttc assessment - ilsi...
TRANSCRIPT
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Computational Modelling for TTC Assessment
Andrew Worth1 and Chihae Yang2 1) European Commission - Joint Research Centre Institute for Health & Consumer Protection, Systems Toxicology Unit
2) Altamira LLC, Columbus Ohio, USA and Molecular Networks GmbH, Erlangen, Germany
Eurotox 2015 Continuing Education Course on
“Thresholds of Toxicological Concern – Basics and Latest Developments”
Porto, Portugal, 13 September 2015
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Overview
• Computational tools for the application of the TTC approach
• Chemoinformatics in the development of the TTC approach
• Quality-controlled datasets for modelling
• Investigation of chemical space
• Identification of chemotypes
• Route to route extrapolation
• COSMOS-ILSI decision tree for oral to dermal extrapolation
• Internal TTC approach and biokinetic modelling
• Take home messages
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Computational tools for the application of the TTC approach
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1. Is the substance a non-essential metal or metal containing compound, or is it a polyhalogenated-
dibenzodioxin, -dibenzofuran, or -biphenyl?
3. Is the chemical an aflatoxin-like-, azoxy-, or
N-nit roso- compound?
2. Are there structural alerts that raise
concern for potential genotoxicity?Risk assessment requires
compound-specific toxicity data
4. Does estimated intake exceed TTC of
0.15g/day?
Negligible risk (low probability of a life -time
cancer risk greater than 1 in 106 – see text)
5. Does estimated intake exceed TTC
of 1.5g/day?
6. Is the compound an organophosphate?
10. Is the compound
in Cramer structural
class II?
8. Is the compound in
Cramer structural class
III?
12. Does estimated intake
exceed 1800g/day?
YESNO
NO
7. Does estimated intake exceed
TTC of 18g/day?YES
NO
Substance would not be expected
to be a safety concern
YES
YES
YES
11. Does estimated intake
exceed 540g/day?
NO
9. Does estimated intake
exceed 90g/day?
NO YES
NO
YES
YES
YES
YES
NO
NO
Risk assessment requires
compound-specific toxicity data
Substance would
not be expected to
be a safety concern
YESNOYESRisk assessment requires
compound-specific toxicity data
NO
NO
Substance would not be
expected to be a safety concern
NO
1. Is the substance a non-essential metal or metal containing compound, or is it a polyhalogenated-
dibenzodioxin, -dibenzofuran, or -biphenyl?
3. Is the chemical an aflatoxin-like-, azoxy-, or
N-nit roso- compound?
2. Are there structural alerts that raise
concern for potential genotoxicity?Risk assessment requires
compound-specific toxicity data
4. Does estimated intake exceed TTC of
0.15g/day?
Negligible risk (low probability of a life -time
cancer risk greater than 1 in 106 – see text)
5. Does estimated intake exceed TTC
of 1.5g/day?
6. Is the compound an organophosphate?
10. Is the compound
in Cramer structural
class II?
8. Is the compound in
Cramer structural class
III?
12. Does estimated intake
exceed 1800g/day?
YESNO
NO
7. Does estimated intake exceed
TTC of 18g/day?YES
NO
Substance would not be expected
to be a safety concern
YES
YES
YES
11. Does estimated intake
exceed 540g/day?
NO
9. Does estimated intake
exceed 90g/day?
NO YES
NO
YES
YES
YES
YES
NO
NO
Risk assessment requires
compound-specific toxicity data
Substance would
not be expected to
be a safety concern
YESNOYESRisk assessment requires
compound-specific toxicity data
NO
NO
Substance would not be
expected to be a safety concern
NO
Cancer
endpoints
Non-
cancer
endpoints
Kroes et al. (2004).
Food Chem Toxicol 42, 65-83.
High potency
carcinogen
Structural alert
for genotoxicity
Organophosphate
neurotoxicant
Kroes
decision tree
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Is the substance a member of an
exclusion category?
Is there a structural alert for
genotoxicity (including
metabolites) ?
Exposure > 0.3 µg/kg bw/day?
Is substance an OP/Carbamate?
Exposure > 1.5 µg/kg bw/day?
Is substance in Cramer Class II or III?
Exposure
> 0.0025 µg/kg bw/day?
Substance
requires non-TTC approach
(toxicity data, read-across, etc)
Low probability of
health effect
Low probability of
health effect
Exposure > 30 µg/kg bw/day?
No
No
No
Yes
No
Yes
No
Yes
Yes
Yes
Yes
Yes
No
No
No
Yes
Does the substance have a known structure and are exposure data available?
Yes
No TTC approach cannot
be applied
EFSA website: http://www.efsa.europa.eu/en/efsajournal/doc/2750.pdf
EFSA
decision tree
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Structural Alerts (SAs)
• 30 SAs for genotoxic carcinogens
• 5 SAs for non-genotoxic carcinogens
+
Benigni-Bossa rules for genotoxicity & carcinogenicity
Three QSAR models
Probability
of effect = F Hydrophobic Electronic Steric + +
Descriptors: logP HOMO, LUMO MR
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• Statistically-based
• Support Vector Machine classification method + some SAs from the Benigni-
Bossa rulebase
• Training set: 4225 compounds from the Kazius-Bursi database
http://www.caesar-project.eu
http://www.vega-qsar.eu/
CAESAR mutagenicity model
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• Statistically-based
• Classification model based on a Counter-Propagation Artificial Neural Network
(CP-ANN)
• Training set: 805 compounds from the Carcinogenic Potency Database (CPDB )
http://www.caesar-project.eu
http://www.vega-qsar.eu/
CAESAR carcinogenicity model
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• Downloadable versions from JRC and Sourceforge (http://toxtree.sourceforge.net)
• Current version 2.6.6 (Jun3 2014) includes Cramer, Cramer with Extensions, genotoxicity and carcinogenicity (Benigni-Bossa, In Vivo Micronucleus, Ames), Kroes
• Toxtree online: http://toxtree.sf.net/predict
Toxtree
•Prediction
•Compound structure
•Compound properties
•Reasoning
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• naturally occurring in beer, sweet corn, corn tortillas, milk
• proposed flavouring agent
• EFSA opinion
(2008):http://www.efsa.europa.eu/en/efsajournal/pub/797.htm
• Estimated intake:
Maximised Survey-derived Daily Intake (MSDI) of 0.012 g/p/day
Modified Theoretical Added Maximum Daily Intake (mTAMDI) of 1600
g/p/day
SMILES: O=C(C)c1ccccc1N
2-aminoacetophenone
Example: 2-aminoacetophenone
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• Cramer Class III
• 1N,2N,3N,5N,6N,7N,16N,17N,19N,23Y,27Y,28N,30Y,31N,32N,22N,33N
• TTC for CC III is 90 g / person / day
• Estimated intake:
MSDI of 0.012 g/p/day < TTC of 90 g/p/ day for CC III
BUT mTAMDI of 1600 g/p/day > TTC of 90 g/p/ day for CC III
Example: 2-aminoacetophenone
SMILES: O=C(C)c1ccccc1N
2-aminoacetophenone
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• Many of the original Cramer rules are written in a confusing and inter-dependent way,
which leads to difficulties in the rationalisation of the predictions they make.
• Two rules are not based on chemical features, but simply make reference to look-up
lists of chemicals (Q1, normal body constituents; Q22, common food components).
• Some rules make ambiguous references to chemical features (e.g. steric hindrance)
which need to be clarified and possibly revised/deleted.
• Several studies have identified outliers (e.g. Class I compounds that have low
NOELs). A revised / alternative classification scheme should be more discriminating
in terms of NOEL values.
→ need to update Cramer classification scheme
Lapenna & Worth (2011). JRC report EUR 24898 EN
Evaluation of Toxtree-Cramer
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Cramer classifications:
computer-based predictions vs expert judgement
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Toxtree
Class II
QSAR Toolbox
Class III
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Cramer classifications:
computer-based predictions vs expert judgement
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Chemoinformatics in the development of the TTC approach
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COSMOS
Integrated In Silico Models for the Prediction of Human Repeated
Dose Toxicity of Cosmetics to Optimise Safety
• Collection of toxicological data
• Development of the Threshold of Toxicological Concern (TTC) approach
• Development of novel in silico methods
• Multiscale modelling:
mitochondrial (dys)function, virtual cell-based assay, 2D liver,
Physiologically Based Biokinetic (PBBK) models
• In silico workflows based on open-source and open-access tools
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COSMOS database v1.0
• Open-access
• High-quality toxicity data (quality
control, structure curation)
• User-friendly query builder (chemical
name, structure, toxicity data)
• 44,765 unique chemical structures
• 12,538 toxicity studies for 1,660
compounds across 27 endpoints
Webinar and tutorial:
http://www.cosmostox.eu/what/COSMOSdb/
http://cosmosdb.cosmostox.eu/
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COSMOS Cosmetics Inventory
• Over 5,500 substances
• 66 unique use functions
Chemical classes
Chemical classes
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Munro dataset (1996)
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TTC
dataset
NOAEL database
Munro 1996
• Study reliability • NOAEL selection criteria
• Substance & Study inclusion criteria
• Study relevance • NOAEL decision • Expert review
V1.8 current version
2. NOAEL database 3. TTC dataset 1. Toxicity database
Filter 1 Filter 2
oRepeatTox DB
COSMOS TTC dataset
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Compound classes COSMOS TTC v1.8 (all tentative counts)
Munro
All ̴560 613
Cosmetics inventory 495 190
Cramer Class I: Class II: Class III* 244: 35: 281 (> 40% Class I)
119: 28: 448 (<25 % Class I)
Nutrients (removed) - Lipid soluble vitamins - Essential amino acids
- Vitamin A,D,E,K removed - removed
- retinol - phenyl alanine
Compound categories - Hair dyes - Parabens - Phthalates
110 10 7
13 7 5
* Cramer Classes assigned by Toxtree v2.6.0
Description of COSMOS TTC v1.8
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% in dataset (ratio)
alcohol alcohol, phenol
alcohol - 1,1; 1,2; 1,3 aldehyde
amine amine, aromatic halides, organo
ketones phosphorus containing
sulfonyl (S=O) pyran ring, generic
silicon urea
Chain - aliphatic chain >= C8 Chain - oxyalkane (EO-PO)
Surfactants - nonionic Surfactants - anionic Surfactants - cationic
Carbohydrate Steroid ring
Parabens Phthalates
Hair-dyes - amine_ethanol Hair-dyes - amine_bis_ethanol
Hair-dyes - azo Hair-dyes - benzene_amino_nitro_alcohol
Hair-dyes - benzene_diamino Hair-dyes - benzene_nitro
> 4x organohalides
> 3x phosphporus
> 2x urea
steroid ring
Surfactant - cationic
ToxPrint chemotypes
Organo silicon
COSMOS (v1.8) MUNRO
Chemical space –
structural features
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Chemotypes
• Structural fragments with atom/bond properties (partial charges, polarizability,
electronegativity, etc.)
• Improved ability to predict reactivity and toxicity (compared with structural
alerts)
• Example: association of diazoles and triazoles with cleft palate formation
X= nitrogen or carbon
Pi charge < zero Sigma charge > zero
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Chemical space – physicochemical descriptors
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The Chemotyper
• The Chemotyper • feature searching
• profiling datasets / inventories
• building prediction models
• ToxPrint
• library of public chemotypes
• Generic fragments
• Genotoxic carcinogens
(Ashby-Tennant)
• Cancer TTC (Kroes et al)
Developed by Altamira LLC and Molecular Networks GmbH under FDA contract
Publicly available from:
Chemotyper: https://www.chemotyper.org
ToxPrint: https://toxprint.org 26
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AOPs for liver toxicity (fibrosis and steatosis)
Landesmann et al (2012). Description of Prototype Modes-of-Action Related to Repeated Dose
Toxicity. JRC report EUR 25631 EN.
Protein alkylation to fibrosis
MolecularTissueCellularOrganelle
Biological
Organization
Level
PPAR-α
antagonism
binding
Steatosis
> 5–10% by
liver weight
Fatty liver
cells
Cytoplasm
displacement
Nucleus
distortion
Mitochondrial
disruption
TGs
accumulation
Inhibition of the
mitochondrial
b-oxidation
PPAR-γ
activation
ER
binding
Inhibition of
respiration =
NAD+ deplition
AhR
agonism
CD36 up-
regulation
Increase of the
fat influx from
peripheral
tissues
Peroxisomal
AOX inhibition
MIE Intermediate Effects
Inhibition of the
microsomal
b-oxidation
PXR
activationInduction of
CYP3A4
LXR
activationAOP from LXR
De novo FA
synthesis
ChREBP
SREBP-1c FAS
ACC
SCD-1
L-PK
ModulatorsKey
events
Intermediate
events
Adverse
Outcome
Molecular
Initiating Event
Angptl3
PTLP
Inhibition of the
TG excretion
ApoE
LXR activation to steatosis
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Liver toxicity – pathology and mechanisms
Pathological
changes
Molecular
mechanisms
STEATOSIS STEATOHEPATITIS FIBROSIS
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COSMOS oRepeatToxDB
• 228 cosmetics-related chemicals
• 340 oral studies
(subacute, subchronic, chronic, reproductive &
developmental)
• Controlled vocabulary for toxicological effects
COSMOS database publicly accessible from:
http://cosmosdb.cosmostox.eu/accounts/login
Liver toxicity – mining the COSMOS database (1)
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Query for liver effects (chronic, subchronic,subacute)
59 chemicals with liver effects
Liver toxicity – mining the COSMOS database (2)
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39 chemicals with liver steatosis / steatohepatitis / fibrosis
Liver toxicity – mining the COSMOS database (3)
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Identifying chemotypes for liver toxicity
O
O
OO
O
Cl
N
O
Cl
Cl Cl
• Alcohols, diols, glycol ethers
• Michael acceptors
• Amino phenols, aromatic amines, aromatic
halides
• Polychlorinated short alkanes
• Halogenated amines
O
N
Cl
N
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Route to route extrapolation
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• To add
COSMOS-ILSI Decision tree
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External Dose
HTS - In vitro
concentration
Biologically-based modelling for an internal TTC
Internal concentration External dose – internal
response
Internal (cellular) response
Gajewska et al (2014).
Toxicology Letters 227, 189-202.
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Take home messages
• Various software tools are available to support the application of TTC
• Computational predictions are not intended to be the solution – need to apply
expert judgement, especially for certain chemical classes
• Computational methods also provide a means of further developing the TTC
approach
• Chemistry-based modelling can be supplemented with biological modelling
• Ongoing research is aiming to:
• refine or replace the traditional Cramer tree
• extend the applicability of the approach to new chemical classes
• extend the applicability of the approach to non-oral routes of exposure
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• Aleksandra Mostrag-Szlichtyng; Altamira LLC, Columbus, Ohio, USA
• Vessela Vitcheva; Molecular Networks GmbH, Erlangen, Germany
• Kirk Arvidson; US FDA, Center for Food Safety & Applied Nutrition, Maryland, USA
• Alicia Paini; European Commission, Joint Research Centre, Italy
Acknowledgements
37
Some of the research leading to these results has received funding from the
European Community’s Seventh Framework Program (FP7/2007-2013)
COSMOS Project under grant agreement no. 266835 and from Cosmetics
Europe.