Computational Modelling for TTC Assessment Andrew Worth 1 and Chihae - - PowerPoint PPT Presentation

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Computational Modelling for TTC Assessment Andrew Worth 1 and Chihae Yang 2 1) European Commission - Joint Research Centre Institute for Health & Consumer Protection, Systems Toxicology Unit 2) Altamira LLC, Columbus Ohio, USA and

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SLIDE 1

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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SLIDE 2

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

2

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SLIDE 3

Computational tools for the application

  • f the TTC approach
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SLIDE 4
  • 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-nitroso- 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

0.15g/day?

Negligible risk (low probability of a life-time cancer risk greater than 1 in 106 – see text)

  • 5. Does estimated intake exceed TTC
  • f 1.5g/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 1800g/day?

YES NO NO

  • 7. Does estimated intake exceed

TTC of 18g/day?

YES NO Substance would not be expected to be a safety concern YES YES YES

  • 11. Does estimated intake

exceed 540g/day?

NO

  • 9. Does estimated intake

exceed 90g/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 YES NO YES Risk 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-nitroso- 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

0.15g/day?

Negligible risk (low probability of a life-time cancer risk greater than 1 in 106 – see text)

  • 5. Does estimated intake exceed TTC
  • f 1.5g/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 1800g/day?

YES NO NO

  • 7. Does estimated intake exceed

TTC of 18g/day?

YES NO Substance would not be expected to be a safety concern YES YES YES

  • 11. Does estimated intake

exceed 540g/day?

NO

  • 9. Does estimated intake

exceed 90g/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 YES NO YES Risk 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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SLIDE 5

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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SLIDE 6

Structural Alerts (SAs)

  • 30 SAs for genotoxic carcinogens
  • 5 SAs for non-genotoxic carcinogens

+

Benigni-Bossa rules for genotoxicity & carcinogenicity

Three QSAR models

Probability

  • f effect

= F Hydrophobic Electronic Steric + + Descriptors: logP HOMO, LUMO MR

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SLIDE 7
  • 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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SLIDE 8
  • 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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SLIDE 9
  • 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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SLIDE 10
  • 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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SLIDE 11
  • 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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SLIDE 12
  • 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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SLIDE 13

Cramer classifications: computer-based predictions vs expert judgement

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SLIDE 14

Toxtree Class II QSAR Toolbox Class III

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SLIDE 15

Cramer classifications: computer-based predictions vs expert judgement

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SLIDE 16

Chemoinformatics in the development

  • f the TTC approach
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SLIDE 17

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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SLIDE 18

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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SLIDE 19

COSMOS Cosmetics Inventory

  • Over 5,500 substances
  • 66 unique use functions

Chemical classes

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SLIDE 20

Munro dataset (1996)

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SLIDE 21

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

  • RepeatTox

DB

COSMOS TTC dataset

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SLIDE 22

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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SLIDE 23

% 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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SLIDE 24

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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SLIDE 25

Chemical space – physicochemical descriptors

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SLIDE 26

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

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SLIDE 27

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

Molecular Tissue Cellular Organelle

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 activation Induction of CYP3A4 LXR activation

AOP from LXR De novo FA synthesis

ChREBP SREBP-1c FAS ACC SCD-1 L-PK Modulators Key events Intermediate events Adverse Outcome Molecular Initiating Event Angptl3 PTLP Inhibition of the TG excretion ApoE

LXR activation to steatosis

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SLIDE 28

Liver toxicity – pathology and mechanisms

Pathological changes Molecular mechanisms

STEATOSIS STEATOHEPATITIS FIBROSIS 28

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SLIDE 29

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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SLIDE 30

Query for liver effects (chronic, subchronic,subacute) 59 chemicals with liver effects

Liver toxicity – mining the COSMOS database (2)

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SLIDE 31

39 chemicals with liver steatosis / steatohepatitis / fibrosis

Liver toxicity – mining the COSMOS database (3)

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SLIDE 32

Identifying chemotypes for liver toxicity

O O O O 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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SLIDE 33

Route to route extrapolation

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SLIDE 34
  • To add

COSMOS-ILSI Decision tree

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SLIDE 35

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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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SLIDE 36

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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SLIDE 37
  • 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

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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.