Computational Modelling for TTC Assessment

Transcription

1 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 Molecular Networks GmbH, Erlangen, Germany Eurotox 2015 Continuing Education Course on Thresholds of Toxicological Concern Basics and Latest Developments Porto, Portugal, 13 September 2015

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

3 Computational tools for the application of the TTC approach

4 1. Is the substance a non-essential metal or metal containing compound, or is it a polyhalogenateddibenzodioxin, -dibenzofuran, or -biphenyl? NO YES Kroes decision tree Structural alert for genotoxicity Organophosphate neurotoxicant 2. Are there structural alerts that raise concern for potential genotoxicity? NO 5. Does estimated intake exceed TTC of 1.5 g/day? YES 6. Is the compound an organophosphate? NO 8. Is the compound in Cramer structural class III? NO YES 10. Is the compound in Cramer structural class II? NO YES NO 12. Does estimated intake exceed 1800 g/day? YES YES YES Substance would not be expected to be a safety concern NO YES 7. Does estimated intake exceed TTC of 18 g/day? 9. Does estimated intake exceed 90 g/day? NO Risk assessment requires compound-specific toxicity data 3. Is the chemical an aflatoxin-like-, azoxy-, or N-nitroso- compound? 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 10 6 see text) NO YES Substance would not be expected to be a safety concern 11. Does estimated intake exceed 540 g/day? NO YES NO NO YES YES Risk assessment requires compound-specific toxicity data Risk assessment requires compound-specific toxicity data Cancer endpoints High potency carcinogen Noncancer endpoints Kroes et al. (2004). Food Chem Toxicol 42, Substance would not be expected to be a safety concern

5 EFSA decision tree Low probability of health effect Does the substance have a known structure and are exposure data available? Yes Is the substance a member of an exclusion category? No Is there a structural alert for genotoxicity (including metabolites)? No Yes No Yes Exposure > µg/kg bw/day? No Low probability of health effect TTC approach cannot be applied Yes Substance requires non-ttc approach (toxicity data, read-across, etc) No No No Exposure > 0.3 µg/kg bw/day? Yes Is substance an OP/Carbamate? No Exposure > 1.5 µg/kg bw/day? Yes Is substance in Cramer Class II or III? No Exposure > 30 µg/kg bw/day? Yes Yes Yes EFSA website:

6 Benigni-Bossa rules for genotoxicity & carcinogenicity Structural Alerts (SAs) 30 SAs for genotoxic carcinogens 5 SAs for non-genotoxic carcinogens + Three QSAR models Probability of effect = F Hydrophobic + Electronic + Steric Descriptors: logp HOMO, LUMO MR

7 CAESAR mutagenicity model Statistically-based Support Vector Machine classification method + some SAs from the Benigni- Bossa rulebase Training set: 4225 compounds from the Kazius-Bursi database

8 CAESAR carcinogenicity model Statistically-based Classification model based on a Counter-Propagation Artificial Neural Network (CP-ANN) Training set: 805 compounds from the Carcinogenic Potency Database (CPDB )

9 Toxtree Compound properties Prediction Compound structure Reasoning Downloadable versions from JRC and Sourceforge ( Current version (Jun3 2014) includes Cramer, Cramer with Extensions, genotoxicity and carcinogenicity (Benigni-Bossa, In Vivo Micronucleus, Ames), Kroes Toxtree online:

10 Example: 2-aminoacetophenone 2-aminoacetophenone SMILES: O=C(C)c1ccccc1N naturally occurring in beer, sweet corn, corn tortillas, milk proposed flavouring agent EFSA opinion (2008): Estimated intake: Maximised Survey-derived Daily Intake (MSDI) of g/p/day Modified Theoretical Added Maximum Daily Intake (mtamdi) of 1600 g/p/day

11 Example: 2-aminoacetophenone 2-aminoacetophenone SMILES: O=C(C)c1ccccc1N 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 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

12 Evaluation of Toxtree-Cramer 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 EN

13 Cramer classifications: computer-based predictions vs expert judgement

14 Toxtree Class II QSAR Toolbox Class III

15 Cramer classifications: computer-based predictions vs expert judgement

16 Chemoinformatics in the development of the TTC approach

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 17

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: 18

19 COSMOS Cosmetics Inventory Chemical classes Over 5,500 substances 66 unique use functions 19

20 Munro dataset (1996)

21 1. Toxicity database COSMOS TTC dataset 2. NOAEL database 3. TTC dataset Substance & Study inclusion criteria Study reliability NOAEL selection criteria Study relevance NOAEL decision Expert review Munro 1996 orepeattox DB NOAEL database TTC dataset Filter 1 Filter 2 V1.8 current version

22 Compound classes COSMOS TTC v1.8 (all tentative counts) Munro All Cosmetics inventory Cramer Class I: Class II: Class III* 244: 35: 281 (> 40% Class I) Nutrients (removed) - Lipid soluble vitamins - Essential amino acids Compound categories - Hair dyes - Parabens - Phthalates * Cramer Classes assigned by Toxtree v2.6.0 Description of COSMOS TTC v1.8 - Vitamin A,D,E,K removed - removed : 28: 448 (<25 % Class I) - retinol - phenyl alanine

23 alcohol alcohol, phenol alcohol - 1,1; 1,2; 1,3 aldehyde ToxPrint chemotypes amine amine, aromatic halides, organo ketones phosphorus containing sulfonyl (S=O) pyran ring, generic Organo silicon silicon urea Chain - aliphatic chain >= C8 Chain - oxyalkane (EO-PO) Surfactants - nonionic Surfactants - anionic Surfactant - cationic 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 COSMOS (v1.8) MUNRO Chemical space structural features > 4x organohalides > 3x phosphporus > 2x urea steroid ring % in dataset (ratio)

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 24

25 Chemical space physicochemical descriptors 25

26 The Chemotyper feature searching profiling datasets / inventories building prediction models The Chemotyper 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: ToxPrint: 26

27 AOPs for liver toxicity (fibrosis and steatosis) LXR activation to steatosis Biological Organization Level MIE Molecular Intermediate Effects Organelle Cellular Tissue ER binding Inhibition of respiration = NAD+ deplition PPAR-α antagonism binding Peroxisomal AOX inhibition Inhibition of the mitochondrial b-oxidation L-PK Inhibition of the microsomal b-oxidation Cytoplasm displacement Protein alkylation to fibrosis AOP from LXR LXR activation ChREBP SREBP-1c SCD-1 ACC FAS De novo FA synthesis TGs accumulation Nucleus distortion Mitochondrial disruption Fatty liver cells Steatosis > 5 10% by liver weight PPAR-γ activation CD36 upregulation Increase of the fat influx from peripheral tissues AhR agonism ApoE Angptl3 PTLP Inhibition of the TG excretion PXR activation Induction of CYP3A4 Modulators Molecular Initiating Event Intermediate events Key events Adverse Outcome Landesmann et al (2012). Description of Prototype Modes-of-Action Related to Repeated Dose Toxicity. JRC report EUR EN. 27

28 Liver toxicity pathology and mechanisms Pathological changes STEATOSIS STEATOHEPATITIS FIBROSIS Molecular mechanisms 28

29 Liver toxicity mining the COSMOS database (1) COSMOS orepeattoxdb 228 cosmetics-related chemicals 340 oral studies (subacute, subchronic, chronic, reproductive & developmental) Controlled vocabulary for toxicological effects COSMOS database publicly accessible from: 29

30 Liver toxicity mining the COSMOS database (2) Query for liver effects (chronic, subchronic,subacute) 59 chemicals with liver effects 30

31 Liver toxicity mining the COSMOS database (3) 39 chemicals with liver steatosis / steatohepatitis / fibrosis 31

32 Identifying chemotypes for liver toxicity O O O O O N N O N Cl Cl Cl Cl Cl O Alcohols, diols, glycol ethers Michael acceptors Amino phenols, aromatic amines, aromatic halides Polychlorinated short alkanes Halogenated amines 32

33 Route to route extrapolation

34 To add COSMOS-ILSI Decision tree

35 Biologically-based modelling for an internal TTC HTS - In vitro concentration External Dose Internal (cellular) response Gajewska et al (2014). Toxicology Letters 227, Internal concentration External dose internal response 35

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

37 Acknowledgements 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 Some of the research leading to these results has received funding from the European Community s Seventh Framework Program (FP7/ ) COSMOS Project under grant agreement no and from Cosmetics Europe. 37