EU FP6 Integrated Project (IP) ITS Schemes for Human and Environmental Toxicology in REACH

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1 EU FP6 Integrated Project (IP) ITS Schemes for Human and Environmental Toxicology in REACH Gerrit Schüürmann Brussels, 9 November 2011 UFZ Department of Ecological Chemistry, Leipzig & Technical University Bergakademie Freiberg

2 Table of Contents 1 OSIRIS project overview Goal & approach 2 Integrated Testing Strategies (ITS) ITS components with examples 3 Five OSIRIS ITS Schemes Human: Skin sens, RepDose & Muta/Carc Environmental: Aquat. Tox & Bioconcentration 4 OSIRIS Webtool

3 OSIRIS Optimized Strategies for Risk Assessment of Industrial Chemicals through Integration of Non-Test and Test Information 31 Partners 14 Countries including 4 New Member States 5 SMEs, 2 large industrial companies

4 OSIRIS Consortium 31 Partner: 24 Research institutions & universities 5 SMEs 2 Industrial enterprises Seite 4

5 3R Principle Russel & Burch 1959: Reduce Reduce the number of animal tests Refine Decrease the animal burden during testing Replace Replace animal tests by non-animal approaches Seite 5

6 From 3R to ITS: Paradigm Shift 3R Principle: 1:1 Approach e.g. 1 in vivo replaced by 1 in vitro Integrated Testing Strategy (ITS): 1:n approach e.g. 1 in vivo replaced by n non-animal Seite 6

7 Goal To develop Integrated Testing Strategies (ITS) fit for REACH that enable to increase the use of non-testing information for regulatory decision making, and to reduce animal testing to the level needed from a risk perspective SEITE 7

8 Approach Management Board Pillar 1: Chemical Domain Pillar 5: Case Studies Pillar 2: Biological Domain Advisory Board Advisory Board Pillar 3: Exposure Pillar 4: Integration Strategies and Tools Dissemination : Integrated Testing Strategies fit for REACH SEITE 8

9 Integrated Testing Strategy (ITS) Target- and context-specific approach Consider all (potentially) relevant information Non-test & test methods, non-standard methods, Assess combined information content WoE, qwoe, decision theory and statistics 3R principle Animal test as last resort Actually: Integrated Assessment Strategy (IAS)

10 ITS Components In silico (Non-test methods) Read-across Chemical categories Structural alerts Structure-activity relationship Threshold of toxicological concern (Interpolation) (Cpd class analysis) (Critical substructures) (QSAR) (TTC) Non-animal testing In chemico (Chemoassay) In vitro (Bioassay) Omics (Genomics, Proteomics, Metabolomics,..) Information theory WoE, consensus, level of uncertainty

11 Electrophilic Toxicity Endogenous Compounds Proteins DNA Membrane components Substrates contain numerous nucleophilic sites such as SH, NH 2 and OH (electron-rich) Chemical Reaction between electrophilic xenobiotic or metabolite and endogenous nucleophilic compound

12 Structural Alerts: Electrophiles Michael-type acceptors Vinyl halides Schiff base formers Z X X O Nu-H Nu-H NH 2 -R Nu Nu H Z X X H N R + H 2 O Acylating agents Nu-H O X O Nu O Nu-H C C O CH C + HX Nu S N 2 (+ S N 1) electrophiles S N Ar electrophiles Nu-H R 2 CH X R 2 CH Nu Z X Nu-H Z Nu + HX + HX

13 EINECS Compounds: Structural Alerts for Excess Toxicity Fathead minnow: Acute Toxicity, 96-h exposure, log LC 50 (n = 72520) all cpds not strictly out of domain in domain Alert No alert No result Prediction for (only) 70%, with 63% narcosis-level, and 7% excess-toxic Page 13

14 Chemoassay: Toxicity Screening Class-specific models: Log EC 50 = a log K ow + b log k GSH + c Predicted log EC 50 [mol/l] Experimental log EC 50 [mol/l] Methyl tiglat (unreactive) 46 α,β-unsaturated Carbonyls = Michael acceptors: 15 Aldehydes 15 Ketones 16 Esters Böhme et al., Chem. Res. Toxicol. 2010

15 In silico: Reactive Toxicity Screening Log EC 50 = a log K ow + b E + c I α + d -1 Predicted log EC Exp. log EC Aldehydes r 2 = Ketones r 2 = Esters r 2 = 0.83 Mulliner et al., Org. Biomol. Chem. 2011, in press

16 Page 16 Read-across

17 Fish Toxicity: Method Comparison Fathead Minnow: Acute Toxicity, 96-h exposure, log LC 50 (n = 692) n valid r 2 q 2 rms bias mne mpe ECOSAR Read-Across Read-Across (high sim.) Schüürmann et al., Environ. Sci. Technol Page 17

18 Mutagenicity: Structural Alerts vs. Read-across Combined dataset from Bursi & ISSCAN (n = 4536): active (+) = 2493 non-active ( ) = 2043 Sens Pred Sens + Pred + Concordance Kazius et al Benigni / Bossa Read-across Page 18

19 5 ITS Schemes for REACH Human Toxicology Skin Sensitization Mutagenicity & Carcinogenicity Repeated-Dose Toxicity Environmental Toxicology Aquatic Toxicity Bioconcentration Page 19

20 5 ITS Schemes for REACH Human Toxicology 1) Skin Sensitization Emiel Rorije Tom Aldenberg Page 20

21 REACH Threshold Level for (Bayesian) Probability REACH prefers LLNA, but also accepts GPMT Statistics for GPMT predicting LLNA Probability; substance IS LLNA+ given GPMT+ 82 % Probability; substance IS LLNA- given GPMT- 91 % Proposed REACH Threshold for reaching a sufficiently certain conclusion Substance IS a skin sensitizer P>80% Substance is NOT a skin sensitizer P>90% Otherwise: WoE analysis yields Equivocal result Page 21

22 ITS for Skin Sens.: Performance Reproducing thresholds, Sensitivity>80%, Specificity>90%. However, XX substances will be Equivocal (e.g need more testing) Example: Performance of Independent WoE model, using only three QSAR models, n = 522 LLNA+ LLNAlocal WoE pred local WoE pred local WoE pred. Equivocal: 235 (46%) Sensitivity 0.82 Specificity 0.90 PPV 0.90 NPV 0.83 Individual QSAR performance PPV NPV SMARTs DEREK TIMES-SS Page 22

23 5 ITS Schemes for REACH Human Toxicology 2) Repeated-Dose Toxicity Inga Tluczkiewicz Monika Batke Sylvia Escher Inge Mangelsdorf Page 23

24 Non-Guideline Studies May Differ from Guideline Studies with Regard to: Basic requirements (Substance identity, route, species, administration) Knock out criteria Scope of examination (Number of targets/organs examined) Coverage approach Overall study quality (Documentation of methods, number of animals, study duration, transparency of statistics... QUANTOS Page 24

25 Page 25 ITS RepDose: Step by Step

26 5 ITS Schemes for REACH Human Toxicology 3) Mutagenicity & Carcinogenicity TNO Innovation for life Harrie Buist Dinant Kroese Page 26

27 ITS for Mutagenicity POS START In vivo human data mutagenicity NEG Not sufficient NEG ITS In vitro bacterial mutagenicity test POS ITS In vivo mammalian mutagenicity test (UDS) NO 10 t/y? 10 t/y? NO ITS In vitro mammalian mutagenicity test POS YES YES POS In vivo human data chromosome aberration NEG Not sufficient ITS In vitro chromosome aberration test POS NEG ITS In vivo chromosome aberration test (MNS) All in vivo results negative? NEG NO Classify for mutagenicity Perform ITS carcinogenicity All in vitro tests negative? YES No classification - No further testing genotoxicity YES Classify for mutagenicity Perform ITS carcinogenicity Page 27

28 Quantitative Weight-of-Evidence (WoE) for Mutagenicity (Semi)quantitative quality assessment of individual pieces of information Bayesian statistics used to calculate individual and combined Weight-of-Evidence In all but one cases independent Bayesian approach Dependent Bayesian approach for 4 Ames QSARs Cut-off posterior probability (= Weight-of- Evidence) set to 85% Page 28

29 Interdependence of Ames QSARs Results QSARs Ames(DATA) Kazius Benigni-Bossa UFZ NN-strong sim CAESAR pos neg Total pos pos pos pos pos pos pos neg pos pos neg pos pos pos neg neg Page 29

30 ITS for Carcinogenicity START Human or animal carcinogenicity data? YES NO germ cell mutagen cat 1 or 2? YES NO Mutagen cat 3? NO YES Widespread dispersive use or frequent/ long-term human expo? YES EBW module in webtool YES NO readacross? Exposure Based Waiving? YES NO Adequate for C&L and RA? YES 1000 t/y? NO YES Rep. dose: Hyperplasia and/or preneoplastic lesions? qwoe NO in webtool YES NO Adequate for C&L and RA? YES No (further) carcinogenicity testing NO Adequate for C&L and RA? YES NO NO Testing technically possible? YES Propose in vivo carcinogenicity study Page 30

31 5 ITS Schemes for REACH Environmental Toxicology 4) Aquatic Toxicity 5) Bioconcentration Emilio Benfenati Anna Lombardo Page 31

32 ITS for BCF Performance Statistics Proposed 1 st waiving scheme tested on 701 compounds with experimental values Method No. of waived comp. No. of outliers Nendza et al (35%) 0 Dimitrov et al (61%) 2 Phys-chem properties 326 (47%) 0 Combination of methods 341 (49%) 0 Consensus model tested on the same 701 compounds with experimental values Performance in terms of correctly classified compounds nb = 88% B = 82% vb = 72% Page 32

33 5 ITS Schemes for REACH Human & Environmental Toxicology OSIRIS Webtool Eduard Paune Page 33

34 Page 34 Webtool Access

35 ITS design: Conclusions Intelligent combination of non-animal, non-test & test info WoE and/or consensus & uncertainty evaluation essential Ready to decrease testing needs Non-test methods (in silico): Read-across, structural alerts, grouping, QSAR, TTC Consider application domain & potential interdependence Non-animal test methods: In chemico, in vitro, omics Consider application domain & mechanistic basis

36 Acknowledgements OSIRIS consortium partners UFZ team European Commission for funding Page 36