Biological Read-Across: Species-Species and Endpoint- Endpoint Extrapolation

Similar documents
Comparative study of mechanism of action of allyl alcohols for different endpoints

RSC Publishing. Principles and Applications. In Silico Toxicology. Liverpool John Moores University, Liverpool, Edited by

Grouping and Read-Across for Respiratory Sensitisation. Dr Steve Enoch School of Pharmacy and Biomolecular Sciences Liverpool John Moores University

PERFORMANCE STANDARDS FOR THE ASSESSMENT OF PROPOSED SIMILAR OR MODIFIED IN VITRO SKIN SENSITISATION DPRA AND ADRA TEST METHODS

User manual Strategies for grouping chemicals to fill data gaps to assess acute aquatic toxicity endpoints

Good Read-Across Practice 1: State of the Art of Read-Across for Toxicity Prediction. Mark Cronin Liverpool John Moores University England

OECD QSAR Toolbox v.3.3

OECD QSAR Toolbox v.4.1

OECD QSAR Toolbox v.3.4

OECD QSAR Toolbox v.3.3. Predicting skin sensitisation potential of a chemical using skin sensitization data extracted from ECHA CHEM database

OECD QSAR Toolbox v.4.1. Tutorial on how to predict Skin sensitization potential taking into account alert performance

QMRF# Title. number and title in JRC QSAR Model Data base 2.0 (new) number and title in JRC QSAR Model Data base 1.0

OECD QSAR Toolbox v.4.0. Tutorial on how to predict Skin sensitization potential taking into account alert performance

OECD QSAR Toolbox v.4.1. Step-by-step example for predicting skin sensitization accounting for abiotic activation of chemicals

OECD QSAR Toolbox v.4.1. Tutorial of how to use Automated workflow for ecotoxicological prediction

OECD QSAR Toolbox v.4.1. Implementation AOP workflow in Toolbox: Skin Sensitization

OECD QSAR Toolbox v.3.2. Step-by-step example of how to build and evaluate a category based on mechanism of action with protein and DNA binding

Read-Across or QSARs?

OECD QSAR Toolbox v.3.4. Step-by-step example of how to build and evaluate a category based on mechanism of action with protein and DNA binding

OECD QSAR Toolbox v.3.3. Step-by-step example of how to build and evaluate a category based on mechanism of action with protein and DNA binding

OECD QSAR Toolbox v.4.1. Step-by-step example for building QSAR model

OECD QSAR Toolbox v.3.3. Step-by-step example of how to build a userdefined

Data Bases and Data Modeling

OECD QSAR Toolbox v.3.3

BIOAUTOMATION, 2009, 13 (4),

AOPs to support in silico predictions: the link from chemistry to adverse effects

OECD QSAR Toolbox v.3.3. Predicting acute aquatic toxicity to fish of Dodecanenitrile (CAS ) taking into account tautomerism

OECD QSAR Toolbox v.3.4. Example for predicting Repeated dose toxicity of 2,3-dimethylaniline

OECD QSAR Toolbox v.4.1

Human Health Models Skin sensitization

1.3.Software coding the model: QSARModel Molcode Ltd., Turu 2, Tartu, 51014, Estonia

Case study: Category consistency assessment in Toolbox for a list of Cyclic unsaturated hydrocarbons with respect to repeated dose toxicity.

Use of (Q)SAR and read across for assessment of genotoxicity of pesticides metabolites

OECD QSAR Toolbox v.3.3. Step-by-step example of how to categorize an inventory by mechanistic behaviour of the chemicals which it consists

OECD QSAR Toolbox v.3.0

Mode of action approaches to mixtures. Joop Hermens Institute for Risk Assessment Sciences Utrecht University

OECD QSAR Toolbox v.3.4

QSAR APPLICATION TOOLBOX ADVANCED TRAINING WORKSHOP. BARCELONA, SPAIN 3-4, June 2015 AGENDA

Sensitizer potency prediction based on Key event Andreas Natsch, Givaudan Schweiz AG Presented by: David Basketter, DABMED consultancy

Adverse Outcome Pathways in Ecotoxicology Research

8 th BioDetectors. Applications of bioassays to prioritize chemical food safety issues. Maricel Marin-Kuan

ISSN Volume 2, Number 2 June 2010

Exploration of alternative methods for toxicity assessment of pesticide metabolites

LIFE Project Acronym and Number ANTARES LIFE08 ENV/IT/ Deliverable Report. Deliverable Name and Number

LMC PUBLICATIONS

Technical information on alternative methods

Chemical Categories and Read Across Grace Patlewicz

Amino acid Derivative Reactivity Assay (ADRA) JaCVAM Validation Study Report. Version 1.2. July, 2018

1.QSAR identifier 1.1.QSAR identifier (title): QSAR model for Toxicokinetics, Transfer Index (TI) 1.2.Other related models:

OECD QSAR Toolbox v4.0 Simplifying the correct use of non-test methods

CURRICULUM VITAE Terry W. Schultz

Regulatory use of (Q)SARs under REACH

1.3.Software coding the model: QSARModel Molcode Ltd., Turu 2, Tartu, 51014, Estonia

How to decide whether a substance is a polymer or not and how to proceed with the relevant registration

Recent Advances in QSAR Studies

Developing Molecular Design Guidelines for Reduced Toxicity. Adelina Voutchkova Yale University, George Washington University

External Validation of Common Aquatic Toxicity Prediction Tools:

OECD QSAR Toolbox v.4.1. Tutorial illustrating new options for grouping with metabolism

Introduction to Chemoinformatics

1.QSAR identifier 1.1.QSAR identifier (title): Nonlinear QSAR model for acute oral toxicity of rat 1.2.Other related models:

OECD Conceptual Framework for Testing and Assessment of Endocrine Disrupters (as revised in 2012)

Opportunities to Incorporate Toxicology into the Chemistry Curriculum Report from the Field

1.QSAR identifier 1.1.QSAR identifier (title): Nonlinear QSAR: artificial neural network for acute oral toxicity (rat 1.2.Other related models:

Screening and prioritisation of substances of concern: A regulators perspective within the JANUS project

MS Program in Environmental and Green Chemistry at GWU

In Silico Assessment of Adverse Effects of a Large Set of 6-Fluoroquinolones Obtained from a Study of Tuberculosis Chemotherapy

Neural and Neuro-Fuzzy Models of Toxic Action of Phenols

Validation of the GastroPlus TM Software Tool and Applications

1.QSAR identifier 1.1.QSAR identifier (title): QSAR for female rat carcinogenicity (TD50) of nitro compounds 1.2.Other related models:

QMRF identifier (JRC Inventory): QMRF Title: QSAR model for acute oral toxicity-in vitro method (IC50) Printing Date:

1.2.Other related models: Same endpoint and dataset as presented model in reference [1].

Recent Advances in QSAR Studies

Applications of multi-class machine

Assessment and Regulation of Nanomaterials under the European Biocides Regulation Isabel Günther

QMRF identifier (JRC Inventory): QMRF Title: QSAR model for female rat carcinogenicity (TD50) of nitro compounds Printing Date:21.01.

One Size Doesn't Fit All: Tailoring Read-across Methodology for TSCA and Other Contexts

QSAR Model for Eye irritation (Draize test)

Introduction to the Globally Harmonized System of Classification and Labelling of Chemicals (GHS)

KATE2017 on NET beta version Operating manual

Extrapolating New Approaches into a Tiered Approach to Mixtures Risk Assessment

Ecorisk Dilemma. ES/RP 532 Applied Environmental Toxicology. EPA Approach. EPA Objective. Hazard Identification. Hazard ID

Announcement ECB-Workshop on Biology-Based Modelling

1.QSAR identifier 1.1.QSAR identifier (title): QSAR for the bioconcentration factor of non-ionic organic compounds 1.2.Other related models:

RISKCYCLE (#226552) Deliverable 4.2. List of databases and meta-databases

1.QSAR identifier 1.1.QSAR identifier (title): QSARINS model for aquatic toxicity of organic chemicals in Pimephales

1.3.Software coding the model: QSARModel Molcode Ltd., Turu 2, Tartu, 51014, Estonia

Quantitative Structure Activity Relationships: An overview

(e.g.training and prediction set, algorithm, ecc...). 2.9.Availability of another QMRF for exactly the same model: No other information available

Review of the Priority Substances under the Water Framework Directive

Arizona Division of Occupational Safety and Health Administration. 800 W. Washington Street, Phoenix, AZ Consultation:

Tuning Neural and Fuzzy-Neural Networks for Toxicity Modeling

Nano-Ecotoxicology Assessment of Potential Effects of Engineered Nanomaterials in the Environment

Section II Assessing Polymers

Background 3. Regulatory History for Assessments of Ethers 5

1.3.Software coding the model: QSARModel Turu 2, Tartu, 51014, Estonia

In Silico Quantitative Structure Toxicity Relationship of Chemical Compounds: Some Case Studies

All information in full detail is available. 2.9.Availability of another QMRF for exactly the same model: No other QMRF available for the same model

1.QSAR identifier 1.1.QSAR identifier (title): QSAR model for Bioaccumulation, BAF carp for PCBs 1.2.Other related models:

Reactive e Metabolite Trapping

Medicinal Chemistry/ CHEM 458/658 Chapter 3- SAR and QSAR

Transcription:

Biological Read-Across: Species-Species and Endpoint- Endpoint Extrapolation Mark Cronin School of Pharmacy and Chemistry Liverpool John Moores University England m.t.cronin@ljmu.ac.uk

Integrated Testing Strategies (ITS) Existing Data In Silico Assessment In Chemico Assessment In Vitro Assessment In Vivo Assessment

An Initial Stage of an ITS is the Use of In Silico and In Chemico Techniques Existing data Category formation Filling data gaps Existing Data In Silico Assessment In Chemico Assessment In Vitro Assessment In Vivo Assessment

Category Formation Developing groups of similar compounds btaining toxicological data and information Performing read-across to interpolate toxicological endpoints

Category Formation Structural Analogues H H H H Mechanistic Analogues H N Toxicologically Meaningful Analogues H H H H H

Chemical Read-Across H H H H Toxicity Toxicity SAR / Read- Across Interpolation ECD Guidance on Grouping of Chemicals

Biological Read-Across Read-Down H H H H Species 1 Species 2 Single Endpoint Species 3

Inter-Species Relationships Fish to Fish Miscellaneous Chemicals 3 Rainbow Trout Toxicity 2 1 0 Trout LD 50 = 0.97 Bluegill LD 50 0.11 n = 13 r 2 = 0.93-1 -1 0 1 2 Bluegill Toxicity 3 4 Data from: LeBlanc GA (1984) Environ. Toxicol. Chem. 3: 47-60

Biological Read-Across Trophic Level Read-Down H H H H Species 1 Species 2 Single Endpoint Species 3 Single Effect H H H H Species 1 Species 2 Single Endpoint Species 3

Fathead Minnow vs Tetrahymena pyriformis Toxicity 5 4 Fish Toxicity 3 2 1 0-1 -2-3 Fish LD 50 = 1.00 Protozoan IGC 50 + 0.56 n = 364 r 2 = 0.75-3 -2-1 0 Protozoan Toxicity 1 2 3 Kahn I et al (2007) ATLA 35: 1-10

Fathead Minnow vs Tetrahymena pyriformis Toxicity of Non-Polar Narcotics 3 2 Fish Toxicity 1 0-1 -2 Fish LD 50 = 1.15 Protozoan IGC 50 + 0.44 n = 54 r 2 = 0.96-3 -3-2 -1 0 Protozoan Toxicity 1 2 3 Cronin MTD et al (1991) Sci Tot Environ 109-110: 431-439

Between Taxa Extrapolations are Stronger Within a Mechanism Narcotic potency is often consistent across trophic levels, only differing by species sensitivity Reactive chemicals show more significant inter-species variability Species specific metabolism can be identified Esterase in fish and not in Tetrahymena Form categories on mechanisms for biological read-across

Tools to Form Categories: Acute Aquatic Toxicity Verhaar rules and updates US EPA (Aster) Protein reactivity rules Metabolic groups

Biological Read-Across Endpoint to Endpoint Read-Down H H H H Species 1 Species 2 Species 3 Single Endpoint Single Effect H H H H Species 1 Species 2 Species 3 Single Endpoint H H H H Species 1 Species 2 Species 3 Single Endpoint Single Effect

(Protein) Reactive Toxicity A number of toxic effects are a result of reactivity with biological macromolecules Skin sensitisation, excess acute aquatic toxicity, mutagenicity Reactivity is the formation of a covalent bond with e.g. a protein or DNA Reactive toxicity has been a challenge to model (quantitatively) in silico

Predicting Reactive Toxicity Reactivity can be associated with mechanistic organic chemistry Relative reactivity can be quantified by in chemico reactivity Domains of reactivity have been defined SMARTS strings for five classic mechanisms Endpoints superimposed across a domain

Cl Br H Cl H Cl Category: Michael Acceptors Cl Cl Cl - N + Cl Cl Cl - N + - N + N H Cl N + - Br

Michael Acceptors Show Excess Acute Aquatic Toxicity 2 Non-Polar Narcosis 1 Toxicity 0-1 -2 0 1 Log P 2 3 4

Skin Sensitisers? LLNA Moderate Sensitiser Read-Across LLNA Weak Sensitiser H Br

Further Lines of Evidence: In Chemico Reactivity Read-Across Reactivity with a nucleophile e.g. glutathione is associated with sensitisation In chemico reactivity can be measured (and predicted) In chemico reactivity will extend the domain of the category and assist in readacross Br H

Category Formation: Filling an Incomplete Data Matrix by Weight of Evidence H H H H Species 1 Species 2 Species 3 Single Endpoint Structure H H H H Species 1 Species 2 Single Endpoint In Chemico Species 3 Species 1 Species 2 H H H H Single Endpoint In Vitro Species 3

A special supplement of ATLA is available with details of these ITS Integrated Testing Strategies 1. Are there existing data to suggest that the substance is, or is not, sensitising to the skin? No Yes C&L and/or RA 2. Define mechanistic domain (if applicable) and collect any available data on the reactive chemistry of the test substance (or its chemical class; nonvalidated). 3. Use in silico methods (such as DEREK, TIMES, ECD QSAR Application Toolbox) to make predictions on skin sensitisation (non-validated). 4. Perform in vitro skin penetration study (ECD TG 428). 5. Perform in vitro protein binding test (in house method). 6. Perform in vitro cell based assays such as those involving dendritic/langerhans cells and/or T Lymphocytes (non-validated). Grindon C et al (2006) ATLA 34: 651-664 7. Perform weight of evidence evaluation on all data so far. Does this show whether the substance is a skin sensitiser or not? No 8. Is a full quantitative risk assessment required? Yes C&L and/or RA

Conclusions Mechanisms of action can be used to form categories Categories allow for biological readacross Species to species Endpoint to endpoint Tools are available to assist in the formation of robust categories Categories can be implemented through ITS

Acknowledgements Terry Schultz, Steve Enoch, Mark Hewitt, Yana Koleva, Judith Madden EU FP6 SIRIS Integrated Project (GCE-CT-2007-037017) EU FP6 InSilicoTox Marie Curie Project (MTKD-CT-2006-42328)

References Aptula A, Patlewicz G, Roberts DW, Schultz TW (2006) Non-enzymatic glutathione reactivity and in vitro toxicity: A non-animal approach to skin sensitization. Toxicology in Vitro 20: 239-247. Cronin MTD, Dearden JC, Dobbs AJ (1991) QSAR studies of comparative toxicity in aquatic organisms. Science of the Total Environment 109/110:431-439. Ellison CM, Cronin MTD, Madden JC, Schultz TW (2008) Definition of the structural domain of the baseline non-polar narcosis model for Tetrahymena pyriformis. SAR and QSAR in Environmental Research. 19: In Press. Enoch SJ, Cronin MTD, Schultz TW, Madden JC (2008) Quantitative and mechanistic read across for predicting the skin sensitization potential of alkenes acting via Michael addition. Chemical Research in Toxicology 21: 513-520. Enoch SJ, Madden JC, Cronin MTD (2008) Identification of mechanisms of toxic action for skin sensitisation using a SMARTS pattern based approach. SAR and QSAR in Environmental Research. 19: 555-578. Gerberick F, Aleksic M, Basketter D, Casati S, Karlberg A-T, Kern P, Kimber I, Lepoittevin JP, Natsch A, vigne JM, Rovida C, Sakaguchi H, Schultz T (2008) Chemical reactivity measurement and the predictive identification of skin sensitisers. Alternatives to Laboratory Animals 36: 215-242. Grindon C, Combes R, Cronin MTD, Roberts DW, Garrod J (2006) Integrated decisiontree testing strategies for environmental toxicity with respect to the requirements of the EU REACH Legislation. Alternatives to Laboratory Animals 34: 651-664. Kahn I, Maran U, Benfenati E, Netzeva TI, Schultz TW, Cronin MTD (2007) Comparative quantitative structure activity activity relationships for toxicity to Tetrahymena pyriformis and Pimephales promelas. Alternatives to Laboratory Animals 35: 15-24. LeBlanc GA (1984) Interspecies relationships in acute toxicity of chemicals to aquatic organisms. Environmental Toxicology and Chemistry 3: 47-60. ECD (2007) Series on Testing and Assessment No. 80. Guidance on groupings of chemicals, September 26 (2007) (ENV/JM/MN(2007)28).