Good Read-Across Practice 1: State of the Art of Read-Across for - PowerPoint PPT Presentation

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

Acknowledgement

What I am Going to Say… • Background and context • State of the art of read-across – Practical issues – Quantification Good Read- – Supporting read-across Across – Tools Practice – Guidance – Case Studies – Acceptance These are my views and others may wish to dissociate themselves from them

Category Formation (Grouping) for Read Across • Read-across uses information from members of a group of similar compounds, with known activity, to predict activity of unknown(s) OH OH OH OH Toxicity SAR / Read- Across Toxicity Interpolation

Some Good Reasons for Using Read-Across • Its simple, cheap and transparent • It has regulatory acceptance (if done correctly) • Provides solutions to problems

Potential Uses of Read-Across – REACH and other global legislation • New and existing chemicals • Prioritisation, C&L, Risk Assessment – New product risk assessment (e.g. industry) – New product registration – AOP / IATA Framework – Nanomaterials – Pharmaceuticals – development – Pharmaceuticals – impurities – Legal highs / illicit drugs – Others

Example of a Category: Long-Chain Alcohols Veenstra G et al (2009) Ecotox Environ Saf 72: 1016-1030.

Example of a Category: Terephthalic Acid and Esters Ball GL et al (2012) Crit Rev Toxicol 42: 28-67.

QUESTION: Can We Fill These Data Gaps? Ball GL et al (2012) Crit Rev Toxicol 42: 28-67.

Can We Fill These Data Gaps? Probably…. If we have…. • High quality “source” data • Consistency within the data for the category • We are interpolating • There is a good reason and justification for data gap filling • We can demonstrate similarity Ball GL et al (2012) Crit Rev Toxicol 42: 28-67.

Well Known, and Worrying, Activity Cliffs Exist Which Demonstrate Problem of Identifying Similar Compounds Teratogen Sedative

Are These Similar Molecules? Fingerprint Tanimoto maccs 0.77 fp4 0.67 fp2 0.64 fp3 0.50

Similarity is More Than Similarity in Chemical Structure Non Sensitiser Strong Skin Sensitiser

Guide to Grouping Chemicals Structural Analogues OH OH OH OH Mechanistic Analogues O O OH N O O Mode of Action Analogues OH OH H O H O H O

Other Options for Grouping Chemicals • Compounds that are metabolised to a common molecule • Compounds that are degraded rapidly to common products • Metrics of molecular similarity

Like it or Not… The Use of Read-Across is a Reality Toxicity to reproduction (1 882 dossiers covering phase – in substances 100-1 000 tpa) Developmental toxicity (1 882 dossiers) Full Report Published 2 June 2014 available at: http://echa.europa.eu/documents/10162/13639/alternatives_test_animals_2014_en.pdf

Growth in Publications Web of Science literature search using key words “Read - Across” and “Toxic” performed 20 February 2016

Like it or Not… The Use of Read-Across is a Reality Toxicity to reproduction (1 882 However the dossiers covering phase – in substances acceptance of read- 100-1 000 tpa) across predictions is not fully known Developmental toxicity (1 882 dossiers) Full Report Published 2 June 2014 available at: http://echa.europa.eu/documents/10162/13639/alternatives_test_animals_2014_en.pdf

Key Issues with Read-Across • How can we support a read-across prediction? – i.e. provide further (biological) evidence that chemicals belong to a group • When do read-across predictions become acceptable to replace an animal test?

State of the Art of Read-Across and Good Read-Across Practice

Practical Issues with Undertaking Read-Across Similarity is a Simple and Confirmation Fundamental Concept and Evidence Required Difficult and Subjective Assuring Category Membership Good Read-Across Practice Proof is essential for regulatory Well recognised approaches acceptance Identification and reduction of Much guidance uncertainties Consideration of endpoint to Support from New Methods identify best approach Data / Biological Similarity

Practical Issues with Undertaking Read-Across Can be R-A Arguments, Described Data, TK etc Defining Uncertainty Good Read-Across Practice Some uncertainty, context dependent, must be considered acceptable Criteria for defining uncertainty proposed but not necessarily accepted Further effort required

Practical Issues with Undertaking Read-Across Various Resources Assessing and Assigning Quality Biological Data Good Read-Across Practice Increasing data availability e.g. eChemPortal, ECHA DB etc New methods data e.g. HTS, Tox21 Biological profiling will support read-across

Specific Use Case Scenarios Difficult to Many Case Confirm No Studies Toxicity Confirming the No / Low Presence of Toxicity Toxicity Good Read-Across Practice Few robust categories, map onto Good examples for e.g. OECD / HPVC? reactive toxicity Similarity in toxicokinetics may need to be proven Some areas more effort e.g. receptor mediated Effort needed in using biological toxicity similarity Other Area: Nanomaterials, Mixtures, UVCBs

Supporting Mechanistically-Based Read-Across Clear Linkages to Category Supports Hypothesis Formation of Toxicity AOPs Good Read-Across Practice Molecular Initiating Events form the basis of grouping Data from assays for key events may confirm category membership Data from key events may be quantitative May form the basis of ITS / IATA, case studies required

Quantification of Read-Across Very important, Qualitative R-A is little addressed the current norm Some Few data examples How to Toxicokinetics Quantify R-A Good Read-Across Practice Appreciation of (PB)PK Requires more data modelling will be required and understanding Effort needed on how to incorporate new methods data May support quantification, More understanding, e.g. similarity assessment through case studies, is needed

Chemoinformatics: Tools for Grouping, Databases, Predictions of Toxicity, Metabolism etc

Tools and Databases – Not An Exhaustive List Tool Grouping Tox ADME Mechanism Free Data Yes Partial Some Yes Yes Tox/Track • Many bespoke tools for grouping and read-across DrugMatrix • May need further guidance / illustrated case studies Some Yes Few Yes Few No Yes Yes

Tools and Databases – Not An Exhaustive List Tool Grouping Tox ADME Mechanism Free Data Yes Partial Some Yes Yes Tox/Track DrugMatrix Some Yes Few • Many data sources support read-across • Always opportunities for further data sharing Yes Few No Yes Yes

Tools and Databases – Not An Exhaustive List Tool Grouping Tox ADME Mechanism Free Data Yes Partial Some Yes Yes Tox/Track DrugMatrix Some Yes Few • (Quantitative) metabolite and PK property prediction requires development and better integration into read-across Yes Few No Yes Yes

Tools and Databases – Not An Exhaustive List Tool Grouping Tox ADME Mechanism Free Data Yes Partial Some Yes Yes Tox/Track DrugMatrix Some Yes Few • A mechanistic basis to read-across is desirable Yes Few No • AOPs may support read-across in a number of ways Yes Yes

Current Guidance • Many sources • Need for consistent approach to reporting and assessing read-across • Adoption of ECHA’s Read -Across Assessment Framework (RAAF) and ensure effectiveness Several Other Initiatives

Case Studies • Many examples • Need for more to address issues such as RAAF, uncertainty, reporting, Four repeat dose RA biological profiling etc case studies Ten safety assessments using RA Several Other Initiatives

Acceptance of Read-Across • Variable • Addressed in next talk (Some) Key Points • Getting the documentation right • Read-across argument • Acceptable level of uncertainty

Conclusions • Practical issues affecting read-across have been identified, if not resolved • Useful tools and databases • Much guidance and opinion • Less certainty about certainty… • Acceptance variable

Acknowledgements • The European Community’s Seventh Framework Program (FP7/2007-2013) COSMOS Project under grant agreement n° 266835 and Cosmetics Europe • The CAAT GRAP Drafting Groups • Co-workers in Liverpool, EU, USA