Challenge 19: QSARs Mix

Objective

To use existing toxicological data repositories to develop in silico tools which can be used to predict toxicology endpoints for substances of interest in order to waive in vivo studies. The developed tools will be of benefit to any company that imports or manufactures chemicals. The tools will also be of use to any company seeking to bridge data gaps in skin and eye irritation.

Background

Where substances are manufactured in quantities of over ten tonnes per year, the European REACH regulation requires specific toxicological information for the substance to be registered. Similar toxicological information is also required for registering substances in other regions of the world. Companies also routinely conduct toxicology testing for candidate selection during product development. In one area of mandatory toxicity testing, the prediction skin and eye irritation effects, investigators may utilise in vitro models for prioritisation of candidates and potential classification, however in vivo models may be required for classification of substances. Such animal tests can only be avoided with strong scientific justification. The use of in silico prediction methods such as (Quantitative) Structure Activity Relationships ((Q)SARs) and expert systems are starting to build a toolbox for providing scientific justification, but more development is needed to provide adequate information to waive the in vivo studies.

(Q)SARs and expert systems for human health endpoints are based on the assumption that the toxicity of a compound is related to its chemical structure. (Q)SARs must meet quality standards and be scientifically validated according to the OECD principles. A 2006 European Commission report concluded ‘that the further development, validation and documentation of in silico systems for local toxicity to the skin and eye are necessary’. Expert systems are sets of ‘if-then’ rules or criteria to classify a chemical into various categories (e.g., eye irritant). Currently, these rules are defined by physical and chemical property thresholds. A more promising way forward is the use of structural alerts. These are sets of sub-structures that are identified in a molecular formula; an occurrence of any of these triggers the alert (e.g., skin or eye irritant). It is the sub-structures within a substance that dictate its physical properties, therefore it is important to correlate toxicity directly with these structures rather than the physical chemical parameters that may not always be available.

The current (Q)SAR/ structural alert tools are limited in the data sources used to develop them and are not amenable to mixtures. A large proportion of substances imported or manufactured in the EU are mixtures. The development of computational models that can be used to interrogate mixtures of chemicals would present a significant innovation in this area, broaden their application and reduce animal use.

The aim of this Challenge is that a model or expert system be created which allows for the reliable prediction of skin and eye irritation based on structural information or ‘structural alerts’. Specifically, this tool should be able to predict the toxicity associated with a mixture of substances assuming a proper compositional analysis of the test mixture and applying a weighted average score approach.

If successful these tools will:

  • Provide a more predictive and relevant tool-set to predict potential toxicity related to skin and eye irritation that can be used to reduce in vivo studies.
  • Enable rapid screening of potential candidates.
  • Decrease development costs and time-to-market.
  • With further development, the approach could be translated to other toxicity endpoints such as reproductive and developmental toxicity.

3Rs benefits

  • Companies performing in vivo studies for skin and eye irritation for registration of new substances can utilise >250 rabbits per year.
  • These irritation studies are invasive, further supporting replacement of the in vivo models.

The proposed model will improve the predictive capacity of the current in silico models, permitting the early identification of potential toxicities in candidate selection without having to use in vivo studies and contribute to the scientific justification to waive the in vivo studies for skin and eye irritation for those taken forward to registration.

Single Phase Challenge winner

Project team led by

Full Challenge information

Assessment information

The following Panel considered applications submitted to this Challenge.

Member Name Institution
Professor Mark Cronin (Chair) Liverpool John Moores University
Tom Austin (Sponsor) Shell
Charles Eadsforth (Sponsor) Shell
Dr Satinder Sarang (Sponsor) Shell
Professor Emilio Benfenati Istituto di Ricerche Farmacologiche Mario Negri
Dr Cecilia Bossa Istituto Superiore di Sanità
Dr Qasim Chaudhry The Food & Environment Research Agency
Professor Daniel Dietrich University of Konstanz
Dr David Lovell St George's, University of London

 

 

 

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Budget information

Up to £100k

Sponsor(s)

Shell

Duration

Up to one year