Challenge 26: DARTpaths



  • To develop a data strategy and supporting data management structure to properly integrate available information on the relation between specific genes and specific physiology, or specific compounds and specific effects, for model organisms to include human, mouse, rat, rabbit, zebrafish, fruitfly, nematode and D. discoideum.
  • To map those gene-to-physiology or compound-effect relations properly between these organisms all with a focus on humans.
  • To build and use this framework to establish two proof-of-concepts (PoCs):
    1. Identify key data gaps in mechanistic understanding and demonstrate that newly generated mechanistic data for classes of substances with these gaps (e.g. UVCBs) can be integrated.
    2. To evaluate whether this framework and mechanistic data could have predicted a developmental toxicity de novo in a novel chemical class (e.g. of a crop protection chemical).
  • To employ the strategy and provide a mapping of the DART pathways and genes discussed in the National Research Council report (2000) and appendices 1 and 2 between mammalian and non-mammalian model organisms.


Developmental and reproductive toxicity (DART) testing assesses the potential effects of new chemicals on adult fertility and sexual behaviour, embryo implantation and the development of the foetus. These studies use large numbers of animals (primarily rats and rabbits), are expensive and time consuming, and the relevance to humans of effects seen in these studies is not always clear.

In recent years, there has been increasing interest in using cell-based, non-mammalian and computational approaches to improve our ability to predict chemicals with DART potential. This has coincided with the development of the Adverse Outcome Pathway (AOP) concept which links molecular initiating event(s) (MIE) caused by a chemical interaction at a molecular level, with adverse effects in an organism or population through a scientifically proven set of biochemical pathways. Advances in this area could enable the prediction of an adverse outcome within an organism in the absence of animal studies.

However, the current understanding of DART AOPs is incomplete and, potentially, many mechanisms could result in DART effects (see National Research Council, 2000 and Appendices 1 and 2 for a partial list of potential MIEs and the processes they participate in that, if perturbed, may cause DART effects). Integrating DART data generated in human cells (ToxCast program, Lamb J et al., 2006) and alternative non-mammalian model organisms (e.g. zebrafish, C. elegans, D. discoidium) (van der Laan JW et al., 2012, PREDART, 2012) in DART-specific AOPs may improve the translation of data generated in these systems to effects in humans. With many of the related mechanisms and developmental routes assumed to be shared, at least in part, by most eukaryotic species, the expectation is that a common response measured at the molecular level can be translated to a molecular response in human and thus more easily, to a properly predicted effect in human.

The key to unlocking the potential of these alternative test systems is to understand the conservation of key pathways across different species, including non-mammalian models, conventional mammalian test species and humans, and to identify and link genes with the same function and role in driving DART between organisms. The challenge here is to develop an approach to integrate the available information on the relation between particular genes and particular physiology in the various model organisms and the mapping of those relations between model organisms in functional AOPs.

3Rs benefits

DART testing requires the use of large numbers of animals. An OECD 414 prenatal development test requires ~800 rats, and ~900 rabbits for a second species test. An OECD 443 extended one generation test requires ~1400 rats for a minimal basic design, i.e. no cohorts and extension to F2 generations.

Currently, there is no overall systematic approach (to enable data mining or query ‘what if’ questions) to assess the species relevance, usefulness and applicability domain of the available model systems (conventional in vivo mammalian animal models and alternative non-mammalian systems) used to assess DART. Therefore key questions such as those listed in appendix 3 often remain unanswered, requiring companies to use conventional regulatory animal DART tests. Reliably linking data of specific genes with specific responses between organisms is essential to the development of reliable alternative non-mammalian DART test systems, as well as defining the applicability domains of these models.

Addressing this Challenge offers the following 3Rs benefits:

  • A reduction in the number of regulatory studies carried out in animals by using alternative non-mammalian test systems to pre-screen compounds for DART, allowing better compound pre-selection and at the same time providing a better insight into the mode of action of tested compounds.
  • In the longer term, as more information is gathered about the applicability domain of the alternative non-mammalian test systems, there may be an opportunity to replace animal studies with alternative non-mammalian studies.
  • Although this Challenge focuses on DART, the work conducted and the strategy developed could impact other areas of toxicology, pharmacology and chemical development, potentially increasing the 3Rs impact substantially.

Phase 1 winners

  • Dr Paul Andrews, SimOmics Ltd, £99,606.
  • Dr Barry Hardy, Douglas Connect GmbH, £99,834.
  • Dr Raymond Pieters, University of Applied Sciences Utrecht (UASU)/Institute for Risk, £100,000.

Full Challenge information



Assesment information

The following Challenge Panel Panel will consider Phase 2 applications submitted to this Challenge:

Member Name Institution
Dr Ian Ragan (Chair) NC3Rs Board Member
Dr Martijn Rooseboom (Sponsor) Shell
Dr Richard Currie (Sponsor) Syngenta
Dr Martino Picardo Independent
Professor Nigel Brown St George's, University of London
Dr Helen Parkinson European Molecular Biology Laboratory
Professor Dr Gerhard Ecker University of Vienna
Professor Dr Dick de Ridder Wageningen University and Research



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In progress

Budget information

Phase 1: up to £100k
Phase 2: up to £1 million




Phase 1: six months. Phase 2: up to three years