International STakeholder NETwork (ISTNET): creating a developmental neurotoxicity (DNT) testing road map for regulatory purposes

Abstract

A major problem in developmental neurotoxicity (DNT) risk assessment is the lack of toxicological hazard information for most compounds. Therefore, new approaches are being considered to provide adequate experimental data that allow regulatory decisions. This process requires a matching of regulatory needs on the one hand and the opportunities provided by new test systems and methods on the other hand. Alignment of academically and industrially driven assay development with regulatory needs in the field of DNT is a core mission of the International STakeholder NETwork (ISTNET) in DNT testing. The first meeting of ISTNET was held in Zurich on 23-24 January 2014 in order to explore the concept of adverse outcome pathway (AOP) to practical DNT testing. AOPs were considered promising tools to promote test systems development according to regulatory needs. Moreover, the AOP concept was identified as an important guiding principle to assemble predictive integrated testing strategies (ITSs) for DNT. The recommendations on a road map towards AOP-based DNT testing is considered a stepwise approach, operating initially with incomplete AOPs for compound grouping, and focussing on key events of neurodevelopment. Next steps to be considered in follow-up activities are the use of case studies to further apply the AOP concept in regulatory DNT testing, making use of AOP intersections (common key events) for economic development of screening assays, and addressing the transition from qualitative descriptions to quantitative network modelling.

Fig. 1

Summary of data available for conducting health-hazard assessments of chemicals (adapted and modified from NRC ; reprinted from Crofton et al. 2012)

Fig. 2

Chemical category formation and toxicant assessment. A traditional chemistry-driven approach of classification/category formation is based on quantitative structure–activity relationships (QSAR). A complementary approach uses the actual activity of a compound (i.e. the effect in a test system) to relate it to its potential hazard (QAHR). Multiple QSAR/QAHR may be combined into test batteries or into integrated testing strategies (ITS)/integrated approaches to testing and assessment (IATA). All category formation approaches require some form of evaluation of their performance. This may take the form of a classical validation or mechanistic validation or merely a technical validation. Simple classification outcomes are “no effect”, “adverse effect” or “adaptive effect”. An adverse effect may be defined in different ways (left bottom). At the bottom right, different logical approaches to hazard prediction within the context of a biological pathway or AOP are indicated. In probabilistic risk assessment, the likelihood of a certain hazard (p(B)) would be a function of the test outcome (f(A)). The orange boxes exemplify a specific choice of approaches that may be used in the context of test structuring according to the AOP concept: one may choose to take the approach of an ITS that is mechanistically validated. Hazard would be defined on the basis of the biological thresholds relevant to the key events of the AOP. Focus for hazard prediction would be on events that are sufficient by themselves to explain/result in hazard (color figure online)

Fig. 3

Concept of adverse outcome pathways (AOPs). A complete AOP spans the events linking a chemical’s structure and properties to the adverse outcome (AO) it triggers in an organism. The decisive first step is a defined molecular initiating event (MIE), an interaction of the chemical with a target. This triggers cellular responses through metabolic and signalling pathway perturbations; these cellular responses result in changes in tissues, organs and the organism. A pivotal element of the concept is the assumption of key events (KE). Complexity may arise, when reality suggests that one KE is directly upstream of two or more other KE, or when one of the KE is involved in a feedback loop

Fig. 4

Anchor and context dependence of different chemical assessment methods. Different approaches for hazard testing and classification may be distinguished by their dependence on anchoring (x axis), i.e. relating the results to other information not delivered by the test method. For instance, most classical assays and models (QAHR/QSAR) require high numbers of already known compounds for calibrations. In contrast to this, testing of biological processes (e.g. neurite growth) does not necessitate such information. Approaches may also be distinguished (y axis) by the extent to which they use networks or simple clustering approaches to categorize information form multiple sources. A third dimension (z axis) distinguishes methods by the context dependence of the endpoint measured. For instance, receptor binding constants or the polarity of a compound would be only to a small extent dependent on the assay used. In contrast to this, gene expression changes triggered by a compound will depend on the cell type, the culture conditions and many other factors

Fig. 5

Concept of common key events (CKEs). CKEs are identical to KEs altered within multiple AOPs. When those are chosen as testing endpoints, the number of assays/AOPs can be drastically reduced