Abstract
Currently, the identification of chemicals that have the potential to induce developmental neurotoxicity (DNT) is based on animal testing. Since at the regulatory level, systematic testing of DNT is not a standard requirement within the EU or USA chemical legislation safety assessment, DNT testing is only performed in higher tiered testing triggered based on chemical structure activity relationships or evidence of neurotoxicity in systemic acute or repeated dose toxicity studies. However, these triggers are rarely used and, in addition, do not always serve as reliable indicators of DNT, as they are generally based on observations in adult rodents. Therefore, there is a pressing need for developing alternative methodologies that can reliably support identification of DNT triggers, and more rapidly and cost-effectively support the identification and characterization of chemicals with DNT potential.We propose to incorporate mechanistic knowledge and data derived from in vitro studies to support various regulatory applications including: (a) the identification of potential DNT triggers, (b) initial chemical screening and prioritization, (c) hazard identification and characterization, (d) chemical biological grouping, and (e) assessment of exposure to chemical mixtures. Ideally, currently available cellular neuronal/glial models derived from human induced pluripotent stem cells (hiPSCs) should be used as they allow evaluation of chemical impacts on key neurodevelopmental processes, by reproducing different windows of exposure during human brain development. A battery of DNT in vitro test methods derived from hiPSCs could generate valuable mechanistic data, speeding up the evaluation of thousands of compounds present in industrial, agricultural and consumer products that lack safety data on DNT potential.
Highlights
The developing nervous system is known to be more vulnerable to chemical exposure as compared to the adult nervous system (Spyker, 1975; NRC, 1993; Rodier, 1995; Grandjean and Landrigan, 2006)
It has to be pointed out that while such an approach is suitable for screening purposes, it is not applicable for the development of Quantitative structure-activity relationships (QSARs) models aimed at identifying chemicals triggering a specific MIE, in light of the fact that common key events (KEs) are triggered by various MIEs
This can be achieved by using a battery of in vitro assays which permit evaluation of a range of human key pathways that mediate developmental neurotoxicity (DNT) effects, critical neurodevelopmental processes at different developmental time points and KEs identified in the existing Adverse outcome pathways (AOPs) relevant to DNT (Table 1A in (Bal-Price and Meek, 2017)), preferably by using human models derived from human induced pluripotent stem cells (hiPSCs), rather than rodent test systems to avoid interspecies differences (Fritsche et al, 2015)
Summary
The developing nervous system is known to be more vulnerable to chemical exposure as compared to the adult nervous system (Spyker, 1975; NRC, 1993; Rodier, 1995; Grandjean and Landrigan, 2006). Robust human stem cell-based in vitro models are used to evaluate key neurodevelopmental processes, known to be specific for normal brain development and maturation These include commitment and proliferation of neural stem cells, apoptosis, cell migration, neuronal and glial differentiation, neurite outgrowth, myelination, axonal and dendritic elongation, synapse formation, synapse pruning, neurotransmitter receptor profiling, development of neuronal connectivity, spontaneous electrical activity, etc. Based on the current knowledge it can be stated that in vitro human neuronal models, such as those derived from hiPSCs, can recapitulate a sequence of neurodevelopmental processes starting from NPC proliferation until an advanced stage of neuronal and glial differentiation and maturation If these processes are impaired as a result of chemical exposure, they can be assessed in a quantitative manner and serve as reliable readouts for in vitro DNT evaluation. Notwithstanding, further efforts should be made to upscale the throughput applicability of some measured endpoints, when 3D systems are required
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