E-waste: mechanisms of toxicity and safety testing
Chukwuebuka Eze1, Mathieu Vinken1
1Entity of In Vitro Toxicology and Dermato-Cosmetology, Department of Pharmaceutical and Pharmacological Sciences, Faculty of Medicine and Pharmacy, Vrije Universiteit Brussel, Brussels, Belgium.
Abstract:
Currently, information on the toxicity profile of the majority of the identified e-waste chemicals, while extensive and growing, is admittedly fragmentary, particularly at the cellular and molecular levels. Furthermore, the toxicity of the chemical mixtures likely to be encountered by humans during and after informal e-waste recycling, as well as their underlying mechanisms of action, is largely unknown. This review paper summarizes state-of-the-art knowledge of the potential underlying toxicity mechanisms associated with e-waste exposures, with a focus on toxic responses connected to specific organs, organ systems, and overall effects on the organism. To overcome the complexities associated with assessing the possible adverse outcomes from exposure to chemicals, a growing number of new approach methodologies have emerged in recent years, with the long-term objective of providing a human-based and animal-free system that is scientifically superior to animal testing, more effective, and acceptable. This encompasses a variety of techniques, typically regarded as alternative approaches for determining chemical-induced toxicities and holds greater promise for a better understanding of key events in the metabolic pathways that mediate known adverse health outcomes in e-waste exposure scenarios. This is crucial to establishing accurate scientific knowledge on mixed e-waste chemical exposures in shorter time frames and with greater efficacy, as well as supporting the need for safe management of hazardous chemicals. The present review paper discusses important gaps in knowledge and shows promising directions for mechanistically anchored effect-based monitoring strategies that will contribute to the advancement of the methods currently used in characterizing and monitoring e-waste-impacted ecosystems.
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