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A novel toolbox supports rational design of nanomaterials for safe use

Despite several decades of research, understanding of the mechanisms of nanomaterial toxicity and its integration in safety assessments was still quite limited. A breakthrough is helping scientists predict the development of diseases based on a nanomaterial’s physicochemical properties.

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Nanomaterials are a diverse, virtually limitless class of materials that vary in composition and physicochemical attributes. Their common feature is their small size. This gives them their unique and often exotic properties but poses a challenge in terms of protecting people and the environment throughout the materials’ life cycle. The current safety screening procedures are expensive and time consuming and often include animal experiments. The EU-funded SmartNanoTox project has developed a simpler and faster way to predict the specific nanotoxic effects a given material might have, even before it is used in products.

Two sides of the same coin

SmartNanoTox analysed an organism’s state over time following exposure in the lung to different nanomaterials and developed flowcharts showing how the biological responses to toxicity progress, referred to as the adverse outcome pathways (AOPs). Scientists also followed the state of nanoparticles in the exposed tissues using high resolution imaging, chemical analysis and computer simulation. After mapping one onto the other, they determined which molecular interactions triggered specific, rather simple biological events – for example, misfolding of a protein, membrane disruption, catalysis of a chemical reaction or cell penetration. Machine learning and statistical models helped connect the biological toxicity data to the physicochemical description (for example, long rigid needles versus chemically aggressive, small, insoluble particles).

Nothing left to chance

The AOP framework describes causal linkages between a molecular initiating event, a series of intermediate key events and the adverse outcome. The team aimed to describe several independent pathways leading to serious diseases. However, they were surprised to find how interconnected the AOPs were, with several adverse outcomes sharing similar intermediate key events. The insight added up to a significantly enhanced predictive ability. Project coordinator Vladimir Lobaskin of University College Dublin explains: “For the first time, we can directly connect a nanomaterial’s measured or computed properties to the key steps of disease development and predict whether the specific material is likely to cause fibrosis, cardiovascular problems or cancer.” SmartNanoTox used the powerful, new safety assessment paradigm to show that a chronic lung inflammation caused by nanoparticles could be understood in detail and predicted using tests on cell cultures and computer simulation without actual animal experiments. The results have been published in Advanced Materials. Further, the team showed that this chronic condition can be caused by a single exposure to certain biopersistent materials.

Predicting chronic disease with in vitro and in silico tools

In vitro aerosol exposure test systems and sensorial cellular devices harnessing tissue-on-chip technology are available from partners. Other tools are available in freely accessible databases or open-source software repositories, including NanoCommons KnowledgeBase, the AOP-Wiki and the Gene Expression Omnibus. “Our unique effort linking physics, chemistry and biology to medicine has connected the elementary structures of nanomaterials to their biological actions. With this, we demonstrated that it is possible to predict chronic health conditions resulting from nanomaterial exposure with simple in vitro tests and computer simulation,” Lobaskin concludes. The SmartNanoTox tools will have a far-reaching impact on any application of nanomaterials that can affect living systems at any point in their life cycle.

Keywords

SmartNanoTox, nanomaterial, AOP, physicochemical, toxicity, safety assessment, nanoparticle, biopersistent, adverse outcome pathway

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