Lang Tran

The development and application of ENMs promises many benefits to both society and the global economy. However, to ensure responsible development of this emerging technology, governance must be put into place to ensure that any potential risk posed by ENMs are fully understood and controlled. The regulatory landscape is constantly evolving for all substances and products. However, in the case of ENMs, challenges are greater because on the nanoscale, properties of a material relevant to its safety and effectiveness may differ from those on the bulk scale.

The idea for NANOSOLUTIONS is that it offers a solution to the needs of industry operating within necessary regulatory circles to be able to classify the hazards of nanomaterials. Classification simplifies and rationalises the description of nanomaterials. Different materials can be put into different boxes depending on their physical and chemical characteristics in order to facilitate their management. If this doesn’t exist, each nanomaterial has to be dealt with on a case-by-case basis.

The challenge for us is to do two things: not only will we classify the materials as being safe or not, but also maintain an awareness of the composition of the materials. That is something that takes a long time to work out.

WP12 will develop an ENM Safety Classifier that can be used for the reliable assessment of ENM safety. Based upon the resulting design and data requirements of the model, specification for a high throughput system for future testing and analysis of further refined or new ENMs will also be produced. The development and design of this will depend upon access to large volumes of data and information being retrieved and abstracted from the data repository in the systems biology work package (WP11), in which all the data produced by the partners, including the characterisation of engineered nanomaterials (ENM) and the study of their effect on cell and animal models by a number of in vitro and in vivo experiments, will be systematically collected, organised and analysed.

There are a lot of materials that can be described as hazardous. But, if there is no exposure to humans, they can be safe; there will be no risk. But we are not doing a risk classification. We are doing a hazard classification, and in order to do that we need a lot of toxicology data. Data mining and neural networking will be used to find the traits that link the physical and chemical characteristics of the nanomaterials with these toxicology results. Those correlations will provide the basis of the Classifier.

At present, the data that will be used for the algorithms that will provide the backbone of the classifier is still being collected. Once this is done, the Classifier will be presented to industrial experts in the form of a prototype computer programme. There are a lot people from industry, academia and regulatory bodies who have an interest in the production of nanomaterials, and so we will bring these people together for a workshop where we can demonstrate how the classifier works and also get feedback from them, so that we can improve on it and also demonstrate that we have a viable solution to their needs.

Nanomaterials are being used in an increasing variety of industries, and it won’t be long before they are incorporated into the mainstream. 20 years ago we were talking about micro-technology. Now we just call it technology. We are now going through the same cycle with nanotechnology, and if the NANOSOLUTIONS classifier helps to speed up the process by giving people confidence in the safety of these materials, we will have achieved our goal.