Objectives & Impact

Seven interconnected objectives define our goals

We work towards the following objectives:

1. Mode of Action (MoA) based Integrated Approaches to Testing and Assessment (IATAs) for advanced materials by combining conventional and New Approach Methodologies; 2. IATAs anchored by comprehensive data analysis on Adverse Outcome Pathways and nanomaterial grouping from animal models; 3. Data analysis and data integration supported by big data management solutions; 4. Development of the Safe Innovation Approach (SIA); 5. Development of validated Safe-by-Design (SbD) tools; 6. Verifying the robustness of tools under real-world conditions assessed in different case studies; 7. Engagement with stakeholders and collaboration with national and international initiatives.

Objective 1

Establish Mode-of-Action based Integrated Approaches to Testing and Assessment (IATAs) for advanced materials (AMs) by integrating conventional and New Approach Methodologies (NAMs)

HARMLESS develops MoA-based IATAs, building on existing knowledge as much as possible. Partners of HARMLESS are past and present partners in more than 40 EU nanosafety projects. Thus, we consider available information and data from Adverse Outcome Pathways (AOPs), including in vivo and in vitro multi-omics datasets and comprehensive High Throughput Screening Approaches. Exisiting data combined with nano-relevant AOPs constitute the basis to develop the HARMLESS IATAs. New data generation is focussed on potential lung toxicity of NMs, as inhalation is the primary route of exposure, but also relevant secondary target organs such as liver, cardiovascular and immune systems is considered (as well as primary ecotoxicological compartments, e.g., water and soil). Our IATAs integrate computational methods with conventional and New Approach methodologies (NAMs). They are tiered in different levels of complexity at each stage of innovation, to aid innovators during product development supported by user-friendly tools.

Objective 2

Comprehensive data analysis to support adverse outcome pathways and NM grouping

HARMLESS applies a multifaceted New Approach Methodologies (NAMs) toolbox to generate millions of modeling-permissive data points as required to generate a new standard SbD framework that can inform on the intimate relationships between NM descriptors and AOPs. Data selection and toxicokinetic/toxicodynamic modelling is coupled to point-of-departure assessments at low doses, typically below the levels of significant perturbation detectable in conventional assays. We develop our training and validation algorithms on diverse data, including high-throughput screening of cellular toxicity endpoints and high-throughput transcriptomics and proteomics. We use machine learning to recognise toxic key events and molecular probabilistic component modelling to characterise MoA, leading to computable AOP schemata with qualitative and quantitative toxicity pathways data. The new Decision Support System (DSS) is developed iteratively from the case studies, and NAM results are anchored to in vivo hazard assessments.

Objective 3

Support data analysis with big data management solutions

The HARMLESS database builds on the open source data management solution eNanoMapper, currently the largest searchable compilation of nanoEHS (Nano Environment, Health and Safety) data in Europe. Within HARMLESS, we implement a proper description (including ontology) for multi-component nanomaterials and enable handling of the massive quantities of data that are obtained by High Throughput -cellular and -omics methods. We adopt a modern big data architecture with a high performing intake and processing layers, while the eNanoMapper data solutions stay as the final serving layers, providing search and retrieval to end users via user-friendly interfaces and Application Programming Interface (API).

Objective 4

Safe-Innovation-Approach for advanced materials

Safe Innovation Approaches (SIA) aim to provide a proactive system to minimise the gap between the pace of innovation development and the pace of developing nano-specific risk governance. Thus, SIA supports both Safe-by-Design and Regulatory Preparedness and evolves to multicomponent, hybrid nanomaterials and HARNs. The developed SIA framework uses descriptive models and tools, innovative in vitro and in silico approaches (NAMs), suitable for supporting risk assessment in industrial settings, both for the Cooper Stage Model and Triple-A Agile System. By this, we can cover more than 80% of industrial design processes. The inclusion of NAMs allows us to generate “big data” early in the design process, namely on relevant end points for risk assessment. To encourage dialog between innovators and regulators, we incorporate tools for risk communication based on risk matrices on consumer, environmental and occupational safety. The framework is improved by application of reference and case study nanomaterials. Thus, HARMLESS provides industry with guidelines and tools to devise SbD at all stages of the development of a new product.

Objective 5

Provide validated, user-friendly SbD tools for advanced materials

We build upon our Safe-by-Design strategies in combination with the available data from previous and ongoing projects, complemented by newly generated HARMLESS data, with the aim to develop computational tools for different scales and stages of product development. Available risk assessment tools have just recently been able to semi-quantitatively assess the risks of “conventional” first generation nanomaterials. However, none of these tools are suitable for assessing the risk of next generation multicomponent NMs and HARNs. The big data generation in HARMLESS allows for next generation decision support tools to be developed by using machine learning techniques on all available and newly generated data, with the aim to derive simple-to-use, user-friendly tools that can be easily implemented and used by SMEs and other industries. The guidance material  assists companies in making decisions throughout the all stages of product development whether to proceed with material development or not based on a transparent decision between the benefits and the risks.

Objective 6

Verify and facilitate SIA and SbD tools in real-world industry scenarios

Our industrial partners are involved in all stages of the project, to assure a real-world and sector-specific perspective. To simplify the implementation of Safe-by-Design, to enable SMEs to benefit, we test three features: a) flexible 3-stage or 6-stage implementation, b) concepts of analogy (such as grouping and categories) and c) sector-specific categories (like risk matrices). We carry out different well-defined case studies. Careful monitoring of exposure levels and hazard assessment of the multi-component nanomaterials, and their fragments, found in-situ at the industry sites provide unique datasets for developing and validating our tools.

Objective 7

Engage with stakeholders and establish collaboration with national and international initiatives

We strive for collaboration with different stakeholders, i.e., relevant EU and international bodies, platforms, clusters and projects, building on already existing networks and ongoing activities on Safe-by-Design and advanced materials. Cooperation with other relevant projects is facilitated via the EU NanoSafety Cluster Working Groups. We aim to organise dedicated workshops and other dialogue formats, collecting feedback and refining project outcomes to match stakeholder needs. Special focus is put on the transfer of knowledge from public research to industry. Our partners are active members of several EU or international panels and committees such as the Nanomaterial Expert Group at ECHA and the OECD WPMN.

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EU-funded H2020 Research & Innovation Action addressing Safe-by-Design of multicomponent nanomaterials running from January 2021 - January 2025