Authors: Dimitrios Zouraris | Angelos Mavrogiorgis | Andreas Tsoumanis | Laura Aliisa Saarimäki | Giusy del Giudice | Antonio Federico | Angela Serra | Dario Greco | Ian Rouse | Julia Subbotina | Vladimir Lobaskin | Karolina Jagiełło | Krzesimir Ciura | Beata Judzińska | Alicja Mikołajczyk | Anita Sosnowska | Tomasz Puzyn | Mary Gulumian | Victor Wepener | Diego Stéfani T. Martinez | Romana Petry | Naouale El Yamani | Elise Rundén‐Pran | Sivakumar Murugadoss | Sergey Shaposhnikov | Vasileios Minadakis | Periklis Tsiros | Haralambos Sarimveis | Eleonora Longhin | Tanima SenGupta | Ann‐Karin Olsen | Viera Skakalova | Peter Hutár | Mária Dušinská | Anastasios G. Papadiamantis | L Cristiana Gheorghe | Katie Reilly | Émilie Brun | Sami Ullah | Sébastien Cambier | Tommaso Serchi | Kaido Tämm | Candida Lorusso | Francesco Dondero | Evangelos Melagrakis | Muhammad Moazam Fraz | Georgia Melagraki | Iseult Lynch | Antreas Afantitis
Source: Computational and Structural Biotechnology Journal (article, 2024-12-25)
Field: Materials Science — Materials Chemistry

CompSafeNano project: NanoInformatics approaches for safe-by-design nanomaterials The CompSafeNano project, a Research and Innovation Staff Exchange (RISE) project funded under the European Union's Horizon 2020 program, aims to advance the safety and innovation potential of nanomaterials (NMs) by integrating cutting-edge nanoinformatics, computational modelling, and predictive toxicology to enable design of safer NMs at the earliest stage of materials development. The project leverages Safe-by-Design (SbD) principles to ensure the development of inherently safer NMs, enhancing both regulatory compliance and international collaboration. By building on established nanoinformatics frameworks, such as those developed in the H2020-funded projects NanoSolveIT and NanoCommons, CompSafeNano addresses critical challenges in nanosafety through development and integration of innovative methodologies, including advanced <i>in vitro</i> models, <i>in silico</i> approaches including machine learning (ML) and artificial intelligence (AI)-driven predictive models and 1st-principles computational modelling of NMs properties, interactions and effects on living systems. Significant progress has been made in generating atomistic and quantum-mechanical descriptors for various NMs, evaluating their interactions with biological systems (from small molecules or metabolites, to proteins, cells, organisms, animals, humans and ecosystems), and in developing predictive models for NMs risk assessment. The CompSafeNano project has also focused on implementing and further standardising data reporting templates and enhancing data management practices, ensuring adherence to the FAIR (Findable, Accessible, Interoperable, Reusable) data principles. Despite challenges, such as limited regulatory acceptance of New Approach Methodologies (NAMs) currently, which has implications for predictive nanosafety assessment, CompSafeNano has successfully developed tools and models that are integral to the safety evaluation of NMs, and that enable the extensive datasets on NMs safety to be utilised for the re-design of NMs that are inherently safer, including through prediction of the acquired biomolecule coronas which provide the biological or environmental identities to NMs, promoting their sustainable use in diverse applications. Future efforts will concentrate on further refining these models, expanding the NanoPharos Database, and working with regulatory stakeholders thereby fostering the widespread adoption of SbD practices across the nanotechnology sector. CompSafeNano's integrative approach, multidisciplinary collaboration and extensive stakeholder engagement, position the project as a critical driver of innovation in NMs SbD methodologies and in the development and implementation of computational nanosafety. Computer science Nanoparticles: synthesis and applications ; Nanotechnology research and applications ; Graphene and Nanomaterials Applications