TY - JOUR
T1 - CompSafeNano Project: NanoInformatics Approaches for Safe-by-Design Nanomaterials
AU - Zouraris, Dimitrios
AU - Mavrogiorgis, Angelos
AU - Tsoumanis, Andreas
AU - Saarimäki, Laura Aliisa
AU - del Giudice, Giusy
AU - Federico, Antonio
AU - Serra, Angela
AU - Greco, Dario
AU - Rouse, Ian
AU - Subbotina, Julia
AU - Lobaskin, Vladimir
AU - Jagiello, Karolina
AU - Ciura, Krzesimir
AU - Judzinska, Beata
AU - Mikolajczyk, Alicja
AU - Sosnowska, Anita
AU - Puzyn, Tomasz
AU - Gulumian, Mary
AU - Wepener, Victor
AU - Martinez, Diego S. T.
AU - Petry, Romana
AU - El Yamani, Naouale
AU - Rundén-Pran, Elise
AU - Murugadoss, Sivakumar
AU - Shaposhnikov, Sergey
AU - Minadakis, Vasileios
AU - Tsiros, Periklis
AU - Sarimveis, Harry
AU - Longhin, Eleonora Marta
AU - SenGupta, Tanima
AU - Olsen, Ann-Karin Hardie
AU - Skakalova, Viera
AU - Hutar, Peter
AU - Dusinska, Maria
AU - Papadiamantis, Anastasios G.
AU - Gheorghe, L. Cristiana
AU - Reilly, Katie
AU - Ullah, Sami
AU - Cambier, Sebastien
AU - Serchi, Tommaso
AU - Tämm, Kaido
AU - Lorusso, Candida
AU - DONDERO, Francesco
AU - Fraz, Muhammad Moazam
AU - Melagraki, Georgia
AU - Lynch, Iseult
AU - Afantitis, Antreas
PY - 2024
Y1 - 2024
N2 - 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 in vitro models, in silico 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 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) 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. The project’s integrative approach, multidisciplinary collaboration and extensive stakeholder engagement, positions CompSafeNano as a critical driver of innovation in NMs SbD methodologies and in the development and implementation of computational nanosafety.
AB - 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 in vitro models, in silico 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 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) 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. The project’s integrative approach, multidisciplinary collaboration and extensive stakeholder engagement, positions CompSafeNano as a critical driver of innovation in NMs SbD methodologies and in the development and implementation of computational nanosafety.
UR - https://iris.uniupo.it/handle/11579/199962
U2 - 10.1016/j.csbj.2024.12.024
DO - 10.1016/j.csbj.2024.12.024
M3 - Article
SN - 2001-0370
JO - Computational and Structural Biotechnology Journal
JF - Computational and Structural Biotechnology Journal
ER -