دورية أكاديمية

In silico assessment of nanoparticle toxicity powered by the Enalos Cloud Platform: Integrating automated machine learning and synthetic data for enhanced nanosafety evaluation.

التفاصيل البيبلوغرافية
العنوان: In silico assessment of nanoparticle toxicity powered by the Enalos Cloud Platform: Integrating automated machine learning and synthetic data for enhanced nanosafety evaluation.
المؤلفون: Varsou DD; NovaMechanics MIKE, Piraeus 18545, Greece.; Entelos Institute, Larnaca 6059, Cyprus., Kolokathis PD; NovaMechanics MIKE, Piraeus 18545, Greece.; Entelos Institute, Larnaca 6059, Cyprus., Antoniou M; NovaMechanics Ltd, Nicosia 1070, Cyprus., Sidiropoulos NK; NovaMechanics MIKE, Piraeus 18545, Greece.; Entelos Institute, Larnaca 6059, Cyprus., Tsoumanis A; Entelos Institute, Larnaca 6059, Cyprus.; NovaMechanics Ltd, Nicosia 1070, Cyprus., Papadiamantis AG; Entelos Institute, Larnaca 6059, Cyprus.; NovaMechanics Ltd, Nicosia 1070, Cyprus.; School of Geography, Earth and Environmental Sciences, University of Birmingham, B15 2TT Birmingham, UK., Melagraki G; Division of Physical Sciences and Applications, Hellenic Military Academy, Vari 16672, Greece., Lynch I; Entelos Institute, Larnaca 6059, Cyprus.; School of Geography, Earth and Environmental Sciences, University of Birmingham, B15 2TT Birmingham, UK., Afantitis A; NovaMechanics MIKE, Piraeus 18545, Greece.; Entelos Institute, Larnaca 6059, Cyprus.; NovaMechanics Ltd, Nicosia 1070, Cyprus.
المصدر: Computational and structural biotechnology journal [Comput Struct Biotechnol J] 2024 Mar 30; Vol. 25, pp. 47-60. Date of Electronic Publication: 2024 Mar 30 (Print Publication: 2024).
نوع المنشور: Journal Article
اللغة: English
بيانات الدورية: Publisher: Elsevier B.V. on behalf of Research Network of Computational and Structural Biotechnology Country of Publication: Netherlands NLM ID: 101585369 Publication Model: eCollection Cited Medium: Print ISSN: 2001-0370 (Print) Linking ISSN: 20010370 NLM ISO Abbreviation: Comput Struct Biotechnol J Subsets: PubMed not MEDLINE
أسماء مطبوعة: Publication: Amsterdam : Elsevier B.V. on behalf of Research Network of Computational and Structural Biotechnology
Original Publication: Gothenburg, Sweden : Research Network of Computational and Structural Biotechnology
مستخلص: The rapid advance of nanotechnology has led to the development and widespread application of nanomaterials, raising concerns regarding their potential adverse effects on human health and the environment. Traditional (experimental) methods for assessing the nanoparticles (NPs) safety are time-consuming, expensive, and resource-intensive, and raise ethical concerns due to their reliance on animals. To address these challenges, we propose an in silico workflow that serves as an alternative or complementary approach to conventional hazard and risk assessment strategies, which incorporates state-of-the-art computational methodologies. In this study we present an automated machine learning (autoML) scheme that employs dose-response toxicity data for silver (Ag), titanium dioxide (TiO 2 ), and copper oxide (CuO) NPs. This model is further enriched with atomistic descriptors to capture the NPs' underlying structural properties. To overcome the issue of limited data availability, synthetic data generation techniques are used. These techniques help in broadening the dataset, thus improving the representation of different NP classes. A key aspect of this approach is a novel three-step applicability domain method (which includes the development of a local similarity approach) that enhances user confidence in the results by evaluating the prediction's reliability. We anticipate that this approach will significantly expedite the nanosafety assessment process enabling regulation to keep pace with innovation, and will provide valuable insights for the design and development of safe and sustainable NPs. The ML model developed in this study is made available to the scientific community as an easy-to-use web-service through the Enalos Cloud Platform (www.enaloscloud.novamechanics.com/sabydoma/safenanoscope/), facilitating broader access and collaborative advancements in nanosafety.
Competing Interests: DDV, PK, MA, NKK, AT, AGA and AA are affiliated with NovaMechanics, a cheminformatics and materials informatics company
(© 2024 The Authors.)
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فهرسة مساهمة: Keywords: Automated machine learning; Nanoinformatics; Safety and sustainability by design; Synthetic data
تواريخ الأحداث: Date Created: 20240422 Latest Revision: 20240426
رمز التحديث: 20240426
مُعرف محوري في PubMed: PMC11026727
DOI: 10.1016/j.csbj.2024.03.020
PMID: 38646468
قاعدة البيانات: MEDLINE
الوصف
تدمد:2001-0370
DOI:10.1016/j.csbj.2024.03.020