ReviewMolecules (Basel, Switzerland)2019
Nano-(Q)SAR for Cytotoxicity Prediction of Engineered Nanomaterials.
Review in Molecules (Basel, Switzerland), 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
16 citing papers in PubMed.
- A Nano-QSAR Model for the Cytotoxicity of Doxorubicin Prodrug Micelles in Multidrug-Resistant Cancer Cells.ACS omega · 2026Article
- AI-Driven Nano-QSAR Framework for Predicting Carbon Nanotube Cytotoxicity: Overcoming High Dimensionality and Data Imbalance.International journal of nanomedicine · 2026Article
- Silver integrated hybrids and nanocomposites for next-generation biomedicine: Beyond antimicrobial coatings toward smart sense-response-heal platforms.Materials today. Bio · 2025Review
- Do We Know Enough About the Safety Profile of Silver Nanoparticles in Oncology? A Focus on Novel Methods and Approaches.International journal of molecular sciences · 2025Review
- Predicting zeta potential of liposomes from their structure: A nano-QSPR model for DOPE, DC-Chol, DOTAP, and EPC formulations.Computational and structural biotechnology journal · 2024Article
- Nanoparticles-induced potential toxicity on human health: Applications, toxicity mechanisms, and evaluation models.MedComm · 2023Review
- Experimental Methods for the Biological Evaluation of Nanoparticle-Based Drug Delivery Risks.Pharmaceutics · 2023Review
- Integrating structure annotation and machine learning approaches to develop graphene toxicity models.Carbon · 2023Article
- In Silico Simulation of Impacts of Metal Nano-Oxides on Cell Viability in THP-1 Cells Based on the Correlation Weights of the Fragments of Molecular Structures and Codes of Experimental Conditions Represented by Means of Quasi-SMILES.International journal of molecular sciences · 2023Article
- Machine Learning for Evaluating the Cytotoxicity of Mixtures of Nano-TiOMolecules (Basel, Switzerland) · 2022Article
- Experimental and Computational Nanotoxicology-Complementary Approaches for Nanomaterial Hazard Assessment.Nanomaterials (Basel, Switzerland) · 2022Review
- Transcriptome Profile Alterations with Carbon Nanotubes, Quantum Dots, and Silver Nanoparticles: A Review.Genes · 2021Review
- Current Strategies in Assessment of Nanotoxicity: Alternatives to In Vivo Animal Testing.International journal of molecular sciences · 2021Review
- Article
- Novel Methods and Approaches for Safety Evaluation of Nanoparticle Formulations: A Focus TowardsFrontiers in pharmacology · 2021Review
- The rapid development of computational toxicology.Archives of toxicology · 2020Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
Abstract
Although nanotechnology is a new and rapidly growing area of science, the impact of nanomaterials on living organisms is unknown in many aspects. In this regard, it is extremely important to perform toxicological tests, but complete characterization of all varying preparations is extremely laborious. The computational technique called quantitative structure-activity relationship, or QSAR, allows reducing the cost of time- and resource-consuming nanotoxicity tests. In this review, (Q)SAR cytotoxicity studies of the past decade are systematically considered. We regard here five classes of engineered nanomaterials (ENMs): Metal oxides, metal-containing nanoparticles, multi-walled carbon nanotubes, fullerenes, and silica nanoparticles. Some studies reveal that QSAR models are better than classification SAR models, while other reports conclude that SAR is more precise than QSAR. The quasi-QSAR method appears to be the most promising tool, as it allows accurately taking experimental conditions into account. However, experimental artifacts are a major concern in this case.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.