Predicting the toxicity of nanoparticles using artificial intelligence tools: a systematic review

Yazdipour, Alireza Banaye; Masoorian, Hoorie; Ahmadi, Mahnaz; Mohammadzadeh, Niloofar; Ayyoubzadeh, Seyed Mohammad

doi:10.1080/17435390.2023.2186279

Cited by 9 publications

(3 citation statements)

References 72 publications

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“…QSAR models facilitate the screening of large numbers of nanomaterials for toxicity, prioritize testing, and optimize experimental design. 55 Furthermore, QSAR models offer insights into the mechanisms underlying nanotoxicity, which inform the development of safer and more efficient nanomaterials. Several studies have reported the successful use of QSAR models for predicting the toxicity of nanomaterials in different biological systems.…”

Section: Quantitative Structure−activity Relationship For Nanomateria...mentioning

confidence: 99%

“…One of the advantages of QSAR models for nanotoxicology is that they can reduce the need for time-consuming and expensive in vitro and in vivo toxicity testing. QSAR models facilitate the screening of large numbers of nanomaterials for toxicity, prioritize testing, and optimize experimental design . Furthermore, QSAR models offer insights into the mechanisms underlying nanotoxicity, which inform the development of safer and more efficient nanomaterials.…”

Section: Quantitative Structure–activity Relationship For Nanomateria...mentioning

confidence: 99%

See 1 more Smart Citation

Harmonization Risks and Rewards: Nano-QSAR for Agricultural Nanomaterials

Singh,

Shelar,

Rai

et al. 2024

J. Agric. Food Chem.

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This comprehensive review explores the emerging landscape of Nano-QSAR (quantitative structure−activity relationship) for assessing the risk and potency of nanomaterials in agricultural settings. The paper begins with an introduction to Nano-QSAR, providing background and rationale, and explicitly states the hypotheses guiding the review. The study navigates through various dimensions of nanomaterial applications in agriculture, encompassing their diverse properties, types, and associated challenges. Delving into the principles of QSAR in nanotoxicology, this article elucidates its application in evaluating the safety of nanomaterials, while addressing the unique limitations posed by these materials. The narrative then transitions to the progression of Nano-QSAR in the context of agricultural nanomaterials, exemplified by insightful case studies that highlight both the strengths and the limitations inherent in this methodology. Emerging prospects and hurdles tied to Nano-QSAR in agriculture are rigorously examined, casting light on important pathways forward, existing constraints, and avenues for research enhancement. Culminating in a synthesis of key insights, the review underscores the significance of Nano-QSAR in shaping the future of nanoenabled agriculture. It provides strategic guidance to steer forthcoming research endeavors in this dynamic field.

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Section: Quantitative Structure−activity Relationship For Nanomateria...mentioning

confidence: 99%

Section: Quantitative Structure–activity Relationship For Nanomateria...mentioning

confidence: 99%

Harmonization Risks and Rewards: Nano-QSAR for Agricultural Nanomaterials

Singh,

Shelar,

Rai

et al. 2024

J. Agric. Food Chem.

View full text Add to dashboard Cite

show abstract

“…A recent systematic study demonstrated that machine learning algorithms can predict the cytotoxicity of nanomaterials based on their physicochemical properties, thus guiding the optimization of nanomaterial designs. 739 , 740 For example, researchers developed the NanoTox model, which uses features like particle size and zeta potential to predict the toxicity of metal oxide NPs. 741 Another study showcased how deep learning models can enhance the interpretation of immune responses and lung burdens caused by NPs, providing support for nanosafety assessments.…”

Section: Conclusion and Perspectivementioning

confidence: 99%

Nanotechnology’s frontier in combatting infectious and inflammatory diseases: prevention and treatment

Huang,

Guo,

et al. 2024

Sig Transduct Target Ther

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Inflammation-associated diseases encompass a range of infectious diseases and non-infectious inflammatory diseases, which continuously pose one of the most serious threats to human health, attributed to factors such as the emergence of new pathogens, increasing drug resistance, changes in living environments and lifestyles, and the aging population. Despite rapid advancements in mechanistic research and drug development for these diseases, current treatments often have limited efficacy and notable side effects, necessitating the development of more effective and targeted anti-inflammatory therapies. In recent years, the rapid development of nanotechnology has provided crucial technological support for the prevention, treatment, and detection of inflammation-associated diseases. Various types of nanoparticles (NPs) play significant roles, serving as vaccine vehicles to enhance immunogenicity and as drug carriers to improve targeting and bioavailability. NPs can also directly combat pathogens and inflammation. In addition, nanotechnology has facilitated the development of biosensors for pathogen detection and imaging techniques for inflammatory diseases. This review categorizes and characterizes different types of NPs, summarizes their applications in the prevention, treatment, and detection of infectious and inflammatory diseases. It also discusses the challenges associated with clinical translation in this field and explores the latest developments and prospects. In conclusion, nanotechnology opens up new possibilities for the comprehensive management of infectious and inflammatory diseases.

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Predicting the potential toxicity of the metal oxide nanoparticles using machine learning algorithms

Sayed,

Alshater,

Hassanien

2024

Soft Comput

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Over the years, machine learning (ML) algorithms have proven their ability to make reliable predictions of the toxicity of metal oxide nanoparticles. This paper proposed a predictive ML model of the potential toxicity of metal oxide nanoparticles. A dataset consisting of 79 descriptors including 24 metal oxide nanoparticles (MexOy NPs) and their physicochemical and structural characteristics is adopted. The proposed model comprises of three main phases. The first phase is used to analyze the characteristics of nanoparticles along with their toxicity behavior. In the second phase, the problems associated with the metal oxide nanoparticles dataset are tackled. The first problem namely the class imbalance problem is handled through utilizing synthetic minority over-sampling technique (SMOTE). The second problem namely the outliers is handled through applying a novel feature selection algorithm based on the enhanced binary version of the sine tree-seed algorithm (EBSTSA). The proposed EBSTSA is used to find the relevant features affecting toxicity. The density-based spatial clustering of applications with noise (DBSCAN) is utilized as a tool for identifying outliers in the dataset and for visualizing the impact of the feature selection on the performance of the subsequent classification. Finally, in the third phase, the support vector machine (SVM) supervised machine learning algorithm and k-fold cross-validation method are applied to classify the mode of action of each instance of nanoparticle as toxic or nontoxic. The simulation results showed that the EBSTSA-based feature selection algorithm is reliable and robust across 23 benchmark datasets from the UCI machine learning repository. The results also showed that proposed EBSTSA can effectively find the relevant descriptors for nano-particles. Furthermore, the results demonstrated the efficacy of the proposed ML toxicity prediction model. It is obtained on average 1.02% of error rate, 100% of specificity, 98.87% of sensitivity, and 99.47% of f1-score.

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Predicting the toxicity of nanoparticles using artificial intelligence tools: a systematic review

Cited by 9 publications

References 72 publications

Harmonization Risks and Rewards: Nano-QSAR for Agricultural Nanomaterials

Harmonization Risks and Rewards: Nano-QSAR for Agricultural Nanomaterials

Nanotechnology’s frontier in combatting infectious and inflammatory diseases: prevention and treatment

Predicting the potential toxicity of the metal oxide nanoparticles using machine learning algorithms

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