Developing random forest based QSAR models for predicting the mixture toxicity of TiO2 based nano-mixtures to Daphnia magna

Trinh, Tung X.; Seo, Myungwon; Yoon, Tae Hyun; Kim, Jongwoon

doi:10.1016/j.impact.2022.100383

Cited by 14 publications

(12 citation statements)

References 57 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The weighted descriptor approach in eq represents a preferred approach to developing descriptors for chemical mixtures ( D mix ). , Then, a generic QSAR model for the prediction of activities of chemical mixtures can be expressed by eq where A mix represents the activity of the chemical mixtures to be modeled, x i represents the molar fraction of a component ( i ) in the mixtures, D 1 and D 2 are the structural descriptors used for each component, and a , b , and z are the coefficients of the regression function. A QSAR approach with mixture descriptors was implemented in a user-friendly application for assessing the aquatic toxicity of nanomixtures containing nTiO 2 and one of the selected inorganic/organic compounds …”

Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

“…A QSAR approach with mixture descriptors was implemented in a user-friendly application for assessing the aquatic toxicity of nanomixtures containing nTiO 2 and one of the selected inorganic/organic compounds. 87 Assessing and predicting the toxicity of mixtures of multiple ENPs is facing unprecedented opportunities and challenges.…”

Section: Assessment and Predictionmentioning

confidence: 99%

See 2 more Smart Citations

Review and Prospects on the Ecotoxicity of Mixtures of Nanoparticles and Hybrid Nanomaterials

Zhang

Wang

Peijnenburg

et al. 2022

Environ. Sci. Technol.

View full text Add to dashboard Cite

The rapid development of nanomaterials (NMs) and the emergence of new multicomponent NMs will inevitably lead to simultaneous exposure of organisms to multiple engineered nanoparticles (ENPs) at varying exposure levels. Understanding the joint impacts of multiple ENPs and predicting the toxicity of mixtures of ENPs are therefore evidently of importance. We reviewed the toxicity of mixtures of ENPs to a variety of different species, covering algae, bacteria, daphnia, fish, fungi, insects, and plants. Most studies used the independent-action (IA)-based model to assess the type of joint effects. Using co-occurrence networks, it was revealed that 53% of the cases with specific joint response showed antagonistic, 25% synergistic, and 22% additive effects. The combination of nCuO and nZnO exhibited the strongest interactions in each type of joint interaction. Compared with other species, plants exposed to multiple ENPs were more likely to experience antagonistic effects. The main factors influencing the joint response type of the mixtures were (1) the chemical composition of individual components in mixtures, (2) the stability of suspensions of mixed ENPs, (3) the type and trophic level of the individual organisms tested, (4) the biological level of organization (population, communities, ecosystems), (5) the exposure concentrations and time, (6) the endpoint of toxicity, and (7) the abiotic field conditions (e.g., pH, ionic strength, natural organic matter). This knowledge is critical in developing efficient strategies for the assessment of the hazards induced by combined exposure to multiple ENPs in complex environments. In addition, this knowledge of the joint effects of multiple ENPs assists in the effective prediction of hybrid NMs.

show abstract

Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Section: Assessment and Predictionmentioning

confidence: 99%

See 1 more Smart Citation

Review and Prospects on the Ecotoxicity of Mixtures of Nanoparticles and Hybrid Nanomaterials

Zhang

Wang

Peijnenburg

et al. 2022

Environ. Sci. Technol.

View full text Add to dashboard Cite

show abstract

“…7d). Nano-mixture QSAR models in previous studies 4,7,8,11,14 were limited to only TiO 2 -based nano-mixtures and limited in nano-size. The QSAR models in the present study have exhibited an extended applicability domain in which toxicity is based on TiO 2 , SiO 2 , and ZnO of various sizes up to 140 nm.…”

Section: Model Evaluationmentioning

confidence: 99%

“…Several nano-QSAR models, based on metallic and metal oxide nanoparticles, have been developed for predicting cytotoxicity. 4,7,8,11–13 Furthermore, Trinh et al (2022) 14 developed QSAR models to predict the mixture toxicity of TiO 2 -based nano-mixtures to Daphnia magna , and the descriptors were calculated using the semi-empirical parametric method seven (PM7) based on the TiO 2 anatase nanocluster (0.75 × 0.75 × 1.35 nm 3 ). Although the descriptors mentioned above are widely used in nano-QSAR studies, there is a limitation regarding their applicable size.…”

Section: Introductionmentioning

confidence: 99%

Development of a nano-QSAR model for predicting the toxicity of nano-metal oxide mixtures to Aliivibrio fischeri

Nam

Moon

et al. 2023

Environ. Sci.: Nano

Self Cite

View full text Add to dashboard Cite

show abstract

Computational modeling approaches for developing a synergistic effect prediction model of estrogen agonistic activity

Seo,

Choi,

Park

et al. 2024

Chemosphere

View full text Add to dashboard Cite

Developing random forest based QSAR models for predicting the mixture toxicity of TiO2 based nano-mixtures to Daphnia magna

Cited by 14 publications

References 57 publications

Review and Prospects on the Ecotoxicity of Mixtures of Nanoparticles and Hybrid Nanomaterials

Review and Prospects on the Ecotoxicity of Mixtures of Nanoparticles and Hybrid Nanomaterials

Development of a nano-QSAR model for predicting the toxicity of nano-metal oxide mixtures to Aliivibrio fischeri

Computational modeling approaches for developing a synergistic effect prediction model of estrogen agonistic activity

Contact Info

Product

Resources

About