Computational ecotoxicology: Simultaneous prediction of ecotoxic effects of nanoparticles under different experimental conditions

Abstract

Nanotechnology has brought great advances to many fields of modern science. A manifold of applications of nanoparticles have been found due to their interesting optical, electrical, and biological/chemical properties. However, the potential toxic effects of nanoparticles to different ecosystems are of special concern nowadays. Despite the efforts of the scientific community, the mechanisms of toxicity of nanoparticles are still poorly understood. Quantitative-structure activity/toxicity relationships (QSAR/QSTR) models have just started being useful computational tools for the assessment of toxic effects of nanomaterials. But most QSAR/QSTR models have been applied so far to predict ecotoxicity against only one organism/bio-indicator such as Daphnia magna. This prevents having a deeper knowledge about the real ecotoxic effects of nanoparticles, and consequently, there is no possibility to establish an efficient risk assessment of nanomaterials in the environment. In this work, a perturbation model for nano-QSAR problems is introduced with the aim of simultaneously predicting the ecotoxicity of different nanoparticles against several assay organisms (bio-indicators), by considering also multiple measures of ecotoxicity, as well as the chemical compositions, sizes, conditions under which the sizes were measured, shapes, and the time during which the diverse assay organisms were exposed to nanoparticles. The QSAR-perturbation model was derived from a database containing 5520 cases (nanoparticle-nanoparticle pairs), and it was shown to exhibit accuracies of ca. 99% in both training and prediction sets. In order to demonstrate the practical applicability of our model, three different nickel-based nanoparticles (Ni) with experimental values reported in the literature were predicted. The predictions were found to be in very good agreement with the experimental evidences, confirming that Ni-nanoparticles are not ecotoxic when compared with other nanoparticles. The results of this study thus provide a single valuable tool toward an efficient prediction of the ecotoxicity of nanoparticles under multiple experimental conditions.

Nano-(Q)SAR for Cytotoxicity Prediction of Engineered Nanomaterials

“…Ecotoxicity of three nickel-based nanoparticles was predicted. The predictions were found to be in very good agreement with the experimental evidence, confirming that Ni-nanoparticles are not ecotoxic when compared with other NPs [67]. Further, a unified in silico machine learning model based on artificial neural networks was developed by Concu and co-authors [68]; the model was aimed to simultaneously predict general toxicity profiles of NPs under diverse experimental conditions.…”

A Mechanism-based QSTR Model for Acute to Chronic Toxicity Extrapolation: A Case Study of Antibiotics on Luminous Bacteria

“…For this reason, the E T in Equation 2 will not be able to discriminate the toxic effect of a defined drug when the target protein varied. In this sense, the moving average approach 27–30 was employed to generate new descriptors ΔE ij , as follows:In this equation, E i denotes the interaction energy of drug i with the protein, j denotes the target (e.g., DHPS and DHF), and (E ij ) avg is the average value of Ebind for all drugs with the same j. Using this new descriptor, another QSAR model for lgK c /K a was obtained with high R 2 (0.803) as below:n = 15, R 2  = 0.803, RMSE = 0.056, F = 24.490, P = 0.000,  = 0.715, RMSE loo  = 0.060,  = 0.709, RMSE lto  = 0.061.…”

Toxicity of engineered metal oxide nanomaterials mediated by nano–bio–eco–interactions: a review and perspective

“…Theoretical/ computational studies are increasingly involved in nanotoxicological researches, to circumvent the problems associated with field research, i.e., the limitations of resources, and reproducibility and reliability of first-hand data collected by experiments. 142 Oftentimes, nanotoxicological profiles vary not only on biological models, but also on biological endpoints. 120 Herein, we mainly discuss the development of QSAR in nanotoxicology.…”