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

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.

Current Strategies in Assessment of Nanotoxicity: Alternatives to In Vivo Animal Testing

“…The computational technique helps to reduce the costs associated with labor, resources, and time in nanotoxicity assessments [ 82 ]. QSAR modeling is an efficient method of predicting the biological activity or toxicity of chemical substances on the basis of mathematical statistics and knowledge of machine learning ( Figure 2 ).…”

Artificial Intelligence and Machine Learning Empower Advanced Biomedical Material Design to Toxicity Prediction

“…The calculation of the quasi‐SMILES model using the Monte Carlo equation provides ample statistical parameters. [ 98 ] In this case, the cytotoxicity calculation endpoint is the percentage of viability of the cells. In another case, i.e., an in vitro ML‐based algorithm, as shown in Figure , it was shown that single‐walled CNTs (SWCNTs) can amend the cellular motility and biological chirality of the cells, an anomaly that can be related to abnormal in vivo fetal development.…”

Artificial Intelligence and Machine Learning in Computational Nanotoxicology: Unlocking and Empowering Nanomedicine

“…Quasi‐SMILES was used to represent the physicochemical properties and experimental conditions: diameter, length, surface area, in vitro toxicity assay, cell line, exposure time, and dose. [ 60 ] The quasi‐SMILES‐based nano‐QSAR model calculation using Monte Carlo method, provided sufficient statistical parameters. In this case, the endpoint of cytotoxicity measurement is the cell viability (%).…”

“…It was also demonstrated that the RF approach is applicable to modeling the cytotoxicity of silica. [ 60 ] Later, better nano‐QSAR models based on quasi‐SMILES were built using CORAL software with high determination coefficients (0.8–0.95). [ 65 ]…”