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

Abstract

In this study, we developed nano-mixture QSAR models using molecular dynamic (MD) descriptors to predict the toxicity of MONPs to A. fischeri.

The nanosafety assessment of ENMs under a dermal exposure scenario: from key molecular events to in silico modeling tools

“…This involves the identification of a subset of representative descriptors describing the essential information of the original dataset while eliminating the missing, constant, and highly correlated variables. 215 A variety of algorithms, e.g., genetic algorithm, recursive feature elimination, principal component analysis, partial least squares, and genetic function approximation, can be employed to optimize the variable selection process. 159,215 In Table 5, both experimental and theoretical descriptors have been used for nano-QSAR modeling of the dermal contact ENMs.…”

“…215 A variety of algorithms, e.g., genetic algorithm, recursive feature elimination, principal component analysis, partial least squares, and genetic function approximation, can be employed to optimize the variable selection process. 159,215 In Table 5, both experimental and theoretical descriptors have been used for nano-QSAR modeling of the dermal contact ENMs. For instance, dissolution and redox characteristics of MeOx NPs, e.g., the enthalpy of a standard formation of metal oxide nanocluster, the absolute value of Fermi energy, and Mulliken electronegativity were identified as important descriptors for the induction of hydroxyl radical formation and the resultant cytotoxicity in human keratinocytes.…”

“…, genetic algorithm, recursive feature elimination, principal component analysis, partial least squares, and genetic function approximation, can be employed to optimize the variable selection process. 159,215 In Table 5, both experimental and theoretical descriptors have been used for nano-QSAR modeling of the dermal contact ENMs. For instance, dissolution and redox characteristics of MeOx NPs, e.g.…”

A structure–activity approach towards the toxicity assessment of multicomponent metal oxide nanomaterials

“…The descriptors used to predict the MCNM toxic class were computed using the additive mixture approach. 47 Although some works have tested the use of descriptors computed using an independent action approach for the case of nanomaterial or chemical mixtures, 100,101 the additive mixture approach has been the main approach used to build predictive models for nanomaterial mixtures, 66,67,102 as well as for the case of MCNMs. [47][48][49][50]52 Nevertheless, the list of MCNMs has been more limited.…”

Predicting the Time-Dependent Toxicities of Binary Mixtures of Five Antibiotics to Vibrio qinghaiensis sp.-Q67 Based on the QSAR Model

“…20,21 QSAR modeling has proven effective in predicting the toxicity of binary cosmetic and personal care additives (CPCAs) 22 and mixtures of metal oxide nanoparticles. 23 QSAR modeling is commonly employed for quantitative analyses of mixture toxicity in antibiotics. 24−27 However, challenges remain, such as the selection of appropriate descriptors and equations for calculating mixture descriptors.…”