Wang Dali,Gu Yue,Zheng Min,Zhang Wei,Lin Zhifen,Liu Ying
The determination of the chronic toxicity is time-consumed and costly, so it’s of great interest to predict the chronic toxicity based on acute data. Current methods include the acute to chronic ratios (ACRs) and the QSTR models, both of which have some usage limitations. In this paper, the acute and chronic mixture toxicity of three types of antibiotics, namely sulfonamides, sulfonamide potentiators and tetracyclines, were determined by a bioluminescence inhibition test. A novel QSTR model was developed for predicting the chronic mixture toxicity using the acute data and docking-based descriptors. This model revealed a complex relationship between the acute and chronic toxicity, i.e. a linear correlation between the acute and chronic lg(−lgEC50)s, rather than the simple EC 50 s or −lgEC 50 s. In particular, the interaction energies (E bind ) of the chemicals with luciferase and LitR in the bacterial quorum sensing systems were introduced to represent their acute and chronic actions, respectively, regardless of their defined toxic mechanisms. Therefore, the present QSTR model can apply to the chemicals with distinct toxic mechanisms, as well as those with undefined mechanism. This study provides a novel idea for the acute to chronic toxicity extrapolation, which may benefit the environmental risk assessment on the pollutants.
In this study, 38 organic compounds were used as lubricants in the GCr15 steel/polyimide (PI) friction system to systematically compare the influence mechanism of lubricant molecular structures on the friction performance of the system. Quantitative structure tribo-ability relationship (QSTR) models linking structural descriptors with coefficients of friction were established using random forest (RF) and extreme gradient boosting (XGBoost) algorithms, respectively. Based on comparisons of performance metrics such as fitting degree and generalization ability, the QSTR model developed by the RF algorithm was selected to quantitatively analyze the nonlinear influence mechanism of lubricant molecular structures on coefficients of friction. The study preliminarily reveals that the ability of lubricant molecules to form hydrogen bonds with PI macromolecules, the strength of van der Waals forces, and the influence of molecular geometric conformation on the effective interfacial interaction distance collectively constitute the key factors affecting the friction performance of the system.
危险品化学品经营许可证（带存储）