Details

Autor(en) / Beteiligte

• Jiang, Jie-Ru
• Cai, Wen-Xi
• Chen, Zhi-Feng
• Liao, Xiao-Liang
• Cai, Zongwei

Titel

Prediction of acute toxicity for Chlorella vulgaris caused by tire wear particle-derived compounds using quantitative structure-activity relationship models

Ist Teil von

• Water research (Oxford), 2024-06, Vol.256, p.121643-121643, Article 121643

Ort / Verlag

England: Elsevier Ltd

Erscheinungsjahr

2024

Quelle

MEDLINE

Beschreibungen/Notizen

• •EC50 values for 21 TWP-derived compounds on C. vulgaris growth were 0.04‒8.60 mg/L.•A QSAR model, employing GA and MLR, was constructed with 112 compounds.•The consensus and q-RASAR models were developed to enhance prediction reliability.•The q-RASAR model was selected to predict the toxicity of 128 emerging compounds.•Predicted higher toxicity of diphenylamines linked to SpMax2_Bhm and LogBCF. Tire wear particles (TWPs) enter aquatic ecosystems through various pathways, such as rainwater and urban runoff. Additives in TWPs can harm aquatic organisms in these ecosystems. Therefore, it is essential to investigate their toxicity to aquatic organisms. In our study, we initially recorded the median effective concentrations of 21 TWP-derived compounds on Chlorella vulgaris growth, ranging from 0.04 to 8.60 mg/L. Subsequently, through an extensive review of the literature, we incorporated 112 compounds with specific toxicity endpoints to construct the QSAR model using genetic algorithm and multiple linear regression techniques, followed by the construction of the consensus model and the quantitative read-across structure-activity relationship (q-RASAR) model. Meanwhile, we employed rigorous internal and external validation measures to assess the performance of the model. The results indicated that the developed q-RASAR model exhibited strong adaptation, robustness, and reliable prediction, with q-RASAR indicators of Q2LOO = 0.7673, R2tr = 0.8079, R2test = 0.8610, Q2Fn = 0.8285−0.8614, and CCCtest = 0.9222. Based on an external dataset containing 128 emerging TWP-derived compounds, the model's applicability domain coverage was 90.6 %. The q-RASAR model predicted that the structure of diphenylamine was associated with higher toxicity, possibly liked to the SpMax2_Bhm and LogBCF descriptors. The established model reliably provides prediction and fills a critical data gap. These findings highlight the potential risks posed by emerging TWP-derived compounds to aquatic organisms. [Display omitted]