{"title":"定量读取-交叉结构-属性关系(q-RASPR):一种估算不同类别工业化学品在水生生物体内生物累积潜力的新方法。","authors":"Prodipta Bhattacharyya, Pabitra Samanta, Ankur Kumar, Shubha Das, Probir Kumar Ojha","doi":"10.1039/d4em00374h","DOIUrl":null,"url":null,"abstract":"<p><p>The Bioconcentration Factor (BCF) is used to evaluate the bioaccumulation potential of chemical substances in reference organisms, and it directly correlates with ecotoxicity. Traditional <i>in vivo</i> BCF estimation methods are costly, time-consuming, and involve animal sacrifice. Many <i>in silico</i> technologies are used to avoid the problems associated with <i>in vivo</i> testing. This study aims to develop a quantitative read across structure-property relationship (q-RASPR) model using a structurally diverse dataset consisting of 1303 compounds by combining quantitative structure-property relationship (QSPR) and read-across (RA) algorithms. The model incorporates simple, interpretable, and reproducible 2D molecular descriptors along with RASAR descriptors. The PLS-based q-RASPR model demonstrated robust performance with internal validation metrics (<i>R</i><sup>2</sup> = 0.727 and <i>Q</i><sup>2</sup><sub>(LOO)</sub> = 0.723) and external validation metrics (<i>Q</i><sup>2</sup><sub>F1</sub> = 0.739, <i>Q</i><sup>2</sup><sub>F2</sub> = 0.739, and CCC = 0.858). These results indicate that the q-RASPR model is statistically superior to the corresponding QSPR model. Furthermore, screening of 1694 compounds from the Pesticide Properties Database (PPDB) was performed using the PLS-based q-RASPR model for assessing the eco-toxicological bioaccumulative potential of various compounds, ensuring the external predictability of the developed model and confirming the real-world application of the developed model. This model offers a reliable tool for predicting the BCF of new or untested compounds, thereby helping to develop safe and environment-friendly chemicals.</p>","PeriodicalId":74,"journal":{"name":"Environmental Science: Processes & Impacts","volume":null,"pages":null},"PeriodicalIF":4.3000,"publicationDate":"2024-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Quantitative read-across structure-property relationship (q-RASPR): a novel approach to estimate the bioaccumulative potential for diverse classes of industrial chemicals in aquatic organisms.\",\"authors\":\"Prodipta Bhattacharyya, Pabitra Samanta, Ankur Kumar, Shubha Das, Probir Kumar Ojha\",\"doi\":\"10.1039/d4em00374h\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><p>The Bioconcentration Factor (BCF) is used to evaluate the bioaccumulation potential of chemical substances in reference organisms, and it directly correlates with ecotoxicity. Traditional <i>in vivo</i> BCF estimation methods are costly, time-consuming, and involve animal sacrifice. Many <i>in silico</i> technologies are used to avoid the problems associated with <i>in vivo</i> testing. This study aims to develop a quantitative read across structure-property relationship (q-RASPR) model using a structurally diverse dataset consisting of 1303 compounds by combining quantitative structure-property relationship (QSPR) and read-across (RA) algorithms. The model incorporates simple, interpretable, and reproducible 2D molecular descriptors along with RASAR descriptors. The PLS-based q-RASPR model demonstrated robust performance with internal validation metrics (<i>R</i><sup>2</sup> = 0.727 and <i>Q</i><sup>2</sup><sub>(LOO)</sub> = 0.723) and external validation metrics (<i>Q</i><sup>2</sup><sub>F1</sub> = 0.739, <i>Q</i><sup>2</sup><sub>F2</sub> = 0.739, and CCC = 0.858). These results indicate that the q-RASPR model is statistically superior to the corresponding QSPR model. Furthermore, screening of 1694 compounds from the Pesticide Properties Database (PPDB) was performed using the PLS-based q-RASPR model for assessing the eco-toxicological bioaccumulative potential of various compounds, ensuring the external predictability of the developed model and confirming the real-world application of the developed model. This model offers a reliable tool for predicting the BCF of new or untested compounds, thereby helping to develop safe and environment-friendly chemicals.</p>\",\"PeriodicalId\":74,\"journal\":{\"name\":\"Environmental Science: Processes & Impacts\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":4.3000,\"publicationDate\":\"2024-11-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Environmental Science: Processes & Impacts\",\"FirstCategoryId\":\"93\",\"ListUrlMain\":\"https://doi.org/10.1039/d4em00374h\",\"RegionNum\":3,\"RegionCategory\":\"环境科学与生态学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"CHEMISTRY, ANALYTICAL\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Environmental Science: Processes & Impacts","FirstCategoryId":"93","ListUrlMain":"https://doi.org/10.1039/d4em00374h","RegionNum":3,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"CHEMISTRY, ANALYTICAL","Score":null,"Total":0}
Quantitative read-across structure-property relationship (q-RASPR): a novel approach to estimate the bioaccumulative potential for diverse classes of industrial chemicals in aquatic organisms.
The Bioconcentration Factor (BCF) is used to evaluate the bioaccumulation potential of chemical substances in reference organisms, and it directly correlates with ecotoxicity. Traditional in vivo BCF estimation methods are costly, time-consuming, and involve animal sacrifice. Many in silico technologies are used to avoid the problems associated with in vivo testing. This study aims to develop a quantitative read across structure-property relationship (q-RASPR) model using a structurally diverse dataset consisting of 1303 compounds by combining quantitative structure-property relationship (QSPR) and read-across (RA) algorithms. The model incorporates simple, interpretable, and reproducible 2D molecular descriptors along with RASAR descriptors. The PLS-based q-RASPR model demonstrated robust performance with internal validation metrics (R2 = 0.727 and Q2(LOO) = 0.723) and external validation metrics (Q2F1 = 0.739, Q2F2 = 0.739, and CCC = 0.858). These results indicate that the q-RASPR model is statistically superior to the corresponding QSPR model. Furthermore, screening of 1694 compounds from the Pesticide Properties Database (PPDB) was performed using the PLS-based q-RASPR model for assessing the eco-toxicological bioaccumulative potential of various compounds, ensuring the external predictability of the developed model and confirming the real-world application of the developed model. This model offers a reliable tool for predicting the BCF of new or untested compounds, thereby helping to develop safe and environment-friendly chemicals.
期刊介绍:
Environmental Science: Processes & Impacts publishes high quality papers in all areas of the environmental chemical sciences, including chemistry of the air, water, soil and sediment. We welcome studies on the environmental fate and effects of anthropogenic and naturally occurring contaminants, both chemical and microbiological, as well as related natural element cycling processes.