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<p>This study presents a quantitative read-across structure-property relationship (q-RASPR) approach that integrates the chemical similarity information used in read-across with traditional quantitative structure-property relationship (QSPR) models. This novel framework is applied to predict the physicochemical properties and environmental behaviors of persistent organic pollutants, specifically polychlorinated biphenyls (PCBs) and polybrominated diphenyl ethers (PBDEs). By utilizing a curated dataset and incorporating similarity-based descriptors, the q-RASPR approach improves the accuracy of predictions, particularly for compounds with limited experimental data. The models’ performances were assessed using internal cross-validation and external testing, demonstrating significant enhancements in predictive reliability compared to conventional QSPR models. The findings highlight the potential of q-RASPR for use in regulatory risk assessments and optimizing remediation strategies by providing more precise insights into the environmental fate of these contaminants.</p>

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The q-RASPR approach for predicting the property and fate of persistent organic pollutants

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年1月8日

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  • Chia Ming Chang,
  • Arkaprava Banerjee,
  • Vinay Kumar,
  • Kunal Roy,
  • Emilio Benfenati

摘要

This study presents a quantitative read-across structure-property relationship (q-RASPR) approach that integrates the chemical similarity information used in read-across with traditional quantitative structure-property relationship (QSPR) models. This novel framework is applied to predict the physicochemical properties and environmental behaviors of persistent organic pollutants, specifically polychlorinated biphenyls (PCBs) and polybrominated diphenyl ethers (PBDEs). By utilizing a curated dataset and incorporating similarity-based descriptors, the q-RASPR approach improves the accuracy of predictions, particularly for compounds with limited experimental data. The models’ performances were assessed using internal cross-validation and external testing, demonstrating significant enhancements in predictive reliability compared to conventional QSPR models. The findings highlight the potential of q-RASPR for use in regulatory risk assessments and optimizing remediation strategies by providing more precise insights into the environmental fate of these contaminants.