Drug Theoretics and Cheminformatics Laboratory, Department of Pharmaceutical Technology, Jadavpur University, Kolkata-700032, India.
Drug Theoretics and Cheminformatics Laboratory, Department of Pharmaceutical Technology, Jadavpur University, Kolkata-700032, India.
Aquat Toxicol. 2023 Apr;257:106429. doi: 10.1016/j.aquatox.2023.106429. Epub 2023 Feb 25.
Polychlorinated naphthalenes (PCNs) are produced from a variety of industrial sources, and they reach the aquatic ecosystems by the dry-wet deposition from the atmosphere and also by the drainage from the land surfaces. Then the PCNs can be transmitted through the food chain to humans and show toxic effects on different aquatic animals as well as humans. Considering this scenario, it is an obligatory task to explore the toxicity data of PCNs more deeply for the species of an aquatic ecosystem (green algae-Daphnia magna-fish), and to extrapolate those data for humans. But the toxicity data for different aquatic species are quite limited. The laboratory experimentations are complicated and ethically troublesome to fill toxicity data gaps; therefore, different in silico methods (e.g., QSAR, quantitative read-across predictions) are emerging as crucial ways to fill the data gaps and hazard assessments. In the present study, we developed individual toxicity models as well as interspecies models from the 75 PCN toxicity data against three aquatic species (green algae-Daphnia magna-fish) by employing easily interpretable 2D descriptors; these models were validated rigorously employing different globally accepted internal and external validation metrics. Then we interpreted the modelled descriptors mechanistically with the endpoint values for better understanding. And finally, we endeavored to improve the prediction quality in terms of external validation metrics by employing a novel quantitative read-across approach by pooling the descriptors from the developed individual QSAR models.
多氯萘（PCNs）由多种工业来源产生，通过大气干湿沉降和陆地表面排水进入水生生态系统。然后，PCNs 可以通过食物链传递给人类，并对不同的水生动物以及人类产生毒性作用。考虑到这种情况，对于水生生态系统（绿藻-大型溞-鱼）的物种，深入探索 PCNs 的毒性数据并将这些数据外推到人类身上是一项必要的任务。但是，不同水生物种的毒性数据非常有限。实验室实验由于毒性数据的填补较为复杂且存在伦理问题，因此不同的计算方法（例如 QSAR、定量结构活性关系预测）正成为填补数据空白和进行危害评估的重要手段。在本研究中，我们利用易于解释的 2D 描述符，从针对三种水生物种（绿藻-大型溞-鱼）的 75 种 PCN 毒性数据中开发了个体毒性模型和种间模型；我们采用了不同的全球公认的内部和外部验证指标，对这些模型进行了严格的验证。然后，我们对模型描述符进行了机制解释，以便更好地理解终点值。最后，我们通过从开发的个体 QSAR 模型中汇集描述符，采用一种新的定量结构活性关系预测方法，努力提高外部验证指标的预测质量。
