Modelling quantitative structure activity–activity relationships (QSAARs): auto-pass-pass, a new approach to fill data gaps in environmental risk assessment under the REACH regulation

Reviewing the toxicological literature for over the past decades, the key elements of QSAR modelling have been the mechanisms of toxic action and chemical classes. As a result, it is often hard to design an acceptable single model for a particular endpoint without clustering compounds. The main aim here was to develop a Pass-Pass Quantitative Structure-Activity-Activity Relationship (PP QSAAR) model for direct prediction of the toxicity of a larger set of compounds, combing the application of an already predicted model for another species, and molecular descriptors. We investigated a large acute toxicity data set of five aquatic organisms, fish, <i>Daphnia magna</i>, and algae from the VEGA-Hub, as well as <i>Tetrahymena pyriformis</i> and <i>Vibrio fischeri</i>. The statistical quality of the models encouraged us to consider this alternative for the prediction of toxicity using interspecies extrapolation QSAAR models without regard to the toxicity mechanism or chemical class. In the case of algae, the use of activity values from a second species did not improve the models. This can be attributed to the weak interspecies relationships, due to different aquatic toxicity mechanisms in species.

回顾过去数十年的毒理学文献可知，定量构效关系（QSAR）建模的核心要素始终为毒性作用机制与化学类别。正因如此，若不对化合物进行聚类，往往难以针对特定毒性终点构建出合格的单一模型。本研究的核心目标是构建双传递定量结构-活性-活性关系（Pass-Pass Quantitative Structure-Activity-Activity Relationship, PP QSAAR）模型，以直接预测更大规模化合物集合的毒性，该模型结合了已针对另一物种构建的预测模型的应用与分子描述符的使用。本研究从VEGA-Hub获取了涵盖5种水生生物的大型急性毒性数据集，受试生物包括鱼类、大型溞（<i>Daphnia magna</i>）、藻类、梨形四膜虫（<i>Tetrahymena pyriformis</i>）以及费氏弧菌（<i>Vibrio fischeri</i>）。所构建模型的统计性能优异，这促使我们考虑采用这种无需考虑毒性作用机制或化学类别的种间外推PP QSAAR模型开展毒性预测。针对藻类而言，使用第二物种的活性值并未对模型性能产生提升效果，这可归因于不同水生生物的毒性作用机制存在差异，导致物种间的相关性较弱。