Jouanneau Sulivan, Thouand Gerald
Nantes Université, Oniris, Centre National de la Recherche Scientifique, Génie des Procédés-Environnement-Agroalimentaire, Unité Mixte de Recherche 6144, F-85000 La Roche sur Yon, France.
Sensors (Basel). 2025 May 20;25(10):3215. doi: 10.3390/s25103215.
Evaluating the impact of pollutants on ecosystems and human health is crucial. To achieve this, a wide range of bioassays, using organisms of different trophic levels, are available. Extrapolating the results of these bioassays to real environmental conditions remains a major challenge. This study addresses this challenge by aiming to develop an algorithm capable of predicting the effect of environmental conditions on the impact of a toxicant, pentachlorophenol (PCP). Three abiotic factors were considered: pH, temperature, and conductivity. In the absence of the toxicant, the activity of is influenced only by pH and temperature. However, exposed to PCP, the sensitivity of the bacteria was affected by these three factors. From these data, a predictive model was established to assess the intensity of the toxic effect induced by PCP. This model was validated using a validation dataset and demonstrated a strong correlation between the experimental and predicted values (r ≈ 0.9). Thus, this approach enables the effective prediction of PCP's effects by accounting for environmental variations. This proof of concept constitutes a potential alternative, complementary to conventional models like BLMs (focused on water chemistry for metals) and QSARs (linking structure to intrinsic toxicity), which often overlook the complexities of real-world environmental conditions.
评估污染物对生态系统和人类健康的影响至关重要。为此，可以使用多种生物测定方法，这些方法使用不同营养级别的生物。将这些生物测定的结果外推到实际环境条件仍然是一项重大挑战。本研究旨在开发一种算法，能够预测环境条件对有毒物质五氯苯酚（PCP）影响的作用，从而应对这一挑战。研究考虑了三个非生物因素：pH值、温度和电导率。在没有有毒物质的情况下， 的活性仅受pH值和温度的影响。然而，暴露于五氯苯酚时，细菌的敏感性受到这三个因素的影响。根据这些数据，建立了一个预测模型来评估五氯苯酚诱导的毒性作用强度。该模型使用验证数据集进行了验证，结果表明实验值和预测值之间存在很强的相关性（r≈0.9）。因此，这种方法能够通过考虑环境变化有效地预测五氯苯酚的影响。这一概念验证构成了一种潜在的替代方法，它是对传统模型（如侧重于金属水化学的生物配体模型（BLMs）和将结构与内在毒性联系起来的定量构效关系模型（QSARs））的补充，传统模型往往忽视了现实世界环境条件的复杂性。
