Prediction of Influence of Environmental Factors on the Toxicity of Pentachlorophenol on E. coli-Based Bioassays.

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 Escherichia coli 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(2) ≈ 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.