TY - JOUR
T1 - AQUA Tox: A web tool for predicting aquatic toxicity in rotifer species using intrinsic explainable models
AU - Dieguez Santana, Karel
AU - Casanola-Martin, Gerardo M.
AU - Torres-Gutiérrez, Roldán
AU - Rasulev, Bakhtiyor
AU - González-Díaz , Humberto
PY - 2025/3/25
Y1 - 2025/3/25
N2 - The widespread use of chemicals in various industries, including agriculture, cosmetics, pharmaceuticals, and textiles, poses significant environmental risks, particularly in aquatic ecosystems. This study focuses on the toxicity of organic compounds on two rotifer species, Brachionus calyciflorus and Brachionus plicatilis, widely used as bioindicators in ecotoxicology. A database of toxicity data (LC50) was compiled and QSAR/QSTR models were developed to predict chemical toxicity in both freshwater (FW) and saltwater (SW) environments. Using molecular descriptors, the study identified critical factors influencing toxicity, such as hydrophobicity and the presence of chlorine atoms. The models demonstrated strong predictive performance, with R² values exceeding 70 % for both FW and SW conditions. Key descriptors influencing toxicity included hydrophobicity and chlorine content. The models demonstrated strong predictive performance, with R² values exceeding 70 %. A user-friendly web application was developed, enabling the scientific community to assess the aquatic toxicity of chemicals. This tool aids in the design of safer, more sustainable substances, facilitating regulatory compliance and minimizing environmental impacts. The findings highlight the importance of combining computational methods with
technological applications for environmental protection.
AB - The widespread use of chemicals in various industries, including agriculture, cosmetics, pharmaceuticals, and textiles, poses significant environmental risks, particularly in aquatic ecosystems. This study focuses on the toxicity of organic compounds on two rotifer species, Brachionus calyciflorus and Brachionus plicatilis, widely used as bioindicators in ecotoxicology. A database of toxicity data (LC50) was compiled and QSAR/QSTR models were developed to predict chemical toxicity in both freshwater (FW) and saltwater (SW) environments. Using molecular descriptors, the study identified critical factors influencing toxicity, such as hydrophobicity and the presence of chlorine atoms. The models demonstrated strong predictive performance, with R² values exceeding 70 % for both FW and SW conditions. Key descriptors influencing toxicity included hydrophobicity and chlorine content. The models demonstrated strong predictive performance, with R² values exceeding 70 %. A user-friendly web application was developed, enabling the scientific community to assess the aquatic toxicity of chemicals. This tool aids in the design of safer, more sustainable substances, facilitating regulatory compliance and minimizing environmental impacts. The findings highlight the importance of combining computational methods with
technological applications for environmental protection.
U2 - 10.1016/j.jhazmat.2025.138050
DO - 10.1016/j.jhazmat.2025.138050
M3 - Artículo
SN - 0304-3894
VL - 492
JO - Journal of Hazardous Materials
JF - Journal of Hazardous Materials
IS - 138050
ER -