TY - JOUR
T1 - Unveiling first report on in silico modeling of aquatic toxicity of organic chemicals to Labeo rohita (Rohu) employing QSAR and q-RASAR
AU - Gallagher, Andrea
AU - Kar, Supratik
N1 - Publisher Copyright:
© 2023 Elsevier Ltd
PY - 2024/2
Y1 - 2024/2
N2 - Labeo rohita, a fish species within the Carp family, holds significant dietary and aquacultural importance in South Asian countries. However, the habitats of L. rohita often face exposure to various harmful pesticides and organic compounds originating from industrial and agricultural runoff. It is challenging to individually investigate the effects of each potentially harmful compound. In such cases, in silico techniques like Quantitative Structure-Activity Relationship (QSAR) and quantitative Read-Across Structure-Activity Relationship (q-RASAR) can be employed to construct algorithmic models capable of simultaneously assessing the toxicity of numerous compounds. We utilized the US EPA's ToxValDB database to curate data regarding acute median lethal concentration (LC50) toxicity for L. rohita. The experimental variables included study type (mortality), study duration (ranging from 0.25 h to 4 h), exposure route (static, flowthrough, and renewal), exposure method (drinking water), and types of chemicals (industrial chemicals and pharmaceuticals). Using this dataset, we developed regression-based QSAR and q-RASAR models to predict chemical toxicity to L. rohita based on chemical descriptors. The key descriptors for predicting the toxicity of L. rohita in the regression-based QSAR model include F05[S–Cl], SpMax_EA(ri), s4_relPathLength_2, and SpDiam_AEA(ed). These descriptors can be employed to estimate the toxicity of untested compounds and aid in the development of compounds with lower toxicity based on the presence or absence of these descriptors. Both the QSAR and q-RASAR models serve as valuable tools for understanding the chemicals' structural features responsible for toxicity and for filling gaps in aquatic toxicity data by predicting the toxicity of newly untested compounds in relation to L. rohita. Finally, the developed best model was employed to predict 297 external chemicals, the most toxic substances to L. rohita were identified as cyhalothrin, isobornyl thiocyanatoacetate, and paclobutrzol, while the least toxic ones included ethyl acetate, ethylthiourea, and n-butyric acid.
AB - Labeo rohita, a fish species within the Carp family, holds significant dietary and aquacultural importance in South Asian countries. However, the habitats of L. rohita often face exposure to various harmful pesticides and organic compounds originating from industrial and agricultural runoff. It is challenging to individually investigate the effects of each potentially harmful compound. In such cases, in silico techniques like Quantitative Structure-Activity Relationship (QSAR) and quantitative Read-Across Structure-Activity Relationship (q-RASAR) can be employed to construct algorithmic models capable of simultaneously assessing the toxicity of numerous compounds. We utilized the US EPA's ToxValDB database to curate data regarding acute median lethal concentration (LC50) toxicity for L. rohita. The experimental variables included study type (mortality), study duration (ranging from 0.25 h to 4 h), exposure route (static, flowthrough, and renewal), exposure method (drinking water), and types of chemicals (industrial chemicals and pharmaceuticals). Using this dataset, we developed regression-based QSAR and q-RASAR models to predict chemical toxicity to L. rohita based on chemical descriptors. The key descriptors for predicting the toxicity of L. rohita in the regression-based QSAR model include F05[S–Cl], SpMax_EA(ri), s4_relPathLength_2, and SpDiam_AEA(ed). These descriptors can be employed to estimate the toxicity of untested compounds and aid in the development of compounds with lower toxicity based on the presence or absence of these descriptors. Both the QSAR and q-RASAR models serve as valuable tools for understanding the chemicals' structural features responsible for toxicity and for filling gaps in aquatic toxicity data by predicting the toxicity of newly untested compounds in relation to L. rohita. Finally, the developed best model was employed to predict 297 external chemicals, the most toxic substances to L. rohita were identified as cyhalothrin, isobornyl thiocyanatoacetate, and paclobutrzol, while the least toxic ones included ethyl acetate, ethylthiourea, and n-butyric acid.
KW - Aquatic toxicity
KW - Labeo rohita
KW - LC
KW - q-RASAR
KW - QSAR
KW - Risk assessment
KW - USEPA
UR - http://www.scopus.com/inward/record.url?scp=85179095004&partnerID=8YFLogxK
U2 - 10.1016/j.chemosphere.2023.140810
DO - 10.1016/j.chemosphere.2023.140810
M3 - Article
C2 - 38029938
AN - SCOPUS:85179095004
SN - 0045-6535
VL - 349
JO - Chemosphere
JF - Chemosphere
M1 - 140810
ER -