TY - JOUR
T1 - First report on chemometric modeling of tilapia fish aquatic toxicity to organic chemicals
T2 - Toxicity data gap filling
AU - Yang, Siyun
AU - Kar, Supratik
N1 - Publisher Copyright:
© 2023 Elsevier B.V.
PY - 2024/1/10
Y1 - 2024/1/10
N2 - The Toxic Substances Control Act (TSCA) mandates the Environmental Protection Agency (EPA) to document chemicals entering the US. Due to the vast range of toxicity endpoints, experimental toxicological study for all chemicals is impossible to conduct. To address this, in silico methods like QSAR and read-across are strategically used to prioritize testing for chemicals lacking ecotoxicity data. Aquatic toxicity is one of the most critical endpoints directly related to aquatic species, mainly fish, followed by direct to indirect effects on humans through drinking water and fish as food, respectively. Therefore, we have employed the ToxValDB database to curate acute LC50 toxicity data for three Tilapia species covering two different genera, an ideal species for aquatic toxicity testing. Employing the curated dataset, we have developed multiple robust and predictive QSAR and quantitative read-across structure-activity relationship (q-RASAR) models for Tilapia zillii, Oreochromis niloticus, and Oreochromis mossambicus which helped to understand the toxicological mode of action (MoA) of the modeled chemicals and predict the aquatic toxicity of new untested chemicals followed by toxicity data gap filling. The best three QSAR models showed encouraging statistical quality in terms of determination coefficient R2 (0.94, 0.74, and 0.77), cross-validated leave-one-out Q2 (0.90, 0.67 and 0.70), and predictive capability in terms of R2pred (0.95, 0.77, and 0.74) for T. zillii, O. niloticus, and O. mossambicus datasets, respectively. The developed best mathematical models were used for the prediction of aquatic toxicity in terms of pLC50 for 297 untested organic chemicals across three major Tilapia species ranging from 1.841 to 8.561 M in terms of environmental risk assessment.
AB - The Toxic Substances Control Act (TSCA) mandates the Environmental Protection Agency (EPA) to document chemicals entering the US. Due to the vast range of toxicity endpoints, experimental toxicological study for all chemicals is impossible to conduct. To address this, in silico methods like QSAR and read-across are strategically used to prioritize testing for chemicals lacking ecotoxicity data. Aquatic toxicity is one of the most critical endpoints directly related to aquatic species, mainly fish, followed by direct to indirect effects on humans through drinking water and fish as food, respectively. Therefore, we have employed the ToxValDB database to curate acute LC50 toxicity data for three Tilapia species covering two different genera, an ideal species for aquatic toxicity testing. Employing the curated dataset, we have developed multiple robust and predictive QSAR and quantitative read-across structure-activity relationship (q-RASAR) models for Tilapia zillii, Oreochromis niloticus, and Oreochromis mossambicus which helped to understand the toxicological mode of action (MoA) of the modeled chemicals and predict the aquatic toxicity of new untested chemicals followed by toxicity data gap filling. The best three QSAR models showed encouraging statistical quality in terms of determination coefficient R2 (0.94, 0.74, and 0.77), cross-validated leave-one-out Q2 (0.90, 0.67 and 0.70), and predictive capability in terms of R2pred (0.95, 0.77, and 0.74) for T. zillii, O. niloticus, and O. mossambicus datasets, respectively. The developed best mathematical models were used for the prediction of aquatic toxicity in terms of pLC50 for 297 untested organic chemicals across three major Tilapia species ranging from 1.841 to 8.561 M in terms of environmental risk assessment.
KW - Aquatic toxicity
KW - LC
KW - Q-RASAR
KW - QSAR
KW - Risk assessment
KW - Tilapia
KW - USEPA
UR - http://www.scopus.com/inward/record.url?scp=85175813618&partnerID=8YFLogxK
U2 - 10.1016/j.scitotenv.2023.167991
DO - 10.1016/j.scitotenv.2023.167991
M3 - Article
C2 - 37898216
AN - SCOPUS:85175813618
SN - 0048-9697
VL - 907
JO - Science of the Total Environment
JF - Science of the Total Environment
M1 - 167991
ER -