In this study, Quantitative structure-toxicity relationship (QSTR) models were developed to predict the toxicity of nitrobenzene to the algae (Scenedesmus obliguus). Quantum chemical descriptors computed by PM3 Hamiltonian were used as predictor variables. The cross-validated Q²(cum) value for the optimal QSTR models is 0.867, indicating good predictive capability. The toxicity of nitrobenzenes (pC) was found to be affected by the molecular structure, the heat of formation (ΔH(f)) and dipole moment (μ(z)). Contrary to the μ(z) values of nitrobenzenes, the ΔH(f) values increase with increase in pC values and the energy of the highest occupied molecular orbital. Increasing the largest positive atomic charge on a nitrogen atom and the most positive net atomic charge on a hydrogen atom of the nitrobenzene leads to decrease in pC values. Nitrobenzenes with larger absolute hardness tend to be more stable and less toxic to the algae.
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http://dx.doi.org/10.1016/j.etap.2011.09.003 | DOI Listing |
Toxicol Appl Pharmacol
December 2024
Department of Predictive Toxicology, Korea Institute of Toxicology, 141 Gajeong-ro, Yuseong-gu, Daejeon 34114, Republic of Korea; Department of Human and Environmental Toxicology, University of Science and Technology, Daejeon, Republic of Korea; Department of Environmental Health, Harvard T.H. Chan School of Public Health, Boston, MA, USA. Electronic address:
New approach methods (NAMs) are required to predict human toxicity effectively, particularly due to limitations in conducting in vivo studies. While NAMs have been established for various industries, such as cosmetics, pesticides, and drugs, their applications in natural products (NPs) are lacking. NPs' complexity (multiple ingredients and structural differences from synthetic compounds) complicates NAM development.
View Article and Find Full Text PDFMol Divers
December 2024
QSAR Research Unit On Environmental Chemistry and Ecotoxicology, Department of Theoretical and Applied Sciences (DiSTA), University of Insubria, Varese, Italy.
A bibliometric analysis of the Cheminformatics/QSAR articles published in the present century (2000-2023) is presented based on a SCOPUS search made in October 2024 using a given set of search criteria. The obtained results of 52,415 documents against the specific query are analyzed based on the number of documents per year, contributions of different countries and Institutes in Cheminformatics/QSAR publications, the contributions of researchers based on the number of documents, appearance in the top-cited articles, h-index, composite c-score (ns), and the newly introduced q-score. Finally, a list of the top 50 Cheminformatics/QSAR researchers is presented.
View Article and Find Full Text PDFToxicol Res (Camb)
December 2024
Laboratory of Drug Discovery and Ecotoxicology, Department of Pharmacy, Guru Ghasidas Vishwavidyalaya (A Central University), Koni, Bilaspur Chhattisgarh-495009, India.
Aquat Toxicol
December 2024
Laboratory of Environmental Chemoinformatics, Faculty of Chemistry, University of Gdansk, Wita Stwosza 63, 80-308 Gdansk, Poland. Electronic address:
Tadpoles, as early developmental stages of frogs, are vital indicators of toxicity and environmental health in ecosystems exposed to harmful organic compounds from industrial and runoff sources. Evaluating each compound individually is challenging, necessitating the use of in silico methods like Quantitative Structure Toxicity-Relationship (QSTR) and Quantitative Read-Across Structure-Activity Relationship (q-RASAR). Utilizing the comprehensive US EPA's ECOTOX database, which includes acute LC toxicity and chronic endpoints, we extracted crucial data such as study types, exposure routes, and chemical categories.
View Article and Find Full Text PDFJ Hazard Mater
December 2024
Drug Discovery and Development Laboratory (DDD Lab), Department of Pharmaceutical Technology, Jadavpur University, Kolkata 700032, India. Electronic address:
The increasing presence of active pharmaceutical ingredients (APIs) in aquatic ecosystems, driven by widespread human use, poses significant risks, including acute and chronic toxicity to aquatic species. However, the scarcity of experimental toxicity data on APIs and related compounds due to the high costs, time requirements, and ethical concerns associated with animal testing hinders comprehensive risk assessment. In response, we developed quantitative structure-toxicity relationship (QSTR) and interspecies quantitative structure toxicity-toxicity relationship (i-QSTTR) models for three key aquatic species: zebrafish, water fleas, and green algae, using NOEC as an endpoint, following OECD guidelines.
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