scholarly journals Assessment of predictive models for estimating the acute aquatic toxicity of organic chemicals

2016 ◽  
Vol 18 (16) ◽  
pp. 4432-4445 ◽  
Author(s):  
Fjodor Melnikov ◽  
Jakub Kostal ◽  
Adelina Voutchkova-Kostal ◽  
Julie B. Zimmerman ◽  
Paul T. Anastas
Keyword(s):  
Predictive Models ◽  
In Silico ◽  
Aquatic Toxicity ◽  
Organic Chemicals ◽  
Toxicity Data ◽  
Acute Aquatic Toxicity ◽  
Data Gaps

In silico toxicity models are critical in addressing experimental aquatic toxicity data gaps and prioritizing chemicals for further assessment.

scholarly journals Comparison of Seven in Silico methods for Evaluating of Ecotoxicological Acute Toxicity of Daphnia magna and Pimephales promelas: Case study on Chinese Priority Controlled Chemicals

2019 ◽  
Author(s):  
Linjun Zhou ◽  
Deling Fan ◽  
Wei Yin ◽  
Wen Gu ◽  
Zhen Wang ◽  
...  
Keyword(s):  
Risk Assessment ◽  
Acute Toxicity ◽  
Daphnia Magna ◽  
In Silico ◽  
Aquatic Toxicity ◽  
Pimephales Promelas ◽  
Chemical Screening ◽  
Structure Activity ◽  
Acute Aquatic Toxicity ◽  
In Silico Methods

Abstract Background: The acute toxicity on aquatic organisms are indispensable parameters in the ecological risk assessment priority chemical screening process (e.g. persistent, bioaccumulative and toxic chemicals). Currently, a number of predictive models for aquatic toxicity are available, however, the accuracy of in silico tools in priority assessment and risk assessment still remains to be further studied. Herein, this study evaluated the performance of seven Quantitative Structure–Activity Relationship (QSAR) in silico methods (Danish QSAR Database, Ecological Structure Activity Relationships, KAshinhou Tool for Ecotoxicity on PAS, Toxicity Estimation Software Tool, QSAR Toolbox, Read Across, and Virtual models for property Evaluation of chemicals within a Global Architecture) for assessing acute aquatic toxicity to Daphnia magna and Pimephales promelas using the first batch list of Priority Controlled Chemicals in China. Results: Based on the values for the median lethal dose and the US Environmental Protection Agency’s acute aquatic toxicity categories of concern, the acute toxicity grade was classified into six categories. According to the comparative prediction results, the accuracy of the Daphnia magna toxicity categories prediction was 25%–56%, the correlation coefficient ranged from 0.1236 to 0.6349, and the correlation coefficients of the applicability domain were 0.040 and 0.5148. The corresponding values for the Pimephales promelas toxicity categories prediction were 22%–44%, 0.1495–0.4144, 0.2156 and 0.6793. Conclusion: As the structure of chemicals of first batch list of Priority Controlled Chemicals in China are complex, the accuracy of model prediction is low, which depends on the quality of the constructed model and application domain. Although in silico methods can be used to preliminarily estimate aquatic toxicity, experimental data validation is still required for prioritizing environmental hazards assessments and risk assessments.

In silicoestimation of chemical aquatic toxicity on crustaceans using chemical category methods

2018 ◽  
Vol 20 (9) ◽  
pp. 1234-1243 ◽  
Author(s):  
Qianqian Cao ◽  
Lin Liu ◽  
Hongbin Yang ◽  
Yingchun Cai ◽  
Weihua Li ◽  
...  
Keyword(s):  
Machine Learning ◽  
In Silico ◽  
Aquatic Toxicity ◽  
Learning Methods ◽  
Molecular Fingerprints ◽  
Machine Learning Methods ◽  
Acute Aquatic Toxicity

A series ofin silicomodels were developed to estimate chemical acute aquatic toxicity on crustaceans using machine learning methods combined with molecular fingerprints.

scholarly journals Ecotoxicological assessment of nanoparticle-containing acrylic copolymer dispersions in fairy shrimp and zebrafish embryos

2017 ◽  
Vol 4 (10) ◽  
pp. 1981-1997 ◽  
Author(s):  
Tamara S. Galloway ◽  
Yuktee Dogra ◽  
Natalie Garrett ◽  
Darren Rowe ◽  
Charles R. Tyler ◽  
...  
Keyword(s):  
Aquatic Toxicity ◽  
Zebrafish Embryos ◽  
Fairy Shrimp ◽  
Acrylic Copolymer ◽  
Acrylic Polymer ◽  
Ecotoxicological Assessment ◽  
Acute Aquatic Toxicity ◽  
Polymer Dispersions

Nanoparticle-containing acrylic polymer dispersions showed virtually no acute aquatic toxicity in fairy shrimp and zebrafish embryos.

scholarly journals HUBUNGAN KUANTITATIF STRUKTUR TOKSISITAS : REVIEW

2020 ◽  
Vol 3 (2) ◽  
pp. 107-126
Author(s):  
Purwaniati Purwaniati
Keyword(s):  
In Silico ◽  
Toxicity Data ◽  
Quantitative Structure ◽  
Training Set ◽  
Drug Discovery And Development ◽  
Independent Variables ◽  
Drug Molecules ◽  
Structure Toxicity Relationship ◽  
Toxicity Relationship ◽  
In Silico Methods

AbstrakProses penemuan dan pengembangan obat merupakan proses panjang yang memerlukan banyak waktu dan biaya. Ada banyak calon molekul obat yang gagal mencapai pasaran karena alasan toksisitasnya yang tinggi, sehingga harus dapat diidentifikasi sedini mungkin. Hubungan kuantitatif struktur toksisitas (HKST) merupakan salah satu metode in silico yang cukup tangguh untuk memprediksi toksisitas. HKST merupakan persamaan matematis yang dibentuk dari variabel data endpoint toksisitas seperti LD50 sebagai variabel terikat dan sejumlah deskriptor sebagai variable bebas yang dihitung dari senyawa-senyawa dalam training set. Persamaan HKST kemudian digunakan untuk memprediksi toksisitas senyawa baru.Kata kunci : toksisitas, hubungan kuantitatif struktur toksisitas (HKST)AbstractThe process of drug discovery and development is a long process that requires a lot of time and costly. There are many prospective drug molecules that fail to reach the market due to high toxicity reasons, so they must be identified as early as possible. The quantitative structure toxicity relationship  (QSTR) is one of the in silico methods that is strong enough to predict toxicity. QSTR is a mathematical equation formed from endpoint toxicity data variables such as LD50 as a bound variable and a number of descriptors as independent variables calculated from the compounds in the training set. The QSTR equation is then used to predict the toxicity of new compounds.Keywords: toxicity, quantitative structure toxicity relationship (QSTR)

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