scholarly journals Comparison of seven in silico tools for evaluating of daphnia and fish acute toxicity: case study on Chinese Priority Controlled Chemicals and new chemicals

2021 ◽  
Vol 22 (1) ◽  
Author(s):  
Linjun Zhou ◽  
Deling Fan ◽  
Wei Yin ◽  
Wen Gu ◽  
Zhen Wang ◽  
...  
Keyword(s):  
Risk Assessment ◽  
Acute Toxicity ◽  
In Silico ◽  
Water Solubility ◽  
Expert Knowledge ◽  
Aquatic Toxicity ◽  
Predictive Ability ◽  
Validation Dataset ◽  
In Silico Models ◽  
In Silico Tools

Abstract Background A number of predictive models for aquatic toxicity are available, however, the accuracy and extent of easy to use of these in silico tools in risk assessment still need further studied. This study evaluated the performance of seven in silico tools to daphnia and fish: ECOSAR, T.E.S.T., Danish QSAR Database, VEGA, KATE, Read Across and Trent Analysis. 37 Priority Controlled Chemicals in China (PCCs) and 92 New Chemicals (NCs) were used as validation dataset. Results In the quantitative evaluation to PCCs with the criteria of 10-fold difference between experimental value and estimated value, the accuracies of VEGA is the highest among all of the models, both in prediction of daphnia and fish acute toxicity, with accuracies of 100% and 90% after considering AD, respectively. The performance of KATE, ECOSAR and T.E.S.T. is similar, with accuracies are slightly lower than VEGA. The accuracy of Danish Q.D. is the lowest among the above tools with which QSAR is the main mechanism. The performance of Read Across and Trent Analysis is lowest among all of the tested in silico tools. The predictive ability of models to NCs was lower than that of PCCs possibly because never appeared in training set of the models, and ECOSAR perform best than other in silico tools. Conclusion QSAR based in silico tools had the greater prediction accuracy than category approach (Read Across and Trent Analysis) in predicting the acute toxicity of daphnia and fish. Category approach (Read Across and Trent Analysis) requires expert knowledge to be utilized effectively. ECOSAR performs well in both PCCs and NCs, and the application shoud be promoted in both risk assessment and priority activities. We suggest that distribution of multiple data and water solubility should be considered when developing in silico models. Both more intelligent in silico tools and testing are necessary to identify hazards of Chemicals.

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.

Role of in silico tools and text mining in the risk assessment of selected alkaloids

2018 ◽  
Vol 295 ◽  
pp. S159
Author(s):  
L. Sousselier ◽  
G. Raitano ◽  
M. Petoumenou ◽  
E. Benfenati ◽  
N. Nguyen ◽  
...  
Keyword(s):  
Risk Assessment ◽  
Text Mining ◽  
In Silico ◽  
In Silico Tools

scholarly journals Framework for risk assessment of PFAS utilizing experimental studies and in-silico models

2022 ◽  
pp. 112722
Author(s):  
Deepika Deepika ◽  
Joaquim Rovira ◽  
Óscar Sabuz ◽  
Jordina Balaguer ◽  
Marta Schuhmacher ◽  
...  
Keyword(s):  
Risk Assessment ◽  
In Silico ◽  
Experimental Studies ◽  
In Silico Models

Wildfire susceptibility assessment: evaluation of the performance of different machine learning algorithms

2021 ◽  
Author(s):  
Andrea Trucchia ◽  
Sara Isnardi ◽  
Mirko D'Andrea ◽  
Guido Biondi ◽  
Paolo Fiorucci ◽  
...  
Keyword(s):  
Machine Learning ◽  
Risk Assessment ◽  
Expert Knowledge ◽  
Susceptibility Mapping ◽  
Machine Learning Algorithms ◽  
Validation Dataset ◽  
Burned Area ◽  
Support Vector ◽  
Testing Dataset ◽  
Wildfire Susceptibility

<p><span>Wildfires constitute a complex environmental disaster triggered by several interacting natural and human factors that can affect the biodiversity, species composition and ecosystems, but also human lives, regional economies and environmental health. Therefore, wildfires have become the focus on forestry and ecological research and are receiving considerable attention in forest management. Current advances in automated learning and simulation methods, like machine learning (ML) algorithms, recently aroused great interest in wildfires risk assessment and mapping. This quantitative evaluation is carried out by taking into account two factors: the location and spatial extension of past wildfires events and the geo-environmental and anthropogenic predisposing factors that favored their ignition and spreading. When dealing with risk assessment and predictive mapping for natural phenomena, it is crucial to ascertain the reliability and validity of collected data, as well as the prediction capability of the obtained results. In a previous study (Tonini et al. 2020) authors applied Random Forest (RF) to elaborate wildfire susceptibility mapping for Liguria region (Italy). In the present study, we address to the following outstanding issues, which are still unsolved: (1) the vegetation map included a class labeled “burned area” that masked to true burned vegetation; (2) the implemented model based on RF gave good results, but it needs to be compared with other ML based approaches; (3) to test the predictive capabilities of the model, the last three years of observations were taken, but these are not fully representative of different wildfires regimes, characterizing non-consecutives years. Thus, by improving the analyses, the following results were finally achieved. 1) the class “burned areas” has been reclassified based on expert knowledge, and the type of vegetation correctly assigned. This allowed correctly estimating the relative importance of each vegetation class belonging to this variable. (2) Two additional ML based approach, namely Multi-Layer Perceptron (MLP) and Support Vector Machine (SVM), were tested besides RF and the performance of each model was assessed, as well as the resulting variable ranking and the predicting outputs. This allowed comparing the three ML based approaches and evaluating the pros and cons of each one. (3) The training and testing dataset were selected by extracting the yearly-observations based on a clustering procedure, allowing accounting for the temporal variability of the burning seasons. As result, our models can perform on average better prediction in different situations, by taking into considering years experiencing more or less wildfires than usual. The three ML-based models (RF, SVM and MLP) were finally validated by means of two metrics: i) the Area Under the ROC Curve, selecting the validation dataset by using a 5-folds cross validation procedure; ii) the RMS errors, computed by evaluating the difference between the predicted probability outputs and the presence/absence of an observed event in the testing dataset. </span></p><p><strong><span>Bibliography: </span></strong></p><p><span>Tonini, M.; D’Andrea, M.; Biondi, G.; Degli Esposti, S.; Trucchia, A.; Fiorucci, P. A Machine Learning-Based Approach for Wildfire Susceptibility Mapping. The Case Study of the Liguria Region in Italy. </span><span><em>Geosciences</em></span><span> </span><span>2020</span><span>, </span><span><em>10</em></span><span>, 105.</span> <span>https://doi.org/10.3390/geosciences10030105</span></p>

(Eco)toxicological maps: A new risk assessment method integrating traditional and in silico tools and its application in the Ledra River (Italy)

2018 ◽  
Vol 119 ◽  
pp. 275-286 ◽  
Author(s):  
Giuseppa Raitano ◽  
Daniele Goi ◽  
Valentina Pieri ◽  
Alice Passoni ◽  
Michele Mattiussi ◽  
...  
Keyword(s):  
Risk Assessment ◽  
In Silico ◽  
Assessment Method ◽  
Risk Assessment Method ◽  
In Silico Tools

Identification of BAP1-associated MicroRNAs and Implications in Cancer Development

2019 ◽  
pp. 1-4
Author(s):  
Tikam Chand ◽  
Tikam Chand
Keyword(s):  
Gene Regulation ◽  
In Silico ◽  
Mirna Target ◽  
Target Prediction ◽  
Differential Regulation ◽  
Cancer Development ◽  
Mirna Target Prediction ◽  
Cancer Types ◽  
In Silico Tools

Having role in gene regulation and silencing, miRNAs have been implicated in development and progression of a number of diseases, including cancer. Herein, I present potential miRNAs associated with BAP1 gene identified using in-silico tools such as TargetScan and Exiqon miRNA Target Prediction. I identified fifteen highly conserved miRNA (hsa-miR-423-5p, hsa-miR-3184-5p, hsa-miR-4319, hsa-miR125b-5p, hsa-miR-125a-5p, hsa-miR-6893-3p, hsa-miR-200b-3p, hsa-miR-200c-3p, hsa-miR-505-3p.1, hsa-miR-429, hsa-miR-370-3p, hsa-miR-125a-5p, hsa-miR-141-3p, hsa-miR-200a-3p, and hsa-miR-429) associated with BAP1 gene. We also predicted the differential regulation of these twelve miRNAs in different cancer types.

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