Name: in silico modeling method for computational aquatic toxicology of endocrine disruptors: a software-based approach using qsar toolbox
Uploaded: 2019-08-28T14:00:00.000+00:00
Duration: 5 s
Description: Watch this Scientific Journal Video about In Silico Modeling Method for Computational Aquatic Toxicology of Endocrine Disruptors: A Software-Based Approach Using QSAR Toolbox at JoVE.com

This research was supported by the National Research Council of Science &#38; Technology (NST) grant by the South Korean government (MSIP) (No. CAP-17-01-KIST Europe) and Project 11911.

1. Equipment
<ol>
	<li>Software: use OECD QSAR Toolbox 4.0 or newer (free download from &#60;https://qsartoolbox.org/download/?) and data analysis software.</li>
	<li>Computer: for the OECD QSAR Toolbox, use: (i) system type: 64 bit, Windows 7 or newer; (ii) processor: I5 at 2.4 GHz, or a faster processor or equivalent AMD CPU; (iii) installed memory (RAM): 6 GB; (iv) hard disk drive (HDD): 20 GB of free hard drive space (OECD QSAR Toolbox 4.3 Release Notes: &#60;https://qsartoolbox.org/file/2019/02/Toolbox-4.3-Release-Notes-1.pdf&#62;).</li>
</ol>
2. Procedure
<ol>
	<li>OECD QSAR Toolbox 
	NOTE: The QSAR Toolbox operates in six consecutive flow modules starting from Input and followed by Profiling, Data, Category Definition, Data Gap Filling, then Report, located at the top of the program interface.
	<ol>
		<li>Explore the aforementioned six stages through six toolbar icons by left-clicking. First, look over the stages of Input, Data Gap Filling, and Report that are necessary to perform the automated workflow &#8220;Ecotoxicological endpoint&#8221; and to document its results.</li>
		<li>Take a short look over optional stages Profiling and Data. The Profiling stage provides an initial insight into the target substance&#8217;s (eco)toxicity potential and environmental fate characteristics. Optional Data stage enables searching for available experimental data related to the target substance.</li>
	</ol>
	</li>
	<li>Input
	<ol>
		<li>Upon starting the QSAR Toolbox, the user begins at the Input toolbox stage by default. The QSAR Toolbox creates a working file named &#8220;Document 1&#8221; automatically, which is displayed in the stage option panel on the left of the program interface. Rename the file, if desired, by right-clicking the working file.</li>
		<li>Click on the CAS# button in the actions toolbar, enter the chemical abstract service (CAS) number of the target substance in the available text field, and click Search. The tool will then search&#160;for the target substance by CAS number.</li>
		<li>If required, choose other search options that are available in the action toolbar such as searching by substance name or simplified molecular-input line-entry system (SMILES) code. SMILES can be entered as 2D non-stereochemical or 3D stereochemical containing forms. Click Name or Structure, respectively. Use the Structure tool to draw the target substance.</li>
		<li>The search tool displays the search results through database records in a pop-up window. Choose the record reporting a &#8220;high&#8221; CAS-SMILES relation (CS Relation field) if multiple records are retrieved for the target substance by checking the box on the left of the record. Click OK. 
		NOTE: Proceeding from this point is possible only if the retrieved record contains a SMILES code, as the SMILES code (2D non-stereochemical containing form) is the basis for computation.</li>
		<li>Batch mode: to perform the in silico assay for multiple target substances, write a simple substance list in a text editor in which each CAS number is listed in a single row (Supplementary Figure S3). Save the text file with an appropriate name and extension .txt on the computer.</li>
		<li>Batch mode: click Data. Then, go to Databases in the stage option panel on the left of the program interface. Make sure databases that are listed under Ecotoxicological Information are checked.</li>
		<li>Batch mode: click Input. Select Query from the actions toolbar. Accept the settings set in step 2.2.6 by clicking Yes in the dialog window.</li>
		<li>Batch mode: choose the CAS tab. Upload the substance list saved as text file through Load list from your computer.</li>
		<li>Batch mode: there are two Add buttons available; click the Add button on the bottom of the pop-up menu and then click Execute. The QSAR Toolbox will display a message on the number of substances that have been retrieved for the search. 
		NOTE: Some substances of the loaded list may not be found by the search tool or that several entries may be available for one CAS number. It is not possible to delete substances from the retrieved set of substances.</li>
	</ol>
	</li>
	<li>Profiling 
	NOTE: The following section is optional. If this is not required, skip to section 2.5.
	<ol>
		<li>Click on the toolbox stage button Profiling. Go to Profiling methods in the stage option panel on the left of the program interface.</li>
		<li>Click Unselect All. Check all profilers listed under Predefined and those related to aquatic toxicity listed under Endpoint specific such as &#8220;Acute aquatic toxicity classification by Verhaar (Modified).&#8221;</li>
		<li>Finish the selection. Then click on the Apply button in the Actions toolbar. 
		NOTE: The QSAR Toolbox provides recommendations on a set of profilers. These are highlighted in green (suitable) and orange (plausible) when choosing Options &#62; Color by: &#62; Endpoint selected in the data matrix in the upper left corner of Profiling methods. Left-click the data matrix field next to the endpoint of interest.&#160;Available endpoints are listed in the endpoint tree next to the stage option panel. The profiler Substance type will indicate whether the target substance is a &#8220;discrete chemical.&#8221; The information is displayed in the expanded endpoint tree &#8220;Profile&#8221;, &#8220;Predefined&#8221;, and &#8220;Substance type&#8221;. Only if the target substance is a discrete chemical can the automated workflow run successfully. &#8220;Acute aquatic toxicity classification by Verhaar (modified)&#8221; provides a first estimate of the acute aquatic toxicity mechanism of the target substance19,20. The information is displayed in the expanded endpoint tree &#8220;Profile&#8221;, &#8220;Endpoint Specific&#8221;, and &#8220;Acute aquatic toxicity classification by Verhaar (modified)&#8221;. Five classes are available: (class 1) inert chemicals (baseline toxicity); (class 2) less inert chemicals; (class 3) reactive chemicals; (class 4) specifically acting chemicals; and (class 5) for chemicals not possible to classify.</li>
		<li>Right-click Parameter in the endpoint tree to run integrated 2D and 3D QSAR models available in the QSAR Toolbox, if desired. Click Calculate/extract all parameters for all chemicals in the pop-up menu.</li>
		<li>2D and 3D QSAR models compiled in Parameter provide numeric values. Use &#8220;Profiling methods&#8221; for qualitative information (see step 2.3.1).</li>
	</ol>
	</li>
	<li>Data 
	NOTE: This section is optional. If it is not required, skip to section 2.5.
	<ol>
		<li>Click on the toolbox stage button Data. Then, click Gather from the Actions toolbar.</li>
		<li>Select All endpoints to gather all experimental data, then Choose to gather endpoint specific experimental data. As an example, if aquatic toxicity is the user&#8217;s focus, click Choose &#62; Ecotoxicological Information &#62; Aquatic toxicity &#62; OK. 
		NOTE: Choosing to gather experimental data for all endpoints may lead to extended processing time. The user can adapt the hierarchy of the endpoint tree to the specific purpose. This changes the manner in which data are displayed.</li>
		<li>If desired, right-click the endpoint of interest in the endpoint tree area. Choose Set tree hierarchy in the pop-up menu. Organize the endpoint tree in the preferred manner using the available terms and arrows and click OK.</li>
		<li>If desired, export the gathered data as an Excel file. Right-click on the endpoint of interest and choose Export Data matrix in the pop-up menu.</li>
		<li>A &#8220;Matrix export&#8221; wizard opens and enables adding other endpoints to the export list. Finish the selection, click Export, and save the file on the computer. 
		NOTE: Exporting data from all databases is not possible. For example, data retrieved from the database &#8220;ECHA CHEM&#8221; cannot be saved.</li>
	</ol>
	</li>
	<li>Data gap filling
	<ol>
		<li>Click on the toolbox stage button Data gap filling. Then, click Automated in the Actions toolbar.</li>
		<li>Select Ecotoxicological Endpoint &#62; Fish, LC50 (lethal concentration, 50%) at 96 h for Pimephales promelas (mortality). Click OK. A &#8220;Workflow Controller&#8221; will appear, and processing will take up to several minutes, especially in batch mode. 
		NOTE: The QSAR Toolbox automatically applies a defined set of profilers when searching for suitable substances with available experimental data for the prediction. The experimental data [e.g., effect concentrations 96 h LC50 (P. promelas) or 96 h EC50 (P. promelas, mortality)] are used to generate the prediction for the target substance by either linear approximation or nearest neighbor method. Note that the methods of linear approximation and nearest neighbor are referred to as trend analysis (labeled as &#8220;T&#8221;) and read-across (labeled as &#8220;R&#8221;), respectively.</li>
		<li>The user will receive a message if the prediction is performed successfully. Click OK and close the workflow controller indicating &#8220;Finished workflow&#8221; by clicking x in the upper right corner.</li>
		<li>Batch mode: upon starting the automated workflow, the user will be asked to specify the range of substances over which to execute the workflow. Accept the full range of substances selected by default in the dialog window by clicking OK.</li>
		<li>Batch mode: the user will not receive a message indicating whether a prediction was run successfully or unsuccessfully. Close the workflow controller indicating &#8220;Finished workflow&#8221; at the end of the batch processing by clicking x in the upper right corner.</li>
	</ol>
	</li>
	<li>Report
	<ol>
		<li>If a prediction was successfully executed, click on the toolbox stage button Report. 
		NOTE: No reports can be generated in batch mode.</li>
		<li>Scroll down and find the prediction value in matrix field located in a yellow highlighted row next to endpoint &#8220;96-h.&#8221; The predicted value is labelled with &#8220;T&#8221; or &#8220;R.&#8221; Activate this specific data matrix field by left-clicking it.</li>
		<li>Click Prediction in the Actions toolbar. Customize the report content and appearance in the pop-up wizard. Three types of reports are available: (i) prediction, (ii) category, and (iii) data matrix.</li>
		<li>The wizard allows the user to fill in the author&#8217;s name and contact details. Write a short summary, provide a detailed explanation of the mechanistic interpretation, or provide justification for the adequacy of the prediction.</li>
		<li>Include additional information related to the executed prediction, if desired. The extent of additional information depends on the user.</li>
		<li>Go through the wizard by clicking Next. Finally, click Create report and save the prediction and category reports as PDF files and the data matrix as an Excel spreadsheet on the computer.</li>
		<li>Find additional details on the functionalities of the QSAR Toolbox and automated workflows in the application manual for the OECD QSAR Toolbox v.4 (F1 help on the keyboard). Details on the algorithms and rationale behind the automated workflow are described by Dimitrov et al.8 and Yordanova et al.9.</li>
	</ol>
	</li>
</ol>
3. Application
<ol>
	<li>If using the predicted effect concentration (i.e., 96-h LC50 of P. promelas) in the environmental risk assessment, use the lower limit of the 95% confidence interval. Find the data on the first page of the saved prediction report (PDF) in &#8220;Prediction summary&#8221;, &#8220;Predicted value: &#60;mean&#62; (from &#60;lower_limit&#62; to &#60;upper_limit&#62;).&#8221; 
	NOTE: The notes given here are based on results of the comparison between predicted and experimental data for a set of target substances reported in this study. Selecting the lower end of the 95% confidence range will increase the likelihood that the predicted effect concentration will not underestimate the real toxicity of the substance (see the representative results). The predicted effective concentration of the lower limit of the 95% confidence interval will therefore present a safer basis for risk assessment.</li>
</ol>

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J. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Role+of+quinones+in+toxicology.">Role of quinones in toxicology.</a> Chemical Research in Toxicology. 13, 135-160 (2000).</li><li>Panche, A. N., Diwan, A. D., Chandra, S. R. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Flavonoids:+an+overview.">Flavonoids: an overview.</a> Journal of Nutritional Science. 5, e47 (2016).</li><li>Li, J., Gramatica, P. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=QSAR+classification+of+estrogen+receptor+binders+and+pre-screening+of+potential+pleiotropic+EDCs.">QSAR classification of estrogen receptor binders and pre-screening of potential pleiotropic EDCs.</a> SAR QSAR Environmental Research. 21, 657-669 (2010).</li><li>Bohl, M. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=.">.</a> Molecular Structure and Biological Activity of Steroids. , (2017).</li><li>Kaminuma, T., Takai-Igarashi, T., Nakano, T., Nakata, K. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Modeling+of+signaling+pathways+for+endocrine+disruptors.">Modeling of signaling pathways for endocrine disruptors.</a> BioSystems. 55, 23-31 (2000).</li><li>Lillicrap, A., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Alternative+approaches+to+vertebrate+ecotoxicity+tests+in+the+21st+century:+a+review+of+developments+over+the+last+2+decades+and+current+status.">Alternative approaches to vertebrate ecotoxicity tests in the 21st century: a review of developments over the last 2 decades and current status.</a> Environmental Toxicology and Chemistry. 35, 2637-2646 (2016).</li><li>Kim, J. W., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Acute+toxicity+of+pharmaceutical+and+personal+care+products+on+freshwater+crustacean+(Thamnocephalus+platyurus)+and+fish+(Oryzias+latipes).">Acute toxicity of pharmaceutical and personal care products on freshwater crustacean (Thamnocephalus platyurus) and fish (Oryzias latipes).</a> Journal of Toxicological Sciences. 34, 227-232 (2009).</li></ol>

The authors have nothing to disclose.

The versatility of the OECD QSAR Toolbox as analytic software for ecotoxicology is shown here with specific interest in the adverse effects of endocrine disrupting chemicals on aquatic vertebrates. In addition, a simple and standard protocol was demonstrated for predicting acute toxicity (96-h LC50) of 74 representative EDs (Table 1) for fish species. This was achieved by applying category building, data gap filling, and ER profiling modules embedded in the QSAR Toolbox (Figure 1, Figure 2).
The linear correlation between log10LC50 and log10KOW with a negative slope (as shown in Supplementary Figure S1) has long been known as a standard quantitative relationship in QSAR analyses25, where higher toxicity is shown the more hydrophobic a given chemical is. As can be seen from a simple calculation, the general mathematical relation that includes Equation S1 and Equation S2&#160;(Supplementary Information) is a converted expression from the following power function26:
<img alt="Equation 1" src="/files/ftp_upload/60054/60054equ01.jpg" />
<img alt="Equation 2" src="/files/ftp_upload/60054/60054equ02.jpg" />
From the plot of (Equation 2), characterizing an intermediate range of KOW26 may be possible by adjusting the parameters a and b, where a certain variation in hydrophobicity (or hydrophilicity) does not significantly change the endpoint of acute toxicity.
Comparative analyses between the computational predictions and experimental observations on the LC50, as shown in Figure 3 and Figure 4, have been typically reported in studies of QSAR for various aquatic toxicants, including technical nonionic surfactants27, triazole fungicides28, and pesticide metabolites21. This type of retrospective validation provides information on how far a given QSAR tool can reach in terms of comparative performance to experimental results. In this study of acute toxicity in fish, the QSAR Toolbox was proven to provide protective predictions for over 90% of tested EDs in all fish and in a single species, Pimephales promelas.
Further identifying the three outlier chemicals in Figure 3 and Figure 4, which showed higher predicted LC50 on average and at a minimum, respectively, is required. First, the 3&#39;,5,7-trihydroxy-4&#39;,6-dimethoxyisoflavone is a type of flavonoid (more specifically, an isoflavone), which is considered to be generally safe and used in herbal pharmaceuticals; however, it still has estrogen-related concerns29 and may cause acute toxicity probably through oxidative phosphorylation uncoupling30. Next, the 1,4-benzenediol, called hydroquinone, is a phenolic compound that can trigger a non-specific and cytotoxic immune response in fish31. Finally, the 4-hexylphenol has been known to exhibit sufficient positive estrogenic activity to be classified as an ED32. It has been well-studied that the main reason of the acute toxicity of hydroquinone is the reduction-oxidation (redox) cycling. The hydroquinone is oxidized to benzoquinone and reduced back to semi-quinone or hydroquinone repeatedly, with depleting cofactors and generating reactive oxygen species33. The other two chemicals may require deeper investigations to reveal their mechanisms of action in acute ecotoxicity using molecular docking approaches such as that used by Panche et al.34, which cannot be covered by the QSAR Toolbox.
EDs interfere with the endocrine system mainly through physicochemical interactions with steroid receptors such as estrogen and androgen receptors, which are of considerable interest in QSAR modeling studies35. Considering this, the QSAR Toolbox is robust in terms of facile and rapid classification of ER binding affinities for a set of chemicals based only on the 2D descriptors of molecular structures. When this ER profiler system was applied to our list of EDs, no clear correlation was found between ER binding affinity and hydrophobicity (Supplementary Figure S2). This result may be explained by the fact that the formation of a steroid-receptor complex is not a direct consequence of a hydrophobic bonding contribution but should be accompanied by a conformational change in the active-site receptor structure36. The receptor binding can be also due to hydrogen-bonding and &#960;-stacking.
Additionally, the position of each chemical group on the molecule may affect the receptor binding, even if the hydrophobicity and number of hydrogen-bond acceptors-donors remain the same. Second, the ER profiler produced contrary trends between predicted and experimental LC50 mean levels with increasing ER binding affinity (Figure 5). This may be because the lethality of parents in an acute toxicity test are not due to ER binding but rather to narcosis in most cases, or to redox cycling in the case of hydroquinone. For example, more extensive analysis, including the chronic toxicity, is required for a larger set of EDs to define predictive limitations of the current version of the QSAR Toolbox.
This preliminary research may also have public health implications because steroids (androgens, estrogens, progestines, and corticoids) and their receptors exhibit similar or even identical macromolecular structures across vertebrates5. These types of analogous endocrine signaling systems may operate using a common mechanism in key events of EDs5. Nevertheless, additional and complementary methodologies are required to illuminate this vast and complex aspect [for example, by performing computational modeling of absorption, distribution, metabolism, and excretion (ADME), and/or adverse outcome pathway (AOP)]38. Furthermore, because most of the scientific and public concerns raised about the adverse effects of EDs are related to their chronic toxicities, improving the databases and algorithms in the QSAR Toolbox and producing reliable long-term ecotoxicology predictions for EDs are both necessary.
This paper demonstrates the application of QSAR Toolbox to compare ecotoxicological LC50 values for fish with log10Kow values of EDs. Throughout the protocol, it results in weak relationships between the two parameters, as it has been revealed by previous studies (e.g., Kim et al.39) that log10Kow is not a good direct predictor of aquatic LC50. In spite of this limitation, this protocol provides a general review or &#8220;vignette&#8221; to describe how to use the dashboard for a given purpose, since it is a valid application to use the QSAR Toolbox for investigating correlations between LC50 (or ER binding affinity) and log10Kow,&#160;or as a tool for rapid acute ecotoxicity screening.&#160;Nevertheless, it should be noted that (1) illuminating the link between estrogen receptor binding and chronic toxicity, rather than acute toxicity (lethality), is more relevant so that clearer correlations may be found, and (2) the androgen receptor, together with that of estrogen, also plays a critical role in reproductive toxicity. Therefore, it is required for the future version of the QSAR Toolbox to improve the prediction functions in light of those two points.

New developments in informatics and computational technology have empowered the biological sciences with quantitative methodologies that offer high precision and reliability1. In particular, algorithms used in molecular taxonomy and property classification have resulted in quantitative structure-activity relationship (QSAR) models2. These models automatically correlate the chemical structures and biological activities of a given chemical database and implement rapid in silico screening of a wide range of chemical substrates according to their medicinal or toxicological actions3. QSAR tools can produce predictive toxicity profiles as a function of feature vectors of molecular descriptors (i.e., physicochemical parameters) of chemicals of interest to numerically create categorical endpoints4. Usually, each quantitative endpoint is displayed as a 2D scatterplot vs. changes in descriptor values. A QSAR model is then generated using (multiple) linear regression analyses. Once a dataset has been fully exploited to construct a QSAR model (called the training set), then the model is statistically validated by predicting the endpoints of a group of chemicals not included in the training set (called the test set). The model can then be used to predict the biological activities of untested compounds3.
Among many harmful chemicals, endocrine disruptors (EDs) have been highlighted as a group of toxicants that may interfere in numerous hormone-related metabolisms in mammals, amphibians, and fish5,6. EDs are known to induce a variety of adverse effects, such as cancers and malformations, by blocking or altering normal hormonal pathways or activating abnormal hormone synthesis/degradation signals. As a consequence, these hormone-mimicking chemicals can perturb endocrine systems such that biological development and reproduction of wildlife animal populations are hampered. In particular, the ecotoxicological effects of EDs have been extensively investigated in aquatic vertebrates, which have nearly identical hormone receptor structures to those of mammals, including humans. Because all hormonal actions occur at low doses in vivo, predicting the potential toxicities of ED candidates using rapid in silico screening is critical to public and environmental health.
QSAR models based on the toxicology of EDs have been conducted utilizing both 2D and 3D descriptors (known as 2D and 3D QSAR, respectively), which reveal the ED ligand binding affinities of estrogen, androgen, and progesterone receptors7. Despite the high-precision advantages of 3D QSAR, in which conformational and electrostatic interactions are considered, 2D QSAR retains its own robustness in direct mathematical algorithms, rapid calculations, and extremely low computational loads. In addition, 2D-QSAR models are flexible for use in a wide range of applications while achieving relatively accurate prediction performance.
The OECD QSAR Toolbox is currently one of the most utilized computer software tools, providing freely available and pre-built QSAR models8,9. Its profiler uses 2D descriptor databases. Since the release of the first version in 2008, the software has been applied in the fields of chemical and biological industries, public health, and environmental safety for full or partial analysis of the potential risks of natural and synthetic compounds, with special interests in carcinogenesis10,11,12, mutagenicity13,14,15, and developmental toxicity16. The application to aquatic toxicology has also been demonstrated, with focus on bioaccumulation and biotransformation17.
The QSAR Toolbox has been proven useful in predicting the short-term toxicity of a broad range of chemicals17, as well as the estrogen receptor (ER) binding affinities of EDs18. However, the acute ecotoxicities of EDs in aquatic vertebrates has not been analyzed using the QSAR Toolbox. In this study, a typical and facile protocol is presented to perform QSAR modeling on the acute adverse effects of EDs with a focus in fish species. The study shows that the QSAR Toolbox is a highly accessible software for calculating and predicting the lethality/mortality of aquatic vertebrates for some representative EDs. Statistical treatment methods for the derived in silico datasets are presented. Figure 1 shows the overall scheme for the general operation of the QSAR Toolbox. The workflow shown in Figure 2 provides straightforward instructions on how to operate the in silico assay to predict acute ecotoxicity of target substances such as endocrine disrupting chemicals.

<table><tbody><tr><td>Adobe Acrobat Reader DC</td><td>Adobe Systems Software Ireland Limited</td><td>NA</td><td>Required to view prediction and category report</td></tr><tr><td>Computer</td><td>System: Microsoft Corporation</td><td>NA</td><td>Recommended system properties: (i) system type: 64 bit, Microsoft Windows 7 or newer, (ii) processor: I5 at 2.4 GHz or faster processor or equivalent AMD CPU, (iii) Installed memory (RAM): 6 GB of RAM, (iv) Hard Disk Drive (HDD): 20 GB free hard drive space</td></tr><tr><td>Microsoft Editor</td><td>Microsoft Corporation</td><td>NA</td><td>Required to upload a substance list of CAS numbers (batch mode) to the OECD QSAR Toolbox as .txt file (text file)</td></tr><tr><td>Microsoft Excel 2016</td><td>Microsoft Corporation</td><td>NA</td><td>Required to export data from OECD QSAR Toolbox as .cvs, .xls or .xlsx files</td></tr><tr><td>OECD QSAR Toolbox version 4.0 or newer</td><td>Organisation for Economic&lt;br/&gt; Co-operation and Development</td><td>NA</td><td>Required to run OECD QSAR Toolbox Automated Workflows; free download:&lt;br/&gt; https://qsartoolbox.org/download/</td></tr><tr><td>OriginPro 9</td><td>OriginLab Corporation</td><td>NA</td><td>Optional program for data analysis; similar tools possible</td></tr></tbody></table>

in silico modeling method for computational aquatic toxicology of endocrine disruptors: a software-based approach using qsar toolbox

Computational analyses of toxicological processes enables high-throughput screening of chemical substances and prediction of their endpoints in biological systems. In particular, quantitative structure-activity relationship (QSAR) models have been increasingly applied to assess the environmental effects of a plethora of toxic materials. In recent years, some more highlighted types of toxicants are endocrine disruptors (EDs, which are chemicals that can interfere with any hormone-related metabolism). Because EDs may significantly affect animal development and reproduction, rapidly predicting the adverse effects of EDs using in silico techniques is required. This study presents an in silico method to generate prediction data on the effects of representative EDs in aquatic vertebrates, particularly fish species. The protocol describes an example utilizing the automated workflow of the QSAR Toolbox software developed by the Organization for Economic Co-operation and Development (OECD) to enable acute ecotoxicity predictions of EDs. As a result, the following are determined: (1) calculation of the numerical correlations between the concentration for 50% of lethality (LC50) and octanol-water partition coefficient (Kow), (2) output performances in which the LC50 values determined in experiments are compared to those generated by computations, and (3) the dependence of estrogen receptor binding affinity on the relationship between Kow and LC50.

Watch this Scientific Journal Video about In Silico Modeling Method for Computational Aquatic Toxicology of Endocrine Disruptors: A Software-Based Approach Using QSAR Toolbox at JoVE.com

In Silico Modeling Method for Computational Aquatic Toxicology of Endocrine Disruptors: A Software-Based Approach Using QSAR Toolbox

Quantitative structure-activity relationship (QSAR) modeling is a representative bioinformatics-assisted method in toxicological screening. This protocol demonstrates how to computationally assess the risks of endocrine disruptors (EDs) in aquatic environments. Utilizing the OECD QSAR Toolbox, the protocol implements an in silico assay for analyzing toxicity of EDs in fish.

Computational analyses of toxicological processes enables high-throughput screening of chemical substances and prediction of their endpoints in biological ...

in-silico-modeling-method-for-computational-aquatic-toxicology

Nanyang Technological University

Research

JoVE Journal

Bioengineering

13.8K Views.  KIST Europe Forschungsgesellschaft mbH. Ons protocol maakt gebruik van een zeer toegankelijk en open softwareprogramma om een snelle berekening van de negatieve effecten op korte termijn van hormoonontregelende chemicaliën mogelijk te maken. Deze OESO QSAR Toolbox is nuttig voor het voorspellen van de toxiciteit van chemische stoffen en biedt een efficiënte methode voor het beoordelen van acute ecotoxiciteit van hormoonontregelaars op een gebruiksvriendelijke manier. Bij het starten van de QSAR Toolbox begint de gebruiker standaa...

Video: In Silico Modeling Method for Computational Aquatic Toxicology of Endocrine Disruptors: A Software-Based Approach Using QSAR Toolbox

Use of a Battery of Chemical and Ecotoxicological Methods for the Assessment of the Efficacy of Wastewater Treatment Processes to Remove Estrogenic Potency

Endocrine Disrupting Compounds pose a substantial risk to the aquatic environment. Ethinylestradiol (EE2) and estrone (E1) have recently been included in a watch list of environmental pollutants under the European Water Framework Directive. Municipal wastewater treatment plants are major contributors to the estrogenic potency of surface waters. Much of the estrogenic potency of wastewater treatment plant (WWTP) effluents can be attributed to the discharge of steroid estrogens including estradiol (E2), EE2 and E1 due to incomplete removal of these substances at the treatment plant. An evaluation of the efficacy of wastewater treatment processes requires the quantitative determination of individual substances most often undertaken using chemical analysis methods. Most frequently used methods include Gas Chromatography-Mass Spectrometry (GCMS/MS) or Liquid Chromatography-Mass Spectrometry (LCMS/MS) using multiple reaction monitoring (MRM). Although very useful for regulatory purposes, targeted chemical analysis can only provide data on the compounds (and specific metabolites) monitored. Ecotoxicology methods additionally ensure that any by-products produced or unknown estrogenic compounds present are also assessed via measurement of their biological activity. A number of in vitro bioassays including the Yeast Estrogen Screen (YES) are available to measure the estrogenic activity of wastewater samples. Chemical analysis in conjunction with in vivo and in vitro bioassays provides a useful toolbox for assessment of the efficacy and suitability of wastewater treatment processes with respect to estrogenic endocrine disrupting compounds. This paper utilizes a battery of chemical and ecotoxicology tests to assess conventional, advanced and emerging wastewater treatment processes in laboratory and field studies.

Endocrine Disrupting Compounds (EDC) pose a substantial risk to the aquatic environment. Municipal wastewater treatment plants are major contributors to the estrogenic potency of surface waters. The methodology provided in this paper allows for an assessment of the efficacy and suitability of wastewater treatment processes with respect to EDC removal.

Endocrine Disrupting Compounds pose a substantial risk to the aquatic environment. Ethinylestradiol (EE2) and estrone (E1) have recently been included in ...

Environment

Screening for Endocrine Activity in Water Using Commercially-available In Vitro Transactivation Bioassays

In vitro transactivation bioassays have shown promise as water quality monitoring tools, however their adoption and widespread application has been hindered partly due to a lack of standardized methods and availability of robust, user-friendly technology. In this study, commercially available, division-arrested cell lines were employed to quantitatively screen for endocrine activity of chemicals present in water samples of interest to environmental quality professionals. A single, standardized protocol that included comprehensive quality assurance/quality control (QA/QC) checks was developed for Estrogen and Glucocorticoid Receptor activity (ER and GR, respectively) using a cell-based Fluorescence Resonance Energy Transfer (FRET) assay. Samples of treated municipal wastewater effluent and surface water from freshwater systems in California (USA), were extracted using solid phase extraction and analyzed for endocrine activity using the standardized protocol. Background and dose-response for endpoint-specific reference chemicals met QA/QC guidelines deemed necessary for reliable measurement. The bioassay screening response for surface water samples was largely not detectable. In contrast, effluent samples from secondary treatment plants had the highest measurable activity, with estimated bioassay equivalent concentrations (BEQs) up to 392 ng dexamethasone/L for GR and 17 ng 17&#946;-estradiol/L for ER. The bioassay response for a tertiary effluent sample was lower than that measured for secondary effluents, indicating a lower residual of endocrine active chemicals after advanced treatment. This protocol showed that in vitro transactivation bioassays that utilize commercially available, division-arrested cell &#34;kits&#34;, can be adapted to screen for endocrine activity in water.

Screening for Endocrine Activity in Water Using Commercially-available In Vitro Transactivation Bioassays

A protocol to screen for endocrine activity in organic extracts of water samples, including treated wastewater effluent and surface (receiving) water, was adapted using commercially available division-arrested ("freeze and thaw") in vitro transactivation bioassays.

In vitro transactivation bioassays have shown promise as water quality monitoring tools, however their adoption and widespread application has been ...

A High-throughput Assay for the Prediction of Chemical Toxicity by Automated Phenotypic Profiling of Caenorhabditis elegans

Applying toxicity testing of chemicals in higher order organisms, such as mice or rats, is time-consuming and expensive, due to their long lifespan and maintenance issues. On the contrary, the nematode Caenorhabditis elegans (C. elegans) has advantages to make it an ideal choice for toxicity testing: a short lifespan, easy cultivation, and efficient reproduction. Here, we describe a protocol for the automatic phenotypic profiling of C. elegans in a 384-well plate. The nematode worms are cultured in a 384-well plate with liquid medium and chemical treatment, and videos are taken of each well to quantify the chemical influence on 33 worm features. Experimental results demonstrate that the quantified phenotype features can classify and predict the acute toxicity for different chemical compounds and establish a priority list for further traditional chemical toxicity assessment tests in a rodent model.

A High-throughput Assay for the Prediction of Chemical Toxicity by Automated Phenotypic Profiling of Caenorhabditis elegans

A quantitative method has been developed to identify and predict the acute toxicity of chemicals by automatically analyzing the phenotypic profiling of Caenorhabditis elegans. This protocol describes how to treat worms with chemicals in a 384-well plate, capture videos, and quantify toxicological related phenotypes.

Applying toxicity testing of chemicals in higher order organisms, such as mice or rats, is time-consuming and expensive, due to their long lifespan and ...

Using Tg(Vtg1:mcherry) Zebrafish Embryos to Test the Estrogenic Effects of Endocrine Disrupting Compounds

There are many endocrine disrupting compounds (EDC) in the environment, especially estrogenic substances. The detection of these substances is difficult due to their chemical diversity; therefore, increasingly more effect-detecting methods are used, such as estrogenic effect-sensitive biomonitor/bioindicator organisms. These biomonitoring organisms include several fish models. This protocol covers the use of zebrafish Tg(vtg1: mCherry) transgenic line as a biomonitoring organism, including the propagation of fish and the treatment of embryos, with an emphasis on the detection, documentation, and evaluation of fluorescent signals induced by EDC. The goal of the work is the demonstration of the use of the Tg(vtg1: mCherry) transgenic line embryos to detect estrogenic effects. This work documents the use of transgenic zebrafish embryos Tg(vtg1: mCherry) for the detection of estrogenic effects by testing two estrogenic substances, &#945;- and &#946;-zearalenol. The described protocol is only a basis for designing assays; the test method can be varied according to the test endpoints and the samples. Moreover, it can be combined with other assay methods, thereby facilitating the future use of the transgenic line.

Using TgVtg1:mcherry Zebrafish Embryos to Test the Estrogenic Effects of Endocrine Disrupting Compounds

Present here is a detailed protocol for the use of zebrafish embryos Tg(vtg1: mCherry) for the detection of estrogenic effects. The protocol covers the propagation of the fish and treatment of embryos, and emphasizes the detection, documentation, and the evaluation of fluorescent signals induced by endocrine disrupting compounds (EDC).

There are many endocrine disrupting compounds (EDC) in the environment, especially estrogenic substances. The detection of these substances is difficult ...

Demonstration of the Sequence Alignment to Predict Across Species Susceptibility Tool for Rapid Assessment of Protein Conservation

The US Environmental Protection Agency Sequence Alignment to Predict Across Species Susceptibility (SeqAPASS) tool is a fast, freely available, online screening application that allows researchers and regulators to extrapolate toxicity information across species. For biological targets in model systems such as human cells, mice, rats, and zebrafish, toxicity data are available for a variety of chemicals. Through the evaluation of protein target conservation, this tool can be used to extrapolate data generated from such model systems to thousands of other species lacking toxicity data, yielding predictions of relative intrinsic chemical susceptibility. The latest releases of the tool (versions 2.0-6.1) have incorporated new features that allow for the rapid synthesis, interpretation, and use of the data for publication plus presentation-quality graphics.
Among these features are customizable data visualizations and a comprehensive summary report designed to summarize SeqAPASS data for ease of interpretation. This paper describes the protocol to guide users through submitting jobs, navigating the various levels of protein sequence comparisons, and interpreting and displaying the resulting data. New features of SeqAPASS v2.0-6.0 are highlighted. Furthermore, two use-cases focused on transthyretin and opioid receptor protein conservation using this tool are described. Finally, SeqAPASS&#39; strengths and limitations are discussed to define the domain of applicability for the tool and highlight different applications for cross-species extrapolation.

Here, we present a protocol to utilize the latest version of the US Environmental Protection Agency Sequence Alignment to Predict Across Species Susceptibility (SeqAPASS) tool. This protocol demonstrates the application of the online tool to rapidly analyze protein conservation and provide customizable and easily interpretable predictions of chemical susceptibility across species.

The US Environmental Protection Agency Sequence Alignment to Predict Across Species Susceptibility (SeqAPASS) tool is a fast, freely available, online ...

Genetics

A High-Throughput Screening Approach for Evaluating the Binding Affinity of Environmental Pollutants to Nuclear Receptors

Increasing levels of compounds have been detected in the environment, causing widespread pollution and posing risks to human health. However, despite their high environmental occurrence, there is very limited information regarding their toxicological effects. It is urgent to develop high-throughput screening (HTS) methods to guide toxicological studies. In this study, a receptor-ligand binding assay using an HTS system was developed to determine the binding potency of environmental pollutants on nuclear receptors. The test is conducted using a microplate reader (i.e., a 96-well plate containing various chemicals) by measuring the fluorescence polarization (FP) of a specific fluorescent probe. This assay consists of four parts: the construction and transformation of recombinant vectors, the expression and purification of the receptor protein (ligand-binding domain), receptor-probe binding, and competitive binding of chemicals with the receptor. The binding potency of two environmental pollutants, perfluorooctanesulfonic acid (PFOS) and triphenyl phosphate (TPHP), with peroxisome proliferator-activated receptor gamma (PPAR&#947;) was determined to illustrate the assay procedure. Finally, the advantages and disadvantages of this method and its potential applications were also discussed.

This protocol describes a high-throughput screening system that uses fluorescence polarization of a specific fluorescent probe binding to a nuclear receptor as a readout for screening environmental pollutants.

Increasing levels of compounds have been detected in the environment, causing widespread pollution and posing risks to human health. However, despite ...

Quantitative Structure-Activity Relationship, Activity Prediction, and Molecular Dynamics of Non-nucleotide Reverse Transcriptase Inhibitors

The increasing incidence of HIV-1 drug resistance presents a challenge to the effectiveness of combination antiretroviral therapy, particularly in Southern Africa. The development of resistance to non-nucleotide reverse transcriptase inhibitors (NNRTIs) threatens the long-term success of antiretroviral therapy. In 2019, antimicrobial resistance directly accounted for an estimated 1.27 million deaths globally. This study employed an in-silico approach to investigate NNTRI drugs and their derivatives. Techniques used included density functional theory calculations, molecular docking, enumeration, quantitative structure-activity relationship (QSAR) analysis, molecular dynamics simulation (MDS), and molecular mechanics with generalized Born and surface area methods. The analysis focused on various pyrimidine derivatives and six NNRTI drugs, examining their interactions with the HIV-1 protein (PDB code 1HQU).
A QSAR model was developed to predict the biological activity of the six NNRTIs under investigation. Using 94 pyrimidine derivatives, the QSAR model achieved an R2 OF 0.822 and a Q2 of 0.815, indicating a high level of predictive accuracy.
MDS were conducted to assess the stability of various ligands and their newly developed alternatives, ensuring they remained bound to the protein&#39;s active site over a 200-nanosecond simulation timeframe. Etravirine exhibited root mean square deviation (RMSD) fluctuations of approximately 4.5 &#197;, while its enumerated derivatives showed RMSD fluctuations of 3.5 &#197;. Through molecular docking, MDS, and free energy computations, enumerated Etravirine demonstrated the best performance, with an activity value of 7.373 and a docking score of -10.517 kcal/mol. Furthermore, the calculated free energy of binding for enumerated Etravirine was -89.684 kcal/mol, outperforming other ligands under investigation. The significant improvement suggests that the modified Etravirine holds promising potential as a novel agent in antiretroviral therapy.
The obtained lower RMSD value, the enhanced amino acid interactions, and the highest binding free energy indicate that enumerated Etravirine could serve as a viable alternative for HIV/AIDS treatment.

This study used in-silico strategies to identify Enumerated Etravirine as a promising therapeutic agent for HIV. Our findings on molecular interactions and dynamics support the rational design of novel NNRTIs as possible HIV treatment alternatives.

The increasing incidence of HIV-1 drug resistance presents a challenge to the effectiveness of combination antiretroviral therapy, particularly in ...

Chemistry

A Chemical Screening Procedure for Glucocorticoid Signaling with a Zebrafish Larva Luciferase Reporter System

Glucocorticoid stress hormones and their artificial derivatives are widely used drugs to treat inflammation, but long-term treatment with glucocorticoids can lead to severe side effects. Test systems are needed to search for novel compounds influencing glucocorticoid signaling in vivo or to determine unwanted effects of compounds on the glucocorticoid signaling pathway. We have established a transgenic zebrafish assay which allows the measurement of glucocorticoid signaling activity in vivo and in real-time, the GRIZLY assay (Glucocorticoid Responsive In vivo Zebrafish Luciferase activitY). The luciferase-based assay detects effects on glucocorticoid signaling with high sensitivity and specificity, including effects by compounds that require metabolization or affect endogenous glucocorticoid production. We present here a detailed protocol for conducting chemical screens with this assay. We describe data acquisition, normalization, and analysis, placing a focus on quality control and data visualization. The assay provides a simple, time-resolved, and quantitative readout. It can be operated as a stand-alone platform, but is also easily integrated into high-throughput screening workflows. It furthermore allows for many applications beyond chemical screening, such as environmental monitoring of endocrine disruptors or stress research.

We describe the procedure and data analysis of a chemical screening system for glucocorticoid stress hormone signaling using zebrafish larvae: the Glucocorticoid Responsive In vivo Zebrafish Luciferase activitY (GRIZLY) assay. The assay sensitively and specifically detects effects on glucocorticoid signaling by compounds that require metabolization or affect endogenous glucocorticoid production.

Glucocorticoid stress hormones and their artificial derivatives are widely used drugs to treat inflammation, but long-term treatment with glucocorticoids ...

Biology

High Throughput SiRNA Screening for Chloropicrin and Hydrogen Fluoride-Induced Cornea Epithelial Cell Injury

Toxicant-induced ocular injury is a true ocular emergency because chemicals have the potential to rapidly inflict significant tissue damage. Treatments for toxicant-induced corneal injury are generally supportive as no specific therapeutics exist to treat these injuries. In the efforts to develop treatments and therapeutics to care for exposure, it can be important to understand the molecular and cellular mechanisms of these injuries. We propose that utilization of high throughput small inhibitory RNA (siRNA) screening can be an important tool that could help to more rapidly elucidate the molecular mechanisms of chemical cornea epithelial injury. siRNA are double stranded RNA molecules that are 19-25 nucleotides long and utilize the post-transcriptional gene silencing pathway to degrade mRNA which have homology to the siRNA. The resulting reduction of expression of the specific gene can then be studied in toxicant exposed cells to ascertain the function of that gene in the cellular response to the toxicant. The development and validation of in vitro exposure models and methods for the high throughput screening (HTS) of hydrogen fluoride- (HF) and chloropicrin- (CP) induced ocular injury are presented in this article. Although we selected these two toxicants, our methods are applicable to the study of other toxicants with minor modifications to the toxicant exposure protocol. The SV40 large T antigen immortalized human corneal epithelial cell line SV40-HCEC was selected for study. Cell viability and IL-8 production were selected as endpoints in the screening protocol. Several challenges associated with the development of toxicant exposure and cell culture methods suitable for HTS studies are presented. The establishment of HTS models for these toxicants allows for further studies to better understand the mechanism of injury and to screen for potential therapeutics for chemical ocular injury.

High throughput small inhibitory RNA screening is an important tool that could help to more rapidly elucidate the molecular mechanisms of chemical cornea epithelial injury. Herein, we present the development and validation of exposure models and methods for the high throughput screening of hydrogen fluoride- and chloropicrin-induced cornea epithelial injury.

Toxicant-induced ocular injury is a true ocular emergency because chemicals have the potential to rapidly inflict significant tissue damage. Treatments ...

Startle Assay for Toxicity Evaluation in Zebrafish Embryo

Evaluating Toxicity of Chemicals using a Zebrafish Vibration Startle Response Screening System

We developed a simple screening system for the evaluation of neuromuscular and general toxicity in zebrafish embryos. The modular system consists of electrodynamic transducers above which tissue culture dishes with embryos can be placed. Multiple such loudspeaker-tissue culture dish pairs can be combined. Vibrational stimuli generated by the electrodynamic transducers induce a characteristic startle and escape response in the embryos. A belt-driven linear drive sequentially positions a camera above each loudspeaker to record the movement of the embryos. In this way, alterations to the startle response due to lethality or neuromuscular toxicity of chemical compounds can be visualized and quantified. We present an example of the workflow for chemical compound screening using this system, including the preparation of embryos and treatment solutions, operation of the recording system, and data analysis to calculate benchmark concentration values of compounds active in the assay. The modular assembly based on commercially available simple components makes this system both economical and flexibly adaptable to the needs of particular laboratory setups and screening purposes.

Author Spotlight: High-Throughput Toxicity Screening Using Zebrafish Embryo Startle Response Assay

We describe a screening system's workflow and data analysis for evaluating chemical compound toxicity based on the zebrafish embryo vibration startle response. The system records the movements of zebrafish embryos upon exposure to a vibration stimulus and allows for an integrated evaluation of general toxicity/lethality and neuromuscular toxicity.

We developed a simple screening system for the evaluation of neuromuscular and general toxicity in zebrafish embryos. The modular system consists of ...

In Silico Modeling Method for Computational Aquatic Toxicology of Endocrine Disruptors: A Software-Based Approach Using QSAR Toolbox

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