Predicting Aquatic Toxicity By Trend Analysis

QSAR Application QSAR Application Toolbox:Toolbox:

Second steps: Second steps: Predicting Predicting aquaticaquatic toxicity by trend toxicity by trend

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Background

• This is a step-by-step presentation designed to take the first time user of the Toolbox through the workflow of a data filling exercise.

Objectives

• This presentation demonstrates a number of functionalities of the Toolbox :– Identify analogues for a target

chemical,

– Retrieve experimental results available for those analogues, and

– Fill data gaps by trend-analysis.

Specific Aims

• To introduce the first time user to the work flow of the Toolbox,

• To familiarize the first time user with the six modules of the Toolbox,

• To familiarize the first time user with the basic functionalities within each module,

• To explain to the first time user the rationale behind each step of the exercise.

The Exercise• In this exercise we will predict the acute

toxicity to daphnids for an untested compound, (3-ethyl-5-methyl-4-methoxyphenol), which will be the “target” chemical.

• This prediction will be accomplished by collecting a small set of test data for chemicals considered to be in the same category as the target molecule.

• The category will be defined using the following categorization schemes:

- EcoSAR – for structural grouping- OASIS acute aquatic MOA – mechanistic

grouping

Trend analysis

• For a given (eco)toxicological endpoint, the members of a category are often related by a trend (e.g. increasing, decreasing or constant). The trend could be related to molecular mass, carbon chain length, or to some other physicochemical property.

• A demonstration of consistent trends in the behaviour of a group of chemicals is one of the desirable attributes of a chemical category and one of the indicators that a common mechanism for all chemicals is involved. When some chemicals in a category have measured values and a consistent trend is observed, missing values can be estimated by simple scaling from the measured values to fill in the data gaps.

Tracks

• After opening the Toolbox, the user has to choose between three use tracks (or workflows):– (Q)SAR Track– Category Track– Flexible Track

• For a first time users of the Toolbox, it is recommended to select the Category Track.

• As the user becomes more familiar with the functionalities of the Toolbox, it is recommended to use the Flexible Track.

Workflow

• Each track follows the same general six-step workflow:– Chemical Input– Profiling– Endpoints– Category Definition– Filling Data Gaps– Reporting

Tracks and Workflow

Chemical Input• Click on the “Category Track”.• This takes you to the first module,

which is “Chemical input”. • This module provides the user with

several means of entering the chemical of interest or the target chemical.

• Since all subsequent functions are based on chemical structure, the goal here is to make sure the molecular structure assigned is the correct one.

Chemical Input Screen

Ways of Entering a Chemical

• There are several ways to enter a target chemical.

• Most often used are:– Chemical Abstract Services (CAS) number (#),– SMILES (simplified molecular information line

entry system) notation, and– Drawing the structure using the structural

editor included in the Toolbox.

• Click on Drawing.

Drawing target chemical 3-ethyl-5-methyl-4-methoxyphenol

Target Chemical• The Toolbox now searches the Toolbox databases

and inventories for the presence of the drawn chemical structure. It is displayed as a 2D image.

• Click on the box next to “Substance Information”; this displays the chemical identification information.

• Note that no CAS number or name is displayed for this chemical. It means that it is not listed in the chemical inventories implemented in the Toolbox.

• The workflow on the first module is now complete, click “Next”.

Chemical Identification Information

Profiling

• “Profiling” refers to the electronic process of retrieving relevant information on the target compound, other than environmental fate, ecotoxicity, and toxicity data, which are stored in the Toolbox database.

• Available information includes likely mechanism(s) of action.

Side-Bar to Profiling

• Detailed explanations of the different profilers are provided in the Manual accompanying the Toolbox.

• In addition, for most of the profilers, background information can be retrieved by highlighting one of the profilers (for example OASIS acute aquatic MOA, Protein Binding ) and clicking on the button “Show Category Boundaries’’.

Highlight “Protein binding“ andClick on ‘’Show Category Boundaries’’

Insert Window Appears with the Explanation

of Protein binding category

Profiling Target Chemical• Select the “Profiling methods” you wish

to use by clicking on the box before the name of the profiler.

• This selects (a red check mark appears) or deselects(red check disappears) profilers.

• For this example check the following mechanistic categorization methods which are relevant for aquatic toxicity:

• - EcoSAR – for structural grouping- OASIS acute aquatic MOA – mechanistic grouping- Protein binding – mechanistic grouping- Verhaar categorization – grouping by reactivity

• Click on “Apply” (see next slide).

Profilers for 3-ethyl-5-methyl-4-methoxyphenol

Profiling• The actual profiling will take several seconds

depending on the number and type of profilers selected.

• The results of profiling automatically appear as a dropdown box under the target chemical (see next slide).

• Please note the specific profiling results by ECOSAR Classification and OASIS Acute Toxicity MOA.

• These results will be used to search for suitable analogues in the next steps of the exercise.

• Click on “Next” to move to the module “Endpoints”.

Profiles of 3-ethyl-5-methyl-4-methoxyphenol

Endpoints

• “Endpoints” refer to the electronic process of retrieving the environmental fate, ecotoxicity and toxicity data that are stored in the Toolbox database.

• Data gathering can be executed in a global fashion (i.e., collecting all data of all endpoints) or on a more narrowly defined basis (e.g., collecting data for a single or limited number of endpoints).

This Example

• In this example, we limit our data gathering to common aquatic toxicity endpoints from databases containing aquatic toxicity data (ECETOX, ECOTOX and OASIS Aquatic).

• Click on the box with a list of database names and select them.

• Click on “Gather data” (see next slide).

Data Gathering

Next Step in Data Gathering

• Toxicity information on the target chemical is electronically collected from the selected datasets.

• In this example, an insert window appears stating there was “no data found” for the target chemical (see next slide).

• Close the insert window.

No data for Target Chemical

Recap • You have entered the target chemical

being sure of the correct structure.

• You have profiled the target chemical and found no experimental data is currently available for this structure.

• In other words, you have identified a data gap, which you would like to fill.

• Click on “Next” to move to the category definition module.

Category Definition

• This module provides the user with several means of grouping chemicals into a toxicologically meaningful category that includes the target molecule.

• This is the critical step in the workflow.

• Several options are available in the Toolbox to assist the user in defining the category definition.

Grouping Methods

• The different grouping methods allow the user to group chemicals into chemical categories according to different measures of “similarity” so that within a category data gaps can be filled by trend-analysis.

• For example, starting from a target chemical for which a specific EcoSAR classification is identified, analogues can be found with the same classification and for which experimental results are available.

Side Bar to EcoSAR Categorization

• EcoSAR has been used by the U.S. Environmental Protection Agency since 1981 to predict the aquatic toxicity of new industrial chemicals in the absence of test data.

• SARs are developed for chemical classes (e.g. phenols) based on measured test data that have been submitted by industry or they are developed from other data sources.

• Toxicity values for new chemicals may then be calculated from the resulting regression equation.

Side Bar to EcoSAR Categorization

• To date, over 150 SARs have been developed for more than 50 chemical classes.

• These chemical classes range from the very large, e.g., neutral organics, to the very small, e.g., aromatic diazoniums. Some chemical classes have only one SAR, such as acid chlorides, for which only an equation for 96-hour LC50 for fish has been developed.

• The EcoSAR classification in the Toolbox is used for grouping of chemicals by structural (mechanistic) similarity

EcoSAR category definition

To define a category based on an EcoSAR classification:

• Highlight “EcoSAR classification”.

• Click on “Defining Category” (see next slide).

Defining the Category

Confirmation

• An insert window confirming the EcoSAR phenols classification of the target chemical appears.

• Click on “OK”.

Naming Category

• Another insert window listing the default category name appears.

• Click on “OK”.

Analogues

• The Toolbox now identifies all chemicals corresponding to the ECOSAR classification of “phenols” which are listed in the databases selected under “Endpoints”.

• 559 analogues are identified.• These 559 compounds along with the

target chemical form a category (Phenols), which can be used for data filling (see next slide).

Mechanistic Analogues

Recap

• You have identified a mechanistic category (EcoSAR “phenols” classification) for the target chemical (3-ethyl-5-methyl-4-methoxyphenol).

• The available experimental results for these 559 chemicals can now be collected from the previously selected databases (ECETOC, ECOTOX and OASIS Aquatic).

Next Step in Gathering Data

• Highlight the category “[559] Phenols (EcoSAR classification)”.

• The inserted window entitled “Read Data?” appears (see next slide).

• Click OK.

Reading the Selected Data

Reading the Selected Data

• Select mode of reading data.

• Click OK.

Due to the overlap between the Toolbox databases same data for intersecting chemicals could be found simultaneously in more than one database.  The data redundancy is identified and the user has the opportunity to select either a single data value or all data values.

Summary of Aquatic toxicity Information for Analogues

Side-Bar on Data

• Note the structure of the 599 compounds with experimental results are shown.

• Double clicking on a structure enlarges the view of the structure.

Side-Bar on DataDouble clicking on a structure enlarges

the view of the structure

Navigating Through the Data Tree

• The user can navigate through the data tree by closing or opening the nodes of the tree.

• In this example, results from aquatic toxicity are available.

• By double clicking on a cell in the data matrix, additional information on the test result (for example Daphnia magna for LC50 48h) is made available (see next slide).

Navigating Through the Data Tree

By double clicking on a cell in the data matrix, additional

information on the test result (for example Daphnia magna

for LC50 48h) is made available

Recap• You have now retrieved the available

experimental data on aquatic toxicity for 599 chemicals with classified as “phenols” by EcoSAR, which were found in the databases ECETOC,ECOTOX and OASIS Aquatic.

• You are ready to fill the data gap.

• Click on “Next” to access the module “Filling data gaps”.

The Filling Data Gap Window

• Take a moment to examine the filling data matrix on the next slide.

• Note it contains– information on the chemicals, which

form the category,– the 3 options for data filling, and– a means of selecting data points used

to fill the data gap (see next slide).

The Filling Data Gap Window

Filling Data Gaps• This step in the work flow provides the

user with three options for making an endpoint-specific prediction for the target molecule.

• As noted earlier, these options, in increasing order of complexity, are– by read-across,– by trend analysis, and– through the use of external QSAR

models.• In this example we only use trend

analysis.

Filling Data Gaps

Selecting the Data Points

• Before applying trend analysis, the Toolbox allows the user to decide which type of results should be used in case more than one result is available for any analogue, (i.e., all values, average values, minimum or maximum results) (see next slide).

• Select “Average value”• It should be noted that averaging results

is only useful for quantitative endpoints.

Data Point Selection

Applying Trend-analysis

• Highlight the data endpoint box corresponding to Daphnia magna / LC50 / 48 h under the target chemical. It will be empty as it is the data gap.

• Next, with the “trend analysis” box highlighted, click “Apply” (see next slide).

Apply Trend-analysis

Results of Trend-analysis

Interpreting the Trend-analysis• The resulting plot outlines the experimental

LC50 results of all analogues (Y axis) according to a descriptor (X axis) with Log Kow being the default descriptor (see next slide).

• The RED dot represents the estimated result for the target chemical.

• The BLUE dots represent the experimental results available for the analogues.

• The GREEN dots (see following slides) represent analogues belonging to different subcategories.

• Before accepting the estimated result for the target chemical, the trend analysis should be further refined by subcategorisation (see following slides)

Side-Bar to Subcategorisation• In the Toolbox, a category refers to a

group of chemicals which have the same profiling result according to one of the profilers listed in the module “Profiling”

• Subcategorisation refers to the process of applying additional profilers to the previously defined category, identifying chemicals which have differing profiling results and eventually eliminating these chemicals from the category.

The analogues which are dissimilar to the target chemical with respect to:- Substance type (mixtures and hydrolizing chemicals)

The categorization based on substance type allows keeping among the analogues only those that are of the same chemical type: discrete chemicals, mixtures, polymers, inorganics, organometalics. The current target is a discrete chemical hence the analogues should also be discrete chemicals.

- OASIS Mode of action (all except phenols and anilines)The categorization based on mode of action identifies analogues having the same mode of action as the target which is in the group of phenols and anilines.

can be removed from the initial list of analogues previously defined by EcoSAR classification.

Subcategorization

Subcategorization (1)

• Click on “Subcategor”

• Select “Substance type” from the Grouping methods list.

• Click on “Remove” to eliminate chemicals with a different substance type.

Subcategorization (1)One salt (hydrolyzing chemical) has to be eliminated among the analogues being of a different substance type compared to the target (discrete chemical)

Subcategorization (2)

• Click on “Subcategor.”

• Select “OASIS acute aquatic MOA” from the Grouping methods list.

• Click on “Remove” to eliminate chemicals with a different mode of action.

Subcategorization(2)

Results

Filled Data Gap

• The remaining chemicals in the graph now all have a consistent profile relevant for aquatic toxicity (i.e. substance type, ECOSAR classification and OASIS Acute Toxicity MOA)

• By accepting the prediction (click on “Accept”) the data gap is filled (see next slide).

• Click on “Next” to access the report module.

Filled Data Gap

Report• The final step in the workflow,

report, provides the user with a downloadable written audit trail of what the Toolbox did to arrive at the prediction.

• Click on “Show history”.• This study history can be printed

or copied to be inserted in a more detailed report (see next slide).

• Click on “Finish”.

Finished