QSAR Application Toolbox: Third Step Data Gap Filling ReadAcross by Molecular Similarity

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QSAR Application ToolboxThird Step - Data Gap
Filling(Read-Across by Molecular Similarity)
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Background

• This is a step-by-step presentation designed to
  take the you through the workflow of the Toolbox
  in a data-gap filling exercise using read-across
  based on molecular similarity with data pruning.
• If you are a novice user of the Toolbox you may
  wish to review the Getting Started document
  available at www.oecd.org/env/existingchemicals/q
  sar

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Objectives-1

• This presentation reviews a number of
  functionalities of the Toolbox
• Entering and Profiling a target chemical,
• Identifying analogues for a target chemical,
• Retrieving experimental results available for
  those analogues, and
• Filling data gaps by read-across.

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Objectives-2

• This presentation also introduces several other
  functionalities of the Toolbox
• Use of the Flexible Track
• Entering a target chemical by SMILES notation,
• Identify analogues for a target chemical by
  molecular similarity,
• Retrieve experimental results for multiple
  endpoints

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Specific Aims

• To review the work flow of the Toolbox.
• To review the use of the six modules of the
  Toolbox.
• To review the basic functionalities within each
  module.
• To introduce the user to new functionalities with
  selected modules
• To explain to the rationale behind each step of
  the exercise.

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The Exercise

• In this exercise we will predict the Ames
  mutagenicity potential for an untested compound,
  (n-hexanal) SMILES CCCCCCO), which is 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 by molecular
  similarity, in particular Organic functional
  groups.
• The prediction itself will be made by
  read-across.

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Read-across the Analogue Approach

• Read-across can be used to estimate missing data
  from a single or limited number of chemicals
  using an analogue approach.
• In the analogue approach, endpoint information
  for a single or small number of tested chemicals
  is used to predict the same endpoint for an
  untested chemical that is considered to be
  similar.

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Analogous Chemicals

• Previously you learned that analogous sets of
  chemicals are often selected based on the
  hypothesis that the toxicological effects of each
  member of the set will show a common behavior.
• For this reason mechanistic profilers and
  grouping methods have been shown to be of great
  value in using the Toolbox.
• However, there are cases where the mechanistic
  profilers and grouping methods are inadequate and
  one is forced to rely on molecular similarity to
  form a category.
• The Toolbox allows one to develop a category by
  using either organic functional groups or
  structural similarity.
• Since there is no preferred way of identifying
  structural similarity the user is guided to use
  organic functional groups as a first option.

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Side-Bar On Mutagenesis

• Mutagens do not create mutations.
• Mutagens create DNA damage.
• Mutations are changes in nucleotide sequence.
• Mutagenesis is a cellular process requiring
  enzymes and/or DNA replication, thus cells create
  mutations.

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Tracks

• After opening the Toolbox, the user has to choose
  between three use tracks (or workflows)
• (Q)SAR Track
• Category Track
• Flexible Track
• Since you are becoming more familiar with the
  functionalities of the Toolbox, select the
  Flexible Track.

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Tracks and Workflow
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Workflow

• Remember each track follows the same workflow
• Chemical Input
• Profiling
• Endpoints
• Category Definition
• Filling Data Gaps
• Reporting

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Chemical Input

• Click on the Flexible 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.

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Chemical Input Screen
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Ways of Entering a Chemical

• Remember there are several ways to enter a target
  chemical and the most often used are
• CAS,
• SMILES (simplified molecular information line
  entry system) notation, and
• Drawing the structure.
• Click on SMILES.
• This inserts the window entitled Structure
  editor (see next slide).

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Blank Structure Editor Screen
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Entering a SMILES

• In the Aqua-colored area type in the SMILES in
  this example enter CCCCCCO
• Note as you type the SMILES code the structure is
  being drawn in the center of the field (see next
  slide).
• Click OK to accept the target chemical.

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SMILES STRUCTURE
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Target Chemical

• You have now selected your target chemical.
• Click on the box next to Substance Information
  this displays the chemical identification
  information.
• It is important to remember from here on the
  workflow will be based on the structure coded in
  SMILES.
• The workflow on the first module is now complete
  click Profiling to move to the next module.

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Chemical Identification Information
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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.
• Available information includes likely
  mechanism(s) of action and a survey of organic
  functional group, which form the target chemical.

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Profiling Target Chemical

• Select the Profiling methods you wish to use by
  red-checking the box before the name of the
  profiler you wish to use.
• For this example, select all the profilers for
  the mechanistic methods (see next slide).
• Click on Apply.

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Profilers for 1-Hexanal
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Profiling

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

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Profiles of 1-Hexanal (1)
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Profiles of 1-Hexanal (2)

• Very specific profiling results are obtained for
  the target compound.
• Please note that no DNA-binding mechanisms was
  identified (see side-bar on mutagenicity above).
• These results will be used to search for suitable
  analogues in the next steps of the exercise.

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Side-Bar on the Data Tree

• As one moves through the different modules of the
  workflow the information on the target chemical
  increases.
• One may find it advantageous to conceal some of
  that information.
• For example we can hide the substance information
  by double clicking on the

-
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Side-Bar on Retrieving Concealed Information

• One can retrieve hidden information by double
  clicking on the
• This is demonstrated in the next two slides.

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Double click on small box next to substance
information
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Substance informationreappears on screen
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Endpoints

• Click on Endpoints to move to the next module.
• Endpoints refer to the electronic process of
  retrieving the environmental fate, ecotoxicity
  and toxicity data that are stored in the Toolbox.
  
• 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).

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Side-Bar on Gene Mutation

• Mutations within a gene are generally
  base-substitutions or small deletions/insertions
  (i.e., frameshifts).
• Such alteration are generally called point
  mutations.
• The Ames scheme based on strains of Salmonella
  provide the corresponding experimental data.

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This Example

• In this example, we focus our data gathering to
  the-multi-endpoint of mutagenicity and the
  databases OASIS Genotox and ISSCAN.
• Click on the boxes next to all the databases
  except those entitled ISSCAN Gentox and OASIS
  Genotox.
• This leaves a black check mark in the box next to
  these two database (the ones we want to search).
• Click on Gather data.

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Oasis Genotox Data Gathering
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Next Step in Data Gathering

• Toxicity information on the target chemical is
  electronically collected from the selected
  dataset(s).
• In this example, an insert window appears stating
  there was no data found for the target chemical
  (see next slide).
• Close the insert window.

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No data for Target Chemical
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Recap

• You have entered the target chemical by SMILES
  and found it to be 1-hexanal with the CAS
  66-25-1.
• You have profiled the target chemical and found
  no experimental data is currently available for
  1-hexanal.
• In other words, you have identified a data gap,
  which you would like to fill.
• Click on Category definition to move to the
  next module.

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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 refining the category
  definition.

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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.
• For example, starting from a target chemical for
  which a specific DNA binding mechanism is
  identified, analogues can be found which can bind
  by the same mechanism and for which experimental
  results are available.

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Side-Bar on Mutagens

• It is important to remember that mutagens are
  really cell-damaging agents, which can create a
  wide array of adverse effects beyond damage to
  DNA.
• Lets take a moment to review our mechanistic
  profile of the target chemical (see next slide).

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No DNA Binding
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Defining the Category

• In the case of 1-hexanal there is no structural
  evidence that it is a DNA binding compound.
• Therefore, no grouping by a DNA mechanism is
  possible.
• We elect to define the category by using
  molecular similarity.
• Highlight Organic functional groups.
• Click on Defining Category.

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Defining the Category
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Confirmation of Groups

• An insert window listing the organic function
  groups of the target chemical appears.
• Click on OK.

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Naming Category

• Another insert window listing the default
  category name appears.
• Click OK.

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Analogues Identified
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Recap

• You have identified a structurally similar
  category for the target chemical
  (1-hexanal).
• There were 34 similar chemicals identified.
• Available data on these similar chemicals can now
  be collected.

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Next Step in Gathering Data

• Highlight the 35Aldehydes ltANDgtMethyl under
  Single Chemical in the Defined Categories
  box.
• The inserted window entitled Read Data?
  appears (see next slide).

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What data to collect?
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Side-Bar to Data Collection

• Data can be collected for a wide variety of
  endpoints or for narrowly defined (e.g.,
  endpoint, test scheme) ones.
• Since data is endpoint specific the data
  selection is presented in a drop-down menu.
• By double clicking on an endpoint, the data tree
  is expanded.

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Data Selection

• To select the data to be read you click on the
  box(s) before the name of the data type.
• This selects (a red check mark appears) or
  deselects (red check disappears) the data type.
• Click on the box next to Toxicological
  Information.
• This places a red check mark in the box next to
  this data type (the one we want to read).
• Click on OK (see next slide).

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Reading the Selected Data
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Analogues

• The data is automatically collated.
• There is genotox data on only 15 of the 34
  structurally similar analogues.
• However, multiple entries of the same test result
  were found and one wants to eliminate
  duplications (see next slide).

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Click Select Single then Click OK
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Summary of Toxicological Information for Analogues
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Side-Bar on Data

• Note the structure of the compounds with
  experimental results is shown.
• Double clicking on any structure enlarges the
  view of the structure.
• Details on the experimental results can be
  retrieved by double-clicking on any cell in the
  data matrix line.

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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 genotox testing are
  available.
• By double clicking on a cell in the data matrix,
  additional information on the test result (Ames)
  is made available (see next slide).

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Data Tree
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Side-Bar on Data Tree

• Details about the specific assays, in this case
  the different strains of Salmonella typhimurium
  can be observed at the bottom of the screen by
  placing the cursor on the text fragment of the
  test you want more information about (see next
  slide).

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Filling Data Gap

• You are ready to fill the data gap. Click on
  Filling data gap.
• In this step in the work flow the user is
  provided three options for making a prediction
  for the target molecule.
• In this example with qualitative mutagenicity
  data we can only use read-across. Click on
  Read-across
• Highlight the blank space in the
  AMES_mutagenicity line under the column for the
  target chemical
• Click on All values under data.
• Click on Apply (see next slide).

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Filling Data Gap
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Possible Data Inconsistencies

• An insert window alerting you to possible data
  inconsistencies appears.
• Click on the small box before Endpoint.

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Multiple Endpoint Data

• In this example what appears is a red checked
  listing of all the Ames data, which you are
  trying to model at the same time.
• Click OK.

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Results of Read-across
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Interpreting the Read-across Figure

• The resulting plot is experimental results of all
  analogues (Y axis) according to a descriptor (X
  axis) with the default descriptor of log Kow.
• Note the dots along the bottom of the previous
  screen. The RED dot represents the target
  chemical, while the PURPLE dots the experimental
  results available for the analogues, which are
  used for the read-across the BLUE dots represent
  the experimental results available for the
  analogues but not used for read-across.

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Interpreting the Read-across Figure

• Upon further examination of the read-across
  results, noted in an upper corner is an
  aqua-color dot.
• This represents the lone positive result in the
  Ames tests.
• By placing the cursor on this dot and double left
  clicking the structural details of this chemical
  appear (see next slide).

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Details of Outlier
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Can One Prune the Outlier?

• Clearly this outlier is structurally dissimilar
  to all the other compounds in the category,
  including the target chemical.
• It contains a number of functional groups which
  are not present in the target molecule.
• This dissimilarity is justification for deleting
  (pruning) it from the category.

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Pruning the Outlier

• Close the structure detail window.
• Place the cursor on outlying dot and right click.
• then click on remove focused.
• This removes the outlier.
• Note the read-across results are automatically
  re-tabulated (see next slide).

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Re-evaluated Read-across
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Interpretation of Read-across

• In pruned data, all 10 analogues are
  non-mutagenic in all the Ames assays.
• The same non-mutagenic potential (value 0f -1.0)
  is, therefore, predicted with confidence for the
  target chemical.

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Filled Data Gap

• Click Accept.
• By accepting the prediction the data gap is
  filled (see next slide).
• You are now ready to complete the final module
  and download the report.
• Click on Report to move to the last module.

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Filled Data Gap
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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).

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Report