QSAR Application Toolbox: Step 4: Category pruning capabilities - PowerPoint PPT Presentation

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QSAR Application Toolbox: Step 4: Category pruning capabilities

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... proteins are relevant to grouping chemicals that may act as ... Highlight 'Protein binding' in the list of 'Grouping methods' Click on 'Defining category' ... – PowerPoint PPT presentation

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Title: QSAR Application Toolbox: Step 4: Category pruning capabilities


1
QSAR Application ToolboxStep 4 Category
pruning capabilities
2
Objectives
  • This presentation demonstrates a number of
    functionalities of the Toolbox
  • Category definition by protein binding mechanism
  • Identifying and removing (pruning) from the
    category chemicals that have additional
    protein-binding mechanisms
  • Filling data gaps by trend-analysis.

3
The Exercise
  • In this exercise we will predict the toxicity
    towards the cilliate Tetrahymena pyriformis, of
    the substance Hexanal, 4-Methyl (CAS Nr
    41065-97-8), which is called the target
    chemical.
  • This prediction will be accomplished by
    collecting experimental results for a set of
    chemicals considered to be in the same category
    as the target molecule.
  • The category definition will be based on Protein
    binding mechanism.
  • The Trend-analysis will be used for data gap
    filling.

4
Input of target chemical by CAS Number
5
Chemical Identification Information
6
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.

7
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 all 8 mechanistic
    methods.

8
Profilers for Hexanal, 4-Methyl
9
Profiles of Hexanal, 4-Methyl
Profiling results for hexanal,4-methyl -Very
specific profiling results are obtained for the
target compound -Please note the specific
protein-binding profile -These results will be
used to search for suitable analogues in the next
steps of the exercise
10
Endpoints
  • Endpoints refer to the electronic process of
    retrieving the measured data for environmental
    fate, ecotoxicity and toxicity 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).
  • In this example, we limit our data gathering to
    common toxicity endpoints from all databases
    except Danish EPA (where only estimated results
    are stored).

11
Data Gathering
12
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.

13
No data for target chemical
14
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.
  • You have identified a data gap, which you would
    like to fill.

15
Category Definition
  • This module provides the user with several means
    of grouping chemicals into a toxicologically
    meaningful category that includes the target
    molecule
  • The target chemical (Hexanal,4-methyl) could
    react with proteins and thus has a potential for
    excess aquatic toxicity.
  • Therefore mechanisms by which the target
    chemical binds with proteins are relevant to
    grouping chemicals that may act as aquatic
    toxicants, so we have mechanistic reasons for
    defining our category based on a specific
    protein-binding mechanism.
  • Highlight Protein binding in the list of
    Grouping methods
  • Click on Defining category

16
Defining the Category
17
Category results
The category of chemicals with the same protein
binding mechanism (Schiff base formation)
consists of 184 mechanistic analogues.
18
Gathering Data
  • Highlight the category of 184 analogues with
    the same Protein binding mechanism
  • The inserted window entitled Read Data?
    appears (see next slide).
  • Click OK.

19
Summary of Aquatic toxicity Information for
Analogues
20
Reading the Selected Data
  • Select the mode of reading data. In this case
    click on Select single to eliminate any double
    entries in the databases.
  • Click OK.

21
Data Tree
  • All the analogues with their available
    experimental results are inserted into the data
    matrix (see next slide).
  • Open the data tree by double-clicking on the
    nodes of the data tree, to access the results for
    Tetrahymena pyriformis
  • Ecotoxicological information
  • Aquatic Toxicity
  • Protozoa
  • Tetrahymena pyriformis
  • IGC50

22
Data Tree
23
The Filling Data Gap Window
  • Move to the module Filling data gap
  • 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.

24
The Filling Data Gap Window
25
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 QSAR models.
  • In this example we only use trend analysis.

26
Filling Data Gaps
27
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) .
  • It should be noted that averaging results is
    only useful for quantitative endpoints, which is
    the case in this example.

28
Data Point Selection
29
Applying Trend-analysis
  • Highlight the data endpoint box corresponding to
    IGC50 for Tetrahymena pyriformis under the
    target chemical.
  • It should be empty as this is the data gape we
    are trying to fill.
  • Next with the trend analysis box highlighted,
    click Apply.

30
Data Point Selection
The trend analysis is chosen, because we have a
quantitative endpoint and enough data.
31
Results of Trend-analysis
32
Interpreting the Trend-analysis
  • The resulting plot shows the experimental results
    (IGC50-48h) of all analogues (Y axis) according
    to the default descriptor Log Kow (X axis).
  • The RED dot represents the target chemical.
  • The BLUE dots represent the experimental results
    available for the analogues.
  • The GREEN dots (see following slides) represent
    the analogues belonging to different
    subcategories.

33
An Accurate Trend Analysis of the Data set
  • Due to the polyfunctionality of the molecules,
    there are analogues with additional protein
    binding mechanisms (e.g. Michael-type
    nucleophilic addition and nucleophilic addition
    to azomethynes) different from those of the
    target compound.
  • There are analogues with organic functional
    group different than those of the target - for
    example alkane, arene, benzyl.
  • These analogues can be identified via
    subcategorisation.

34
An Accurate Trend Analysis of the Data set
  • Two subsequent subcategorisations are applied to
    prune the analogues
  • Having protein binding mechanism different than
    that of the target (Subcategorisation 1)
  • Which are structurally dissimilar to target
    i.e., have different organic functional groups
    than those of the target (Subcategorisation 2).

35
Subcategorization(1)
36
An Accurate Trend Analysis of the Data set
  • A mechanistic transparency is provided for
    different subsets of analogues
  • Highlight the different interaction mechanisms
    associated with analogues in the
    subcategorisation window
  • By right clicking one could see the chemicals
    with the specified mechanism
  • Also, by double clicking on a dot in the graph,
    detailed information can be obtained for the
    structural and parametric boundaries of
    underlying binding mechanism.

37
An Accurate Trend Analysis of the Data set
By right clicking one can see the chemicals with
the specified mechanism
38
An Accurate Trend Analysis of the Data set
39
An Accurate Trend Analysis of the Data set
By double clicking on a dot in the graph,
detailed information can be obtained for the
structural and parametric boundaries of
underlying binding mechanism
Click to see detailed information
Click to see detailed information
40
Subcategorization(1)
41
Subcategorization(2)
42
Results
43
Filled Data Gap
  • The predicted target value can be accepted.
  • Click on Accept.
  • The estimated result is inserted into the data
    matrix (see next slide)

44
Filled Data Gap
45
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 Study history.
  • This study history can be printed or copied to be
    inserted in a more detailed report (see next
    slide).

46
Report
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