Introduction to OECD QSAR Toolbox

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  • The simplest exercise in QSAR is canonical ordering which starts with choosing a group of chemicals, and a selected property or biological activity for each. In this slide, nine chemicals are listed with their boiling points. If we think we understand how chemical structure relates to boiling point, we would expect that those molecular descriptors would place the chemicals in the same order as would the boiling point.
  • In this slide, the chemicals are sorted by increasing boiling point. Can we identify molecular descriptors that create the same order. If not, we do understand the inter and intramolecular forces that control boiling point. If QSAR can order them properly, the task is then to find chemicals that fit between these values an test the QSAR model. Through numerous iterations , theoretical explanations can be evaluated for relevance and the important molecular descriptors are discovered. This came approach can be used for toxicity data provided a similar toxicity mechanism can be expected for the chemicals.
  • In this example, I am illustrating that there are many toxicity mechanisms, and if all the chemicals having the same mechanism are compiled, it would not be unusual for the potency of those chemicals to range over 8-10 orders of magnitude. Even if the range were much less, the first challenge for QSAR would be to identify a molecular descriptor that places the chemicals in the same order as the potency measures (LC50). To illustrate, I am using aquatic lethality with fish just to move away from the rodent inhalation example, but keep in mind that a fish test is just an inhalation test with aquatic organisms.
  • For many mechanisms, uptake of the chemicals is controlled by passive transport and one would expect the octanol/water partition coefficient to covary with passive transport. When the entire range of potency values are plotted vesus Log Ko/w, the chemicals remain in the same order and quantitative relationship between LC50 and Ko/w can be derived exactly like that for the rodent inhalation data.
  • Introduction to OECD QSAR Toolbox

    1. 1. Introduction to QSAR<br /><ul><li>Welcome to this online introduction of QSAR which gives a basic understanding of QSAR and why a QSAR Toolbox is needed
    2. 2. Risk assessments are based on test data, and QSAR is not needed if you have lot’s of data
    3. 3. However, if data gaps exist, one can defer the hazard assessment or use QSAR </li></li></ul><li>Introduction<br /><ul><li>Fewer than 10,000 chemicals have been tested for the major hazards
    4. 4. The world inventory of produced chemicals exceeds 160,000 chemicals
    5. 5. The world capacity for SIDS initial hazard assessments is only ~500 chemicals/year</li></li></ul><li>Introduction<br /><ul><li>Therefore, even initial assessments based on test ing discrete chemicals is not possible for most chemicals
    6. 6. Also, priority setting for 130,000 chemicals will require faster testing or better models
    7. 7. In QSAR, estimating behavior of untested chemicals has been used for >60 years </li></li></ul><li>An Overview of QSAR<br /><ul><li>Chemistry is based on the premise that similar chemicals will behave similarly
    8. 8. Like most systems, the behavior of a chemical is derived from its structure
    9. 9. Chemical behavior in biological systems is described as biological activity of chemicals </li></li></ul><li>An Overview of QSAR<br /><ul><li>QSAR research searches for relationships between chemical structure and activity
    10. 10. QSAR is the acronym for Quantitative Structure-Activity Relationship
    11. 11. log LC50 (rat, 4hr) = 0.69 log VP + 1.54 is an example of QSAR for lethality in rats</li></li></ul><li>An Overview of QSAR<br /><ul><li>Here, “VP” is the vapor pressure which is measured or estimated from the structure
    12. 12. There are more than 15,000 published QSARs for biological activity endpoints
    13. 13. The term “quantitative” most often pertains to the statistical quality of the “relationship”</li></li></ul><li>An Overview of QSAR<br /><ul><li>QSARs are always associated with endpoint ( i.e.LC50) and an toxicity mechanism (i.e. narcosis, AChE inhib)
    14. 14. Only chemicals causing common toxicity mechanisms lead to a reliable QSAR
    15. 15. Therefore, QSAR must group chemical behavior in terms of toxicity mechanisms </li></li></ul><li>Overview Conclusions<br /><ul><li>QSAR predicts biological activity (endpoints) directly from models of chemical structure
    16. 16. Each QSAR predicts a specific endpoint and only for chemicals with the same mechanism
    17. 17. Errors of choosing the wrong QSAR (mechanism) are larger than model errors</li></li></ul><li>Process for Creating QSAR <br />Choose a well-defined endpoint for biological activity needed in your work<br />Compile measured values using consistent methods for the endpoint --OR<br />Select a series of relevant chemicals and systematically test all for the endpoint<br />Identify “molecular descriptors” which quantify structural attributes for endpoint<br />Statistically evaluate the molecular descriptor-- endpoint relationships (QSAR)<br />
    18. 18. Example for Lethality in Mice<br /><ul><li>Compile data for 30-minute lethality with mice from the anesthesiology literature
    19. 19. Data restricted to alkyl ethers to increase likelihood of a similar toxicity mechanism
    20. 20. Estimate or measure vapor pressure as molecular descriptor (selected from theory or by trial-n-error)
    21. 21. Correlate LC50 with VP to get:</li></ul> log LC50 = 0.57 x log VP + 2.08 <br />
    22. 22.
    23. 23. Example<br /><ul><li>Notice the dependence on VP (slope) is almost the same as with the rat QSAR
    24. 24. Notice the intercept is about 0.5 log units greater for 30 min mouse vs 4 hr rat LC50
    25. 25. Can you suggest reasons for the greater LC50 (lower toxicity) for 30 min mouse ?</li></li></ul><li>Some Important Lessons<br /><ul><li> Vapor pressure correlates with LC50, but many molecular descriptors would not correlate
    26. 26. This QSAR implies vapor pressure is important to the lethality mechanism for these chemicals
    27. 27. Chemicals with other mechanisms (i.e. acrolein, phosgene) will appear as statistical outliers
    28. 28. QSAR provides insights into chemical similarity in terms of common “effect” mechanisms</li></li></ul><li>Some Important Lessons<br /><ul><li> QSAR is an exploration of chemical attributes which reliably predicts their biological activity (biological effects) under specific test conditions
    29. 29. QSAR is also a tool to group chemicals which can be expected to behave similarity (same toxicity pathway under specific test conditions</li></li></ul><li>The Chemical Category Solution<br /><ul><li>Grouping chemicals by similar behavior extrapolates from tested to untested chemicals within a given chemical category
    30. 30. Entire categories of chemicals can be assessed when only a few are tested
    31. 31. Filling missing data (gaps) involves read-across & trend or correlation analysis </li></li></ul><li>What do we mean by Chemical Categories?<br />A group of chemicals that have some features that are common<br />Structurally similar e.g. common substructure<br />Property e.g. similar physicochemical, topological, geometrical, or surface properties<br />Behaviour e.g. (eco)toxicological response underpinned by common modes of action<br />Functionality e.g. preservatives, flavourings, detergents, fragrances<br />
    32. 32. Annex IX of REACH <br />Substances whose physicochemical, toxicological and ecotoxicological properties are likely to besimilar or follow a regular patternas a result of structural similarity may be considered as a group, or “category” of substances. <br />Application of the group concept requires that physicochemical properties, human health effects and environmental effects or environmental fate may bepredicted from data for a reference substancewithin the group by interpolation to other substances in the group (read-across approach). Thisavoids the need to test every substance for every endpoint.<br />
    33. 33. OECD Definition of Category<br /><ul><li>A chemical category is a group of chemicals whose physicochemical and toxicological properties are likely to besimilar or follow a regular patternas a result ofstructural similarity
    34. 34. These structural similarities may create a predictable pattern in any or all of the following parameters: physicochemical properties, environmental fate and environmental effects, and human health effects</li></ul>OECD Manual for Investigation of High Production Volume (HPV) Chemicals. <br />
    35. 35. Forming Chemical Categories<br />Chemical categories have boundary conditions which vary with endpoints<br />Without detailed understanding of metabolism or mechanisms, grouping similarity of behavior is difficult to define.<br />Ironically, examining data trends with different category boundaries is a flexible way to define categories<br />
    36. 36. Canonical Ordering<br />Chemical<br />Amyl amine<br />Amyl chloride<br />Dibromobenzene<br />Ethyl bromide<br />n-Heptanol<br />Methacrolein<br />Methyl-p-anisylketone<br />n-Octane<br />n-Nonane<br />Boiling Point °C<br />103-4<br />98-9<br />219-2<br />38.4<br />192<br />68<br />267-9<br />126<br />151<br />
    37. 37. Canonical Ordering<br />Chemical<br />Ethyl bromide<br />Methacrolein<br />Amyl chloride<br />Amyl amine<br />n-Octane<br />n-Nonane<br />n-Heptanol<br />Dibromobenzene<br />Methyl-p-anisylketone<br />Boiling Point °C<br />38.4<br />68<br />98-9<br />103-4<br />126<br />151<br />192<br />219-2<br />267-9<br />
    38. 38. Modeling Chemical Potency<br />10+2<br />10 0<br />10_2<br />1/LC50<br />(Moles/L)<br />It is not uncommon to find endpoint<br />values spanning 6-10 orders for a <br />single toxicity mechanism <br />10_4<br />10_6<br />10-8<br />1<br />2<br />3<br />4<br />5<br />N &lt; 10,000<br />…....<br />TOXICITY “MECHANISMS”<br />
    39. 39. Modeling Chemical Potency<br />10+2<br />10 0<br />10_2<br />1/LC50<br />(Chemical<br />Activity)<br />10_4<br />10_6<br />10-8<br />0<br />2<br />4<br />6<br />8<br />LOG K o/w<br />
    40. 40. QSAR Methods<br />QSAR fills data gaps by first grouping chemicals and then using existing data within a group to estimate missing values<br />When the chemical group is identified by a common mechanism, QSAR models can accurately describe the trends<br />
    41. 41. Why Do We Need the QSAR Toolbox<br />Defining category boundaries requires the calculation of complex attributes of chemicals to determine which best explains available data<br />In many cases, metabolic simulators are needed to provide metabolic maps and active metabolites <br />To do trend analysis, hundreds of available data must be compiled and flexibly analyzed for trends<br />
    42. 42. Which Metabolite should we use in modeling interactions?<br />Simulated <br />2-Acetylaminofluorene<br /> Metabolism<br />
    43. 43. Adverse Outcome Pathway For<br />A Well-Defined Endpoint<br />Molecular<br />Initiating <br />Event<br />Speciation,<br />Metabolism<br />Reactivity<br />Etc.<br />In Vitro <br />and <br />System <br />Effects<br />In Vivo<br />Adverse <br />Outcomes<br />Parent<br />Chemical<br />Up-Stream Down-Stream<br />CHEMISTRYBIOLOGY<br /> Structure-Activity Levels of Organization<br />
    44. 44. MolecularInitiating Event<br />Macro<br />-Molecular Interactions<br />Toxicant<br />Chemical Reactivity Profiles<br />Receptor, DNA,<br />Protein<br />Interactions<br />Biological Responses<br />Mechanistic Profiling<br />The Adverse Outcome Pathway<br />
    45. 45. MolecularInitiating Event<br />Biological Responses<br />Macro<br />-Molecular Interactions<br />Toxicant<br />Cellular<br />Gene Activation<br />Protein Production<br />Signal Alteration<br />Chemical Reactivity Profiles<br />Receptor, DNA,<br />Protein<br />Interactions<br />NRC Toxicological Pathway<br />The Adverse Outcome Pathway<br />
    46. 46. MolecularInitiating Event<br />Biological Responses<br />Macro<br />-Molecular Interactions<br />Tissue/ Organ<br />Toxicant<br />Cellular<br />Gene Activation<br />Protein Production<br />Signal Alteration<br />Receptor, DNA,<br />Protein<br />Interactions<br />Altered<br />Function <br />Altered Development<br />Chemical Reactivity Profiles<br />Mechanistic Profiling<br />In Vitro &<br />HTP Screening<br />The Adverse Outcome Pathway<br />
    47. 47. MolecularInitiating Event<br />Biological Responses<br />Macro<br />-Molecular Interactions<br />Toxicant<br />Cellular<br />Organism<br />Organ<br />Population<br />Lethality<br />Sensitization<br />Birth Defect<br />Reproductive Impairment<br />Cancer<br />Gene Activation<br />Protein Production<br />Signal Alteration<br />Altered<br />Function <br />Altered Development<br />Chemical Reactivity Profiles<br />Receptor, DNA,<br />Protein<br />Interactions<br />Structure<br />Extinction<br />Mechanistic Profiling<br />In Vivo<br />Testing<br />In Vitro &<br />HTP Screening<br />The Adverse Outcome Pathway<br />
    48. 48. Major Pathways for Reactive Toxicity from Moderate Electrophiles<br />Interaction<br />Mechanisms<br />Molecular<br />Initiating<br />Events<br />In vivo<br />Endpoints<br />Exposed<br />Surface<br />Irritation<br />Michael<br />Addition<br />Schiff base<br />Formation<br />SN2<br />Acylation<br />Atom<br />Centered<br /> Irreversible<br />(Covalent)<br />Binding <br />Necrosis<br />Which Tissues?<br />Pr-S Adducts<br />GSH Oxidation<br />GSH Depletion<br />NH2 Adducts<br />RN Adducts<br />DNA Adducts<br />Oxidative <br />Stress<br />Systemic<br /> Responses<br />Skin<br />Liver<br />Lung<br />Systemic<br />Immune<br />Responses<br />Dose-Dependent Effects<br />
    49. 49. Organization for Economic Co-operation and Development<br />QSARApplication Toolbox<br />-filling data gaps using available information- <br />Training Workshop<br />Barcelona<br />
    50. 50.
    51. 51. Organization for Economic Co-operation and Development<br />QSAR Application Toolbox<br />-filling data gaps using available information- <br />Historical Notes<br /><ul><li>First “organized” discussions – ‘Red Lobsters’, Duluth - 1992
    52. 52. Organized actions of EU and OECD – coming with REACH
    53. 53. The role of the “revolutionary” notions – category, analogues
    54. 54. OECD and EU Guidance documents on ‘Category’, ‘QSAR’
    55. 55. Need for translation documents into a working machinery</li></li></ul><li>Organization for Economic Co-operation and Development<br />QSAR Application Toolbox<br />-filling data gaps using available information- <br />General Objectives<br /><ul><li>Improve accessibility of (Q)SAR methods and databases
    56. 56. Facilitate selection of chemical analogues and categories
    57. 57. Integrate metabolism/mechanisms with categories/(Q)SAR
    58. 58. Assist in the estimation of missing values for chemicals</li></ul>-ENV/JM(2006)47<br />
    59. 59. Typical queries included in the (Q)SAR Application Toolbox<br />Is the chemical included in regulatory inventories or existing chemical categories?<br />Has the chemical already been assessed by other agencies/organisations?<br />Would you like to search for available data on assessment endpoints for each chemical?<br />
    60. 60. Typical Queries included in the (Q)SAR Application Toolbox<br />Explore a chemical list for possible analogues using predefined, mechanistic, empiric and custom built categorization schemes?<br />Group chemicals based on common chemical/toxic mechanism and/or metabolism?<br />Design a data matrix of a chemical category?<br />
    61. 61. QSAR Toolbox Workflow<br />The workflow in the first version of the QSAR Toolbox is<br /> to facilitate hazard assessors in the creating of chemical<br /> categories which enable data to be extrapolated from<br /> tested chemicals to untested members of categories <br />
    62. 62. Logical sequence of components usage<br />Chemical<br />input<br />Profiling<br />Category<br />Definition<br />Filling<br />data gap<br />Report<br />Endpoints<br />
    63. 63. Logical sequence of components usage<br />Chemical<br />input<br />Profiling<br />Category<br />Definition<br />Filling<br />data gap<br />Report<br />Endpoints<br />User Alternatives for Chemical ID:<br />A. Single target chemical<br /><ul><li>Name
    64. 64. CAS#
    65. 65. SMILES/InChi
    66. 66. Draw Chemical Structure
    67. 67. Select from User List/Inventory</li></ul>B. Group of chemicals<br /><ul><li>User List
    68. 68. Inventory
    69. 69. Specialized Databases</li></li></ul><li>Logical sequence of components usage<br />Chemical<br />input<br />Profiling<br />Category<br />Definition<br />Filling<br />data gap<br />Report<br />Endpoints<br />General characterization by the following grouping schemes:<br /><ul><li>Substance information
    70. 70. Predefined
    71. 71. Mechanistic
    72. 72. Empirical
    73. 73. Custom
    74. 74. Metabolism</li></li></ul><li>Logical sequence of components usage<br />Chemical<br />input<br />Profiling<br />Category<br />Definition<br />Filling<br />data gap<br />Report<br />Endpoints<br />General characterization by the following grouping schemes:<br /><ul><li>Substance information
    75. 75. Predefined:
    76. 76. US EPA categorization
    77. 77. OECD categorization
    78. 78. Database affiliation
    79. 79. Inventory affiliation
    80. 80. Substance type: polymers, mixtures, discrete, hydrolyzing</li></li></ul><li>Logical sequence of components usage<br />Chemical<br />input<br />Profiling<br />Category<br />Definition<br />Filling<br />data gap<br />Report<br />Endpoints<br />Finding Data for SIDS and Other Endpoints<br /><ul><li> Selecting Data Base(s):
    81. 81. Toolbox databases
    82. 82. Publicly available
    83. 83. Proprietary databases
    84. 84. Toolbox Links to External Databases (DSSTOX)
    85. 85. Selecting type of extracting data:
    86. 86. Measured Data
    87. 87. Estimated Data
    88. 88. Both</li></li></ul><li>Logical sequence of components usage<br />Chemical<br />input<br />Profiling<br />Category<br />Definition<br />Filling<br />data gap<br />Report<br />Endpoints<br />Forming and Pruning Categories:<br /><ul><li>Predefined
    89. 89. Mechanistic
    90. 90. Empirical
    91. 91. Custom
    92. 92. Metabolism</li></li></ul><li>Logical sequence of components usage<br />Chemical<br />input<br />Profiling<br />Category<br />Definition<br />Filling<br />data gap<br />Report<br />Endpoints<br />Forming and Pruning Categories:<br /><ul><li>Predefined
    93. 93. OECD categorization
    94. 94. US EPA categorization
    95. 95. Inventory affiliation
    96. 96. Database affiliation
    97. 97. Substance type</li></li></ul><li>Logical sequence of components usage<br />Endpoints<br />Chemical<br />input<br />Profiling<br />Category<br />Definition<br />Filling<br />data gap<br />Report<br />Data gaps filling approaches<br /><ul><li>Read-across
    98. 98. Trend analysis
    99. 99. QSAR models</li></li></ul><li>Logical sequence of components usage<br />Chemical<br />input<br />Profiling<br />Category<br />Definition<br />Filling<br />data gap<br />Report<br />Endpoints<br />Report the results:<br /><ul><li>QMRF/QPRF
    100. 100. IUCLID 5 Harmonized Templates
    101. 101. SIDS Dossiers (Data matrix)
    102. 102. History of the Toolbox Application
    103. 103. User-Defined Reports
    104. 104. Documentation:
    105. 105. Description of the system
    106. 106. Description of Category Editor</li>

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