Introduction to OECD QSAR Toolbox
- 1. Introduction to QSARWelcome to this online introduction of QSAR which gives a basic understanding of QSAR and why a QSAR Toolbox is needed
- 2. Risk assessments are based on test data, and QSAR is not needed if you have lot’s of data
- 3. However, if data gaps exist, one can defer the hazard assessment or use QSAR IntroductionFewer than 10,000 chemicals have been tested for the major hazards
- 4. The world inventory of produced chemicals exceeds 160,000 chemicals
- 5. The world capacity for SIDS initial hazard assessments is only ~500 chemicals/yearIntroductionTherefore, even initial assessments based on test ing discrete chemicals is not possible for most chemicals
- 6. Also, priority setting for 130,000 chemicals will require faster testing or better models
- 7. In QSAR, estimating behavior of untested chemicals has been used for >60 years An Overview of QSARChemistry is based on the premise that similar chemicals will behave similarly
- 8. Like most systems, the behavior of a chemical is derived from its structure
- 9. Chemical behavior in biological systems is described as biological activity of chemicals An Overview of QSARQSAR research searches for relationships between chemical structure and activity
- 10. QSAR is the acronym for Quantitative Structure-Activity Relationship
- 11. log LC50 (rat, 4hr) = 0.69 log VP + 1.54 is an example of QSAR for lethality in ratsAn Overview of QSARHere, “VP” is the vapor pressure which is measured or estimated from the structure
- 12. There are more than 15,000 published QSARs for biological activity endpoints
- 13. The term “quantitative” most often pertains to the statistical quality of the “relationship”An Overview of QSARQSARs are always associated with endpoint ( i.e.LC50) and an toxicity mechanism (i.e. narcosis, AChE inhib)
- 14. Only chemicals causing common toxicity mechanisms lead to a reliable QSAR
- 15. Therefore, QSAR must group chemical behavior in terms of toxicity mechanisms Overview ConclusionsQSAR predicts biological activity (endpoints) directly from models of chemical structure
- 16. Each QSAR predicts a specific endpoint and only for chemicals with the same mechanism
- 17. Errors of choosing the wrong QSAR (mechanism) are larger than model errorsProcess for Creating QSAR Choose a well-defined endpoint for biological activity needed in your workCompile measured values using consistent methods for the endpoint --ORSelect a series of relevant chemicals and systematically test all for the endpointIdentify “molecular descriptors” which quantify structural attributes for endpointStatistically evaluate the molecular descriptor-- endpoint relationships (QSAR)
- 18. Example for Lethality in MiceCompile data for 30-minute lethality with mice from the anesthesiology literature
- 19. Data restricted to alkyl ethers to increase likelihood of a similar toxicity mechanism
- 20. Estimate or measure vapor pressure as molecular descriptor (selected from theory or by trial-n-error)
- 21. Correlate LC50 with VP to get: log LC50 = 0.57 x log VP + 2.08
- 23. ExampleNotice the dependence on VP (slope) is almost the same as with the rat QSAR
- 24. Notice the intercept is about 0.5 log units greater for 30 min mouse vs 4 hr rat LC50
- 25. Can you suggest reasons for the greater LC50 (lower toxicity) for 30 min mouse ?Some Important Lessons Vapor pressure correlates with LC50, but many molecular descriptors would not correlate
- 26. This QSAR implies vapor pressure is important to the lethality mechanism for these chemicals
- 27. Chemicals with other mechanisms (i.e. acrolein, phosgene) will appear as statistical outliers
- 28. QSAR provides insights into chemical similarity in terms of common “effect” mechanismsSome Important Lessons QSAR is an exploration of chemical attributes which reliably predicts their biological activity (biological effects) under specific test conditions
- 29. QSAR is also a tool to group chemicals which can be expected to behave similarity (same toxicity pathway under specific test conditionsThe Chemical Category SolutionGrouping chemicals by similar behavior extrapolates from tested to untested chemicals within a given chemical category
- 30. Entire categories of chemicals can be assessed when only a few are tested
- 31. Filling missing data (gaps) involves read-across & trend or correlation analysis What do we mean by Chemical Categories?A group of chemicals that have some features that are commonStructurally similar e.g. common substructureProperty e.g. similar physicochemical, topological, geometrical, or surface propertiesBehaviour e.g. (eco)toxicological response underpinned by common modes of actionFunctionality e.g. preservatives, flavourings, detergents, fragrances
- 32. Annex IX of REACH 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. 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.
- 33. OECD Definition of CategoryA 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. 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 effectsOECD Manual for Investigation of High Production Volume (HPV) Chemicals.
- 35. Forming Chemical CategoriesChemical categories have boundary conditions which vary with endpointsWithout detailed understanding of metabolism or mechanisms, grouping similarity of behavior is difficult to define.Ironically, examining data trends with different category boundaries is a flexible way to define categories
- 36. Canonical OrderingChemicalAmyl amineAmyl chlorideDibromobenzeneEthyl bromiden-HeptanolMethacroleinMethyl-p-anisylketonen-Octanen-NonaneBoiling Point °C103-498-9219-238.419268267-9126151
- 37. Canonical OrderingChemicalEthyl bromideMethacroleinAmyl chlorideAmyl aminen-Octanen-Nonanen-HeptanolDibromobenzeneMethyl-p-anisylketoneBoiling Point °C38.46898-9103-4126151192219-2267-9
- 38. Modeling Chemical Potency10+210 010_21/LC50(Moles/L)It is not uncommon to find endpointvalues spanning 6-10 orders for a single toxicity mechanism 10_410_610-812345N < 10,000…....TOXICITY “MECHANISMS”
- 39. Modeling Chemical Potency10+210 010_21/LC50(ChemicalActivity)10_410_610-802468LOG K o/w
- 40. QSAR MethodsQSAR fills data gaps by first grouping chemicals and then using existing data within a group to estimate missing valuesWhen the chemical group is identified by a common mechanism, QSAR models can accurately describe the trends
- 41. Why Do We Need the QSAR ToolboxDefining category boundaries requires the calculation of complex attributes of chemicals to determine which best explains available dataIn many cases, metabolic simulators are needed to provide metabolic maps and active metabolites To do trend analysis, hundreds of available data must be compiled and flexibly analyzed for trends
- 42. Which Metabolite should we use in modeling interactions?Simulated 2-Acetylaminofluorene Metabolism
- 43. Adverse Outcome Pathway ForA Well-Defined EndpointMolecularInitiating EventSpeciation,MetabolismReactivityEtc.In Vitro and System EffectsIn VivoAdverse OutcomesParentChemicalUp-Stream Down-StreamCHEMISTRYBIOLOGY Structure-Activity Levels of Organization
- 44. MolecularInitiating EventMacro-Molecular InteractionsToxicantChemical Reactivity ProfilesReceptor, DNA,ProteinInteractionsBiological ResponsesMechanistic ProfilingThe Adverse Outcome Pathway
- 45. MolecularInitiating EventBiological ResponsesMacro-Molecular InteractionsToxicantCellularGene ActivationProtein ProductionSignal AlterationChemical Reactivity ProfilesReceptor, DNA,ProteinInteractionsNRC Toxicological PathwayThe Adverse Outcome Pathway
- 46. MolecularInitiating EventBiological ResponsesMacro-Molecular InteractionsTissue/ OrganToxicantCellularGene ActivationProtein ProductionSignal AlterationReceptor, DNA,ProteinInteractionsAlteredFunction Altered DevelopmentChemical Reactivity ProfilesMechanistic ProfilingIn Vitro &HTP ScreeningThe Adverse Outcome Pathway
- 47. MolecularInitiating EventBiological ResponsesMacro-Molecular InteractionsToxicantCellularOrganismOrganPopulationLethalitySensitizationBirth DefectReproductive ImpairmentCancerGene ActivationProtein ProductionSignal AlterationAlteredFunction Altered DevelopmentChemical Reactivity ProfilesReceptor, DNA,ProteinInteractionsStructureExtinctionMechanistic ProfilingIn VivoTestingIn Vitro &HTP ScreeningThe Adverse Outcome Pathway
- 48. Major Pathways for Reactive Toxicity from Moderate ElectrophilesInteractionMechanismsMolecularInitiatingEventsIn vivoEndpointsExposedSurfaceIrritationMichaelAdditionSchiff baseFormationSN2AcylationAtomCentered Irreversible(Covalent)Binding NecrosisWhich Tissues?Pr-S AdductsGSH OxidationGSH DepletionNH2 AdductsRN AdductsDNA AdductsOxidative StressSystemic ResponsesSkinLiverLungSystemicImmuneResponsesDose-Dependent Effects
- 49. Organization for Economic Co-operation and DevelopmentQSARApplication Toolbox-filling data gaps using available information- Training WorkshopBarcelona
- 51. Organization for Economic Co-operation and DevelopmentQSAR Application Toolbox-filling data gaps using available information- Historical NotesFirst “organized” discussions – ‘Red Lobsters’, Duluth - 1992
- 52. Organized actions of EU and OECD – coming with REACH
- 53. The role of the “revolutionary” notions – category, analogues
- 54. OECD and EU Guidance documents on ‘Category’, ‘QSAR’
- 55. Need for translation documents into a working machineryOrganization for Economic Co-operation and DevelopmentQSAR Application Toolbox-filling data gaps using available information- General ObjectivesImprove accessibility of (Q)SAR methods and databases
- 56. Facilitate selection of chemical analogues and categories
- 57. Integrate metabolism/mechanisms with categories/(Q)SAR
- 58. Assist in the estimation of missing values for chemicals-ENV/JM(2006)47
- 59. Typical queries included in the (Q)SAR Application ToolboxIs the chemical included in regulatory inventories or existing chemical categories?Has the chemical already been assessed by other agencies/organisations?Would you like to search for available data on assessment endpoints for each chemical?
- 60. Typical Queries included in the (Q)SAR Application ToolboxExplore a chemical list for possible analogues using predefined, mechanistic, empiric and custom built categorization schemes?Group chemicals based on common chemical/toxic mechanism and/or metabolism?Design a data matrix of a chemical category?
- 61. QSAR Toolbox WorkflowThe workflow in the first version of the QSAR Toolbox is to facilitate hazard assessors in the creating of chemical categories which enable data to be extrapolated from tested chemicals to untested members of categories
- 62. Logical sequence of components usageChemicalinputProfilingCategoryDefinitionFillingdata gapReportEndpoints
- 63. Logical sequence of components usageChemicalinputProfilingCategoryDefinitionFillingdata gapReportEndpointsUser Alternatives for Chemical ID:A. Single target chemicalName
- 64. CAS#
- 65. SMILES/InChi
- 67. Select from User List/InventoryB. Group of chemicalsUser List
- 68. Inventory
- 69. Specialized DatabasesLogical sequence of components usageChemicalinputProfilingCategoryDefinitionFillingdata gapReportEndpointsGeneral characterization by the following grouping schemes:Substance information
- 70. Predefined
- 71. Mechanistic
- 72. Empirical
- 73. Custom
- 74. MetabolismLogical sequence of components usageChemicalinputProfilingCategoryDefinitionFillingdata gapReportEndpointsGeneral characterization by the following grouping schemes:Substance information
- 75. Predefined:
- 76. US EPA categorization
- 80. Substance type: polymers, mixtures, discrete, hydrolyzingLogical sequence of components usageChemicalinputProfilingCategoryDefinitionFillingdata gapReportEndpointsFinding Data for SIDS and Other Endpoints Selecting Data Base(s):
- 88. BothLogical sequence of components usageChemicalinputProfilingCategoryDefinitionFillingdata gapReportEndpointsForming and Pruning Categories:Predefined
- 89. Mechanistic
- 90. Empirical
- 91. Custom
- 92. MetabolismLogical sequence of components usageChemicalinputProfilingCategoryDefinitionFillingdata gapReportEndpointsForming and Pruning Categories:Predefined
- 97. Substance typeLogical sequence of components usageEndpointsChemicalinputProfilingCategoryDefinitionFillingdata gapReportData gaps filling approachesRead-across
- 98. Trend analysis
- 99. QSAR modelsLogical sequence of components usageChemicalinputProfilingCategoryDefinitionFillingdata gapReportEndpointsReport the results:QMRF/QPRF
- 100. IUCLID 5 Harmonized Templates
- 101. SIDS Dossiers (Data matrix)
- 102. History of the Toolbox Application
- 103. User-Defined Reports
- 104. Documentation:
- 105. Description of the system
- 106. Description of Category Editor
Editor's Notes
- #21: 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.
- #22: 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.
- #23: 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.
- #24: 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.