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.
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 Categories Chemical categories have boundary conditions which vary with endpoints Without 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 Ordering Chemical Amyl amine Amyl chloride Dibromobenzene Ethyl bromide n-Heptanol Methacrolein Methyl-p-anisylketone n-Octane n-Nonane Boiling Point °C 103-4 98-9 219-2 38.4 192 68 267-9 126 151
37. Canonical Ordering Chemical Ethyl bromide Methacrolein Amyl chloride Amyl amine n-Octane n-Nonane n-Heptanol Dibromobenzene Methyl-p-anisylketone Boiling Point °C 38.4 68 98-9 103-4 126 151 192 219-2 267-9
38. Modeling Chemical Potency 10+2 10 0 10_2 1/LC50 (Moles/L) It is not uncommon to find endpoint values spanning 6-10 orders for a single toxicity mechanism 10_4 10_6 10-8 1 2 3 4 5 N < 10,000 ….... TOXICITY “MECHANISMS”
40. QSAR Methods QSAR fills data gaps by first grouping chemicals and then using existing data within a group to estimate missing values When the chemical group is identified by a common mechanism, QSAR models can accurately describe the trends
41. Why Do We Need the QSAR Toolbox Defining category boundaries requires the calculation of complex attributes of chemicals to determine which best explains available data In 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 For A Well-Defined Endpoint Molecular Initiating Event Speciation, Metabolism Reactivity Etc. In Vitro and System Effects In Vivo Adverse Outcomes Parent Chemical Up-Stream Down-Stream CHEMISTRYBIOLOGY Structure-Activity Levels of Organization
44. MolecularInitiating Event Macro -Molecular Interactions Toxicant Chemical Reactivity Profiles Receptor, DNA, Protein Interactions Biological Responses Mechanistic Profiling The Adverse Outcome Pathway
45. MolecularInitiating Event Biological Responses Macro -Molecular Interactions Toxicant Cellular Gene Activation Protein Production Signal Alteration Chemical Reactivity Profiles Receptor, DNA, Protein Interactions NRC Toxicological Pathway The Adverse Outcome Pathway
46. MolecularInitiating Event Biological Responses Macro -Molecular Interactions Tissue/ Organ Toxicant Cellular Gene Activation Protein Production Signal Alteration Receptor, DNA, Protein Interactions Altered Function Altered Development Chemical Reactivity Profiles Mechanistic Profiling In Vitro & HTP Screening The Adverse Outcome Pathway
47. MolecularInitiating Event Biological Responses Macro -Molecular Interactions Toxicant Cellular Organism Organ Population Lethality Sensitization Birth Defect Reproductive Impairment Cancer Gene Activation Protein Production Signal Alteration Altered Function Altered Development Chemical Reactivity Profiles Receptor, DNA, Protein Interactions Structure Extinction Mechanistic Profiling In Vivo Testing In Vitro & HTP Screening The Adverse Outcome Pathway
48. Major Pathways for Reactive Toxicity from Moderate Electrophiles Interaction Mechanisms Molecular Initiating Events In vivo Endpoints Exposed Surface Irritation Michael Addition Schiff base Formation SN2 Acylation Atom Centered Irreversible (Covalent) Binding Necrosis Which Tissues? Pr-S Adducts GSH Oxidation GSH Depletion NH2 Adducts RN Adducts DNA Adducts Oxidative Stress Systemic Responses Skin Liver Lung Systemic Immune Responses Dose-Dependent Effects
49. Organization for Economic Co-operation and Development QSARApplication Toolbox -filling data gaps using available information- Training Workshop Barcelona
58. Assist in the estimation of missing values for chemicals-ENV/JM(2006)47
59. Typical queries included in the (Q)SAR Application Toolbox Is 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 Toolbox Explore 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 Workflow The 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 usage Chemical input Profiling Category Definition Filling data gap Report Endpoints
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 QSAR ,[object Object]](https://image.slidesharecdn.com/toolboxonlineintro-100121175539-phpapp02/85/Introduction-to-OECD-QSAR-Toolbox-1-320.jpg)
