Introduction to OECD QSAR Toolbox
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Transcript of Introduction to OECD QSAR Toolbox
Introduction to QSARWelcome to this online introduction of QSAR
which gives a basic understanding of QSAR and why a QSAR Toolbox is needed
Risk assessments are based on test data, and QSAR is not needed if you have lot’s of data
However, if data gaps exist, one can defer the hazard assessment or use QSAR
Introduction
Fewer than 10,000 chemicals have been tested for the major hazards
The world inventory of produced chemicals exceeds 160,000 chemicals
The world capacity for SIDS initial hazard assessments is only ~500 chemicals/year
IntroductionTherefore, even initial assessments based on
test ing discrete chemicals is not possible for most chemicals
Also, priority setting for 130,000 chemicals will require faster testing or better models
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
Like most systems, the behavior of a chemical is derived from its structure
Chemical behavior in biological systems is described as biological activity of chemicals
An Overview of QSAR
QSAR research searches for relationships between chemical structure and activity
QSAR is the acronym for Quantitative Structure-Activity Relationship
log LC50 (rat, 4hr) = 0.69 log VP + 1.54 is an example of QSAR for lethality in rats
An Overview of QSAR
Here, “VP” is the vapor pressure which is measured or estimated from the structure
There are more than 15,000 published QSARs for biological activity endpoints
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)
Only chemicals causing common toxicity mechanisms lead to a reliable QSAR
Therefore, QSAR must group chemical behavior in terms of toxicity mechanisms
Overview ConclusionsQSAR predicts biological activity (endpoints)
directly from models of chemical structure
Each QSAR predicts a specific endpoint and only for chemicals with the same mechanism
Errors of choosing the wrong QSAR (mechanism) are larger than model errors
Process for Creating QSAR 1. Choose a well-defined endpoint for biological
activity needed in your work2. Compile measured values using consistent
methods for the endpoint --OR3. Select a series of relevant chemicals and
systematically test all for the endpoint4. Identify “molecular descriptors” which
quantify structural attributes for endpoint5. Statistically evaluate the molecular
descriptor-- endpoint relationships (QSAR)
Example for Lethality in Mice
• Compile data for 30-minute lethality with mice from the anesthesiology literature
• Data restricted to alkyl ethers to increase likelihood of a similar toxicity mechanism
• Estimate or measure vapor pressure as molecular descriptor (selected from theory or by trial-n-error)
• Correlate LC50 with VP to get: log LC50 = 0.57 x log VP + 2.08
Table 1. LC50-30 min of aliphatic ethers in mice
Chemical name CAS MWVP
(mmHg)LC50
(mmol/m3)
Diisobutyl ether 628-55-7 130.2 15 1200
Disec-butyl ether 6863-58-7 130.2 17 1000
Diethyl ether 60-29-7 74.1 537 6000
Diisopropyl ether 108-20-3 102.2 170 1500
Dimethyl ether 115-10-6 46.1 4450 37000
Dipropyl ether 111-43-3 102.2 60 1600
Divinyl ether 109-93-3 70.1 684 4700
Ethyl amyl ether 17952-11-3 116.2 18 1000
Ethyl butyl ether 628-81-9 102.2 52 1500
Ethyl cyclopropyl ether 5614-38-0 86.1 150 1000
Ethyl isoamyl ether 628-04-6 116.2 30 1000
Ethyl isobutyl ether 627-02-1 102.2 98 1500
Ethyl isopropyl ether 625-54-7 88.1 250 2500
Ethyl propyl ether 628-32-0 88.1 185 2500
Ethyl- sec-butyl ether 2679-87-0 102.2 98 1400
Ethyl tert-amyl ether 919-94-8 116.2 43 700
Ethyl tert-butyl ether 637-92-3 102.2 155 1200
Ethyl vinyl ether 109-92-2 72.1 500 4500
Methyl amyl ether 628-80-8 102.2 55 1300
Methyl butyl ether 628-28-4 88.1 160 2000
Methyl cyclopropyl ether 540-47-6 72.1 410 1750
Methyl ethyl ether 540-67-0 60.1 1493 18000
Methyl isobutyl ether 625-44-5 88.1 210 1600
Methyl isopropyl ether 598-53-8 74.1 550 5500
Methyl propyl ether 557-17-5 74.1 500 3500
Methyl sec-butyl ether 6795-87-5 88.1 230 1600
Methyl tert-butyl ether 1634-04-4 88.1 244 1600Propyl isopropyl ether 627-08-7 102.2 85 1500
Example• Notice the dependence on VP (slope) is
almost the same as with the rat QSAR
• Notice the intercept is about 0.5 log units greater for 30 min mouse vs 4 hr rat LC50
• 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
• This QSAR implies vapor pressure is important to the lethality mechanism for these chemicals
• Chemicals with other mechanisms (i.e. acrolein, phosgene) will appear as statistical outliers
• QSAR provides insights into chemical similarity in terms of common “effect” mechanisms
Some Important Lessons• QSAR is an exploration of chemical
attributes which reliably predicts their biological activity (biological effects) under specific test conditions
• QSAR is also a tool to group chemicals which can be expected to behave similarity (same toxicity pathway under specific test conditions
The Chemical Category Solution• Grouping chemicals by similar behavior
extrapolates from tested to untested chemicals within a given chemical category
• Entire categories of chemicals can be assessed when only a few are tested
• 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
Substances whose physicochemical, toxicological and ecotoxicological properties are likely to be similar or follow a regular pattern as 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 be predicted from data for a reference substance within the group by interpolation to other substances in the group (read-across approach). This avoids the need to test every substance for every endpoint.
Annex IX of REACH
OECD Definition of Category
• A chemical category is a group of chemicals whose
physicochemical and toxicological properties are likely to
be similar or follow a regular pattern as a result of
structural similarity
• 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
OECD Manual for Investigation of High Production Volume (HPV) Chemicals.
Forming Chemical CategoriesChemical 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
Canonical Ordering
ChemicalAmyl amineAmyl chlorideDibromobenzeneEthyl bromiden-HeptanolMethacroleinMethyl-p-anisylketonen-Octanen-Nonane
Boiling Point °C103-498-9219-238.419268267-9126151
Canonical Ordering
ChemicalEthyl bromideMethacroleinAmyl chlorideAmyl aminen-Octanen-Nonanen-HeptanolDibromobenzeneMethyl-p-anisylketone
Boiling Point °C38.46898-9103-4126151192219-2267-9
1 2 3 4 5 N < 10,000…....
1/LC50(Moles/L)
TOXICITY “MECHANISMS”
10-8
10_4
10_2
10+2
10 0
10_6
Modeling Chemical Potency
It is not uncommon to find endpointvalues spanning 6-10 orders for a single toxicity mechanism
0 2 4 6 8
1/LC50(ChemicalActivity)
LOG K o/w
10-8
10_4
10_2
10+2
10 0
10_6
Modeling Chemical Potency
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
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
Simulated 2-Acetylaminofluorene
Metabolism
NH
O
NH
O
OH
NH
O
O
NH2
O
HO
O
NHOH
O
N+HO
NH
OHO
NH
O
O
NH
O
O
NH
OHO
NH
OHO
OHNH
OHO
OH
NH
OHO
O
NH
OHO
O
N+H
HO
ON+H
OH
O
. . . . . .
NHX
OO
X = H, OH,
O
Activated metabolites
Which Metabolite should we use in modeling interactions?
MolecularInitiating Event
Speciation,MetabolismReactivityEtc.
In Vitro and System Effects
In VivoAdverse Outcomes
ParentChemical
Adverse Outcome Pathway ForA Well-Defined Endpoint
Up-Stream Down-Stream CHEMISTRY BIOLOGY Structure-Activity Levels of Organization
Chemical Reactivity Profiles
Receptor, DNA,ProteinInteractions
Mechanistic Profiling
Biological Responses
The Adverse Outcome Pathway
ToxicantMacro-Molecular Interactions
Molecular Initiating Event
NRC Toxicological Pathway
Biological Responses
The Adverse Outcome Pathway
Molecular Initiating Event
Chemical Reactivity Profiles
Receptor, DNA,ProteinInteractions
Macro-Molecular Interactions Gene
Activation
Protein Production
Signal Alteration
CellularToxicant
AlteredFunction
Altered Development
Gene Activation
Protein Production
Signal Alteration
Cellular
Mechanistic Profiling
In Vitro &HTP Screening
Biological Responses
The Adverse Outcome Pathway
Molecular Initiating Event
Chemical Reactivity Profiles
Receptor, DNA,ProteinInteractions
Macro-Molecular Interactions
Tissue/ OrganToxicant
Lethality
Sensitization
Birth Defect
Reproductive Impairment
Cancer
AlteredFunction
Altered Development
Gene Activation
Protein Production
Signal Alteration
Chemical Reactivity Profiles
Receptor, DNA,ProteinInteractions
Structure
Extinction
Cellular Organ
Mechanistic Profiling
In VivoTesting
Biological Responses
The Adverse Outcome Pathway
Toxicant OrganismMacro-Molecular Interactions
Molecular Initiating Event
Population
In Vitro &HTP Screening
Major Pathways for Reactive Toxicity from Moderate Electrophiles
Systemic Responses
SkinLiverLung
MichaelAddition
Schiff baseFormation
SN2
Acylation
AtomCentered
Irreversible(Covalent)Binding
InteractionMechanisms
MolecularInitiatingEvents Exposed
SurfaceIrritation
SystemicImmune
Responses
NecrosisWhich
Tissues?
In vivoEndpoints
Pr-S AdductsGSH OxidationGSH DepletionNH2 AdductsRN AdductsDNA Adducts
Oxidative Stress
Dose-Dependent Effects
Organization for Economic Co-operation and Development
QSAR Application Toolbox-filling data gaps using available information-
Training WorkshopBarcelona
• First “organized” discussions – ‘Red Lobsters’, Duluth - 1992
Historical Notes
• Organized actions of EU and OECD – coming with REACH
• The role of the “revolutionary” notions – category, analogues
• OECD and EU Guidance documents on ‘Category’, ‘QSAR’
• Need for translation documents into a working machinery
QSAR Application ToolboxOrganization for Economic Co-operation and Development
-filling data gaps using available information-
QSAR Application ToolboxOrganization for Economic Co-operation and Development
Improve accessibility of (Q)SAR methods and databases
Facilitate selection of chemical analogues and categories
Integrate metabolism/mechanisms with categories/(Q)SAR
Assist in the estimation of missing values for chemicals
-ENV/JM(2006)47
-filling data gaps using available information-
General Objectives
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?
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?
Typical Queries included in the
(Q)SAR Application Toolbox
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
Chemicalinput
Profiling CategoryDefinition
Fillingdata gap
ReportEndpoints
Logical sequence of components usage
User Alternatives for Chemical ID:A. Single target chemical• Name• CAS# • SMILES/InChi• Draw Chemical Structure• Select from User List/InventoryB. Group of chemicals• User List• Inventory• Specialized Databases
Chemicalinput
Profiling CategoryDefinition
Fillingdata gap
ReportEndpoints
Logical sequence of components usage
General characterization by the following grouping schemes:• Substance information• Predefined• Mechanistic• Empirical• Custom• Metabolism
Chemicalinput
Profiling CategoryDefinition
Fillingdata gap
ReportEndpoints
Logical sequence of components usage
General characterization by the following grouping schemes:• Substance information• Predefined:
• US EPA categorization• OECD categorization• Database affiliation • Inventory affiliation• Substance type: polymers, mixtures, discrete, hydrolyzing
Chemicalinput
Profiling CategoryDefinition
Fillingdata gap
ReportEndpoints
Logical sequence of components usage
Chemicalinput
Profiling CategoryDefinition
Fillingdata gap
Report
Finding Data for SIDS and Other Endpoints• Selecting Data Base(s):
Toolbox databases Publicly available Proprietary databases
Toolbox Links to External Databases (DSSTOX)
• Selecting type of extracting data: Measured Data Estimated Data Both
Endpoints
Logical sequence of components usage
Forming and Pruning Categories:• Predefined• Mechanistic• Empirical• Custom• Metabolism
Chemicalinput
Profiling CategoryDefinition
Fillingdata gap
ReportEndpoints
Logical sequence of components usage
Forming and Pruning Categories:
• Predefined• OECD categorization• US EPA categorization• Inventory affiliation• Database affiliation• Substance type
Chemicalinput
Profiling CategoryDefinition
Fillingdata gap
ReportEndpoints
Logical sequence of components usage
Data gaps filling approaches• Read-across• Trend analysis• QSAR models
Chemicalinput
Profiling CategoryDefinition
Fillingdata gap
ReportEndpoints
Logical sequence of components usage
Report the results:• QMRF/QPRF• IUCLID 5 Harmonized Templates• SIDS Dossiers (Data matrix)• History of the Toolbox Application• User-Defined Reports• Documentation:
• Description of the system• Description of Category Editor
Chemicalinput
Profiling CategoryDefinition
Fillingdata gap
ReportEndpoints
Logical sequence of components usage