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Altam raLLC
Predictive Toxicology & Risk Assessment using
SAR/QSAR Methods and Cheminformatics
Chihae YangMarch 5, 2012
Outline
• Computational toxicology and knowledgebase• Historical perspectives• Reality check• Future
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Safety/Risk assessment deals with extremely diverse and disparate data and information!
• In vivo and in vitro experimental data– GLP and non‐GLP…
• Exposure information• Knowledge from past
– Structural alerts– QSAR predictions – Read‐across– Threshold of toxicological concern
• ….3
Need for a computational system
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Knowledge derivation/extraction
Requirements to build knowledge
StructuredDatabase
VisualizationRetrievalSearching
Knowledge dissemination
Chemical structuresToxicity data
In vitro and qHTS assay data
Data miningHypothesis testing
Computational system (knowledgebase)
Read‐across…SAR/QSAR
Structural alertsPathways
Use of knowledgebase in safety/risk assessment workflow
Knowledgebase‐ database
‐ estimations‐ rationale
found quality data on query for a particular endpoint of interest
found no databut found data for analogs
‐ estimate the quality of analogs‐ estimate the quality of tox data
found no datafound no quality analogs databut can apply knowledge from past
Search database
Read‐across
PathwaysStructural alerts
SAR/QSAR‐ look for alerts‐ look for known pathways‐ estimate the endpoint by prediction
O
H2C O
CH3
CH3
5
Computational systems – knowledge source
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• All computational systems are expert systems providing derived knowledge– End‐users are provided with answers (hopefully with rationale)
• Predictions (knowledge) can be either – deductive from mechanistic knowledge
– inductive: learned from data
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Derived knowledge: predictions
• Knowledge from deductive process– Structural alerts, MoA Pathways…
• Knowledge from inductive learning process– QSAR (quantitative structure activity relationship) models, categorization, structural alerts…
Historical perspective
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20001970 198019601950194019301870 1990 2010
Historical perspective
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20001970 198019601950194019301870 1990 2010
Canadian DSL
EU REACH
EU Cosm
etics Directive
Current initiatives and tools development
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SEURAT CLUSTER
• Numerous international initiatives for sharing data and tools
• Common methods reflecting broad needs of safety/risk assessment
• Sustainable, configurable workflow system
We propose a shift from primarily in vivo animal studies to in vitro assays, in vivo assays with lower organisms, and computational modeling for toxicity assessments.
Regulatory science in 21st century ‐A paradigm shift
ToxCast_PhaseII (industrial, food additives, cosmetics)yellow: ToxCast_PhaseII (pharmaceuticals)
ToxCast_PhaseI
courtesy of Ann Richard, US EPA
ToxCast™
Science vol 319, Feb 15, 2008. www.sciencemag.org
Chemical representations intoxicity & risk evaluation
structural featuresphysicochemical properties
chemical reactivitymetabolic reactivity
structural rulesproperty rules
models
Thresholds of Toxicological Concernexposure routes
in collaboration with Ann Richard, US EPA, NCCT
Biology representation: mode‐of‐actions in toxicity & risk evaluation
MIE
Cellular
Response
Organs &
O
rganisms
Toxicity pathways
Mode of Actions (MoA)
MIE= molecular initiating event
Adverse Outcomein population
• Aquatic (fish) toxicity• Genetic toxicity• Genotoxic carcinogenicity• Skin sensitization• Skin irritation
Better understood MoAs and chemistry
• Aquatic (fish) toxicity• Genetic toxicity• Genotoxic carcinogenicity• Skin sensitization• Skin irritation
• Progress has been made in developmental toxicity.• Target organ toxicity is still being investigated.
History of SAR/QSAR: Meyer‐Overton theory
• Narcosis of tadpoles– accumulation of molecules in the lipid biophase is the only prerequisite for activity
• Potency of anesthetics‐ depends on lipid partitioning
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http://en.wikipedia.org/wiki/File:The_Meyer‐Overton_correlation.png
A humble beginning!
History of SAR/QSAR: electronic effect
• Substituent effects of on chemical reactions and equilibria
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O
OH
O
O+ H+
X X
- +[CO ][H ]2[CO H]2
Ka
xH
KxLogK
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Historical example: hydrophobic Effect
• Corwin Hansch 1962– Extended QSAR approach to biological systems, which in part involve diffusion processes (e.g., transport across a membrane)
• Hansch parameter,
X H
octanol
water
P andP :Partition coefficient of derivatives and parent
XX
H
drug XP
drug X
PLog
P
• Electronic effects• Steric and size effects• Hydrophobicity
History of SAR/QSAR : Descriptors
QSAR 50 years after Hansh and Fujita
• 1.5 million hits in Google search for “QSAR”• Activity QSAR is a workhorse
– Discovery during lead optimization process– Some physicochemical properties are not longer measured.
• In combination with chemoinformatics and computer science, we had an explosion of descriptor calculators.
• Many commercial systems with toxicity predictions
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The fundamental problem of (Q)SAR
molecularstructures activities
moleculardescriptors
//
representationCalculation/Estimation
response = f (predictors)
activities = f (molecular descriptors)
• Inherent deficiency:– Very highly aggregated biology into one number– Meant to relate structures to biological experiments, not to risk assessment decisions
• Training set issues– Disconnect between training set builders and model builders– Extremely difficult to build so mostly owned proprietarily– Not enough data
• Computational approaches– “Global” and “Blackbox” mode of operations– Lack of rationale and inferences
• Lack of clear standardized mode of operations
Other shortcomings toxicity QSAR
• Inherent deficiency– Very highly aggregated biology into one number– Meant to relate structures to biological experiments, not to risk assessment decisions
• Training set issues– Disconnect between training set builders and model builders– Not enough public data, very difficult to build so mostly owned proprietarily
• Computational approaches– “global” and “blackbox” mode of operations– Lack of rationale and inference
Def
ined
app
licab
ility
dom
ain
Una
mbi
guou
s al
gorit
hm
Mod
el ro
bust
ness
met
ric
Historic perspective: structural alerts
Ashby and Tennant (1985)
Issues with structural alerts
• What do they alert? • Current paradigm
– Presence/Absence – All substructure‐based
• coding and applying require software system
– Lack of potency information
• Quantitative likelihood of events
• Multiple alerts – Likelihood from a joint probability
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O
N+ OH
NH2
O–
0
aapLikelihood
p
+=positive prediction for an endpoint(e.g., Ames); aa=aromatic amine; an=aromatic nitro
, , ,,
,p an p aa an n aa an
p aa ann aa anp aa an
Quantitative assessment of aromatic amines
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endpoints Probability P‐values fromFisher’s Exact Test
All ArAmine ArNH2 All ArAmine ArNH2SAL+ 0.37 0.60 0.69 ‐ <0.0001 <0.0001RAT+ 0.55 0.62 0.64 ‐ 0.011 0.0234MUS+ 0.49 0.49 0.62 ‐ 0.513 0.0025
• Aromatic amine is a salmonella‐positive rodent tumorigen alert • Aromatic amine is not a mouse tumorigen alert, but ArNH2 is.
‐ Probabilities were calculated from a large reference database. Ames assay contains >8,000 and tumorigenicity >2,400 chemicals.
100 98 1535 1537
Alcohol, aromtaticAromatic amine, ArNH2Aromatic amine, ArNHRAromatic amine, ArNR2Azo and azoxy groupNitroso and nitrosaminePAH (benzene, fused ring)CarbamateCarboxamide(NH2), aromaticCarboxamide(NHR), aromaticCarboxylateEtherHalide, aromaticHalide, alkylImidazolePyridineQuinolineQuinoneThiazole
S9 Differentiating FeaturesMutation +S9/Mutation ‐S9
SALRat
TumorMus Tumor
• S9 analysis based on NTP salmonella assay and calls from Errol Zeiger (> 2000 chemicals).• SAL, Tumor (Rat, Mus) data are based on the data described in previous slide. 25
Aromatic am
inesHalides
Structural alerts differentiating effects of S9 metabolic activation
• Marry deductive reasoning with inductive learning
• MoA QSAR– Mechanistically biased
• MoA category• Structural alerts
– Biological assays as descriptors
– Mechanistic descriptors
QSAR 21: Tox QSAR in 21st century
Structure diversity
Mechanism
com
plexity X
“The Data Cube”
Activity QSAR
The Hopeless region
Global region
MoA
cheminformatics can help find where we are
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Mode‐of‐action QSAR
MIE
Cellular Response
Organs &
Organism
s
Toxicity pathways
mode of action (MoA)
O
O
O
O
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chemical MoA category
training set
Selected examples: MoA categories in genotoxic/tumorigenicity
• Aliphatic halide
• Aromatic amine
• PAH
alkylation DNA
adduct formationDNA
intercalationadduct formation
DNA
mutagenesis
• Flavonoid
• Steroid
• Retinoid
clastogenesis
CH3
CH3CH3
Topoisomerase 2
non‐specific
inhibition
DNA polymerase
hydrophobicinteraction
inhibition
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Chemical class Biological events
Aldehydes PhenolsAromatic Amines Alkyl halidesGlobal
Alerts
High mutagenic likelihood
Example of MoA QSAR workflowAmes mutagenicity
NH2
OH
...
Likely mutagenic
Combine
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N H2
OH
MoACategories
Possible Mode‐of‐Actions in vivo skin irritation
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• Surfactants• Acids• Alkali (base)
– bleaches…• Organic solvent
– xylene, ketones, alcohols…
• Reactive chemicals– aldehydes…
lysisdelipidationprotein denaturation
ROS
Chemical class Biological events Phenotype effects
cell deathinflammation…..
Biological events
oedema, erythema
erythemaoedema
MoA modeling approach for in vivo skin irritation
Amines SurfactantsAcids AlcoholsGlobal
Combine
Corrosive Filter corrosives
Reactives
Alerts
Non Corrosives
irritancy alerts
Alkenes
Query
skin irritant!31
MoACategories
Examples of overall model performance
% Concordance % Sensitivity % Specificity
91 95 84
% Concordance % Sensitivity % Specificity
84 82 85
overall salmonella mutagenicity overall skin irritation
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Example of post‐market analysis: applied to “Cosmetics Inventory”
• Cosmetics inventory– COSING and PCPC– 4,979 compounds diverse cosmetics ingredients
– many do not have oral systemic toxicity data
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alcoholalcohol, phenolalochol, glycol
aldehydeamine
amine, aromatic (NH2)azo
halideketone
ketone, ACACphthalate ester
organometalphosphorus
pyran, genericsiliconsteroidsulfide
sulfonyl groupurea
aliphatic chain >= C8non-ionic surfactant
anionic surfactantcationic surfactant -
QUAT
Chemical space of Cosmetics Inventory by physicochemical properties
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% in cosmetics inventory
350.10 1.00 10.00 100.00
decr
easi
ng o
rder
PAH (benzene, fused ring)Halide, alphiphaticNitroso and nitrosamineQuinonesEpoxideQuinolineAromatic amine, ArNH2Alcohol, aromtaticAzo and azoxy groupPyridineThiazoleImidazoleEtherAromatic amine, ArNHRAromatic amine, ArNR2CarbamateCarboxamide(NHR), aromaticHalide, aromaticCarboxylate
Structural alerts and fragments
SAL Rat Tumor
Structural alerts for salmonella/rat tumor
Profiling of Cosmetics Inventoryusing toxicity predictions
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• Computational methods represent knowledge derived from past experiments and theory.
• Inherent problems and limitations must be recognized and addressed.
• Need for a quantitative measure for the “Safety/Risk Assessment” process – Formal quantitative treatment of weight of Evidence
• Structural alerts• QSAR• Read‐across
Summary and Future
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Summary and Future ‐ QSAR 21
• MoA QSAR– Chemical MoA categories group training sets more mechanistically
– Use mechanistic and biological (assay) descriptors
• QSAR results as a chemical and biological space profiler– Useful in read‐across
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• Ann Richard, US EPA NCCT• James Rathman, the Ohio State University• Scott Boyer, AstraZeneca, Sweden• Kirk Arvidson, US FDA CFSAN• Andrew Worth, EC JRC
Acknowledgement
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