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

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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

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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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