A Systems Approach to Alternatives for Toxicity TestingGeorge Daston

Toxicology: From an Empirical to a Predictive Science

O

OH

Traditional Approach (Black box): Use a model that we have (some) confidence in, but incomplete understanding of how it works

NOH

Desired Approach:Predictions based on deep, fundamental understanding

Changes in Safety Approaches, 2000-2010

2000 2005 2010

In vivo testing

In vitro testing

Computationalmethods

Models and simulations

Changing data streamsChanging technology

Systems Toxicology at P&GAdds foundational capabilities to, and learns from, SB platform

Cheminformatics- computational approach to identify analogs

Modeling weak chem-biological interactions- a key to understanding toxicity and efficacy at a non-Rx level

Application of microarray data for rapid prediction of toxicity

Dynamic model of local effects- adds a kinetic component essential for epithelial biology,

Beauty Technology

Dynamic models of systemic toxicity- a rational approach to non-animal evaluation of organ toxicity

Cheminformatics: Alternatives to Alternatives

• Substructure searching– Genotoxicity (19,300)– Carcinogenicity (15,800)– Skin Sensitization (9,400)– Skin Irritation (10,400)– Reproductive/Developmental Toxicity (11,300)– Subchronic/Chronic Toxicity (15,100)– Acute Toxicity (68,500)

• All assessment captured in CHS

• External Data Sources: BIBRA*, Cal Prop 65*, CTFA*, HERA*, HPV*, OECD*, IPCS*, NICNAS*, RIFM/FEMA*, SCCP*, WHO/JECFA*, SciFinder, ToxNet, ATSDR, CPDB, ECETOC, ECB, IARC , Thompson/MicroMedix, NTP, RTECS/NIOSH, Scopus, TSCATS, others

O N

O

Flow chart of new analog identification & evaluation framework

Structural similarityReactivity similarity

Metabolism similarityPhys-Chem properties

Final analog packageincludes:

1. Categorized analogs w/shortrational explanation & their

phys-chem properties2. Major metabolic pathways &

majormetabolites of the target

Ranking based upon

The targetchemical

Metabolic routes& major

metabolites of thetarget

Candidateanalogs with

relevant toxicitydata

Searchstrategy

Initial searchresultsRevised

strategy

Chemical &ToxicologicalDatabases

1.DiscoveryGateTM, MetabolismTM

2. Literature reports3. Substructure search results4. Expert judgment5. Meteor softwareTM

1. DEREKTM

2. Ashby structural alerts3. Principles of Chem,

Biochem-toxicology4. Expert judgment5. ACD/LabsTM

1. Structuralfeatures

2. Key functionalgroups

Submission for toxdata & uncertainty

ranking review to fillthe data gap

Decision tree for categorizing analogs

Do the target & analoghave similar metabolic

pathways?

Do the target & analoghave similar phys-chem

properties?

Yes

SuitableSuitable

withinterpretation

No

Could metabolism resultin different

bioactivation pathways?Not

suitableYes

Could the target & analogmetabolize to each

other or converge to acommon stable metabolite orreactive metabolite with the

same mode of action?

Yes

No Could these phys-chemdifferences fundmently

alter toxicological profile?

No

Yes

Suitablewith

preconditions

No

Yes

Yes

No

No

No

Do the target & analoghave similar structural

features &chemical reactivity?

Yes

Yes

Could thedifferent alert groupspotentially change thetoxicity of the analogrelative to the target?

Yes

No

No

Yes

No

Could any other part ofthe molecule have thepotential to change the

toxicity of theanalog relative to the

target?

CandidateAnalogs with

relevant toxicitydata

Do the target & analogcontain different orpotentially different

alert functional groups?

1

2

3

4

6

7

8

5 9

10

Do the target & analogshare a

major substructuralfeature or key functional

group?

Searching GRASP- Substructure Searching

Search Structure

N

O

O

O N

O

O

OHNOH

N

O

O

F

Output – Substructure Searching

Expert system decision tree for repro/dev toxicity

Organiccompds

Yes

Contains a cyclicring

Yes

Yes

Yes

ER binding chemicals:steroids, f lavonesalkylphenols,DES-like deriv.biphenyls with OH,DDT-like, salicylates,parabens, phthalates,tamoxifen-like,alkoxy phenols,diphenyl alkanesAR binding chemicals:N-aryl subst. urea,carbamides

Yes

No

Belongs to 1) chloro subst. (6 Cls)cyclohexane; 2) cyclophosphamide,cycloheximide; 3) arabinopyranose;

4) isotretinoin & retinoids; 5)pyrimidine & purine derivatives

No

YesNo

Belongs to < C8carboxylic acids, theirprecursors (alcohols,aldehydes, esters) or

amides, ureas &carbamates

No

Belongs to di-functionalgroup (NH2, SH, OH,OR, CN) subst. C2 toC6 hydrocarbon orrepeating C2 units

Yes

Precedentedreproductive &developmentaltoxic potential

Unprecedentedrepro/dev toxic

potential

No

Belongs to saturated, <C8 carboxylic acids or

their precursors(alcohols, aldehydes,

esters)

Yes

Yes

Yes

No

No

No

No

No

NoCore structure

contains aromatic orheteroaromatic ring

Belongs to: multi-halogenated (Cl, Br)

< C4 alkanes oralkenes

Metallicderivatives Yes

Miscellaneous Drugs:diphenylhydantoin, thalidoamide, benzhydryl

piperazine, leucoalkyl violet, nitrofural,chloromazine, codine, morphine,

xanthotoxin; Antibiotics: actinomycinD, mitomycin C, puromycin,

streptomycin, lincomycin, gentiahviolet, oxytetracycline; Naturalchemicals: berberine, emodin,

hinokitiol

No

Yes

No

1

2

3

4

56

7

9

10

11

Belongs to: 1) small orbranched alkyl benzene andPAHs; 2) poly-subst. (Cl, Br,NO2) benzene, oxdibenzene;bi-benzene; 3) BMHCA-like;

4) alpha aryloxy subst.aliphatic acid derivatives

Belongs to: 1) alphahalogens (Cl, Br); alpha-

alkoxyl (-OR, R is < C5 alkylchain); alpha-alkyl (C2 toC3) substituted carboxylic

acids or their precursors; 2)adipate derivates; 3) C1-C4

non-branched alcohols

Belongs to: 1) vinyl amides,aldehydes & esters; 2) C1-

C4 amides and N-alkylsubst. amides, ureas,thioureas, carbamates

ChemicalsBelong to Al, As, B, Cd,

Cu, Cr, Zn, Mn,acids, oxides chlorides orPb, Hg chlorides & Me, Et

derivatives

Belongs to multi-functional group subst.(at the terminal carbon)

< C8 hydrocarbons(substituents: halogens,NH2, SH, OH, OR, CN)

Miscellaneous chemicals:Belongs to alkylation agents,bis(2-ethylhexyl) hexanedioicester, methyl carbamodithioicacid etc.

Yes Yes

Yes

Yes

No

8

VI

I

II

IV

III

V

1) Sulfamoyl, sulfonic acid, subst.benzoic acid, toluenesulfonamide;

2) benzidineazo and ethyl aminoazocompds; 3) Triazoles & pyridyl

triazenes; 4) 2,4-diamino pyrimidine

No No

Unprecedentedrepro/dev toxic

potential

Precedentedreproductive &developmentaltoxic potential

Key criteria for ER binding

Hydrogen bonding abilityof the phenolic ring.

Hydrogen bond donorand O-O distance.

Precise sterichydrophobic centers.

Satisfactoryhydrophobicity (log p).

Ring structure

1

2

4

3

Expert system decision tree for ER binding (EPA)

Current research in supporting biological activity assessments

• Toxicogenomics– Predictive toxicology

• Evaluation of global gene changes in tissues of interest in vitro, with comparisons to known toxicants

– Ishikawa cells– Rat hepatocytes– Human hepatocytes– connectivity mapping

– Mechanistic understanding– Improved dose-response assessment

From Liu et al., 2005

1 2 8 24 48 72 96

Time (h)

Fluid imbibition

CellProliferation

Epitheliumremodeling

Regression to basal level

Transcription factors, cell signaling, vascular permeability, growth factors

mRNA and protein synthesis

Cell growth, differentiation, suppression apoptosis,

Cell cycle regulators

DNA replication and cell division

Tissue remodeling and cytoarchitecture

Immune response

Adapted from Fertuck et al. (2003); Moggs et al. (2004); and Naciff et al. (2007)

EE-RAT-24hr (Up-regulated)

KEGG Cell cycle Example

EE

BPA

Gen

Connectivity Mapping

• Establish connections between biological states using gene expression data

• A simpler approach to analyzing toxicogenomics data to identify common mechanisms

• Data are from simple cell types, but these contain a diverse range of small molecule receptors

• Most highly up- and down-regulated genes are considered

• High degree of concordance across mechanisms

Lovastatin as an example

From Smalley, Gant and Zhang, 2010

Dynamic Modeling of Local Effects

• Linking:– Pharmacokinetic

model of dermal absorption

– Quantitative metabolism predictions

– Kinetics of peptide reactivity

Concentration of Butyl Paraben (µM)

0 20 40 60 80 100

Form

atio

n of

p-H

ydro

xybe

nzoi

c ac

id(n

mol

/min

/mg)

0

2

4

6

8 Vmax = 8.8 Km = 28.6

Acknowledgements

• Cheminformatics– Karen Blackburn– Shengde Wu– AIM and ACES Teams

• Dynamic Models– Joanna Jaworska– John Troutman

• Toxicogenomics– Jorge Naciff– Gary Overmann– Greg Carr– Yuching Shan– Xiahong Wang– Nadira deAbrew– Blad Ovando