Use of Toxicogenomics as Support for Structure Activity Relationships (SAR) for

Predictive Toxicology

Jorge M. Naciff, PhD Procter and Gamble

Global Product Stewardship

Predicting Toxicity

In the absence of data, we are increasingly relying on Structure Activity Relationships (SAR)-based assessments to evaluate the safety of new ingredients.

SAR-based assessments rely on assumptions that the new chemical’s biological activity, and consequently its toxicological profile, is the same as a close analog(s) that has been evaluated; or that is metabolized to a well-studied compound.

In order to support these assumptions, the identification of

the specific cellular pathways modified by exposure to this chemical (which dictates its biological activity), in a dose and time-dependent fashion, are paramount.

High information content analysis of biological systems at the molecular

level

Reference Data Base

Integrating Large Scale, External Biological, Chemical and Clinical Databases

Relevant Biomarkers and Mechanistic Information of

Toxicity

Predicting Chemical Toxicity: Goal

Predictive Toxicity for Chemical Exposure

Genomics in Toxicology

Changes in gene expression are sensitive indicators of biological responses.

Specific mechanisms of toxicity elicit specific patterns of gene expression.

Conservation of biological response across species (or even across cell types within an organism) is at the level of the gene/gene product.

Toxicity Phenotype

New Chemical Analog 1 Analog 2 Analog 3

Predicting Toxicity by Gene Expression Analysis: Goal

O

CH3

CH3

CH3

CH3

O

CH3

CH3

CH3

CH3

CH3

CH3

CH3

CH3

CH3

OH

OH

CH3

CH3

CH3

CH3

H3C

H3C

CH3

H3C

OH

H3C

CH3H3C

H3C

HO

CH3H3C

H3C

HO

H3C

CH3

H3C

CH3H3C

H3C

OH

CH3

H3CCH3

And…

Biological Function

0

20

40

60

80

100

120

140

160

0.001 0.01 0.1 1.0 10.0

EE (mg/kg/day)

Nu

mb

er

of

gen

es

aff

ec

ted

0

10

20

30

40

50

60

70

80

LL L M MH H

Doses

Nu

mb

er

of

Gen

es a

ffecte

d

Ges

BPA

Dose-Response of Gene Expression Changes Induced by EE, Ges or BPA in Testis/Epididymis of the Developing Rat

0.002 0.02 0.5 50 400

BPA (mg/kg/day)

Gene expression changes induced by a single dose of EE* in the immature uterus of the rat

* S.C. 10 mgEE/kg; p<0.0001, t-test, n = 5 for all groups

0

500

1000

1500

2000

2500

3000

3500

1 2 8 24 48 72 96

Time (h)

Num

ber

of G

ene

s

Time (h)

1 2 8 24 48 72 96

Time (h)

Fluid imbibition

Cell Proliferation

Epithelium remodeling

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; 2010)

Experimental Approach

RNA RNA

Human U219 Array

( Affymetrix GeneChip ™ )

Ishikawa cells cultured and

transferred from flask to 6 - well plates to a cell density ~1x10

6

cells/ mL for EE - dosing

Ishikawa cells

cultured in flasks

to generate

stock

Ishikawa cells

cultured in flasks

to generate

stock

C C vL vL L L H H vH vH EtOH 10

- 12 M 10

- 10 M 10

- 8 M 10

- 6 M

C C vL vL L L H H vH vH EtOH 10

- 12 M 10

- 10 M 10

- 8 M 10

- 6 M

Ishikawa cells challenged with various levels of

EE: very low [ vL ; 10 - 12

M], low [L; 10 - 10

M], high [H; 10

- 8 M], and very high [ vH ; 10

- 6 M] over 8h, 24h or

48h

C C vL vL L L H H vH vH EtOH 10

- 12 M 10

- 10 M 10

- 8 M 10

- 6 M

C C vL vL L L H H vH vH EtOH 10

- 12 M 10

- 10 M 10

- 8 M 10

- 6 M

Ishikawa cells challenged with various levels of

EE: very low [ vL ; 10 - 12

M], low [L; 10 - 10

M], high [H; 10

- 8 M], and very high [ vH ; 10

- 6 M] over 8h, 24h or

48h

cDNA cDNA cRNA cRNA

Statistical analyses and

Annotation tools

49,294 probe sets 38,500 genes

control 1 1 1 1 Vehicle

17aEE 10x10-12 10x10-10 10x10-8 10x10-6 M

Ges 10x10-11 10x10-9 10x10-7 10x10-5 M

BPA 10x10-10 10x10-8 10x10-6 10x10-4 M

Time and dose response of gene expression changes induced by chemical exposure in vitro

EE

BPA

Gen

EE-RAT (Up-regulated)

KEGG Cell cycle Example (24hr)

EE

BPA

Gen

Diethylstilbestrol EE

Estrone* Methoxychlor Octylphenol

Cortisol Methimazole Ethylene thiourea Phenylthiourea

BPA Gen

Estrogenic

No estrogenic

Pre-validation of estrogenic fingerprint

Estrogenic Fingerprint Gene Symbol Gene Name EE DES Estrone Methoxychlor Octylphenol Cortisol ETU PTU Methimazole

MLPH melanophilin 8.4 6.5 5.9 2.4 1.0 1.1 1.1 -1.0 1.0

NRTN neurturin 5.8 4.5 4.0 2.3 1.0 -1.2 1.0 -1.2 1.2

TGFA transforming growth factor, alpha 4.6 2.3 2.3 1.4 -1.1 1.0 1.0 -1.0 -1.1

CA2 carbonic anhydrase II 4.3 2.9 3.0 1.4 1.1 1.0 -1.0 -1.0 -1.0

DEPDC6 DEP domain containing 6 4.2 2.5 3.0 2.2 -1.0 -1.0 -1.0 -1.0 -1.0

CAPS calcyphosine 3.9 2.6 3.0 1.5 -1.0 1.1 -1.0 1.0 1.1

PCCA propionyl Coenzyme A carboxylase, alpha polypeptide 3.2 1.9 1.7 1.2 1.0 1.1 1.0 1.0 1.1

PION pigeon homolog (Drosophila) 2.8 1.9 1.7 1.2 -1.0 1.2 1.0 -1.1 -1.0

TIAF1 TGFB1-induced anti-apoptotic factor 1 2.8 1.5 1.5 1.3 1.1 1.0 1.0 1.0 1.1

IL6R interleukin 6 receptor 2.7 1.7 2.3 1.8 1.1 -1.0 1.1 -1.1 1.1

ATP2C1 ATPase, Ca++ transporting, type 2C, member 1 2.5 1.8 1.4 1.3 1.1 1.0 -1.0 -1.1 1.1

PIPOX pipecolic acid oxidase 2.4 2.7 2.8 1.6 -1.0 1.1 1.1 -1.1 1.1

RAN RAN binding protein 3 2.4 3.4 3.6 1.6 -1.0 1.0 -1.0 -1.1 -1.1

RBBP8 retinoblastoma binding protein 8 2.2 1.6 1.7 1.3 -1.0 -1.0 -1.0 -1.0 -1.0

CA12 carbonic anhydrase XII 2.0 1.4 1.7 1.2 -1.0 1.0 1.0 -1.0 1.0

ASRGL1 asparaginase like 1 1.9 1.5 1.5 1.2 -1.1 1.1 -1.1 -1.0 1.0

G0S2 G0/G1switch 2 1.9 1.6 1.9 1.3 1.0 1.0 -1.1 1.0 -1.2

NRIP1 nuclear receptor interacting protein 1 1.7 1.3 1.4 1.3 -1.0 -1.0 -1.0 -1.1 1.0

RP1-21O18.1 kazrin 1.6 1.8 1.5 1.3 1.0 -1.1 1.0 -1.0 -1.0

C1orf168 chromosome 1 open reading frame 168 1.5 1.7 1.8 1.3 1.0 -1.2 1.1 -1.1 1.0

MAL mal, T-cell differentiation protein -1.6 -1.9 -1.4 -1.4 -1.0 1.0 1.0 1.0 1.0

RBP1 retinol binding protein 1, cellular -1.6 -1.8 -1.5 -1.3 -1.1 -1.1 -1.0 1.0 1.1

TRIL TLR4 interactor with leucine rich repeats -1.6 -1.7 -1.4 -1.2 1.0 -1.1 1.0 -1.0 1.0

DYSF dysferlin, limb girdle muscular dystrophy 2B (autosomal recessive) -1.7 -1.9 -1.3 -1.4 -1.1 1.1 -1.0 -1.0 1.0

LIMK2 LIM domain kinase 2 -1.7 -1.5 -1.3 -1.2 1.0 -1.0 1.0 1.0 1.0

STON2 stonin 2 -1.7 -1.6 -1.3 -1.2 -1.0 -1.1 -1.0 1.0 1.0

ODZ1 odz, odd Oz/ten-m homolog 1(Drosophila) -1.7 -2.4 -1.8 -1.6 1.1 1.0 1.1 -1.0 1.1

ERP27 endoplasmic reticulum protein 27 -1.9 -2.4 -1.8 -1.3 -1.0 -1.1 1.0 1.0 -1.0

MXRA5 matrix-remodelling associated 5 -2.0 -1.8 -1.5 -1.3 1.0 1.0 1.1 1.1 -1.0

SOX4 SRY (sex determining region Y)-box 4 -2.2 -1.7 -1.5 -1.2 1.0 -1.1 1.0 -1.0 1.1

FRZB frizzled-related protein -2.3 -3.0 -2.4 -1.7 1.0 1.1 1.0 -1.0 1.0

SLC40A1 solute carrier family 40 (iron-regulated transporter), member 1 -2.4 -2.0 -1.7 -1.4 -1.1 -1.2 1.0 1.0 -1.1

MMP10 matrix metallopeptidase 10 (stromelysin 2) -2.7 -2.1 -1.4 -1.4 1.0 1.0 1.0 -1.1 1.1

VGLL1 vestigial like 1 (Drosophila) -3.0 -2.1 -1.9 -1.5 -1.1 -1.1 1.0 -1.0 1.1

E E

/b

Estrogen-responsive genes

Organ Response

Transporters Extracellular matrix Enzymes

Cellular Response

Cytosol

Receptors

Specific mRNAs (Up- or Down-regulated)

Estrogen: Mechanism of Action

Chemicals Tested in Rat and Human Primary Hepatocytes

Acetaminophen Di(2-ethylhexyl ) phthalate

Sodium Valproate Phenobarbital Clofibrate

Diisononyl phthalate WY-14,643 Chlorpromazine Methapyrilene b-Naphthoflavone

Structurally “unrelated” hepatotoxicants, with two clear exceptions (phthalates)

Peroxisomal branched chain fatty acid oxidation

Mitochondrial long chain fatty acid beta oxidation

Peroxisome proliferators Cytotoxicants

CYPs inducers

Hepatotoxicants with similar mode of action segregate together:

Connectivity Map Approach: using gene-expression signatures to connect small

molecules, genes, and disease.

Lamb et al., 2006 (Broad Institute)

Gene expression signature is obtained from testing one dose and at one time point/chemical

Chemical Name Mode of Action 6 Aminonicotinamide Inhibitor of the pentose pathway

Amoxicillin β-lactam antibiotic used to treat bacterial infections Tetrachlorodibenzo p dioxin AhR agonist Dehydrorepiandrosterone AR agonist Trenbolone AR agonist Flutamide AR-antagonist Troglitazone CAR/PXR agonists Phenobarbital CAR/PXR agonists Bisphenol A ER agonist Ethenyl Estradiol ER agonist Genistein ER agonist; kinase inhibitor, etc Tamoxifen ER-antaganist Chenodeoxycholic acid Farnesoid X receptor (FXR) receptor agonist Farnesol Farnesoid X receptor (FXR) receptor agonist Methotrexate Inhibitor of dihydrofolate reductase Clobetasol Glucocorticoid receptor agonist Valproic Acid Histone deacetylase (HDAC) inhibitor Vorinostat Histone deacetylase (HDAC) inhibitor Desferrioxamine Iron chelator ANIT Liver Cholestasis inducer Griseofulvin Liver Cholestasis inducer Vinblastine Microtubule inhibitor Imidacloprid Nicotinic acetylcholine receptors antagonist; neonicotinoid, inhibits ACh pathway Nicotine Nicotinic acetylcholine receptors agonist Metformin Oxidative phosphorylation/mitochondrial inhibitor; it seems that it binds to an orphan receptor Phenformin Oxidative phosphorylation/mitochondrial inhibitor; it seems that it binds to an orphan receptor Clofibrate PPAR agonist DHP (diethylhexyl phthalate) PPAR agonist Progesterone Progesterone receptor agonist RU 486 (mefepristone) Progesterone receptor agonist Retinoic Acid RAR agonist Ketoconazole Steroid synthesis inhibitor Thyroxine TR agonist Dihydroxyvitamin 3 Vitamin D agonist

C-Map: Chemicals Tested

Cell lines evaluated: MCF7 • Human breast adenocarcinoma cell line •Exhibits characteristics of differentiated mammary epithelium • Steroid receptor positive (i.e. Estrogen Receptor positive) HepaRG and HepG2 • Human hepatocelluar carcinoma cell line • Exhibits many characteristics of primary hepatocytes

• Similar morphology • Express metabolic enzymes (metabolically active) • Express nuclear receptors • Express drug transporter

Ishikawa •Human endometrial adenocarcinoma cell line • Exhibits many characteristics of human endometrium

• Similar morphology and metabolic competency • Express nuclear receptors

• No donor variability • Available on-demand

HepG2 cells’ response to 34 chemicals

Key points of expert system decision tree for screening reproductive and developmental toxicity

Expert system decision tree for DART effects

Positive Hit

Maps Structures

with Known

Precedent for

DART

Scaffold Map

Maps

Substructures

Associated with

Structures with a

Known Precedent

for DART

Requires further

interpretation

No Mappings

This result means

the structure is not

covered by the

decision tree (out of

domain). It does

not demonstrate

the absence of

DART endpoint

effects.

Not Covered

Contains P, Si

Inorganic

Output

Draw Structure

LCAS Number

Lists Structure data (sd)

Excel (CAS#)

Input

The chemical or structure of interest (SOI):

“Is associated with structures known to have DART activity”.

“Is not associated with structures known to have DART activity”.

“Has core structures outside the chemical domain of the DART decision tree”.

One example: Camphor (1,7,7-Trimethylbicyclo[2.2.1]heptan-2-one,CAS# 76-22-2)

Category 17: Heterocyclic, cyclic compounds contain nitrogen, oxygen/sulfur atoms. Subcategory 17c includes: Piperazine-, dioxane-, morpholine-, tetrahydrothiopyran-like derivatives and cyclohexanamine.

How to use the DART decision tree?

The decision tree is not intended to be used as a stand-alone tool, and by design

is intended to broadly capture chemicals with features that are similar to

chemicals with precedent for DART effects.

The decision tree can be used as part of the weight of evidence in an integrated SAR read across assessment.

We propose that this decision tree could be used both as a component of a

screening system, to identify chemicals of potential concern, and as a

component of weight of evidence decisions based on SAR, to fill data gaps

without generating additional test data.

The use of the tree can help to “broaden” the net to find useable analogs

(substructures of the parent compound, potential metabolites, etc.).

The DART decision tree can be used to reduce the uncertainty in the strength of the analog data by triggering the use of the data from a worst case analog within the broader class identified by the decision tree:

p-tert-Butylbenzoic acid

2-(4-tert-Butylphenyl)ethanol no DART but, p-tert-Butylbenzoic acid could be used as worst case analog!

The DART decision tree can be used as a starting point to select groups of chemicals to explore mode of action hypotheses:

Are all these N-Aromatic substituted urea, carbamides able to affect AR system and elicit DART?

SOI

(+) SOI

(+/-) SOI

(-) SOI

How can we use the DART decision tree?

DART

Team: • George Daston

• Nadira De Abrew

• Yuching Shan

• Xiaohong Wang

• Jay Tiesman

• Rachel Adams

• Ryan Estep

• Greg Carr

• Raja Settivari

• Edward Carney

• Barbara Wetmore

• Rusty Thomas

• Karen Blackburn

• Joan Fisher

• Michael Laufersweiler

• Cathy Lester

• Shengde Wu