Use of Toxicogenomics as Support for Structure Activity...
Transcript of Use of Toxicogenomics as Support for Structure Activity...
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