INTRODUCTION

MATERIALS AND METHODS

CONCLUSIONS

RESULTS

FUTURE DIRECTIONS

TOXICOLOGICAL CATEGORIZATION OF P- AND E-SERIES GLYCOL ETHERS USING HIGH-CONTENT SCREENING OF HUMAN INDUCED PLURIPOTENT STEM CELL (iPSC)-DERIVED CELLS

Iwata Y1, Grimm FA1, Wilson M1, Chappell GA1, Bittner M2, Sirenko O3, Rowlands JC4, Ball N4, Rusyn I11Veterinary Integrative Biosciences, Texas A&M University, College Station, TX, USA; 2Engineering Experiment Station, Texas A&M University, College Station, TX, USA;

3Molecular Devices, LLC, Sunnyvale, CA, USA; 4The Dow Chemical Company, Midland, MI, USA

• High-content screening (HCS) assays utilizing novel organotypiccell culture models1,2,3,4 are an attractive approach for chemicalsafety assessments using biological data-based category read-across.

• In order to test the hypothesis that HCS represents a feasibleapproach to categorize chemicals based on similarities in theirin vitro toxicity profiles, we applied a series of phenotypic andtranscriptomic screening assays to screen eight propylene (P-series) and twelve ethylene (E-series) glycol ethers, a structurallyrelated yet toxicologically diverse group of prototypical industrialhigh production volume chemicals.

• We used two human induced pluripotent stem cell (iPSC)-derived cell types, cardiomyocytes and hepatocytes. Cells wereexposed to glycol ethers for various durations in concentration-response.

• Concentration-response data from phenotypic screening assayswere used to derive point-of-departure values5 that were usedas quantitative descriptors for categorization in ToxicologicalPrioritization Index (ToxPi).

P-Series and E-Series Glycol Ethers

The P-series glycol ether category

Mono- Di- Tri-

Methyl Ether PGME DPGME TPGME

Ethyl Ether

Propyl Ether PGnPE DPGnPE

Butyl Ether PGBE DPGBE TPGBE

•Data-rich, well established category (e.g., OECD acceptance)

•Read across used to address several endpoints within category

•Low order of toxicity throughout

•Toxicity limited to liver/kidney hypertrophy (adaptive adverse)

Mono- Di- Tri-

Methyl Ether EGME DEGME TEGME

Ethyl Ether EGEE DEGEE TEGEE

Propyl Ether EGPE

Butyl Ether EGBE DEGBE TEGBE

Hexyl Ether EGHE DEGHE

•Data-rich, well established category (e.g., OECD acceptance)

•Read across already used for several endpoints within the category

•Significant variance in toxicity exists (includes DART, immunotoxicity, blood toxicity, hepatotoxicity)

• In general, acid metabolite (e.g., methoxyacetic acid) is the toxicant

High-Content in vitro Screening for P-Series and E-Series Glycol Ethers using iPSC Cardiomyocytes and Hepatocytes

384-well plate design

2x or 5x working dilutions

Experimental Design Data Acquisition

Chemical Master Plate

20 Glycol Ethers2 Symple Glycols

Data Evaluation

EXPERIMENT 1

ImageXPress® XLS Micro

High

-Con

tent

Cel

l Im

agin

g

Cytotoxicity

EXPERIMENT 1

Nucl

ei

Hoe

chst

Mito

chon

dria

Mito

Trac

ker

ROS

form

atio

n

CellR

OX

Nor

mal

izatio

nDo

se-R

espo

nse

Toxicity Profiling

Cardiophysiology

Calc

ium

-Flu

x

FLIPR Tetra®

Cytotoxicity

Nuc

lei

Hoe

chst

Mito

chon

dria

Mito

Trac

ker

EXPERIMENT 2 EXPERIMENT 2Hepatocyte physiology

Cyto

skel

eton

Phal

loid

in

Lipi

d Ac

cum

ul.

Lipi

dTO

X

iCell HepatocytesiCell Cardiomyocytes

0 10 mM0 10.10.010.0010.0001

Nuc

lei

100 mM301010.10.01

High-Throughput Gene Expression Analysis Data Integration/ Visualization

•A targeted RNA sequencing technology, TempO-Seq™ (BioSpyder Technologies, Inc., Carlsbad, CA), were used.

• iPSC hepatocytes were exposed to test chemicals in concentration-response for 48 hours.

•mRNA targets were “barcoded” by a specific primer pair in PCR process for the following sequencing step.

•Libraries are sequenced using a HiSeq 2500 Ultra-High-Throughput Sequencing System (Illumina, San Diego, CA).

mRNA

Detector oligo annealing

Detector oligo ligation

PCR with barcodedprimers

Sequence

Concentration-response plots for representative phenotypes cardiomyocyte peak frequency (A) and viability (B) are shown. Plots are shown in each alcohol group. Data were normalized to media controls (100%). Data points represent means of duplicate

determinations (n=2). [BPM = beats per minute, cardiomyocyte beat frequency, Total cells (Hoechst) = the total number of nuclei]

In vitro toxicity profile categorization was performed using the ToxPi approach. ToxPi data derived from all endpoints and cell-types were integrated to a ToxPi score. The higher the ToxPi score, the higher the relative toxicity of the glycol ethers. There is a positive correlation between ToxPi scores and the length of the alcohol group (simple glycols, methyl-, ethyl-, propyl-, butyl-, and hexyl ethers). This trend was more prominent

than when the data was expressed by individual cell-type.

• There was a correlation between the length of the alcohol group and induced effects such that glycol ethers can be categorized based on the alcohol group.

• Within each alcohol group, there was a trend of increasing cytotoxicity between mono-, di-, and tri-substituted glycol ethers.

• These trend were more prominent when cell typed and all endpoints were integrated.• However, these trends did not align well with trends based on in vivo toxicity data with P- and E-series glycol ethers.• Gene expression analysis using TempO-Seq revealed clustering trends of E-series glycol ethers and simple glycols.• There was not a clear separation between E- and P- series glycol ethers in terms of biological activity using in vitro data.

• To conduct a biological and physico-chemical data-integrative categorization to further improve product groupings• To assess the relevance of the in vitro concentrations to in vivo dose• Pathway-integrative differential gene expression analysis to provide insights for mechanistic toxicology evaluation

ACKNOWLEDGEMENTSThis work was supported by institutional funding from Texas A&M University. Fabian Grimm is a recipient of SOT Colgate-Palmolive postdoctoralfellowship. We would like to thank the Dow Chemical Company for providing glycol ethers and reagents used in this study. In addition, we wouldlike to acknowledge technical assistance from Dr. Michael Bittner (Texas A&M University). We Thank BioSpyder for providing assistance withtranscriptomics data analysis.

REFERENCES

Simple Glycols & Methyl Ethers Ethyl & Propyl Ethers Butyl & Hexyl Ethers

Simple Glycols & Methyl Ethers Ethyl & Propyl Ethers Butyl & Hexyl Ethers

Hexyl Ethers; Eor

Butyl Ethers; P

Hexyl Ethers; Eor

Butyl Ethers; P

Concentration-response plots for representative phenotypes hepatocyte cytoskeletal integrity (A) and viability (B) are shown. Plots are shown in each alcohol group. Data points represent means of duplicate determinations (n=2). [% positive (phalloidin) = percentage of

cells with intact cytoskeleton, Total cells (Hoechst) = the total number of nuclei]

A

B

Simple Glycols & Methyl Ethers Ethyl & Propyl Ethers Butyl & Hexyl Ethers

Simple Glycols & Methyl Ethers Ethyl & Propyl Ethers Butyl & Hexyl Ethers

Hexyl Ethers; E& Butyl Ethers; P

Butyl Ethers; E

Hexyl Ethers; E

Methyl Ethers; P Propyl Ethers; P

Ethyl & Propyl Ethers; E

A

B

Concentration Response Profiling - iPSC Cardiomyocytes

Concentration Response Profiling - iPSC Hepatocytes

Biological data-based read-across of P-Series and E-Series Glycol Ethers using in vitro data from iPSC cells

•Data were normalized to media controls and fitted to a concentration-response.

•To determine point-of-departure (POD) values at 1 standard deviation from baseline level, we used a custom script in R.

•POD values were transformed and visualized in ToxPi6.

•The larger the area of a ToxPi slice (on a 0-1 scale), the higher the toxicity of a substance on the respective phenotype.

1. Sirenko et al. (2013) J Biomol Screen. 18: 39-53 2. Sirenko et al. (2013) Toxicol Appl Pharmacol. 273: 500-507 3. Sirenko et al. (2015) Assay Drug Dev Technol. 12: 43-52 4. Grimm et al. (2015) Assay Drug Dev Technol. 13: 529-5465. US EPA (2011) Benchmark Dose Technical Guidance Document6. Reif et al. (2013) Bioinformatics. 29:402-403

Cell culture: iCell Cardiomyocytes and iCell Hepatocytes 2.0 were plated and maintained in 384-well plates.

Cardiophysiology screening: Cardiac physiological change were assessed by measuring the intracellular Ca2+-flux of synchronously beating iPSC cardiomyocytes using the FLIPR Tetra (Molecular Devices).

Treatments: Cells were incubated at the following concentrations for up to 48 hours. Cardiomyocyte screening: 0.01, 0.1, 1, 10, 30, 100 mM; Hepatocyte screening: 0.0001, 0.001, 0.01, 0.1, 1, 10 mM.

High-content imaging Screening: We conducted cytotoxicity by using the ImageXpress Micro XL high-content imaging system (Molecular Devices). Nuclei and mitochondria integrity were assessed for both cell types. Cytoskeletal integrity, reactive oxygen species formation (ROS) and lipid accumulation were assessed for hepatocytes.

Principal components analysis (PCA) for global changes in hepatocyte gene expression at the highest concentration is shown. There was a clustering trend of E-series glycol ethers and simple glycols. [A: 2D plot of PCA 1 and 2 ; B: 2D plot of PCA 1 and 3]

Gene Expression Profiling by TempO-Seq - iPSC Hepatocytes

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A B

Simple Glycols SimpleGlycols

E-Series

E-Series

The E-series glycol ether category

Hex

yl-

EGHEDEGHE

E

PGBEDEGBEEGBETEGBE DPGBETPGBE

E P

But

yl-

EGPE PGnPEDPGnPE

E P

Simple Glycols

Ethy

l-

EGEEDEGEETEGEE

E

Met

hyl-

PGMEDEGMETEGMEEGME DPGMETPGME

E P

EG PG

E P

Prop

yl-

Lege

nd

Methyl Ethers; P Propyl Ethers; P

Butyl Ethers; E