In Vitro Screening Approaches to Estimate Human Hepatic Responses to Xenobiotic Exposures
Stephen S. Ferguson, Ph.D.
National Toxicology Program (NTP) Division
National Institute of Environmental Health Sciences (NIEHS)
• I have no financial relationships to disclose.
• The statements, opinions or conclusions contained therein do not necessarily represent
the statements, opinions or conclusions of NIEHS, NIH or the US government.
Tissues and Organs: a text of scanning electron microscopy, Kessel, RG and Kardon,RH, 1979.
Immortal liver cells
Physiological architecture of liver
=?
Cell reporter assays
Human-derived In Vitro Liver Models for Estimation of Human DMPK/DDI
• Hepatic clearance
− Suspensions of PHHs (typically pooled donor)
• Retain liver-like xenobiotic metabolism & uptake transport
• Ineffective models of efflux transport
• Very short longevity (few hours)
• Liver enzyme induction - DDI
− Sandwich-cultured primary human hepatocytes
• Effective to model liver enzyme induction via AhR,
CAR, PXR, and PPAR pathways
• Useful models of biliary excretion
• ~90% reduced baseline metabolic competence
• High variabilities
− donor prep variations
− technically variations
− Short longevities (days, sporadic cell death) Time (hrs)
Blo
od C
oncentr
ati
on
Ineffective level
Therapeutic Window
(drug efficacy)
Toxic / side-effect level
Seeding Density
1.5
0.75 0.
50.
25
0.17
5
CY
P1A
2 A
cti
vit
y
(pm
ol/m
in/1
06 c
ells)
0.0
0.5
1.0
1.5
2.0
2.5
3.0
Impact of cell density on cytoskeleton and xenobiotic metabolism with
primary cultures of human hepatocytes
Hamilton et al., Cell Tissue Res, 2001; 306: 85-99.
CY
P3
A4
/5 I
nd
uce
d
En
zym
atic A
ctivity
LeCluyse et al., 2010. Chapter 8: Cytochrome P450 Induction. Enzyme Inhibition in Drug
Discovery and Development: The Good and the Bad. John Wiley & Sons.
Mechanism-based CYP3A Inhibitors/Inducers Over Time in SC-PHHs
CYP3A4 mRNA
0
2
4
6
8
10
12
2 4 8 24 48 72 96
Time (Hrs)
(CY
P3A
4 m
RN
A C
on
ten
t}
Rela
tiviv
e F
old
-Over-
Co
ntr
ol
TAO 0.1 µM
TAO 10 µM
CYP3A4 Activity
0
50
100
150
200
250
2 4 8 24 48 72 96
Time (Hrs)
(Testo
ste
ron
e 6
ß-H
yd
roxyla
tio
n)
Perc
en
t o
f V
eh
icle
Co
ntr
ol
TAO 0.1 µM
TAO 10 µM
0
20
40
60
80
100
120
2 4 8 24 48 72 96Time (Hrs)
(Testo
ste
ron
e 6
ß-H
yd
roxyla
tio
n)
Perc
en
t o
f V
eh
icle
Co
ntr
ol
Ritonavir 0.1 µM
Ritonavir 10 µM
CYP3A Activity CYP3A4 mRNA
0
5
10
15
20
25
30
35
2 4 8 24 48 72 96
Time (Hrs)
(CY
P3A
4 m
RN
A C
on
ten
t}
Rela
tiviv
e F
old
-Over-
Co
ntr
ol
Ritonavir 0.1 µM
Ritonavir 10 µM
0
0.5
1
1.5
2
2 4 8 24 48 72 96
In Vitro Disposition of Omeprazole in SC-PHHs
50 µM Omeprazole in Cultures of Primary Human Hepatocytes
0
20
40
60
80
100
120
0.25 0.5 2 6 24 48 72
Time (hr)
[OM
P]
µM
Extracellular
Intracellular
Total
Time[Inducer]
Added
Percent in
Monolayer
Supernatant
Concentratio
n (µM)
Est. Monolayer
Concentration
(µM)†
15 min OMP (50 µM) 24.4 80.9 26
30 min OMP (50 µM) 23.4 75.2 23
2 hr OMP (50 µM) 21.40 75.2 20
6 hr OMP (50 µM) 22.1 48.6 14
24 hr OMP (50 µM) 21.47 15.66 4.28
48 hr OMP (50 µM) 17.28 7.51 1.57
72 hr OMP (50 µM) 14.36 2.12 0.36
†Assumed 4 µL volume per million cells
YALE JOURNAL OF BIOLOGY AND MEDICINE 69 (1996), pp. 203-209.
Smith et al. J. Pharm. Sci. 2012. v.101(10):3898.
Isolated Primary Liver Cells
Rapidly De-differentiate Once
Removed from Liver Tissue
Tox21 Evolution: Predictive Toxicology Screening
• Physiologically-relevant in vitro screening models
– improved cellular differentiation/functionality
– xenobiotic metabolism & bioactivation/detoxification
– longevity to model response progressions towards apical outcomes
– cross-species parallelogram comparisons
• Multi-dimensional assay platforms (time/concentration)
− high throughput transcriptomics
− high content imaging
− metabolomics
• Quantitative translation of biological responses to humans
– Cmax/BMC ratios
– Pathway Analyses
– IVIVE
• Extend approach to:
–Extrahepatic tissues: kidney & intestine
–Susceptibility models: developmental, disease, population
primary hepatocytes
spheroids
HepaRG
3D HepaRG spheroids
Hepatopac
Hanging drop
(Insphero)
Magnetic levitation
HµRELflowSandwich cultures
Perfused bioreactors
Hydrogel-based
3D models
Bioreactors
3D Bioprinting
(e.g., Organovo)
Throughput
ULA microplate
(Corning)
Mimetas
Emulate/
Wyss
3D HepaRG Spheroids (384-well)
Dr. Sreenivasa Ramaiahgari
Postdoctoral Fellow1 vial of 10 million cells
= 1 X 2D 384-well plate
= 12-25 X 384-well platesFrom the Cover: Ramaiahgari et al., Toxicol Sci (2017) v.159 (1): 124-136
HepaRG Cells
Liver Progenitor Cell Line (INSERM/BioPredic)
− Derived from female patient with hepatocellular carcinoma & hepatitis C
− Differentiate to two distinct cell populations
− hepatocyte-like cells
− cholangiocyte-like cells
Differentiated Hepatocyte Functionality
- Transporters▪ uptake (e.g. OATP, NTCP)
▪ efflux (e.g. MRPs, MDR)
- DMEs▪ Phase I (e.g. P450, FMO)
▪ Phase II (e.g. UGTs, SULT)
- Receptor Pathways▪ functional CAR, PXR, AhR
▪ induction of DMEs and Transporters
Advantages over PHHs– Year-over-year availability
– Markedly reduced lot-to-lot variability
– Ability to transdifferentiate & proliferate
Primary Human HepatocytesHepaRG Cells
Jackson et. al, DMD, (2016) v.44(9): 1463-79.
CAR Translocation
pm
ol/m
in-m
illio
n c
ells
Nature Reviews Gastroenterology & Hepatology 9, 231-240 (April 2012)
MRP2 Localization in HepaRG Spheroids
MRP2 Nuclei Merge
100 um
Z-a
xis
MRP2 Nuclei Merge
Ramaiahgari et al., Toxicol Sci (2017) v.159 (1): 124-136
HepaRG cells form polarized spheroids
PAS: Glycogen storage
Poly CEA: Glycoprotein-1 on Bile Canaliculi (BC)
MRP2: Luminal transporter found at BC surfaces
CK19: Marker for Cholangiocytes
Bell et al., Sci Rep. 2016 May 4;6:25187.
Ramaiahgari et al., Toxicol Sci (2017) v.159 (1): 124-136
Suspension PHHs
SC-PHHs 2D HepaRG
Me
tab
olic
Co
mp
ete
nce
~
~
iPSC-derived hepatocytes
Transformed cell lines
(e.g., HepG2)
Human Liver
Proliferating HepaRG
Legacy Tox21
n.a.
AhR-, CAR-, & PXR-Mediated Liver Enzyme Induction
Cytoplasm
Omeprazole
Phenobarbital
Rifampin
CAR
PXR
AhR
Nucleus
XREM
PBREM
DRE
AhR Arnt
CAR RXR
PXR RXR
CYP1A2
CYP2B6
CYP3A4
Ramaiahgari et al., Toxicol Sci (2017) v.159 (1): 124-136
2D HepaRG 3D HepaRG
3D HepaRG repeat
exposure
TC50 46.54 µM 7.94 µM 2.83 µM
HepaRG spheroids sensitive to metabolically-activated aflatoxin B1 cytotoxicity
-0 .5 0 .0 0 .5 1 .0 1 .5 2 .0
0
2 5
5 0
7 5
1 0 0
1 2 5
A fla to x in B 1
C o n c e n tra t io n (lo g M )
Pe
rce
nta
ge
ce
ll v
iab
ilit
y
to
ve
hic
le c
on
tro
l
2 D H e p a R G
3 D H e p a R G s p h e ro id
3 D H e p a R G s p h e ro id R P T
Ramaiahgari et al., Toxicol Sci (2017) v.159 (1): 124-136
Figure 8.14: https://what-when-how.com/human-drug-
metabolism/type-b3-reactions-role-of-metabolism-in-cancer-role-
of-metabolism-in-drug-toxicity-human-drug-metabolism-part-2/
CYP3A4/2A6
CYP1A2
HepaRG Spheroid Culture Responses to Compounds
HepaRG Spheroid Culture Responses to ‘Case Study’ Comparisons
High Throughput Transcriptomics (HTT) Paired with HepaRG Cultures
• 3 Culture Configurations of HepaRG Cells (384-well formats)
• 24 Compounds
• Liver injury/metabolically-activated toxicity
• Hepatic receptor activators
• Case-study analogue comparisons
• ‘Negatives’ for liver injury
• Assays:
– cell morphology (Incucyte, daily for each culture well)
• Image classifications, quantitative masking of confluence
– cytotoxicity (LDH leakage)
– high throughput transcriptomics
3D
3D
Proliferated
acetaminophen caffeine diphenhydramine DMN rifampicin tamoxifen
aflatoxin B1 CDCA fenofibric acid omeprazole ritonavir troglitazone
aspirin chlorpromazine levofloxacin phenobarbital rosiglitazone trovafloxacin
benzo(a)pyrene cyclophosphamide menadione KCl sucrose valproic acid
hepatocytes
cholangiocytes
2D(confluent/differentiated)
2D(confluent/differentiated)
Proliferated
Progenitor/EMT cells
3D
Aflatoxin B1 Cell Culture Photomicrographs (Incucyte Zoom)
Aflatoxin B1-96h, 1.5µM, ProliferatedMedia, 0h, Proliferated Media-96h, Proliferated
Media, 96h, 2DMedia, 0h, 2D Aflatoxin B1-96h, 1.5µM, 2D
hepatocytes
cholangiocytes
Damaged/repairing
areas
High-throughput Transcriptomics (HTT)
Platform:
TempO-Seq™ (Biospyder Inc.)
• modified RASL-Seq
Gene set:
S1500+ ~ 3000 sentinel transcripts
Samples:
NO RNA EXTRACTION!
25K cells/well
20 µL Lysis Buffer
TempO-Seq
HiSeq
1A_C03 1A_C04 1A_D03
AARS_3 64 32 71
ABCB1_12 17 9 17
ABCC2_22 195 129 254
ABCC5_26 67 51 58
ABCF1_35 135 83 138
ABCF3_36 24 16 25
384-well plate
Mapped Read counts
2D-DIFF & PROLIF HepaRG HTT (Run1)
2D Differentiated (Red)
Proliferating (Blue)
Omeprazole
Metabolism-dependent
CYP3A4 induction by OMP
• Elevated basal CYP1A1 expression in PROLIF
HepaRG; linked to liver development
• AhR functionality in 2D & PROLIF
• Reduced xenobiotic metabolism competence
impacts CYP3A4 inducibility (PXR)
Classical Hepatic Receptor Pathway
Functionality Summary: IPA vs. Sentinel
Transcripts
Compound Ref. Pathway Mode Inter-run
Mean Emax Inter-run
Mean Emin Emax/Emin Inter-run
Mean EC50 (µM)
OMP AhR 2D-DIFF 865 5 166 61.7
OMP AhR PROLIF 3050 147 20.8 25.8
PB CAR 2D-DIFF 51.5 5.4 9.47 463
PB CAR PROLIF 11.9 4.2 2.80 306
RIF PXR 2D-DIFF 9607 185 51.9 1.31
RIF PXR PROLIF 484 37 12.9 64.3
CDCA FXR 2D-DIFF 716 6 117 207
CDCA FXR PROLIF 189 6 30.0 110
FFA PPARα 2D-DIFF 73.5 16.1 4.57 88.8
FFA PPARα PROLIF 27.5 8.5 3.25 9.87
• 2D-DIFF HepaRG produced enhanced dynamic
ranges of response vs. PROLIF, with the exception
of PPARα
OMP PB RIF FFA CDCA
-1 .5 -1 .0 -0 .5 0 .0 0 .5 1 .0 1 .5
0
2
4
6
8
C Y P 3 A 4 - 3 D H e p a R G
C o n c e n tra tio n o f R ifa m p in (lo g M )
Fo
ld C
ha
ng
e t
o v
eh
icle
0 1 2 3 4
0
5
1 0
1 5
C Y P 3 A 4 - 3 D H e p a R G
C o n c e n tra t io n o f P h e n o b a rb ita l ( lo g M )
Fo
ld C
ha
ng
e t
o v
eh
icle
0 1 2 3 4
0
1
2
3
4
5
C Y P 2 B 6 - 3 D H e p a R G
C o n c e n tra t io n o f P h e n o b a rb ita l ( lo g M )
Fo
ld C
ha
ng
e t
o v
eh
icle
-1 .5 -1 .0 -0 .5 0 .0 0 .5 1 .0 1 .5
0
2
4
6
8
C Y P 2 B 6 - 3 D H e p a R G
C o n c e n tra tio n o f R ifa m p in (lo g M )
Fo
ld C
ha
ng
e t
o v
eh
icle
TempoSeq High Throughput Transcriptomics Analysis with HepaRG 3D Spheroids
Rifampicin Phenobarbital
CYP3A4 BMC Curve
Concentration (nM) of Each Identified Respective BMD
Benchmark Concentration Analysis of HTT
Prolif
2D-DIFF
Estimation of Liver Injury Potential with 2D-Differentiated HepaRG Through
Benchmark Concentration Analysis of High Throughput Transcriptomics (S1500+)
Estimated
Liver Injury
HTT Threshold
Ramaiahgari et al., 2019 (Jun) Toxicological Sciences, v.169 (2), 553-566.
trovafloxacin vs. levofloxacin
Trovafloxacin
Levofloxacin
(Cmax~5.3µM)
(Cmax~18µM)
(Pathway-level)(Gene-level)
Troglitazone vs. Rosiglitazone Identified/Resolved ‘Pathways’
Troglitazone Rosiglitazone
Therapeutic Target ID
via BMC Modeling
PPARg report gene assay potencies:
Troglitazone: EC50 = ~550 nM
Rosiglitazone: EC50 = ~18 nM
Chen, R. et al. Rational screening of peroxisome proliferator-
activated receptor-gamma agonists from natural products: potential
therapeutics for heart failure. Pharm Biol 55, 503-509 (2017).
FABP4
ADIPOQ
FABP4
ADIPOQ
PROLIF-
HepaRG
2D-DIFF
HepaRG
Cyclophosphamide-induced HTT Pathways with 2D-DIFF vs. PROLIF HepaRG
Cmax~120µM
Opportunities & Challenges with 3D Liver Spheroid Models
Opportunities
• Simple model system readily
compatible with most cell culture labs
• Efficient use of hepatocytes
• Liver-like hepatocyte functionality (e.g.,
drug metabolism, receptor signaling)
• Long-term viability/differentiation for
repeated exposure and reversibility
studies
• Pathology-compatible model with
emerging utility to model liver disease
states (e.g., steatosis, cholestasis,
fibrosis) & link to molecular pathways
Challenges
• Recent plate manufacturing issues
• Insufficient knowledge of spheroid maturation and toxicological utility over longer time (when to expose?)
• Understanding of the reversibility of biological responses over time
• Allometric scaling the biomass necessary for metabolic profiling & activation of toxicity (e.g., DNA damage)
• Lack of knowledge of the applicability domains towards human translation
• Inadequate optimization of cell culture media, largely adopted from 2D hepatocyte models, (e.g., DMSO, hydrocortisone, glucose, serum, insulin)
Biliary Efflux Transporter MRP-2
Immunostaining of HepaRG Spheroids
(21d)
Summary
• Evolving approaches within the Tox21 Program
• physiologically-relevant in vitro models
• xenobiotic metabolism proficiency
• high throughput transcriptomics, high content imaging, metabolomics
• Future additional models (e.g., rodent liver, kidney, intestine) and susceptibility
states (disease, developmental, population) coming soon
• High throughput transcriptomics paired with 3D spheroid models offers an
efficient and interpretable survey of hepatocyte biological response space
that can be further trained with reference compounds & phenotypic
characterizations
• Upcoming focus on maturation kinetics, time-response, and reversibility
Tox21: A Collaboration of Many …
Biomolecular Screening Branch
Rick Paules (Branch Chief)
Scott Auerbach
Trey Saddler
Alison Harrill
Jui-Hua Hsieh
Fred Parham
Kristine Witt
Stephanie Smith-Roe
Alex Merrick
Stephen Ferguson
Sreenivasa Ramaiahgari
Katelyn Lavrich
Nisha Sipes
Marianna Rosentsvit
Julie Foley
Pierre Bushel
NTP Labs
Mike DeVito (Branch Chief)
Paul Dunlap
Julie Rice
David Crizer
Wei Qu
Will Gwinn
Nancy Urbano
Janice Harvey
Sreenivasa Ramaiahgari
Stephen Ferguson
US FDA
Weida Tong
US EPA
Josh Harrill
Rusty Thomas
John Wambaugh
NIEHS
NIEHS/NTP Colleagues & Collaborators
Georgia Roberts
Jennifer Fostel
Brad Collins
Suramya Waidyanatha
Windy Boyd
BioSpyder
Jo Yeakley
Harper VanSteenhouse
Bruce Seligman
Jason Downing
Sciome
Ruchir Shah
Deepak Mav
Dhiral Padke
Jason Phillips
ICF
Joanne Trogovich
Battelle
Barney Sparrow
Jenni Gorospe
Numerous colleagues
CellzDirect/
Life Technologies
LifeNet
Ed LeCluyse
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