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PIONEERING NEW DISCOVERIES

IN HUMAN DISEASE BIOLOGY.

THINK HUMAN.

HEMOSHEAR NONCONFIDENTIAL

In Vitro Liver Models And Their Applications For NASH

30th June 2016

Ajit Dash, MD, PhD Senior Scientific Director

HemoShear Therapeutics


Non Alcoholic Steatohepatitis: Disease Problem and Unmet Needs

An ideal in vitro liver

model would fulfill various

unmet needs:

Unbiased novel target

discovery

Development of non-

invasive translational

biomarkers for diagnosis

and monitoring disease.

Understanding/predicting

efficacy differences, in

stratified sub-populations

(Personalized medicine).

Safety assessment under

disease-like conditions.

Target 2

Target 1

Target 3

Target 4

Source: Catenion analysis based on ADIS database,

2014

1 in 3 adults in the U.S. has non-alcoholic fatty liver

disease

75% of people with NASH also have type 2 diabetes

Fastest growing disease in China and India.

Approximately 50 active programs with 38 distinct

therapeutic targets


Underlying Mechanisms of Steatohepatitis are Complex

1 2

3

4

Fatty acid

β-oxidation ( )

Peripheral fatty

acid mobilization

and hepatic

uptake ( )

Lipoprotein

export ( )

PXR

LXR

PPARG

ChREBP

SREBP-1c

MTP activity

VLDL

Secretion

De novo

Fatty acid and

triglyceride

synthesis ( )

CD36

FASN

ACC

SCD1

ELOV

CPT-1

VLCAD

LCAD

SCAD PGC1A

PPARA

SIRT1

AMPK

+ -

+ -

FABP

Lipid

Peroxidation and

Oxidative stress

(ROS) ( )

Peroxisomal

FA oxidation,

CYP2E1

Cytokines (TFGβ, IL-8,

TNFα)

Inflammatory

Cell

Recruitment

Stellate Cell

Activation and

Extracellular

Matrix

Metabolic

Imbalance

Drugs

Viruses

Mechanisms of

Steatosis

1. Synthesis of

lipids/cholesterol

2. β-Oxidation

of fatty acids

3. Export of

lipoproteins

4. Uptake of

fatty acids

Steatosis Oxidative

Stress

Inflammatory

Cytokines

Macrophage/St

ellate Activation

Extracellular

Matrix

Deposition


Existing in vitro Models: Challenges and Opportunities

Species Cell Type(s) Origin

Human

Hepatocytes Primary

(Healthy/Patient)

Huh7 Hepatoma

HepG2 Hepatocellular Ca

Hepatic Stellate Cells Healthy/Patient

LX2 Stellate Cell line Immortalized

Hepatocytes +

Adipose Cells

Huh7 + LX2

Canine Hepatocytes Primary

Rat

Primary Hepatocytes

H4IE Immortalized

H4IEC3 Immortalized

PAV-1 Immortalized

Mouse

RAW 264.7

Macrophages and

AML-12 Cell co-

cultures

Immortalized

Challenges of existing models employing

static flat-plate cell cultures:

Dedifferentiation and loss of CYP activity.

Non-physiological levels of glucose and

insulin and loss of insulin sensitivity.

Altered baseline inflammatory state.

Hypoxia-reperfusion on media change.

Non-relevant drug and metabolite

concentration profiles

Opportunities for improvement:

Organotypic approaches (3-D, heterotypic

cell interactions, flow).

Physiological media formulations and drug

concentrations based on clinical

pharmacokinetics

Use of Translational biomarkers.

Big data –omic approaches


Recreating Physiological Milieu and Parameters in a 3D Culture

Configuration

3D cell configuration - modeled on sinusoid

with hepatocytes ± non-parenchymal cells.

Simultaneous perfusion and hemodynamics -

allows control of drug, nutrient and oxygen

gradients

Effluent and cells can be assessed from top

and bottom separately.

Hemodynamic Flow Transmural Flow

Interstitial Flow

Transport Inflow

Transport Outflow

Hemodynamic Flow

Interstitial Flow

Adapted from: Nature Reviews Immunology 14, 181–194 (2014)

Dash et al, AJP 2013, Terelius et al Chem Bio 2015, Chapman et al 2016

Hepatocytes Plated

1. RNA-Seq Analysis

2. Functional Endpoints e.g. MTT, Imaging, CYP Assays

3. Secreted Biomarkers e.g. Albumin, Cytokines, FGF19

Treatment

(3- 7 days) (2-7 days)

Restoration of Biology

Day 14 Controlled Hemodynamics: In vivo liver:

MRP-2, HNF-4α

Day 14 Controlled Hemodynamics: In vivo liver:

MRP-2, HNF-4α


Liver-like Polarized Morphology and Function Maintained Over Time

Dash et al SOT 2013, Marukian et al AASLD 2013

6

In vivo Liver

E-cadherin HNF4α CD26, Draq5 MRP2 HNF4α CD81, Draq5

0

5

10

15

20

25

30

Day 2 Day 4 Day 7 Day 9

HemoShear

Static

0

100

200

300

400

500

600

700

Day 2 Day 4 Day 7 Day 9

HemoShear

Static

0

2

4

6

8

10

12

CYP1A2 CYP2B6 CYP3A4/5 CYP2C9 CYP2D6

Controlled Hemodynamics

Static

CYP Activity

10#μm#

Transporter Activity CYTOCHROME P450

ACTIVITY

CDFDA -> CDF

TRANSPORTER

ACTIVITY

ALBUMIN SECRETION

(ANABOLIC)

UREA SECRETION

(CATABOLIC)

TIGHT JUNCTIONAL

PROTEIN

SURFACE ANTIGENIC

ENZYME

BILIARY EFFLUX

TRANSPORTER

SURFACE

GLYCOPROTEIN


Drug Responses Exhibited at Clinically Relevant Concentrations

7

Figler et al AASLD 2015.

Rumack-Matthews nomogram for serum concentration thresholds for clinical treatment of Acetaminophen poisoning.

B

C

Toxic in HemoShear System

A Safe in HemoShear System

Toxicity seen in Other Systems

FDA guidelines

for antidote

administration.

Normal ‘safe’

concentration

Eff

ica

cy

/To

xic

ity

Log Scale Concentration

In vivo/Clinical Toxicity

In vivo/Clinical Efficacy

Therapeutic Window

Cmax 10Cmax

Toxicity

Subclinical Efficacy

100Cma

x

In vitro Toxicity

In vitro Efficacy

Exp

res

sio

n/A

cti

vit

y L

ev

el

Time (Days)

Localized Transporter Activity In vitro

Phase I and II enzyme levels/activity in Static

Phase I and Phase II enzyme levels/activity

under controlled hemodynamics

5 10

A

B

Efficacy and toxicity responses seen at

concentrations that match clinical

therapeutic exposures.

Over 30 drugs assessed for mechanistic

differences using transcriptomics. (NIH

SBIR Award R44 DK091104-02)

Dash et al Expert Opinion 2012

HEMOSHEAR NONCONFIDENTIAL Deering et al AASLD, 2012, Dash et al ADA 2013, Cole et al AASLD 2015

0

20

40

60

80

100

120

140

160

Healthy Disease Disease + Pioglitazone 1.5 uM Healthy Diabetes Diabetes

+ Pio

0

10

20

30

40

50

60

70

80

90

100

Healthy + DMSO Steatotic + DMSO Steatotic + Pioglitazone 1.5 uM Healthy Diabetes Diabetes

+ Pio

Gluconeogenesis

Different ially Regulated CYP Transcripts

HG/HI LG/LI ‘Diabet ic’ MilieuHealthy Milieu

CYPs

involved in

drug

metabolism

CYPs

involved in

cholesterol/

lipid

synthesis

ug/m

g o

f pro

tein

F

old

over

sta

tic

8

Insulin Sensitivity And Lipogenic Responses Maintained

Actin Lipid (Nile Red) Nucleus

Normal Glucose/Insulin

(Healthy Milieu) High Glucose/Insulin

(Steatotic Milieu)

Days in-device (or in static cultur e)

0

5

10

15

20

25

day2 day4 day7 day9 day10

ng

/day/1

0^

6c

ells

Static HG-HI

Device LI-LG

Static conditions: High insulin/glucose

HemoShear: Low insulin/glucose

HS System

Static Culture

0

0.5

1

1.5

2

2.5

noRx Ins LP LP+In

Medium 1

ng

/mg

pro

tein

Glycogenolysis

Gluconeogenesis Glycogenolysis +

Gluconeogenesis

Lactate/

Pyruvate

Basal Insulin Lactate/

Pyruvate +

Insulin

LIPOPROTEIN (a) SECRETION

GLUCONEOGENESIS DE NOVO LIPOGENESIS

TRIGLYCERIDES

CYP3A2

CYP GENES

Healthy Steatotic

Healthy Steatotic Healthy Steatotic

Insulin sensitivity allows culture in a close to physiologic milieu and altered disease-like

steatotic phenotype under hyperglycemic, hyperinsulinemic conditions.


Applications of a Physiologically Responsive Liver Model

9

After demonstrating that the system maintained differentiated liver phenotype

as evidenced by polarized morphology, liver specific functions, drug

metabolizing enzyme and transporter activity and responsiveness to insulin,

we tested the model for the following applications:

1. Assessing on-target and off-target pharmaco-toxicology of drugs at

clinically relevant concentrations.

2. Distinguishing transcriptomic signatures of various phenotypes of drug

induced liver injury (DILI).

3. Studying underlying mechanisms of drug induced steatohepatitis that

could help understand potential NASH targets.

4. Developing a lipotoxic model with milieu mimicking metabolic disease.


Assessing On-target Pharmacology of Obeticholic Acid

10

18

74

8

comparison

Responses (NA)

CYP7A1

SLC27A5

CYP27A1

AKR1D1

HSD3B7

ACOT8

AKR1C4

BAAT

CYP8B1

AMACR

SCP2

ACOX2

HSD17B4

SLC27A2

ABCB11

Gene

s (

0.9

8)

. .

. .

. .

.

.

.

. .

.

. .

. .

. .

Gene Response Heatmap

−2 −1 0 1 2

Value

02

4

Co

un

t

16

44

3

comparison

Responses (NA)

CYP7A1

SLC51B

SLC51A

NR0B2

ABCB11

ABCB4

RXRA

SLCO1B1

ADCY3

ADCY8

SLCO1B3

ADCY1

CFTR

PRKX

NR1H4

ABCC2

GNAS

SLC10A1

ADCY6

ADCY9

PRKACA

PRKACB

SCTR

ADCY5

Gene

s (0.96

)

. .

. .

. .

. .

. .

. .

. .

. .

.

.

.

. .

.

.

.


−2 −1 0 1 2

Value

04

8

Co

unt

Bile

Syn

the

sis

B

ile S

ecre

tio

n

18

74

8

comparison

Responses (NA)

CYP7A1

SLC27A5

CYP27A1

AKR1D1

HSD3B7

ACOT8

AKR1C4

BAAT

CYP8B1

AMACR

SCP2

ACOX2

HSD17B4

SLC27A2

ABCB11

Gene

s (

0.9

8)

. .

. .

. .

.

.

.

. .

.

. .

. .

. .


−2 −1 0 1 2

Value

02

4

Co

un

t

18

74

8

comparison

Responses (NA)

CYP7A1

SLC27A5

CYP27A1

AKR1D1

HSD3B7

ACOT8

AKR1C4

BAAT

CYP8B1

AMACR

SCP2

ACOX2

HSD17B4

SLC27A2

ABCB11

Gene

s (

0.9

8)

. .

. .

. .

.

.

.

. .

.

. .

. .

. .


−2 −1 0 1 2

Value

02

4

Co

un

t

16

44

3

comparison

Responses (NA)

CYP7A1

SLC51B

SLC51A

NR0B2

ABCB11

ABCB4

RXRA

SLCO1B1

ADCY3

ADCY8

SLCO1B3

ADCY1

CFTR

PRKX

NR1H4

ABCC2

GNAS

SLC10A1

ADCY6

ADCY9

PRKACA

PRKACB

SCTR

ADCY5

Gene

s (0.96

)

. .

. .

. .

. .

. .

. .

. .

. .

.

.

.

. .

.

.

.


−2 −1 0 1 2

Value

04

8

Co

unt

16

44

3

comparison

Responses (NA)

CYP7A1

SLC51B

SLC51A

NR0B2

ABCB11

ABCB4

RXRA

SLCO1B1

ADCY3

ADCY8

SLCO1B3

ADCY1

CFTR

PRKX

NR1H4

ABCC2

GNAS

SLC10A1

ADCY6

ADCY9

PRKACA

PRKACB

SCTR

ADCY5

Gene

s (

0.9

6)

. .

. .

. .

. .

. .

. .

. .

. .

.

.

.

. .

.

.

.


−2 −1 0 1 2

Value

04

8

Co

un

t

10 μM

Obeticholic Acid

0.5 μM comparison

Responses (NA)

FGFR4

NR5A2

NR1H4

NR0B2

FGF19

Gene

s (0

.98

)

. .

. .

.

. .

. .


−2 −1 0 1 2

Value

02

Co

unt

FX

R S

ign

alin

g

100

1000

DMSO INT_LOW INT_HIGH

TreatmentC

once

ntra

tion Donor

HC5−30

Hu8160

QHu0032

TRL4038

FGF19Crossbars: 95% Confidence Inter val based on model

Secreted FGF19 Protein

OCA 0.5μM 0.5μM 10μM 0.1%

Obeticholic Acid DMSO

***

*** p<0.001

***

***

Concentr

ation (

pg/m

l)

Donor

2

5

1

3

1000

100

SHP

NTCP

FXR-RXR

FXR

CYP7A1 HMGCR

Cholesterol Synthesis

Bile Acid Synthesis

FXR

BSEP

Bile Acid Secretion

ABCB4

CYP27A1

FGF19

Obeticholic Acid

Relative to DMSO Controls

-2 2

LOG2 Fold Change

FGF19

CYP7A1

Strongly induced

FGF19 in hepatocytes,

both at a gene and

protein level, confirming

a direct hepatic effect in

addition to the widely

appreciated FGF19

loop through the gut.

CYP7A1 was the most

down-regulated

differentially expressed

gene in the

transcriptome, with

simultaneous down-

regulation of the bile

synthesis pathway

genes.

Sanyal, Oral presentation AASLD 2015


Pharmaco-toxicological Signature of Obeticholic Acid and Impact of CYP

Activity

11

0,0

0,5

1,0

1,5

2,0

Bile Secretion (*,#)

Bile Synthesis (*)

FXR GeneTranscription (*,#)

CholeseterolBiosynthesis (*)

TriglycerideSynthesis (*,#)

IL6 Signaling (*)

Reactive OxygenSpecies (*)

Apoptosis

ExtracellularMatrix

Vehicle Control

0.5uM Obeticolic Acid

10uM Obeticolic Acid

1

10

DMSO OCA_0.5_uM OCA_10_uM

Treatment

Con

cent

ratio

n man_don

1

2

3

5

CYP3a4Crossbars: 95% CI based on linear model

Error bars: +/− SE

1

10

DMSO OCA_0.5_uM OCA_10_uM

Treatment

Con

cent

ratio

n man_don

1

2

3

5

CYP1a2Crossbars: 95% CI based on linear model

Error bars: +/− SE

OCA 0.5μM OCA 10μM

OCA 0.5μM OCA 10μM

0.5μM 10μM 0.1%


0.5μM 10μM 0.1%


Concentr

ation

(nM

/μg p

rote

in)

Donor 1 2 3 5

Donor 1 2 3 5

**

***

**

** p<0.01

*** p<0.001

10

1

Concentr

ation

(nM

/μg p

rote

in)

10

1

CYP3A4 Activity

(Hydroxy-Testosterone Formation)

CYP1A2 Activity

(Acetaminophen Formation)

Pathway analysis and scoring confirmed beneficial effects

of obeticholic acid on reducing steatotic indices and

inflammatory signaling.

Functional CYP assays revealed that obeticholic acid

suppressed CYP1A2 and CYP3A4 activity.


Distinguishing Drug Induced Steatohepatitis Signatures From Other Forms

of Drug Induced Liver Injury

12

18

405

18

559

comparison

Responses (0.83)

GPD1LDGAT2LPIN3GPD1FDPSELOVL6FDFT1DHCR7HSD17B7MVDCYP51A1ACLYLPCAT1MVKNSDHLMSMO1SQLESC5DHMGCRIDI1ELOVL1HSD17B12TM7SF2HMGCS1EBPLSSLPIN1AGPAT5AGPAT6ACSL1

Gene

s (0.87

)

. .. .

. .. . .

. . .

. . . .

. .

. .

. . .

. . .

. . .

. .

. .

. .

. . .

. .

. . . .

. .. .

. .

. .

. .

. . .

. . .

. .

. .. .

.. . . .

. .


−2 −1 0 1 2

Value

06

Co

unt

APAP RIT AMI TVX

0,00

1,00

2,00

3,00

4,00

5,00

6,00

Vehicle Amiodarone


-2 2

LOG2 Fold Change HSB-101

(Steatoinflammatory biomarker)

Figler et al AASLD 2015.

Transcriptomic analysis allowed us to characterize distinct

signatures for different drugs having different DILI phenotypes.

Assays like Nile Red (neutral lipid) and secreted protein

biomarkers in effluent media were confirmatory functional

endpoints that defined the steatohepatitic phenotype.

0

50

100

150

200

Lipid &Choleste

rolSynth…

TCAcycleand

Respir…

Oxidative Stress

Glutathione

Detoxification

Coagulation

NFkBPathwayActivatio

n

TNF-aStress

Signaling

TGF-BSignalin

g

Unfolded

ProteinRespo…

Bilemetabolism andsecreti…

Trovafloxacin

Acetaminophen

Amiodarone

Ritonavir

Vehicle

NFkB-

Signaling

Coagulation

Glutathione

Detoxification

Oxidative

Stress

TCA Cycle

and

Respiration

Lipid and

Cholesterol

Synthesis

Bile

Metabolism

and

Secretion

Unfolded

Protein

Response

TGF-B

Signaling

TNF-A

Stress

Signaling

Phalloidin, Nile Red, ToPro

Vehicle

Obeticholic Acid 10µM

Ketoconazole 65.9µM

Amiodarone 39µM


Understanding Mechanisms and Potential Targets of Steatohepatitis

13

0

1

2

3

4

5

6

Vehicle Amiodarone Ketoconazole NASH-like milieu

1699

2

comparison

Responses (NA)

MAP2K6CDK1CD36DUSP6PPP2R1BRPS6KA2JUNPPP2CBFOSRPS6KA5SIGIRRTYK2MAP2K7NOD1MAPK7MAPK11IRAK1ECSITIRAK2CD14IL6RNOD2PELI3DUSP4LY96MAPK3TLR2PELI2MAPKAPK3RPS6KA1

Gene

s (0.7

6)

. .

.

. .

. . .

. . .

. .

. . ..

. . ..

. . ...

.

..

. . .

. . .

. . .

. .

. .

. . .

. .

. .

. .

. .

. .

. .

. .

. .


−2 −1 0 1 2

Value

04

Co

un

t

0

50

100

150

200

TriglycerideSynthesis

Fatty Acid Oxidation

Lipid Transport

MitochondrialDysfunction

Respiration and ATPSynthesis

Bile Secretion

myd88 Signaling

NRf2 signaling

IL6 Signaling

TNF Signaling

AMI

KET

OCA

Vehicle

2430

0

comparison

Responses (NA)

GPD1

DGAT2

AGPAT1

ACSL6

ELOVL2

ACSL4

FASN

ELOVL6

ELOVL7

GPAM

AGPAT2

ACSL1

ELOVL1

ACSL5

ACACA

TECR

SLC25A1

LPCAT4

ELOVL5

AGPAT5

LPIN2

AGPAT9

ACSL3

AGPAT6

LPCAT1

LPIN1

HSD17B12

ACLY

Gen

es (0

.8

)

. . .

. . .

. . ..

. . .. .. .

. . .

. . ..

. .. . .. . .. . .. . .

. .

. .

.. . .. .. .. .. .. ... . .. .. .


−2 −1 0 1 2

Value

04

Co

unt

TR

IGL

YC

ER

IDE

SY

NT

H

MY

D8

8 S

IGN

AL

ING

1712

0

comparison

Responses (NA)

NDUFS7NDUFB7ATP5DNDUFV1COX8ANDUFA3UQCR10UQCR11NDUFS8CYC1NDUFA6NDUFA11NDUFV2ATP5C1SDHCNDUFB4ETFDHNDUFAB1ATP5J2UQCRHUQCRFS1NDUFS3NDUFS5NDUFS2ETFACOX5ACYCSNDUFA8COX7A2LUCP2

Gene

s (0.91

)

.

.. .

.

.

.

.. .. .

. .

. . .. .

. . .... .. .

. .

. .

. .

. .

. ....

.

. .

. . .

. . ..


−2 −1 0 1 2

Value

06

Co

unt

comparison

Responses (NA)

TRAF2RPS6KA4MAP2K7MAP3K14AKT1MAP3K5MLKLMAPK11AKT2MAPK12RPS6KA5CREB3L1PIK3CDPIK3R1MAP2K6BIRC3CREB5CREB3L4NFKB1MAP3K8CASP10JUNTNFRSF1BMAGI2ATF4PIK3R3TRAF5RIPK3MAPK3CREB3L3

Gene

s (0.77

)

.

.

.. .. .

.. .

.. . .

.

.. .

. .

. . .

. .

. . .

. .

. . .

. .

. . ..

. . .

. ..

. .

. .

. .

. .

. .

. . .


−2 −1 0 1 2

Value

04

Co

unt

RE

SP

CH

AIN

/ A

TP

TN

F-α

SIG

NA

LIN

G


-2 2

LOG2 Fold Change

AMI KET OCA AMI KET OCA

AMI KET OCA AMI KET OCA

AMI KET Healthy Lipotoxic

Media

HSB-101

(Steatoinflammatory biomarker)

Differential analysis of transcriptomic signatures for different drugs causing steatohepatitis

versus those causing NASH may offer insights into mechanisms and targets.

Dash et al EASL 2016.


Liver Models to Recapitulate Metabolic Disease Spectrum

14

0,5

1

2

4

HEALTHY NAFLD NASH NASH+TNF

Rela

tive N

ile R

ed (

FO

LD

) INFLAMMATORY CYTOKINES LIPID ACCUMULATION

2

8

32

128

CX

CL

10

(p

g/m

l)

1

1

2

4

Healthy NAFLD NASH

Rela

tive T

rigly

cerides

(FO

LD

)

HEPATIC TRIGLYCERIDES

FBP1 FBP1 -

-

-

-

+

+

-

-

+

+

+

-

+

+

+

+

-

-

-

-

+

+

-

-

+

+

+

-

+

+

+

+

FBP1 FBP1 GLUCOSE:

INSULIN:

FFA:

-

-

-

+

+

-

+

+

+

GLUCOSE:

INSULIN:

FFA:

TNF:

GLUCOSE:

INSULIN:

FFA:

TNF:

HEALTHY FATTY

LIVER

FATTY LIVER +

INFLAMMATION

ADVANCED

LIPOTOXIC

Healthy Milieu Steatotic Milieu

(Glucose, Insulin)

Lipotoxic Milieu

(Steatotic + Fatty

Acids)

Lipotoxic Milieu +

TNF

Lipotoxic metabolic disease model has Kupffer cells and stellate cells added on opposite side of

the membrane


Ongoing Validation of Drug Responses in the Advanced Lipotoxic Liver

System

In the advanced lipotoxic liver system, OCA

Reduced ALT levels (a clinical biomarker for

NASH)

Promoted a robust increase in downstream

targets of FXR signaling, including FGF19

Reduced several markers of inflammation

Reduced markers of fibrosis

ON-TARGET FXR

SIGNALING

0,5

1

VEH VEH OCA

Healthy NASH

ALT

(F

OLD

)

CLINICAL

BIOMARKERS p=0.12

**

**p<0.005,* p<0.05 relative to VEH

**

0,25

0,5

1

IL8 CHI3L1 IL6 MCP1 GRO VEGF

Secre

ted P

rote

in

Concentr

ations (

FO

LD

)

INFLAMMATORY BIOMARKER

PANEL

*

p=

0.1

3

** **

*

0,5

1

2

4

8

16

32

VEH OCA

NASH

FG

F19 (

FO

LD

)

0,125

0,25

0,5

1

Secre

ted P

rote

in C

oncentr

ations

(FO

LD

)

FIBROSIS

BIOMARKER PANEL

TGFβ OPN Pro-

Col

*

-100%

-50%

0%

50%

100%

150% Cholesterol Biosynthesis

Fatty Acid Synthesis

Cytokine-cytokine receptor interaction

TGFB Signaling

Glucose Regulation

-100%

-50%

0%

50%

100%

150% Cholesterol Biosynthesis

Fatty Acid Synthesis

Cytokine-cytokine receptor interaction

TGFB Signaling

Glucose Regulation

NASH

Diabetic

Healthy

OCA

LY2109761

15

Advanced

Lipotoxic Advanced

Lipotoxic Advanced Lipotoxic Advanced Lipotoxic


Conclusions and Future Directions

16

• The physiologically responsive liver model allows assessment of on-

target and off-target pharmaco-toxicology of drugs at clinically relevant

concentrations.

• Comparative analysis of transcriptomic responses of drug induced

steatohepatitis, lipotoxic NASH-like conditions and drugs that impact

NASH could help identify and understand potential NASH targets.

• Ongoing/Future activities include:

• Benchmarking signatures against clinical samples.

• Analysis of non-parenchymal cell response within system.

• Lipidomic analysis to gain a better understanding of lipid fractions

under lipotoxic milieu and how they correlate with transcriptomics.

• Characterization of translatable functional responses such as

histology and extracellular matrix composition measurements.

• Comparative analysis of drug response under healthy versus

lipotoxic conditions and stratified patient derived hepatocytes

versus human hepatocytes could provide additional insights about

useful applications of this system.


Acknowledgements and Funding

Entire HemoShear Scientific Team

17

Brian Wamhoff, PhD Head of Innovation

Robert Figler PhD Senior Director

Ryan Feaver, PhD Program Leader, NASH

Banu Cole, PhD Director

NIH SBIR Grants:

• R43/R44 DK100136:

Development of an in vitro system

of human hepatic steatosis.

• R44 DK104456-01: Development

of a human physiological multi-

cellular liver platform for drug-

induced liver injury and disease.

• R43/R44DK091104: Development

of a human hepatocyte predictive

pharmacology and toxicology

system.

• R44GM109539: Development of

an iPSC-derived human hepatocyte

platform for drug development.

Arun Sanyal, MD Consultant & Collaborator

pioneering new discoveries in human ... - paris-nash.org · 2. distinguishing transcriptomic...

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