Learn about the latest advances in Biomarkers for NASH.

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NON-INVASIVE BIOMARKERS IN NASH
(NON-ALCOHOLIC STEATOHEPATITIS)
Latest Advances and
Potential Clinical Applications
@Covance
Non-alcoholic #Steatohepatitis
#NASH

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Agenda
► The Urgent Need for Non-Invasive Biomarkers in NASH
 Claudia Filozof, MD, PhD
► Non-invasive Diagnosis of NASH and Liver Fibrosis:
Present and Future
 Marge Connelly, PhD, MBA
► Leveraging Genomics in NASH Biomarker Development
 Mark Parrish, BS
► Strategic Use of Non-Invasive Testing for NASH Drug Development
 Claudia Filozof, MD, PhD
► Role of Non-Invasive Biomarkers in Clinical Practice
 Arun Sanyal, MD
► Q&A
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The Urgent Need for Non-Invasive
Biomarkers in NASH
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Claudia Filozof, MD, PhD

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Biomarker
► A characteristic that is objectively measured and
evaluated as an indication of normal biologic processes,
pathogenic processes or pharmacologic responses to
a therapeutic intervention
► Surrogate biomarker or endpoint
 A biomarker intended to substitute for a clinical endpoint
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NIH Definitions Working Group. Biomarkers and Surrogate Endpoints. 2000
J K Aronson Br J Clin Pharmacol 2005

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Liver Biopsy
► Remains the gold standard test for NASH diagnosis,
grading and staging
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 Several limitations
– Sampling error
 Length: >15 mm long, 16 gauge
needle are recommended
– Intra- and interobserver reading variability
 Reduced when performed by a NASH
expert pathologist
– Invasive, unpleasant for the patient
– Procedure-related morbidity and mortality
– Relatively high cost
Liver

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Non-Invasive Biomarkers in NASH:
Methodological considerations
► Liver biopsy: imperfect reference standard
► Taking into account a range of accuracies of
the biopsy, even in the best possible
scenario, an AUROC* >0.90 might
not be achieved for a perfect marker
of liver disease
► Spectrum bias: The AUROC can
vary based on the prevalence of
each stage of fibrosis
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*Area under the receiver operator characteristic curve
AUROC curve of ELF predicting stages 0,1 Vs 2-6 in NAFLD
cohort (none or mild fibrosis from significant fibrosis Ishak
classification)

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Non-Invasive Diagnosis of NASH and
Liver Fibrosis: Present and Future
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Marge Connelly, PhD

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Fibronectin
Hyaluronic acid (HA)
Type IV collagen S
Procollagen type III N-terminal Peptide (PIIINP)
Tissue inhibitor of metalloproteinase 1 (TIMP-1)
Cytokines: TNF-α, IL-1β, IL-6, IL-8, IFNγ, TGFβ
Adipokines: Adiponectin, Leptin, Resistin
Acute phase reactants: hsCRP, GlycA
Chemoattractants: MCP-1
Ferritin, Soluble CD14
Present Biomarkers of NAFLD
Alanine aminotransferase (ALT)
Aspartate aminotransferase (AST)
Alkaline phosphatase (ALP)
Gamma-glutamyl-transpeptidase (GGT)
Acute phase reactants: α1-antitrypsin,
ceruloplasmin, haptoglobin, GlycA
Albumin, Lipid panel, Platelet count
Measures of insulin resistance
Malondialdehyde, TBARS, Oxidized LDL
Cytokeratin-18 (CK-18) fragments
Ferritin
1. Adapted from Fitzpatrick et al., Noninvasive biomarkers in NAFLD World, J Gastroenterol 2014;20(31):10851-10863.
2. Armutcu et al., Adv in Clin Chem 2013;61:67-125.
Oxidative Stress
and Apoptosis
Fibrosis
Inflammation
Hepatocyte
Function

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Diagnosis of NASH
► Liver function tests: ALT, AST, GGT, albumin
► Measures of insulin resistance (HOMA-IR, adiponectin)
► NASH diagnostic: cleaved and intact CK-18, adiponectin, resistin
(AUROC 0.70-0.85)
► NASH diagnostic panel: gender, BMI, diabetes,
triglycerides, cleaved and intact CK-18 (AUROC 0.81)
► ION: Index of NASH >55 (AUROC 0.88)
Limitations
► Need more rigorous clinical validation (e.g. larger,
more clearly defined study populations without spectrum bias)
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1. Buzzetti et al., Int J Endocrinol 2015; 343828. 2. Otgonsuren et al., Hepatol 2014;29:2006-2013.
3. Armutcu et al., Adv in Clin Chem 2013;61:67-125. 4. Yilmaz et al., Curr Drug Targets 2013;14:1357-1366.
5. Pearce et al., Biomarker Res 2013:1:7-17. 6. Image adapted from Mayo Foundation for Medical Education
and Research, www.mayoclinic.org.

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NASH FibroSure Test
► Includes 10 biomarkers in combination with age, gender, BMI
 SteatoTest: Marker of hepatic steatosis grade S0-S3 (0.00-1.00)
 Steatosis score >0.5, sensitivity= 71%, specificity= 72% for identification of steatosis1
 NASHTest: Diagnostic assessment of the presence of NASH
 N0 = No NASH, N1 = Borderline NASH, N2 = NASH per the Kleiner classification2
 Prediction of NASH, sensitivity = 88%, specificity = 50% (AUROC 0.69-0.83)3,4
 FibroTest: Quantitative surrogate fibrosis marker (0.00-1.00)
 Corresponds to Metavir F0-F4 fibrosis staging
 Fibrosis score of >0.3, sensitivity = 83%, specificity = 78% for ≥F3 (AUROC 0.88)4,5
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FibroSure Content
Alpha-2-macroglobulin Alanine transaminase (ALT)
Haptoglobin Aspartate aminotransferase (AST)
Apolipoprotein A1 Total cholesterol
γ-glutamyl transpeptidase (GGT) Triglycerides
Total bilirubin Fasting glucose
Widely used ~100,000 FibroSure Tests were conducted at LabCorp in 2014
1. Poynard et al., Comp Hepatol, 2005;4:10. 2. Kleiner et al., Hepatol, 2005;41(6):1313-1321. 3. Poynard et al.,
41st Annual EASLD Meeting 2006;Abstract 86. 4. Buzzetti et al., Int J Endocrinol 2015;343828. 5. Ratziu et al.,
BMC Gastroenterol, 2006;6:6.

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Diagnosis of Liver Fibrosis
► NFS: NAFLD Fibrosis Score - age, BMI, IFG/diabetes, AST/ALT ratio, platelet
count, albumin (AUROC 0.82-0.88)
► Hepascore: Bilirubin, GGT, HA, α2-macroglobulin, age, gender
(AUROC 0.81)
► APRI: AST to platelet ratio index (AUROC 0.62-0.94)
► FIB-4 Index: Age, AST, ALT, platelet count (AUROC 0.87, 0.88)
► BARDI: BMI, AST/ALT ratio, diabetes, INR (AUROC 0.88)
Limitations
► Performance may be affected by morbid obesity
► Well validated in patients with chronic viral hepatitis, less validated in NAFLD
► Assay performance better for detecting significant than moderate fibrosis
► May also reflect extra-hepatic fibrosis
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1. EASL-ALEH Clinical Practice Guidelines: Non-invasive tests for evaluation of liver disease severity and prognosis.
J Hepatol 2015;63:237-264. 2. Shah et al., Clin Gastroenter Hepatol 2009;7:1104-1112. 3. Buzzetti et al.,
Int J Endocrinol 2015; 343828. 4. Image adapted from Mayo Foundation for Medical Education and Research,
www.mayoclinic.org.

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Enhanced Liver Fibrosis (ELF) Score
► 3 direct biomarkers of fibrosis combined into one score:
 Hyaluronic acid
 Procollagen III N terminal peptide (PIIINP)
 Tissue inhibitor of metalloproteinase 1 (TIMP1)
► Good at diagnosing severe fibrosis in patients with chronic hep B, C, HIV
► Not as much validation in subjects with NAFLD
► Significant overlap with normal ranges, which may confound interpretation
of results in mild to moderate fibrosis range
► Used in NASH trials as a surrogate marker for fibrosis; preliminary results
encouraging
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1. ELF Test is trademarked by Siemens Healthcare Diagnostics, Inc. Licensed for use outside of US; not currently
available in the US. 2. Yoo et al, Liver Int. 2013;33:706-713.
ELF Score Severity of Liver Fibrosis Fibrosis Stage AUROC
< 7.7 None to mild ≥F2 0.82
≥ 7.7 - < 9.8 Moderate ≥F3 0.90
≥ 9.8 Severe ≥F4 0.87
≥ 11.3 Cirrhosis

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Future Assays May Include NMR Biomarkers
of Insulin Resistance, NASH and Liver Fibrosis
► LP-IR, lipoprotein insulin resistance index1,2
 Multivariable algorithm, 6 lipoprotein parameters related
to insulin resistance
 Validated in hyperinsulinemic-euglycemic clamp studies
and multiple clinical cohorts
 Simple, inexpensive way to assess insulin resistance in
large clinical studies
► NASH specific analytes3
 VLDL size and small LDL particle number
► Liver fibrosis specific analytes3,4
 Small VLDL particle number
 Branched chain amino acid (BCAA) concentrations
associated with reduced liver function
► GlycA, glycoprotein marker of inflammation5-8
 Reduced with later stage liver fibrosis and cirrhosis
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1. Shalaurova et al. Met Syn Rel Dis 2014;12(8):422-429. 2. Mackey et al. Diab Care 2015;38(4):628-636. 3. Jiang et al. Liver Int 2016; doi: 10.1111/liv.13076.
4. Cheng et al., PLoS One 2015;10(10);e0138889. 5. Otvos et al., Clin Chem 2015;61(5):714-23. 6. Akinkuolie, et al., JAHA 2014;3(5):e001221. 7. Akinkuolie et
al., ATVB 2015;35(6):1544-50. 8. Sands et al, Amer J Gastroenterology 2015;110:159-169.
BCAA
VLDL
particle
number
(VLDL-P)
Large VLDL
Medium VLDL
Small VLDL
IDL
Medium LDL
Small LDL
Large HDL
Medium HDL
Small HDL
LIPOPROTEIN SUBCLASS PARTICLE NUMBERS
Positive Association Negative Association
LDL
particle
number
(LDL-P)
HDL
particle
number
(HDL-P)
VLDL Size
LDL Size
HDL Size

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Leveraging Genomics in NASH
Biomarker Development
Mark Parrish, BS
| Non-Alcoholic Steatohepatitis14

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NASH in a Genomic Context
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Underlying every protein or phenotype is a
genetic component
Genes influence proteins and each other
The genomics toolbox is expanding and
highly flexible
Genomics has influence at every level of the
progression of NASH

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PNPLA3
̶ Gene with the strongest association with NASH
̶ Genotype increases odds for NASH and HCC
̶ Associated with all aspects of progression
̶ Higher prevalence of disease-causing genotype in
some populations
TM6SF2
̶ Increased risk for NASH
̶ Associated with ALT/AST levels
̶ Involved in hepatic triglyceride secretion
Other genes of metabolism
̶ ADIPOQ, LEPR, INS-1, NCAN, FDFT1
̶ Increase likelihood of other liver diseases and comorbidities
̶ Strength of association varies, highly dependent on the
phenotype studied and population
Genes Associated with NASH
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Biochimica et Biophysica Acta 1812 (2011) 1557–1566

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cfDNA
̶ High levels of cfDNA are correlated to various liver indications
̶ cfDNA is nonspecific to NASH
̶ May indicate later stages of liver disease, HCC
Microbiome Profiling
̶ The composition of gut/stool bacteria correlates with
NASH status
̶ NASH patients tend to have higher levels of alcohol-producing
bacteria
Emerging/Exploratory DNA Biomarkers
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Adv Anthro (2012) Vol.2, No.4, 214-220
Hepatology (2013) Vol. 57, No. 2

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miRNAs are signaling molecules
̶ miRNAs regulate intracellular gene expression
̶ mRNAs are also packaged and released as signaling molecules
Sensitive markers of liver health
̶ miR-122 is highly indicative to liver injury
• Viral, alcohol or chemical
̶ Regulates lipid metabolism genes
̶ Localized to the lipid droplets and cell membranes
Surrogate markers of NAFLD and NASH
̶ miR-122 is not sufficient to distinguish NASH
̶ miR-192 and miR-375 are also increased in NASH serum
miroRNA Profiling
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Gut. 2015 May ; 64(5): 800–812

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Considerations for Genomic Profiling
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You can’t analyze what you don’t collect
̶ New techniques are always evolving
̶ Cost of collection and storage is cheaper than repeating a study
Consider IRB requirements for genetic testing
̶ Clear use of samples needs to be identified up front
̶ Don’t assume broad use, especially across geographies
Consider appropriate collection matrix
̶ Certain collection agents limit future use
Timing and storage is critical
̶ RNA/miRNA expression is dynamic

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SpecificPredictiveNon-Specific
Microbiome
cfDNA
PNPLA3
TM6SF2
miR-122
miR-192, miR-375
Healthy
liver
15-30% NAFLD 12-40% NASH 15-25% Cirrhosis ~7%
50%
Liver transplant/death
Hepato-cellular
carcinoma

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Strategic Use of Non-Invasive Testing
for NASH Drug Development
Claudia Filozof, MD, PhD
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Imaging
Liver Fat
► Ultrasound low sensitivity ( <20%-30% of liver fat)
► Magnetic resonance-based
 MRI-PDFF / MRS
 Multiscan
Liver Stiffness
► Vibration-controlled transient elastography (VCTE)
► US elastography (pSWE or ARFI and 2D-SWD)
► Magnetic resonance elastography
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MRI-PDFF: Magnetic resonance imagins-protocol density fat fraction
MRS: Magnetic resonance spectroscopy
pSWE: point shear wave elastography-ARFI: acoustic radiation force impulse imaging
2D-SWD 2D-shear wave elastography

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Non-Invasive Biomarkers: Utility in
Clinical Development
► Enrich target population: In a POC study when a population
of biopsy-confirmed NASH patients may not be feasible
► Potential endpoints in proof of concept trials/facilitate
decision making during interim analysis
► Identification of potential progression to cirrhosis
 In Phase III/IV trials: Fibroscan, MRE or other markers of
fibrosis can help identify patients with cirrhosis prior to a
liver biopsy confirmation
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1-Enrich Target Population
2-Endpoints in POC Trials
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1-Target Population:
Adult (≥18 years) with fatty liver AND
► T2DM
► Metabolic syndrome
► High ALT levels
► Other serum biomarkers > predefined threshold
► Multiscan with LIF > 3
2-Endpoints: Changes in liver
fat by MRI, changes in ALT
and other biomarkers
(metabolic/ inflammation/
fibrosis)
weeks
Placebo=XX
R
N XX
N=XX dose 3
-4 -2 0 12/16/24
Screening
period
Run-in
N=XX dose 2
N=XX dose 1

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Non-Invasive Biomarkers Can Help in Decision
Making During Interim Analysis in Protocols
Using an Adaptive Design
Population:
• Adult (≥18 years) with biopsy-confirmed NASH (within 90 to 180 days)
• NAS ≥ 4
• Enrich the population with a minimum number of patients with F2/F3
N=XX dose 2
N=XX placebo
weeks
N=XX dose 3
-4 -2 0 48/52
Screening
period
Run-in
IA when X% of the population completes week 12/16-24:
futility/efficacy criteria based on changes in liver fat by MRI,
changes in ALT and other non-invasive biomarkers facilitate
decision making and potential adaptations
N=XX dose 1
Placebo=XX
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R
N XX

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Non-Invasive Biomarkers Can Help Identify
Patients with Compensated Cirrhosis in a
Long-Term Outcome Trial
R
N XX
N=XX placebo
N=XX dose 3
-4 -2 0 72 months
N=XX Dose X
12 24 36 48 60
Fibroscan, MRE, biomarkers of fibrosis for
early identification of progression to F4
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Limitations Of Non-Invasive Biomarkers
► Limited data for prediction of treatment response
 Resolution of or improvement in NASH
(inflammation/ballooning)
 Improvement in one stage of fibrosis
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Transient elastography
Requires a dedicated device
Affected by obesity, ascites, operator experience
False positive in case of acute hepatitis, extra hepatic cholestasis, liver congestion, food intake
and excessive alcohol intake
Unable to discriminate between intermediate stages of fibrosis
MRE
Requires MRI facility
Not applicable in case of iron overload
Time-consuming and high cost
Further validation warranted

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Role of Non-Invasive Biomarkers in
Clinical Practice
Arun Sanyal, MD
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The Clinician’s Perspective
Population at risk
Symptoms or
accidental finding
Progression to HCC
End-stage liver disease
Ignored Triaged for care
Lifestyle
Vit E/TZD
clinical trials
DEATH OR OLT
LARGELY IN
REALM OF
PRIMARY CARE
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Barriers to Better NASH-Related Clinical Care
► Awareness
► Lack of point-of-care diagnostics
► Lack of easy ways to risk stratify and triage subjects
► Lack of approved therapies
► Need for a liver biopsy to determine whether treatments
are effective
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The Clinical Questions Involved in the Area
of NASH
Clinical question Target population Provider population
Is NAFLD present? General population Primary care providers
Will it lead to
outcomes?
Those where NAFLD
present
Primary care providers
Diabetologists
Cardiovascular clinics
Is disease progressing,
stable or regressing?
Those with NAFLD Primary care, diabetologists,
GI-Hep, cardiovascular clinics
Is drug therapy
warranted?
Those with NAFLD and
at risk for outcomes
Primary care, diabetologists,
GI-Hep, cardiovascular clinics
What drug to choose? NASH with some fibrosis Hepatology, primary care,
diabetologists
How is the subject
responding?
NASH and fibrosis Hepatologists, GI, primary care,
diabetes
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Biomarker Qualification
► Define the clinical question and use
► Identify the population to be studied
► Validation:
 biological plausibility
 face validity
 scale of measure
 sensitivity to change
 methodological quality controls
► Ability to predict the outcome of interest in rigorously
designed trials
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What Will Make a Biomarker Useful
for a Clinician?
► Validated to outcomes
► Biological plausibility
► Ease of use:
 point of care trumps need to make a separate appointment
 balance between ease of use versus accuracy
► Ability to guide actionable next step in clinical care
► Ability to reduce unnecessary testing, exposure to potentially
harmful drugs
► Contribution of improving overall cost of clinical care
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CONCLUSION: What We Would Like the
Care Flow to Look Like in the Future
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If risk profile improves
and drugs can be stopped Worsening risk profile
outcomes
outcomes
Low-risk profile
for outcomes High-risk profile
Population at risk
Intermediate
risk profile
Immediate intervention
Escalation of intervention
Low-risk interventions
Healthy living
Periodic monitoring
of risk vs Intervention
outcomes

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Rinella and Sanyal, Nature Reviews Gastroenterology and Hepatology In press 2016
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Suspected NAFLD
(Hepatic steatosis on imaging ± elevated serum ALT)
Exclude alternate
causes of ↑ ALT
Evaluate alcohol
consumption
Low-risk profile
• BMI <29.9
• Age <40 years
• No T2DM or metabolic syndrome features
• Non-invasive fibrosis estimation:**
- FIB4 <1.3
- APRI < 0.5
- NFS < -1.455
• Fibroscan® <5 kPa *
High-risk profile
• AST>ALT
• Platelets <150k
• Non-invasive fibrosis estimation:**
- FIB4 >2.67
- APRI >1.5
- NFS >0.675
• Fibroscan® >11 kPa *
Intermediate-risk profile
• BMI >29.9
• Age >40
• Multiple features metabolic syndrome
• Non-invasive fibrosis estimation:**
- FIB4 :1.3-2.67
- APRI: 0.5-1.5
- NFS: - 1.455-0.675
• Fibroscan® 6–11 kPa *
Consider liver biopsy
Follow patient and reassess
as risk factors evolve
Risk stratification for liver-related outcomes
Consider liver biopsy or
confirmatory testing for cirrhosis
such as MR elastography
Confirmation of NAFLD

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Panel Q&A
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Claudia Filozof, MD, PhD
Senior Medical Director,
Cardiovascular/Metabolic
Covance
Margery A. Connelly, PhD, MBA
Vice President,
Translational Research
LabCorp (LipoScience)
Mark L. Parrish, BS
Associate Scientific
Director of Genomic
Solutions
Covance
Arun Sanyal, MD
Executive Director,
Education Core
Center for Clinical and
Translational Research
Virginia Commonwealth
University, USA

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