Bi k “OMICS” A li ti iBiomarker “OMICS” Applications in ... · Drug Discovery and...

Bi k “OMICS” A li ti iBiomarker “OMICS” Applications in Drug Discovery and Development

Shashi K. Ramaiah, DVM, PhD, DACVP, DABTHead-Translational Biomarker Lab

Drug Safety Research & DevelopmentDrug Safety Research & DevelopmentPfizer-BioTx

Cambridge, MAShashi Ramaiah@Pfizer [email protected]

ASIO Webinar: December 13, 2013

Today’s Goal and OutlineBriefly summarize “omics” profiling approaches in the context of

biomarkers for utility in pharmacology and toxicology in Drug Di & D l tDiscovery & Development

Outline Bi k• Biomarkers– Definition & Key questions?

• Applications

– General concepts & Examples

• Summary Pfizer Confidential │ 2

Identity crisis in the general post-genomic nomenclaturePubmed search

EpigenomicsOrganellomicsMetabalomics

>50 omic/omes terms appearing online in literature

642230 hit f bi kPharmacogenomicsTranscriptomicsClinomicsOperomics

642230 hits for biomarkers

44649 hits for proteomicsOperomicsMethylomicsIntegromicsSecretomicsE i

104711 hits for genomics6192 hits for metabolomics

341 hits for peptidomeExonomicsRegulatomeFunctomicsFoodomics

341 hits for peptidome

2216 hits for transcriptomics35 hits for miRNAomeFoodomics

ProteomicsmiRNAomeMicrobiomeM t

3637 hits for chemical proteomics3032 hit f i

Pfizer Confidential │ 3

Metagenome 3032 hits for omics128384 hits for toxicology

What is a biomarker? And where do you measure?Biomarker ≠ TestBiomarker = Characteristic

S b ll l

Biomarker Characteristic

Tissue

Saliva

U i

Blood SubcellularFractions

S t

Tissue Extract

Urine

CerebralS Sputum

Tissue Feces

Cerebral Spinal Fluid

Serum

sectionsTissue Culture

Feces

PlasmaVarious ManufacturingCulture

MediumPlasma Manufacturing

Medium

“Biomarkers” to understand translation(NIH Workshop definition).

“DISEASE”Biomarker:

A characteristic that is objectively measuredand evaluated as an indicator of normal

h i l i th iphysiologic processes, pathogenic processes, or pharmacologic responses to a

therapeutic intervention.

Translational Medicine

BIOMARKERS

Physiology DRUGPharmacology/Safety Mechanism Biomarker: a biomarker

that reports a downstream effect of a drug Safety Biomarkers; a biomarker thatSafety Biomarkers; a biomarker that

can mitigate safety risk with low TI

What are some of the key questions that biomarker (s) may address in drug discovery & development?

1. Is there pharmacologic modulation in vivo?

2. Can a biomarker be linked to clinical efficacy?

3. Can a biomarker or set of biomarkers stratify or enrich patients?

4. Can a safety risk be identified and mitigated by biomarker measurement?

Considering the complex human biology, disease pathogenesis and toxicity mechanisms, a set of biomarker parameters rather than a

single biomarker may provide a better picture

OMICS & Biomarker Life-Line

Exploratory /Esoteric

Bi k

Routine Biomarker

Discovery ValidationQualification Regulatory approval

Biomarkers

Throughput

CLIA-CAP

AutomatedLIMS and reports

$/assay

LIMS and reports“safety lab tests”

$/assay

FDA 109 protein BMs cleared in plasma/ser m (2009)

Discovery Omics Targeted “omics” Clinical verification

•FDA: 109 protein BMs cleared in plasma/serum (2009)•Home Brew: 96+ for a total of 205 proteins

Human Data is Critical: Why is “OMICS” relevant?

Genetics ‘Omics’‘Right pathway’

Human Biology

Endophenotypes Functional Biology

Validated Targets

Endophenotypes Functional Biology

‘Right target’

Patient Stratification Biomarkers (Imaging)‘Right molecule’

Precision/Personalized Medicine

Pharmacogenomics Companion Diagnostics

‘Right patients’

What is OMICS?• ‘OMICS’ encompasses several

disciplines in which high-

2400Chemicals

dimensional data are generated from molecules such as DNAs (genomics), RNAs

Metabolomics

>100,000 >100,000

Chemicals as DNAs (genomics), RNAs (transcriptomics), proteins (proteomics), or metabolites (metabolomics)

Proteomics,,

proteinsproteins(metabolomics) .

• These high dimensional dataTranscriptomics

~30,000 ~30,000 GenesGenesThese high dimensional data are typically reduced to a profile or ‘signature’, using a computational model that can

Transcriptomics

computational model that can be considered a biomarker

Genomics(mRNA, miRNA…)

“Signatures” are derived as part of discovery and unbiased approaches

• Ultimate goal: To develop a narrow list of biomarkers to be further validated with independent assays & sample cohortindependent assays & sample cohort

• Signature is a combination of one or more markers, when applied to an empirical model, predicts an outcome of interest.

• Multiplex analyses considered a starting point for biomarker identification

• Simultaneous analysis of all proteins in a d fi d t i l ti th thdefined protein population, rather than one protein at a time, as in traditional “biochemistry”

Transcriptomics viaTLDA, Affy chip, RNA seq etc

RNA

Coding RNA(Messenger)

Non-coding RNA

Rounak Nassirpour

How will transcriptomics benefit understanding of disease phenotype?

• Disease is usually reflected at a cellular and molecular level

• Some efforts to understand cellular changes at the tissue level – Blood cellular microarray data may be surrogate to

changes at the tissue level

• Microarray expression deconvolution can quantify proportion of cells in a complex tissuep


Transcriptional Data

Abbas et al, PLoS ONE: 4(7) 2009

Specificity of different genes

•Systematic large scale characterization of cellular

p y gfor different cell types used for deconvolution

•Systematic large scale characterization of cellular composition of SLE blood would measure quantitative differences relevant to the disease pathophysiology


IFN inducible genes and IFN signature• Type 1 interferons will induce hundreds

of genes in vitro and in vivoTLR7/8/9TLR7/8/9

• IFN signature has been defined as IFN inducible genes that are also up-

Myd88 / IRAK4 / IRF5Myd88 / IRAK4 / IRF5

IRF5, IRF7, NFkB

inducible genes that are also upregulated in SLE patientsCytokines: IFN

IL-6, TNF IFN • IFN gene expression signature in blood cells of patients appears to be a more sensitive readout for activation of thisJAK-1 / Tyk-2

IFRNARIFN

6, IFN

sensitive readout for activation of this pathway than cytokine levels in serum

yJAK 1 / Tyk 2

IFN signature

STAT1,2STAT1,2

IFN signature

What is the approach to generating IFN signature?

37oC

PBSControl Identify panel of genes that are

• IFN inducible•Up-regulated in lupus patients •Sensitive to anti-IFN drug

IFN

37oC

RNA Isolation

Affymetrix GenechipHuman Genome U133 plus 2 arrayex vivo with SLE sera +/- anti-IFN drug

RNAlater~54,000 qualifiers

~47,000 transcripts

37oCHealthy

ex vivo with SLE sera plus anti-IFN drug

37oC

Data analysisDisease

Gene Expression Signature Stratifying SLE Patients Identified from Peripheral Blood Cells

SLE patients segregate into two subtypes: IFN+ and IFN-

• Half of SLE patients had elevated expression levels of IFN related transcripts in peripheral bloodrelated transcripts in peripheral blood • This IFN gene expression ‘‘signature’’ served as a marker for more severe disease involving the kidneys, hematopoietic cells, and/or the central nervous system.

A subset of 21 selected to be used as candidate

(Baechler, PNAS, 100, 2610‐5, 2003)

PD biomarkers for anti IFN drug in SLEArthritis Research & Therapy 2010, 12(Suppl 1):S6

Use of type I interferon-inducible mRNAs as pharmacodynamic markers in trials with an anti-IFNα antibody, in SLE

Anti IFN drug rapidly reduces the type I interferon signature in whole blood of SLE patients, in a dose-dependent mannerdependent manner

Phase 1 trial in 62 mild-to-moderately active adult PlaceboySLE subjects who were receiving standard-of-care therapy

Placebo

0.3

Intravenously administered anti IFN

d f 0 3

30

Arthritis Research & Therapy 2010, 12(Suppl 1):S6Higgs BW, et al., 2014: 73; 256‐262

over a dose range of 0.3 to 30.0 mg/kg,

Anti-INF drug is a fully human IgG1κ monoclonal antibody

Utility of IFN signature?•Usefulness of using the type I interferon signature as a pharmacodynamic marker to evaluate activity of anti-IFNαtherapy in SLEtherapy in SLE.

•Expression of the type I interferon signature in whole blood reflects involved tissue in SLE

•Possibility of testing the type I interferon signature as a potential predictive biomarker to identify a subset of SLEpotential predictive biomarker to identify a subset of SLE patients who may preferentially respond to anti-IFNα treatment.


Is there value to assessing circulating miRNAssignature?

Metzinger-Le Meuth et al. 2012Rounak Nassirpour

Regulation of mRNA by Regulation of mRNA by miRNAsmiRNAs

Highly conserved, single stranded RNAs (~22 nucleotides)

Protein‐coding gene miRNA gene

Translation of mRNAs can be regulated by miRNA

mRNAmRNA

Pri‐miRNAregulated by miRNA

miRNA expression profiles can be influenced by chemicals

Pre‐miRNA

influenced by chemicals

Emerging serum‐based biomarkers Nucleus

Cytoplasm Dicerin various biological and toxicological processes

Cytoplasm DicerMature miRNA

RISCRibosomeRISC

AAAA

Translational inhibition / mRNA degradation

boso e

ORF

20

Rounak Nassirpour

miRNA biomarkers should also add value tothe already available biomarkers

Tissue specific

Pl iR 122 d

expression ofmiRNAs.

Plasma miR-122 and miR-133a are specific for liver and muscle toxicity, respectively. y, p yThey outperformed traditional biomarkers, ALT and AST, which were both increasedwere both increased with either organ toxicity in animal models. TMPD, Statin A = Muscle toxicant

Laterza et al. 2009

CBrCl3 = Hepatotoxicant

Targeted discovery of tissue specific Targeted discovery of tissue specific miRNAomemiRNAome

3 adult Wistar Han male rats (~ 12 weeks) 18 tissues 74 miRNAs identified from literature as potential testicular 74 miRNAs identified from literature as potential testicular specific

10 miRNAs specific multiple organsA i l Appropriate controls

Assay platform:

Lin et al., 2013; Previously Presented at the Annual SOT Meeting Poster, 2013

y p Custom ABI TaqMan TLDA, 384 wells, 96 probes, 4 samples

Total RNAs were extracted using Qiagen miRNeasy kit then QC’d Total RNAs were extracted using Qiagen miRNeasy kit, then QC dwith Agilent pico/smallRNA chips

Spiked‐in Cel‐miR‐39 was included to determine RT efficiency, and as nomalizer

22

Hank LinBob Chapin

Tissue specificity of miRNA precursor clustersTissue specificity of miRNA precursor clusters

mmu‐463 cluster

Next-generation sequencing data

mmu 463 cluster

23Landgraf et al., 2007; Cell. 2007 Jun 29;129(7):1401-14

mi463 highly expressed in the reproductive system which consists of mir-741 and 471

Heat map shows the expression of 22 miRNAs across liver, epididymis, testis and also in plasma.

Other tissue specific miRNA controls confirmed miR‐122 – Liver, miR‐1/208 – Heart/Muscle

miR‐513miR‐201

dct (normalized to U6)

miR‐202‐5pmiR‐471miR‐743b‐3prno‐miR‐742miR‐742miR‐883a‐5pmiR‐122miR‐1

Expression

miR‐743b‐5pmiR‐883a‐3pmiR‐878miR‐463miR‐883b‐3pmiR‐743amiR‐34b‐3pmiR 449amiR‐449amiR‐34cmiR‐741miR‐449cmiR‐34b‐5pmiR‐871miR‐202‐3p

Bra

in

Epid

idym

is

Fat

Hea

rt

Kid

ney

Live

r

Mus

cle

Pla

sma

Sple

en

Stom

ach

Test

is

Mir-463 and other members of the clusters 471 741 have increased expression

24

Mir 463 and other members of the clusters, 471, 741 have increased expression in the testis Hank Lin

Bob Chapin

E in Healthy E in AKI

BE in Healthy BE in AKIHEALTHY AKI

INE in Healthy NE in AKI

Pooled Pooled

RNA ISOLATION

URINES URINES58

28E in both248

REVERSE TRANSCRIPTION

PRE-AMPLIFICATION

39 5

8

PRE AMPLIFICATION

MIRNA PROFILING (1809 miRNAS) 378 miRNAs selected

HEALTHY AKIExpressed [E] 345 281Borderline expressed [BE] 275 223

Work flow for processing pooled urines from 6 healthy volunteers and 6 AKI patients and then

Borderline-expressed [BE] 275 223Non-expressed [NE] 1287 1402E = 19-30 (Ct); BE = 30-32 (Ct); NE = >32 (Ct)

Vishal S. Vaidya, Ph.D., Harvard Medical School

Ramachandran et al., Clin Chem, 2013

screening 1809 miRNAs

-5 51.5miR-502-5p

miR-4640-5pmiR 21 5p

HEALTHY AKI

miR 502 5pmiR-21-5p

miR-4698

miR-4650-3p

iR 200b 3

A panel of 17 miRNAs identified with values >5-fold higher in AKI patients than in healthy individuals

filin

g

miR-200b-3plet-7d-5p

miR-23a-3p378 79 17

NA

Pro

f

Fold Change Melt Curve Std Dev > 1.5

miR-3679-5p

miR-4724-5p

miR-4301

378 79 25 17

78 m

iR

8 l d d

54 l d d

g< 5-fold

299 l d d

Failures p > 0.01 let-7b-5p

let-7c

miR-191-5p

3 excludedexcludedexcludedmiR-373-5pmiR-1301miR-320b

Fold change over healthy93 60

miR-3620-3p


Expression levels of miRExpression levels of miR‐‐21, 200c, 423 and 4640 are 21, 200c, 423 and 4640 are significantly different in patients AKI (n=117) as compared to significantly different in patients AKI (n=117) as compared to

healthy volunteers (n=97)healthy volunteers (n=97)y 97y 97


The combined cross‐validated area under the receiver operator curve for miR‐21, ‐200c, ‐423 and ‐4640 was computed to be 0 914640 was computed to be 0.91.

miR-21 miR-200c miR-423 and miR-miR 21, miR 200c, miR 423 and miR4640 are capable of differentiating between patients with AKI and patients without AKI

Vishal S. Vaidya, Ph.D., Harvard Medical School


p

What is proteomics? Global analysis of proteins that make up a cell or tissue Systematic analysis of proteins for their identity, quantity, and

f tifunction.

Investigating populations of proteins rather than one t i t tiprotein at a time

Global profiling-based proteomics Targeted proteomics

o peptide fragments; MRM or SRMo Chemoproteomics, phosphoproteomics

Proteome is complex Huge expression range (106-9); Heterogeneous, sub-

stoichiometric modifications12/13/2013 29

Clinical Serum/plasma proteome~ 1% of the human protein gene products, defining a practical clinical plasma proteome.clinical plasma proteome.

•Functional categories of the 109 unique proteins measured by FDA-unique proteins measured by FDAcleared or -approved tests in plasma or serum

• 96 LDT tests available for clinical use in U.S

Pfizer Confidential │ 30Anderson N, Clinical Chemistry 56: 177–185 (2010)

Mass Spectrometry-Based Proteomic Analysis

Discovery proteomics (Shot gun/Bottom up)

Targeted proteomics

•Broad & unbiased: Uncover as •Pre-defined list of proteotypicmany proteins as possible

ypeptides •Immunoaffinitity and peptide enrichment

•Large dynamic range•Resource intensive•Lower abundance proteins

•Focused on verification and validation•Greater sensitivity, detection of

difficult to capture•Missing data•Need extensive fractionation, li it th h t l l

low abundance proteins, high throughput and accurate quantificationM lti l i bilit ith tlimit throughput, large sample

needs•Multiplexing capability without a need for antibody

Quantitative strategies include SILAC iTRAQ

Stable isotope internal standardsT i l d l MRMSILAC or iTRAQ

Orbitrap; improved mass accuracyTriple quadrupole, MRM

Transcriptional vs proteomics profiling

The proteins likely the most ubiquitously affected in disease, drug response and recovery

Transcriptional profiling vs proteomics Transcriptional profiling vs proteomics Proteomics complex with large span of analyte concentations:

measurements will need wide dynamic range (~12 orders ofmeasurements will need wide dynamic range ( 12 orders of magnitude)

o ~1010 pg/mL for albumin to <10 pg/mL for cytokines and interleukins

o Most FDA approved protein biomarkers; ~ 102 to 105 pg/mLrange

No PCR equivalents in protein identification

I ffi it t d t t t ti b l / Lo Immunoaffinity to detect concentations below ng/mL

o Difficulty in developing antibody reagents32

Proteomics application in Diabetic NephropathyCKD 273 Peptidome classifier

Questions: •Can we identify patients at risk of renal fibrosis? y p•Are there urinary biomarkers of renal fibrosis that that can be detected prior to albuminuria or decline in eGFR?

Technology: Capillary electrophoresis -mass spectrometry (CE-MS)

• Urine samples obtained from >20 clinical centers (230 CKD patients and 379 healthy subjects)

• Focus on LMW proteins by eliminating >25 kd MW proteins.

• Urine is not trypsinized to focus on naturally occurring


Good DM et al. Molecular and Cellular Proteomics 2010, 9:2424-2437

Urine is not trypsinized to focus on naturally occurring peptides

CKD 273 Classifier Score Development

CKD 273

HV (379) CKD (230)

•Applied to blinded, multicenter test set

f ti t ith

Anal ed each rine sample (A erage of

HV (379) CKD (230) of patients with various kidney disease (n=110) and

Analyzed each urine sample (Average of 1174 peptides/sample) HV (n=34)

634 peptides identified as signficantly different Selected subset of 273 peptides with known

sequence information

•Result: 85.5% sensitivity and 100% ifi it

•Sample considered positive if CKD273 score >0.373•CKD 273 signature uses a classification score based on the

100% specificity


CKD 273 signature uses a classification score based on the amplitudes of all 273 biomarkers

Good DM et al. Molecular and Cellular Proteomics 2010, 9:2424-2437

Lori FitzJulie Lee

Mechanistic basis for CKD 273 peptide changes?

CKD peptide signature contains fragments of different collagens (major constituents, 74%), serum proteins andcollagens (major constituents, 74%), serum proteins and

kidney-specific proteins (e.g. uromodulin)

Compared to controls RationaleCollagen fragments

In CKD…….reflect disease-induced changes to protease/MMP

Rationale…

fragments

Serum protein

activities in the kidney

fl t di i d dSerum protein fragments

….reflect disease-induced alternations in glomerularfilteration

Good DM et al. Molecular and Cellular Proteomics 2010, 9:2424-2437.

Lori FitzJulie Lee

CKD273 signature identifies progressors in diabetic nephropathy earlier than microalbuminuria

• CKD273 classifier scores in T1D and T2D patients who developed DN were

T2D progressor

patients who developed DN were consistently higher than those patients that did not progress in a 9 year period

CKD273 l ifi id tifi d• CKD273 classifier scores identified progressors 1.5 years earlier on average than microalbuminuria

• Decrease in collagen fragments observed 3-5 years before onset of macroalbuminuria

Suggests the CKD273 could be used to

T2D nonprogressor

• Suggests the CKD273 could be used to identify at risk patients for targeted intervention before the development of microalbuminuria

CKD classifier (0.373), macroabluminuria (200ug/mlcutoff from normo- to microalbuminuria (20ug/min)Zurbig et al. Diabetes 2012 v61

Lori FitzJulie Lee

Proteomic classifier 273 performance in CKD

•53 anuric out of 76 CKD patients

Urinary proteome/peptidome analysedby CE-MS of selected patients.

CKD273 scoring and eGFR values in the patients included in the study

•Validation of CKD273 in a large independent cohort for prognostic value

•CKD273 ≤0.55 no dialysis or death while CKD273≥0.55 reached endpoint

CKD 273 classifier separated CKD patients according to renal f nction

Argiles A et al., PLoS ONE 2013, 8, e62837

•CKD 273 classifier separated CKD patients according to renal function and informed adverse outcome

Take Home Messages

• High-dimensional, high content (and low-cost) platforms such as targeted or unbiased “omics” approaches are no longer

id d “fi hi i t ” d b l bl if thconsidered “fishing experiments” and can be valuable if they are applied to address key questions

• “Omics” biomarker strategies and approaches are critical to address biology/pharmacology (and safety) question (s) either clinically or during early drug discoveryclinically or during early drug discovery

• Multiplex/signature/high dimensional data generated by “omics” approaches improves disease classification compared with single biomarkers


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