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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 ComShashi.Ramaiah@Pfizer.Com
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
Pfizer Confidential │ 12
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
Pfizer Confidential │ 13
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.
Pfizer Confidential │ 18
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
Ramachandran et al., Clin Chem, 2013
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
Ramachandran et al., Clin Chem, 2013
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
Ramachandran et al., Clin Chem, 2013
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
Pfizer Confidential │ 33
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
Pfizer Confidential │ 34
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
Pfizer Confidential │ 38
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