Integrated genomic approaches to repositioning drugs for ...
Transcript of Integrated genomic approaches to repositioning drugs for ...
D R D AV I D C H AM B E R S
P R I N C I PAL I N V E S T I G AT O R & L E C T U R E R I N F U N C T I O N AL
G E N O M I C S
G E N O M I C S D R U G D I S C O V E RY U N I T
W O L F S O N C E N T R E F O R AG E - R E L AT E D D I S E AS E S ( C AR D )
K I N G ’ S C O L L E G E L O N D O N
S E 1 1 U L U K
Integrated genomic approaches to repositioning drugs
for neurodegenerative disorders
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Lab themes & research areas
Investigative biomarker approachesDrug discovery
Drug repurposing:
CMAP &candidate
approaches
Pathway ID & validation:RA, RTKs,
GPCRs
Large-scalehigh-resolution
biomarkers:Single cell
FFPE
EmergingPathways:miRNA & exosomalsignaling
Genomics-baseddrug
repositioning
AD PD NDD Regen Pain
Alzheimer’s Disease: the unmet need
• 35 million people worldwide with dementia
• 78 million by 2040
• >60% have Alzheimer’s Disease (AD)
• Huge human and financial cost: Global cost estimated > $600 billion
• Symptomatic treatments give modest but important benefit
• Disease-modifying drugs are urgently needed to:
• Delay the onset of Alzheimer’s disease
• Improve long term outcomes
‘Current drugs help mask the symptoms of Alzheimer's, but do not treat the underlying disease or delay its progression’:
Alzheimer’s Association 2016
Polyproteinopathies (Ab, NFT, aSyn) Synaptodendritic rarefaction Inflammation Mitochondrial dysfunction Multiple transmitter deficits Aberrant neural network activity Reduced neurogenesis Degeneration of specific neuronal cells Epigenetics Lysosomal proteolysis Dysregulation intracellular Ca2+ Levels Oxidative damage Perpetuated cell-cell spread
Why is it so difficult to find a drug with multiple disease targets?
AD: biological targets for drug discovery
The drug discovery pipeline: why aren’t there many new drugs?
High costs: $1.5 billion to bring a new compound to marketLong timelines: around 12 years; patents valid for 25 years
Cumulative number of new drugs (NMEs) approved by FDA circa 2013
Circa 2012: 1700 CT cancer vs 30 CT AD
Can we use other drugs?
Drug repositioning or repurposing.
Identifies already existing compounds which may have benefit in treating target disease
Benefits include saving time and money: $5-10m making it accessible for research charities
The dosage, tolerability & side-effects are known
Potential new delivery mechanisms
How do we go about repurposing studies?
Sildenafil
Specific enzyme (PDE5) inhibitor
Unsuccessful for angina
Successful for male
impotence
‘On’ target approach: reiterated mechanism of action of the drug
‘Off’ target approach: identify novel targets for existing drugs
Amantadine
Licensed for influenza: M2
Proton channel blocker
Discovered NMDA receptor
antagonist
Used for Parkinson’s
Disease
How do we go about repurposing studies?
A genomic approach to drug repurposing
The Connectivity Map (CMAP)
[Broad Institute]
An ‘Off’ target approach
The CMAP in a nutshell
Disease gene expression signature
Disease gene expression signature
1. Generate via Array or NGS2. Generate via GWAS, WES3. Generate manual list4. Generate via metadata (Spied)5. Efficacious Drug Mimetic
Drug Gene Expression profile
1. Generated by Affymetrix Array2. Non parametric ‘ranking’3. Generated by Bead studio
(LINCs)4. Cancer cell focussed
Drug Gene Expression profile
Connectivity Mapping
Justin Lamb et al. Science 2006;313:1929-1935
Accordingly, the Cmap resource has the potential to connect human diseases or degenerative states with the genes that underlie them and the drugs that treat
them
CMAP: key parameters
All treatments of cells are 6h
Original CMAP: dosing distributionHuman: MCF7
breast adenocarcinoma cell line
Justin Lamb et al. Science 2006;313:1929-1935
CMAP = >1300 FDA approved compound profiles in MCF7
Does CMAP work: cancer proof of concept
Cim
eti
din
e
Human lung adenocarcinoma
signature generated
Experimental validation of cimetidine for lung adenocarcinoma
Sirota, M., et al.,. Sci Transl Med, 2011. 3(96): p. 96ra77
Can we do better than CMAP for NDD?
A Systematic Approach to Develop and Evaluate the Best Candidate Treatments for Repositioning
as Therapies for Alzheimer’s Disease: SMART-AD
Prof Clive Ballard
Prof Pat Doherty
Prof Jonathan Corcoran
Dr Gareth Williams
Dr Anne Corbett
Dr David Chambers
Prof Paul Francis
Prof Simon Lovestone
SMART AD
SMART AD is driven by human genetics: SPIED
Searchable platform-independent expression database (SPIED)
SPIED uses deposited profiles as surrogates for biology comparison across all platforms and species
Can query SPIED to identify all experiments relevant to specific questions and then generate consensus signature:
Generate gene expression signatures for different classifications of AD
Human: Early
Human: Moderate
Human: Severe
Mouse: most representative AD model to human AD
SMART AD
Query CMAP with Human Early AD signature: anticorrelates
Approximately 200 drugs significantly anti correlate with early AD signature
SMART AD
CMAP Drug Candidates from multiple independent drug
classes
Heatmap: transcriptional similarity of the 200 SMART AD candidates to
each other reveals distinct classes of drugs including:
anti-inflammatory, anti-bacterial, analgesics & anti-depressives
corr
ela
tion
SMART_AD: Cell type
Human: cerebral corticaliPSC* neurons
Rat: hippocampal neurons
Human: MCF7 breast adenocarcinoma cell line
Increasing relevance for SMART AD initiative
Do candidate drugs generate an anti correlating profilein human neuronal cells?: NMAP & ApoE4 NMAP
NMAPCMAP
Human: cerebral corticaliPSC* neurons: ApoE4
ApoE4: NMAP
SMART AD
The distribution of significantly altered gene expression values over the assayed
drugs is shown [left] The distributions are relatively symmetric,
with ~1000 up and down regulated genes on average, shown right.
SMART_AD candidate compounds induce robust and genome-wide gene expression
changes in neurons (hyCCN IPSCs) : Affymetrix U133 2.0
Generate an AD-relevant neuronal connectivity map: NMAP
The effects are not
necessarily
mediated by
classic ligand-
receptor
pharmacology
SMART AD
SMART AD: NMAP summary
1300 CMAP Candidates
200 CMAP hits –ve AD
160 NMAP –ve AD
40 retain –veAD
Systematic review &
Steering Panel
Triage
~ 1000 Transcriptomic
profiles generated:
NMAP
SPIED: AD
‘Early’
Signature
Ab 1-42 Cell Death Assay characterisation in mouse cortical
neurons: 3 Day
10uM
3uM
1uM
diluen
t
untrea
ted
0.0
0.2
0.4
0.6
Abeta42 titre
**
***
***
**
**
3 day
ab
so
rban
ce (
570n
m)
Plate 3
Abet
a42
only
Dilu
ent
C18
C34
C37
0
50
100
***
***
3 day%
of
co
ntr
ol
C18 > Abeta42: p=0.0003
SMART AD
SMART AD: NMAP summary
1300 CMAP Candidates
200 CMAP hits –ve AD
160 NMAP –ve AD
40 retain –veAD
12 pass in vitro
Systematic review &
Steering Panel
Triage
~ 1000 Transcriptomic
profiles generated
SMART AD: What classes of Drugs?
SMART AD: select hits to progress based upon diverse drug classes and interaction with different pathways
Antibiotics
NSAIDS
Receptor antagonists
Histone deacetylase inhibitors
Naturally-occurring compounds
SMART AD
NMAP data predicts the pathways candidates target in human neuronal cells: Drug F
Pathways enriched in the top 500 responders. Immune system, WNT signalling and Amyloids are
notable pathways.
PATHWAY p N n
IMMUNE SYSTEM 0.025892 868 31
HEMOSTASIS 0.041398 445 17
METABOLISM OF PROTEINS 0.027839 414 17
CELL CYCLE 0.014612 375 17
CELL CYCLE MITOTIC 0.046736 290 12
CYTOKINE SIGNALING IN IMMUNE SYSTEM 0.045332 256 11
TRANSCRIPTION 0.010066 191 11
POST TRANSLATIONAL PROTEIN MODIFICATION 0.00603 176 11
JAK STAT SIGNALING PATHWAY 0.002425 154 11
CLASS I MHC MEDIATED ANTIGEN PROCESSING PRESENTATION 0.046481 226 10
FOCAL ADHESION 0.021307 190 10
FATTY ACID TRIACYLGLYCEROL AND KETONE BODY METABOLISM 0.007743 158 10
SYSTEMIC LUPUS ERYTHEMATOSUS 0.002735 134 10
RNA POL I RNA POL III AND MITOCHONDRIAL TRANSCRIPTION 0.000943 115 10
ENDOCYTOSIS 0.022208 164 9
FACTORS INVOLVED IN MEGAKARYOCYTE DEVELOPMENT AND PLATELET PRODUCTION
0.005261 125 9
RNA POL I TRANSCRIPTION 0.000329 82 9
WNT SIGNALING PATHWAY 0.029008 146 8
SIGNALING BY NOTCH 0.003218 94 8
CELL ADHESION MOLECULES CAMS 0.043728 133 7
CELL CYCLE 0.025335 115 7
HYPERTROPHIC CARDIOMYOPATHY HCM 0.005809 83 7
SIGNALING BY NOTCH1 0.001344 63 7
MEIOSIS 0.049235 109 6
DNA REPAIR 0.039739 102 6
MEIOTIC RECOMBINATION 0.017968 82 6
ST INTEGRIN SIGNALING PATHWAY 0.016297 80 6
AMYLOIDS 0.015497 79 6
SMART AD
SMART AD: NMAP summary & current stage
1300 CMAP Candidates
200 CMAP hits –ve AD
160 NMAP –ve AD
40 retain –ve AD
12 pass in vitro
in vivo
6
5 x FAD
Apply genomic profiling to
determine BBB penetration
Systematic review &
Steering Panel
Triage~ 1000 Transcriptomic
profiles generated
Determine histopathology and
behavioural impact of
candidates
Mile
sto
nes 1
-4
Proof of Concept II: Retinoid signalling and ageing
Generate signature of efficacious drug with undesirable off-target effects:
Reduction in adult neurogenesis is concomitant with decline in atRA levels
Exogenous retinoid signalling can reverse age-related decline in hippocampal neurogenesis
Target process neuroprotection and neurogenesis
Target Disease: Neurodegeneration (AD)
ApoE4 impairs adult hippocampal neurogenesis
Internally Generated Query Signature
Use CMAP to find Drugs that correlate: mimetic
Validation: in vitro assay of neuronal cell death
RETINOIDS AND AD
Retinoid-based compounds: drugs of gene expression
Retinoids signal via steroid hormone-like receptors (RARabg & RXRabg) to directly modulate gene expression in target cells
Accordingly they are ideal targets for CMAP-based approach as they control defined
cohorts of genes that can be correlated with disease signature
Using CMAP to find RA bio-mimetics
Exposure to RAR agonists over a period of 28 days: stable signature
Chambers & Maden CRC Press 2017
Co-ordinating compounds across models
Drug PDrug IDrug NDrug P2Drug UDrug MDrug SP
some are concentration dependent e.g. Drug P, but many
of them work across concentrations from 100 mM to 1
nM
Chambers, Maden & SMART AD
SMART AD
Proprietary
cell to cell signaling
RAR a/b/g Neurogenesis
2-6
Acknowledgements
SMART AD: Clive Ballard (Lead PI), Patrick Doherty (PI), Gareth Williams (PI), Jonathon Cocoran (PI), Paul Francis (PI), Anne Corbett
RA a/b/g: Malcolm Maden, University of Florida
EGFR NSC SPIED: Pat Doherty, Gareth William & Phil Sutterlin
Exosomal Signalling & Regeneration: Ketan Patel
Micregen & University of Reading
Exosomal miRNA-21-5p & Pain: Marcia Malcangio
Funding
Wellcome Trust, BBSRC, KHP, Micregen, PE & PF