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

Declaration

Thermo Fisher Scientific and its affiliates are not endorsing, recommending, or promoting any use or

application of Thermo Fisher Scientific products presented by third parties during this seminar. Information and materials presented or provided by third parties are

provided as-is and without warranty of any kind, including regarding intellectual property rights and reported results. Parties presenting images, text and

material represent they have the rights to do so. If applicable; modify as appropriate, (for which you must

have a signed speaker engagement agreement) including if free products/support were provided: Speaker was provided travel and hotel support by Thermo Fisher Scientific for this presentation, but no remuneration.

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

In vitro assays for AD Candidates

Ab 1-42

P Tau

Cell Death

H202

Neuro genesis

Wnt

SMART AD

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

Using CMAP to find bio-mimetics

David Chambers & Malcolm Maden

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