Integrated genomic approaches to repositioning drugs for neurodegenerative disorders 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 ATO 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
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Lab themes & research areas Drug discovery Investigative biomarker approaches Drug Large-scale repurposing: high-resolution CMAP & biomarkers: candidate Single cell approaches Genomics-based FFPE drug repositioning Emerging Pathway ID Pathways: & validation: miRNA & RA, RTKs, exosomal GPCRs signaling 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
AD: biological targets for drug discovery Polyproteinopathies (A b , NFT, a Syn) Synaptodendritic rarefaction Inflammation Mitochondrial dysfunction Multiple transmitter deficits Aberrant neural network activity Reduced neurogenesis Degeneration of specific neuronal cells Epigenetics Lysosomal proteolysis Dysregulation intracellular Ca 2+ Levels Oxidative damage Perpetuated cell-cell spread Why is it so difficult to find a drug with multiple disease targets?
The drug discovery pipeline: why aren’t there many new drugs? High costs: $1.5 billion to bring a new compound to market Long 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? ‘On’ target approach: reiterated mechanism of action of the drug Specific Successful for enzyme Unsuccessful Sildenafil male (PDE5) for angina impotence inhibitor ‘Off’ target approach: identify novel targets for existing drugs Licensed for Discovered Used for influenza: M2 Parkinson’s Amantadine NMDA receptor Proton channel antagonist Disease blocker
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 Drug Gene Expression profile Disease gene expression signature Drug Gene Expression profile Disease gene expression signature 1. Generated by Affymetrix Array 1. Generate via Array or NGS 2. Non parametric ‘ranking’ 2. Generate via GWAS, WES 3. Generated by Bead studio 3. Generate manual list (LINCs) 4. Generate via metadata (Spied) 4. Cancer cell focussed 5. Efficacious Drug Mimetic
Connectivity Mapping 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 Justin Lamb et al. Science 2006;313:1929-1935
CMAP: key parameters Original CMAP: dosing distribution Human: MCF7 All treatments of cells are 6h breast adenocarcinoma cell line CMAP = >1300 FDA approved compound profiles in MCF7 Justin Lamb et al. Science 2006;313:1929-1935
Does CMAP work: cancer proof of concept Human lung Experimental validation of cimetidine for lung adenocarcinoma adenocarcinoma signature generated Cimetidine 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 A lzheimer’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 correlation
Do candidate drugs generate an anti correlating profile in human neuronal cells?: NMAP & ApoE4 NMAP SMART_AD: Cell type Human: MCF7 breast Rat: Human: cerebral cortical Human: cerebral cortical iPSC* neurons iPSC* neurons: ApoE4 adenocarcinoma cell line hippocampal neurons NMAP ApoE4: NMAP CMAP Increasing relevance for SMART AD initiative SMART AD
Generate an AD-relevant neuronal connectivity map: NMAP SMART_AD candidate compounds induce robust and genome-wide gene expression changes in neurons (hyCCN IPSCs) : Affymetrix U133 2.0 The effects are not necessarily mediated by classic ligand- The distribution of significantly altered gene expression values over the assayed receptor drugs is shown [left] The distributions are relatively symmetric, with ~ 1000 up and down regulated genes on average, shown right. pharmacology SMART AD
SMART AD: NMAP summary 1300 CMAP Candidates 200 CMAP hits – ve AD SPIED: AD ‘Early’ 160 NMAP – ve AD Systematic review & Signature Steering Panel Triage 40 retain – ve ~ 1000 Transcriptomic AD profiles generated: NMAP
In vitro assays for AD Candidates A b 1-42 Wnt P Tau Neuro Cell genesis Death H202 SMART AD
A b 1-42 Cell Death Assay characterisation in mouse cortical neurons: 3 Day Abeta42 titre Plate 3 *** *** 100 ** ** *** % of control *** absorbance (570nm) ** 0.6 50 0.4 0 0.2 Abeta42 only Diluent C18 C34 C37 0.0 10uM 3uM 1uM diluent untreated 3 day C18 > Abeta42: p=0.0003 3 day SMART AD
SMART AD: NMAP summary 1300 CMAP Candidates 200 CMAP hits – ve AD 160 NMAP – ve AD Systematic review & 40 retain – ve Steering Panel AD Triage ~ 1000 Transcriptomic 12 pass in profiles generated vitro
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
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