Download ppt - Drug Discovery: Proteomics, Genomics

Drug Discovery: Proteomics, Genomics

Philip E. BourneProfessor of Pharmacology UCSD

[email protected] 858-534-8301

1SPPS273

mailto:[email protected]

Agenda

• Where my perspective comes from• The interplay between omics, IT and drug

discovery• The omics revolution• Changes in IT and open science and software

licensing• Applying the new biology to drug discovery

– Example 1 – Drug repositioning– Example 2 - Determining side-effects

• Words of caution

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Some Background• We work in the area of structural

bioinformatics• We distribute the equivalent to ¼

the Library of Congress to approx. 250,000 scientists each month

• We are interested in improving the drug discovery process through computationally driven hypotheses on the complete biological system

• Personally:– Open science advocate– Started 4 companies – Spent whole life in the ivory tower

SPPS273 3The Source of My Perspective

Observations

• Glass ½ Empty: drug discovery in the traditional sense is in a woeful state

• Glass ½ Full:– We have an explosion of

data and hence a new emerging understanding of complex biological systems

– Information technology is advancing rapidly

• Let optimism rule – let traditional computational chemistry and cheminfomatics meet bioinformatics, systems biology and information science to discover drugs in new ways

SPPS273 4The Take Home Message

Biological Experiment Data Information Knowledge Discovery

Collect Characterize Compare Model Infer

Sequence

Structure

Assembly

Sub-cellular

Cellular

Organ

Higher-life

Year90 05

Computing Power

Sequencing

Data1 10 100 1000 105

95 00

Human Genome Project

E.ColiGenome

C.ElegansGenome

1 Small Genome/Mo.

ESTs

YeastGenome

Gene Chips

Virus Structure

Ribosome

Model Metaboloic Pathway of E.coli

Complexity Technology

Brain Mapping

Genetic Circuits

Neuronal Modeling

Cardiac Modeling

Human Genome

# People/Web Site

106 102 1

VirtualCommunities

The Drivers of Change – Data & IT

106

BlogsFacebook

1000’sGWAS

The Omics Revolution

Num

ber

of r

elea

sed

entr

ies

Year

Its Not Just About Numbers its About Complexity

The Omics Revolution Courtesy of the RCSB Protein Data Bank

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Metagenomics - 2007• New type of genomics

• New data (and lots of it) and new types of data– 17M new (predicted

proteins!) 4-5 x growth in just few months and much more coming

– New challenges and exacerbation of old challenges


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Metagenomics: Early Results

• More then 99.5% of DNA in very environment studied represent unknown organisms– Culturable organisms are

exceptions, not the rule• Most genes represent

distant homologs of known genes, but there are thousands of new families

• Everything we touch turns out to be a gold mine

• Environments studied:– Water (ocean, lakes)– Soil– Human body (gut, oral

cavity, human microbiome)


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Metagenomics New DiscoveriesEnvironmental (red) vs. Currently Known PTPases (blue)

Higher eukaryotes

1

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4The Omics Revolution

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The Good News and the Bad News

• Good news– Data pointing towards function are growing at

near exponential rates– IT can handle it on a per dollar basis

• Bad news– Data are growing at near exponential rates– Quality is highly variable– Accurate functional annotation is sparse


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Example of the Interplay Between Bioinformatics & Proteomics - The Structural Genomics Pipeline

Basic Steps

Target Selection

Crystallomics• Isolation,• Expression,• Purification,• Crystallization

DataCollection

StructureSolution

StructureRefinement

Functional Annotation Publish


Structural biology moves from being functionally driven to genomically driven

Fill inprotein fold

space

Robotics-ve data

Software engineering Functional prediction

Notnecessarily

Towards Open Science

• Open access publishing• Open source software• Generation of scientists weaned on social

networks• Blogs, wikis, social bookmarking etc. are

becoming a valid form of scientific discourse

SPPS273 13http://www.osdd.net/

University Tech Transfer Offices are Slow to Embrace this Change

• Overvalue disclosures• Inability to market disclosures appropriately• Protracted negotiations in a fast moving

market• Disable rather than enable startups

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So Why is All of This So Important to Drug Discovery?

We are beginning to piece together a complex living system and we need

to understand that to do better

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A.L. Hopkins Nat. Chem. Biol. 2008 4:682-690

Why Don’t we Do Better?A Couple of Observations

• Gene knockouts only effect phenotype in 10-20% of cases , why? – redundant functions – alternative network routes – robustness of interaction networks

• 35% of biologically active compounds bind to more than one target

Paolini et al. Nat. Biotechnol. 2006 24:805–815

Why Don’t we Do Better?A Couple of Observations

• Tykerb – Breast cancer

• Gleevac – Leukemia, GI cancers

• Nexavar – Kidney and liver cancer

• Staurosporine – natural product – alkaloid – uses many e.g., antifungal antihypertensive

Collins and Workman 2006 Nature Chemical Biology 2 689-700

Implications

• Ehrlich’s philosophy of magic bullets targeting individual chemoreceptors has not been realized

• Stated another way – The notion of one drug, one target, one disease is a little naïve in a complex system

So How Can We Exploit All The New Data We are Collecting on This

Complex System?

Lets Work Through a Couple of Examples

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What if…

• We can characterize a protein-ligand binding site from a 3D structure (primary site) and search for that site on a proteome wide scale?

• We could perhaps find alternative binding sites (off-targets) for existing pharmaceuticals and NCEs?

Exploiting the Structural Proteome

What Do These Off-targets Tell Us?

• Potentially many things:1. Nothing2. How to optimize a NCE3. A possible explanation for a side-effect of a drug

already on the market4. A possible repositioning of a drug to treat a

completely different condition5. The reason a drug failed 6. A multi-target strategy to attack a pathogen


Need to Start with a 3D Drug-Receptor Complex - The PDB Contains Many Examples

Generic Name Other Name Treatment PDBid

Lipitor Atorvastatin High cholesterol 1HWK, 1HW8…

Testosterone Testosterone Osteoporosis 1AFS, 1I9J ..

Taxol Paclitaxel Cancer 1JFF, 2HXF, 2HXH

Viagra Sildenafil citrate ED, pulmonary arterial hypertension

1TBF, 1UDT, 1XOS..

Digoxin Lanoxin Congestive heart failure

1IGJ


A Reverse Engineering Approach to Drug Discovery Across Gene Families

Characterize ligand binding site of primary target (Geometric Potential)

Identify off-targets by ligand binding site similarity(Sequence order independent profile-profile alignment)

Extract known drugs or inhibitors of the primary and/or off-targets

Search for similar small molecules

Dock molecules to both primary and off-targets

Statistics analysis of docking score correlations

…

Xie and Bourne 2009 Bioinformatics 25(12) 305-312


The Problem with Tuberculosis

• One third of global population infected• 1.7 million deaths per year• 95% of deaths in developing countries• Anti-TB drugs hardly changed in 40 years• MDR-TB and XDR-TB pose a threat to

human health worldwide• Development of novel, effective, and

inexpensive drugs is an urgent priority

Example 1 – Repositioning The TB Story

Found..

• Evolutionary linkage between: – NAD-binding Rossmann fold– S-adenosylmethionine (SAM)-binding domain of SAM-

dependent methyltransferases• Catechol-O-methyl transferase (COMT) is SAM-

dependent methyltransferase• Entacapone and tolcapone are used as COMT

inhibitors in Parkinson’s disease treatment• Hypothesis:

– Further investigation of NAD-binding proteins may uncover a potential new drug target for entacapone and tolcapone

Kinnings et al. 2009 PLoS Comp Biol 5(7) e1000423Example 1 – Repositioning The TB Story

Functional Site Similarity between COMT and InhA

• Entacapone and tolcapone docked onto 215 NAD-binding proteins from different species

• M.tuberculosis Enoyl-acyl carrier protein reductase ENR (InhA) discovered as potential new drug target

• InhA is the primary target of many existing anti-TB drugs but all are very toxic

• InhA catalyses the final, rate-determining step in the fatty acid elongation cycle

• Alignment of the COMT and InhA binding sites revealed similarities ...

Repositioning - The TB Story Kinnings et al. 2009 PLoS Comp Biol 5(7) e1000423

Binding Site Similarity between COMT and InhA

COMT

SAM (cofactor)

BIE (inhibitor)

NAD (cofactor)

InhA

641 (inhibitor)


Summary of the TB Story

• Entacapone and tolcapone shown to have potential for repositioning

• Direct mechanism of action avoids M. tuberculosis resistance mechanisms

• Possess excellent safety profiles with few side effects – already on the market

• In vivo support• Assay of direct binding of entacapone and tolcapone to

InhA reveals a possible lead with no chemical relationship to existing drugs


Summary from the TB Alliance – Medicinal Chemistry

• The minimal inhibitory concentration (MIC) of 260 uM is higher than usually considered

• MIC is 65x the estimated plasma concentration

• Have other InhA inhibitors in the pipeline


Predicted protein-ligand interaction network of M.tuberculosis. Proteins that are predicted to have similar binding sites are connected. Squares represent the top 18

most connected proteins.

The TB DruggomeBioinformatics 2009 25(12) 305-312

The TB DruggomeBioinformatics 2009 25(12) 305-312

SMAP p-value < 1e-5

drugs

TB proteins p < 1e-7p < 1e-6p < 1e-5

The TB Druggome

New Ways of Thinking

• Polypharmacology – One or multiple drugs binding to multiple targets for a collective effect aka Dirty Drugs

• Network Pharmacology – Measuring that effect on the whole biological network

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Example 2 - The Torcetrapib Story PLoS Comp Biol 2009 5(5) e1000387

Cholesteryl Ester Transfer Protein (CETP)

• collects triglycerides from very low density or low density lipoproteins (VLDL or LDL) and exchanges them for cholesteryl esters from high density lipoproteins (and vice versa)

• A long tunnel with two major binding sites. Docking studies suggest that it possible that torcetrapib binds to both of them.

• The torcetrapib binding site is unknown. Docking studies show that both sites can bind to torcetrapib with the docking score around -8.0.

HDLLDL

CETP

CETP inhibitor

X

Bad Cholesterol Good Cholesterol

PLoS Comp Biol 2009 5(5) e1000387Example 2 - The Torcetrapib Story

http://en.wikipedia.org/wiki/Very_low_density_lipoprotein

http://en.wikipedia.org/wiki/Low_density_lipoprotein

http://en.wikipedia.org/wiki/High_density_lipoprotein

Off-target PDB Ids Torcetrapib Anacetrapib JTT705 Complex ligand

CETP 2OBD -11.675 / -5.72 -11.375 / -8.15 -7.563 / -6.65 -8.324 (PCW)

Retinoid X receptor 1YOW1ZDT

-11.420 / -6.600 -6.74

-8.696 / -7.68 -7.35

-6.276 / -7.28 -6.95

-9.113 (POE)

PPAR delta 1Y0S -10.203 / -8.22 -10.595 / -7.91 -7.581 / -8.36 -10.691(331)

PPAR alpha 2P54 -11.036 / -6.67 -0.835 / -7.27 -9.599 / -7.78 -11.404(735)

PPAR gamma 1ZEO -9.515 / -7.31 > 0.0 / -8.25 -7.204 / -8.11 -8.075 (C01)

Vitamin D receptor 1IE8 >0.0/ -4.73 >0.0 / -6.25 -6.628 / -9.70 -8.354 (KH1) -7.35

Glucocorticoid Receptor

1NHZ1P93

/-4.43 /-5.63

/-7.08 /-0.58

/-7.09 /-9.42

Fatty acid binding protein

2F732PY12NNQ

>0.0/ -4.33>0.0/-6.13 /-6.40

>0.0/ -7.81>0.0/ -6.98 /-7.64

-7.191 / -8.49 /-6.33 /6.35

???

T-Cell CD1B 1GZP -8.815 / -7.02 -13.515 / -7.15 -7.590 / -8.02 -6.519 (GM2)

IL-10 receptor 1LQS / -4.59 / -6.77 / -5.95 ???

GM-2 activator 2AG9 -9.345 / -6.26 -9.674 / -6.98 -8.617 / -6.17 ??? (MYR) -4.16

(3CA2+) CARDIAC TROPONIN C

1DTL /-5.83 /-6.71 /-5.79

cytochrome bc1 complex

1PP9 (PEG) /-6.97 /-9.07 /-6.64

1PP9 (HEM) /-7.21 /8.79 /-8.94

human cytoglobin 1V5H /-4.89 /-7.00 /-4.94

Docking Scores eHits/Autodock


RAS PPARα

RXR

VDR

+

–

High blood pressure

FABPFA

+

Anti-inflammatory function

?

Torcetrapib Anacetrapib JTT705

JNK/IKK pathwayJNK/NF-KB pathway

?

Immune response to infection

JTT705

PPARδ

PPARγ

?


Chang et al. 2009 Mol Sys Biol Submitted

The Future?

Modifications to Early Stage Drug Discovery

SPPS273 39http://www.celgene.com/images/celgene_drug_arrow.gif

Off-targets Systems Biology

Some Known Limitations

• Structural coverage of the given proteome• False hits / poor docking scores• Literature searching• It’s a hypothesis – need experimental

validation• Money

Known Limitations

Perceived Limitations

• Mistrust of computational approaches

• Bioinformatics was previously oversold

• Omics was previously oversold

• Still too cutting edge

• No interest in drug resistance

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Questions?

[email protected]

mailto:[email protected]