New Targets for Old Drugs: Ideas from in silico Analysis

Philip E. BourneUniversity of California San Diego

pbourne@ucsd.edu

WPS-AMEFAR Meeting February 10, 2010

Agenda

• Motivation

• Computational Methodology

• Repositioning an Existing Drug - The TB Story

• The Future? - The Human vs Pathogen Drugome

Big Questions in the Lab

1. Can we improve how science is disseminated and comprehended?

2. What is the ancestry of the protein structure universe and what can we learn from it?

3. Are there alternative ways to represent proteins from which we can learn something new?

4. What really happens when we take a drug? August 14, 2009

• The truth is we know very little about how the major drugs we take work

• We know even less about what side effects they might have

• Drug discovery seems to be approached in a very consistent and conventional way

• The cost of bringing a drug to market is huge >$800M

• The cost of failure is even higher e.g. Vioxx - >$4.85Bn

Motivation

Motivation

• The truth is we know very little about how the major drugs we take work – receptors are unknown

• We know even less about what side effects they might have - receptors are unknown

• Drug discovery seems to be approached in a very consistent and conventional way

• The cost of bringing a drug to market is huge ~$800M – drug reuse is a big business

• The cost of failure is even higher e.g. Vioxx - $4.85Bn - fail early and cheaply

Motivation

Motivation

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

Motivation

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 biological system

Motivation

How Can we Begin to Address the Problem?

• Systematic screening for multiple targets

• Integration of knowledge from multiple sources

• Analyze the impact of multiple targets on the complete biological network

Motivation

2. What is the ancestry of the protein structure universe?

4. What really happens when we take a drug?

Valas, Yang & Bourne 2009 Current Opinions in Structural Biology 19:1-6

What if only the binding pocket was conserved and the global structure of the protein

has changed?

A drug could potentially bind to distinctly different gene families

Put More Simply:Can We Find Off-targets and What Do

They Tell Us?

• They tell us one of four things:1. Nothing

2. A possible explanation for a side-effect of a drug

3. A possible repositioning of a drug to treat a completely different condition

4. A multi-target strategy to attack a pathogen

Today I will give you examples of 3 and 4 while illustrating the complexity of the problem

Agenda

• Motivation

• Computational Methodology

• Repositioning an Existing Drug - The TB Story

• The Future? - The Human vs Pathogen Drugome

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

ExamplesGeneric 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

Computational Methodology

A Reverse Engineering Approach to Drug Discovery Across Gene FamiliesCharacterize 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

…

Computational Methodology

• Initially assign C atom with a value that is the distance to the environmental boundary

• Update the value with those of surrounding C atoms dependent on distances and orientation – atoms within a 10A radius define i

0.2

0.1)cos(

0.1

i

Di

PiPGP

neighbors

Conceptually similar to hydrophobicity or electrostatic potential that is dependant on both global and local environments

Characterization of the Ligand Binding Site - The Geometric Potential

Xie and Bourne 2007 BMC Bioinformatics, 8(Suppl 4):S9Computational Methodology

Discrimination Power of the Geometric Potential

0

0.5

1

1.5

2

2.5

3

3.5

4

0 11 22 33 44 55 66 77 88 99

Geometric Potential

binding site

non-binding site

• Geometric potential can distinguish binding and non-binding sites

100 0

Geometric Potential Scale

Computational Methodology Xie and Bourne 2007 BMC Bioinformatics, 8(Suppl 4):S9

Local Sequence-order Independent Alignment with Maximum-Weight Sub-Graph Algorithm

L E R

V K D L

L E R

V K D L

Structure A Structure B

• Build an associated graph from the graph representations of two structures being compared. Each of the nodes is assigned with a weight from the similarity matrix

• The maximum-weight clique corresponds to the optimum alignment of the two structures

Xie and Bourne 2008 PNAS, 105(14) 5441

Similarity Matrix of Alignment

Chemical Similarity• Amino acid grouping: (LVIMC), (AGSTP), (FYW), and

(EDNQKRH)• Amino acid chemical similarity matrix

Evolutionary Correlation• Amino acid substitution matrix such as BLOSUM45• Similarity score between two sequence profiles

ia

i

ib

ib

i

ia SfSfd

fa, fb are the 20 amino acid target frequencies of profile a and b, respectivelySa, Sb are the PSSM of profile a and b, respectively Computational Methodology Xie and Bourne 2008 PNAS, 105(14) 5441

Nothing in Biology {Including Drug Discovery} Makes Sense

Except in the Light of Evolution

Theodosius Dobzhansky (1900-1975)

Agenda

• Motivation

• Computational Methodology

• Repositioning an Existing Drug - The TB Story

• The Future? - The Human vs Pathogen Drugome

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

Repositioning an Existing Drug - 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 ...

Kinnings et al. 2009 PLoS Comp Biol 5(7) e1000423

Repositioning an Existing Drug - The TB Story

Binding Site Similarity between COMT and InhA

COMT

SAM (cofactor)

BIE (inhibitor)

NAD (cofactor)

InhA

641 (inhibitor)

Repositioning an Existing Drug - The TB Story

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

Kinnings et al. 2009 PLoS Comp Biol 5(7) e1000423

Repositioning an Existing Drug - The TB Story

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• The chemistry is novel and may be revisited• Interested in our approach

Repositioning an Existing Drug - The TB Story

Agenda

• Motivation

• Computational Methodology

• Repositioning an Existing Drug - The TB Story

• The Future? - The Human vs Pathogen Drugome

1. StructuralDetermination

& Modeling

2. Binding site Similarity

3. Protein-ligandDocking

TB Genome

TB StructuralProteome

TB Protein-drugInteractome

TB Metabolome4.1 Network

Reconstruction

Drugome/TB

4.2 Network Integration

Existing Drugs

…

Target identification

Drug resistance mechanism

Drug repurposing

Side effect prediction

New

therapeutics for M

DR

and XD

R-

TB

The TB Drugome Bioinformatics 2009 25(12) 305-312

The Future? - The Human vs Pathogen Drugome

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 Future? - The Human vs Pathogen Drugome

Some Limitations

• Structural coverage of the given proteome

• False hits / poor docking scores

• Literature searching

• It’s a hypothesis – need experimental validation

• Money

Summary

• We have established a protocol to look for off-targets for existing therapeutics and NCEs

• Understanding these off-targets in the context of pathways and complete biological systems would seem to be the next step towards a new understanding – cheminfomatics meets systems biology

Example On-going Collaborations

• Metabolic Modeling of CETP inhibitor-induced hypertension (Roger Chang / Bernhard Palsson)

• Drug target identification in P. aeruginosa using an associated metabolic network (Josh Lerman / Bernhard Palsson)

• Detecting off-targets of NSC45208 an inhibitor of T. brucei RNA editing ligase I (Jacob Durant / Rommie Amaro / J. Andrew McCammon)

• Organic Anion Transporters (OATs) towards determining substrate specificity (Sanjay Nigam)

Acknowledgements

Sarah Kinnings

Lei Xie

Li Xie

Jian Wang

http://funsite.sdsc.edu