the role of biopathways in drug repositioning and determining side effects philip e. bourne...

The Role of Biopathways in Drug Repositioning and Determining

Side Effects

Philip E. Bourne

University of California San Diego

[email protected] Open Access

BioPathways 2008

What We Know & What We Don’t Know

• We know how to do functional annotation of proteins

• We know little about biopathways

• A side effect of our annotation work relates to drug repositioning

• That work highlights our need to explore pathways – this is what I hope to show today and perhaps get you interested

proteome.sdsc.edu

• The truth is we know very little about how the major drugs we take work – most drugs bind to a variety of targets with varying affinity

• 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 ~$800M

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

What Motivates Us

What Has Evolution Taught Us?

• Global 3D similarity and sequence similarity do not tell the whole story

• Perhaps a ligand binding site is what has passed from generation to generation while virtually all other aspects of the protein have changed?

What Has Evolution Taught Us About Drug Discovery?

• If that were true and evolutionarily related ligand binding sites could be found, they presumably would exist across very diverse gene families

• From the perspective of drug discovery such sites would have significant implications

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?

• We could use it for lead optimization and possible ADME/Tox prediction

What Do Off-targets Tell Us?

• One of three 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

Today I will give you examples of both 2 and 3 and illustrate how pathways come into play

Agenda

• Computational Methodology

• Side Effects - The Tamoxifen Story

• Repositioning an Existing Drug - The TB Story

• Salvaging $800M – The Torcetrapib Story

• The need to introduce pathway analysis

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

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

Boundary Accuracy of Ligand Binding Site Prediction

0

5

10

15

20

25

10 20 30 40 50 60 70 80 90 100

Sensitivity (%)

Dis

trib

uti

on

(%

)

0

10

20

30

40

50

60

70

10 20 30 40 50 60 70 80 90 100

Specificity (%)

Dis

trib

uti

on

(%

)

• ~90% of the binding sites can be identified with above 50% sensitivity

• The specificity of ~70% binding sites identified is above 90%Computational Methodology

So Far…

• Geometric potential dependant on local environment of a residue – relative to other residues and the environmental boundary

• Geometric potential reasonably good at discriminating between ligand binding sites and non-ligand binding sites

• Boundary of the binding site reasonably well defined

• How to compare sites ???

Computational Methodology

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

So What is the Potential of this Methodology?

Finding Secondary Binding Sites for Major Pharmaceuticals

• Scan known binding sites for major pharmaceuticals bound to their receptors against the human and other “druggable” proteomes

• Try and correlate strong hits with known data from the literature, databases, clinical trials etc. and now pathways to provide molecular evidence of secondary effects

Agenda






Tuberculosis (TB)

• One third of global population infected• Kills 2 million people each 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

Repositioning an Existing Drug - The TB Story

Hypothesis Drawn from the Study of Evolution

• We were looking for connections (evolutionary linkages) across fold and functional space through an all-by-all comparison of ligand binding sites

Repositioning an Existing Drug - The TB StoryRepositioning an Existing Drug - 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

Repositioning an Existing Drug - The TB StoryRepositioning an Existing Drug - The TB Story

Functional Site Similarity between COMT and ENR

• 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

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

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

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


Binding Site Similarity between COMT and ENR

COMT

SAM (cofactor)

BIE (inhibitor)

NAD (cofactor)

ENR

641 (inhibitor)


In Vivo Studies

• Quantitative and microplate assays of Mtb agree

• Entacapone - 80% growth inhibition with 62 ug/ml; 100% inhibition with 2x the dose

• Tolcapone – similar results

Courtesy Nancy BuchmeierRepositioning 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

• At least some in vivo support• Assay of direct binding of Entacapone and

tolcapone to ENR under way


Agenda






Selective Estrogen Receptor Modulators (SERM)

• One of the largest classes of drugs

• Breast cancer, osteoporosis, birth control etc.

• Amine and benzine moiety

Side Effects - The Tamoxifen Story PLoS Comp. Biol., 2007 3(11) e217

Adverse Effects of SERMs

cardiac abnormalities

thromboembolic disorders

ocular toxicities

loss of calcium homeostatis

?????

Side Effects - The Tamoxifen Story PLoS Comp. Biol., 3(11) e217

Ligand Binding Site Similarity Search On a Proteome Scale

• Searching human proteins covering ~38% of the drugable genome against SERM binding site

• Matching Sacroplasmic Reticulum (SR) Ca2+ ion channel ATPase (SERCA) TG1 inhibitor site

• ER ranked top with p-value<0.0001 from reversed search against SERCA

ER

0 20 40 60 80

0.0

00

.02

0.0

40

.06

Score

De

nsi

ty

SERCA


Structure and Function of SERCA

• Regulating cytosolic calcium levels in cardiac and skeletal muscle

• Cytosolic and transmembrane domains

• Predicted SERM binding site locates in the TM, inhibiting Ca2+ uptake


The Challenge

• Design modified SERMs that bind as strongly to estrogen receptors but do not have strong binding to SERCA, yet maintain other characteristics of the activity profile


Agenda






Consider in any of these cases there are likely multiple

secondary sites

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 trocetrapib with the docking score around -11.0.

HDLLDL

CETP

CETP inhibitor

X

Bad Cholesterol Good Cholesterol

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

EP distributions in binding pockets


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


-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)



1NHZ1P93

/-4.43 /-5.63

/-7.08 /-0.58

/-7.09 /-9.42


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 ???



1DTL /-5.83 /-6.71 /-5.79


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

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



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

?


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


-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)



1NHZ1P93

/-4.43 /-5.63

/-7.08 /-0.58

/-7.09 /-9.42


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 ???



1DTL /-5.83 /-6.71 /-5.79


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

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



RAS PPARα

RXR

VDR

+–

High blood pressure

FABPFA

+

Anti-inflammatory function

?


JNK/IKK pathwayJNK/NF-KB pathway

?


JTT705

PPARδ

PPARγ

?

Summary

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

• Understanding these in the context of pathways would seem to be the next step towards a new understanding

• Lots of other opportunities to examine existing drugs

Bioinformatics Final Examples..

• Donepezil for treating Alzheimer’s shows positive effects against other neurological disorders

• Orlistat used to treat obesity has proven effective against certain cancer types

• Ritonavir used to treat AIDS effective against TB

• Nelfinavir used to treat AIDS effective against different types of cancers

Acknowledgements

Support Open Access

Eric Scheeff

Lei Xie

Li Xie

Jian Wang

Sarah Kinnings

Nancy Buchmeier

43,738Human Proteins

3,158Human Proteins

(10,730 PDB Structures)

13,865Human Proteins

(2,002 Drug Targets)

1,585PDB Structures

(929 Drug Targets)

remove redundant structures with 30% sequence identity,

map human proteins to PDB structures with >95% sequence identity

map human proteins to drug targets with BLAST e-value < 0.001

map drug targets to PDB structures

cover 929/2,002 = 46.4% drug targets structurally

remove redundant structures with 30% sequence identity

2,586PDB Structures

825PDB Structures

(druggable)

Lead Discovery from Fragment Assembly

• Privileged molecular moieties in medicinal chemistry

• Structural genomics and high throughput screening generate a large number of protein-fragment complexes

• Similar sub-site detection enhances the application of fragment assembly strategies in drug discovery

1HQC: Holliday junction migration motor protein from Thermus thermophilus1ZEF: Rio1 atypical serine protein kinase from A. fulgidus

Lead Optimization from Conformational Constraints

• Same ligand can bind to different proteins, but with different conformations

• By recognizing the conformational changes in the binding site, it is possible to improve the binding specificity with conformational constraints placed on the ligand

1ECJ: amido-phosphoribosyltransferase from E. Coli1H3D: ATP-phosphoribosyltransferase from E. Coli

Angiotensinogen

Angiotensin I

Angiotensin II

Renin

ACE

+

+

High blood pressure

++

Aldosteronesecretion

Hydrolyzation

Peptide cleavage

Renin-angiotensin system(RAS)

GCR

Inhibition of NF-KB

anti-cancer and anti-inflammatory

JTT705

Cytochrome bc1 complex

Q cycle

ATP generation, cell repair, cell death


X

Hypertension

excessive activation

Cardiac hypertrophy,hypertension

?

Anacetrapib

X

T-cell CD1B

CD1B+antigen



X

Cardiac TnC

Troponin conformation change

Ca2+

Heart muscle contraction

X

Torcetrapib

M. Dickson & J. P. Gagnon, Nature Review Drug Discovery 3(2004) p417-429

Summary Estimated Capitalized Costs for New Chemical Entities

(NCEs) Entering Each Phase

• Estimated costs for a drug withdrawal:

~ 60.0 millions• Phase III is most

costly: fail fast, fail cheap

Swiss-Prot - 20 Year Celebration

www.pdb.org • [email protected]

Northwestern Jan 2008

53

Implications on Drug Development

Affinity (ER Site) Affinity (SERCA) Affinity Difference

Bazedoxifene(BAZ) -9.44 +/- 0.54 -7.23 +/- 0.13 2.21

Lasofoxifene(LAS) -8.66 +/- 0.40 -6.54 +/- 0.20 2.12

Ormeloxifene(ORM) -8.67 +/- 0.18 -5.84 +/- 0.33 2.83

Raloxifene(RAL) -8.08 +/- 0.64 -5.78 +/- 0.23 2.30

4-hydroxytamoxifen(OHT) -7.67 +/- 0.47 -5.40 +/- 0.15 2.27

Tamoxifen(TAM) -7.30 +/- 0.28 -5.64 +/- 0.28 1.66

• Taking account of both target and off-target for lead optimization

• Drug delivery and administration regime

Improved Performance of Alignment Quality and Search Sensitivity and Specificity

0

10

20

30

40

50

60

70

80

90

<1.0 <3.0 <5.0 <7.0 <9.0 <11.0

RMSD (Angsgroms)

Fre

qu

en

cy

(%

)

Amino Acid GroupingChemical SimilaritySubstitution MatrixProfile-Profile

0

0.005

0.01

0.015

0.02

0.025

0.03

0 0.04 0.08 0.12 0.16 0.2

True Positive Ratio

Fal

se P

osi

tive

Rat

io

Amino Acid Group

Chemical Similarity

Substitution Matrix

Profile-Profile

RMSD distribution of the aligned common fragments of ligands from 247 test cases showing four scores: amino acid grouping, chemical similarity, substitution matrix and profile-profile.

.

0.00 0.05 0.10 0.15 0.20 0.25 0.30

05

10

15

2D Similarity to Entacapone

Tanimoto Coefficient

De

nsi

ty

2D small molecule similarity between existing and potential

ENR inhibitors

0.00 0.05 0.10 0.15 0.20 0.25 0.30 0.35

02

46

81

01

2

2D Similarity to Tolcapone

Tanimoto Coefficient

De

nsi

tyAYMp=0.065

ZAMp=0.205

Tanimoto Coefficient Tanimoto Coefficient

Den

sity

Den

sity

Entacapone Tolcapone

Docking existing and potential InhA inhibitors onto COMT and InhA

InhA inhibitorDocking score

with InhADocking score

with COMT

468 -6.57 +/- 1.27 -4.42

566 -6.24 +/- 0.92 -3.96

641 -6.00 +/- 1.51 -5.92

665 -5.18 +/- 0.72 -4.20

744 -6.07 +/- 1.28 -5.47

5PP -5.99 +/- 0.48 -3.90

8PS -6.51 +/- 0.95 -4.04

GEQ -6.29 +/- 1.61 -4.45

Triclosan -6.34 +/- 0.68 -4.05

Entacapone -4.91 +/- 0.97 -4.49

Entacapone (N5) -5.25 +/- 0.93 -4.02

Entacapone (N6) -5.10 +/- 1.03 -3.81

Tolcapone -5.85 +/- 0.74 -4.68

Correlation of binding affinity profiles between COMT and InhA

• Tolcapone-like molecules

COMT Docking Score

InhA

Doc

king

Sco

re

COMT Docking Score

Con

trol

Doc

king

Sco

re

-2 0 2 4 6

-20

24

6

Tolcapone-like molecules (R=0.36)

COMT Docking Score

EN

R D

ock

ing

Sco

re

-2 0 2 4 6

-3-2

-10

12

3

Tolcapone-like molecules (R=0.35)

COMT Docking Score

Co

ntro

l Do

ckin

g S

core

Binding pose analysis of potential InhA inhibitors with InhA

Asp110

Glu210

Asp115

11.54Å

15.25Å

14.53Å

Comparison of surface electrostatic potential between

COMT and InhA functional sites

• Electrostatic potentials of COMT and InhA calculated using APBS

• Predicted binding poses of entacapone and tolcapone inserted into proteins

• Qualitative similarities between COMT and InhA functional sites observed

• In both cases, nitrite groups of entacapone and tolcapone associated with positively charged region of active site

Comparison of surface electrostatic potential between COMT and InhA

functional sitesCOMT InhA

Ent

acap

one

Tol

capo

ne

Advantage to Using Ligand Site Similarity

ProteinSequence/Structure

Similarity

ProteinFunctional Site

Similarity

Small molecule

Similarity

. Not adequately reflecting functional relationship. Not directly addressing drug design problem

• Poor correlation between structure and activity• Infinite chemical space

. Build closer structure- function relationships . Limit chemical space through co-evolution

Correlation of Binding Affinity Profiles between COMT and ENR

0 2 4 6

02

46

8

Entacapone-like molecules (R=0.42)

COMT Docking Score

EN

R D

ock

ing

Sco

re

• Entacapone-like molecules

COMT Docking Score

EN

R D

ocki

ng S

core

0 2 4 6

-3-2

-10

12

3

Entacapone-like molecules (R=0.38)

COMT Docking ScoreC

ont

rol D

ock

ing

Sco

re

COMT Docking Score

Con

trol

Doc

king

Sco

re


Linear regression 2 identical sites

the role of biopathways in drug repositioning and determining side effects philip e. bourne...

Documents

drug repositioning

existing drug

igj slide

play slide

perspective of drug

proteinligand binding

pathway analysis slide

targets search