Off-Targets Philip E. Bourne University of California San Diego [email protected] Support Open...

Off-Targets

Philip E. BourneUniversity of California San Diego

[email protected]://www.sdsc.edu/pb/edu/biom230/off-targets.ppt

Support Open Access

mailto:[email protected]

Motivation

When you add a foreign chemical into something as complex as a human

being do you really believe that drug is binding to only a single receptor?

The Drug Discovery Pipeline

VerySelective

Collective Effect

• 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 - Hence fail early and cheaply

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

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 the complexity of the problem

Agenda

• Computational Methodology

• Side Effects - The Tamoxifen Story

• Repositioning an Existing Drug - The TB Story

• Salvaging $800M – The Torcetrapib Story

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

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

Nothing in Biology {including Drug Discovery} Makes Sense

Except in the Light of Evolution

Theodosius Dobzhansky (1900-1975)

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

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

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

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

• 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 ...


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 InhA 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

Structure and Function of SERCASacroplasmic Reticulum (SR) Ca2+ ion channel

ATPase

• 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





The Torcetrapib Story

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 -8.0.

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

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

?

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

?

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

Sarah Kinnings

Nancy Buchmeier

Lei Xie

Li Xie

Jian Wang

Off-Targets Philip E. Bourne University of California San Diego [email protected] Support Open...

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