Polypharmacology: Drug Discovery in the Era of Genomics and Proteomics Philip E. Bourne University of California San Diego [email protected]

Polypharmacology: Drug Discovery in the Era of Genomics

and Proteomics

Philip E. BourneUniversity of California San Diego

[email protected]

Big Questions in the Lab1. 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?

5. Can we contribute to the treatment of neglected {tropical} diseases?

August 14, 2009

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

• 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 not to have moved into the omics era

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

• The cost of failure is even higher e.g., Vioxx ~ $5Bn

• Fatal diseases are neglected because they do not make money

Motivation

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

• We know even less about what side effects they might have - receptors/mechanism is unknown

• Drug discovery seems not to have moved into the omics era – systems biology can help but as yet is unproven

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

• The cost of failure is even higher e.g., Vioxx ~ $5Bn - receptors/mechanism is unknown

• Fatal diseases are neglected because they do not make money – there must be a workable business model

Motivation - Reasoning

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

How Can we Begin to Address the Problem?

• Systematic screening for multiple targets by multiple drugs

• Integration of knowledge from multiple sources

• Analyze the impact on the complete living system– Statically– Dynamically

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?

What Do These Off-targets Tell Us?

• Potentially many things:1. Nothing

2. How to optimize a NCE

3. A possible explanation for a side-effect of a drug already on the market

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

5. The reason a drug failed

6. A multi-target strategy to attack a pathogen

Today I will give you brief vignettes of each of these scenarios, but first the bioinformatics guts of the approach

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 Quick Aside – RCSB PDB Pharmacology/Drug View Mid 2010

• Establish linkages to drug resources (FDA, PubChem, DrugBank, etc.)

• Create query capabilities for drug information

• Provide superposed views of ligand binding sites

• Analyze and display protein-ligand interactions

Drug Name Asp

Aspirin

Has Bound Drug% Similarity to Drug Molecule 100

Mockups of drug view features

RCSB PDB Ligand View Peter Rose et al

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

…


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

• 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)

What Do Off-targets Tell Us?








How to Optimize a NCE• African trypanosomiasis

(sleeping sickness)• Carried by the tsetse fly• Trypanosoma brucei is the

active agent• Endemic to Africa• 300,000 new cases each

year• Sleep cycle disturbed• Neurological phase

deadly

Durrant et al 2009 PLoS Comp Biol in pressHow to Optimize a NCE

Optimize: Find Secondary Targets of TbREL1

IC50: 1.95 ± 0.33 μM

TbREL1 – T. brucei RNA editing ligase I

NCS45208Aka Compound 1

How to Optimize a NCE Durrant et al 2009 PLoS Comp Biol in press

Workflow


Mitochondrial 2-enoyl Thioester Reductase (HsETR1)

• Neither FATCAT nor CLUSTALW2 judged HsETR1 to be homologous to the primary target.

• Both SOIPPA and AutoDock predicted it was a secondary target.





• HsETR1 is thought to be essential for fatty acid synthesis (FAS) type II.

• In the process of optimizing Compound 1 to make it more drug-like, modifications that reduce binding to human HsETR1 may diminish unforeseen side effects.


T. brucei UDP-galactose 4-epimerase (TbGalE)

• Neither FATCAT nor CLUSTALW2 judged TbGalE to be homologous to the primary target.

• AutoDock predicted it was a secondary target, and it was homologous to a protein that SOIPPA identified as a secondary target.





• Like TbREL1, TbGalE (galactose metabolism) is essential for T. brucei survival.

• Compound 1 inhibits two essential T. brucei enzymes.










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

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

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 ...


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









Today I will give you brief vignettes of each {some } of these scenarios, but first the bioinformatics guts of the approach

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

Bioinformatics 2009 25(12) 305-312

Multi-target strategy Kinnings et al in Preparation

The TB Drugome

Structural coverage of the TB proteome

284

1, 446

3, 996 2, 266

TB proteome

homology

models

solve

d

structu

res

• High quality homology models from ModBase (http://modbase.compbio.ucsf.edu) increase structural coverage from 7.1% to 43.3%


Drug binding sites in the PDB

• Searched the PDB for protein crystal structures bound with FDA-approved drugs

• 268 drugs bound in a total of 931 binding sites

No. of drug binding sites

MethotrexateChenodiol

AlitretinoinConjugated estrogens

DarunavirAcarbose


SMAP p-value < 1e-5

drugs

TB proteins

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

Multi-target drugs?• Similarities between drug binding sites and

TB proteins are found for 61/268 drugs• 41 of these drugs could potentially inhibit

more than one TB protein

No. of potential TB targets

raloxifenealitretinoin

conjugated estrogens &methotrexate

ritonavir

testosteronelevothyroxine

chenodiol


Top 5 most highly connected drugs

Drug Intended targets Indications No. of connections TB proteins

levothyroxine transthyretin, thyroid hormone receptor α & β-1, thyroxine-binding globulin, mu-crystallin homolog, serum albumin

hypothyroidism, goiter, chronic lymphocytic thyroiditis, myxedema coma, stupor

14

adenylyl cyclase, argR, bioD, CRP/FNR trans. reg., ethR, glbN, glbO, kasB, lrpA, nusA, prrA, secA1, thyX, trans. reg. protein

alitretinoin retinoic acid receptor RXR-α, β & γ, retinoic acid receptor α, β & γ-1&2, cellular retinoic acid-binding protein 1&2

cutaneous lesions in patients with Kaposi's sarcoma 13

adenylyl cyclase, aroG, bioD, bpoC, CRP/FNR trans. reg., cyp125, embR, glbN, inhA, lppX, nusA, pknE, purN

conjugated estrogens estrogen receptor

menopausal vasomotor symptoms, osteoporosis, hypoestrogenism, primary ovarian failure

10

acetylglutamate kinase, adenylyl cyclase, bphD, CRP/FNR trans. reg., cyp121, cysM, inhA, mscL, pknB, sigC

methotrexatedihydrofolate reductase, serum albumin

gestational choriocarcinoma, chorioadenoma destruens, hydatidiform mole, severe psoriasis, rheumatoid arthritis

10

acetylglutamate kinase, aroF, cmaA2, CRP/FNR trans. reg., cyp121, cyp51, lpd, mmaA4, panC, usp

raloxifeneestrogen receptor, estrogen receptor β

osteoporosis in post-menopausal women 9

adenylyl cyclase, CRP/FNR trans. reg., deoD, inhA, pknB, pknE, Rv1347c, secA1, sigC








Today I will give you brief vignettes of each {some } of these scenarios, but first the bioinformatics guts of the approach

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) e1000387The Torcetrapib Story

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

Some Limitations

• Structural coverage of the given proteome

• False hits / poor docking scores

• Literature searching

• It’s a hypothesis – need experimental validation

• Money

Limitations









Acknowledgements

Sarah Kinnings

Lei Xie

Li Xie

http://funsite.sdsc.edu

Roger ChangBernhard Palsson

Jacob DurantAndy McCammon

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)

What we Search AgainstThe Human Target List


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 StoryPLoS Comp. Biol., 2007 3(11) e217

Adverse Effects of SERMs

cardiac abnormalities

thromboembolic disorders

ocular toxicities

loss of calcium homeostatis

?????

PLoS Comp. Biol., 3(11) e217

Side Effects - The Tamoxifen Story

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



Binding Poses of SERMs in SERCA from Docking Studies

• Salt bridge interaction between amine group and GLU

• Aromatic interactions for both N-, and C-moiety

6 SERMS A-F (red)

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

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











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

Lots of Opportunities

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 – cheminfomatics meets systems biology

• Lots of other opportunities to examine existing drugs – DrugX and the Recovery Act

Documents

Polypharmacology: Drug Discovery in the Era of Genomics and Proteomics Philip E. Bourne University of California San Diego [email protected]