what really happens when i take a drug? · 2012. 4. 18. · off-targets) and other atp or...

What Really Happens When I

Take a Drug?

Philip E. Bourne

University of California San Diego

[email protected]

http://www.sdsc.edu/pb

Vancouver April 12, 2012

mailto:[email protected]

http://www.sdsc.edu/pb

Big Questions in the Lab

{In the spirit of Hamming} 1. Can we improve how

science is disseminated

and comprehended?

2. What is the ancestry and

organization 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?

Motivators

Erren et al 2007 PLoS Comp. Biol., 3(10): e213

Our Motivation • 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 Motivators

Our Broad Approach

• Involves the fields of:

– Structural bioinformatics

– Cheminformatics

– Biophysics

– Systems biology

– Pharmaceutical chemistry

• L. Xie, L. Xie, S.L. Kinnings and P.E. Bourne 2012 Novel Computational Approaches to Polypharmacology

as a Means to Define Responses to Individual Drugs, Annual Review of Pharmacology and Toxicology 52:

361-379

• L. Xie, S.L. Kinnings, L. Xie and P.E. Bourne 2012 Predicting the Polypharmacology of Drugs: Identifying

New Uses Through Bioinformatics and Cheminformatics Approaches in Drug Repurposing M. Barrett and

D. Frail (Eds.) Wiley and Sons. (available upon request)

Disciplines Touched & 2012 Reviews

A Quick Aside – RCSB PDB

Pharmacology/Drug View 2012

• Establish linkages to

drug resources (FDA,

PubChem, DrugBank,

ChEBI, BindingDB 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’s Drug Work RCSB PDB Team

Led by Peter Rose

A Quick Aside PDB Scope/Deliverables

• Part I: small molecule drugs, nutraceuticals, and their targets ( DrugBank) - 2012

• Part II: peptide derived compounds (PRD)- tbd

• Part III: toxins and toxin targets (T3DB), human metabolites (HMDB)

• Part IV: biotherapeutics, i.e., monoclonal antibodies

• Part V: veterinary drugs (FDA Green Book)

RCSB PDB’s Drug Work

Our Approach

• We characterize a known protein-ligand binding site from a 3D structure (primary site) and search for similar sites (secondary sites) on a proteome wide scale independent of global structure similarity

• We try a static and dynamic network-based approach to understand the implications of drug binding to multiple sites

Methodology

Applications Thus Far

• Repositioning existing pharmaceuticals

and NCEs (e.g., tolcapone, entacapone,

nelfinavir)

• Early detection of side-effects (J&J)

• Late detection of side-effects

(torcetrapib)

• Lead optimization (e.g., SERMs,

Optima, Limerick)

• Drugomes (TB, P. falciparum, T. cruzi)

Applications

Approach - Need to Start with a 3D Drug-

Receptor Complex – Either Experimental or

Modeled

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

Computational Methodology

Some Numbers to Show

Limitations

TB-drugome Pf-

Drugome

Target gene 3996 5491

Target protein in PDB 284 136

Solved structure in PDB 749 333

Reliable homology models 1446 1236

Structure coverage 43.29% 25.02%

Drugs 274 321

Drug binding sites 962 1569

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

…

Computational Methodology 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):S9 Computational 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

For Residue Clusters

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 Computational Methodology

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

i

a

i

i

b

i

b

i

i

a SfSfd

fa, fb are the 20 amino acid target frequencies of profile a

and b, respectively

Sa, Sb are the PSSM of profile a and b, respectively

Computational Methodology Xie and Bourne 2008 PNAS, 105(14) 5441

Scoring

The Point is this Approach Can Now be Applied

on a Proteome-wide Scale

• Scores for binding site

matching by SOIPPA follow

an extreme value distribution

(EVD). Benchmark studies

show that the EVD model

performs at least two-orders

faster and is more accurate

than the non-parametric

statistical method in the

previous SOIPPA version

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

a) Blosum45 and

b) b) McLachlan substitution matrices.




nelfinavir)



(torcetrapib)


Optima, Limerick)


Applications

Nelfinavir

• Nelfinavir may have the most potent antitumor activity of the HIV protease inhibitors

Joell J. Gills et al, Clin Cancer Res, 2007; 13(17)

Warren A. Chow et al, The Lancet Oncology, 2009, 10(1)

• Nelfinavir can inhibit receptor tyrosine kinase(s)

• Nelfinavir can reduce Akt activation

• Our goal:

• to identify off-targets of Nelfinavir in the human proteome

• to construct an off-target binding network

• to explain the mechanism of anti-cancer activity

Possible Nelfinavir Repositioning PLoS Comp. Biol., 2011 7(4) e1002037

Possible Nelfinavir Repositioning

binding site comparison

protein ligand docking

MD simulation & MM/GBSA Binding free energy calculation

structural proteome

off-target?

network construction & mapping

drug target

Clinical

Outcomes

1OHR


Binding Site Comparison

• 5,985 structures or models that cover approximately

30% of the human proteome are searched against

the HIV protease dimer (PDB id: 1OHR)

• Structures with SMAP p-value less than 1.0e-3 were

retained for further investigation

• A total 126 structures have significant p-values <

1.0e-3

Possible Nelfinavir Repositioning PLoS Comp. Biol., 2011 2011 7(4) e1002037

Enrichment of Protein Kinases

in Top Hits

• The top 7 ranked off-targets belong to the same EC family - aspartyl proteases - with HIV protease

• Other off-targets are dominated by protein kinases (51 off-targets) and other ATP or nucleotide binding proteins (17 off-targets)

• 14 out of 18 proteins with SMAP p-values < 1.0e-4 are protein kinases


p-value < 1.0e-3

p-value < 1.0e-4

Distribution of

Top Hits on the

Human Kinome

Manning et al., Science,

2002, V298, 1912


1. Hydrogen bond with main chain amide of Met793 (without it 3700 fold loss of inhibition) 2. Hydrophobic interactions of aniline/phenyl with gatekeeper Thr790 and other residues

H-bond: Met793 with quinazoline N1 H-bond: Met793 with benzamide hydroxy O38

EGFR-DJK

Co-crys ligand

EGFR-Nelfinavir

Interactions between Inhibitors and Epidermal Growth

Factor Receptor (EGFR) – 74% of binding site resides

are comparable

DJK = N-[4-(3-BROMO-PHENYLAMINO)-QUINAZOLIN-6-YL]-ACRYLAMIDE

Off-target Interaction Network

Identified off-target

Intermediate protein

Pathway

Cellular effect

Activation

Inhibition

Possible Nelfinavir Repositioning PLoS Comp. Biol., 2011 7(4) e1002037

Other Experimental Evidence to Show Nelfinavir inhibition on

EGFR, IGF1R, CDK2 and Abl is Supportive

The inhibitions of Nelfinavir on IGF1R, EGFR, Akt activity

were detected by immunoblotting.

The inhibition of Nelfinavir on Akt activity is less than a

known PI3K inhibitor

Joell J. Gills et al.

Clinic Cancer Research September 2007 13; 5183

Nelfinavir inhibits growth of human melanoma cells

by induction of cell cycle arrest

Nelfinavir induces G1 arrest through inhibition

of CDK2 activity.

Such inhibition is not caused by inhibition of Akt

signaling.

Jiang W el al. Cancer Res. 2007 67(3)

BCR-ABL is a constitutively activated tyrosine kinase that causes chronic myeloid leukemia (CML)

Druker, B.J., et al New England Journal of Medicine, 2001. 344(14): p. 1031-1037

Nelfinavir can induce apoptosis in leukemia cells as a single agent

Bruning, A., et al. , Molecular Cancer, 2010. 9:19

Nelfinavir may inhibit BCR-ABL


Summary

• The HIV-1 drug Nelfinavir appears to be

a broad spectrum low affinity kinase

inhibitor

• Most targets are upstream of the

PI3K/Akt pathway

• Findings are consistent with the

experimental literature

• More direct experiment is needed





nelfinavir)



(torcetrapib)


Optima, Limerick)


Applications

Case Study: Torcetrapib Side Effect

• Cholesteryl ester transfer protein (CETP) inhibitors treat

cardiovascular disease by raising HDL and lowering LDL

cholesterol (Torcetrapib, Anacetrapib, JTT-705).

• Torcetrapib withdrawn due to occasional lethal

side effect, severe hypertension.

• Cause of hypertension undetermined; off-target effects

suggested.

• Predicted off-targets include metabolic enzymes. Renal function

is strong determinant of blood pressure. Causal off-targets may be

found through modeling kidney metabolism.

Constraint-based Metabolic

Modeling

S · v = 0

Matrix representation of network

Metabolic network reactions Flux space

Change in system capacity

Perturbation constraint

HEX1 ?

PGI ?

PFK ?

FBA ?

TPI ?

GAPD ?

PGK ?

PGM ?

ENO ?

PYK ?

Steady-state

assumption

Flux

Recon1: A Human Metabolic

Network

(Duarte et al Proc Natl Acad Sci USA 2007)

http://bigg.ucsd.edu

Global Metabolic Map

Comprehensively represents

known reactions in human cells Pathways

(98)

Reactions

(3,311) Compounds

(2,712)

Genes (1,496)

Transcripts (1,905)

Proteins (2,004)

Compartments (7)

Context-specific Modeling Pipeline

metabolic

network

metabolomic

biofluid & tissue

localization data

constrain

exchange

fluxes preliminary

model

gene

expression

data

refine

based on

capabilities

set flux

constraints

objective

function

literature

GIMME

normalize &

set threshold

set minimum

objective flux

model

metabolic

influx

metabolic

efflux

Predicted Hypertension Causal

Drug Off-Targets

Official

Symbol Protein

Off-Target

Prediction

Functional

Site

Overlap

Reactions

Limited by

Expression

Impacts

Renal

Function in

Simulation

Stronger

Drug

Binding

Affinity Cryptic Genetic Risk Factors

PTGISProstacyclin

synthasex x x x x

ACOX1 Acyl CoA oxidase x x x x x

AK3L1 Adenylate kinase 4 x x x x

HAO2 Hydroxyacid oxidase 2 x x x xSLC3A1; SLC7A9; SLC7A10;

ABCC1

MT-COIMitochondrial

cytochrome c oxidase Ix x x CYP27B1; ABCC1

UQCRC1Ubiquinol-cytochrome c

reductase core protein Ix x x CYP27B1; ABCC1

*Clinically linked to hypertension.




nelfinavir)



(torcetrapib)


Optima, Limerick)


Applications

The Future as a High

Throughput Approach…..

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

Repositioning - The TB Story

The TB-Drugome

1. Determine the TB structural proteome

2. Determine all known drug binding sites

from the PDB

3. Determine which of the sites found in 2

exist in 1

4. Call the result the TB-drugome

A Multi-target/drug Strategy Kinnings et al 2010 PLoS Comp Biol 6(11): e1000976

1. Determine the TB Structural

Proteome

284

1, 446

3, 996 2, 266

• High quality homology models from ModBase

(http://modbase.compbio.ucsf.edu) increase structural

coverage from 7.1% to 43.3%


0

20

40

60

80

100

120

140

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37

2. Determine all Known 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

No.

of dru

gs

Methotrexate Chenodiol

Alitretinoin Conjugated estrogens

Darunavir

Acarbose


Map 2 onto 1 – The TB-Drugome http://funsite.sdsc.edu/drugome/TB/

Similarities between the binding sites of M.tb proteins (blue),

and binding sites containing approved drugs (red).

0

2

4

6

8

10

12

14

16

18

20

1 2 3 4 5 6 7 8 9 10 11 12 13 14

From a Drug Repositioning Perspective

• 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

No.

of

dru

gs

raloxifene alitretinoin

conjugated estrogens & methotrexate

ritonavir

testosterone levothyroxine

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

methotrexate dihydrofolate 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

raloxifene estrogen receptor, estrogen receptor β

osteoporosis in post-menopausal women

9

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

Vignette within Vignette

• 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

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

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

Acknowledgements

Sarah Kinnings

Lei Xie

Li Xie

http://funsite.sdsc.edu

Roger Chang

Bernhard Palsson

Jian Wang

http://funsite.sdsc.edu

what really happens when i take a drug? · 2012. 4. 18. · off-targets) and other atp or...

Documents