towards systems pharmacology

Towards Systems Pharmacology

Philip E. BourneUniversity of California San Diego

[email protected]://www.sdsc.edu/pb

BIOTEC Forum Dresden Dec. 6, 2012

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

What Really Happens When You Take a Drug?

• Can we predict drug efficacy and toxicity?• Can we reuse old drugs?• Can we design personalized medicines?

Motivators

One Drug, One Gene, One Disease

Bernard M. Nat Rev Drug Disc 8(2009), 959-968

Motivators

Polypharmacology• 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 2 689-700Motivators

A.L. Hopkins Nat. Chem. Biol. 2008 4:682-690

Polypharmacology is Not Rare but Common

• Single gene knockouts only affect phenotype in 10-20% of cases

• 35% of biologically active compounds bind to two or more targets that do not have similar sequences or global shapesPaolini et al. Nat. Biotechnol. 2006 24:805–815

Kaiser et al. Nature 462 (2009) 175-81

Motivators

Predict side effects Repurpose drugs

Drug Binding is Dynamic

• Drug effect dependents on not only how strong (binding affinity) but also how long the drug is “stuck” in the protein (residence time).

• Molecular Dynamics (MD) simulation is powerful but computationally intensive.

~ns 1 day simulation

~ms – hours >106 days

D. Huang et al. (2011), PLoS Comp Biol 7(2):e1002002

Motivators

Systems Pharmacology

Target binding

Affect protein function

Systemic response

Drug molecules

×Uptake

Secretion(or biomass components)

× × ×× ××

Enzyme inhibition

Metabolic network

Catalytic site

Slide from Roger Chang

Multiscale Modeling of Drug Actions

physiological process

Understanding of dynamics and kinetics of protein-ligand interactions

physiological processphysiological processphysiological process

Knowledge representation and discovery & model integration

Prediction of molecular interaction network on

a genome scale

Reconstruction, analysis and simulation of

biological networks

Traditional Approach

Systems-based Approach

Motivators

How to Explore a Huge Conformational, Molecular and Functional Space?

Approach

Constraint-based Modeling Framework

Approach

• Geometric and topological constraints• Evolutionary constraints• Dynamic constraints• Physiochemical constraints

Detecting Protein Binding Promiscuity in a Given Proteome

HASSTRVCTVREPRTSEQAENCE

SMAP v2.0

Approach

Geometric Potential of the Protein Structure

• Challenge: inherent flexibility and uncertainty in homology models

• Representation of the protein structure - C atoms only- Delaunay tessellation - Graph representation

• Geometric Potential (GP)

0.2

0.1)cos(

0.1

i

Di

PiPGP

neighbors

L. Xie & P. E. Bourne, BMC Bioinformatics, 8(2007):S9

100 0

Geometric Potential Scale

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

Approach

Sequence-order Independent Profile-Profile Alignment (SOIPPA)

L E R

V K D L

L E R

V K D L

Structure A Structure B

S = 8

S = 4

Xie & Bourne, PNAS, 105(2008):5441Approach

Similarity Matrix of Alignment

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

(EDNQKRH)• Amino acid chemical similarity matrix

Constraint - 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

Extreme Value Distribution of SOIPPA Scores

EVD:

P(s>S) = 1 - exp(-exp(-Z))

Z = (S2 - μ)/σ

Xie et al. 2009 Bioinformatics, 25:i305

Approach

Detection of Remote Functional Relationships across Fold Space

0

0.01

0.02

0.03

0.04

0.05

0.06

0 0.1 0.2 0.3 0.4

True Positive Ratio

Fal

se P

osi

tive

Rat

io

PSI-BlastCESOIPPA

0

0.01

0.02

0.03

0.04

0.05

0.06

0 0.1 0.2 0.3 0.4

True Positive Ratio

Fal

se P

osi

tive

Rat

io

PSI-BlastCESOIPPA

Same CATH Topology Different CATH Topology

Xie & Bourne, PNAS, 105(2008):5441

Approach

Some Applications

• Lead optimization (e.g., SERMs, Optima, Limerick)

• Early detection of side-effects (J&J)• Repositioning existing pharmaceuticals

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

• Late detection of side-effects (torcetrapib)• Drugomes (TB, P. falciparum, T. cruzi)

Applications

Nelfinavir Story

Drug discovery using chemical systems biology: weak inhibition of multiple kinases may contribute to the anti-cancer effect of nelfinavir.

Xie L, Evangelidis T, Xie L, Bourne PEPLoS Comput Biol. 2011 (4):e1002037

Possible Nelfinavir Repositioning

binding site comparison

protein ligand docking

MD simulation & MM/GBSABinding 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

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

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 benzamidehydroxy O38

EGFR-DJKCo-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(Derived from Kegg)

Identified off-target

Intermediate protein

Pathway

Cellular effect

Activation

Inhibition

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

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

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

Some Applications





Applications

Torcetrapib Side Effects

• Targets Cholesteryl ester transfer protein (CETP) which raises HDL and lowers LDL cholesterol

• Torcetrapib withdrawn due to occasional lethal side effects, 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.

Applications

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

Flux

Flux AFluxB

Flu

x C

P1

P2

P3

P4

Flux AFlux B

Flu

x C

P2

P3

P4

Recon1: A Human Metabolic Network

(Duarte et al Proc Natl Acad Sci USA 2007)http://bigg.ucsd.edu

Global Metabolic MapComprehensively 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)

Approach

Human Kidney Modeling Pipeline

Recon1metabolic network

metabolomic blood/urine & kidney

localization data

constrain exchange

fluxes preliminary model

refine based on

capabilities

literature

set flux constraints

normalize & set threshold

renal objectives

set minimum objective flux

GIMME metabolic influx

metabolic efflux

kidney model

healthy kidney gene expression

data

Approach

Predicted Hypertension Causal Drug Off-Targets

OfficialSymbol Protein

Off-TargetPrediction

FunctionalSiteOverlap

ImpactsRenalFunction inSimulation

PTGIS Prostacyclinsynthase

x x x

ACOX1 Acyl CoA oxidase x x x

AK3L1 Adenylate kinase 4 x x x

HAO2 Hydroxyacid oxidase 2 x x x

MT-COIMitochondrialcytochrome c oxidase I

x x x

UQCRC1Ubiquinol-cytochrome creductase core protein I

x x x

*Clinically linked to hypertension.

Applications

Prostacyclin Synthase (PTGIS)

• In silico inhibition blocks renal prostaglandin I2 secretion.

• Associated with essential hypertension in humans.

• Expression of human PTGIS decreases mean pulmonary arterial pressure in hypertensive rats.

Prostaglandin H2 Prostaglandin I2

Applications

Conclusions

• Framework for perturbation phenotype simulation capable of predicting metabolic disorders, causal drug targets, and genetic risk factors for drug treatment (including cryptic risk factors).

• Pipeline established for in silico prediction of systemic drug response.

• Torcetrapib hypertension side effect may result from renal metabolic off-target effects.

Some Applications





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

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%


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

MethotrexateChenodiol

AlitretinoinConjugated estrogens

DarunavirAcarbose


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

Similarities between the binding sites of M.tb proteins (blue), and binding sites containing approved drugs (red).

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

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

Vignette within Vignette • Entacapone and tolcapone used in the treatment of

Parkinsons disease (COMT inhibitors) shown to have potential for repositioning

• 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

Summary

• We are entering an era of systems pharmacology where drug action is computationally analyzed relative to the complete constrained system at a spectrum of biological scales not just at the level of the single receptor molecule and patient.

Interesting Questions

• Are similar binding sites and different global structures the result of convergent evolution or extreme divergent evolution?

• Will/how soon drug discovery become patient centric?

Acknowledgements

Sarah Kinnings

Lei Xie

Li Xie

http://funsite.sdsc.edu

Roger ChangBernhard Palsson

Chirag Krishna(Chagas Disease)

Yinliang Zhang(Malaria)

towards systems pharmacology

Education