Dresden December 5, 2012

1. Towards Systems
Pharmacology
Philip E. Bourne
University of California San Diego
pbourne@ucsd.edu
http://www.sdsc.edu/pb
BIOTEC Forum Dresden Dec. 6, 2012

2. 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
Erren et al 2007 PLoS Comp. Biol., 3(10): e213 {tropical} diseases?
Motivators

3. 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

4. One Drug, One Gene, One Disease
Bernard M. Nat Rev Drug Disc 8(2009), 959-968
Motivators

5. 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 2 689-700
Motivators

6. Polypharmacology is Not Rare but Common
• Single gene knockouts only
affect phenotype in 10-20%
of cases
A.L. Hopkins Nat. Chem. Biol. 2008 4:682-690
• 35% of biologically active
compounds bind to two or
more targets that do not
have similar sequences or
global shapes
Paolini et al. Nat. Biotechnol. 2006 24:805–815
 Predict side effects
 Repurpose drugs
Kaiser et al. Nature 462 (2009) 175-81
Motivators

7. 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

8. Systems
Pharmacology
Enzyme
inhibition
×× ×
Uptake
×
Systemic ×
×
×
response
Secretion
Catalytic (or biomass
components)
site
Affect protein
Metabolic
function network
Target binding
Slide from Roger Chang Drug molecules

9. Multiscale Modeling of Drug
Actions
Understanding of Prediction of molecular
dynamics and interaction network on
kinetics of protein- a genome scale
ligand interactions
Traditional
Approach
physiological process
Knowledge Reconstruction,
representation analysis and
and discovery & simulation of
model integration biological networks
Systems-based
Approach
Motivators

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

11. Constraint-based Modeling
Framework
Approach

12. Detecting Protein Binding
Promiscuity in a Given Proteome
• Geometric and topological constraints
• Evolutionary constraints
• Dynamic constraints
• Physiochemical constraints
SMAP v2.0
PRTSEQAENCE HASSTRVCTVRE
Approach

13. Geometric Potential of the Protein Structure
4
binding site
• Challenge: inherent flexibility
3.5
non-binding site and uncertainty in homology
models
3
• Representation of the protein
2.5
structure
- Cα atoms only
2
- Delaunay tessellation
1.5
- Graph representation
1 • Geometric Potential (GP)
0.5
Pi cos(αi) + 1.0
100 0 0
GP = P + ∑
neighbors Di + 1.0
×
2.0
11
22
33
44
55
66
77
88
99
0
Geometric Potential Scale Geometric Potential
L. Xie & P. E. Bourne, BMC Bioinformatics, 8(2007):S9
Approach

14. Sequence-order Independent
Profile-Profile Alignment (SOIPPA)
Structure A Structure B
LER
VKDL
S=8
LER
VKDL
S=4
Xie & Bourne, PNAS, 105(2008):5441
Approach

15. 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
d = ∑ f a Sb + ∑ f b S a
i i i i
i i
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

16. 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

17. Detection of Remote Functional Relationships
across Fold Space
0.06 0.06
False Positive Ratio
0.05 0.05
False Positive Ratio
0.04 0.04
0.03 0.03
0.02 0.02 PSI-Blast
PSI-Blast
CE 0.01 CE
0.01
SOIPPA SOIPPA
0 0
0 0.1 0.2 0.3 0.4 0 0.1 0.2 0.3 0.4
True Positive Ratio True Positive Ratio
Same CATH Topology Different CATH Topology
Xie & Bourne, PNAS, 105(2008):5441
Approach

19. 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

20. 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 PE
PLoS Comput Biol. 2011 (4):e1002037

21. Possible Nelfinavir Repositioning

22. drug target
off-target?
structural proteome
binding site comparison
1OHR protein ligand docking
MD simulation & MM/GBSA
Binding free energy calculation
network construction
& mapping
Clinical
Outcomes
Possible Nelfinavir Repositioning

23. 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

24. 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

25. Distribution of
Top Hits on the
Human Kinome
p-value < 1.0e-4
p-value < 1.0e-3
Manning et al., Science,
2002, V298, 1912
Possible Nelfinavir Repositioning

26. Interactions between Inhibitors and Epidermal Growth
Factor Receptor (EGFR) – 74% of binding site resides
are comparable
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
EGFR-DJK EGFR-Nelfinavir
Co-crys ligand H-bond: Met793 with benzamide
H-bond: Met793 with quinazoline N1 hydroxy O38
DJK = N-[4-(3-BROMO-PHENYLAMINO)-QUINAZOLIN-6-YL]-ACRYLAMIDE

27. Off-target Interaction Network
(Derived from Kegg)
Identified off-target Pathway Activation
Intermediate protein Cellular effect Inhibition
Possible Nelfinavir Repositioning
PLoS Comp. Biol., 2011 7(4) e1002037

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

29. 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

30. 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
Applications enzymes. Renal

31. Metabolic Modeling
etabolic network reactions Flux space
P1 P3
FluxC
P2
P4
FluxB
ux A
Fl
Steady-state
Perturbation constraint
S·v=0
Flux
HEX1 ?
PGI ? P3
Flux C
PFK ?
FBA ? P2
TPI ? P4
GAPD ?
PGK ? A Flux B
ux
PGM ? Fl
ENO ?
trix representation of network PYK ?
Change in system capacity

32. Recon1: A Human Metabolic
Network
Global Metabolic Map
Reactions Compounds
Comprehensively represents Pathways (3,311) (2,712)
known reactions in human cells (98)
Genes (1,496)
Transcripts (1,905)
Proteins (2,004)
Compartments (7)
http://bigg.ucsd.edu
Approach (Duarte et al Proc Natl Acad Sci USA 2007)

33. Human Kidney Modeling Pipeline
metabolomic
blood/urine & kidney
localization data
Recon1
metabolic
network
healthy kidney
constrain gene expression
exchange
fluxes
data
preliminary
model normalize &
set threshold
kidney
refine set flux model
based on constraints GIMME metabolic
capabilities influx
set minimum
renal objective flux
objectives
literatur metabolic
e efflux
Approach

34. Predicted Hypertension Causal
Drug Off-Targets
Impacts
Functional Renal
Official Off-Target Site Function in
Symbol Protein Prediction Overlap Simulation
Prostacyclin
PTGIS x x x
synthase
ACOX1 Acyl CoA oxidase x x x
AK3L1 Adenylate kinase 4 x x x
HAO2 Hydroxyacid oxidase 2 x x x
Mitochondrial
MT-COI x x x
cytochrome c oxidase I
Ubiquinol-cytochrome c
UQCRC1 x x x
reductase core protein I
*Clinically linked to hypertension.
Applications

35. Prostacyclin Synthase
(PTGIS)
• In silico inhibition blocks renal
prostaglandin I2 secretion.
Prostaglandin H2 Prostaglandin I2
• Associated with essential hypertension in
humans.
• Expression of human PTGIS decreases
mean pulmonary arterial pressure in
hypertensive rats.
Applications

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

37. 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

38. The Future as a High
Throughput Approach…..

39. 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

40. 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

41. 1. Determine the TB Structural
Proteome
e
m
t eo
p ro y
TB og
ol ls
hom ode
m ed es
olv tur
s c
3, 996 2, 266 tru 284
s
1, 446
• High quality homology models from ModBase
(http://modbase.compbio.ucsf.edu) increase structural
coverage from 7.1% to 43.3%
A Multi-target/drug Strategy Kinnings et al 2010 PLoS Comp Biol 6(11): e1000976

42. 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
Acarbose
Darunavir Alitretinoin
Conjugated
estrogens
Chenodiol
Methotrexate
No. of drug binding sites
A Multi-target/drug Strategy Kinnings et al 2010 PLoS Comp Biol 6(11): e1000976

43. 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).

44. 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
conjugated
estrogens &
chenodiol methotrexate
levothyroxine
testosterone raloxifene
ritonavir alitretinoin
No. of potential TB targets
A Multi-target/drug Strategy Kinnings et al 2010 PLoS Comp Biol 6(11): e1000976

45. Top 5 Most Highly Connected
Drugs
No. of
Drug Intended targets Indications TB proteins
connections
levothyroxine transthyretin, thyroid
hypothyroidism, goiter,
adenylyl cyclase, argR, bioD,
hormone receptor α & β-1, CRP/FNR trans. reg., ethR,
chronic lymphocytic
thyroxine-binding globulin, 14 glbN, glbO, kasB, lrpA, nusA,
thyroiditis, myxedema coma,
mu-crystallin homolog, prrA, secA1, thyX, trans. reg.
stupor
serum albumin protein
alitretinoin retinoic acid receptor RXR-α,
adenylyl cyclase, aroG,
β & γ, retinoic acid receptor
cutaneous lesions in patients bioD, bpoC, CRP/FNR trans.
α, β & γ-1&2, cellular 13
with Kaposi's sarcoma reg., cyp125, embR, glbN,
retinoic acid-binding protein
inhA, lppX, nusA, pknE, purN
1&2
conjugated menopausal vasomotor acetylglutamate kinase,
estrogens symptoms, osteoporosis, adenylyl cyclase, bphD,
estrogen receptor 10
hypoestrogenism, primary CRP/FNR trans. reg., cyp121,
ovarian failure cysM, inhA, mscL, pknB, sigC
methotrexate gestational choriocarcinoma, acetylglutamate kinase, aroF,
dihydrofolate reductase, chorioadenoma destruens, cmaA2, CRP/FNR trans. reg.,
10
serum albumin hydatidiform mole, severe cyp121, cyp51, lpd, mmaA4,
psoriasis, rheumatoid arthritis panC, usp
raloxifene adenylyl cyclase, CRP/FNR
estrogen receptor, estrogen osteoporosis in post- trans. reg., deoD, inhA, pknB,
9
receptor β menopausal women pknE, Rv1347c, secA1, sigC

46. 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

47. 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

48. 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.

49. 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?

50. Acknowledgements
Lei Xie
Li Xie
Roger Chang
Bernhard Palsson
Chirag Krishna
(Chagas Disease)
Yinliang Zhang Sarah
(Malaria) Kinnings
http://funsite.sdsc.edu