Excerpts from our drug repositioning and drug targeting work with an emphasis on cancer treatment.

1. Polypharmacology: The Good
News and Bad News of Possible
Cancer Therapy
Philip E. Bourne
University of California San Diego
pbourne@ucsd.edu
http://www.sdsc.edu/pb
Cancer Therapeutics Training Program - November 23, 2010

2. Big Questions in the Lab
1. 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?

3. What Really Happens When We
Take a Drug?
• If we knew the answer we could:
– Contribute to the design of improved drugs
with minimal side effects
– Contribute to how existing drugs and NCEs
might be repositioned
Motivation

4. Why We Think This is Important
• Ehrlich’s philosophy of magic bullets targeting
individual chemoreceptors has not been
realized in most cases – witness the recent
success of big pharma
• Stated another way – The notion of one drug,
one target, to treat one disease is a little naïve
in a complex system
Motivation

5. Polypharmacology - One Drug Binds to Multiple
Targets
• 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
Motivation

6. We Have Developed a Theoretical
Approach to Address Polypharmacology
• Involves the fields of:
– Structural bioinformatics
– Cheminformatics
– Systems-level biology
– Pharmaceutical chemistry
Our Approach

7. Our Approach
• We can characterize a known protein-
ligand binding site from a 3D structure
(primary site) and search for that site on
a proteome wide scale independent of
global structure similarity
Our Approach

8. Which Means …
• We could perhaps find alternative
binding sites (off-targets) for existing
pharmaceuticals and NCEs?
• If we can make this high throughput we
could rationally explore a large network
of protein-ligands interactions
Our Approach

9. What Have These Off-targets and
Networks Told Us So Far?
1. Nothing
2. A possible explanation for a side-effect of a drug
already on the market (SERMs - PLoS Comp. Biol.,
3(11) e217)
3. The reason a drug failed (Torcetrapib - PLoS Comp Biol
2009 5(5) e1000387)
4. How to optimize a NCE (NCE against T. Brucei PLoS
Comp Biol. 2010 6(1): e1000648)
5. A possible repositioning of a drug (Nelfinavir) to
treat a completely different condition (under
review)
6. A multi-target/drug strategy to attack a pathogen
(TB-drugome PLoS Comp Biol 6(11): e1000976)
Our Approach

10. More Specifically & Related to Cancer
• Tamoxifen and other SERMs
have side effects why would
that be?
• Why would Nelfinavir – a
protease inhibitor used in AIDS
treatment have reported
positive effects against
different cancer cell types?
Application to Cancer

11. Application to Cancer

12. Need to Start with a 3D Drug-Receptor
Complex - The PDB Contains Many
Examples
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

13. Numberofreleasedentries
Year:

14. 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

15. • Initially assign Cα atom with
a value that is the distance
to the environmental
boundary
• Update the value with those
of surrounding Cα atoms
dependent on distances and
orientation – atoms within a
10A radius define i
0.2
0.1)cos(
0.1
+
×
+
+= ∑
i
Di
Pi
PGP
neighbors
α
 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

16. 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

17. 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) 5441Computational Methodology

18. 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

19. What Do These Off-targets and Networks
Tell Us?
1. Nothing
2. A possible explanation for a side-effect of a drug
already on the market (SERMs - PLoS Comp. Biol.,
3(11) e217)
3. The reason a drug failed (Torcetrapib - PLoS Comp Biol
2009 5(5) e1000387)
4. How to optimize a NCE (NCE against T. Brucei PLoS
Comp Biol. 2010 6(1): e1000648)
5. A possible repositioning of a drug (Nelfinavir) to
treat a completely different condition (under
review)
6. A multi-target/drug strategy to attack a pathogen
(TB-drugome PLoS Comp Biol 6(11): e1000976)
Our Approach

20. 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., 3(11) e217

21. 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

22. Ligand Binding Site Similarity
Search On a Proteome Scale
• Searching human proteins covering ~38% of the
drugable genome against SERM binding site
• Matching Sacroplasmic Reticulum (SR) Ca2+ ion
channel ATPase (SERCA) TG1 inhibitor site
• ERα ranked top with p-value<0.0001 from reversed
search against SERCA
ERα
0 20 40 60 80
0.000.020.040.06
Score
Density
SERCA
Side Effects - The Tamoxifen Story PLoS Comp. Biol., 3(11) e217

23. Structure and Function of SERCA
• Regulating cytosolic
calcium levels in cardiac
and skeletal muscle
• Cytosolic and
transmembrane
domains
• Predicted SERM
binding site locates in
the TM, inhibiting Ca2+
uptake
Side Effects - The Tamoxifen Story PLoS Comp. Biol., 3(11) e217

24. 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)
Side Effects - The Tamoxifen Story PLoS Comp. Biol., 3(11) e217

25. Off-Target of SERMs
cardiac abnormalities
thromboembolic
disorders
ocular toxicities
loss of calcium
homeostatis
SERCA !
 in vivo and in vitro Studies
 TAM play roles in regulating calcium uptake activity of cardiac SR
 TAM reduce intracellular calcium concentration and release in the
platelets
 Cataracts result from TG1 inhibited SERCA up-regulation
 EDS increases intracellular calcium in lens epithelial cells by
inhibiting SERCA
 in silico Studies
 Ligand binding site similarity
 Binding affinity correlation PLoS Comp. Biol., 3(11) e217

26. 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
Side Effects - The Tamoxifen Story PLoS Comp. Biol., 3(11) e217

27. What Do These Off-targets and Networks
Tell Us?
1. Nothing
2. A possible explanation for a side-effect of a drug
already on the market (SERMs - PLoS Comp. Biol.,
3(11) e217)
3. The reason a drug failed (Torcetrapib - PLoS Comp Biol
2009 5(5) e1000387)
4. How to optimize a NCE (NCE against T. Brucei PLoS
Comp Biol. 2010 6(1): e1000648)
5. A possible repositioning of a drug (Nelfinavir) to
treat a completely different condition (under
review)
6. A multi-target/drug strategy to attack a pathogen
(TB-drugome PLoS Comp Biol 6(11): e1000976)

28. 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
• Neifinavir can reduce Akt activation
• Our goal:
• to identify off-targets of Nelfinavir in human
proteome
• to construct off-target binding network
• to explain the mechanism of anti-cancer activity
Possible Nelfinavir Repositioning

29. 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
Possible Nelfinavir Repositioning

30. Binding Site Comparison
• 5,985 structures or models that cover approximately
30% of the human proteome are searched against
HIV protease dimer (PDB id: 1OHR)
• Structures with SMAP p-value less than 1.0e-3 were
remained for further investigation
• Total 126 Structures have significantly p-value < 1.0e-
3
Possible Nelfinavir Repositioning

31. 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

32. 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
Possible Nelfinavir Repositioning

33. 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

34. Possible Nelfinavir Repositioning

35. Off-target Interaction Network
Identified off-target
Intermediate protein
Pathway
Cellular effect
Activation
Inhibition
Possible Nelfinavir Repositioning

36. Inhibition rate of Nelfinavir on EGFR, ErbB2, ErbB4,
Akt1, Akt2 Akt3
HTRF® TranscreenerTM ADP
Assay is performed for Nelfinavir
on 20μM by GenScript
Results are inconclusive
Non-specific aggregation problem?
Possible Nelfinavir Repositioning

37. 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
Possible Nelfinavir Repositioning

38. 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 (dose
response inhibition assays)
Possible Nelfinavir Repositioning

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

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
284
1, 446
3, 996 2, 266
TB proteom
e
hom
ology
m
odels
solved
structures
• 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
No. of drug binding sites
Methotrexate
Chenodiol
Alitretinoin
Conjugated
estrogens
Darunavir
Acarbose
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
No. of potential TB targets
raloxifene
alitretinoin
conjugated
estrogens &
methotrexate
ritonavir
testosterone
levothyroxine
chenodiol
A Multi-target/drug Strategy Kinnings et al 2010 PLoS Comp Biol 6(11): e1000976

45. 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

46. The Future as a Dynamical
Network Approach

47. Drug Failure - The Torcetrapib Story PLoS Comp Biol 2009 5(5) e1000387

48. 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) e1000387Drug Failure - The Torcetrapib Story

49. Computational Evaluation of Drug Off-Target Effects
Proteome
Drug binding
site alignments
SMAP
Predicted drug targets
Drug and endogenous
substrate binding site analysis
Competitively inhibitable targets
Inhibition simulations in
context-specific model
COBRA Toolbox
Predicted causal targets
and genetic risk factors
Metabolic
network
Scientific
literature
Tissue and biofluid
localization data
Gene
expression
data
Physiological
objectives
System
exchange
constraints
Flux states
optimizing
objective
Physiological
context-specific
model
Influx
Efflux
Drug response phenotypes
Drugta
Physiological
objectives
Causal drug targets
All targets
336 genes
1587 reactions

50. Acknowledgements
Sarah Kinnings
Lei Xie
Li Xie
http://funsite.sdsc.edu
Roger Chang
Bernhard Palsson
Jian Wang