What Really Happens When I
Take a Drug?
Philip E. Bourne
University of California San Diego
pbourne@ucsd.edu
http://www.sdsc.edu/pb
Vancouver April 12, 2012
Big Questions in the Lab
{In the spirit of Hamming}
1.
2.
3.
4.
5.
Erren et al 2007 PLoS Comp. Biol., 3(10): e213
Motivators
Can we improve how
science is disseminated
and comprehended?
What is the ancestry and
organization of the protein
structure universe and
what can we learn from it?
Are there alternative ways
to represent proteins from
which we can learn
something new?
What really happens when
we take a drug?
Can we contribute to the
treatment of neglected
{tropical} diseases?
Our Motivation
• Tykerb – Breast cancer
• Gleevac – Leukemia, GI
cancers
• Nexavar – Kidney and liver
cancer
• Staurosporine – natural product
– alkaloid – uses many e.g.,
antifungal antihypertensive
Motivators
Collins and Workman 2006 Nature Chemical Biology 2 689-700
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
Asp
Drug Name
Aspirin
% Similarity to
Drug Molecule
Has Bound Drug
100
Mockups of drug view features
RCSB PDB’s Drug Work
• 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
Led by Peter Rose
RCSB PDB Team
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 networkbased 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 DrugReceptor 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
Drugome
Target gene
3996
Target protein in PDB
284
Solved structure in PDB
749
Reliable homology models 1446
Structure coverage
43.29%
Drugs
274
Drug binding sites
962
Pf5491
136
333
1236
25.02%
321
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
Characterization of the Ligand Binding
Site - The Geometric Potential
 Conceptually similar to hydrophobicity
or electrostatic potential that is
dependant on both global and local
environments
• Initially assign Ca atom with
a value that is the distance
to the environmental
boundary
• Update the value with those
of surrounding Ca atoms
dependent on distances and
orientation – atoms within a
10A radius define i
GP  P 

neighbors
Pi
cos(ai)  1.0

Di  1.0
2.0
Computational Methodology
Xie and Bourne 2007 BMC Bioinformatics, 8(Suppl 4):S9
Discrimination Power of the Geometric
Potential
4
binding site
non-binding site
3.5
• Geometric
potential can
distinguish
binding and
non-binding
sites
3
2.5
2
1.5
1
0.5
100
99
88
77
66
55
44
33
22
11
0
0
Geometric Potential
0
Geometric Potential Scale
For Residue Clusters
Computational Methodology
Xie and Bourne 2007 BMC Bioinformatics, 8(Suppl 4):S9
Local Sequence-order Independent Alignment
with Maximum-Weight Sub-Graph Algorithm
Structure A
Structure B
LER
VKDL
LER
VKDL
• 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
Computational Methodology
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
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
Scoring
The Point is this Approach Can Now be Applied
on a Proteome-wide Scale
a)
b)
Blosum45 and
b) McLachlan substitution matrices.
• 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
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
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
drug target
off-target?
structural proteome
binding site comparison
1OHR
protein ligand docking
MD simulation & MM/GBSA
Binding free energy calculation
network construction
& mapping
Possible Nelfinavir Repositioning
Clinical
Outcomes
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
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
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
Co-crys ligand
H-bond: Met793 with quinazoline N1
DJK = N-[4-(3-BROMO-PHENYLAMINO)-QUINAZOLIN-6-YL]-ACRYLAMIDE
EGFR-Nelfinavir
H-bond: Met793 with benzamide
hydroxy O38
Off-target Interaction Network
Identified off-target
Intermediate protein
Possible Nelfinavir Repositioning
Pathway
Cellular effect
Activation
Inhibition
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
Possible Nelfinavir Repositioning
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
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
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
suggested.
hypertension
undetermined;
off-target
effects
• 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
Flux space
Metabolic network reactions
Steady-state
assumption
S·v=0
Perturbation constraint
Flux
HEX1
?
PGI
?
PFK
?
FBA
?
TPI
?
GAPD
?
PGK
?
PGM
?
ENO
?
Matrix representation of network PYK
?
Change in system capacity
Recon1: A Human Metabolic
Network
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)
http://bigg.ucsd.edu
Compartments (7)
(Duarte et al Proc Natl Acad Sci USA 2007)
Context-specific Modeling Pipeline
metabolomic
biofluid & tissue
localization data
metabolic
network
gene
expression
data
constrain
exchange
fluxes
preliminary
model
refine
based on
capabilities
model
set flux
constraints
objective
function
literature
normalize &
set threshold
GIMME
metabolic
influx
set minimum
objective flux
metabolic
efflux
Predicted Hypertension Causal
Drug Off-Targets
Official
Symbol
PTGIS
Protein
Prostacyclin
synthase
Impacts
Functional Reactions Renal
Off-Target Site
Limited by Function in
Prediction Overlap
Expression Simulation
Stronger
Drug
Binding
Affinity
Cryptic Genetic Risk Factors
x
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
x
SLC3A1; SLC7A9; SLC7A10;
ABCC1
x
x
x
CYP27B1; ABCC1
x
x
x
CYP27B1; ABCC1
Mitochondrial
cytochrome c oxidase I
Ubiquinol-cytochrome c
UQCRC1
reductase core protein I
MT-COI
*Clinically linked to hypertension.
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
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
3, 996
2, 266
284
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
2. Determine all Known Drug
Binding Sites in the PDB
No. of drugs
• Searched the PDB for protein crystal structures
bound with FDA-approved drugs
• 268 drugs bound in a total of 931 binding sites
140
120
Acarbose
Darunavir
Alitretinoin
Conjugated
estrogens
Chenodiol
100
80
60
40
Methotrexate
20
0
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
No. of drug binding sites
A Multi-target/drug Strategy
Kinnings et al 2010 PLoS Comp Biol 6(11): e1000976
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).
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
20
18
16
chenodiol
No. of
drugs
14
12
testosterone
10
conjugated
estrogens &
methotrexate
raloxifene
ritonavir
8
levothyroxine
alitretinoin
6
4
2
0
1
2
3
4
5
6
7
8
9
10
11
12
13
14
No. of potential TB targets
A Multi-target/drug Strategy
Kinnings et al 2010 PLoS Comp Biol 6(11): e1000976
Top 5 Most Highly Connected
Drugs
Drug
Intended targets
Indications
levothyroxine
transthyretin, thyroid
hormone receptor α & β-1,
thyroxine-binding globulin,
mu-crystallin homolog,
serum albumin
hypothyroidism, goiter,
chronic lymphocytic
thyroiditis, myxedema coma,
stupor
alitretinoin
conjugated
estrogens
retinoic acid receptor RXR-α,
β & γ, retinoic acid receptor cutaneous lesions in patients
α, β & γ-1&2, cellular retinoic with Kaposi's sarcoma
acid-binding protein 1&2
estrogen receptor
menopausal vasomotor
symptoms, osteoporosis,
hypoestrogenism, primary
ovarian failure
dihydrofolate reductase,
serum albumin
gestational choriocarcinoma,
chorioadenoma destruens,
hydatidiform mole, severe
psoriasis, rheumatoid arthritis
methotrexate
No. of
TB proteins
connections
14
adenylyl cyclase, argR, bioD,
CRP/FNR trans. reg., ethR,
glbN, glbO, kasB, lrpA, nusA,
prrA, secA1, thyX, trans. reg.
protein
13
adenylyl cyclase, aroG,
bioD, bpoC, CRP/FNR trans.
reg., cyp125, embR, glbN,
inhA, lppX, nusA, pknE, purN
10
acetylglutamate kinase,
adenylyl cyclase, bphD,
CRP/FNR trans. reg., cyp121,
cysM, inhA, mscL, pknB, sigC
10
acetylglutamate kinase, aroF,
cmaA2, CRP/FNR trans. reg.,
cyp121, cyp51, lpd, mmaA4,
panC, usp
raloxifene
estrogen receptor, estrogen
receptor β
osteoporosis in postmenopausal 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
Lei Xie
Li Xie
Roger Chang
Bernhard Palsson
Jian Wang
Sarah Kinnings
http://funsite.sdsc.edu