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What Really Happens When I
Take a Drug?
Philip E. Bourne
University of California San Diego
http://www.sdsc.edu/pb
Vancouver April 12, 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
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.
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
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
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
Possible Nelfinavir Repositioning
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
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 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.
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
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%
A Multi-target/drug Strategy Kinnings et al 2010 PLoS Comp Biol 6(11): e1000976
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
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).
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
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 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