Polypharmacology - NBIC April 20, 2011

Bioinformatics Meets Systems Biology for Early Stage Drug Discovery Philip E. Bourne University of California San Diego [email_address] http://www.sdsc.edu/pb NBIC – April 20, 2011

Big Questions in the Lab
Can we improve how science is disseminated and comprehended?
What is the ancestry 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?

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

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

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

We Are Developing a Theoretical Approach to Address This Fundamental Shift in Thinking
Involves the fields of:
Structural bioinformatics
Cheminformatics
Biophysics
Systems-level biology
Pharmaceutical chemistry
Our Approach

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 an integrated approach to understand the implications of drug binding to multiple sites
Our Approach

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

What Have These Off-targets and Networks Told Us So Far? Some Examples…
Nothing
A possible explanation for a side-effect of a drug already on the market (SERMs - PLoS Comp. Biol. , 2007 3(11) e217)
A possible repositioning of a drug (Nelfinavir) to treat a completely different condition (under review)
A multi-target/drug strategy to attack a pathogen (TB-drugome PLoS Comp Biol 2010 6(11): e1000976)
The reason a drug failed (Torcetrapib - PLoS Comp Biol 2009 5(5) e1000387)
How to optimize a NCE ( NCE against T. Brucei PLoS Comp Biol. 2010 6(1): e1000648)
Our Stories

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

Number of released entries Year:

A Quick Aside – RCSB PDB Pharmacology/Drug View 2010-2011
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
Mockups of drug view features RCSB PDB Ligand View RCSB PDB Team Drug Name Asp Aspirin Has Bound Drug % Similarity to Drug Molecule 100

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
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
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
f a , f b are the 20 amino acid target frequencies of profile a and b , respectively S a , S b are the PSSM of profile a and b , respectively Computational Methodology Xie and Bourne 2008 PNAS , 105(14) 5441

Nothing
Our Stories

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. , 2007 3(11) e217

Adverse Effects of SERMs cardiac abnormalities thromboembolic disorders ocular toxicities loss of calcium homeostatis ????? Side Effects - The Tamoxifen Story PLoS Comp. Biol. , 2007 3(11) e217

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
ER  ranked top with p-value<0.0001 from reversed search against SERCA
ER  SERCA Side Effects - The Tamoxifen Story PLoS Comp. Biol. , 2007 3(11) e217

Structure and Function of SERCA
R egulating cytosolic calcium levels in cardiac and skeletal muscle
Cytosolic and transmembrane domains
Predicted SERM binding site locates in the TM, inhibiting Ca2+ uptake

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. , 2007 3(11) e217

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. , 2007 3(11) e217

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

Nothing
Our Stories

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 To Appear

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

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

Distribution of Top Hits on the Human Kinome p-value < 1.0e-3 p-value < 1.0e-4 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 H-bond: Met793 with quinazoline N1 H-bond: Met793 with benzamide hydroxy O38 EGFR-DJK Co-crys ligand EGFR-Nelfinavir 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 To Appear

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

Nothing
Our Stories

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
Determine the TB structural proteome
Determine all known drug binding sites from the PDB
Determine which of the sites found in 2 exist in 1
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
High quality homology models from ModBase (http://modbase.compbio.ucsf.edu) increase structural coverage from 7.1% to 43.3%
284 1, 446 3, 996 2, 266 TB proteome homology models solved structures 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
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

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
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

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

Nothing
Our Stories

The Future as a Dynamical Network Approach

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

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.
HDL LDL CETP CETP inhibitor X Bad Cholesterol Good Cholesterol PLoS Comp Biol 2009 5(5) e1000387 Drug Failure - The Torcetrapib Story

Computational Evaluation of Drug Off-Target Effects 336 genes 1587 reactions Plos Comp. Biol. 2010 6(9): e1000938 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 Drug targets Physiological objectives Causal drug targets All targets

Summary
Generated a large number of testable hypotheses regarding off-targets of known drugs
A few have been tested experimentally (e.g., encapalone against InhA)
A possible methodology for use in early stage drug discovery

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

Polypharmacology - NBIC April 20, 2011

by Philip Bourne

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