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New Targets for Old Drugs: Ideas from in silico Analysis
Philip E. BourneUniversity of California San Diego
WPS-AMEFAR Meeting February 10, 2010
Agenda
• Motivation
• Computational Methodology
• Repositioning an Existing Drug - The TB Story
• The Future? - The Human vs Pathogen Drugome
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? August 14, 2009
• The truth is we know very little about how the major drugs we take work
• We know even less about what side effects they might have
• Drug discovery seems to be approached in a very consistent and conventional way
• The cost of bringing a drug to market is huge >$800M
• The cost of failure is even higher e.g. Vioxx - >$4.85Bn
Motivation
Motivation
• The truth is we know very little about how the major drugs we take work – receptors are unknown
• We know even less about what side effects they might have - receptors are unknown
• Drug discovery seems to be approached in a very consistent and conventional way
• The cost of bringing a drug to market is huge ~$800M – drug reuse is a big business
• The cost of failure is even higher e.g. Vioxx - $4.85Bn - fail early and cheaply
Motivation
Motivation
A.L. Hopkins Nat. Chem. Biol. 2008 4:682-690
Why Don’t we Do Better?A Couple of Observations
• Gene knockouts only effect phenotype in 10-20% of cases , why? – redundant functions – alternative network routes – robustness of interaction networks
• 35% of biologically active compounds bind to more than one target
Paolini et al. Nat. Biotechnol. 2006 24:805–815
Motivation
Implications
• Ehrlich’s philosophy of magic bullets targeting individual chemoreceptors has not been realized
• Stated another way – The notion of one drug, one target, one disease is a little naïve in a complex biological system
Motivation
How Can we Begin to Address the Problem?
• Systematic screening for multiple targets
• Integration of knowledge from multiple sources
• Analyze the impact of multiple targets on the complete biological network
Motivation
2. What is the ancestry of the protein structure universe?
4. What really happens when we take a drug?
Valas, Yang & Bourne 2009 Current Opinions in Structural Biology 19:1-6
What if only the binding pocket was conserved and the global structure of the protein
has changed?
A drug could potentially bind to distinctly different gene families
Put More Simply:Can We Find Off-targets and What Do
They Tell Us?
• They tell us one of four things:1. Nothing
2. A possible explanation for a side-effect of a drug
3. A possible repositioning of a drug to treat a completely different condition
4. A multi-target strategy to attack a pathogen
Today I will give you examples of 3 and 4 while illustrating the complexity of the problem
Agenda
• Motivation
• Computational Methodology
• Repositioning an Existing Drug - The TB Story
• The Future? - The Human vs Pathogen Drugome
Need to Start with a 3D Drug-Receptor Complex - The PDB Contains Many
ExamplesGeneric 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
A Reverse Engineering Approach to Drug Discovery Across Gene FamiliesCharacterize 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
• 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):S9Computational 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
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
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
ia
i
ib
ib
i
ia SfSfd
fa, fb are the 20 amino acid target frequencies of profile a and b, respectivelySa, Sb are the PSSM of profile a and b, respectively Computational Methodology Xie and Bourne 2008 PNAS, 105(14) 5441
Nothing in Biology {Including Drug Discovery} Makes Sense
Except in the Light of Evolution
Theodosius Dobzhansky (1900-1975)
Agenda
• Motivation
• Computational Methodology
• Repositioning an Existing Drug - The TB Story
• The Future? - The Human vs Pathogen Drugome
Found..
• Evolutionary linkage between: – NAD-binding Rossmann fold– S-adenosylmethionine (SAM)-binding domain of SAM-
dependent methyltransferases
• Catechol-O-methyl transferase (COMT) is SAM-dependent methyltransferase
• Entacapone and tolcapone are used as COMT inhibitors in Parkinson’s disease treatment
• Hypothesis:– Further investigation of NAD-binding proteins may
uncover a potential new drug target for entacapone and tolcapone
Repositioning an Existing Drug - The TB Story
Functional Site Similarity between COMT and InhA
• Entacapone and tolcapone docked onto 215 NAD-binding proteins from different species
• M.tuberculosis Enoyl-acyl carrier protein reductase ENR (InhA) discovered as potential new drug target
• InhA is the primary target of many existing anti-TB drugs but all are very toxic
• InhA catalyses the final, rate-determining step in the fatty acid elongation cycle
• Alignment of the COMT and InhA binding sites revealed similarities ...
Kinnings et al. 2009 PLoS Comp Biol 5(7) e1000423
Repositioning an Existing Drug - The TB Story
Binding Site Similarity between COMT and InhA
COMT
SAM (cofactor)
BIE (inhibitor)
NAD (cofactor)
InhA
641 (inhibitor)
Repositioning an Existing Drug - The TB Story
Summary of the TB Story
• 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
Repositioning an Existing Drug - The TB Story
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• The chemistry is novel and may be revisited• Interested in our approach
Repositioning an Existing Drug - The TB Story
Agenda
• Motivation
• Computational Methodology
• Repositioning an Existing Drug - The TB Story
• The Future? - The Human vs Pathogen Drugome
1. StructuralDetermination
& Modeling
2. Binding site Similarity
3. Protein-ligandDocking
TB Genome
TB StructuralProteome
TB Protein-drugInteractome
TB Metabolome4.1 Network
Reconstruction
Drugome/TB
4.2 Network Integration
Existing Drugs
…
Target identification
Drug resistance mechanism
Drug repurposing
Side effect prediction
New
therapeutics for M
DR
and XD
R-
TB
The TB Drugome Bioinformatics 2009 25(12) 305-312
The Future? - The Human vs Pathogen Drugome
Predicted protein-ligand interaction network of M.tuberculosis. Proteins that are predicted to have similar binding sites are connected. Squares represent the
top 18 most connected proteins.
The Future? - The Human vs Pathogen Drugome
Some Limitations
• Structural coverage of the given proteome
• False hits / poor docking scores
• Literature searching
• It’s a hypothesis – need experimental validation
• Money
Summary
• We have established a protocol to look for off-targets for existing therapeutics and NCEs
• Understanding these off-targets in the context of pathways and complete biological systems would seem to be the next step towards a new understanding – cheminfomatics meets systems biology
Example On-going Collaborations
• Metabolic Modeling of CETP inhibitor-induced hypertension (Roger Chang / Bernhard Palsson)
• Drug target identification in P. aeruginosa using an associated metabolic network (Josh Lerman / Bernhard Palsson)
• Detecting off-targets of NSC45208 an inhibitor of T. brucei RNA editing ligase I (Jacob Durant / Rommie Amaro / J. Andrew McCammon)
• Organic Anion Transporters (OATs) towards determining substrate specificity (Sanjay Nigam)
Acknowledgements
Sarah Kinnings
Lei Xie
Li Xie
Jian Wang
http://funsite.sdsc.edu