New Targets for Old Drugs: Ideas from in silico Analysis

New Targets for Old Drugs: Ideas from in silico Analysis Philip E. Bourne University of California San Diego pbourne@ucsd.edu 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 • 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? 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

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 A.L. Hopkins Nat. Chem. Biol. 2008 4:682-690 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: Nothing A possible explanation for a side-effect of a drug A possible repositioning of a drug to treat a completely different condition 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

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

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

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 Computational Methodology Xie and Bourne 2007 BMC Bioinformatics, 8(Suppl 4):S9

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

Local Sequence-order Independent Alignment with Maximum-Weight Sub-Graph Algorithm Structure A Structure B L E R V K D L L E R V K D L • 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 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 Xie and Bourne 2008 PNAS, 105(14) 5441 Computational Methodology

Nothing in Biology {Including Drug Discovery} Makes Sense Except in the Light of Evolution Theodosius Dobzhansky (1900-1975)

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.tuberculosisEnoyl-acyl carrier protein reductaseENR (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) InhA NAD (cofactor) 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

Existing Drugs 3. Protein-ligand Docking … TB Protein-drug Interactome TB Structural Proteome 2. Binding site Similarity Drug resistance mechanism New therapeutics for MDR and XDR-TB Drugome/TB Target identification 1. Structural Determination & Modeling 4.2 Network Integration Drug repurposing TB Metabolome Side effect prediction TB Genome 4.1 Network Reconstruction 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 Lei Xie Li Xie Jian Wang Sarah Kinnings http://funsite.sdsc.edu

New Targets for Old Drugs: Ideas from in silico Analysis