Polypharmacology: Drug Discovery in the Era of Genomics and Proteomics Philip E. Bourne University of California San Diego pbourne@ucsd.edu Big Questions in the Lab 1. 2. 3. 4. 5. August 14, 2009 Valas, Yang & Bourne 2009 Current Opinions in Structural Biology 19:1-6 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? Motivation • 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 not to have moved into the omics era • The cost of bringing a drug to market is huge >$800M • The cost of failure is even higher e.g., Vioxx ~ $5Bn • Fatal diseases are neglected because they do not make money Motivation - Reasoning • The truth is we know very little about how the major drugs we take work – receptors/mechanism is unknown • We know even less about what side effects they might have - receptors/mechanism is unknown • Drug discovery seems not to have moved into the omics era – systems biology can help but as yet is unproven • The cost of bringing a drug to market is huge >$800M • The cost of failure is even higher e.g., Vioxx ~ $5Bn - receptors/mechanism is unknown • Fatal diseases are neglected because they do not make money – there must be a workable business model Why Don’t we Do Better? A Couple of Observations • Gene knockouts only effect phenotype in 1020% of cases , why? – redundant functions – alternative network routes – robustness of interaction networks A.L. Hopkins Nat. Chem. Biol. 2008 4:682-690 • 35% of biologically active compounds bind to more than one target Paolini et al. Nat. Biotechnol. 2006 24:805–815 Why Don’t we Do Better? A Couple of Observations • 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 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 system How Can we Begin to Address the Problem? • Systematic screening for multiple targets by multiple drugs • Integration of knowledge from multiple sources • Analyze the impact on the complete living system – Statically – Dynamically What if… • We can characterize a proteinligand binding site from a 3D structure (primary site) and search for that site on a proteome wide scale? • We could perhaps find alternative binding sites (off-targets) for existing pharmaceuticals and NCEs? What Do These Off-targets Tell Us? • Potentially many things: 1. Nothing 2. How to optimize a NCE 3. A possible explanation for a side-effect of a drug already on the market 4. A possible repositioning of a drug to treat a completely different condition 5. The reason a drug failed 6. A multi-target strategy to attack a pathogen Today I will give you brief vignettes of each of these scenarios, but first the bioinformatics guts of the approach Need to Start with a 3D Drug-Receptor Complex - The PDB Contains Many Examples 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 A Quick Aside – RCSB PDB Pharmacology/Drug View Mid 2010 Asp Drug Name Aspirin % Similarity to Drug Molecule Has Bound Drug 100 • Establish linkages to drug resources (FDA, PubChem, DrugBank, 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 Peter Rose et al 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 Pi cos(ai) 1.0 2.0 neighbors Di 1.0 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 Computational Methodology 0 Geometric Potential Scale 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 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 Nothing in Biology {Including Drug Discovery} Makes Sense Except in the Light of Evolution Theodosius Dobzhansky (1900-1975) What Do Off-targets Tell Us? • Potentially many things: 1. Nothing 2. How to optimize a NCE 3. A possible explanation for a side-effect of a drug already on the market 4. A possible repositioning of a drug to treat a completely different condition 5. The reason a drug failed 6. A multi-target strategy to attack a pathogen Today I will give you brief vignettes of each of these scenarios, but first the bioinformatics guts of the approach How to Optimize a NCE • African trypanosomiasis (sleeping sickness) • Carried by the tsetse fly • Trypanosoma brucei is the active agent • Endemic to Africa • 300,000 new cases each year • Sleep cycle disturbed • Neurological phase deadly How to Optimize a NCE Durrant et al 2009 PLoS Comp Biol in press Optimize: Find Secondary Targets of TbREL1 NCS45208 Aka Compound 1 TbREL1 – T. brucei RNA editing ligase I IC50: 1.95 ± 0.33 μM How to Optimize a NCE Durrant et al 2009 PLoS Comp Biol in press Workflow How to Optimize a NCE Durrant et al 2009 PLoS Comp Biol in press Mitochondrial 2-enoyl Thioester Reductase (HsETR1) • Neither FATCAT nor CLUSTALW2 judged HsETR1 to be homologous to the primary target. • Both SOIPPA and AutoDock predicted it was a secondary target. How to Optimize a NCE Durrant et al 2009 PLoS Comp Biol in press Mitochondrial 2-enoyl Thioester Reductase (HsETR1) How to Optimize a NCE Durrant et al 2009 PLoS Comp Biol in press Mitochondrial 2-enoyl Thioester Reductase (HsETR1) • HsETR1 is thought to be essential for fatty acid synthesis (FAS) type II. • In the process of optimizing Compound 1 to make it more drug-like, modifications that reduce binding to human HsETR1 may diminish unforeseen side effects. How to Optimize a NCE Durrant et al 2009 PLoS Comp Biol in press T. brucei UDP-galactose 4epimerase (TbGalE) • Neither FATCAT nor CLUSTALW2 judged TbGalE to be homologous to the primary target. • AutoDock predicted it was a secondary target, and it was homologous to a protein that SOIPPA identified as a secondary target. How to Optimize a NCE Durrant et al 2009 PLoS Comp Biol in press T. brucei UDP-galactose 4epimerase (TbGalE) How to Optimize a NCE Durrant et al 2009 PLoS Comp Biol in press T. brucei UDP-galactose 4epimerase (TbGalE) • Like TbREL1, TbGalE (galactose metabolism) is essential for T. brucei survival. • Compound 1 inhibits two essential T. brucei enzymes. How to Optimize a NCE Durrant et al 2009 PLoS Comp Biol in press What Do Off-targets Tell Us? • Potentially many things: 1. Nothing 2. How to optimize a NCE 3. A possible explanation for a side-effect of a drug already on the market 4. A possible repositioning of a drug to treat a completely different condition 5. The reason a drug failed 6. A multi-target strategy to attack a pathogen Today I will give you brief vignettes of each of these scenarios, but first the bioinformatics guts of the 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 Found.. • Evolutionary linkage between: – NAD-binding Rossmann fold – S-adenosylmethionine (SAM)-binding domain of SAMdependent methyltransferases • Catechol-O-methyl transferase (COMT) is SAMdependent 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 - The TB Story Kinnings et al. 2009 PLoS Comp Biol 5(7) e1000423 Functional Site Similarity between COMT and InhA • Entacapone and tolcapone docked onto 215 NADbinding 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 ... Repositioning - The TB Story Kinnings et al. 2009 PLoS Comp Biol 5(7) e1000423 Binding Site Similarity between COMT and InhA COMT SAM (cofactor) BIE (inhibitor) InhA NAD (cofactor) 641 (inhibitor) Repositioning - The TB Story Kinnings et al. 2009 PLoS Comp Biol 5(7) e1000423 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 Repositioning - The TB Story 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 What Do Off-targets Tell Us? • Potentially many things: 1. Nothing 2. How to optimize a NCE 3. A possible explanation for a side-effect of a drug already on the market 4. A possible repositioning of a drug to treat a completely different condition 5. The reason a drug failed 6. A multi-target strategy to attack a pathogen Today I will give you brief vignettes of each {some } of these scenarios, but first the bioinformatics guts of the approach The TB Drugome Existing Drugs 3. Protein-ligand Docking TB Structural Proteome … TB Protein-drug Interactome 2. Binding site Similarity Drugome/TB 1. Structural Determination & Modeling TB Genome 4.2 Network Integration 4.1 Network Reconstruction TB Metabolome Target identification Drug repurposing Side effect prediction New therapeutics for MDR and XDR-TB Drug resistance mechanism Bioinformatics 2009 25(12) 305-312 Multi-target strategy Kinnings et al in Preparation Structural coverage of the TB 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% Multi-target strategy Kinnings et al in Preparation 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 Multi-target strategy Kinnings et al in Preparation SMAP p-value < 1e-5 drugs TB proteins p < 1e-7 p < 1e-6 p < 1e-5 Multi-target drugs? • 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 Multi-target strategy Kinnings et al in Preparation 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 What Do Off-targets Tell Us? • Potentially many things: 1. Nothing 2. How to optimize a NCE 3. A possible explanation for a side-effect of a drug already on the market 4. A possible repositioning of a drug to treat a completely different condition 5. The reason a drug failed 6. A multi-target strategy to attack a pathogen Today I will give you brief vignettes of each {some } of these scenarios, but first the bioinformatics guts of the approach The Torcetrapib Story PLoS Comp Biol 2009 5(5) e1000387 Cholesteryl Ester Transfer Protein (CETP) CETP inhibitor X CETP LDL Bad Cholesterol HDL Good Cholesterol • 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. The Torcetrapib Story PLoS Comp Biol 2009 5(5) e1000387 Docking Scores eHits/Autodock Off-target PDB Ids Torcetrapib Anacetrapib JTT705 Complex ligand CETP 2OBD -11.675 / -5.72 -11.375 / -8.15 -7.563 / -6.65 -8.324 (PCW) Retinoid X receptor 1YOW 1ZDT -11.420 / -6.600 -6.74 -8.696 / -7.68 -7.35 -6.276 / -7.28 -6.95 -9.113 (POE) PPAR delta 1Y0S -10.203 / -8.22 -10.595 / -7.91 -7.581 / -8.36 -10.691(331) PPAR alpha 2P54 -11.036 / -6.67 -0.835 / -7.27 -9.599 / -7.78 -11.404(735) PPAR gamma 1ZEO -9.515 / -7.31 > 0.0 / -8.25 -7.204 / -8.11 -8.075 (C01) Vitamin D receptor 1IE8 >0.0/ -4.73 >0.0 / -6.25 -6.628 / -9.70 -8.354 (KH1) -7.35 Glucocorticoid Receptor 1NHZ 1P93 Fatty acid binding protein 2F73 2PY1 2NNQ >0.0/ -4.33 >0.0/-6.13 /-6.40 >0.0/ -7.81 >0.0/ -6.98 /-7.64 -7.191 / -8.49 /-6.33 /6.35 ??? T-Cell CD1B 1GZP -8.815 / -7.02 -13.515 / -7.15 -7.590 / -8.02 -6.519 (GM2) IL-10 receptor 1LQS / -4.59 / -6.77 GM-2 activator 2AG9 -9.345 / -6.26 -9.674 / -6.98 (3CA2+) CARDIAC TROPONIN C 1DTL /-5.83 /-6.71 /-5.79 cytochrome bc1 complex 1PP9 (PEG) /-6.97 /-9.07 /-6.64 1PP9 (HEM) /-7.21 /8.79 /-8.94 1V5H /-4.89 /-7.00 /-4.94 human cytoglobin The Torcetrapib Story /-4.43 /-5.63 /-7.08 /-0.58 /-7.09 /-9.42 / -5.95 -8.617 / -6.17 ??? ??? (MYR) -4.16 PLoS Comp Biol 2009 5(5) e1000387 JTT705 Torcetrapib Anacetrapib JTT705 VDR – RAS + RXR PPARα PPARδ FA ? FABP ? ? PPARγ High blood pressure + Anti-inflammatory function JNK/IKK pathway JNK/NF-KB pathway Immune response to infection The Torcetrapib Story PLoS Comp Biol 2009 5(5) e1000387 Chang et al. 2009 Mol Sys Biol Submitted Some Limitations • Structural coverage of the given proteome • False hits / poor docking scores • Literature searching • It’s a hypothesis – need experimental validation • Money Limitations What Do Off-targets Tell Us? • Potentially many things: 1. Nothing 2. How to optimize a NCE 3. A possible explanation for a side-effect of a drug already on the market 4. A possible repositioning of a drug to treat a completely different condition 5. The reason a drug failed 6. A multi-target strategy to attack a pathogen Today I will give you brief vignettes of each of these scenarios, but first the bioinformatics guts of the approach Acknowledgements Lei Xie Li Xie Roger Chang Bernhard Palsson Jacob Durant Andy McCammon Sarah Kinnings http://funsite.sdsc.edu 43,738 Human Proteins map human proteins to drug targets with BLAST e-value < 0.001 map human proteins to PDB structures with >95% sequence identity 13,865 Human Proteins (2,002 Drug Targets) 3,158 Human Proteins (10,730 PDB Structures) map drug targets to PDB structures 1,585 PDB Structures (929 Drug Targets) cover 929/2,002 = 46.4% drug targets structurally remove redundant structures with 30% sequence identity 2,586 PDB Structures remove redundant structures with 30% sequence identity, 825 PDB Structures (druggable) What we Search Against The Human Target List Computational Methodology Selective Estrogen Receptor Modulators (SERM) • One of the largest classes of drugs • Breast cancer, osteoporosis, birth control etc. • Amine and benzine moiety PLoS Comp. Biol., 2007 3(11) e217 Side Effects - The Tamoxifen Story Adverse Effects of SERMs cardiac abnormalities thromboembolic disorders loss of calcium homeostatis ????? ocular toxicities PLoS Comp. Biol., 3(11) e217 Side Effects - The Tamoxifen Story Structure and Function of SERCA Sacroplasmic Reticulum (SR) Ca2+ ion channel ATPase • Regulating cytosolic calcium levels in cardiac and skeletal muscle • Cytosolic and transmembrane domains PLoS Comp. Biol., 3(11) e217 • Predicted SERM binding site locates in the TM, inhibiting Ca2+ uptake Side Effects - The Tamoxifen Story 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) PLoS Comp. Biol., 3(11) e217 Side Effects - The Tamoxifen Story 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 PLoS Comp. Biol., 3(11) e217 Side Effects - The Tamoxifen Story What Do Off-targets Tell Us? • Potentially many things: 1. Nothing 2. How to optimize a NCE 3. A possible explanation for a side-effect of a drug already on the market 4. A possible repositioning of a drug to treat a completely different condition 5. The reason a drug failed 6. A multi-target strategy to attack a pathogen Today I will give you brief vignettes of each of these scenarios, but first the bioinformatics guts of the approach Bioinformatics Final Examples.. • Donepezil for treating Alzheimer’s shows positive effects against other neurological disorders • Orlistat used to treat obesity has proven effective against certain cancer types • Ritonavir used to treat AIDS effective against TB • Nelfinavir used to treat AIDS effective against different types of cancers Lots of Opportunities Summary • We have established a protocol to look for offtargets for existing therapeutics and NCEs • Understanding these in the context of pathways would seem to be the next step towards a new understanding – cheminfomatics meets systems biology • Lots of other opportunities to examine existing drugs – DrugX and the Recovery Act