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Off-Targets Philip E. Bourne University of California San Diego [email protected] http://www.sdsc.edu/pb/edu/biom230/off-targets.ppt Support Open Access Motivation When you add a foreign chemical into something as complex as a human being do you really believe that drug is binding to only a single receptor? The Drug Discovery Pipeline Very Selective Collective Effect 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 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 - Hence fail early and cheaply 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 What if… • We can characterize a protein-ligand 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? • We could use it for lead optimization and possible ADME/Tox prediction What Do Off-targets Tell Us? • One of three 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 Today I will give you examples of both 2 and 3 and illustrate the complexity of the problem Agenda • Computational Methodology • Side Effects - The Tamoxifen Story • Repositioning an Existing Drug - The TB Story • Salvaging $800M – The Torcetrapib Story 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 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 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 Nothing in Biology {including Drug Discovery} Makes Sense Except in the Light of Evolution Theodosius Dobzhansky (1900-1975) 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 Lead Discovery from Fragment Assembly • Privileged molecular moieties in medicinal chemistry • Structural genomics and high throughput screening generate a large number of proteinfragment complexes • Similar sub-site detection enhances the application of fragment assembly strategies in drug discovery 1HQC: Holliday junction migration motor protein from Thermus thermophilus 1ZEF: Rio1 atypical serine protein kinase from A. fulgidus Lead Optimization from Conformational Constraints • Same ligand can bind to different proteins, but with different conformations • By recognizing the conformational changes in the binding site, it is possible to improve the binding specificity with conformational constraints placed on the ligand 1ECJ: amido-phosphoribosyltransferase from E. Coli 1H3D: ATP-phosphoribosyltransferase from E. Coli Agenda • Computational Methodology • Repositioning an Existing Drug - The TB Story • Side Effects - The Tamoxifen Story • Salvaging $800M – The Torcetrapib Story Tuberculosis (TB) • • • • One third of global population infected Kills 2 million people each 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 an Existing Drug - The TB Story 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 an Existing Drug - The TB Story Functional Site Similarity between COMT and ENR • 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 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 • At least some in vivo support • Assay of direct binding of entacapone and tolcapone to InhA under way Repositioning an Existing Drug - The TB Story Agenda • Computational Methodology • Repositioning an Existing Drug - The TB Story • Side Effects - The Tamoxifen Story • Salvaging $800M – The Torcetrapib Story 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 loss of calcium homeostatis ????? ocular toxicities Side Effects - The Tamoxifen Story PLoS Comp. Biol., 3(11) e217 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 • Predicted SERM binding site locates in the TM, inhibiting Ca2+ uptake Side Effects - The Tamoxifen Story PLoS Comp. Biol., 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 Side Effects - The Tamoxifen Story PLoS Comp. Biol., 3(11) e217 Agenda • Computational Methodology • Repositioning an Existing Drug - The TB Story • Side Effects - The Tamoxifen Story • Salvaging $800M – The Torcetrapib Story The Torcetrapib Story 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 trocetrapib with the docking score around -8.0. The Torcetrapib Story 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 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 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 /-4.43 /-5.63 /-7.08 /-0.58 /-7.09 /-9.42 / -5.95 -8.617 / -6.17 ??? ??? (MYR) -4.16 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 Summary • We have established a protocol to look for off-targets for existing therapeutics and NCEs • Understanding these in the context of pathways would seem to be the next step towards a new understanding • Lots of other opportunities to examine existing drugs 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 Acknowledgements Lei Xie Li Xie Jian Wang Sarah Kinnings Nancy Buchmeier Support Open Access