Network Pharmacology Tri-Con 022212


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Presentation at the Network Pharmacology session of Molecular Med Tri-Con 2012 Meeting San Francisco Feb 22, 2012.

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  • Absorption, distribution, metabolism and excretion
  • P distance to environmental boundary; Pi Di and alphai D distance to central atom alpha direction to central atom
  • This is great data!
  • Tuberculosis, which is caused by the bacterial pathogen Mycobacterium tuberculosis , is a leading cause of mortality among the infectious diseases. It has been estimated by the World Health Organization (WHO) that almost one-third of the world's population , around 2 billion people, is infected with the disease. Every year, more than 8 million people develop an active form of the disease, which claims the lives of nearly 2 million. This translates to over 4,900 deaths per day , and more than 95% of these are in developing countries. Despite the current global situation, antitubercular drugs have remained largely unchanged over the last four decades. The widespread use of these agents has provided a strong selective pressure for M.tuberculosis, thus encouraging the emergence of resistant strains. Multidrug resistant (MDR) tuberculosis is defined as resistance to the first-line drugs isoniazid and rifampin . The effective treatment of MDR tuberculosis necessitates long-term use of second-line drug combinations , an unfortunate consequence of which is the emergence of further drug resistance. Enter extensively drug resistant (XDR) tuberculosis - M.tuberculosis strains that are resistant to both isoniazid plus rifampin, as well as key second-line drugs . Since the only remaining drug classes exhibit such low potency and high toxicity , XDR tuberculosis is extremely difficult to treat. The rise of XDR tuberculosis around the world imposes a great threat on human health , therefore reinforcing the development of new antitubercular agents as an urgent priority. Very few Mtb proteins explored as drug targets
  • 3,996 proteins in TB proteome 749 solved structures in the PDB, representing a total of 284 proteins (7.2% coverage) ModBase contains homology models for entire TB proteome 1,446 ‘high quality’ homology models were added to the data set Structural coverage increased to 43.8% Retained only those models with a model score of > 0.7 and a Modpipe quality score of > 1.1 (2818 models). There were multiple models per protein. For each TB protein, chose the model with the best model score, and if they were equal, chose the model with the best Modpipe quality score (1703 models). However, 251 (+6) models were removed since they correspond to TB proteins that already have solved structures. 1446 models remained) Score for the reliability of a Model, derived from statistical potentials (F. Melo, R. Sanchez, A. Sali,2001 PDF ). A model is predicted to be good when the model score is higher than a pre-specified cutoff (0.7). A reliable model has a probability of the correct fold that is larger than 95%. A fold is correct when at least 30% of its Calpha atoms superpose within 3.5A of their correct positions. The ModPipe Protein Quality Score is a composite score comprising sequence identity to the template, coverage , and the three individual scores evalue , z-Dope and GA341 . We consider a MPQS of >1.1 as reliable
  • (nutraceuticals excluded)
  • Multi-target therapy may be more effective than single-target therapy to treat infectious diseases Most of the proteins listed are potential novel drug targets for the development of efficient anti-tuberculosis chemotherapeutics. GSMN-TB : Genome Scale Metabolic Reaction Network of M.tb (http://sysbio/ 849 reactions, 739 metabolites, 726 genes Can optimize the model for in vivo growth Carry out multiple gene inhibition and compute the maximal theoretical growth rate (if close to zero, that combination of genes is essential for growth)
  • Network Pharmacology Tri-Con 022212

    1. 1. Computational Approaches in Network Pharmacology Philip E. Bourne University of California San Diego [email_address] Tri-Con San Francisco, Feb. 22, 2012
    2. 2. Big Questions in the Lab <ul><li>Can we improve how science is disseminated and comprehended? </li></ul><ul><li>What is the ancestry and organization of the protein structure universe and what can we learn from it? </li></ul><ul><li>Are there alternative ways to represent proteins from which we can learn something new? </li></ul><ul><li>What really happens when we take a drug? </li></ul><ul><li>Can we contribute to the treatment of neglected {tropical} diseases? </li></ul>Motivators
    3. 3. Our Motivation <ul><li>Tykerb – Breast cancer </li></ul><ul><li>Gleevac – Leukemia, GI cancers </li></ul><ul><li>Nexavar – Kidney and liver cancer </li></ul><ul><li>Staurosporine – natural product – alkaloid – uses many e.g., antifungal antihypertensive </li></ul>Collins and Workman 2006 Nature Chemical Biology 2 689-700 Motivators
    4. 4. Our Broad Approach <ul><li>Involves the fields of: </li></ul><ul><ul><li>Structural bioinformatics </li></ul></ul><ul><ul><li>Cheminformatics </li></ul></ul><ul><ul><li>Biophysics </li></ul></ul><ul><ul><li>Systems biology </li></ul></ul><ul><ul><li>Pharmaceutical chemistry </li></ul></ul><ul><li>L. Xie, L. Xie, S.L. Kinnings and P.E. Bourne 2012 Novel Computational Approaches to Polypharmacology as a Means to Define Responses to Individual Drugs, Annual Review of Pharmacology and Toxicology 52 : 361-379 </li></ul><ul><li>L. Xie, S.L. Kinnings, L. Xie and P.E. Bourne 2012 Predicting the Polypharmacology of Drugs: Identifying New Uses Through Bioinformatics and Cheminformatics Approaches in Drug Repurposing M. Barrett and D. Frail (Eds.) Wiley and Sons. (available upon request) </li></ul>Disciplines Touched & 2012 Reviews
    5. 5. A Quick Aside – RCSB PDB Pharmacology/Drug View 2012 <ul><li>Establish linkages to drug resources (FDA, PubChem, DrugBank, ChEBI, BindingDB etc.) </li></ul><ul><li>Create query capabilities for drug information </li></ul><ul><li>Provide superposed views of ligand binding sites </li></ul><ul><li>Analyze and display protein-ligand interactions </li></ul>Mockups of drug view features RCSB PDB’s Drug Work RCSB PDB Team Led by Peter Rose Drug Name Asp Aspirin Has Bound Drug % Similarity to Drug Molecule 100
    6. 6. A Quick Aside PDB Scope/Deliverables <ul><li>Part I: small molecule drugs, nutraceuticals, and their targets ( DrugBank) - 2012 </li></ul><ul><li>Part II: peptide derived compounds (PRD)- tbd </li></ul><ul><li>Part III: toxins and toxin targets (T3DB), human metabolites (HMDB) </li></ul><ul><li>Part IV: biotherapeutics, i.e., monoclonal antibodies </li></ul><ul><li>Part V: veterinary drugs (FDA Green Book) </li></ul>RCSB PDB’s Drug Work
    7. 7. Our Approach <ul><li>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 </li></ul><ul><li>We try a static and dynamic network-based approach to understand the implications of drug binding to multiple sites </li></ul>Methodology
    8. 8. Applications Thus Far <ul><li>Repositioning existing pharmaceuticals and NCEs (e.g., tolcapone, entacapone, nelfinavir ) </li></ul><ul><li>Early detection of side-effects ( J&J ) </li></ul><ul><li>Late detection of side-effects ( torcetrapib ) </li></ul><ul><li>Lead optimization (e.g., SERMs, Optima, Limerick ) </li></ul><ul><li>Drugomes ( TB , P. falciparum, T. cruzi) </li></ul>Applications
    9. 9. Approach - Need to Start with a 3D Drug-Receptor Complex – Either Experimental or Modeled 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
    10. 10. Some Numbers to Show Limitations TB-drugome pF-Drugome Target gene 3996 5491 Target protein in PDB 284 136 Solved structure in PDB 749 333 Reliable homology models 1446 1236 S tructure coverage 43.29% 25.02% Drugs 274 321 Drug binding sites 962 1569
    11. 11. 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
    12. 12. <ul><li>Initially assign C  atom with a value that is the distance to the environmental boundary </li></ul><ul><li>Update the value with those of surrounding C  atoms dependent on distances and orientation – atoms within a 10A radius define i </li></ul><ul><li>Conceptually similar to hydrophobicity </li></ul><ul><li>or electrostatic potential that is </li></ul><ul><li>dependant on both global and local </li></ul><ul><li>environments </li></ul>Characterization of the Ligand Binding Site - The Geometric Potential Xie and Bourne 2007 BMC Bioinformatics, 8(Suppl 4):S9 Computational Methodology
    13. 13. Discrimination Power of the Geometric Potential <ul><li>Geometric potential can distinguish binding and non-binding sites </li></ul>100 0 Geometric Potential Scale Computational Methodology Xie and Bourne 2007 BMC Bioinformatics, 8(Suppl 4):S9 For Residue Clusters
    14. 14. 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 <ul><li>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 </li></ul><ul><li>The maximum-weight clique corresponds to the optimum alignment of the two structures </li></ul>Xie and Bourne 2008 PNAS , 105(14) 5441 Computational Methodology
    15. 15. Similarity Matrix of Alignment <ul><li>Chemical Similarity </li></ul><ul><li>Amino acid grouping: (LVIMC), (AGSTP), (FYW), and (EDNQKRH) </li></ul><ul><li>Amino acid chemical similarity matrix </li></ul><ul><li>Evolutionary Correlation </li></ul><ul><li>Amino acid substitution matrix such as BLOSUM45 </li></ul><ul><li>Similarity score between two sequence profiles </li></ul>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
    16. 16. Applications Thus Far <ul><li>Repositioning existing pharmaceuticals and NCEs (e.g., tolcapone, entacapone, nelfinavir ) </li></ul><ul><li>Early detection of side-effects ( J&J ) </li></ul><ul><li>Late detection of side-effects ( torcetrapib ) </li></ul><ul><li>Lead optimization (e.g., SERMs, Optima, Limerick ) </li></ul><ul><li>Drugomes ( TB , P. falciparum, T. cruzi) </li></ul>Applications
    17. 17. Nelfinavir <ul><li>Nelfinavir may have the most potent antitumor activity of the HIV protease inhibitors </li></ul><ul><li>Joell J. Gills et al, Clin Cancer Res, 2007; 13(17) </li></ul><ul><li>Warren A. Chow et al, The Lancet Oncology, 2009, 10(1) </li></ul><ul><li>Nelfinavir can inhibit receptor tyrosine kinase(s) </li></ul><ul><li>Nelfinavir can reduce Akt activation </li></ul><ul><li>Our goal: </li></ul><ul><li>to identify off-targets of Nelfinavir in the human proteome </li></ul><ul><li>to construct an off-target binding network </li></ul><ul><li>to explain the mechanism of anti-cancer activity </li></ul>Possible Nelfinavir Repositioning PLoS Comp. Biol. , 2011 7(4) e1002037
    18. 18. Possible Nelfinavir Repositioning
    19. 19. 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
    20. 20. Binding Site Comparison <ul><li>5,985 structures or models that cover approximately 30% of the human proteome are searched against the HIV protease dimer (PDB id: 1OHR) </li></ul><ul><li>Structures with SMAP p-value less than 1.0e-3 were retained for further investigation </li></ul><ul><li>A total 126 structures have significant p-values < 1.0e-3 </li></ul>Possible Nelfinavir Repositioning PLoS Comp. Biol. , 2011 2011 7(4) e1002037
    21. 21. Enrichment of Protein Kinases in Top Hits <ul><li>The top 7 ranked off-targets belong to the same EC family - aspartyl proteases - with HIV protease </li></ul><ul><li>Other off-targets are dominated by protein kinases (51 off-targets) and other ATP or nucleotide binding proteins (17 off-targets) </li></ul><ul><li>14 out of 18 proteins with SMAP p-values < 1.0e-4 are protein kinases </li></ul>Possible Nelfinavir Repositioning PLoS Comp. Biol. , 2011 2011 7(4) e1002037
    22. 22. 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
    23. 23. 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
    24. 24. Off-target Interaction Network Identified off-target Intermediate protein Pathway Cellular effect Activation Inhibition Possible Nelfinavir Repositioning PLoS Comp. Biol. , 2011 7(4) e1002037
    25. 25. 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
    26. 26. Summary <ul><li>The HIV-1 drug Nelfinavir appears to be a broad spectrum low affinity kinase inhibitor </li></ul><ul><li>Most targets are upstream of the PI3K/Akt pathway </li></ul><ul><li>Findings are consistent with the experimental literature </li></ul><ul><li>More direct experiment is needed </li></ul>Possible Nelfinavir Repositioning PLoS Comp. Biol. , 2011 2011 7(4) e1002037
    27. 27. Applications Thus Far <ul><li>Repositioning existing pharmaceuticals and NCEs (e.g., tolcapone, entacapone, nelfinavir ) </li></ul><ul><li>Early detection of side-effects ( J&J ) </li></ul><ul><li>Late detection of side-effects ( torcetrapib ) </li></ul><ul><li>Lead optimization (e.g., SERMs, Optima, Limerick ) </li></ul><ul><li>Drugomes ( TB , P. falciparum, T. cruzi) </li></ul>Applications
    28. 28. Case Study: Torcetrapib Side Effect <ul><li>Cholesteryl ester transfer protein ( CETP ) inhibitors treat cardiovascular disease by raising HDL and lowering LDL cholesterol (Torcetrapib, Anacetrapib, JTT-705). </li></ul><ul><li>Torcetrapib withdrawn due to occasional lethal side effect, severe hypertension . </li></ul><ul><li>Cause of hypertension undetermined; off-target effects suggested. </li></ul><ul><li>Predicted off-targets include metabolic enzymes. Renal function is strong determinant of blood pressure. Causal off-targets may be found through modeling kidney metabolism. </li></ul>
    29. 29. Constraint-based Metabolic Modeling S · v = 0 Matrix representation of network Metabolic network reactions Flux space Change in system capacity Perturbation constraint Steady-state assumption Flux
    30. 30. Recon1: A Human Metabolic Network (Duarte et al Proc Natl Acad Sci USA 2007) Global Metabolic Map Comprehensively represents known reactions in human cells Pathways (98) Reactions (3,311) Compounds (2,712) Genes (1,496) Transcripts (1,905) Proteins (2,004) Compartments (7)
    31. 31. Context-specific Modeling Pipeline metabolic network metabolomic biofluid & tissue localization data constrain exchange fluxes preliminary model gene expression data refine based on capabilities set flux constraints objective function literature GIMME normalize & set threshold set minimum objective flux model metabolic influx metabolic efflux
    32. 32. Predicted Hypertension Causal Drug Off-Targets *Clinically linked to hypertension.
    33. 33. Applications Thus Far <ul><li>Repositioning existing pharmaceuticals and NCEs (e.g., tolcapone, entacapone, nelfinavir ) </li></ul><ul><li>Early detection of side-effects ( J&J ) </li></ul><ul><li>Late detection of side-effects ( torcetrapib ) </li></ul><ul><li>Lead optimization (e.g., SERMs, Optima, Limerick ) </li></ul><ul><li>Drugomes ( TB , P. falciparum, T. cruzi) </li></ul>Applications
    34. 34. The Future as a High Throughput Approach…..
    35. 35. The Problem with Tuberculosis <ul><li>One third of global population infected </li></ul><ul><li>1.7 million deaths per year </li></ul><ul><li>95% of deaths in developing countries </li></ul><ul><li>Anti-TB drugs hardly changed in 40 years </li></ul><ul><li>MDR-TB and XDR-TB pose a threat to human health worldwide </li></ul><ul><li>Development of novel, effective and inexpensive drugs is an urgent priority </li></ul>Repositioning - The TB Story
    36. 36. The TB-Drugome <ul><li>Determine the TB structural proteome </li></ul><ul><li>Determine all known drug binding sites from the PDB </li></ul><ul><li>Determine which of the sites found in 2 exist in 1 </li></ul><ul><li>Call the result the TB-drugome </li></ul>A Multi-target/drug Strategy Kinnings et al 2010 PLoS Comp Biol 6(11): e1000976
    37. 37. 1. Determine the TB Structural Proteome <ul><li>High quality homology models from ModBase ( increase structural coverage from 7.1% to 43.3% </li></ul>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
    38. 38. 2. Determine all Known Drug Binding Sites in the PDB <ul><li>Searched the PDB for protein crystal structures bound with FDA-approved drugs </li></ul><ul><li>268 drugs bound in a total of 931 binding sites </li></ul>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
    39. 39. Map 2 onto 1 – The TB-Drugome Similarities between the binding sites of M.tb proteins (blue), and binding sites containing approved drugs (red).
    40. 40. From a Drug Repositioning Perspective <ul><li>Similarities between drug binding sites and TB proteins are found for 61/268 drugs </li></ul><ul><li>41 of these drugs could potentially inhibit more than one TB protein </li></ul>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
    41. 41. 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
    42. 42. Vignette within Vignette <ul><li>Entacapone and tolcapone shown to have potential for repositioning </li></ul><ul><li>Direct mechanism of action avoids M. tuberculosis resistance mechanisms </li></ul><ul><li>Possess excellent safety profiles with few side effects – already on the market </li></ul><ul><li>In vivo support </li></ul><ul><li>Assay of direct binding of entacapone and tolcapone to InhA reveals a possible lead with no chemical relationship to existing drugs </li></ul>Kinnings et al. 2009 PLoS Comp Biol 5(7) e1000423
    43. 43. Summary from the TB Alliance – Medicinal Chemistry <ul><li>The minimal inhibitory concentration (MIC) of 260 uM is higher than usually considered </li></ul><ul><li>MIC is 65x the estimated plasma concentration </li></ul><ul><li>Have other InhA inhibitors in the pipeline </li></ul>Repositioning - The TB Story Kinnings et al. 2009 PLoS Comp Biol 5(7) e1000423
    44. 44. Acknowledgements Sarah Kinnings Lei Xie Li Xie Roger Chang Bernhard Palsson Jian Wang