Polypharmacology - NBIC April 20, 2011
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A keynote presentation at the NBIC Annual Meeting. Covers the concept of polypharmacology, a bioinformatics approach to off-target binding and a systems approach to dynamical modeling of the process.

A keynote presentation at the NBIC Annual Meeting. Covers the concept of polypharmacology, a bioinformatics approach to off-target binding and a systems approach to dynamical modeling of the process.

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  • Absorption, distribution, metabolism and excretion
  • Updated for 2009
  • 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/sbs.surrey.ac.uk/tb) 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)

Polypharmacology - NBIC April 20, 2011 Presentation Transcript

  • 1. Bioinformatics Meets Systems Biology for Early Stage Drug Discovery Philip E. Bourne University of California San Diego [email_address] http://www.sdsc.edu/pb NBIC – April 20, 2011
  • 2. 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?
    • Can we contribute to the treatment of neglected {tropical} diseases?
  • 3. What Really Happens When We Take a Drug?
    • If we knew the answer we could:
      • Contribute to the design of improved drugs with minimal side effects
      • Contribute to how existing drugs and NCEs might be repositioned
    Motivation
  • 4. Why We Think This is Important
    • Ehrlich’s philosophy of magic bullets targeting individual chemoreceptors has not been realized in most cases – witness the recent success of big pharma
    • Stated another way – The notion of one drug, one target, to treat one disease is a little naïve in a complex system
    Motivation
  • 5. Polypharmacology - One Drug Binds to Multiple Targets
    • 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 Motivation
  • 6. We Are Developing a Theoretical Approach to Address This Fundamental Shift in Thinking
    • Involves the fields of:
      • Structural bioinformatics
      • Cheminformatics
      • Biophysics
      • Systems-level biology
      • Pharmaceutical chemistry
    Our Approach
  • 7. Our Approach
    • 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
    • We try an integrated approach to understand the implications of drug binding to multiple sites
    Our Approach
  • 8. Which Means …
    • We could perhaps find alternative binding sites ( off-targets ) for existing pharmaceuticals and NCEs?
    • If we can make this high throughput we could rationally explore a large network of protein-ligands interactions
    Our Approach
  • 9.  
  • 10. What Have These Off-targets and Networks Told Us So Far? Some Examples…
      • Nothing
      • A possible explanation for a side-effect of a drug already on the market (SERMs - PLoS Comp. Biol. , 2007 3(11) e217)
      • A possible repositioning of a drug (Nelfinavir) to treat a completely different condition (under review)
      • A multi-target/drug strategy to attack a pathogen (TB-drugome PLoS Comp Biol 2010 6(11): e1000976)
      • The reason a drug failed (Torcetrapib - PLoS Comp Biol 2009 5(5) e1000387)
      • How to optimize a NCE ( NCE against T. Brucei PLoS Comp Biol. 2010 6(1): e1000648)
    Our Stories
  • 11. Need to Start with a 3D Drug-Receptor Complex - The PDB Contains Many Examples 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
  • 12. Number of released entries Year:
  • 13. A Quick Aside – RCSB PDB Pharmacology/Drug View 2010-2011
    • Establish linkages to drug resources (FDA, PubChem, DrugBank, ChEBI, BindingDB 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 RCSB PDB Team Drug Name Asp Aspirin Has Bound Drug % Similarity to Drug Molecule 100
  • 14. 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
  • 15.
    • 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
    • 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):S9 Computational Methodology
  • 16. 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 For Residue Clusters
  • 17. 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 Computational Methodology
  • 18. 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
    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
  • 19. What Have These Off-targets and Networks Told Us So Far? Some Examples…
      • Nothing
      • A possible explanation for a side-effect of a drug already on the market (SERMs - PLoS Comp. Biol. , 2007 3(11) e217)
      • A possible repositioning of a drug (Nelfinavir) to treat a completely different condition (under review)
      • A multi-target/drug strategy to attack a pathogen (TB-drugome PLoS Comp Biol 2010 6(11): e1000976)
      • The reason a drug failed (Torcetrapib - PLoS Comp Biol 2009 5(5) e1000387)
      • How to optimize a NCE ( NCE against T. Brucei PLoS Comp Biol. 2010 6(1): e1000648)
    Our Stories
  • 20. 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
  • 21. Adverse Effects of SERMs cardiac abnormalities thromboembolic disorders ocular toxicities loss of calcium homeostatis ????? Side Effects - The Tamoxifen Story PLoS Comp. Biol. , 2007 3(11) e217
  • 22. Ligand Binding Site Similarity Search On a Proteome Scale
    • Searching human proteins covering ~38% of the drugable genome against SERM binding site
    • Matching Sacroplasmic Reticulum (SR) Ca2+ ion channel ATPase (SERCA) TG1 inhibitor site
    • ER  ranked top with p-value<0.0001 from reversed search against SERCA
    ER  SERCA Side Effects - The Tamoxifen Story PLoS Comp. Biol. , 2007 3(11) e217
  • 23. Structure and Function of SERCA
    • R egulating 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. , 2007 3(11) e217
  • 24. 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) Side Effects - The Tamoxifen Story PLoS Comp. Biol. , 2007 3(11) e217
  • 25. Off-Target of SERMs cardiac abnormalities thromboembolic disorders ocular toxicities loss of calcium homeostatis SERCA !
    • in vivo and in vitro Studies
      • TAM play roles in regulating calcium uptake activity of cardiac SR
      • TAM reduce intracellular calcium concentration and release in the platelets
      • Cataracts result from TG1 inhibited SERCA up-regulation
      • EDS increases intracellular calcium in lens epithelial cells by inhibiting SERCA
    • in silico Studies
      • Ligand binding site similarity
      • Binding affinity correlation
    PLoS Comp. Biol. , 2007 3(11) e217
  • 26. 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. , 2007 3(11) e217
  • 27. What Have These Off-targets and Networks Told Us So Far? Some Examples…
      • Nothing
      • A possible explanation for a side-effect of a drug already on the market (SERMs - PLoS Comp. Biol. , 2007 3(11) e217)
      • A possible repositioning of a drug (Nelfinavir) to treat a completely different condition (under review)
      • A multi-target/drug strategy to attack a pathogen (TB-drugome PLoS Comp Biol 2010 6(11): e1000976)
      • The reason a drug failed (Torcetrapib - PLoS Comp Biol 2009 5(5) e1000387)
      • How to optimize a NCE ( NCE against T. Brucei PLoS Comp Biol. 2010 6(1): e1000648)
    Our Stories
  • 28. Nelfinavir
    • Nelfinavir may have the most potent antitumor activity of the HIV protease inhibitors
    • Joell J. Gills et al, Clin Cancer Res, 2007; 13(17)
    • Warren A. Chow et al, The Lancet Oncology, 2009, 10(1)
    • Nelfinavir can inhibit receptor tyrosine kinase(s)
    • Nelfinavir can reduce Akt activation
    • Our goal:
    • to identify off-targets of Nelfinavir in the human proteome
    • to construct an off-target binding network
    • to explain the mechanism of anti-cancer activity
    Possible Nelfinavir Repositioning PLoS Comp. Biol. , 2011 To Appear
  • 29. Possible Nelfinavir Repositioning
  • 30. 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
  • 31. Binding Site Comparison
    • 5,985 structures or models that cover approximately 30% of the human proteome are searched against the HIV protease dimer (PDB id: 1OHR)
    • Structures with SMAP p-value less than 1.0e-3 were retained for further investigation
    • A total 126 structures have significant p-values < 1.0e-3
    Possible Nelfinavir Repositioning PLoS Comp. Biol. , 2011 To Appear
  • 32. Enrichment of Protein Kinases in Top Hits
    • The top 7 ranked off-targets belong to the same EC family - aspartyl proteases - with HIV protease
    • Other off-targets are dominated by protein kinases (51 off-targets) and other ATP or nucleotide binding proteins (17 off-targets)
    • 14 out of 18 proteins with SMAP p-values < 1.0e-4 are protein kinases
    Possible Nelfinavir Repositioning PLoS Comp. Biol. , 2011 To Appear
  • 33. 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
  • 34. 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
  • 35. Off-target Interaction Network Identified off-target Intermediate protein Pathway Cellular effect Activation Inhibition Possible Nelfinavir Repositioning PLoS Comp. Biol. , 2011 To Appear
  • 36. 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
  • 37. Summary
    • The HIV-1 drug Nelfinavir appears to be a broad spectrum low affinity kinase inhibitor
    • Most targets are upstream of the PI3K/Akt pathway
    • Findings are consistent with the experimental literature
    • More direct experiment is needed
    Possible Nelfinavir Repositioning PLoS Comp. Biol. , 2011 To Appear
  • 38. What Have These Off-targets and Networks Told Us So Far? Some Examples…
      • Nothing
      • A possible explanation for a side-effect of a drug already on the market (SERMs - PLoS Comp. Biol. , 2007 3(11) e217)
      • A possible repositioning of a drug (Nelfinavir) to treat a completely different condition (under review)
      • A multi-target/drug strategy to attack a pathogen (TB-drugome PLoS Comp Biol 2010 6(11): e1000976)
      • The reason a drug failed (Torcetrapib - PLoS Comp Biol 2009 5(5) e1000387)
      • How to optimize a NCE ( NCE against T. Brucei PLoS Comp Biol. 2010 6(1): e1000648)
    Our Stories
  • 39. The Future as a High Throughput Approach…..
  • 40. 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
    Repositioning - The TB Story
  • 41. The TB-Drugome
    • Determine the TB structural proteome
    • Determine all known drug binding sites from the PDB
    • Determine which of the sites found in 2 exist in 1
    • Call the result the TB-drugome
    A Multi-target/drug Strategy Kinnings et al 2010 PLoS Comp Biol 6(11): e1000976
  • 42. 1. Determine the TB Structural Proteome
    • High quality homology models from ModBase (http://modbase.compbio.ucsf.edu) increase structural coverage from 7.1% to 43.3%
    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
  • 43. 2. Determine all Known Drug Binding Sites in the PDB
    • Searched the PDB for protein crystal structures bound with FDA-approved drugs
    • 268 drugs bound in a total of 931 binding sites
    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
  • 44. Map 2 onto 1 – The TB-Drugome http://funsite.sdsc.edu/drugome/TB/ Similarities between the binding sites of M.tb proteins (blue), and binding sites containing approved drugs (red).
  • 45. From a Drug Repositioning Perspective
    • 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
    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
  • 46. 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
  • 47. Vignette within Vignette
    • 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
  • 48. 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
  • 49. What Have These Off-targets and Networks Told Us So Far? Some Examples…
      • Nothing
      • A possible explanation for a side-effect of a drug already on the market (SERMs - PLoS Comp. Biol. , 2007 3(11) e217)
      • A possible repositioning of a drug (Nelfinavir) to treat a completely different condition (under review)
      • A multi-target/drug strategy to attack a pathogen (TB-drugome PLoS Comp Biol 2010 6(11): e1000976)
      • The reason a drug failed (Torcetrapib - PLoS Comp Biol 2009 5(5) e1000387)
      • How to optimize a NCE ( NCE against T. Brucei PLoS Comp Biol. 2010 6(1): e1000648)
    Our Stories
  • 50. The Future as a Dynamical Network Approach
  • 51. Drug Failure - The Torcetrapib Story PLoS Comp Biol 2009 5(5) e1000387
  • 52. Cholesteryl Ester Transfer Protein (CETP )
    • 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.
    HDL LDL CETP CETP inhibitor X Bad Cholesterol Good Cholesterol PLoS Comp Biol 2009 5(5) e1000387 Drug Failure - The Torcetrapib Story
  • 53. Computational Evaluation of Drug Off-Target Effects 336 genes 1587 reactions Plos Comp. Biol. 2010 6(9): e1000938 Proteome Drug binding site alignments SMAP Predicted drug targets Drug and endogenous substrate binding site analysis Competitively inhibitable targets Inhibition simulations in context-specific model COBRA Toolbox Predicted causal targets and genetic risk factors Metabolic network Scientific literature Tissue and biofluid localization data Gene expression data Physiological objectives System exchange constraints Flux states optimizing objective Physiological context-specific model Influx Efflux Drug response phenotypes Drug targets Physiological objectives Causal drug targets All targets
  • 54. Summary
    • Generated a large number of testable hypotheses regarding off-targets of known drugs
    • A few have been tested experimentally (e.g., encapalone against InhA)
    • A possible methodology for use in early stage drug discovery
  • 55. Acknowledgements Sarah Kinnings Lei Xie Li Xie http://funsite.sdsc.edu Roger Chang Bernhard Palsson Jian Wang