Towards Systems Pharmacology


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Towards Systems Pharmacology

  1. 1. Towards Systems Pharmacology Philip E. BourneUniversity of California San Diego BIOTEC Forum Dresden Dec. 6, 2012
  2. 2. Big Questions in the Lab {In the spirit of Hamming} 1. Can we improve how science is disseminated and comprehended? 2. What is the ancestry and organization of the protein structure universe and what can we learn from it? 3. Are there alternative ways to represent proteins from which we can learn something new? 4. What really happens when we take a drug? 5. Can we contribute to the treatment of neglected Erren et al 2007 PLoS Comp. Biol., 3(10): e213 {tropical} diseases?Motivators
  3. 3. What Really Happens When You Take a Drug? • Can we predict drug efficacy and toxicity? • Can we reuse old drugs? • Can we design personalized medicines?Motivators
  4. 4. One Drug, One Gene, One Disease Bernard M. Nat Rev Drug Disc 8(2009), 959-968Motivators
  5. 5. Polypharmacology • 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 2 689-700Motivators
  6. 6. Polypharmacology is Not Rare but Common • Single gene knockouts only affect phenotype in 10-20% of cases A.L. Hopkins Nat. Chem. Biol. 2008 4:682-690 • 35% of biologically active compounds bind to two or more targets that do not have similar sequences or global shapes Paolini et al. Nat. Biotechnol. 2006 24:805–815  Predict side effects  Repurpose drugs Kaiser et al. Nature 462 (2009) 175-81Motivators
  7. 7. Drug Binding is Dynamic • Drug effect dependents on not only how strong (binding affinity) but also how long the drug is “stuck” in the protein (residence time). • Molecular Dynamics (MD) simulation is powerful but computationally intensive. ~ns 1 day simulation ~ms – hours >106 daysD. Huang et al. (2011), PLoS Comp Biol 7(2):e1002002Motivators
  8. 8. Systems Pharmacology Enzyme inhibition ×× × Uptake × Systemic × × × response Secretion Catalytic (or biomass components) siteAffect protein Metabolicfunction networkTarget bindingSlide from Roger Chang Drug molecules
  9. 9. Multiscale Modeling of Drug Actions Understanding of Prediction of molecular dynamics and interaction network on kinetics of protein- a genome scale ligand interactions Traditional Approach physiological process Knowledge Reconstruction, representation analysis and and discovery & simulation of model integration biological networks Systems-based ApproachMotivators
  10. 10. How to Explore a Huge Conformational, Molecular and Functional Space?Approach
  11. 11. Constraint-based Modeling FrameworkApproach
  12. 12. Detecting Protein Binding Promiscuity in a Given Proteome • Geometric and topological constraints • Evolutionary constraints • Dynamic constraints • Physiochemical constraints SMAP v2.0 PRTSEQAENCE HASSTRVCTVREApproach
  13. 13. Geometric Potential of the Protein Structure 4 binding site • Challenge: inherent flexibility 3.5 non-binding site and uncertainty in homology models 3 • Representation of the protein 2.5 structure - Cα atoms only 2 - Delaunay tessellation 1.5 - Graph representation 1 • Geometric Potential (GP) 0.5 Pi cos(αi) + 1.0 100 0 0 GP = P + ∑ neighbors Di + 1.0 × 2.0 11 22 33 44 55 66 77 88 99 0Geometric Potential Scale Geometric Potential L. Xie & P. E. Bourne, BMC Bioinformatics, 8(2007):S9Approach
  14. 14. Sequence-order Independent Profile-Profile Alignment (SOIPPA) Structure A Structure B LER VKDL S=8 LER VKDL S=4 Xie & Bourne, PNAS, 105(2008):5441Approach
  15. 15. Similarity Matrix of Alignment Constraint - Chemical Similarity • Amino acid grouping: (LVIMC), (AGSTP), (FYW), and (EDNQKRH) • Amino acid chemical similarity matrix Constraint - 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, respectivelyComputational Methodology Xie and Bourne 2008 PNAS, 105(14) 5441
  16. 16. Extreme Value Distribution of SOIPPA Scores EVD: P(s>S) = 1 - exp(-exp(-Z)) Z = (S2 - μ)/σ Xie et al. 2009 Bioinformatics, 25:i305Approach
  17. 17. Detection of Remote Functional Relationships across Fold Space 0.06 0.06 False Positive Ratio 0.05 0.05 False Positive Ratio 0.04 0.04 0.03 0.03 0.02 0.02 PSI-Blast PSI-Blast CE 0.01 CE 0.01 SOIPPA SOIPPA 0 0 0 0.1 0.2 0.3 0.4 0 0.1 0.2 0.3 0.4 True Positive Ratio True Positive Ratio Same CATH Topology Different CATH Topology Xie & Bourne, PNAS, 105(2008):5441Approach
  18. 18. Some Applications • Lead optimization (e.g., SERMs, Optima, Limerick) • Early detection of side-effects (J&J) • Repositioning existing pharmaceuticals and NCEs (e.g., tolcapone, entacapone, nelfinavir) • Late detection of side-effects (torcetrapib) • Drugomes (TB, P. falciparum, T. cruzi)Applications
  19. 19. Nelfinavir StoryDrug discovery using chemical systems biology:weak inhibition of multiple kinases may contribute tothe anti-cancer effect of nelfinavir.Xie L, Evangelidis T, Xie L, Bourne PEPLoS Comput Biol. 2011 (4):e1002037
  20. 20. Possible Nelfinavir Repositioning
  21. 21. drug target off-target? structural proteome binding site comparison 1OHR protein ligand docking MD simulation & MM/GBSA Binding free energy calculation network construction & mapping Clinical OutcomesPossible Nelfinavir Repositioning
  22. 22. 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-3Possible Nelfinavir Repositioning PLoS Comp. Biol., 2011 2011 7(4) e1002037
  23. 23. 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 kinasesPossible Nelfinavir Repositioning PLoS Comp. Biol., 2011 2011 7(4) e1002037
  24. 24. Distribution of Top Hits on the Human Kinome p-value < 1.0e-4 p-value < 1.0e-3 Manning et al., Science, 2002, V298, 1912Possible Nelfinavir Repositioning
  25. 25. Interactions between Inhibitors and Epidermal Growth Factor Receptor (EGFR) – 74% of binding site resides are comparable1. Hydrogen bond with main chain amide of Met793 (without it 3700 fold loss ofinhibition)2. Hydrophobic interactions of aniline/phenyl with gatekeeper Thr790 and otherresidues EGFR-DJK EGFR-Nelfinavir Co-crys ligand H-bond: Met793 with benzamide H-bond: Met793 with quinazoline N1 hydroxy O38 DJK = N-[4-(3-BROMO-PHENYLAMINO)-QUINAZOLIN-6-YL]-ACRYLAMIDE
  26. 26. Off-target Interaction Network (Derived from Kegg) Identified off-target Pathway Activation Intermediate protein Cellular effect InhibitionPossible Nelfinavir Repositioning PLoS Comp. Biol., 2011 7(4) e1002037
  27. 27. 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 neededPossible Nelfinavir Repositioning PLoS Comp. Biol., 2011 2011 7(4) e1002037
  28. 28. Some Applications • Lead optimization (e.g., SERMs, Optima, Limerick) • Early detection of side-effects (J&J) • Repositioning existing pharmaceuticals and NCEs (e.g., tolcapone, entacapone, nelfinavir) • Late detection of side-effects (torcetrapib) • Drugomes (TB, P. falciparum, T. cruzi)Applications
  29. 29. Torcetrapib Side Effects • Targets Cholesteryl ester transfer protein (CETP) which raises HDL and lowers LDL cholesterol • Torcetrapib withdrawn due to occasional lethal side effects, severe hypertension. • Cause of hypertension undetermined; off-target effects suggested. • Predicted off- targets include metabolicApplications enzymes. Renal
  30. 30. Metabolic Modelingetabolic network reactions Flux space P1 P3 FluxC P2 P4 FluxB ux A Fl Steady-state Perturbation constraint S·v=0 Flux HEX1 ? PGI ? P3 Flux C PFK ? FBA ? P2 TPI ? P4 GAPD ? PGK ? A Flux B ux PGM ? Fl ENO ?trix representation of network PYK ? Change in system capacity
  31. 31. Recon1: A Human Metabolic Network Global Metabolic Map Reactions Compounds Comprehensively represents Pathways (3,311) (2,712) known reactions in human cells (98) Genes (1,496) Transcripts (1,905) Proteins (2,004) Compartments (7) http://bigg.ucsd.eduApproach (Duarte et al Proc Natl Acad Sci USA 2007)
  32. 32. Human Kidney Modeling Pipeline metabolomic blood/urine & kidney localization data Recon1 metabolic network healthy kidney constrain gene expression exchange fluxes data preliminary model normalize & set threshold kidney refine set flux model based on constraints GIMME metabolic capabilities influx set minimum renal objective flux objectives literatur metabolic e effluxApproach
  33. 33. Predicted Hypertension Causal Drug Off-Targets Impacts Functional Renal Official Off-Target Site Function in Symbol Protein Prediction Overlap Simulation Prostacyclin PTGIS x x x synthase ACOX1 Acyl CoA oxidase x x x AK3L1 Adenylate kinase 4 x x x HAO2 Hydroxyacid oxidase 2 x x x Mitochondrial MT-COI x x x cytochrome c oxidase I Ubiquinol-cytochrome c UQCRC1 x x x reductase core protein I *Clinically linked to hypertension.Applications
  34. 34. Prostacyclin Synthase (PTGIS) • In silico inhibition blocks renal prostaglandin I2 secretion. Prostaglandin H2 Prostaglandin I2 • Associated with essential hypertension in humans. • Expression of human PTGIS decreases mean pulmonary arterial pressure in hypertensive rats.Applications
  35. 35. Conclusions• Torcetrapib hypertension side effect may result from renal metabolic off-target effects.• Framework for perturbation phenotype simulation capable of predicting metabolic disorders, causal drug targets, and genetic risk factors for drug treatment (including cryptic risk factors).• Pipeline established for in silico prediction of systemic drug response.
  36. 36. Some Applications • Lead optimization (e.g., SERMs, Optima, Limerick) • Early detection of side-effects (J&J) • Repositioning existing pharmaceuticals and NCEs (e.g., tolcapone, entacapone, nelfinavir) • Late detection of side-effects (torcetrapib) • Drugomes (TB, P. falciparum, T. cruzi)Applications
  37. 37. The Future as a HighThroughput Approach…..
  38. 38. 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 priorityRepositioning - The TB Story
  39. 39. The TB-Drugome 1. Determine the TB structural proteome 2. Determine all known drug binding sites from the PDB 3. Determine which of the sites found in 2 exist in 1 4. Call the result the TB-drugomeA Multi-target/drug Strategy Kinnings et al 2010 PLoS Comp Biol 6(11): e1000976
  40. 40. 1. Determine the TB Structural Proteome e m t eo p ro y TB og ol ls hom ode m ed es olv tur s c 3, 996 2, 266 tru 284 s 1, 446 • High quality homology models from ModBase ( increase structural coverage from 7.1% to 43.3%A Multi-target/drug Strategy Kinnings et al 2010 PLoS Comp Biol 6(11): e1000976
  41. 41. 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 Acarbose Darunavir Alitretinoin Conjugated estrogens Chenodiol Methotrexate No. of drug binding sitesA Multi-target/drug Strategy Kinnings et al 2010 PLoS Comp Biol 6(11): e1000976
  42. 42. Map 2 onto 1 – The TB-Drugome between the binding sites of M.tb proteins (blue), and binding sites containing approved drugs (red).
  43. 43. 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 conjugated estrogens & chenodiol methotrexate levothyroxine testosterone raloxifene ritonavir alitretinoin No. of potential TB targetsA Multi-target/drug Strategy Kinnings et al 2010 PLoS Comp Biol 6(11): e1000976
  44. 44. Top 5 Most Highly Connected Drugs No. ofDrug Intended targets Indications TB proteins connectionslevothyroxine transthyretin, thyroid hypothyroidism, goiter, adenylyl cyclase, argR, bioD, hormone receptor α & β-1, CRP/FNR trans. reg., ethR, chronic lymphocytic thyroxine-binding globulin, 14 glbN, glbO, kasB, lrpA, nusA, thyroiditis, myxedema coma, mu-crystallin homolog, prrA, secA1, thyX, trans. reg. stupor serum albumin proteinalitretinoin retinoic acid receptor RXR-α, adenylyl cyclase, aroG, β & γ, retinoic acid receptor cutaneous lesions in patients bioD, bpoC, CRP/FNR trans. α, β & γ-1&2, cellular 13 with Kaposis sarcoma reg., cyp125, embR, glbN, retinoic acid-binding protein inhA, lppX, nusA, pknE, purN 1&2conjugated menopausal vasomotor acetylglutamate kinase,estrogens symptoms, osteoporosis, adenylyl cyclase, bphD, estrogen receptor 10 hypoestrogenism, primary CRP/FNR trans. reg., cyp121, ovarian failure cysM, inhA, mscL, pknB, sigCmethotrexate gestational choriocarcinoma, acetylglutamate kinase, aroF, dihydrofolate reductase, chorioadenoma destruens, cmaA2, CRP/FNR trans. reg., 10 serum albumin hydatidiform mole, severe cyp121, cyp51, lpd, mmaA4, psoriasis, rheumatoid arthritis panC, uspraloxifene adenylyl cyclase, CRP/FNR estrogen receptor, estrogen osteoporosis in post- trans. reg., deoD, inhA, pknB, 9 receptor β menopausal women pknE, Rv1347c, secA1, sigC
  45. 45. Vignette within Vignette• Entacapone and tolcapone used in the treatment of Parkinsons disease (COMT inhibitors) shown to have potential for repositioning• 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
  46. 46. 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 pipelineRepositioning - The TB Story Kinnings et al. 2009 PLoS Comp Biol 5(7) e1000423
  47. 47. Summary• We are entering an era of systems pharmacology where drug action is computationally analyzed relative to the complete constrained system at a spectrum of biological scales not just at the level of the single receptor molecule and patient.
  48. 48. Interesting Questions• Are similar binding sites and different global structures the result of convergent evolution or extreme divergent evolution?• Will/how soon drug discovery become patient centric?
  49. 49. Acknowledgements Lei Xie Li Xie Roger Chang Bernhard Palsson Chirag Krishna (Chagas Disease)Yinliang Zhang Sarah(Malaria) Kinnings