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Next Generation Data and Opportunities for Clinical Pharmacologists

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Presentation at the Pre-meeting Workshop Next-Generation Clinical Pharmacology: Integrating Systems Pharmacology, Data-Driven Therapeutics, and Personalized Medicine. American Society for Clinical Pharmacology and Therapeutics Annual Meeting Atlanta GA March 18, 2014.

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Next Generation Data and Opportunities for Clinical Pharmacologists

  1. 1. NEXT GENERATION DATA AND OPPORTUNITIES FOR CLINICAL PHARMACOLOGISTS Philip E. Bourne Ph.D. Associate Director for Data Science National Institutes of Health
  2. 2. As of March 3, 2014
  3. 3. Agenda  Research that Informs my NIH Agenda – The TB drugome – towards reproducibility – Systems pharmacology – towards interoperability  Some Challenges – We have the why, but we lack the how – The how involves: • Representation • Sustainability • Discoverability • Training
  4. 4. Reconstruction of Genome-Scale 3D Drug-Target Interaction Models Integrating chemical genomics and structural systems biology MD simulation Mj Q Mj Q ligENTS SMAP Protein-ligand docking Mj Q Mi 3D model of novel Target 3D model of annotated target interaction model Query chemical Network modeling Experimental support L. Xie and P.E. Bourne 2008 PNAS, 105(14) 5441-5446 http//:funsite.sdsc.edu
  5. 5. • Geometric and topological constraints • Evolutionary constraints • Dynamic constraints • Physiochemical constraints Detecting Protein Binding Promiscuity in a Given Proteome HASSTRVCTVREPRTSEQAENCE SMAP v2.0 Approach
  6. 6. Geometric Potential – A Geometric Constraint  Challenge: inherent flexibility and uncertainty in homology models  Representation of the protein structure - Cα atoms only - Delaunay tessellation - Graph representation  Geometric Potential (GP) GP = P + Pi Di+1.0neighbors ∑ × cos(αi)+1.0 2.0 L. Xie & P. E. Bourne, BMC Bioinformatics, 8(2007):S9 100 0 Geometric Potential Scale 0 0.5 1 1.5 2 2.5 3 3.5 4 0 11 22 33 44 55 66 77 88 99 Geometric Potential binding site non-binding site Approach
  7. 7. Sequence-order Independent Profile-Profile Alignment (SOIPPA) L E R V K D L L E R V K D L Structure A Structure B S = 8 S = 4 Xie & Bourne, PNAS, 105(2008):5441 Approach
  8. 8. Similarity Matrix of Alignment – Chemical & Evolutionary Constraints? 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 i a i i b i b i i a SfSfd ∑∑ += 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 Xie and Bourne 2008 PNAS, 105(14) 5441
  9. 9. 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
  10. 10. 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-drugome Kinnings et al 2010 PLoS Comp Biol 6(11): e1000976
  11. 11. 1. Determine the TB Structural Proteome 284 1, 446 3, 996 2, 266 TB proteom e hom ology m odels solved structures  High quality homology models from ModBase (http://modbase.compbio.ucsf.edu) increase structural coverage from 7.1% to 43.3% Kinnings et al 2010 PLoS Comp Biol 6(11): e1000976
  12. 12. 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 Kinnings et al 2010 PLoS Comp Biol 6(11): e1000976
  13. 13. 3. 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).
  14. 14. 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 Kinnings et al 2010 PLoS Comp Biol 6(11): e1000976
  15. 15. Agenda  Research that Informs my NIH Agenda – The TB drugome – towards reproducibility – Systems pharmacology – towards interoperability  Some Challenges – We have the why, but we lack the how – The how involves: • Representation • Sustainability • Discoverability • Training
  16. 16. Agenda  Research that Informs my NIH Agenda – The TB drugome – towards reproducibility – Systems pharmacology – towards interoperability  Some Challenges – We have the why, but we lack the how – The how involves: • Representation • Sustainability • Discoverability • Training
  17. 17. Characteristics of the Original and Current Experiment  Original and Current: – Purely in silico – Uses a combination of public databases and open source software by us and others  Original: – http://funsite.sdsc.edu/drugome/TB/  Current: – Recast in the Wings workflow system
  18. 18. Considered the Ability to Reproduce by Four Classes of User  REP-AUTHOR – original author of the work  REP-EXPERT – domain expert – can reproduce even with incomplete methods described  REP-NOVICE – basic domain (bioinformatics) expertise  REP-MINIMAL – researcher with no domain expertise Garijo et al 2013 PLOS ONE 8(11): e80278
  19. 19. A Conceptual Overview of the Method Should Be Mandatory Garijo et al 2013 PLOS ONE 8(11): e80278
  20. 20. Time to Reproduce the Method Garijo et al 2013 PLOS ONE 8(11): e80278
  21. 21. Its not that we could not reproduce the work, but the effort involved was substantial Any graduate student could tell you this and little has changed in 40 years Perhaps it is time we did better?
  22. 22. Agenda  Research that Informs my NIH Agenda – The TB drugome – towards reproducibility – Systems pharmacology – towards interoperability  Some Challenges – We have the why, but we lack the how – The how involves: • Representation • Sustainability • Discoverability • Training
  23. 23. Human Kidney Modeling Pipeline Recon1 metabolic network constrain exchange fluxes preliminary model refine based on capabilities literatur e set flux constraints normalize & set threshold renal objectives set minimum objective flux GIMME metabolic influx metabolic efflux kidney model healthy kidney gene expression data Approach metabolomic blood/urine & kidney localization data R.L Chang et al. 2010 PLOS Comp. Biol. 6(9): e1000938
  24. 24. Agenda  Research that Informs my NIH Agenda – The TB drugome – towards reproducibility – Systems pharmacology – towards interoperability  Some Challenges – We have the why, but we lack the how – The how involves: • Representation • Sustainability • Discoverability • Training
  25. 25. Agenda  Research that Informs my NIH Agenda – The TB drugome – towards reproducibility – Systems pharmacology – towards interoperability  Some Challenges – We have the why, but we lack the how – The how involves: • Representation • Sustainability • Discoverability • Training
  26. 26. Representation  Requires community engagement: – RDA – GA4GH – FORCE11 – ……  Policies – Genomic data sharing plan – Machine readable data sharing plans  Particular needs surrounding phenotypic data
  27. 27. Sustainability The How of Data Sharing  More credit to the data scientists  Change to funding models – become less IC based  Public/Private partnerships  Interagency cooperation  International cooperation  Better evaluation and more informed decisions about existing and proposed resources – How are current data being used?  Role of institutional repositories – reward institutions rather than PIs
  28. 28. Discoverability  Calls for data and software registries (e.g., DDI)  Data commons (NIH drive?)  More clinical trial data in the public domain  Facilitate authentication and hence access to clinical data
  29. 29. Training  Calls out for training grants – new and as supplements to existing training efforts  Regional training centers (cf Cold Spring Harbor)?
  30. 30. NIHNIH…… Turning Discovery Into HealthTurning Discovery Into Health

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