Next Generation Data and Opportunities for Clinical Pharmacologists
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Transcript of Next Generation Data and Opportunities for Clinical Pharmacologists
NEXT GENERATION DATA AND OPPORTUNITIES FOR CLINICAL
PHARMACOLOGISTS Philip E. Bourne Ph.D.
Associate Director for Data ScienceNational Institutes of Health
As of March 3, 2014
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
Reconstruction of Genome-Scale 3D Drug-Target Interaction Models
Integrating chemical genomics and structural systems biology
MDsimulation
Mj
Q
MjQ
ligENTS SMAPProtein-liganddocking
Mj
Q
Mi
3D model of novelTarget
3D model ofannotated target
interactionmodel
Querychemical
Networkmodeling
Experimentalsupport
L. Xie and P.E. Bourne 2008 PNAS, 105(14) 5441-5446http//:funsite.sdsc.edu
• Geometric and topological constraints• Evolutionary constraints• Dynamic constraints• Physiochemical constraints
Detecting Protein Binding Promiscuity in a Given Proteome
HASSTRVCTVREPRTSEQAENCE
SMAP v2.0
Approach
Geometric Potential – A Geometric Constraint
Challenge: inherent flexibility and uncertainty in homology models
Representation of the protein structure - C atoms only- Delaunay tessellation - Graph representation
Geometric Potential (GP)
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
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):5441Approach
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
ia
i
ib
ib
i
ia SfSfd
fa, fb are the 20 amino acid target frequencies of profile a and b, respectivelySa, Sb are the PSSM of profile a and b, respectively Xie and Bourne 2008 PNAS, 105(14) 5441
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
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
1. Determine the TB Structural Proteome
284
1, 446
3, 996 2, 266
TB proteome
homology
models
solve
d
structu
res
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
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
MethotrexateChenodiol
AlitretinoinConjugated estrogens
DarunavirAcarbose
Kinnings et al 2010 PLoS Comp Biol 6(11): e1000976
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).
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
raloxifenealitretinoin
conjugated estrogens &methotrexate
ritonavir
testosteronelevothyroxine
chenodiol
Kinnings et al 2010 PLoS Comp Biol 6(11): e1000976
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
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
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
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
A Conceptual Overview of the Method Should Be Mandatory
Garijo et al 2013 PLOS ONE 8(11): e80278
Time to Reproduce the Method
Garijo et al 2013 PLOS ONE 8(11): e80278
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?
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
Human Kidney Modeling Pipeline
Recon1metabolic network
constrain exchange
fluxespreliminary
model
refine based on
capabilities
literature
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
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
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
Representation
Requires community engagement:– RDA
– GA4GH
– FORCE11
– ……
Policies– Genomic data sharing plan
– Machine readable data sharing plans
Particular needs surrounding phenotypic data
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
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
Training
Calls out for training grants – new and as supplements to existing training efforts
Regional training centers (cf Cold Spring Harbor)?
NIHNIH……Turning Discovery Into HealthTurning Discovery Into Health