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