Hoeck – Data Visualizations in the context of a datawarehouse 1

Data VisualizationsA guide to understanding the broader impact of small molecule candidates in the context of a datawarehouse

Data VisualizationsA guide to understanding the broader impact of small molecule candidates in the context of a datawarehouse

Wolfgang G. Hoeck, Ph.D.Amgen Inc. – Dept. of Research Informatics

Wolfgang G. Hoeck, Ph.D.Amgen Inc. – Dept. of Research Informatics

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Outline of this presentationOutline of this presentation

• Spotfire architecture overview and user communities

• Spotfire integration with Assay Datawarehouse

• What can be accomplished using this integration

• Approaches to consider for the future

• Spotfire architecture overview and user communities

• Spotfire integration with Assay Datawarehouse

• What can be accomplished using this integration

• Approaches to consider for the future

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Spotfire DecisionSite ArchitectureSpotfire DecisionSite Architecture

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Amgen’s Spotfire User CommunityAmgen’s Spotfire User Community

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Spotfire integration with AssayDatawarehouseSpotfire integration with AssayDatawarehouse

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Simplified Assay Data FlowSimplified Assay Data Flow

AssayDataStore

ActivityBase

AIMAssay

Datawarehouse

Low-ThroughputAssay Results

High-ThroughputAssay Results

Data In Data Out

• Unique identification for an assay and its results

• Standardized format for assay context (attribute/value pairs)

• Standardized format for assay results (Result types, Units)

• A place to check (QC) the results

• One place for “published” results

• Unique identification for an assay and its results

• Standardized format for assay context (attribute/value pairs)

• Standardized format for assay results (Result types, Units)

• A place to check (QC) the results

• One place for “published” results

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Extensive set of Information LinksExtensive set of Information Links

• One molecule – all assays

• Many molecules – all assays (with

or w/o pivoting)

• One assay – all molecules

• Many assays – all molecules (with

or w/o pivoting)

• One molecule – specific list of

assays

• Many molecules – specific list of

assays (with or w/o pivoting)

• Many more ………………..

• One molecule – all assays

• Many molecules – all assays (with

or w/o pivoting)

• One assay – all molecules

• Many assays – all molecules (with

or w/o pivoting)

• One molecule – specific list of

assays

• Many molecules – specific list of

assays (with or w/o pivoting)

• Many more ………………..

Challenges:- Achieving an acceptable performance- Understanding the path Spotfire chooses- Working with DBA’s- Creating Views- Documenting the Links- Managing Change

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A collection of Process GuidesA collection of Process Guides

Step 1:

Step 2:

Step 3:

Retrieve Vendor Data

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What can be accomplished using this integrationWhat can be accomplished using this integration

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Problem statement(s)Problem statement(s)• I have one or a set of candidates that I am

interested to further pursue. Before starting on any new experiments, can I see what others have done with these molecules? Gain a historical perspective?

• Can we identify patterns of biological behavior that is driven by chemical similarity?

• Can we identify patterns of biological behavior that is NOT driven by chemical similarity?

• Did we screen these compounds against target x?

• I am interested in target x, what are the best compounds we have in our library?

• ….

• I have one or a set of candidates that I am interested to further pursue. Before starting on any new experiments, can I see what others have done with these molecules? Gain a historical perspective?

• Can we identify patterns of biological behavior that is driven by chemical similarity?

• Can we identify patterns of biological behavior that is NOT driven by chemical similarity?

• Did we screen these compounds against target x?

• I am interested in target x, what are the best compounds we have in our library?

• ….

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One Molecule – Many AssaysOne Molecule – Many Assays

Search – Filter – Sort – Annotate - Filter

1.) Search ResultsByRoot Information Link

3.) Sort

2.) InteractiveFilter

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One Molecule – Many AssaysOne Molecule – Many Assays

Search – Filter – Sort – Annotate - Filter

4.) Annotate

5.) Filter

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Many molecules - Many Assays Looking for patterns …Many molecules - Many Assays Looking for patterns …

Similar pattern – Similar StructureSimilar pattern – Dis-similar Structures

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Tools & DataTools & Data

• Spotfire DecisionSite: Broad access to assay data – one molecule, all assays or many molecules, all assays

• AIM Datawarehouse: Published (verified) assay results from high-, medium and low-throughput assays

• ADAPT: Compound clustering by similarity• Excel: Manually curated assay context

(Annotations)• Assay Intelligence: Search and display

assay context (Annotations)

• Spotfire DecisionSite: Broad access to assay data – one molecule, all assays or many molecules, all assays

• AIM Datawarehouse: Published (verified) assay results from high-, medium and low-throughput assays

• ADAPT: Compound clustering by similarity• Excel: Manually curated assay context

(Annotations)• Assay Intelligence: Search and display

assay context (Annotations)

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Tools & Data – Data formatsTools & Data – Data formats

Compounds Assays

Assays

Com

pounds

• What you CAN GET:

– Sorting and filtering by assay annotations: Target, Function, Inducer, Readout, etc.

– Grouping by Compound Similarity and Assay Characteristics; SAR-patterns easier visible

• What you CANNOT GET:

– Structure display

• What you CAN GET:

– Structure display

– Filtering by structural classes

– Manual grouping by assay classes

• What you CANNOT GET:

– Sorting and filtering by assay annotations

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AnnotationsAnnotations

• Structure Class annotation• Target Class annotation• Inducer annotation• Cell Type annotation• ….

• Structure Class annotation• Target Class annotation• Inducer annotation• Cell Type annotation• ….

“Clean annotations are the key to enabling sorting and filtering operations to focus on areas of interest and eliminate unwanted noise that distracts from the important areas.”

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Case StudiesCase Studies

• Target x enzyme inhibitors 1.5 years into screen

• Target Y screen 6 months into screen

• Target x enzyme inhibitors 1.5 years into screen

• Target Y screen 6 months into screen

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Target x enzyme inhibitors <40nMTarget x enzyme inhibitors <40nM

1000+ compounds tested in 219 POC assays, 91300 results

Target Z

ModelCell-basedScreen

Promiscuous Tyr Kinase

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Target x enzyme inhibitorsTarget x enzyme inhibitors

Tyr Kinases

S/T KinasesGleevec and related structures

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Target Y Screen – Top 1000 IC50 HitsTarget Y Screen – Top 1000 IC50 Hits

Target Y

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Target Y Hit Cluster #93Target Y Hit Cluster #93

Target Y

T2

S23T15

Target Y hits of similar structure sorted ascending by selectivity towards target 2.

Target YCounter-target Selectivity

XVX Class of compounds

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Take Home MessageTake Home Message

• What you CAN DO:– Data Visualizations help you survey a larger

number of molecules over a larger number of assays

– Take broader look at your molecules behavior

– Look for patterns that stand out– Encourage others to submit their data

• What you CANNOT DO:– Make one click and get an answer– Take what you see as “Black and White”

• What you CAN DO:– Data Visualizations help you survey a larger

number of molecules over a larger number of assays

– Take broader look at your molecules behavior

– Look for patterns that stand out– Encourage others to submit their data

• What you CANNOT DO:– Make one click and get an answer– Take what you see as “Black and White”

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Approaches to consider for the futureApproaches to consider for the future

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The “3D spreadsheet”The “3D spreadsheet”

Root | AA1223 | AA2234 | AA3445 | AA2312 | AA3411 | AA1244 | Class

cell bioch cell bioch cell cell

T1 T1 T12 T22 T15 T32

Kinase Kinase Kinase Kinase Kinase Kinase

S/T S/T Tyr Tyr S/T S/T

wt wt wt Kin-d wt wt

1232343334545552331

577

3562666777458221

2

0.01000.01200.00541.2237

102.44560.11231.99872.34661.25510.00010.00120.92200.0453

IC50 POC IC50 POC IC50 IC50

Sort to rearrange

Filter to rearrange

11122333334555

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Data Integration for Analytical ProcessingData Integration for Analytical Processing

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The future - Connecting with pathways or interaction networksThe future - Connecting with pathways or interaction networks

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AcknowledgementsAcknowledgements

• Cell-based screen teams

Inflammation• Larry Proctor

• Dave Wolcott

• Simon Chatwin

• Jen Sonnenberg

• Michael Billesbach

• Keith Hoyle

• Ariela Hui

• Bruce Holmen

• Kailash Swarna

• Dave Balaban

RI• Tyrosine Kinase

Screen Team

CRC

What’s Next?

• Computational Chemistry team

MS• Target Screen TeamsHTS

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Go Rallying – Reach new Heights!Go Rallying – Reach new Heights!