allotrope foundation & osthus at smartlab exchange 2015: update on the allotrope framework
TRANSCRIPT
Dana Vanderwall, BMS Research IT & AutomationPatrick Chin, Merck Research Laboratories ITPatrick Chin, Merck Research Laboratories IT
Wolfgang Colsman, OSTHUS
©2015 Allotrope Foundation
web: www.allotrope.orgmail: [email protected]
AbstractTowards A Fully Integrated Lab: Update On The Allotrope Foundation• The mission of the Allotrope Foundation is to create a framework enabling
standardization and integration that will transform the way analytical data i d h d d d Th All F k idis created, shared and managed. The Allotrope Framework provides a toolkit that can be used to create solutions that improve data integrity, improve process efficiency, and allow scientific organizations to realize the full value of their data.
• Our guests from the Foundation will give an overview of their project and what has been achieved so far.
• The scope of the framework includes:p– Simplifying access to data, results, decisions and contextual metadata across
instrument and software platforms
– Reducing costs and complexity by eliminating the need for custom integration g p y y g gof heterogeneous laboratory software platforms
– Enabling knowledge discovery and improve regulatory compliance by creating a better access to and management of knowledge across the full lifecycle
©2015 Allotrope Foundation 2
Reference Architecture & Data StandardsLab Workflow
Forecasting& Capacity Planning
Request Management & Tracking
Collaboration& Distribution
yPlan
AnalysisPrepare Samples
Submit Samples
Acquire Data
Process Data
Store Data
Analyze Data
Reports Results
Allotrope Metadata Allotrope Class
Data Management
Allotrope Data FormatAllotrope MetadataTaxonomies
Allotrope Class Libraries and APIs
TaxonomiesTaxonomies MethodsMethods InstrumentsInstruments SamplesSamples ExperimentsExperiments ResultsResults DataData
D hb d M t d t B D t Vi
Slide 3
Data Analytics
Dashboards Metadata Browser Data Viewer
What problem are we trying to solve?
It’s hard to find data based It’s hard to share, compare or integrate data from
It’s hard to analyze or mine a collection of data becauseon intuitive starting points
[study, project, analyst, technique, etc.]
or integrate data from different labs or instruments
because the file format is different
collection of data because the details of the experiment
and the context is stored somewhere else (metadata)
Can’t understand /interpret data later because the context is incomplete,
inconsistent, often free text
Limited interoperability with instrument & software
AbbVieAmgen
Boehringer IngelheimBristol-Myers Squibb
GlaxoSmithKlineMerck
©2015 Allotrope Foundation 4
BaxterBayerBiogen Idec
Eisai Eli LillyGenentech/Roche
Pfizer
The basic analytical workflow and data flow standardizedProcess
Step
Legend
Data & Metadata
Step
Request ReportSearch
& Reuse Data
Plan Analysis
Prepare Samples
Submit Samples
Control Inst. Acquire
DataProcess
DataAnalyze
DataReports Results
Store, Archive
Data
©2015 Allotrope Foundation 5
The basic analytical workflow and data flow standardizedProcess
Step
Legend
Data & Metadata
Step
Request ReportSearch
& Reuse Data
Plan Analysis
Prepare Samples
Submit Samples
Control Inst. Acquire
DataProcess
DataAnalyze
DataReports Results
Store, Archive
Data
Sample Prep Data
Instrument Instructions
Instrument Data
Processed Data
Analyzed Data
Reported Results Stored DataAnalytical
Method
Ultimately the collective meta data is EVIDENCE that supports a DECISION about your
©2015 Allotrope Foundation 6
Ultimately the collective meta data is EVIDENCE that supports a DECISION about your MANUFACTURING PROCESS or MATERIAL
The basic analytical workflow and data flow standardizedProcess
Step
Legend
Data & Metadata
Step
Request ReportSearch
& Reuse Data
Plan Analysis
Prepare Samples
Submit Samples
Control Inst. Acquire
DataProcess
DataAnalyze
DataReports Results
Store, Archive
Data
Sample Prep Data
Instrument Instructions
Instrument Data
Processed Data
Analyzed Data
Reported Results Stored DataAnalytical
Method
Standard data file format & metadata
©2015 Allotrope Foundation 7
The basic analytical workflow and data flow standardizedProcess
Step
Legend
Data & Metadata
Step
InteroperabilityMore automated reporting,
Powerful searching
Request Report & Share
Search & Reuse
Data
Control Inst. Acquire
DataProcess
DataAnalyze
Data
Plan Analysis
Prepare Samples
Submit Samples
Control Inst. Acquire
DataProcess
DataAnalyze
DataReports Results
Store, Archive
Data
Sample Prep Data
Instrument Instructions
Instrument Data
Processed Data
Analyzed Data
Reported Results Stored DataAnalytical
Method
Standard data file format & metadata
©2015 Allotrope Foundation 8
A lot of good ideas that can be used:More than 100 relevant public standards & ontologies, highly connected
US Library of CongressOpen Archive Initiative
Analytical Data Standardsp
International Standards OrganizationObservations and Measurements
SensorMLAnIML
Metadata Standards
Service Allotrope
FrameworkS88/BatchML
mzML…
Standards
Architecture Standards
©2015 Allotrope Foundation 9
Work completed in 2014• Standards
– Evaluated > 100 public standards against scientific and business requirements across the full data lifecycle, from creation to archiving
– Developed reference architecture for data archiving based on public standards
– Federated select standards and ontologies for use by the Framework
• Development• Development– Created first version of Framework (pre-release), with class libraries for ADF, metadata
repository and data archive
Delivered proof of concept software to all members demonstrating use of the– Delivered proof-of-concept software to all members, demonstrating use of the Framework for HPLC-UV instrument methods, output, data exchange, and archiving
• Benchmarked ADF performance using MS data
L h d th All t P t N t k t t ith i t t d• Launched the Allotrope Partner Network to partner with instrument and software vendors to facilitate adoption
ACD/Labs IDBS Thermo Scientific
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BioviaBSSN
Mettler ToledoSartorius
Waters
ADF Attributes• Semantic approach to metadata (triples) gives context
and meaning to data( )• Data Cube (multi-dimensional data container)
• HDF5 binary format for compact storage and fast access (indexed)(indexed)
• Checksums for security• Portable• Portable• Platform Independent
V d I d d t• Vendor Independent• Highly Extensible!
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Work happening in 2015• Complete the APIs for working with the ADF
• Sharing and rationalizing member company taxonomies as content for the t d t itmetadata repository
• Integration projects with vendors to test the Framework in member company laboratoriesp y– device discovery – Internet of Things (automated inventory, live equipment status)– workflow execution (laboratory automation)– PAT online data– data preservation– projects span Research, Development and Manufacturing
• Embedding common standards in our labs for targeted analytical techniquesec ques– HPLC-UV– MS– Balance– pH Meter
©2015 Allotrope Foundation
– …
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What we’ve learned• We share the same pain points – so we can share the cost
of fixing the root causes of problem (not a “band aid” fix)
• Doing the work and testing assumptions on paper, with code, and in the lab is a great way to make progress
It t k it t d ti f i ti t l b• It takes money, commitment and time from scientists, lab managers, and senior managers
• It takes professionals software engineers architects• It takes professionals – software engineers, architects, laboratory automation, attorneys, scientists, process/domain experts, project managers
• We are making progress, hitting milestones, and will deploy the first production Framework in 2016
©2015 Allotrope Foundation 13
The benefitsLess Manual I d D Seamless dataLess Manual Document
Preparation• Find data quickly/logically
Improved Data Integrity
• Eliminate error due to l
Seamless data exchange & with partners & CROs
• One data file format• Eliminate Copy/paste• No more
Transcription/conversion• Source agnostic
manual text entry or transcription
• Complete, consistent, accurate metadata
• One consistent vocabulary• Reduced cost & complexity
to CROs, CMOs, partnerships
Lower Data M t C t
Facilitate Regulatory C li
Extracting Knowledge & V l f D tManagement Costs
• Interoperability• Future-proof against future
d t i ti
Compliance
• Improved instrument & software validation tracking
& Value from Data
• Greatly enhance speed to answer/decisionR d t ildata migrations
• Reduced technical debt: no more maintenance of legacy systems
• Improved archiving
tracking• Reduced complexity in
system documentation• Simpler to support
questions/investigations
• Remove data silos• Create an ecosystem for
innovation• Facilitates data mining &
analytics
©2014 Allotrope Foundation
• Improved archiving questions/investigations analytics
14What are your priorities?
Questions?Network with Peers: upcoming workshopsNetwork with Peers: upcoming workshops• Allotrope Cross-Industry Workshops
April 24 2015 (Cambridge MA)– April 24, 2015 (Cambridge, MA)– September 16, 2015 (Chicago, IL)
• Allotrope Partner Network Workshops• Allotrope Partner Network Workshops– February 19, 2015 (Philadelphia, PA)– March 12-13, 2015 (New Orleans, LA)March 12 13, 2015 (New Orleans, LA)– September 15, 2015 (Chicago, IL)
To join or get additional information, contact:To join or get additional information, contact:
James Vergis, Ph.D. Science Advisor | Drinker Biddle & Reath LLP1 202 230 5439
James Vergis, Ph.D. Science Advisor | Drinker Biddle & Reath LLP1 202 230 5439
©2014 Allotrope Foundation 15
[email protected]@allotrope.org www.allotrope.org
[email protected]@allotrope.org www.allotrope.org