Allotrope Foundation & OSTHUS at SmartLab Exchange 2015: Update on the Allotrope Framework

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


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



Step

Legend

Data & Metadata

Step


& Reuse Data

Plan Analysis

Prepare Samples

Submit Samples


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


Ultimately the collective meta data is EVIDENCE that supports a DECISION about your MANUFACTURING PROCESS or MATERIAL


Step

Legend

Data & Metadata

Step


& Reuse Data

Plan Analysis

Prepare Samples

Submit Samples


DataProcess

DataAnalyze

DataReports Results

Store, Archive

Data

Sample Prep Data


Instrument Data

Processed Data

Analyzed Data


Method

Standard data file format & metadata



Step

Legend

Data & Metadata

Step

InteroperabilityMore automated reporting,

Powerful searching

Request Report & Share

Search & Reuse

Data


DataProcess

DataAnalyze

Data

Plan Analysis

Prepare Samples

Submit Samples


DataProcess

DataAnalyze

DataReports Results

Store, Archive

Data

Sample Prep Data


Instrument Data

Processed Data

Analyzed Data


Method

Standard data file format & metadata


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


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


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!


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


– …

12

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


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


• 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


[email protected]@allotrope.org www.allotrope.org

[email protected]@allotrope.org www.allotrope.org

Allotrope Foundation & OSTHUS at SmartLab Exchange 2015: Update on the Allotrope Framework

Data & Analytics

Transcript of Allotrope Foundation & OSTHUS at SmartLab Exchange 2015: Update on the Allotrope Framework