During SmartLab Exchange 2015, Allotrope Foundation and OSTHUS presented the latest update on the Allotrope Framework. To learn more, please view the slides below. Presented by: Dana Vanderwall, BMS Research IT & Automation Patrick Chin, Merck Research Laboratories IT Wolfgang Colsman, OSTHUS

1. Dana Vanderwall, BMS Research IT & Automation
Patrick Chin, Merck Research Laboratories ITPatrick Chin, Merck Research Laboratories IT
Wolfgang Colsman, OSTHUS
©2015 Allotrope Foundation
web: www.allotrope.org
mail: more.info@allotrope.org

2. Abstract
Towards 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 integrationg p y y g g
of 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

3. Reference Architecture & Data Standards
Lab Workflow
Forecasting
& Capacity Planning
Request Management
& Tracking
Collaboration
& Distribution
y
Plan
Analysis
Prepare
Samples
Submit
Samples
Acquire
Data
Process
Data
Store
Data
Analyze
Data
Reports
Results
Allotrope Metadata Allotrope Class
Data Management
Allotrope Data Format
Allotrope Metadata
Taxonomies
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

4. 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 because
on 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
AbbVie
Amgen
Boehringer Ingelheim
Bristol-Myers Squibb
GlaxoSmithKline
Merck
©2015 Allotrope Foundation 4
Baxter
Bayer
Biogen Idec
Eisai
Eli Lilly
Genentech/Roche
Pfizer

5. The basic analytical workflow and
data flow standardizedProcess
Step
Legend
Data &
Metadata
Step
Request Report
Search
& Reuse
Data
Plan
Analysis
Prepare
Samples
Submit
Samples
Control Inst.
Acquire
Data
Process
Data
Analyze
Data
Reports
Results
Store,
Archive
Data
©2015 Allotrope Foundation 5

6. The basic analytical workflow and
data flow standardizedProcess
Step
Legend
Data &
Metadata
Step
Request Report
Search
& Reuse
Data
Plan
Analysis
Prepare
Samples
Submit
Samples
Control Inst.
Acquire
Data
Process
Data
Analyze
Data
Reports
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

7. The basic analytical workflow and
data flow standardizedProcess
Step
Legend
Data &
Metadata
Step
Request Report
Search
& Reuse
Data
Plan
Analysis
Prepare
Samples
Submit
Samples
Control Inst.
Acquire
Data
Process
Data
Analyze
Data
Reports
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

8. The basic analytical workflow and
data flow standardizedProcess
Step
Legend
Data &
Metadata
Step
Interoperability
More automated reporting,
Powerful searching
Request Report
& Share
Search
& Reuse
Data
Control Inst.
Acquire
Data
Process
Data
Analyze
Data
Plan
Analysis
Prepare
Samples
Submit
Samples
Control Inst.
Acquire
Data
Process
Data
Analyze
Data
Reports
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

9. A lot of good ideas that can be used:
More than 100 relevant public standards & ontologies, highly connected
US Library of Congress
Open Archive Initiative
Analytical Data
Standards
p
International Standards Organization
Observations and Measurements
SensorML
AnIML
Metadata
Standards
Service
Allotrope
Framework
S88/BatchML
mzML
…
Standards
Architecture
Standards
©2015 Allotrope Foundation 9

10. 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
©2015 Allotrope Foundation 10
Biovia
BSSN
Mettler Toledo
Sartorius
Waters

11. 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!
©2015 Allotrope Foundation 11

12. 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
– …
12

13. 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

14. The benefits
Less 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/decision
R 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
14
What are your priorities?

15. 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 LLP
1 202 230 5439
James Vergis, Ph.D.
Science Advisor | Drinker Biddle & Reath LLP
1 202 230 5439
©2014 Allotrope Foundation 15
1-202-230-5439
James.Vergis@dbr.com
more.info@allotrope.org www.allotrope.org
1-202-230-5439
James.Vergis@dbr.com
more.info@allotrope.org www.allotrope.org