1.
Revolutionizing Laboratory
Instrument Data for the
Pharmaceutical Industry:
How Semantic Technology is Helping Drive New
Standards for Data Management
Eric Little, PhD
VP Data Science
eric.little@osthus.com
Oliver Hesse
Director Lab Automation & Data Mgmt.
Bayer

2.
Slide 2
The Current Situation in the Lab
Many challenges exist for data to be captured, integrated and shared
Data Silos
Incompatible instruments and software
systems, proprietary data formats
Legacy architectures are brittle and
rigid
SME knowledge resides in people’s
heads, little common vocabulary
Data schemas are not explicitly
understood
Lack of common vision between
business units and scientists
2

3.
Slide 3
How do we change this situation? What did the music industry
teach us?
Data in Standard Format
Metadata in a Standard vocabularyRegulatory Guidance
Methods
Recipes
SOPs
…
Vendor-Specific Formats
Process
Material
Equipment
Result

4.
Slide 4
The Structure of Allotrope Is Unique
4
•Subject Matter Experts
•Project Funding
Member
Companies
•Project Management
•Legal & Logistical Support
Secretariat
•Framework Development
•Technical Leadership
Professional
Software Firm
•Requirements & Specifications
•Contributions, PoC Applications
Partner Network

5.
Slide 5
The Structure of Allotrope Is Unique
5
•Subject Matter Experts
•Project Funding
Member
Companies
•Project Management
•Legal & Logistical Support
Secretariat
•Framework Development
•Technical Leadership
Professional
Software Firm
•Requirements & Specifications
•Contributions, PoC Applications
Partner Network
AbbVie
Amgen
Baxter
Bayer
Biogen
Boehringer Ingelheim
Bristol-Myers Squibb
Eli Lilly
Genentech/Roche
GlaxoSmithKline
Merck & Co.
Pfizer

6.
Slide 6
The Structure of Allotrope Is Unique
6
•Subject Matter Experts
•Project Funding
Member
Companies
•Project Management
•Legal & Logistical Support
Secretariat
•Framework Development
•Technical Leadership
Professional
Software Firm
•Requirements & Specifications
•Contributions, PoC Applications
Partner Network
Abbot Informatics
ACD/Labs
Agilent
Biovia
Bruker
BSSN
Cytobank
EPAM
Fraunhofer IPA
Global Value Web
IDBS
LabAnswer
Labware
LEAP Technologies
Mestrelab Research
Mettler Toledo
PerkinElmer
Persistent Systems
Riffyn
Qualitest
Rondaxe
Sartorius
Sciex
Shimadzu
Synthace
TetraScience
Thermo Scientific
Transcriptic
Unchained Labs
Waters
Zifo
Erasmus Univ. Med
Center
J. Paul Getty Trust
(UK) Science and
Technology
Facilities Council
University of
Southampton
University of
Strathclyde
Stanford University

7.
Slide 7
The Allotrope Framework
Allotrope Data Format (ADF)
Allotrope Data Models (ADM)
Allotrope Foundation Ontologies (AFO)

8.
Slide 8
The Allotrope Framework
Allotrope Data Format (ADF)
Graph Instances
Allotrope Data Models (ADM)
Constraints
Allotrope Foundation Ontologies (AFO)
Classes and Properties
is populated by
is structured by
provide standardized
vocabulary for

9.
Slide 9
Allotrope Taxonomies Domain Model [v.1.1.5]

10.
Slide 10
Taxonomies Standardize Metadata Across Domains
Result
Process
Equipment
10

11.
Slide 11
Codes
Terms
Vocabularies
TaxonomiesModels
Ontologies
Reasoning
SEMANTIC METHOD

12.
Slide 12
Allotrope Data Format (ADF)
HDF5
Platform Independent File Format
Allotrope Data Format (ADF)
Descriptive metadata about
• Method, instrument, sample, process, result, etc.
• Provenance, audit trail
• Data Cube, Data Package
Analytical data represented by one- or
multidimensional arrays of homogeneous data
structures.
Data represented by arbitrary formats, incl. native
instrument formats, images, pdf, video, etc.
Specifically designed to store and organize large
amounts of scientific data.
Data Description
Semantic Graph Model
Data Cubes
Universal Data Container
Data Package
Virtual File System
APIs(Java&.NETclasslibraries)

13.
Slide 13
Allotrope Data Format Example
Platform Independent File Format
Data Description
Data Cubes
Data Package
Request Sample Method Data & ResultsRun
Chromatogram 2D HDF
Chromatogram 2D
HDF
Chromatogram: 3DChromatogram: 2D

14.
Slide 14
The Foundation for Data Integrity & Analytics
Plan
Analysis
Prepare
Samples
Submit
Samples
Control Inst.
Acquire
Data
Process
Data
Analyze
Data
Reports
Results
Store,
Archive
Data
Request Report
Find &
Reuse
Sample
Prep Data
Instrument
Instructions
Instrument
Data
Processed
Data
Analyzed
Data
Reported
Results Stored DataAnalytical
Method
Allotrope Foundation Ontologies (AFO)
Taxonomies
Material
Equip-
ment
Process Result
Proper-
ties
Stability
Batch
Release
Solubility …
HPLC MS NMR …
Allotrope Data Models (ADM)
Stability
Study
Batch Rel.
Study
Solubility
Study
…
HPLC-UV
Experiment
MS
Experiment
NMR
Experiment
…

15.
Slide 15
Solubility Testing Example *)
Instrument
Level
LIMS/ELN
Level
Solid
Dispense
Liquid
Dispense
Conditioning Centrifuge Filter Dilution
HPLC
Analysis
Raman
Analysis
xRPD
Analysis
pH
Analysis
LIMS / ELN
Allotrope Foundation Taxonomies
Dispense Ontology
Conditioning
Ontology
Centrifuge
Ontology
Filter
Ontology
Dilution
Ontology
HPLC
Ontology
Raman
Ontology
xRPD
Ontology
pH
Ontology
Solid
Dispense
Data Model
Liquid
Dispense
Data Model
Conditioning
Data Model
Centrifuge
Data Model
Filter
Data Model
Dilution
Data Model
HPLC
Data Model
Raman
Data Model
xRPD
Data Model
pH
Data Model
Solubility Study Data and Metadata
*) Extensions planned after the initial public release
Solubility Testing
Ontology
Solubility Testing
Data Model

16.
Slide 16
Allotrope Provisional Roadmap
4Q 16 1Q 17 2Q 17 3Q 17 4Q 17
ADM
ADM 1.0 – Initial Standardized Data Models + Certification + Governance
Scoping ADM 1.0 Delivered
ADM 1.0 Tested
Public release
extensions
ADF
ADF 1.2 – Regulatory Compliance
ADF 1.2 Delivered
ADF 1.2 Tested
Scoping
ADF 1.3 – Structural Robustness
ADF 1.3 Delivered
ADF 1.3 Tested
Public release
maintenance
AFO
AFO 1.2 – Structural Robustness + Governance
Scoping AFO 1.2 Delivered
AFO 1.2 Tested
Public release
extensions
Design

17.
Allotrope @ Bayer

18.
Bayer • Company Profile 2016Slide 18
Full year sales: €46.3 billion**
115,176 employees*
307 subsidiaries
R&D expenses: €4.3 billion***
As of December 31, 2015 (including Covestro) / Employees: as of September 30, 2016 (including Covestro)
* excluding Covestro: 99,517 employees (in full-time equivalents)
** excluding Covestro: €34.3 billion *** excluding Covestro: €4.0 billion

19.
Strategic areas of interest
Leveraging Benefits of the Allotrope Framework
Bayer – Allotrope @ SmartData 2017Page 19
Allotrope
Implementation
Strategy
Analytical Method
Management
Transfer Analytical Methods
Archiving
Reprocessable data, long
term readable format , Data
Integrity
Instrument Integration
Electronic Workflows /
ELN & LIMS
Taxonomies
as Reference /Master Data
Assets & Instrument
Management
Internet of Things = live inventory
Data Lake
Post-Analysis of data / Data-
mining
External Collaboration
CRO Integration / Data & Method
Exchange
Application Interfaces
LIMS Connectivity, e.g. to CDS

20.
Taxonomies
Reference & Master Data as the Basis
Bayer – Allotrope @ SmartData 2017Page 20
Interview
Research
LIMS/ELN
Publish
Review
Instrument Taxonomies
HPLC / U-HPLC
HPLC-MS
Amino assays
ELISA
HTRF
Electrophoresis
Bioanalyzer
Capillary Electrophoresis
SDS-PAGE/Western Blot
iCIEF / iCE
qPCR
Spectrophotometer
Fortebio Octet/Blitz
Biacore
Mycoplasma
ACL
Multiplex fluorescent
Immunoassay (Mfi)
Microtiter plate readers
Potency Testing
Chromogenic Potency
Cell-based potency
Downstream Process
Taxonomies
Tangential Flow Filtration
(TFF)
Prep. Chromatography

21.
Analytical Method Management
From ‘Text’ to Machine Readable
Bayer – Allotrope @ SmartData 2017Page 21 Taken from: Weller HN, Nirschl DS, Paulson JL, Hoffman SL, Bullock WH., ACS Comb Sci. 2012,14(9),
520-526. doi: 10.1021/co300075g.
Material
Process
(method)
Properties
Device
Results

22.
Analytical Method Management
As-is: Interrupted Process for Setting up Analytics
Bayer – Allotrope @ SmartData 2017Page 22
LIMS
MANUALLY
Assigned
Analysis
TEXT-BASED
Method Description
MANUALLY
transcribed
HPLC-MS
Workstation 1
HPLC-MS
Workstation 2
HPLC-MS
Workstation 3

23.
Analytical Method Management
Our Vision
Bayer – Allotrope @ SmartData 2017Page 23
INTERNAL
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01010010101101011010101010100101011010110101001010101010110101101010101010
01010110101101010101010010101101011010101001010110101101010101010010101101
01101010010101010101101011010101010100101011010110101010101001010110101101
01010010101101011010101010100101011010110101001010101010110101101010101010
01010110101101010101010010101101011010101001010110101101010101010010101101
01101010010101010101101011010101010100101011010110101010101001010110101101
01010010101101011010101010100101011010110101001010101010110101101010101010
01010110101101010101010010101101011010101001010110101101010101010010101101
01101010010101010101101011010101010100101011010110101010101001010110101101
01010010101101011010101010100101011010110101001010101010110101101010101010
0101011010110101010101001010110101101010101010010101101011010
DATA LAKE
Companies’ secret data, IP
Knowledge, Research results
LIMS
DELIVERS work related
methods/data/information
Current work in LIMS/
ELN/etc. triggers
AUTOMATED
SEARCH
Information
Broker
Companies‘
analytical
scientist
PUBLIC
RESEARCH
Published Data,
Published scientific
information, Journals,
Patents
analytical_results.
adf

24.
Slide 24
Moving From Semantics to Data Science

25.
Slide 25
What is Data Science?
At OSTHUS Data Science has a special meaning
 Data Science is more than just statistical analysis
 We combine math-based approaches (statistics) with logic-based approaches (semantics)
 Conceptual + Computational
Semantics
 Provides the vocabularies, definitions, class structures, logical relationships and
conceptual models
Statistics
 Provide computations, trending, analysis, learning over time from the data itself

26.
Slide 26
AT OSTHUS LAB DATA SCIENCE IS
BIG ANALYSIS
STATISTICAL
SEMANTICS
MACHINE
LEARNING
REASONING

27.
Slide 27
Machine Learning is Becoming Increasingly Valuable
Very little is known to be certain
in one’s data – abductive
reasoning is needed
 Capture what you can
semantically
The rest can be gathered directly
from the data (bottom up)
Hypotheses can be driven from
SMEs and past patterns of
success
Often success of predictive
systems rely on testing the
models
 The accuracy of the model can be
helped using semantics
 The tests over time can show
problems of fit (alignment)
“Shelf life” Example:
I have data over 2 years – shows a shelf life of “x”
(I have some level of truth for this compound)
Now I take a similar compound “y”
What is its shelf life?
I can make a better guess based on previous
reasoning (induction)
I make a best guess for the shelf life of “y”
Test hypothesis on new data sets
Outcome:
1. Ability to understand and optimize in a shorter
period of time
2. Taxonomies and ontologies can help
understand the trend over time

28.
Slide 28
Smart Data for Smart Labs in the 21st Century
Smart labs in the future will
provide the enterprise with:
Integrated Data – common
reference data structures
(vocabularies)
Sharable Data – easier interaction
across teams and business units
Scalability – Big data applications
that can be highly elastic
Conceptual Representations –
context and perspective are
captured
Advanced Analytics – complex &
automated problem-solving
capabilities

29.
Thank You? Questions?