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A Journey
with the
ACD/Labs Spectrus System
James Beery
BASF Agricultural Solution Center, RTP, NC USA
Henning Behnken
BASF Agricultural Center, Limburgerhof, Germany
Our Journey
2ACD/Labs NMR Software Symposium4/10/2016
In 2013,
after many years of discussion,
the BASF Crop Protection Global Structure Elucidation group
started a journey to manage the vast quantity
of spectral data being produced by the business.
This talk will present the
Who
Why
How
Where
of this Journey
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Who We Are
BASF Crop Protection
Sites worldwide
* in development
Campinas
UtreraRTP
Pune *
Guaratinguetá
Bay, Laguna
Hannibal
Beaumont Tarragona
Elbeuf
São Paulo
Singapore
Limburgerhof
Ludwigshafen
Schwarzheide
Navi Mumbai
Ames
St. Joseph
Littlehampton
Production center
Global headquarters
Research center
Global agro research stations
Regional headquarters
* in development
Rudong
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Why do we need a solution
Challenges
(and why we are different from other BASF businesses)
Regulated industry each country has their own agency
in United States Environmental Protection Agency (EPA)
governs the process to register a product
also set rules for how testing is to be performed and how data is to be
handled.
In Crop Protection, we are responsible for keeping data for the life of a
product.
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Registration
Apart from medicines, plant protection
products are the most thoroughly scrutinized
substances
About 800 requirements need to be
addressed and more than 200 studies are
filed for a registration
Sale without approval from national
authorities (registration) is not possible
Product Lifecycle
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Development of a
crop protection product
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140,000
costs per active
incredient
225million euros
Years 1 2 3 4 5 6 7 8 9 10
Active
Ingredient
Chemistry
Formulation
Research
Biology
Development
Degradation& residues
Toxicology
Ecotoxicology
Substances
Small-plot trials
Registration
Kilo lab scale
Process development
Synthesis
Production
Process development
Development
Field trials (worldwide)
Plant, animal, soil, water, air
Algae, daphnia, fish, birds,
microorganisms, bees, beneficials
Acute and chronic toxicity, carcinogenicity, mutagenicity,
teratogenicity, reproduction
Screening
Laboratory/greenhouse
1
Source: Industrieverband Agrar, 2008
Data Storage
10ACD/Labs NMR Software Symposium4/10/2016
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How are we going to solve
this problem
‘Live’ Data
Structured
Homogeneous
Unified Data
Metadata for Search
Analyze Repeatedly
Accumulate
Knowledge
Create Intelligence
and Gain Insight
Dead Data
Unstructured
Heterogeneous
Siloed Data
Metadata for
Search?
Analyze
Once
Discrete Knowledge
—
Spectral Data Management
Capture Knowledge Generate Intelligence
ACD/Labs Spectrus System
Utilize Intelligence
Peak Search
Spectral Similarity Search
Structure/Sub-Structure Search
Metadata Search
Analyze
Interpret
Re-Process
Processor Interface
Manage Unified Information
NMR
MS
IR
UV/Vis
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Where are we in the journey
The Global Analytics Database (GLAD)
Started in 2013
Captured older intelligence
Collection of metabolite and by-product information from
Old databases ISIS DB, corporate LIMS
Existing non-digital information (i.e. paper copy)
Newly generated data
Internal
External (from CRO)
Database maintenance and data entry handled within the
group closed system
17
> 6300 substances
> 10000 spectra
more than 20 years
structure elucidation at
BASF captured!
Collected Data
Structures and related data
SMILES
IUPAC name
formula
Fully assigned spectral data
NMR
LC/MS
IR
Retention times & HPLC methods
Meta data
18
Studies covered by GLAD
Guideline
Metabolism
Studies internal &
external
Cold Metabolism
Studies
Indicator
Metabolism
Studies
By-product
Analysis
5 Batch Studies
Reference
Substance
Certification
Automatic
interpretation
19
All structure related information in the life cycle of an a.i. is captured
Purpose of GLAD
Effective state-of-the-art global knowledge
management
Prevention of double work
Faster analytics
Support and data capturing of external
studies
20
The Journey
Setup enterprise database, import existing data
Collect NMR data; process and import to database
Collect MS data; process and import to database
Improve workflow, more efficient interpretation
Exploring use of Structural Elucidator
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ACD/Structure Elucidator
Pilot study on-going
does the tool fit our workflow?
does it work for our samples?
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Initial results
Good tool, but it won’t do the work for you
Steep learning curve
Integrates well with processor interface
Problems with correlations
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NM
RS
yn
cM
an
ua
l
Co
rre
ctio
n GIGO
or more properly
bad/erroneous/incorrect correlations in –
nothing out
missed and
overlapped
correlation
HMBC: one bond
correlation picked
Ginkgolide A
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44 HMBC correlations, no constraints 48 hours
with constraints seconds
26 HMBC correlations, no constraints no structure
with contraints < 1 hour
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Acknowledgements
BASF
John Grimes
ACD Labs
Arvin Moser
Gerd Rheinwald
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Thank you!
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