A Journey with the ACD/Labs Spectrus System · 2016. 4. 28. · Retention times & HPLC methods Meta data 18. Studies covered by GLAD Guideline Metabolism Studies internal & external

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


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


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.


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

8ACD/Labs Software Symposium4/10/2016

Development of a

crop protection product


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



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


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


ACD/Structure Elucidator

Pilot study on-going

does the tool fit our workflow?

does it work for our samples?


Initial results

Good tool, but it won’t do the work for you

Steep learning curve

Integrates well with processor interface

Problems with correlations


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


44 HMBC correlations, no constraints 48 hours

with constraints seconds

26 HMBC correlations, no constraints no structure

with contraints < 1 hour


Acknowledgements

BASF

John Grimes

ACD Labs

Arvin Moser

Gerd Rheinwald


Thank you!

Documents

A Journey with the ACD/Labs Spectrus System · 2016. 4. 28. · Retention times & HPLC methods Meta data 18. Studies covered by GLAD Guideline Metabolism Studies internal & external