Visualizing Large Arrays of Analytical Data in the Pharma and Chemical Industry

Presented By Dimitris Argyropoulos

PANIC 2017

20th February 2017

Hilton Head Island

Information Overload

• Processes in the Chemical and Pharma Industry can take years with huge amounts of information generated– Thousands of individual experiments/tests

– Hundreds of reports, TBytes of data

– Most concise summary is the marketing application but typically still runs to hundreds of pages to describe impurity control strategy

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Information Overload

• There will always be dozens of people involved in a project.– Analytical Chemists

– Medicinal Chemists

– Process Chemists

– Formulators

– Toxicologists

– Etc.

• They all need to have easy access to the data generated.

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Information Overload

• In a global workplace, project transfers are inevitable and managing this transition poses multiple problems

• Trying to share data or pass on project understanding to a new team is difficult– Methods / Specifications

– Knowledge transfers

• Can we make this transfer of knowledge more manageable and efficient?

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Example Case: Impurity Resolution Management• What is the current synthesis route?

• What impurities do we see and how much?

• How are these impurities formed and controlled?– Have we seen this impurity before and where

does it elute?

– What is this other peak?

– Where has it come from and is it a concern?

– What is this new peak?

– What does the spectrum look like?

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Route (Commercial)Route (Development)

Impurities in PhenolImpurities in MethylImpurities in AcetateImpurities in Asprin

0.12

Batch 123

<0.050.080.20

Fate of impurity BFate of impurity A

The information is already there!• ELN

– Excellent repository for experimental detail and observations, difficult to browse/find data unless you have specific identifiers

• LIMS– Great for tracking analysis, batches and numerical results, but difficult to extract

knowledge

• Spreadsheets—everyone loves a spreadsheet– Record batch details & analytical results (imps, assay, water, solvents, etc.)– Doesn’t link stages/batches together, not particularly visual, difficult to understand

rejection

• Access Databases– Ability to link data together (e.g., batch history), limited searching & no overview

• Global Document Management Systems– Normally only generated at key time points in development (not living documents)

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In reality…

ELN / LIMSOther

GDMS Excel

Data to Knowledge – building your control strategy

Quality controlProcess understanding

Data

Knowledge

InstrumentLC, MS, NMR, IR, TGA

Samples containing impurities

Sample preparation

Lab PC or CDSChromatogram, UV spectra, Mass spectra, NMR, IR, TGA

ELNExp. details, method, sample

prep, results, discussion, conclusions

LIMSExperimental details, method, sample prep

GDMSCMC modules, methods, process

descriptions, reports, data summaries, justifications

Pharmaceutical Technology & Development

Accurate MassMSMSMass spectrum

NMR (2D)NMR (Cabon)NMR (Proton)

Route (Commercial)Route (Development)

Fate of impurity BFate of impurity A

ELSDPDAChromatogram (UV at 254nm)

SST

Name Structure Mol Wt Formula

Flexibility to easily navigate around the data and view the results

View into impurity knowledge

Search by project, structure, or reaction

View into impurity knowledge

Select a structure and view all known impurities

View into impurity knowledge

Select a structure and view all known impurities

View into impurity knowledge

Select an impurity and see how they are formed and controlled

from previous experiments

View into impurity knowledge

Select an impurity and see how they are formed and controlled

from previous experiments

Structures are not mandatory

Labels can be used

Synthetic Route

Related Impurities

Composite Chromatogram

Method DetailsImpurity Fate

View into impurity knowledge

Synthetic Route

Analytical Data, Reports &

Parameters

Project Details

View into impurity knowledge

Structure and Route Details

Synthetic Route

Analytical Data, Reports &

Parameters

Project Details

View into impurity knowledge

Structure and Route Details

Synthetic Route

Analytical Data, Reports & Parameters

Project Details

View into impurity knowledge

Structure and Route Details

Synthetic Route

Analytical Data, Reports & Parameters

Project Details

View into impurity knowledge

Structure and Route Details

Peak SearchSpectral Similarity Search

Structure/Sub-Structure Search

Metadata Search

View into impurity knowledge

Solutions Already Implemented

• Astra Zeneca Macclesfield, UK (Steve Coombes, John Nightingale)

• Stay for our Software Demonstration!

• Visit our poster (n. 1)

• Speak with us in the Exhibition Hall

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Thank you!!