Lab Informatics Talking Heads: Data Knowledge Management & Manufacturing

Pharma IQ
Posted: 05/31/2016

Lab operations are at the centre of a pharma firm’s ability to better cater for patients. The intelligence that stems from the lab in many cases is not only valuable but vital to equipping the manufacturers with maximum visibility during production of a pharmaceutical – for example quality by design specifications. (2)

Without this outward thesis towards R&D findings, a labcentric culture in a pharma company could in fact be harmful and lead to intelligence to remain in silos rather than dwelling within end to end informatics systems. For instance ignoring the need for LIMS to be integrated into a manufacturers EPR system could trigger a loss in productivity. (2)

By integrating lab results into the manufacturing structure this can allow for educated change and diversification with decision making. (2)

However, manufacturers face a lot of challenges, including:  issues with readability, the manipulation of data, interfaces and an inability to pull data from the complete set of needed systems. These represent major hurdles within a manufacturer’s shortened timescales they have to rapidly analyse repetitive data.

Lab info collection tools receive criticism for being more tailored to R&D than manufacturing, as a result there is a marked need for LIMS providers that cater for manufacturing streams to avoid these departments facing noise from discovery related functionalities.

While combining the vast volume of data with the expectation of reproducible, transparent, speedy analyses,  a large obstacle for manufacturers is the movement from manual work to sophisticated analyses. Ahead of the 2016 Lab Informatics Summit, Pharma IQ discussed the subject of data knowledge management with lab informatics and more specifically manufacturing with a selection of experts.

Expert Panel

Steve Thomas

Scientific Investigator,

Allan Jordan

Head of Chemistry at the Drug Discovery Unit,
Cancer Research UK Manchester Institute

Roman Affentranger

Head of Small Molecule Discovery Workflows at Pharma Research & Early Development Informatics, 
F. Hoffmann-La Roche AG

Aline Nink

IT Business Partner
Bayer CropScience AG

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Needless to say, laboratory informatics comes with its challenges – but in recent years, as pharma strive to a fully automated, fully integrated laboratory that completely captures and utilises data for multiple purposes across the business, it is no longer just about what informatics you have, but how you use them.

The 15th Annual Laboratory Informatics is one of the most senior, leading event in the laboratory informatics community; showcasing a range of case studies for you to take away practical insight, advice from the associations and dabbling in the cross industry case studies, which will enable you to innovate far beyond the general scope of software and systems.

In terms of increasing efficiency and processing the right data as quickly as possible, what are the main challenges encountered, also what are the hurdles specific to manufacturers?

Roman: “The main challenge is bringing together  the data from the different sources such as planning and documentation tools, reactors, and instruments. The integration of the data across these different sources is necessary for a deep understanding of the processes. Besides problems of technical nature, the biggest hurdle there is the lack of data standardization, that is, the prevalence of proprietary data formats.”

Steve: “We liaise with a number of different instrument manufacturers- with many analysts still preferring to process data in the native software. However, some manufacturers facilitate the output to informatics systems. With a button that says ‘Send to ACD’, (Advanced Chemistry Development, my department’s chosen platform This makes it as seamless as possible to stick data in the database, [it’s] almost as easy as it would be to send data out to the printer and then you get the maximum compliance. If it’s like pulling teeth pushing valuable data to your informatics system, then you end up with holes in your data where people don’t database all their information.”

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Pharma IQ
Posted: 05/31/2016

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