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data integration Analytics in pharma

Simon Tilley, head of pharmaceuticals at SAS UK, on meeting regulation in a big data age

Imagine the vast amounts of data generated from 12,000 or so clinical studies over two decades. Multinational companies often conduct trials across the globe, and also take on old trials conducted by acquired companies. The data is held in different siloed systems, formats and countries, making it difficult to respond to constantly evolving data standards and naming conventions that need data relating to specific studies within a matter of hours. With a sea of data to contend with, and often no idea where to start, one multinational pharmaceutical giant recognised that it was becoming increasingly difficult to meet these demands and it needed a data management solution that would convert the information into easily accessible and usable data.

The benefits Should standards come together, what can we, as an industry, expect? Schaefer believes that one critical benefit will be the lowering of integration costs as companies would only need to interface against that one standard. ‘If there is a normalised exchange mechanism that is open and standardised, the incremental costs of adding to instruments or data systems is greatly reduced,’ he said. Schaefer added that standards will also greatly reduce the total cost of ownership in terms of long-term data retention. ‘Retaining a data archive means that companies must maintain the soſtware and hardware capable



of reading that file type. If you multiply that by the versions of soſtware that have ever contributed a record to that archive, the total cost of ownership is exploding. Te idea is to reduce the number of file formats that need support, and consequently reduce the number of tools in the long term.’ Beyond that is the problem of soſtware

from one vendor being incompatible with hardware from another. ‘But if organisations invest in standard compliance to do the post-


To help consolidate and harmonise all of its data, the company worked with a three- company consortium, including SAS, which built and designed a programme spanning multiple locations across three continents. The challenge was to find a way to integrate data that varied in age by 20 years, during which time relevant data standards had changed massively. The processes also had to be transparent and fully auditable. Through a combination of SAS technology, domain knowledge, training and technical expertise, the pharmaceutical heavyweight was able to drive efficiencies in the way that it was accessing and managing clinical data, through the creation of a central repository. While the repository is physically hosted at SAS’ headquarters in the US, lab workers can access the information needed quickly and easily, and demonstrate a trail of how and when the information is accessed for audit purposes.

processing of the data from all instruments, they can standardise their methods and simply do feature-driven purchasing,’ explained Schaefer. Standardisation may seem like a logical step – but, as he warned, not only are the resources in the community scarce, but the question remains of whether vendors will be able to agree with their competitors that data should be presented in a certain way. ‘A standard is only a piece of paper, and you cannot implement a piece of paper,’ Schaefer said. ‘Tere is the need for tooling and vendor support.’ Randy Bell, director

of operations at LabLite, believes that in order to be effective, standardisation must be driven internally. ‘Externally, I just don’t think it is realistic,’ he said. ‘For example, take a simple environmental lab with a laboratory information management system (LIMS), a few instrument interfaces, that on occasion uses some contract labs, and has to provide compliance reports to the state. Te LIMS will have its own data format and naming convention, and each instrument could output data differently. Te format of

By applying an analytics solution to its data management challenge, the pharmaceutical company can respond to regulatory demands in the fastest, most accurate and cost-effective way. In creating efficiencies within its data storage and analysis capabilities it can avoid penalties from regulatory bodies and, from a pharmacovigilance perspective, react to adverse events, drugs misuse and medication errors as they occur. An advanced solution like visual analytics can help pharmaceutical professionals glean rapid insights from clinical trials for faster and smarter decisions.

Having data represented visually in one centralised hub also means that lab workers can report to key stakeholders or regulatory bodies with easily digestible yet in-depth information, justifying their work and securing a stamp of approval in line with stringent regulation. And that’s easier said than done!

the data received from each of the contract labs will most likely be different and each state has different electronic data deliverable (EDD) reporting formats. ‘Even in this simplistic example both the

Further information

Allotrope Foundation







The Pistoia Alliance


Thermo Fisher Scientific

lab and the LIMS vendor have to handle uniquely named and formatted data. I don’t necessarily look at this as a bad thing. I think it is just something we have to recognise exists in any implementation.’ He continued: ‘We would

probably all agree that data integration standards would be helpful; however, with the number of different lab and test types, equipment manufacturers, LIMS vendors, and client requirements, the reality is that each job poses its own unique data integration challenges. Based on the diversity of lab types and the various formats in which they receive data, I don’t think we will ever get to the point where there is one standard.’ In conclusion, John Wise of

the Pistoia Alliance believes there will be a coming- together of standards, but that standards in life science R&D have historically always been a challenge – one that is set to continue.

@scwmagazine l

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