Capitalising on structured content management in R&D/Regulatory Affairs

Today, there are numerous external and internal drivers for life sciences organisations to be collecting, preparing, publishing/submitting and managing more and more data, and to be doing this more and more using a structured format. Regulatory agencies are demanding increased safety and traceability detail; requirements about what goes on packaging and labelling are becoming more stringent; and the public is calling for greater transparency about the products they use. Meanwhile, companies need to be smarter, faster and more cost-efficient in the way they capture information and create critical content – from clinical trials studies data collection/ reporting, to managing product labelling.

As a result of these growing data-based demands, there has been a surge in life sciences companies asking for help. They see the sizeable challenges ahead but are often unsure where to begin.

We propose the following as a practical roadmap.

1.      Start small, with focus

Companies should approach transformation with a clearly-defined business case in one small, targeted area. Once specific benefits have been demonstrated, this will help to make the case for broader change. But it’s important to keep the bigger picture in focus. A strong technical ‘backbone’ is critical – a master data source that is capable of supporting current and future use of regulated product data from one end of the global organisation to the other.

2.      Engage people in the transformation

Changing the way organisations manage content will inevitably require disruption to the way people behave and work, so identifying corporate-level champions, and creating a team that can showcase successful initial use cases, will be important in creating momentum.

3.      Scoping the challenge

A definitive data backbone is likely to take years to create. Ideally, it would be possible to analyse and transform content by tackling small sections at a time, comparing different sources to look for discrepancies or overlap. However, if respective systems and teams have captured data in different formats and with differing degrees of granularity, comparisons will require too much time. Other potential issues include data ownership. If content ownership has tended to exist at a document level rather than a source-data level, it may not be immediately obvious who should be driving any data transformation initiative.

4.      Define the future, then tackle the legacy

Establishing common structures and templates for data will help put a hard stop to continuing data complexity, by imposing firmer parameters over what and how data is captured from document/report authors. By restricting data input to what is needed by regulators, companies can start to curb the spiralling of free-form content.

This will help to keep everyone focused on building consistent, high-quality data with the potential for extensive re-use - as long as the accuracy and currency of the data is maintained across its lifecycle, so that it remains trusted as a definitive information source.

Establishing a common ‘dictionary’ will be instrumental in transforming existing content so that it can be retro-fitted into the new structured templates, and invested with new business value. A common dictionary sets down agreed rules for referring to products and data around them, and will define any metadata linked to that content which makes assets searchable in context.

Once historic product data has been transformed and assigned proper schema, and processes put in place to ensure that information edits or additions adhere to the new structure, the positive impact should be felt in everyday activity.

5.      Look for ‘easy’ wins first

Start with a proof-of-concept in an area where documents are fairly simple in make-up: factual rather than descriptive; and typically default to a single language. Some CMC/manufacturing documents fall into this category, detailing the composition of a drug and usually in a common language. Descriptions of formulae can be readily transferred to a table format, following standard fields describing the composition of a drug or its manufacturers.

Once companies have learnt what works well and proved the potential of a common, structured way to capturing and managed content, they can broach international labelling management from a structured basis, harnessing reusable master content.

About the author

Agnes Cwienczek is Head of Product Management and Consulting at Amplexor.