A data-driven Regulatory ecosystem has huge potential, beyond the opportunity for operational improvement. However, Regulatory and product teams will need updated data skills to deliver this vision, reports Amplexor’s Renato Rjavec.
Even in today’s eCTD+ world, most life sciences Regulatory teams currently still think and work in terms of documents, paragraphs and sentences when putting together collateral for marketing authorisation and variations submissions. Yet it is data, rather than pre-prepared dossiers, that is moving into central focus now.
That’s as stakeholders across the life sciences and healthcare ecosystem realise that a data-first approach to collecting, managing and communicating product information will be the most efficient and reliable way to maintain a consistent, definitive, current and high-quality record of a product entering or on the market. One that can be interpreted and used in a wide range of use cases, by the broadest possible range of people (from regulators to clinicians, pharmacists and ultimately patients).
Professionals in a range of roles are now used to converting their particular information e.g. about the medicinal product’s clinical properties, chemical composition, or information for patients in the narrative form. But are they ready to adopt new, more structured ways of dealing with such information at the source? Or is there an expectation that the Regulatory role will effectively assume the burden of data extraction and data entry assistance for them?
Adapting to a Data-centric Approach
Given that this data-centric approach will be the new reality before long, the question for existing product information managers/ regulatory teams is whether their skill sets now need to be refreshed to reflect the target new ways of working (first, data and document set needing to be carefully aligned, then a direct flow of good data to the regulators).
So where are companies with all of this today? With the exception of very large pharma organisations with the budget and people resources to have already started exploring the wider possibilities, most companies still lack awareness both of the wider potentials and of the work ahead of them in building the right capabilities.
At one level, this is about how they manage product information so that (a) it fulfills the demands of new IDMP structured data requirements, and (b) becomes sufficiently reliable to form a foundation for not only product registrations and their maintenance, but all sorts of other processes too.
On another level, the opportunity extends to leveraging reporting and analytics to smart effect – first to help users fill gaps and increase the quality of the data; then with a more strategic emphasis, even using AIassisted tools to investigate the scope for process improvement (based on insights into how data is currently being managed and where recurring patterns are emerging).
