5 Best Practices Of Data Management For Emerging Biotechs & Pharma Startups
By Frits Stulp and Duncan van Rijsbergen, Iperion – a Deloitte business
Whether ambitious upcoming pharma startups and biotechs have their sights set on being the world’s first in their field or achieving a lucrative exit through a strategic company sale, starting out as they mean to go on will set them in good stead for the future they envisage. In order to have reliable 360-degree product and process visibility, data management and data-based processes are key. Here are five practical steps to getting this right:
1. Learn what not to do from companies held back by unwieldy content legacies.
Ordinarily, the temptation for any young and aspiring company would be to look to more established players for how to keep records and master regulatory rigor, yet in life sciences the big players are not the best model to follow when it comes to data management. These are companies that have brought in technology systems typically on a targeted, need-oriented basis, often behind the curve and under a series of restrictions, including what was available at the time and what would work with the current siloed legacy IT estate.
2. Manage your data systematically from the start.
The first and immediate challenge many young, ambitious pharma/biotech companies have is that excelling scientifically has been their primary focus up to now. Any investment they have secured will be channeled into the one or two compounds they’re working on and into successfully bringing them to market in the fastest and most efficient way possible. At such an early stage, they may not appreciate the need to capture information in a structured way or to invest in formal infrastructure.
Yet, even at an early investment stage or as an academic startup, startup teams will be generating and compiling information anyway, whether in the form of lab reports or some other record or output. If this activity is unstructured (informal manual notes, spreadsheet-based readings, Word documents), teams will only be storing up problems for later. When, eventually, they’re ready to go to market or collaborate with a large entity, they’ll need to find and collate all of that information to demonstrate that readiness and smooth the path to authorization. And this is where they will begin to experience the first pains of poor information management.
Far better advice is to embrace information or data management considerations and the appropriate technology right from the outset, which starts with appointing a chief technology officer or information/data evangelist and bringing them to the table for even the earliest scientific discussions.
The point is to adopt a data-based mindset and discipline from day one, understanding the value and role of all the information being captured and maintaining this in a way that will make people’s lives easier and help bring products to market in as frictionless a way as possible.
In the old model, followed by Big Pharma, companies might have set out to internally develop or procure a specified IT system, but in reality, the objective should be to develop a data-based mindset and strategy that is intertwined with everything the company does.
Although capturing rich CMC data won’t necessarily speed up product approval, getting into the habit of recording everything in a standardized, accessible, and combinable way will set the company in good stead for that 360-degree transparency all companies need to aim for. (And of course, CMC records reflect a lot of the variation information that regulators will need.) Comprehensive clinical data, meanwhile, could support earlier review and approval of products – the Holy Grail for ambitious startups with important new advanced therapies under development.
Traditionally, Big Pharma has made information management the preserve of individual departments – e.g., quality, CMC, or safety – in support of a targeted use case: most notably marketing authorization holder (MAH) dossiers for regulatory submission. Data flows in each silo to inform individual summaries, overlaid with a product description, when, logically, the information flow should be the other way around.
Even then, the picture isn’t complete: Information should be feeding into this picture from the earliest research stages, from lab-based substance management at a cellular level. Capturing every insight in a structured way would enable teams to track and report on every aspect of what they do, at any point now or in the future.
3. Adopt an architecture-based approach.
Although individual software vendors are making the right noises about making it easier for pharma/biotech companies to combine and manage data, many promote systems that draw from data lakes – e.g., to generate dossiers on the fly. The trouble is that often these platforms fail to make sufficient allowance for context/what the information will be used for. This is an oversight young startups can overcome by taking more of an architectural view of how they’ll capture and use data, which in turn points back to the need to link their technology and data strategies with their scientific ambitions from day one.
To lay the right foundations for data-driven processes, we advocate that these companies take an architecture-based approach to the way they capture and manage information, rather than trying to find the optimum software application to meet all of their needs today and tomorrow. This involves embracing data standards and ensuring that information can be exported and combined easily without risk of overlap or error.
4. Take a proactive role in developing data standards.
One of the huge advantages young startups have today with their greenfield setups is that they can capitalize on the immense amount of work that has gone into setting new data standards and data exchange mechanisms and start as they mean to go on. The latest data standards in pharma include CDISC and ISO IDMP as currently being implemented in the EU.
Just as importantly, there are industry consortia working proactively to encourage standards-based data exchange to accelerate the safe delivery of product benefits to patients. One of the latest and most exciting examples of this, with direct relevance to biotechs, is Accumulus Synergy, which is developing a global information exchange platform to transform how drug innovators and health regulators interact, using comparable parcels of data that have been compiled in a standardized way. Another is Pistoia Alliance, which aims to promote life sciences collaboration and accelerate innovation.
The fact that advanced products, by their very nature, are more complex than more established pharma lines provides a compelling reason for ambitious startups to command a proactive role in these consortia and discussion forums about the future of data management and exchange.
5. Work toward a vision of a “living dossier.”
In terms of “what good looks like,” the ultimate aim should be for young companies (indeed, all pharma companies) to work toward a live, continuously refreshed record of a product that tracks its evolution from conception. We might want to think of this as a “living dossier.” Although this is currently a vision rather than something that exists in practice today, keeping such a goal in mind will help ambitious young startups avoid investing in unwieldy regulatory bureaucracy (the huge administrative overheads that have plagued Big Pharma), so they can focus more of their time and energy on their products and the benefits for patients.
Clearly, standing on the shoulders of giants is not recommended when it comes to life sciences information management – unless it’s to get a good view of the issues involved with siloed, legacy systems and avoid repeating them. Young, dynamic startups that have the luxury of a clean slate are in a great position to capitalize on emerging best practices and start as they mean to go on: driven by standardized data.
About The Authors:
Frits Stulp is managing director of Iperion, a Deloitte company, where he leads a team of regulatory/IDMP experts active in various projects to deliver value to both pharmaceutical companies as well as regulators. In addition to having more than two decades of industry and consultancy experience, he is regarded internationally as a subject matter expert on IDMP and he proactively shares his knowledge and experience wherever he can. You can contact Frits at Frits.stulp@iperon.com.
Duncan van Rijsbergen is a specialist in regulatory affairs business process improvement, structured product data, and data interoperability at Iperion, a Deloitte company. You can contact Duncan at Duncan.vanRijsbergen@iperion.com.