Data Changes from R&D, Preclinical, Clinical and Beyond

Chris Jennings: How do things change as a biotech organization evolves—moving from a resource or organization to preclinical to clinical to commercial, as it pertains to data?

Tony Murabito jumped in with the differences you see in how data is handled, especially at the preclinical and clinical stages where you see a lot of silos in play. Groups tend to work within themselves, be it biology, toxicology or screening. The only information sharing is usually done by ‘throwing it over the wall’ or in the case of output when they're working on publications. But really not a lot of thought at that time into building dossiers or starting to think about regulatory submissions. You tend to start that awareness phase as you enter pre-clinical and phase one. You're getting involved with patients and starting to deal with data that’s GLP regulated, GCP regulated, so you need to have more prescribed processes.  

He added the importance of ‘taking the organization with you’—letting the people in toxicology know why the data is so important to a regulatory submission. Tony cautioned you don't want to end up two weeks before you are planning to get a dossier in place and still not know what the source of truth was. Was it this assay result in this system? Or was it this assay result? Tony concluded with the importance of getting on top of these issues as soon as possible because of the extensive time it takes to get a drug submitted, approved and then marketed.  

Jen Heckman added that thinking about the data strategy is really important. She shared her experience working at previous companies where a big emphasis was put on people and culture during times of growth. She noted the emphasis is important, and that makes it hard because everything is important. When a company has a growing portfolio of products and interest in expanding to new operating sites or other locations, there is a lot of emphasis put on bringing in people to support this growth. You start to see a lot of silos in terms of development because new groups have been hired to focus on and build their part of the organization.  

But in addition to people and culture, it's equally, if not more important, to make sure somebody is focusing on data strategy (for the points Tony brought up earlier). At some point, you will be looking for information. You’ll need data, whether it’s patient data or operational data. If you don't have a mechanism in place for how information is shared and stored within your organization, then you will be in trouble.  

Jackie Fernandes followed up with an IT perspective on the topic stating that as the company grows, IT controls start to evolve to ensure everyone has access to the data they need. And that it's the right data they need. She followed up with an example of what was done at a previous company she worked for: We facilitated data access for the R&D team by pulling in data from different resources and creating a small data hub. The R&D group could then access the right data to run their tests efficiently. She concluded with the fact that IT controls can actually bring structure and cohesion to the process as well as conciseness in decision making.

The following articles provide snapshots of more topics the panelists discussed:

• Data Success Stories in the Biotech Industry
• Avoiding and Mitigating Common Data Risks in Biotechs
• Tackling Portfolio Expansion Hurdles in Life Sciences
• Balancing Data Transparency with Privacy and Security

Watch the panel discussion on demand in its entirety here (1:03:04).

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