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Welcome back to the Forum for EmergingBio's Outsourcing Decisions! There is nothing quite like protocol amendments to put the brakes on a clinical trial’s momentum, which is why, in this blog, we are turning our attention to how to face this challenge head on.
The unplanned costs and delays associated with protocol amendments are a major source of frustration and concern, given their impact on trial budgets and timelines. And now, with the COVID-19 pandemic, dealing with study amendments has become even more challenging, as clinical trial sponsors and their research partners adapt processes and technologies to keep studies moving forward amid the pandemic in an environment where regulations continue to evolve.
The longer your study runs and the more complex it is, the more likely you are to have a mid-study protocol amendment. But even in a pandemic, it’s important to remember that there are preventive and proactive measures that can be taken to reduce the likelihood and impact of some amendments or, at the very least, minimize disruption and ensure the continued productivity of the study team. The more prepared we are for amendments, the more efficient we can be when implementing them and successful in terms of keeping the clinical trial on track.
It all starts with getting the right people around the table
Having a well-defined data strategy with built-in flexibility can curb the high impact of amendments. However, there is a misperception that aligning with a data strategy from study start is not important and it, therefore, gets deprioritized until data starts coming in. Needless to say, this is the common pitfall and trying to plan amendments at this point leads to a number of challenges which have time and cost implications.
The good news is that a flexible data strategy is actually something that can quite easily be achieved, particularly for startups and smaller companies that can avoid the bureaucracy that slows down larger organizations. Working together, in-house experts and trusted partners (covering internal knowledge gaps) should lay out the short and long-term data goals. In doing so, they can define a flexible data strategy that takes in account the risk areas related to data collection and management from the planning phase. For example, this integrated team can, early on, identify mitigations around these areas and supportive tools/applications that can be used to manage unanticipated changes relatively easily (and that are good for function and cost). The key is striking the right balance between following best practices and thinking outside of the box, all while staying within the agreed upon budgets.
Building in flexibility from the start
A flexible data strategy should be able to accommodate anticipated amendments or changes (i.e., known risks) as well as unanticipated changes (i.e., unknown risks) with as little impact as possible. However, if the data strategy is retroactive and not embedded in the clinical program from the very start, it’s nearly impossible to avoid the chaos of substantive amendments that lead to significantly longer clinical trial cycle times and higher costs.
While no two organization or studies are the same and a template approach is never realistic or advisable, there are five major pillars of a data strategy and associated considerations for each that should be taken into account when you start writing a protocol for a clinical trial. This will enable the development of a data management strategy that is flexible in that it meets the specific, individual needs of both the clinical study and the sponsor’s short and long-term goals.
Remember, while working through these steps, do not limit your options based on in-house expertise or budget considerations. These are, of course, important, but the first step is to determine the best approach. From there, you can dive into the details of choosing the right partners to support your data strategy development and its management, and, in doing so, significantly reduce painful amendments or their impact.
It is also important to note that having budget constraints does not necessarily mean that a startup or small company has to give up on using the latest technology tools, standardization and/or consistency in clinical trial execution—a topic we will address in an upcoming blog, with a focus on how to:
Is there anything else you would like to hear about?
We’re open to evolving these discussion starters based on your input and would love to hear your thoughts, so please reach out and feel free to share ideas. Our goal is to create a forum for knowledge sharing that specifically addresses the needs of the small biotech and startup community.
Looking forward to continuing the small- and mid-size EmergingBio conversation!
Tanya du Plessis is vice president of data strategies and solutions at Bioforum the Data Masters. She has vast experience across the industry through her current role at Bioforum, as well as the 14 years she spent with IQVIA (legacy Quintiles). Throughout her career, Tanya has worked with multinational pharmaceutical companies as well as small-to-mid size biotechs and startups. She has led various data management operations and programs, heading numerous innovation teams and spearheading the development of strategies for customized data delivery solutions, focusing on timely, quality data.
A certified clinical data manager (CCDM, SCDM), Tanya holds a M.Med.Sc in Hematology and Cell Biology from University of the Free State in South Africa. She also has a project management professional certification (PMP, PMI).
About Bioforum the Data Masters
Bioforum the Data Masters is a data-focused Contract Research Organization (CRO), supporting clients in the utilization of their clinical data and guaranteeing its integrity and accuracy. Our goal is to consistently improve and innovate data processes to allow for the most efficient data submissions for our clients across the life sciences industry and the patients they serve.