Generating high-quality clinical data is a vital but challenging task in modern drug development. Unfortunately, in the current era of ‘big data’ and global clinical operations, spanning multiple sites and digital systems, protecting the quality of clinical data has become harder than ever.
Planning your data strategy is, therefore, crucial to ensure a high-quality evidence package and increase the chances of successful clinical development. However, as we discuss in our new eBook, planning a data strategy is a complex process involving various considerations that require significant amounts of time and expertise to fully address.
Read our eBook for expert insights on planning a data strategy that can help overcome key challenges in clinical development and boost your success.
In this blog, we discuss the many data-related challenges commonly faced in clinical development and how to implement a fail-safe data strategy that can overcome these challenges, bringing effective new therapies to patients.
Why is data management a key concern in clinical development?
Clinical data management has become a global operation, involving many organizations, dozens of individuals, and numerous systems to collect, transfer, and store the data. Although increasingly vast and complex datasets collected via digital devices are providing richer insights for healthcare, more than ever before these can be tricky to manage and document, especially across multiple sites and systems. Research indicates that without a data strategy, trials experience innumerable delays. [See this article from Tufts CSDD, for an article examining the extensive tribulations of trial delay due to data.]
An added complication is that unique data issues often arise at each of the four phases of clinical development. Take Phase 2, for instance, when the investigational therapy is tested in patient populations with the disease of interest to generate initial evidence of efficacy and safety. At this stage, it is vital to fill any knowledge gaps (such as in the Target Product Profile) and plan for future evidence needs so you can successfully address any regulatory concerns.
Without plans in place to meet these needs, there is a risk that your new therapy will fail to obtain regulatory approval, or there will be delays in approval which will be costly. As such, the continued progress of the therapy to Phase 3 is unlikely, and so there are therefore, lower chances of out-licensing the asset or selling your company. Regardless of the business objective, you could lose considerable resource investment and more importantly, you may have to stop the investigation into a promising therapy that could have helped many patients.
Nonetheless, although these risks might appear difficult to overcome, there is a solution that is both very effective and easy to implement.
The importance of an optimized clinical data strategy
Many companies implement a clinical data strategy to protect the quality of the evidence packages they produce. However, planning often starts quite late in the development process, making it difficult to fully address the various considerations that can affect data quality. These considerations are not only numerous but also involve making tricky decisions, such as:
- What trial design will you use?
- What data will you need to collect to fill in knowledge gaps?
- How will you collect and analyze the data?
- How will you address regulatory and HTA queries about efficacy, safety, and market access?
As such, the complex task of planning your data strategy requires expert input from various specialists, including clinical, biometrics and data management, regulatory, and project management functional groups. While you can draw on your existing expertise, it is worth seeking external guidance when necessary—and this all takes more time than many companies expect.
Your data strategy (and trial design) must also consider another crucial but often overlooked factor - the increasingly connected phases of clinical development, which have traditionally been more separated than they are now. Today’s more interlinked approach means that a data problem occurring in one phase can now more easily affect a later stage, adding another cog into the complex wheel of clinical data management.
The approach you take to planning your data strategy can, therefore, make a big difference to the quality of your evidence packages and the success of your clinical programs.
How to plan a data strategy that maximizes clinical success
One of the simplest but vital changes you can make is to begin formulating a data strategy as early as possible, ideally as you transition from pre-clinical to clinical trials. Just as importantly, your plan will be far more effective if it considers the entire duration of your clinical program (i.e., a program-wide strategy) as well as how to overcome specific challenges in each phase of development.
What else can you do to optimize your data strategy? You can follow the industry’s best practices and other top tips outlined by our experts. For example, implementing a single, centralized electronic data capture system to improve operational efficiency and cost-effectiveness, as well as standardize data quality across sites.
Experts also suggest working with a knowledgeable partner with expertise in planning and executing data strategies and trial designs early on. A skilled statistical team can help to strengthen the quality of your evidence package as well as prepare for unforeseen issues to minimize delays and costs.
"While building a strategy we do not focus only on the study but the entire program. You need to take the time to analyze and check all the boxes before you start building your first program and the backbone of your project." - Marc Lefebvre-Gouy, Lead Statistical Programmer, Cytel France
Additionally, if you use an adaptive trial design, this can create complex statistical issues that will require someone with specialist experience to resolve.
Updating your data strategy to mitigate risk and boost clinical success
Optimizing your data strategy can benefit your programs in numerous ways. One important advantage is that it will give you a ‘roadmap’ that identifies, quantifies, and mitigates potential risks to your programs, so you can easily overcome them as they arise. These signposts can also help you deal with issues faster to streamline the development pathway and accelerate your therapy’s time to market. By maximizing the quality of your evidence packages, you will increase the likelihood of obtaining approval from regulators and other key stakeholders. Consequently, making even small changes to your data strategy planning has the potential to greatly enhance the success of your programs.
Download Cytel’s new eBook to discover tactics and tips on optimizing your data strategy to de-risk your clinical programs and drive the development of new therapies for patients.