Biometrics: Industry State of Affairs
With the last two years marked by COVID-19 disruptions, the industry is now building on the successful innovations that were implemented as part of the industry’s pandemic response to face clinical trial conduct challenges. Powerful computational capabilities and data tools, innovative trial designs, and the use of real-world data (RWD) are increasingly popular and accessible and will all continue to play a key role in clinical research development in 2022. We asked Katell Bernois, Director of Statistical Consulting at Alira Health, to share the key trends that are changing the biometrics industry.
Adaptive clinical trial designs will provide more flexibility and efficiency
The benefits of adaptive designs, particularly Bayesian ones, lie in the ability to use and learn from information while the study is running to improve efficiency and flexibility. The adaptive design provides a greater chance to show a treatment effect, often with a smaller sample size or with a shorter trial duration. In addition, adaptive designs increase the probability of assigning patients to efficacious treatments, minimizing risks and eventually increasing patient willingness to participate in clinical studies.
Master protocols will optimize drug development
As the industry becomes more competitive, pharma and MedTech companies need to optimize their development pathways. Master protocols are a great option to simultaneously investigate multiple hypotheses through concurrent sub-studies and reduce development timelines and costs. We are already seeing growing demand for the following master protocols:
- Basket trials: Multiple indications evaluated for one treatment
- Umbrella trials: Multiple treatment options evaluated for one indication
- Platform trials: Multi-arm, multi-stage with options to add or drop treatment arms
“In short, these master protocols support doing several trials in one,” says Katell Bernois. “Especially in rare disease and complex indications like oncology, and for early phases, these types of trials increase our ability to evaluate and compare treatment options and select the best alternatives for future development even across companies”.
Real-world data will drive more efficient clinical trial design and conduct
With Real-World Data (RWD) more available and accepted, using patient health and treatment pathway data in clinical trial planning and regulatory submission is increasing. RWD has the potential to allow for more efficient trial design and conduct—from identifying key design and feasibility assumptions], like the characterization of the patient population and identification of comparators across countries through treatment pathway analysis, or identification of sites and patients to optimize recruitment rate, and much more. Beyond this, the integration of RWD in regulatory packages can also support evidence generation, like with the use of external and synthetic control arms.
“While randomized controlled trials are still considered the gold standard for regulatory submissions, there could be occasions where these trials are either infeasible or unethical, especially in rare diseases due to small population or for certain indications where the standard of care administration is challenging,” says Katell Bernois. “These situations are examples where implementing synthetic control arm can make a difference, especially as the availability of data sources and methodologies to utilize them have recently evolved.”
Digitalization of data will increase outcomes clarity
The use of digital tools like mobile devices, wearables, and other sensors to collect and store large volumes of health-related data is growing rapidly. These tools offer capabilities to accumulate data in real-time or gather information directly from the patients—both elements that facilitate access and retention in clinical trials. These tools increase our capacity to improve the design and conduct of clinical trials and evaluate outcomes that were previously not available.
The key to maximizing RWD from these new digital tools, like BePatient or Patchai, is appropriate integration with more traditional data, more centralized monitoring to review ongoing data quality, and suitable methodological validation for new endpoints or algorithms to ensure required quality for regulatory purposes.
“With all this digitalization, the development of advanced, new analytical capabilities is key to analyze this data to support product development and approval.” says Katell Bernois.
Increased regulatory support for innovative approaches
Regulators, including the FDA in the U.S., are increasingly promoting the adoption and development of adaptive methods. For example, the FDA designated the Bayesian Optimal Interval (BOIN) design as a statistical methodology for Phase I dose-finding clinical trials as “fit-for-purpose” (FFP) in December 2021, under the Fit-for-Purpose (FFP) Initiative. This initiative provides a regulatory acceptance pathway for dynamic tools used in drug development programs and the BOIN design is only the third tool to be accepted.
The recent publication of three case studies in the framework of the FDA’s Complex Innovative Trial Design (CID) Pilot Meeting Program confirms the industry’s trend toward more innovation in the late-stage development of the trial design. These case studies included a master protocol with information borrowing, a design with adaptive decision rule and Bayesian adaptive features for randomization, and a study with an external control arm.
The need for a more efficient, flexible, and data-driven decision-making process alongside the need for better analytical capacities and new technologies is driving innovation. Expertise in biometrics—where data management and statistical methodology capabilities are key in designing and conducting studies—is a must for success.
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