With 20-25% of the global population affected by metabolic problems, finding effective treatments is crucial for cardiovascular and metabolic (CVM) conditions. Traditional study designs feature predetermined sample sizes, assessments, and analyses that must remain in place over long treatment durations. Although formal protocol amendments are possible, this process is time-consuming and complex.
In contrast, an adaptive trial design allows for modifications to an ongoing trial and its analyses under a pre-specified framework, which is outlined in the FDA’s Adaptive Designs for Clinical Trials for Drugs and Biologics Guidance for Industry, published in 2019. Common outcomes include declaration of futility or overwhelming efficacy, calibration of eligibility criteria updates, and re-estimation of sample size. In summary, adaptive trial methodology allows a sponsor to adjust and modify a study as new information becomes available, thereby maximizing the chance of benefit for both researchers and patients.
Considerations for Adaptive Trial Designs in CVM
Adaptive trial designs are frequently employed in the fields of oncology and rare disease, in which a given mechanism of action may be effective across multiple indications. In recent years, sponsors have increasingly implemented adaptive trial designs in the CVM space. When designing adaptive trials in CVM, a sponsor must consider the following several strategic factors, which are critical for ensuring the trial’s success and generating meaningful, actionable data:
Defining the Adaptive Elements
The first step in designing an adaptive trial is outlining which parts of the study can be adjusted as the study progresses, such as dosage levels, sample size, study duration, eligibility criteria, and the hierarchy of outcome measures. In CVM trials, where the connection between metabolic and cardiovascular factors is often complex, these adjustments need to align closely with the trial’s overall goals.
For example, evaluating dosages based on early metabolic responses (e.g., fluid biomarkers) can identify promising dosages and regimens earlier in a given study. This process can allow more patients to be randomized to select treatment groups to maximize the chance of achieving desired outcomes at later time points.
Regulatory Compliance and Ethical Considerations
Adaptive trials, although flexible and efficient, must adhere to strict regulatory and ethical standards. Regulators are especially concerned about potential bias in the acquisition of data and the preservation of statistical validity, as adaptations are based on newly collected data compared to those anticipated in study planning. This scrutiny is accentuated for modifications comparing unblinded data, which may result in sample size re-estimation or dosing adjustments. To address these concerns, researchers must clearly define trial adjustment criteria, whether the data to be reviewed is blinded or unblinded, the charter to be employed by an independent committee for adaptations, and whether the modifications are appropriate for disclosure.
Ethical considerations are equally important and have been addressed within extant literature. For example, patient inclusion earlier in a study might provide access to data that are different than those obtained later in the study. Therefore, the researcher must maintain clinical equipoise throughout the study. Adaptations made during the trial can directly affect patient safety and well-being, sometimes requiring early termination if significant efficacy or futility is observed.
Data Monitoring and Decision-Making Framework
To ensure the trial has predictable metrics, an adaptive trial demands ongoing data monitoring, a transparent decision-making framework, and pre-defined statistical models. Real-time tracking of patient evaluations is essential to make timely adjustments within a CVM study, which universally requires a Data Monitoring Committee (DMC) to review interim results independently from individuals involved in study design or conduct and to provide recommendations based on clear and pre-specified criteria.
Patient Recruitment and Retention Strategies
Patient recruitment and retention in CVM trials can be challenging, as researchers aim to maximize signal detection as well as generalizability. As a result, the recruitment strategy should be carefully designed to align with the adaptive nature of the trial, ensuring that patients are fully informed about potential changes during the study. Retention strategies must also account for the need of patients to participate over the entire planned duration of the study independent of adaptations that may be introduced.
Future Directions in Adaptive Trial Designs for CVM
Several emerging trends are likely to shape future breakthroughs in CVM adaptive trial designs, including:
Advances in Artificial Intelligence and Machine Learning
Artificial intelligence (AI) and machine learning are set to revolutionize adaptive trials by enabling more sophisticated data analysis and decision-making. These technologies can process large datasets in real-time, offering insights that lead to more accurate and efficient adaptations. For instance, AI can predict patient treatment responses, allowing for more personalized and effective interventions in CVM trials.
AI offers great potential by identifying risks in complex biometric signals, beyond just tabular data. With advancements in deep learning, AI can process unstructured data like ECGs, uncovering patterns that might not be manually detected. AI-guided ECGs, for example, can spot risks like atrial fibrillation or heart disease early, and with portable devices, this technology is becoming more accessible for both chronic and acute conditions.
AI in healthcare has primarily focused on diagnosing diseases and predicting outcomes, but it’s also making strides in identifying new risk markers for diseases before they develop. Digital biomarkers, derived from easily accessible data like wearable devices, offer a non-invasive, scalable way to assess risk, even in healthy individuals. Wearables track metrics like heart rate and sleep quality, which can predict cardiovascular health, while AI-powered ECGs provide insights into risk factors like accelerated aging and heart failure. By integrating data from various sources, AI can create more personalized, accurate risk assessments, paving the way for better, more informed healthcare.
Global Collaboration and Data Sharing
International collaboration and data sharing are crucial for advancing adaptive trial designs. By pooling data from multiple trials across various regions, researchers can increase the power and relevance of their findings. This approach is valuable in addressing patient recruitment challenges through cross-border enrollment and ensuring that trials have the statistical power to have meaningful clinical effects.
Real-World Data Integration
Another emerging trend in clinical research is integrating real-world data (RWD) into the designs of adaptive trials. These data, which may include information from electronic health records, wearable devices, and patient registries, can provide valuable insights into how treatments perform outside of clinical settings.
For CVM, where patient lifestyles and behaviors significantly impact outcomes, incorporating RWD can help researchers design more relevant and applicable trials, ensuring they are effective in controlled environments and everyday activities.
Find Out if Adaptive Trial Designs Are Right for Your Study with Worldwide
Adaptive trial methodology, including both design and operational requirements, represents a notable step forward in research in general and particularly in CVM research. This approach offers greater flexibility and efficiency in the context of ethical rigor. At Worldwide Clinical Trials, we’re passionate in our support of adaptive study design and operation in order to bring new treatments for CVM from discovery to reality.
Our experience with adaptive trial designs enables us to offer customized solutions that address the unique challenges of CVM research. To find out if an adaptive trial design is right for your study, check out our guide or contact us today to learn how we can support your CVM study.
