14.4 Applications of adaptive designs in clinical trials

Adaptive designs in clinical trials are revolutionizing drug development. They allow researchers to modify studies based on interim data, optimizing efficiency and patient outcomes. From dose-finding to biomarker-guided approaches, these designs are transforming how we test new treatments.

Adaptive randomization and platform trials are pushing the boundaries even further. By dynamically adjusting treatment allocation and evaluating multiple therapies simultaneously, researchers can maximize information gained while minimizing patient exposure to ineffective treatments. These innovative approaches are shaping the future of clinical research.

Adaptive Trial Designs for Dose Selection and Efficacy

Efficient Dose-Finding Approaches

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• Dose-finding studies aim to identify the optimal dose of a drug that balances efficacy and safety
• Utilize adaptive designs to efficiently allocate patients to promising dose levels based on observed responses
• Continually update the allocation probabilities as data accumulates, maximizing information gained while minimizing exposure to ineffective or unsafe doses
• Examples include the continual reassessment method (CRM) and the modified toxicity probability interval (mTPI) design

Seamless Integration of Phase II and III Trials

• Seamless Phase II/III trials combine the dose-finding and confirmatory stages into a single study
• Interim analysis conducted at the end of Phase II to select the most promising dose(s) for Phase III evaluation
• Allows for a smooth transition between phases, reducing time and resources compared to traditional separate trials
• Treatment selection at the interim analysis can be based on efficacy, safety, or a combination of both endpoints

Evaluating Multiple Treatment Arms Simultaneously

• Multi-arm multi-stage (MAMS) designs enable the simultaneous evaluation of multiple treatment arms against a common control
• Interim analyses performed at pre-specified stages to drop ineffective arms and focus resources on promising treatments
• Allows for the assessment of multiple hypotheses within a single trial, increasing efficiency and reducing the required sample size
• Examples include the STAMPEDE trial in prostate cancer and the I-SPY 2 trial in breast cancer

Biomarker-Guided Adaptive Trials

Tailoring Treatment Based on Biomarker Status

• Biomarker-adaptive designs incorporate biomarker information to guide treatment allocation and decision-making
• Patients stratified based on their biomarker status (e.g., genetic profile, protein expression) to receive targeted therapies
• Enables the identification of subpopulations most likely to benefit from a specific treatment
• Examples include the BATTLE trial in non-small cell lung cancer and the I-SPY 2 trial in breast cancer

Investigating Multiple Tumor Types with a Common Biomarker

• Basket trials evaluate the efficacy of a targeted therapy across multiple tumor types that share a common biomarker
• Patients enrolled based on the presence of a specific molecular alteration, regardless of the primary tumor site
• Allows for the assessment of treatment efficacy in rare tumor types and the identification of potential new indications
• Examples include the BRAF V600 mutation basket trial and the NCI-MATCH trial

Evaluating Multiple Targeted Therapies within a Single Tumor Type

• Umbrella trials investigate multiple targeted therapies within a single tumor type based on molecular profiling
• Patients assigned to different treatment arms based on their specific biomarker profile
• Enables the identification of the most effective targeted therapy for each molecular subtype
• Examples include the FOCUS4 trial in colorectal cancer and the LUNG-MAP trial in squamous cell lung cancer

Adaptive Randomization Techniques

Dynamically Adjusting Randomization Probabilities

• Response-adaptive randomization adjusts the probability of treatment assignment based on the observed responses
• Patients more likely to be allocated to the treatment arm showing superior outcomes as the trial progresses
• Aims to maximize the number of patients receiving the most effective treatment while maintaining statistical validity
• Examples include the doubly-adaptive biased coin design and the sequential parallel comparison design

Leveraging a Common Master Protocol for Multiple Treatments

• Platform trials utilize a common master protocol to evaluate multiple treatments simultaneously or sequentially
• New treatment arms can be added or dropped based on emerging evidence, allowing for the efficient evaluation of novel therapies
• Shared control arm and standardized trial infrastructure reduce costs and improve comparability across treatment arms
• Examples include the REMAP-CAP trial in community-acquired pneumonia and the GBM AGILE trial in glioblastoma