Ann: Race Appoints George Clinical for Phase 1 Trial of RC220, page-15

What is Bayesian Statistical Design?

Bayesian statistical design is a method in clinical trials that incorporates prior knowledge along with new data collected during the trial to update the probability of outcomes dynamically. Unlike traditional frequentist designs, which rely on fixed sample sizes and pre-determined analysis plans, Bayesian designs are more flexible and adaptive.

Application in the RC220 Bisantrene Trial:

1. Adaptive Design:

   • The Phase 1a/1b trial of RC220 bisantrene will use a Bayesian design to allow for real-time modifications based on interim data.
   • This means the trial can adjust the dosing, patient recruitment numbers, and other parameters as new data are obtained, enhancing efficiency and safety.

2. Safety and Dosing Adjustments:

   • Phase 1a: The trial will start with ascending doses of RC220 bisantrene to determine safety, tolerability, pharmacokinetics, m6A RNA effects, and the maximum tolerated dose.
   • Phase 1b: Optimal dosage in combination with doxorubicin will be assessed for additional safety, tolerability, and preliminary cardioprotective and anticancer efficacy signals.
   • Bayesian design allows for adjusting doses based on the safety and efficacy data gathered during the trial, potentially leading to quicker identification of the optimal dose.

3. Flexibility in Patient Recruitment:

   • Traditional trials often have a fixed number of participants, while Bayesian trials can adapt the number of participants required based on the data collected.
   • This flexibility can lead to quicker completion if the data shows strong evidence early on, or conversely, it may require more participants if the data is less conclusive.

4. Efficiency and Speed:

   • By continuously updating probabilities and incorporating new data, the Bayesian design can potentially reduce the overall time and cost of the trial.
   • This approach can lead to faster decision-making regarding the progression from Phase 1a to Phase 1b, based on accumulating evidence.

5. Ethical Considerations:

   • Bayesian designs are often considered more ethical as they can minimize patient exposure to suboptimal treatments by allowing early stopping for efficacy or futility.
   • In the context of RC220 bisantrene, if strong evidence of cardioprotection and anticancer activity is observed, the trial may progress faster, benefiting patients sooner.

Overall Benefits:

• Dynamic Adjustment: The ability to make real-time adjustments based on ongoing results.
• Resource Efficiency: Potentially reduced patient numbers and costs while maintaining robust data integrity.
• Faster Outcomes: Possibility of earlier trial completion if interim results are promising.

In summary, the Bayesian statistical design provides a flexible, adaptive framework that can optimize the RC220 bisantrene trial’s efficiency, potentially leading to quicker identification of effective treatments while ensuring patient safety.