Dose Individualization for Phase I Cancer Trials With Broadened Eligibility.

Broadening eligibility criteria in cancer trials has been advocated to represent the intended patient population more accurately. The advantages are clear in terms of generalizability and recruitment, however there are some important considerations in terms of design for efficiency and patient safety. While toxicity may be expected to be homogeneous across these subpopulations, designs should be able to recommend safe and precise doses if subpopulations with different toxicity profiles exist.

Dose optimization for cancer treatments with considerations for late-onset toxicities.

Given that novel anticancer therapies have different toxicity profiles and mechanisms of action, it is important to reconsider the current approaches for dose selection. In an effort to move away from considering the maximum tolerated dose as the optimal dose, the Food and Drug Administration Project Optimus points to the need of incorporating long-term toxicity evaluation, given that many of these novel agents lead to late-onset or cumulative toxicities and there are no guidelines on how to handle them. Numerous methods have been proposed to handle late-onset toxicities in dose-finding clinical trials.

Would the Recommended Dose Have Been Different Using Novel Dose-Finding Designs? Comparing Dose-Finding Designs in Published Trials.

Unlabelled: Simulation studies have shown that novel designs such as the continual reassessment method and the Bayesian optimal interval (BOIN) design outperform the 3 + 3 design by recommending the maximum tolerated dose (MTD) more often, using less patients, and allotting more patients to the MTD. However, it is not clear whether these novel designs would have yielded different results in the context of real-world dose-finding trials. This is a commonly mentioned reason for the continuous use of 3 + 3 designs for oncology trials, with investigators considering simulation studies not sufficiently convincing to warrant the additional design complexity of novel designs.