Traditionally, Phase 1 trials commonly utilize 3+3 designs to determine the maximum tolerated dose (MTD) and the recommended Phase 2 dose (RP2D). Studies have shown, however, that two out of three trials employing a 3+3 design failed in identifying the MTD, and better approaches are needed.1
During Premier Research’s recent webinar Alternative Designs to the Traditional 3+3 Design in Phase 1 Dose Escalation Studies, Abie Ekangaki, Vice President, Statistical Consulting, and Andreas Schreiner, Vice President, Medical Affairs, Neuroscience & Analgesia, discuss alternative dose-escalation paradigms introduced into the clinical trial landscape for Phase 1 trials. In this blog post, we share five of their key insights on alternative dose escalation strategies for Phase 1 studies. To watch the full recording, click here.
- Improving the design of phase 1 studies is essential for later-stage success. Overall, the likelihood of approval from Phase 1 is less than 10 percent. The low success rate of Phase 2 trials — less than 31% across all indications — can largely be attributed to failure to identify an optimal therapeutic dose.2 Research has shown that rules-based designs such as 3+3 may result in a higher than expected proportion of study participants treated at subtherapeutic doses.1
- Model-based strategies offer advantages over rules-based designs. Unlike rules-based designs, model-based strategies do not require fixed cohorts, allowing for greater flexibility. They also use objective, statistical approaches for determining the MTD. These approaches are based on either pre-specifying toxicity probability priors at the start of the study or defining a priori Bayesian probability parameters incorporated into the models. Moreover, model-based designs are subject to lower sampling variability and a higher probability of estimating and selecting the true MTD.3
- The continual reassessment method (CRM) may accelerate determination of the MTD. Among the most commonly used model-based designs, CRM can be applied to any cohort size and expresses the probability of toxicity as a function of dose level. Using a pre-specified target toxicity rate, this method uses statistical model-based dose-escalation algorithms to help estimate the MTD. Progressive algorithms allow a change in dose level after each patient is treated, so dose escalations and determination of an MTD or RP2D may occur more quickly.
- The modified toxicity probability interval (mTPI) method simplifies dose-escalation decisions. The mTPI design utilizes a Bayesian statistical framework to compute the posterior probabilities of three intervals — an under-dosing interval, an equivalence interval, and an over-dosing interval — upon which escalation and de-escalation decisions are made. This model simplifies decision-making as these decisions can be tabulated at the start of a trial. It is, however, flawed in its approach to calculating the likelihood of an interval containing the observed toxicity rate, which involves dividing the estimated probability of toxicity by the length of the interval. A modification of this model, known as mTPI-2, seeks to address this flaw.
- The Bayesian optimal interval method (BOIN) may offer the highest chance of selecting a therapeutic RP2D. A hybrid between rules-based and model-based design, this method is a generalization of the 3+3, accelerated titration, and Rolling-6 designs. BOIN compares the observed toxicity rate among a cohort with a set of pre-specified lower and upper toxicity boundaries to determine escalation and de-escalation decisions. If the observed toxicity rate is below the lower boundary, the dose is escalated. If it is above the upper boundary, the dose is de-escalated. In addition to being simple to implement, BOIN is also useful for estimating the cumulative probability of toxicity.
To learn more about the design and implementation of model-based dose-escalation designs, watch the full webinar “Insights on Alternative Designs to the Traditional 3+3 Design in Phase 1 Dose Escalation Studies” here.
1 Reiner E, Paoletti X, O’Quigley J. Operating characteristics of the standard phase I clinical trial design. Comput Stat Data Anal. 1999;30(3):303-315.
2 BIO. Clinical Development Success Rates 2006-2015.
3 Wheeler GM, et al. How to design a dose-finding study using the continual reassessment method. BMC Med Res Methodol. 2019;19(1):18.