In general, the use of Bayesian methods involves planning and simulation to ensure that the design is appropriate for operational execution, based on assumptions around dose-level escalation plans and presumed toxicity rates. This information involves not only statistical, but also preclinical, clinical, pharmacokinetic, and/or pharmacodynamic areas of expertise. Simulation outcomes for candidate dose-escalation designs ensure that protocol reviewers understand the level or risk to patient safety (kept low) and provide insight as to what a team might expect with respect to the operational characteristics of the study.
Briefly, the BLRM and mCRM methods model the probability that a patient might experience a DLT based on an assumed dose-toxicity function, which is repeatedly estimated based on emerging data. After estimation, these posterior probabilities are used to assign subsequent cohorts of patients to an optimal dose. Dose optimality is defined and based on a target toxicity rate or acceptable toxicity interval; additional safeguards can also be incorporated to avoid cohort assignments to dose levels that may be associated with an unacceptably high toxicity risk (i.e., due to estimation variability). For these two methods, ongoing statistical support during the study in support of cohort dose-level recommendations is needed in order to determine the next optimal dose level for exploration. Further, cohort reviews often also involve an analysis of how the study will operationally proceed, given hypothetical outcomes. In short, for these model-based designs, statistical input is not only involved with respect to study setup but is also required for ongoing support with respect to cohort escalation decisions.
Hybrid models, such as the modified toxicity probability interval (mTPI-2) or Bayesian optimal interval (BOIN) methods, involve similar setup and exploration with respect to a review of the trial’s operational characteristics. It should be noted that the latter was identified by the FDA as Fit-for-Purpose in December, 2021 (see https://www.fda.gov/drugs/development-approval-process-drugs/drug-development-tools-fit-purpose-initiative for additional details). However, instead of assuming a single dose-toxicity curve for purposes of posterior DLT probability modeling, these methods consider modeling relative to individual dose levels during the trial. Escalation decisions are dependent on observed DLT rates at explored dose levels, alongside predetermined decision rules. This typically allows for minimal statistical support during operational conduct. The MTD is identified based on an isotonic regression using information accumulated during dose exploration.
All of these designs (model-based and hybrid) also allow for various stopping rules, defined as appropriate for each method, including the maximum number of patients treated at a dose or overall in the study. As mentioned previously, such assumptions would be included in the planning stage within the context of study setup simulations, allowing for an understanding of the operational characteristics of the intended design. Metrics to be explored can include the efficiency with which the MTD can be identified, the likelihood of patient overdosing, or the overall DLT rate, given each set of assumptions and dose escalation methodology.
Comparisons with the Standard Rule-Based Design
The standard 3+3 design assumes a predefined dose-escalation pattern with rules for identifying the MTD, static assumptions for targeted toxicity rate, and fixed cohort sizes for prospective enrollment. As a rule-based model, it does not consider information collected during exploration of previous doses, nor does it incorporate uncertainty. Further, there is no opportunity for dose rechallenge. Thus, in references of simulation studies exploring comparisons of Bayesian designs against the 3+3, the 3+3 design has demonstrated high variability in MTD identification, with a generally lower likelihood of finding the “true” MTD.
In conclusion, the efficiency with which the MTD is identified is improved using Bayesian methods. Determining which method is the best for each situation is based on individual assumptions around potential drug performance and any additional regulatory or safety requirements. Lastly, in comparison to the static 3+3 design, these Bayesian methodologies offer an opportunity to include additional cohorts, alongside a predefined method for including their associated DLT information, for further understanding of early drug performance. Such flexibility for exploration is often amenable to the development of recent, novel cancer therapies (in comparison to those underpinning traditional chemotherapy).
Additional Considerations and Summary
The selection of popular designs described in this blog are used to select a safe, tolerable dose for one investigational agent dependent on full cohort completion of a fixed DLT period. Although this design scenario is supported at Allucent, it is worth mentioning that other design characteristics may be relevant to the selection of alternate methodologies that can meet one or more of the following requirements:
- Dynamic decision-making with respect to cohort DLT period completion (e.g., “rolling” or time-to-event designs)
- Dose exploration of combination therapies
- Dual exploration of dose levels against toxicity rates and efficacy outcomes
The Allucent consulting team can assist with an evaluation of your design needs and help you select a match that best aligns with your drug development objectives.
At Allucent, our team has in-depth experience in the planning, design, and execution of these flexible design trials and stands ready to support you.
Contact us today.
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