P19 PK/PD Assisted Dose Optimization in the Multiple Arms Dose Expansion Early Phase Clinical Trials

Bayesian hierarchical models have emerged as a powerful tool in the field of dose expansion studies, especially when there are multiple arms or multiple indications. The goal of this abstract is to expand our research to guide dose optimization (>= 2 dose levels) in the dose expansion phase after FIH dose escalation using PK, PD, efficacy, and toxicity data. This project will evaluate the following three objectives: reasonable false positive rate, optimize the efficacy, with more manageable toxicity.

To meet FDA's currently thinking on the recommendation of dose optimization strategy in clinical trials, the development of statistical methods for multiple arms dose optimization is critical. Industry has developed Clinical Utility Score (CUS) approach to include both PK/PD, efficacy, and safety information in the dose selection. Pfizer and BMS both have well-established methods in this field. However, the PK/PD information introduces uncertainty for the dose-exposure relationship, and a single score cannot represent the variation of real-world dose-induction of safety, efficacy profile.

This project is planned to focus on the Bayesian utility function (Berry, 2002) to implement the dose optimization process. It will provide more information on the dose selection with a feasible small sample size, which is very practical and with an urgent need. We hope we can also compare different approaches, including two stage Clinical Utility Score, Bayesian HM, to innovatively guide multiple arms to choose the right dose based on the efficacy, safety, and PK/PD information.

We have derived and simulated Bayesian HM with Dirichlet process to choose the admissible doses, regarding to the false go rate at equal sample size scenario. It will expand the approach combines prior information about dose-response relationships, safety, and efficacy with real-time patient data from an ongoing clinical trial. This framework allows for continual learning and adaptation as the trial progresses.

In the Bayesian utility function, we are assuming all efficacy and safety as an ordinal data (e.g., complete response, partial response, minimum response). Two dimensional proportional odds logistic regression will be modeled for the efficacy and safety in the trial, and log-transformed PK/PD will be assumed following normal distribution (Lin, 2023), including patient-specific random effect. It aims to optimize dose selection quickly and efficiently by identifying the optimal dose with fewer patients, reducing the time and resources needed for dose expansion studies.

After we have the utility function posterior probability, we will deploy the decision-making procedure, and operating characteristics (OC) of this approach will be studied through simulation.

There are multiple research objectives for this research project,
1. Develop a Bayesian utility function that is more feasible for small sample sizes, which is very practical and with an urgent need. Perform Bayesian dose optimization simulation, especially for the boundary value for decision making in the dose expansion with reasonable OC false positive rate.
2. Assess the performance of Bayesian utility function (dose cohort number is ≥ 2) in the dose expansion stage. It is a computational tool to iteratively update the dose selection process, even providing a comprehensive sample size justification.
3. Evaluate the new methods in BMS blood disorder study (e.g., Sickel Cell Disease). In this study, PK/PD information will be simultaneously available, together with efficacy, safety data.

Clinical Trial Design (e.g., Innovative/Complex Design, Estimands, Master Protocol)

ASA Biopharmaceutical Section Regulatory-Industry Statistics Workshop 2024