Bayesian adaptive model selection design for optimal biological dose finding in phase I/II clinical trials

Implementing and assessing Bayesian response-adaptive randomisation for backfilling in dose-finding trials

Traditional approaches in dose-finding trials, such as the continual reassessment method, focus on identifying the maximum tolerated dose. In contemporary early-phase dose-finding trials, especially in oncology with targeted agents or immunotherapy, a more relevant aim is to identify the lowest dose level that maximises efficacy whilst remaining tolerable. Backfilling, defined as the practice of assigning patients to dose levels lower than the current highest tolerated dose, has been proposed to gather additional pharmacokinetic, pharmacodynamic and biomarker data to recommend the most appropriate dose to carry forward for subsequent studies. The first formal framework [5] for backfilling proposed randomising backfill patients with equal probability among those doses below the dose level where the study is currently at. Here, we propose to use Bayesian response-adaptive randomisation to backfill patients. This patient-oriented approach to backfilling aims to allocate more patients to dose levels in the backfill set with higher expected efficacy based on emerging data. The backfill set constitutes of the doses below the dose the dose-finding algorithm is at. At study completion, collective patient data inform the dose-response curve, suggesting an optimal dose level balancing toxicity and efficacy. Our simulation study across diverse clinical trial settings demonstrates that a backfilling strategy using Bayesian response-adaptive randomisation can result in a patient-oriented patient assignment procedure which simultaneously enhances the likelihood of correctly identifying the most appropriate dose level. This contribution offers a methodological framework and practical implementation for patient-oriented backfilling, encompassing design and analysis considerations in early-phase trials.

Adaptive phase I-II clinical trial designs identifying optimal biological doses for targeted agents and immunotherapies

Targeted agents and immunotherapies have revolutionized cancer treatment, offering promising options for various cancer types. Unlike traditional therapies the principle of "more is better" is not always applicable to these new therapies due to their unique biomedical mechanisms. As a result, various phase I-II clinical trial designs have been proposed to identify the optimal biological dose that maximizes the therapeutic effect of targeted therapies and immunotherapies by jointly monitoring both efficacy and toxicity outcomes. This review article examines several innovative phase I-II clinical trial designs that utilize accumulated efficacy and toxicity outcomes to adaptively determine doses for subsequent patients and identify the optimal biological dose, maximizing the overall therapeutic effect. Specifically, we highlight three categories of phase I-II designs: efficacy-driven, utility-based, and designs incorporating multiple efficacy endpoints. For each design, we review the dose-outcome model, the definition of the optimal biological dose, the dose-finding algorithm, and the software for trial implementation. To illustrate the concepts, we also present two real phase I-II trial examples utilizing the EffTox and ISO designs. Finally, we provide a classification tree to summarize the designs discussed in this article.

Shotgun-2: A Bayesian phase I/II basket trial design to identify indication-specific optimal biological doses

For novel molecularly targeted agents and immunotherapies, the objective of dose-finding is often to identify the optimal biological dose, rather than the maximum tolerated dose. However, optimal biological doses may not be the same for different indications, challenging the traditional dose-finding framework. Therefore, we proposed a Bayesian phase I/II basket trial design, named "shotgun-2," to identify indication-specific optimal biological doses. A dose-escalation part is conducted in stage I to identify the maximum tolerated dose and admissible dose sets. In stage II, dose optimization is performed incorporating both toxicity and efficacy for each indication. Simulation studies under both fixed and random scenarios show that, compared with the traditional "phase I  +  cohort expansion" design, the shotgun-2 design is robust and can improve the probability of correctly selecting the optimal biological doses. Furthermore, this study provides a useful tool for identifying indication-specific optimal biological doses and accelerating drug development.

Data-dependent early completion of dose-finding trials for drug-combination

PURPOSE

Model-assisted designs for drug combination trials have been proposed as novel designs with simple and superior performance. However, model-assisted designs have the disadvantage that the sample size must be set in advance, and trials cannot be completed until the number of patients treated reaches the pre-set sample size. Model-assisted designs have a stopping rule that can be used to terminate the trial if the number of patients treated exceeds the predetermined number, there is no statistical basis for the predetermined number. Here, I propose two methods for data-dependent early completion of dose-finding trials for drug combination: (1) an early completion method based on dose retainment probability, and (2) an early completion method in which the dose retainment probability is adjusted by a bivariate isotonic regression.

METHODS

Early completion is determined when the dose retainment probability using both trial data and the number of remaining patients is high. Early completion of a virtual trial was demonstrated. The performances of the early completion methods were evaluated by simulation studies with 12 scenarios.

RESULTS

The simulation studies showed that the percentage of early completion was an average of approximately 70%, and the number of patients treated was 25% less than the planned sample size. The percentage of correct maximum tolerated dose combination selection for the early completion methods was similar to that of non-early completion methods with an average difference of approximately 3%.

CONCLUSION

The performance of the proposed early completion methods was similar to that of the non-early completion methods. Furthermore, the number of patients for determining early completion before the trial starts was determined and a program code for calculating the dose retainment probability was proposed.

A Bayesian design for finding optimal biological dose with mixed types of responses of toxicity and efficacy

For molecularly targeted therapy and immunotherapy, the targeted dose in the early phase clinical trial has been shifted from the maximum tolerated dose for the cytotoxic drug to the optimal biological dose where both toxicity and efficacy are considered. In this paper, we consider the situation that the responses of toxicity and efficacy are mixed in binary and continuous types, respectively, where the continuous endpoint bears more magnitude information than the binary endpoint after dichotomization. We propose combining two model-based designs to sequentially identify the most efficacious and tolerably safe dose. The employed designs both take the dose level information into account to achieve high estimation efficiency. We demonstrate the superiority of the proposed method to some existing methods by simulation.

Phase I CAR-T Clinical Trials Review

BACKGROUND/AIM

Chimeric antigen receptor (CAR) T cells with tumor specificity are being increasingly investigated. Phase I trials are the first step of testing for safety of novel CAR-T therapy to determine the maximum tolerated dose (MTD). Several dose escalation methods have been developed over time including rule-based, model-based, and model-assisted designs. The goal of this project is to overview the phase I designs used in current CAR-T trials.

MATERIALS AND METHODS

We searched PubMed for peer-reviewed literature published between January 1, 2015 and December 31, 2021. The search was limited to human studies in the English language using the keywords "CAR-T phase I", "clinical trials", and "full text".

RESULTS

One hundred nine papers with at least partial phase I components were included for analysis. 31.2% of the trials used the traditional 3+3 or a variation of said design, and 60.6% did not mention the dose escalation design. The majority of the manuscripts (59.6%) did not report cohort size while 19.3% did not specify the timing of evaluation. Although most of the studies were registered with CT.gov, only 33.9% had any results submitted or posted to CT.gov These trends persisted even in manuscripts published in journals with high impact factors.

CONCLUSION

Standardizing the publication criteria and providing basic elements of phase I clinical trials are critical to ensure high quality of manuscripts. With the quick development and high costs of CAR-T cell therapy, adoption of advanced designs such as model-based and model-assisted should increase to improve efficiency of clinical trials.

Dosing Regimens of Immune Checkpoint Inhibitors: Attempts at Lower Dose, Less Frequency, Shorter Course

Immune checkpoint inhibitors (ICIs) are a revolutionary breakthrough in the field of cancer by modulating patient's own immune system to exert anti-tumor effects. The clinical application of ICIs is still in its infancy, and their dosing regimens need to be continuously adjusted. Pharmacokinetic/pharmacodynamic studies showed a significant plateau in the exposure-response curve, with high receptor occupancy and plasma concentrations achieved at low dose levels. Coupled with concerns about drug toxicity and heavy economic costs, there has been an ongoing quest to reevaluate the current ICI dosing regimens while preserving maximum clinical efficacy. Many clinical data showed remarkable anticancer effects with ICIs at the doses far below the approved regimens, indicating the possibility of dose reduction. Our review attempts to summarize the clinical evidence for ICIs regimens with lower-dose, less-frequency, shorter-course, and provide clues for further ICIs regimen optimization.