Revisiting isotonic phase I design in the era of model-assisted dose-finding

Isotonic design for phase I cancer clinical trials with late-onset toxicities

This article addresses the problem of identifying the maximum tolerated dose (MTD) in Phase I dose-finding clinical trials with late-onset toxicities. The main design challenge is how best to adaptively allocate study participants to tolerable doses when the evaluation window for the toxicity endpoint is long relative to the accrual rate of new participants. We propose a new design framework based on order-restricted statistical inference that addresses this challenge in sequential dose assignments. We illustrate the proposed method on real data from a Phase I trial of bortezomib in lymphoma patients and apply it to a Phase I trial of radiotherapy in prostate cancer patients. We conduct extensive simulation studies to compare our design's operating characteristics to existing published methods. Overall, our proposed design demonstrates good performance relative to existing methods in allocating participants at and around the MTD during the study and accurately recommending the MTD at the study conclusion.

A Brief Overview of Adaptive Designs for Phase I Cancer Trials

Simple Summary

Phase I cancer trials are important for new drug developments to test the safety and optimal dosage of cancer drugs which are usually toxic. Understanding biostatistical methodologies of these designs is important for developing phase I studies that are both safe for the participants and which use optimal dosages for better outcomes. Currently there are several phase I designs that are being refined and modified for better outcomes and newer designs are being continuously developed. In this review article, we described several important phase I study designs to provide a brief overview of existing methods. Our review could be helpful to the research community who intent to have a better and yet a concise summary of existing methods.

Abstract

Phase I studies are used to estimate the dose-toxicity profile of the drugs and to select appropriate doses for successive studies. However, literature on statistical methods used for phase I studies are extensive. The objective of this review is to provide a concise summary of existing and emerging techniques for selecting dosages that are appropriate for phase I cancer trials. Many advanced statistical studies have proposed novel and robust methods for adaptive designs that have shown significant advantages over conventional dose finding methods. An increasing number of phase I cancer trials use adaptive designs, particularly during the early phases of the study. In this review, we described nonparametric and algorithm-based designs such as traditional 3 + 3, accelerated titration, Bayesian algorithm-based design, up-and-down design, and isotonic design. In addition, we also described parametric model-based designs such as continual reassessment method, escalation with overdose control, and Bayesian decision theoretic and optimal design. Ongoing studies have been continuously focusing on improving and refining the existing models as well as developing newer methods. This study would help readers to assimilate core concepts and compare different phase I statistical methods under one banner. Nevertheless, other evolving methods require future reviews.

Adapting isotonic dose-finding to a dynamic set of drug combinations with application to a phase I leukemia trial

BACKGROUND/AIMS

This article describes the proposed design of a phase I study evaluating the safety of ceramide nanoliposome and vinblastine among an initial set of 19 possible dose combinations in patients with relapsed/refractory acute myeloid leukemia and patients with untreated acute myeloid leukemia who are not candidates for intensive induction chemotherapy.

METHODS

Extensive collaboration between statisticians and clinical investigators revealed the need to incorporate several adaptive features into the design, including the flexibility of adding or eliminating certain dose combinations based on safety criteria applied to multiple dose pairs. During the design stage, additional dose levels of vinblastine were added, increasing the dimension of the drug combination space and thus the complexity of the problem. Increased complexity made application of existing drug combination dose-finding methods unsuitable in their current form.

RESULTS

Our solution to these challenges was to adapt a method based on isotonic regression to meet the research objectives of the study. Application of this adapted method is described herein, and a simulation study of the design's operating characteristics is conducted.

CONCLUSION

The aim of this article is to bring to light examples of novel design applications as a means of augmenting the implementation of innovative designs in the future and to demonstrate the flexibility of adaptive designs in satisfying changing design conditions.

Adaptive dose-finding based on safety and feasibility in early-phase clinical trials of adoptive cell immunotherapy

BACKGROUND/AIMS

Dose feasibility is a challenge that may arise in the development of adoptive T cell therapies for cancer. In early-phase clinical trials, dose is quantified either by a fixed or per unit body weight number of cells infused. It may not be feasible, however, to administer a patient's assigned dose due to an insufficient number of cells harvested or functional heterogeneity of the product. The study objective becomes to identify the maximum tolerated dose with high feasibility of being administered. This article describes a new dose-finding method that adaptively accounts for safety and feasibility endpoints in guiding dose allocation.

METHODS

We propose an adaptive dose-finding method that integrates accumulating feasibility and safety data to select doses for participant cohorts in early-phase trials examining adoptive cell immunotherapy. We sequentially model the probability of dose-limiting toxicity and the probability of feasibility using independent beta-binomial models. The probability model for toxicity borrows information across all dose levels using isotonic regression, allowing participants infused at a lower dose than his or her planned dose to contribute safety data to the dose-finding algorithm. We applied the proposed methodology in a single simulated trial and evaluated its operating characteristics through extensive simulation studies.

RESULTS

In simulations conducted for a phase I study of adoptive immunotherapy for newly diagnosed glioblastoma, the proposed method demonstrates the ability to identify accurately the feasible maximum tolerated doses and to treat participants at and around these doses. Over 10 hypothesized scenarios studied, the percentage of correctly selecting the true feasible and maximum tolerated dose ranged from 50% to 90% with sample sizes averaging between 21 and 24 participants. A comparison to the only known existing method accounting for safety and feasibility yields competitive performance.

CONCLUSION

We have developed a new practical adaptive dose-finding method to assess feasibility in early-phase adoptive cell therapy trials. A design that incorporates feasibility, as a function of the quantity and quality of the product manufactured, in addition to safety will have an impact on the recommended phase II doses in studies that evaluate patient outcomes.