In parametric Bayesian designs of early phase cancer clinical trials with drug combinations exploring a discrete set of partially ordered doses, several authors claimed that there is no added value in including an interaction term to model synergism between the two drugs. In this paper, we investigate these claims in the setting of continuous dose levels of the two agents. Parametric models will be used to describe the relationship between the doses of the two agents and the probability of dose limiting toxicity and efficacy. Trial design proceeds by treating cohorts of two patients simultaneously receiving different dose combinations and response adaptive randomization. We compare trial safety and efficiency of the estimated maximum tolerated dose (MTD) curve between models that include an interaction term with models without the synergism parameter with extensive simulations. Under a selected class of dosetoxicity models and dose escalation algorithm, we found that not including an interaction term in the model can compromise the safety of the trial and reduce the pointwise reliability of the estimated MTDcurve.

Modeling synergism in early phase cancer trials with drug combination with continuous dose levels: is there an added value?

Phase I-II cancer clinical trial designs are intended to accelerate drug development. In cases where efficacy cannot be ascertained in a short period of time, it is common to divide the study in two stages: i) a first stage in which dose is escalated based only on toxicity data and we look for the maximum tolerated dose (MTD) set and ii) a second stage in which we search for the most efficacious dose within the MTD set. Current available approaches in the area of continuous dose levels involve fixing the MTD after stage I and discarding all collected stage I efficacy data. However, this methodology is clearly inefficient when there is a unique patient population present across stages. In this article, we propose a two-stage design for the combination of two cytotoxic agents assuming a single patient population across the entire study. In stage I, conditional escalation with overdose control (EWOC) is used to allocate successive cohorts of patients. In stage II, we employ an adaptive randomization approach to allocate patients to drug combinations along the estimated MTD curve, which is constantly updated. The proposed methodology is assessed with extensive simulations in the context of a real case study.

Combining cytotoxic agents with continuous dose levels in seamless phase I-II clinical trials

Phase I-II cancer clinical trial designs are intended to accelerate drug development. In cases where efficacy cannot be ascertained in a short period of time, it is common to divide the study in two stages: (i) a first stage in which dose is escalated based only on toxicity data and we look for the maximum tolerated dose (MTD) set and (ii) a second stage in which we search for the most efficacious dose within the MTD set. Current available approaches in the area of continuous dose levels involve fixing the MTD after stage I and discarding all collected stage I efficacy data. However, this methodology is clearly inefficient when there is a unique patient population present across stages. In this article, we propose a two-stage design for the combination of two cytotoxic agents assuming a single patient population across the entire study. In stage I, conditional escalation with overdose control is used to allocate successive cohorts of patients. In stage II, we employ an adaptive randomisation approach to allocate patients to drug combinations along the estimated MTD curve, which is constantly updated. The proposed methodology is assessed with extensive simulations in the context of a real case study. 

https://academic.oup.com/jrsssc/article-pdf/71/5/1996/49463853/jrsssc_71_5_1996.pdf

Combining cytotoxic agents with continuous dose levels in seamless phase I‐II clinical trials

In this article, we propose a phase I-II design in two stages for the combination of molecularly targeted therapies. The design is motivated by a published case study that combines a MEK and a PIK3CA inhibitors; a setting in which higher dose levels do not necessarily translate into higher efﬁcacy responses. The goal is therefore to identify dose combination(s) with a prespeciﬁed desirable risk-beneﬁt trade-off. We propose a ﬂexible cubic spline to model the marginal distribution of the efﬁcacy response. In stage I, patients are allocated following the escalation with overdose control (EWOC) principle whereas, in stage II, we adaptively randomize patients to the available experimental dose combinations based on the continuously updated model parameters. A simulation study is presented to assess the design’s performance under different scenarios, as well as to evaluate its sensitivity to the sample size and to model misspeciﬁcation. Compared to a recently published dose ﬁnding algorithm for biologic drugs, our design is safer and more efﬁcient at identifying optimal dose combinations.

A Bayesian design for dual-agent dose optimization with targeted therapies

In recent years, combined therapy shows expected treatment effect as they increase dose intensity, work on multiple targets and benefit more patients for antitumor treatment. However, dose -finding designs for combined therapy face a number of challenges. Therefore, under the framework of phase I-II, we propose a two-stage dose -finding design to identify the biologically optimal dose combination (BODC), defined as the one with the maximum posterior mean utility under acceptable safety. We model the probabilities of toxicity and efficacy by using linear logistic regression models and conduct Bayesian model selection (BMS) procedure to define the most likely pattern of dose-response surface. The BMS can adaptively select the most suitable model during the trial, making the results robust. We investigated the operating characteristics of the proposed design through simulation studies under various practical scenarios and showed that the proposed design is robust and performed well.

A Bayesian phase I-II clinical trial design to find the biological optimal dose on drug combination.

We propose a flexible design for the identification of optimal dose combinations in dual-agent dose finding clinical trials. The design is called AAA, standing for three adaptations: adaptive model selection, adaptive dose insertion and adaptive cohort division. The adaptations highlight the need and opportunity for innovation for dual-agent dose finding and are supported by the numerical results presented in the proposed simulation studies. To our knowledge, this is the first design that allows for all three adaptations at the same time. We find that AAA enhances the chance of finding the optimal dose combinations and shortens the trial duration. A clinical trial is being planned to apply the AAA design and a Web tool is being developed for both statisticians and non-statisticians.

/pdf/aaa-triple-adaptive-bayesian-designs-for-the-identification-27wr9ybp36.pdf

AAA: triple adaptive Bayesian designs for the identification of optimal dose combinations in dual‐agent dose finding trials

We propose a flexible design for the identification of optimal dose combinations in dual-agent dose-finding clinical trials. The design is called AAA, standing for three adaptations: adaptive model selection, adaptive dose insertion, and adaptive cohort divi- sion. The adaptations highlight the need and opportunity for innovation for dual-agent dose finding, and are supported by the numerical results presented in the proposed simulation studies. To our knowledge, this is the first design that allows for all three adaptations at the same time. We find that AAA improves the statistical inference, enhances the chance of finding the optimal dose combinations, and shortens the trial duration. A clinical trial is being planned to apply the AAA design.

Naiqing Zhao

Papers

AAA: triple adaptive Bayesian designs for the identification of optimal dose combinations in dual‐agent dose finding trials

AAA: Triple-adaptive Bayesian designs for the identification of optimal dose combinations in dual-agent dose-finding trials